Effect of gender and personality characteristics on the speed tendency based on advanced driving assistance system (ADAS) evaluation

Cunshu Pan; Jin Xu; Jinghou Fu

doi:10.1108/jicv-04-2020-0003

Effect of gender and personality characteristics on the speed tendency based on advanced driving assistance system (ADAS) evaluation

Pan, Cunshu; Xu, Jin; Fu, Jinghou 2021-04-26 00:00:00 Purpose – This study aims to explore the relationship between speed behavior of participants and driving styles on interchange ramps. A spiral interchange in Chongqing was selected as an experimental road to carry out ﬁeld driving experiment. Design/methodology/approach – The continuous operating speed during experiment was selected by Mobile Eye, and the driving style was selected via two inventories. Findings – Different driving behaviors showed great differences in age, driving mileage and driving experience. During driving process, male pursued driving stimulation more, whereas female pursued driving steadiness more. Therefore, driving characteristics of male were more disadvantageous to driving safety than that of female. Except for the large speed difference at the entrance and exit of the ramps, the differences at other positions were small. And the operating speed of male was slightly higher than that of female. The difference between different genders at the ascending end position achieved 4–5 kph, and the difference at other feature points were mostly 1–2 kph. During driving process, risky participants were more likely to pursue driving stimulation, and the poor speed control behavior was reﬂected in wide range of desired operating speed. Based on the results of analyzing at feature points, melancholy and sanguine participants more tended to take a high operating speed, and the poor speed control behavior was reﬂected in the most widely desired speed range. The speed control behavior of mixed participants was more cautious. Originality/value – Advanced driving assistance system combined with two inventories was used to explore difference of speed behavior. Keywords Driving style, Interchange, MDSI-C, Speed behavior, TTI Paper type Research paper responsibility. Bernstein and Calamia (2019) combined the Introduction participant’s self-reported driving behavior with Driving behavior had always been the focus of research in the information selected via different scales and used ﬁeld of trafﬁc safety, and different subjects had different exploratory factor analysis to reveal the correlation between perspectives on driving behavior. factors. W. Chu et al. (2019) revealed the relationship Scholars who focused on trafﬁc psychology tended to use among external affective demand, functionality, internal different scales to analyze the correlation among the requirement and driving style. Steinbakk et al. (2019) used participant’s sociodemographic factor, personality, self- UPPS-impulsivity scale to discuss the relationship between reported information and the scale factor, as well as to speed choice behavior and personality traits of different investigate the relationship between the driving style and the drivers in the work area. scale factors. Taubman-Ben-Ari and Yehiel (2012); Taubman- To illustrate the relationship between trafﬁc safety and Ben-Ari and Skvirsky (2016);and Taubman-Ben-Ari et al. driving behavior, scholars were more inclined to investigate the (2004) used NEO-ﬁve factor inventory (NEO-FFI) and various operating data (operating speed, lateral acceleration, multidimensional driving style inventory (MDSI) to discuss the pedal force, etc). with theoretical models, driving simulator and correlation between participants with different personality ﬁeld driving experiment to discuss the relationship among the characteristics (such as different genders, different ages, various operating data. Chevalier et al. (2016) investigated the different educational levels, different working status) and ability of elderly drivers with cognitive decline to control their driving behavior. Based on the information selected by brief sensation seeking scale, Zimbardo time perspective inventory and NEO-FFI, Linkov et al. (2019) combined the operating © Cunshu Pan, Jin Xu and Jinghou Fu. Published in Journal of Intelligent data of participant on the driving simulator with ﬁxed scene to and Connected Vehicles. Published by Emerald Publishing Limited. This analyze the relationship between operating speed and article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), The current issue and full text archive of this journal is available on Emerald subject to full attribution to the original publication and authors. The full Insight at: https://www.emerald.com/insight/2399-9802.htm terms of this licence maybe seen at http://creativecommons.org/licences/ by/4.0/legalcode This paper was supported by NSFC (approval number: 51678099). Journal of Intelligent and Connected Vehicles Received 10 April 2020 4/1 (2021) 28–37 Revised 9 July 2020 Emerald Publishing Limited [ISSN 2399-9802] [DOI 10.1108/JICV-04-2020-0003] Accepted 18 August 2020 28 Advanced driving assistance system Journal of Intelligent and Connected Vehicles Cunshu Pan, Jin Xu and Jinghou Fu Volume 4 · Number 1 · 2021 · 28–37 speed during driving process and revealed the impact of least once (completing a travel from start position to end cognitive decline on the speed of older drivers (). Based on position as a round). driving motivation, turn signal usage, duration, urgency and impact, Wang et al. (2019a) established multilevel mixed- Experimental road and vehicle effects linear models to deal with unsafe lane-change Experimental equipment included Mobile Eye and two driving phenomenon. Combined with physiological index and braking recorders. Mobile Eye was an important part of advanced driving assistance system. Its functions included collision requirement of driver, Musicant et al. (2019) revealed the warning, lane departure warning, vehicle speed recording, etc. relationship between driver’s heart rate and deceleration The way of Mobile Eye collected speed data by connecting the intensity during driving. Zhang et al. (2019) discussed the data port of the instrument to the controller area network of the relationship between driving space, a kind of vehicle operating vehicle to obtain the vehicle operating speed transmitted by the on- space originated from driver space during driving process, and board ECU in real-time with the acquisition frequency of about driving emotion, and revealed the inﬂuence of vehicle emotion 10 Hz, and the speed data of Mobile eye was consistent with that on distance from surrounding vehicles during driving. Based on of the instrument panel. the ﬁeld driving experiment, Eboli et al. (2017) divided the Mobile Eye was used to record the continuous operating speed participant into safe, unsafe and safe but potentially dangerous during the experiment. All external environments during the according to the average speed, 50th percentile operating speed experiment were collected by the front and rear driving recorders. and 85th percentile operating speed. Chen et al. (2019) As the ﬁeld driving experiment was affected by many established a graphical approach to reveal driver longitudinal uncontrollable factors (congestion, car accidents, etc.), so the acceleration behavior with different personalities. Wang et al. result affected by uncontrollable factors would be eliminated (2019b) used the Gray relation entropy analysis method to during the data processing process to increase data reliability. analyze the physiological and psychological factors of driving Hyundai Santa Fe was selected as experimental vehicle. process and revealed the sequence of inﬂuencing factors of According to Figure 1, Sujiaba interchange was located in driving tendency . Nanan District of Chongqing, which connected the Caiyuanba In the prior research, in brief, there were few studies combining Yangtze River Bridge and other major roads. Two ramps on the ﬁeld driving experiment and scale. Some of research studies Sujiaba interchange were selected as experimental road, were based on the section velocity and lacked relevant test of including an ascending ramp and a descending ramp, shown in continuous operating speed and driving style tested by scale. The Figure 1. Ascending ramp was composed of an oval curve (blue objective of this paper was to discuss the difference in speed area in Figure 1) and a S-shaped curve (green area in Figure 1), behavior between different types of participant during driving and descending ramp consisted of an oval curve. Both of ramps and the speed behavior characteristics of various types, and the connected Caiyuanba Yangtze River Bridge and Nantong relationship between individual characteristic and operating Road. speed were explored. Different types of participant were classiﬁed by the MDSI and temperament-type inventory (TTI) scales. Multidimensional driving style inventory and temperament-type inventory Methods Orit Taubman-Ben-Ari et al. designed and validated MDSI in The interchange ramp in Nanan District of Chongqing City, 2004, and it was one of the most widely used driving style scales China, was selected as the experimental road, and 30 in 20 years. On the basis of the MDSI, Sun et al. (2014) participants with different individual characteristics were adapted the scale and veriﬁed its reliability and validity and selected from the DiDi (a transportation network company). compiled MDSI-C which was more suitable for China’s According to usual driving mode, each participant should drive on two selected ramps until the end of mission. The Figure 1 Sujiaba interchange relationship between operating speed and driving behavior in Ascending ramp ramp was analyzed via collecting operating speed during driving. Before started the driving experiment, the detailed driving route should inform to participant. To restore the driving behavior of each participant during the driving process to the greatest extent, experimenters would not guide the driver during the experiment, and the participant would control the vehicle completely according to his usual driving style. Every participant needed to complete two designated questionnaires R90m to distinguish the driving style. To prevent the result of random answer from affecting the style judgment, each questionnaire should be completed under the guidance and supervision of experimenters. After completing the questionnaire, participant Descending ramp listened to the instruction of the experimenter to start the driving task. Each participant’s driving task is 2–4 rounds, and each round was required traveling the designated test ramp at Nantong Road Advanced driving assistance system Journal of Intelligent and Connected Vehicles Cunshu Pan, Jin Xu and Jinghou Fu Volume 4 · Number 1 · 2021 · 28–37 national conditions. This paper used MDSI-C to collect the Means and SDs of factors based on MDSI-C and TTI were listed driving style of participants. in Tables 1 and 2, which showed the difference between male and According to TTI, compiled by Zhang and Chen (1985) in female participants under different factors. The factors with higher 1985, participants were divided into four different temperament score were dissociative, angry and risky, and the driving behaviors types (choleric, sanguine, melancholy, phlegmatic participants) related to the three factors were anxious, angry and risky. andseveral mixedtemperaments. Liu et al. (2006) deﬁned the According to the analysis of driving videos, the dissociative factor characteristics of four temperament types in driving style. during driving was mainly caused by communication with other This experiment used above-mentioned scales to divide person, indicating that participant expected to “communicate 1 participants into several descriptions and explored speed trend and driving” model instead of focusing only on driving. The angry behavior characteristics of every description. The trend and factor was mainly caused by improper driving (e.g. jump a queue, characteristics of every description were deeply combined with frequent lane change, etc). of other vehicles and other vulnerable driving video to analyze and summarize behavior characteristics of trafﬁc participants (pedestrians, motorcycles, etc). sudden broken driving style. in, and participants mainly manifested by cursing, whistling and impatience. The main reasons for risk factor were high operating Participant speed during driving (in most cases, it is higher than the speed Taking into account the driving safety during the experiment, limit) and participant’soverconﬁdence. participants were required to have certain driving experience to The results of means and SDs of driving style measured by the avoid accidents and casualties during the experiment. According MDSI-C and self-reported information (age, driving mileage and to all the samples provided by DiDi, 30 drivers were selected as driving experience) were shown in Table 3.As a result,the mean participants, and they were asked to ﬁnish the MDSI-C and TTI. age of anxious participant was the highest, and the mean age of Based on the results of the scale, participants were classiﬁed risk participant was the lowest, and the mean driving mileage was according to demographic characteristics and driving styles. As a also the highest anxious participant, the lowest risk participant, result, the demographic characteristics were mainly based on and anxious participant were often characterized by short driving genders, and the driving style of the participant was mainly based mileage (<100,000 kilometers) and long driving mileage on the results of MDSI-C and TTI. (>500,000 kilometers). Similarly, anxious participant also showed polarization in mean driving experience. Result The means and SDs of four temperament types based on TTI were listed in Table 2. Choleric, sanguine, phlegmatic scores of Analysis of driving styles according to gender males were more discrete than those of females, and choleric, According to gender and personal information, the data selected by the MDSI-C and the TTI were listed by the relationship sanguine, melancholy mean scores of males were higher than that among different factors, gender and self-reported information, as of females. Based on Tables 1 2 and driving video, female was shown in Tables 1–3. more inclined to focus on driving (driving stability was higher), Table 1 MDSI-C Factors according to gender Factors Dissociative Anxious Angry High-velocity Risky Gender Mean SD Mean SD Mean SD Mean SD Mean SD Male(n = 21) 2.74 0.84 1.68 0.68 3.06 1.19 1.59 0.94 3.49 1.09 Female(n = 9) 2.73 0.91 1.67 0.77 2.58 0.72 1.26 0.4 3.07 0.74 Table 2 TTI Factors according to gender Factors Choleric Sanguine Melancholy Phlegmatic Gender Max Min Mean SD Max Min Mean SD Max Min Mean SD Max Min Mean SD Male(n = 21) 16 8 3.67 5.45 21 5 5.90 5.87 19 10 3 7.13 15 2 4.81 4.33 Female(n = 9) 6 4 0.89 3.38 12 5 4.44 4.95 12 11 0.78 7.52 11 3 6.33 2.40 Table 3 MDSI-C Driving style according to self-reported information Age (years) Driving mileage (10 km) Driving experience (years) Mean SD Mean SD Mean SD Risky 32.86 6.85 14.29 10.66 8.24 4.51 Anxious 40.50 7.70 32.50 23.05 11.75 7.22 Angry 34.60 5.54 20.00 5.48 12.40 4.45 30 Advanced driving assistance system Journal of Intelligent and Connected Vehicles Cunshu Pan, Jin Xu and Jinghou Fu Volume 4 · Number 1 · 2021 · 28–37 male was characterized by irritability, impatience and preference characteristics under different feature positions (Fu and Xu, 2019). for conversation, which was not conducive to safety. The author extracted the feature positions and distinguished them The 15th and 85th percentile speed were selected as according to the feature percentile positions (15th, 50th and 85th) feature percentile speed, and the feature percentile speed as the previous research, and the 85th percentile value was selected curves of the descending and ascending ramps were to analyze difference between genders. The results were showed in Figure 2.Male and female were indicated by a summarized in Table 4. solid black line and a black dotted line, respectively. The green area DPS, DEP, ASP and AEP represented decelerating start represented the operating speed male> female, and the orange area position, decelerating end position, accelerating start position represented female > male. The column represented the difference and accelerating end position, respectively. Owning to ascending between the speed difference according to gender and the minimum ramp that had two different accelerating and decelerating operating speed (green: male> female, orange: female> male). processes, the ramp was divided into two sections according to Figure 2(a) and (b) was the 15th operating speed for different the different accelerating and decelerating processes. The feature positions of the descending ramp were 380 m, genders in the descending and ascending ramps. In Figure 2(a), 520 m and 820 m, respectively. In Figure 2(a), the difference at operating speed of male was generally higher than that of female, 380 m was 5% (2 kph), 2% (1 kph) at 820 m and basically same and the differences were within 10% (generally at 2–3kph), and at 520 m. In Figure 2(c), the difference at 380 m was 2% (1 kph), thedifferences of theentranceand exit were 16% and 17%, thesameat520mand7%(5kph)at820m. respectively. In Figure 2(b), due to the good road alignment and The feature points of AR1 were 300, 480 and 740m, and that of line of sight at the entrance, operating speed of female was higher AR2 were 760, 920 and 1020m. In Figure 2(b), the difference at than male between 40 and 160m, and the differences were lower than 10% (1–4kph). At 180–960m, male ran faster than female, 300 m was 6% (3 kph), 5% (2kph) at 480m, 4% (2 kph) at 740m, and the differences were within 10% (generally at 1–4kph). 980– 3% (2 kph) at 760m, 4% (2 kph) at 920 m and 9% (4 kph) at 1020m. In Figure 2(d), the difference at 300m was basically 1100m, the section closed to the exit, female operating speed same, 5% (2 kph) at 480m, 1% (1 kph) at 740m, 2% (1 kph) at higher than male, and the differences were 14% (5kph). 760 m, 5% (2 kph) at 920m and 3% (2 kph) at 1020 m. Figure 2(c) and (d) were the 85th operating speed for different In summary, the difference between different genders was larger genders in the descending and ascending ramps. In Figure 2(c), (4–5 kph) at the AEP, while difference of other feature positions the difference was 28% (7kph) when entering the ramp, which was centralized in 1–2 kph, and the maximum was not more than 3 kph. thelargest differencein entireramp. Thedifferences between 160 and 740 m were maintained in a small region (generally at 1kph, the max was 2kph). Close to the exit (760–840m), the differences Analysis of speed behavior according to were gradually increased (5%–7%, 3–4kph). In Figure 2(d),the multidimensional driving style inventory-C and differences were relatively stable and had been maintained at 1– temperament-type inventory 3kph. At exit, the maximum difference reached 19% (7kph). Analysis of speed behavior according to multidimensional driving In the previous research, the research on the operating speed style inventory-C focused on the difference of operating speed of feature points According to the driving styles selected by MDSI-C, the (DSP, DEP, ASP and AEP) and analyzed the operating speed participants were classiﬁed according to the driving styles, and Figure 2 Speed behavior of participants according to gender Descending ramp Ascending ramp 10% 5% 40 0% –5% – 10% M> F F> M 35 Male Female 10 – 20% 30 –15% 0 100 200 300 400 500 600 700 800 0 200 400 600 800 1,000 Travel distance(m) Travel distance(m) (a) (b) Descending ramp Ascending ramp 10% 10% 0% 0% –10% M> F M> F F> M –10% 40 F> M –20% Male Male Female Female 30 –20% 20 –30% 0 200 400 600 800 1,000 0 100 200 300 400 500 600 700 800 Travel distance(m) Travel distance(m) (c) (d) Notes: a and b represented difference of 15th percentile speed, c and d represented difference of 85th percentile speed Speed(k/h) Speed(k/h) Diﬀerence(%) Diﬀerence(%) Speed(k/h) Speed(k/h) Diﬀerence(%) Diﬀerence(%) Advanced driving assistance system Journal of Intelligent and Connected Vehicles Cunshu Pan, Jin Xu and Jinghou Fu Volume 4 · Number 1 · 2021 · 28–37 Table 4 Feature points of 15th, 50th, 85th percentile distance (R)DSP (m) (Y)DEP/ASP (m) (B)AEP (m) AR 1 AR 2 DR AR 1 AR 2 DR AR 1 AR 2 DR (B)15th 0 680 180 360 820 420 600 940 600 (O)50th 60 720 200 420 860 480 720 980 680 (G)85th 300 760 380 480 920 520 740 1020 820 Note: AR1 – oval curve (ﬁrst curve) of ascending ramp, AR2 – S-shaped curve (second curve) of ascending ramp, DR – descending ramp the operating speed distribution ﬁgures of each style on the relatively stable, and the differences were also maintained at descending and ascending ramps were obtained, as shown in 23–27kph. From 740 m, the difference and operating speed Figure 3. The 15th, 50th and 85th percentile positions in Table 4 were increased. In Figure 3(f), the maximum speed of 0–160m were marked with different colors (Blue-15th, Orange-50th, showed a downward trend, and the minimum speed decreased Green-85th,Red-DSP,Yellow-DEP/ASP, Black-AEP),as ﬁrst and then increased, and the difference at the entrance shown in Figure 3 (e.g. Blue and Red-15th DSP, Orange and Black-50th AEP). Among Figure 3,the ﬁgures on left side were the descending ramp (a, c, e), and the ﬁgures on right side were Figure 3 Speed behavior of participants according to MDSI-C factors the ascending ramp (b, d, f). In Figure 3(a), the operating speed was increased from 0m to 180 m when entering the ramp, the difference at the entrance reached 30kph, and the differences were remained at 17–21kph. At 200–500m, the operating speed showed a downward trend, and the differences were maintained at 12-20kph. At 300–400m, the change of operating speed was relatively stable (difference: 16–20kph, the minimum speed difference and the maximum speed difference were not more than 2kph). After 520 m, the maximum operating speed was gradually increased and the minimum operating speed was gradually decreased, and the differences were increased (difference: 12–40kph). In Figure 3(b), the operating speed from 0 m to 200 m was decreased, and the differences were gradually decreasing (difference: 41-12kph). From 200 m to 920 m, the differences of most sections (200–500m and 740 920m) were maintained in a small interval (10–15kph), the maximum and minimum speeds were distributed in 52–64kph and 38–44kph, respectively. From 940m, the operating speed was decreased, and the difference started to increase gradually (difference: 19–33kph). In Figure 3(c), the speed from the entrance to 220m was gradually increased, the maximum speed of 300–440m was gradually decreased and the minimum speed was gradually increased. The maximum and minimum speeds of 480–640m were gradually increased, and the maximum and minimum speeds of 740–800m were gradually decreased. The differences were relatively small at 0–140m and 460–640m, and the differences at 180–320m and 660–780m were larger, while the maximum difference was lower than 29kph. In Figure 3(d),the maximum and minimum speeds of 0–100m and 120–280m had a same trend. After a period of decline (300–380m), the speed started to increase gradually from 480–740m, the minimum speed remained unchanged, the maximum speed was gradually increased, the maximum speed of 760–860m was decreased, and the minimum speed was showed without change. At 880–1100m, the speed was gradually reduced after a small increased. The speed differences were larger at 100–140m and 760–800m, and the difference from 520 to 760 m was gradually increased, and the remaining differences were around 20kph. Notes: a and b represented angry participants, c and d represented In Figure 3(e), the speed was gradually increased from 0 m to anxious participants, e and f represented risky participants 160 m, the maximum and minimum speeds of 180–640m were Desce Speed - Traveled distance Aesce Speed - Traveled distance a b 10 10 0 100 200 300 400 500 600 700 800 0 200 400 600 800 1000 Traveled distance(m) Traveled distance(m) Desce Speed - Traveled distance Aesce Speed - Traveled distance 70 70 c d 50 50 30 30 10 10 0 100 200 300 400 500 600 700 800 0 200 400 600 800 1000 Traveled distance(m) Traveled distance(m) Desce Speed - Traveled distance Aesce Speed - Traveled distance e f 10 20 0 100 200 300 400 500 600 700 800 0 200 400 600 800 1000 Traveled distance(m) Traveled distance(m) speed(kph) speed(kph) speed(kph) speed(kph) speed(kph) speed(kph) Advanced driving assistance system Journal of Intelligent and Connected Vehicles Cunshu Pan, Jin Xu and Jinghou Fu Volume 4 · Number 1 · 2021 · 28–37 reached 50kph. The maximum speed of 500–700m was In the ﬁrst curve of ascending ramp, the maximum speed of decreased, and the minimum speed was the ﬁrst to decrease DSP: Risky > Anxious > Angry (85th percentile: Anxious > and then increase. The maximum and minimum speeds of Risky > Angry), speed difference: Risky > Angry > Anxious. 740–860m were the same as the decreased trend. The The maximum speed of DEP: Risky > Anxious > Angry, speed maximum and minimum speeds of 880–960m were the same as difference: Risky > Angry > Anxious. The maximum speed of the increased trend, and then declined. AEP: Risky > Anxious > Angry, speed difference: Risky > In summary, in the descending ramp, there was a large speed Anxious > Angry. In the second curve of ascending ramp, the difference at the entrance between the angry and risky maximum speed of DSP: Risky > Angry > Anxious, speed participants, and the differences of risky participants were difference: Risky > Anxious > Angry. The maximum speed of greater. maximum speed when entering the ramp: Risky > DEP: Risky > Anxious > Angry, speed difference: Risky > Angry > Anxious, the operating speed after the accelerating Anxious> Angry. The maximum speed of AEP: Risky> Angry> process of entering the ramp: Risky = Angry > Anxious. There Anxious, speed difference: Risky> Angry = Anxious. was a signiﬁcant decelerating trend for angry participants, and According to the above analysis, the risky participants had the anxious and risky participants were more stable. When the maximum operating speed at the feature points than the driving out of the ramp, there was a speed-up phenomenon for other two types of participants, and the angry driver had the both angry and risky participants, and anxiety participants lowest. Similarly, the speed differences, that was the desired tended to move at a constant speed or slow down. speed interval, of risky participants were higher than the other In the ascending ramp, there was a large speed difference at two types of participants. the entrance of the angry and risky participants, and the difference of the risky participants were greater. The maximum Analysis of speed behavior according to temperament-type speed at the entrance: Risky > Angry > Anxious. The range of inventory speed ﬂuctuation during driving: Risky > Anxious > Angry. Based on the driving factors were selected by the TTI, the The risky and anxious participants had a signiﬁcant speed-up participants were divided into 7 categories, and the distribution phenomenon at 460–760m, and the angry participants only of the operating speed of each type of participants in the decreased with the other two types of participants after a small descending and ascending ramps were investigated, as shown increased in speed. When out of ramp, risky and angry in Figure 4. participants tended to speed up ﬁrst and then slow down, while In Figure 4(a), the operating speed was increased from 0 m Anxious participants tended to slow down until leave the ramp. (max: 28 kph, min: 11 kph) to 180 m (max: 61 kph, min: 46 kph) Based on Table 4 and Figure 3, the maximum and minimum when entering the ramp, 200–500m was slowly decreased, and operating speeds and the speed difference of participants at 520–820m was slowly increased. The speed differences between different feature points were summarized and listed in Table 5. 0 and 440 m were maintained at 14–19kph, the speed differences The speed differences at the feature points were compared between 460 and 700 m were maintained at 6-10kph, and the among different participants, and the difference of expected speed differences between 720 and 820 m were gradually speed among different types of participants at the same position increased to 22kph. and the relationship between expected speed and performance In Figure 4(b), as the maximum speed gradually decreased were discussed. and the minimum speed didn’t change, the difference of 0– According to Table 5, in descending ramp, the maximum 200m was continuously decreasing (44kph to 15 kph), and the speed at the DSP: Risky > Angry > Anxious, and the speed speed of 200 m-820m was the process of two ﬁrst rising and difference: Risky > Anxious > Angry. Maximum speed at the DEP: Risky > Anxious > Angry, and the speed difference: then falling, respectively. It had also grown from small to large Risky > Anxious > Angry (15th percentile: Anxious > Risky > then to small. When driving away from the ramp, operating Angry). Speed difference at AEP: Angry> Risky> Anxious. speed was also the trend of rising ﬁrst and then falling. Table 5 Speed of feature points according to MDSI-C driving styles Speed of DR (kph) Speed of AR 1 (kph) Speed of AR 2 (kph) Angry Anxious Risky Angry Anxious Risky Angry Anxious Risky Max Min Dis Max Min Dis Max Min Dis Max Min Dis Max Min Dis Max Min Dis Max Min Dis Max Min Dis Max Min Dis DSP 15th 64 46 17 59 37 22 65 37 27 74 33 41 65 36 29 84 33 51 63 47 15 67 40 27 71 43 28 50th 63 46 17 61 35 26 63 37 26 70 30 39 58 37 21 80 27 53 63 48 15 68 40 28 72 37 35 85th 59 39 20 58 37 21 62 37 25 56 41 15 61 35 26 60 39 21 60 47 12 69 31 38 72 35 37 DEP 15th 54 38 16 58 34 24 58 37 22 52 38 14 57 30 28 56 34 22 53 40 16 64 33 31 60 27 32 /ASP 50th 51 38 13 54 36 18 57 32 25 52 40 13 52 32 20 56 30 26 52 42 15 56 33 23 59 27 32 85th 52 38 14 56 40 16 59 33 26 53 42 12 55 32 23 58 33 25 54 40 14 56 34 21 63 30 33 AEP 15th 59 27 32 61 43 18 62 38 24 60 44 16 62 41 21 71 38 33 57 39 18 56 38 18 67 29 39 50th 57 31 26 62 35 27 59 40 19 63 48 15 48 40 28 72 37 35 41 38 18 57 39 18 68 26 43 85th 66 26 40 59 35 24 66 35 31 62 48 14 69 36 32 74 35 39 60 32 24 53 29 24 62 25 37 33 Advanced driving assistance system Journal of Intelligent and Connected Vehicles Cunshu Pan, Jin Xu and Jinghou Fu Volume 4 · Number 1 · 2021 · 28–37 In Figure 4(c), the speed of 0–160m was increased. After a Figure 4(i), (k) and (m) were the speed trends of the small decrease of 180 m-480m, the speed was gradually increased descending ramp of three mixed participants (sanguine- away from the ramp, the maximum and minimum speed melancholy, choleric-phlegmatic and melancholy-phlegmatic). 0–180m of mixed participants were on the same trend, and the differences were respectively at the entrance and exit, and the driving speeds of the two types of sanguine-melancholy and speed differences of the remaining positions were maintained at melancholy-phlegmatic participants were the same. The 20kph. In Figure 4(d), the maximum and minimum operating choleric-phlegmatic participants had large speed differences speeds of 0–180m were decreasing. After a stable operation for a between 360 m and 600 m. The speed differences between the certain distance, the maximum and minimum operating speeds sanguine-melancholy and melancholy-phlegmatic participants of 300–760m were the same as the ﬁrst rising and then were larger when driving away from the ramp. Operating speeds decreasing, and the trend of 780–1100m was the same as the of Sanguine-melancholy participants were declined as it leaved decrease-increase- decrease trend. The speed difference reached the ramp, and the other two type participants were on the rise. a maximum (42 kph) at entrance, the 0 m-140m speed Figure 4(j), (l) and (n) was the operating speed trends of three differences were maintained at a high level (>30kph), and the mixed participants (sanguine-melancholy, choleric-phlegmatic remaining positions were remained at 20–25kph. and melancholy–phlegmatic, respectively). The mixed participants In Figure 4(e), the running speed of 0 m-140m was entrance ran at a signiﬁcantly lower speed than the other four types increased, and the speed of 160–520m was maintained in a of participants. The choleric-phlegmatic and melancholy- small range (max: 58–62kph, min: 34–38kph). From 540 m, phlegmatic participants with large differences in speed were at the the speed difference showed a different trend, and the exit. The speed differences of sanguine-melancholy participants differences (> 30kph) increased signiﬁcantly. In Figure 4(f), were generally stable and did not have much ﬂuctuation. the speed differences at the entrance (0–80m) and the middle According to the data in Table 4 and Figure 4, the maximum section of the ramp to the driving ramp (560–1080m) were and minimum speeds and speed difference of different types at maintained at 30 kph. The operating speed trend was the same as different feature points were summarized in Table 6. that of the Sanguine participants, and there was a signiﬁcant According to Table 6, in the descending ramp, maximum drop-up-drop phenomenon. speed at DSP: phlegmatic > sanguine > melancholy > .. . > In Figure 4(g),the speed of 0–180m showed an upward trend, melancholy-phlegmatic, speed difference: melancholy > the maximum speed of 200 m-480m decreased, the minimum sanguine > phlegmatic > .. . > choleric-phlegmatic. Maximum speed was maintained at 40kph, the speed differences decreased speed at DEP: melancholy > sanguine > choleric-phlegmatic > gradually, the speed of 500–800m showed an upward trend, and ... > melancholy-phlegmatic, speed difference: melancholy > the speed difference began to increase gradually at 680m. In sanguine > choleric-phlegmatic > ... > melancholy-phlegmatic. Figure 4(h),0–140m, 480–600m, 1020–1060m speed differences Maximum speed at AEP: sanguine > choleric-phlegmatic > of three sections >25kph, speed differences of 180–320m and melancholy > ... > melancholy-phlegmatic, speed difference: 800–920m <15kph. The maximum and minimum operating melancholy > sanguine > phlegmatic > ... > melancholy- speeds had a same trend from 200m to 1060 m. phlegmatic. In the ﬁrst curve of ascending ramp, maximum speed at DSP: Figure 4 Speed behavior of participants according to TTI factors choleric> sanguine> phlegmatic> ... > melancholy- phlegmatic, speed difference: sanguine > choleric> melancholy > ... > melancholy-phlegmatic. Maximum speed at DEP: sanguine = melancholy = phlegmatic > ... > melancholy-phlegmatic, speed difference: melancholy > phlegmatic > sanguine > ... > melancholy-phlegmatic. Maximum speed at AEP: melancholy > sanguine > phlegmatic > ... > melancholy-phlegmatic, speed difference: melancholy> sanguine> phlegmatic> ... > sanguine- melancholy. In the second curve of ascending ramp, maximum speed at DSP: melancholy > sanguine > phlegmatic > .. . > melancholy-phlegmatic, speed difference: melancholy > sanguine> phlegmatic> .. . > sanguine-melancholy. In summary, the melancholy and sanguine participants were the two fastest types for speed, followed by the phlegmatic participants and the choleric-phlegmatic participants, and the last were the melancholy-phlegmatic participants. The biggest differences were also the melancholy participants and the sanguine participants, followed by the phlegmatic participants, the smallest being the Notes: a and b represented choleric participants, c and d r melancholy-phlegmatic participants. epresented sanguine participants, e and f represented melancholy participants, g and h represented phlegmatic participants, i and j Discussion represented sanguine-melancholy participants, k and l represented choleric-phlegmatic participants, m and n represented melancholy- This paper classiﬁed the participants according to the data phlegmatic participant selected by MDSI-C and TTI. Meanwhile, the speed behavior Desce Speed - Traveled distance Asce Speed - Traveled distance a b 10 20 0 100 200 300 400 500 600 700 800 0 200 400 600 800 1000 Traveled distance(m) Traveled distance(m) Desce Speed - Traveled distance Asce Speed - Traveled distance 70 80 c d 50 60 30 40 10 20 0 100 200 300 400 500 600 700 800 0 200 400 600 800 1000 Traveled distance(m) Traveled distance(m) Desce Speed - Traveled distance Asce Speed - Traveled distance 70 80 e f 50 60 30 40 10 20 0 100 200 300 400 500 600 700 800 0 200 400 600 800 1000 Traveled distance(m) Traveled distance(m) Desce Speed - Traveled distance Asce Speed - Traveled distance g 70 h 10 20 0 100 200 300 400 500 600 700 800 0 200 400 600 800 1000 Traveled distance(m) Traveled distance(m) Desce Speed - Traveled distance Asce Speed - Traveled distance i j 10 20 0 100 200 300 400 500 600 700 800 0 200 400 600 800 1000 Traveled distance(m) Traveled distance(m) Desce Speed - Traveled distance Asce Speed - Traveled distance k l 10 20 0 100 200 300 400 500 600 700 800 0 200 400 600 800 1000 Traveled distance(m) Traveled distance(m) Desce Speed - Traveled distance Asce Speed - Traveled distance m n 10 20 0 100 200 300 400 500 600 700 800 0 200 400 600 800 1000 Traveled distance(m) Traveled distance(m) speed(kph) speed(kph) speed(kph) speed(kph) speed(kph) speed(kph) speed(kph) speed(kph) speed(kph) speed(kph) speed(kph) speed(kph) speed(kph) speed(kph) Advanced driving assistance system Journal of Intelligent and Connected Vehicles Cunshu Pan, Jin Xu and Jinghou Fu Volume 4 · Number 1 · 2021 · 28–37 Table 6 Speed of feature points according to TTI factors Speed of DSP (kph) Speed of DEP/ASP (kph) Speed of AEP (kph) 15th 50th 85th 15th 50th 85th 15th 50th 85th DR Choleric Max 62 60 58 54 49 50 54 58 66 Min 46 43 39 38 42 43 46 50 44 Dis 16 18 19 16 7 7 8 8 22 Sanguine Max 62 62 59 58 54 56 61 62 66 Min 38 39 38 37 35 33 39 40 43 Dis 24 23 21 21 19 23 22 22 23 Melancholy Max 62 60 59 56 57 59 62 59 62 Min 37 35 37 34 36 38 27 31 26 Dis 25 25 22 22 21 21 34 28 36 Phlegmatic Max 65 63 62 58 51 53 59 58 66 Min 45 45 41 40 41 41 42 43 37 Dis 20 18 21 18 10 12 17 15 29 Sanguine-Melancholy Max 59 60 55 54 50 53 56 57 55 Min 45 49 44 44 38 39 46 45 35 Dis 14 11 11 10 12 14 10 11 20 Choleric-Phlegmatic Max 56 53 58 58 53 56 61 59 64 Min 47 48 43 42 33 37 46 47 41 Dis 9 5 15 16 20 19 16 12 24 Melancholy-Phlegmatic Max 53 51 48 46 42 47 51 50 59 Min 38 37 37 37 32 37 42 41 41 Dis 15 14 11 9 10 9 9 9 18 AR 1 Choleric Max 84 80 55 51 52 56 59 61 60 Min 39 40 40 37 35 39 41 43 44 Dis 45 40 15 13 17 17 19 18 16 Sanguine Max 76 68 61 57 52 55 62 68 69 Min 34 27 35 31 34 34 38 47 46 Dis 42 41 26 26 18 21 24 21 22 Melancholy Max 74 66 59 55 55 56 71 72 74 Min 33 30 42 30 32 32 41 40 36 Dis 41 36 17 26 23 24 30 33 37 Phlegmatic Max 73 70 59 55 56 58 65 66 66 Min 33 33 46 34 30 33 40 48 47 Dis 40 36 13 21 25 25 25 19 18 Sanguine-Melancholy Max 60 58 59 56 56 56 58 62 62 Min 44 48 48 44 42 40 50 53 50 Dis 16 10 10 12 14 16 8 9 12 Choleric-Phlegmatic Max 62 57 56 54 51 52 56 59 57 Min 43 41 46 42 40 41 39 44 45 Dis 19 16 10 12 11 11 17 15 12 Melancholy-Phlegmatic Max 65 57 44 41 39 43 47 51 52 Min 46 49 43 38 33 38 45 37 35 Dis 19 8 1 3 7 4 2 14 17 AR 2 Choleric Max 61 61 58 49 48 53 55 57 54 Min 42 43 43 40 42 43 44 39 29 Dis 19 18 16 9 6 10 11 18 24 Sanguine Max 67 68 69 64 57 62 64 63 61 Min 46 47 45 38 38 34 39 37 32 Dis 21 21 24 26 19 28 25 26 30 Melancholy Max 71 72 72 60 59 62 64 66 62 Min 40 40 31 33 33 34 35 30 25 Dis 31 33 42 27 27 27 29 35 37 Phlegmatic Max 65 66 64 55 52 54 57 61 60 Min 46 48 47 42 43 40 42 40 36 Dis 19 19 17 13 9 14 16 21 25 (continued) 35 Advanced driving assistance system Journal of Intelligent and Connected Vehicles Cunshu Pan, Jin Xu and Jinghou Fu Volume 4 · Number 1 · 2021 · 28–37 Table 6 Speed of DSP (kph) Speed of DEP/ASP (kph) Speed of AEP (kph) 15th 50th 85th 15th 50th 85th 15th 50th 85th Sanguine-Melancholy Max 60 62 62 54 50 53 54 57 55 Min 52 53 47 40 35 40 40 41 38 Dis 8 9 14 13 15 13 14 16 17 Choleric-Phlegmatic Max 57 59 58 55 55 63 67 68 56 Min 43 44 43 37 43 42 41 38 36 Dis 14 15 15 18 13 21 26 30 20 Melancholy-Phlegmatic Max 48 51 52 44 44 46 48 52 51 Min 43 37 35 27 27 30 29 26 25 Dis 6 14 17 171716 19 26 26 characteristics of different types of participants during driving difference among different participants speed and the different process and the difference between characteristics and characters in driving with the parameters such as typical descriptions of participants in operating speed were discussed. percentile speed and distance. The continuous operating speed In the past research, some conclusions of MDSI-based research during driving was selected by Mobile Eye, and the driving style studies pointed out that male reckless and angry driving style was selected by the MDSI-C and TTI. The main ﬁndings were was more obvious and prominent than female. Therefore, as follows: female was more anxious and cautious during driving Older participants were more likely to be Anxious, and (Taubman-Ben-Ari and Yehiel, 2012; Taubman-Ben-Ari and driving anxiety was more polarized in driving age and Skvirsky, 2016; Taubman-Ben-Ari et al., 2004). Based on the driving mileage. The Risky driving behavior was results of participants with different characteristics in China, characterized by low age, low mileage and low experience. The Angry driving behavior was characterized by middle- male was more irritated during driving and more stimulating by aged drivers with certain driving experience. high speed. On the contrary, female tended to be more stable in During driving, male was more motivated to drive, and driving. The risky driving behavior that pursued driving pleasure mainly existed in participants with young age, low female was more likely to pursue driving stability. driving mileage and low driving experience. Angry driving Moreover, male traits of driving (prone to anger, irritability, tended to have conversation, etc). were more behavior mainly existed in middle-aged participants with detrimental to driving safety than female. considerable driving experience. According to the analysis In descending and ascending ramps, except for the large speed results, female on some sections drove faster than the male, but differences at the entrance and exit of ramps, the differences at it was generally believed that in most cases, the speed of male other positions were small. In addition, the operating speed of was higher than that of female. Based on the operating speed of different types of male was slightly higher than female. participants and the characteristics of each type, it was found The differences between different genders at the ascending terminal were 4-5 kph, and the difference of that there were some differences between the type other feature points were mostly 1–2 kph. characteristics of participants and the speed behavior. In the The Risky participants had higher requirements for speed ascending ramp, anxious participants tended to slow down than the other two types, Anxious participants tended to from the ramp, and risky and angry participants tended to shift speed and had poor speed control. However, the speed up and then slow down when they leaved the ramp. aggressiveness of the Angry driver was not reﬂected in the However, the driving aggression of angry participants were not speed. manifested in speed behavior, but only in physical behavior Melancholy and sanguine participants were more inclined (cursing, whistling and impatient). to operate at higher speed, and the poor speed control was According to Liu et al. (2006), the deﬁnition of different reﬂected in the most widely desired speed range. Mixed types was founded that choleric participants were not participant speed control was more cautious. prominent in operating speed. Sanguine participants and melancholy participants were more inclined to pursue high speed, and there were certain differences with the deﬁnition. The reason may be that the professional driver would weaken References the inﬂuence of personality on driving behavior during driving Bernstein, J.P.K. and Calamia, M. (2019), “Dimensions of to ensure safety. driving-related emotions and behaviors: an exploratory factor analysis of common self-report measures”, Accident Conclusion Analysis & Prevention, Vol. 124, pp. 85-91. On the designated interchange, from the perspective of driving Chen, C., Zhao, X., Zhang, Y., et al. (2019), “A graphical safety, the ﬁeld driving experiment was held to discuss the modeling method for individual driving behavior and its operating speed of different participants and analyze the application in driving safety analysis using GPS data”, 36 Advanced driving assistance system Journal of Intelligent and Connected Vehicles Cunshu Pan, Jin Xu and Jinghou Fu Volume 4 · Number 1 · 2021 · 28–37 Transportation Research Part F: Trafﬁc Psychology and Sun, L., Yang, C. and Chang, R. (2014), “Revision and Behaviour, Vol. 63, pp. 118-134. preliminary application of multidimensional”, Chinese Chevalier, A., Coxon, K., Rogers, K., et al. 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(2019), Transportation Research Part F: TrafﬁcPsychologyand “Speed preferences in work zones: the combined effect of Behaviour, Vol. 63, pp. 108-117. visible roadwork activity, personality traits, attitudes, risk perception and driving style”, Transportation Research Part F: Corresponding author Trafﬁc Psychology and Behaviour, Vol. 62, pp. 390-405. Jinghou Fu can be contacted at: 383817226@qq.com For instructions on how to order reprints of this article, please visit our website: www.emeraldgrouppublishing.com/licensing/reprints.htm Or contact us for further details: permissions@emeraldinsight.com http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Intelligent and Connected Vehicles Emerald Publishing http://www.deepdyve.com/lp/emerald-publishing/effect-of-gender-and-personality-characteristics-on-the-speed-tendency-f87r4D1K95

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Publisher: Emerald Publishing
Copyright: © Cunshu Pan, Jin Xu and Jinghou Fu.
ISSN: 2399-9802
DOI: 10.1108/jicv-04-2020-0003
Publisher site: See Article on Publisher Site

Abstract

Purpose – This study aims to explore the relationship between speed behavior of participants and driving styles on interchange ramps. A spiral interchange in Chongqing was selected as an experimental road to carry out ﬁeld driving experiment. Design/methodology/approach – The continuous operating speed during experiment was selected by Mobile Eye, and the driving style was selected via two inventories. Findings – Different driving behaviors showed great differences in age, driving mileage and driving experience. During driving process, male pursued driving stimulation more, whereas female pursued driving steadiness more. Therefore, driving characteristics of male were more disadvantageous to driving safety than that of female. Except for the large speed difference at the entrance and exit of the ramps, the differences at other positions were small. And the operating speed of male was slightly higher than that of female. The difference between different genders at the ascending end position achieved 4–5 kph, and the difference at other feature points were mostly 1–2 kph. During driving process, risky participants were more likely to pursue driving stimulation, and the poor speed control behavior was reﬂected in wide range of desired operating speed. Based on the results of analyzing at feature points, melancholy and sanguine participants more tended to take a high operating speed, and the poor speed control behavior was reﬂected in the most widely desired speed range. The speed control behavior of mixed participants was more cautious. Originality/value – Advanced driving assistance system combined with two inventories was used to explore difference of speed behavior. Keywords Driving style, Interchange, MDSI-C, Speed behavior, TTI Paper type Research paper responsibility. Bernstein and Calamia (2019) combined the Introduction participant’s self-reported driving behavior with Driving behavior had always been the focus of research in the information selected via different scales and used ﬁeld of trafﬁc safety, and different subjects had different exploratory factor analysis to reveal the correlation between perspectives on driving behavior. factors. W. Chu et al. (2019) revealed the relationship Scholars who focused on trafﬁc psychology tended to use among external affective demand, functionality, internal different scales to analyze the correlation among the requirement and driving style. Steinbakk et al. (2019) used participant’s sociodemographic factor, personality, self- UPPS-impulsivity scale to discuss the relationship between reported information and the scale factor, as well as to speed choice behavior and personality traits of different investigate the relationship between the driving style and the drivers in the work area. scale factors. Taubman-Ben-Ari and Yehiel (2012); Taubman- To illustrate the relationship between trafﬁc safety and Ben-Ari and Skvirsky (2016);and Taubman-Ben-Ari et al. driving behavior, scholars were more inclined to investigate the (2004) used NEO-ﬁve factor inventory (NEO-FFI) and various operating data (operating speed, lateral acceleration, multidimensional driving style inventory (MDSI) to discuss the pedal force, etc). with theoretical models, driving simulator and correlation between participants with different personality ﬁeld driving experiment to discuss the relationship among the characteristics (such as different genders, different ages, various operating data. Chevalier et al. (2016) investigated the different educational levels, different working status) and ability of elderly drivers with cognitive decline to control their driving behavior. Based on the information selected by brief sensation seeking scale, Zimbardo time perspective inventory and NEO-FFI, Linkov et al. (2019) combined the operating © Cunshu Pan, Jin Xu and Jinghou Fu. Published in Journal of Intelligent data of participant on the driving simulator with ﬁxed scene to and Connected Vehicles. Published by Emerald Publishing Limited. This analyze the relationship between operating speed and article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), The current issue and full text archive of this journal is available on Emerald subject to full attribution to the original publication and authors. The full Insight at: https://www.emerald.com/insight/2399-9802.htm terms of this licence maybe seen at http://creativecommons.org/licences/ by/4.0/legalcode This paper was supported by NSFC (approval number: 51678099). Journal of Intelligent and Connected Vehicles Received 10 April 2020 4/1 (2021) 28–37 Revised 9 July 2020 Emerald Publishing Limited [ISSN 2399-9802] [DOI 10.1108/JICV-04-2020-0003] Accepted 18 August 2020 28 Advanced driving assistance system Journal of Intelligent and Connected Vehicles Cunshu Pan, Jin Xu and Jinghou Fu Volume 4 · Number 1 · 2021 · 28–37 speed during driving process and revealed the impact of least once (completing a travel from start position to end cognitive decline on the speed of older drivers (). Based on position as a round). driving motivation, turn signal usage, duration, urgency and impact, Wang et al. (2019a) established multilevel mixed- Experimental road and vehicle effects linear models to deal with unsafe lane-change Experimental equipment included Mobile Eye and two driving phenomenon. Combined with physiological index and braking recorders. Mobile Eye was an important part of advanced driving assistance system. Its functions included collision requirement of driver, Musicant et al. (2019) revealed the warning, lane departure warning, vehicle speed recording, etc. relationship between driver’s heart rate and deceleration The way of Mobile Eye collected speed data by connecting the intensity during driving. Zhang et al. (2019) discussed the data port of the instrument to the controller area network of the relationship between driving space, a kind of vehicle operating vehicle to obtain the vehicle operating speed transmitted by the on- space originated from driver space during driving process, and board ECU in real-time with the acquisition frequency of about driving emotion, and revealed the inﬂuence of vehicle emotion 10 Hz, and the speed data of Mobile eye was consistent with that on distance from surrounding vehicles during driving. Based on of the instrument panel. the ﬁeld driving experiment, Eboli et al. (2017) divided the Mobile Eye was used to record the continuous operating speed participant into safe, unsafe and safe but potentially dangerous during the experiment. All external environments during the according to the average speed, 50th percentile operating speed experiment were collected by the front and rear driving recorders. and 85th percentile operating speed. Chen et al. (2019) As the ﬁeld driving experiment was affected by many established a graphical approach to reveal driver longitudinal uncontrollable factors (congestion, car accidents, etc.), so the acceleration behavior with different personalities. Wang et al. result affected by uncontrollable factors would be eliminated (2019b) used the Gray relation entropy analysis method to during the data processing process to increase data reliability. analyze the physiological and psychological factors of driving Hyundai Santa Fe was selected as experimental vehicle. process and revealed the sequence of inﬂuencing factors of According to Figure 1, Sujiaba interchange was located in driving tendency . Nanan District of Chongqing, which connected the Caiyuanba In the prior research, in brief, there were few studies combining Yangtze River Bridge and other major roads. Two ramps on the ﬁeld driving experiment and scale. Some of research studies Sujiaba interchange were selected as experimental road, were based on the section velocity and lacked relevant test of including an ascending ramp and a descending ramp, shown in continuous operating speed and driving style tested by scale. The Figure 1. Ascending ramp was composed of an oval curve (blue objective of this paper was to discuss the difference in speed area in Figure 1) and a S-shaped curve (green area in Figure 1), behavior between different types of participant during driving and descending ramp consisted of an oval curve. Both of ramps and the speed behavior characteristics of various types, and the connected Caiyuanba Yangtze River Bridge and Nantong relationship between individual characteristic and operating Road. speed were explored. Different types of participant were classiﬁed by the MDSI and temperament-type inventory (TTI) scales. Multidimensional driving style inventory and temperament-type inventory Methods Orit Taubman-Ben-Ari et al. designed and validated MDSI in The interchange ramp in Nanan District of Chongqing City, 2004, and it was one of the most widely used driving style scales China, was selected as the experimental road, and 30 in 20 years. On the basis of the MDSI, Sun et al. (2014) participants with different individual characteristics were adapted the scale and veriﬁed its reliability and validity and selected from the DiDi (a transportation network company). compiled MDSI-C which was more suitable for China’s According to usual driving mode, each participant should drive on two selected ramps until the end of mission. The Figure 1 Sujiaba interchange relationship between operating speed and driving behavior in Ascending ramp ramp was analyzed via collecting operating speed during driving. Before started the driving experiment, the detailed driving route should inform to participant. To restore the driving behavior of each participant during the driving process to the greatest extent, experimenters would not guide the driver during the experiment, and the participant would control the vehicle completely according to his usual driving style. Every participant needed to complete two designated questionnaires R90m to distinguish the driving style. To prevent the result of random answer from affecting the style judgment, each questionnaire should be completed under the guidance and supervision of experimenters. After completing the questionnaire, participant Descending ramp listened to the instruction of the experimenter to start the driving task. Each participant’s driving task is 2–4 rounds, and each round was required traveling the designated test ramp at Nantong Road Advanced driving assistance system Journal of Intelligent and Connected Vehicles Cunshu Pan, Jin Xu and Jinghou Fu Volume 4 · Number 1 · 2021 · 28–37 national conditions. This paper used MDSI-C to collect the Means and SDs of factors based on MDSI-C and TTI were listed driving style of participants. in Tables 1 and 2, which showed the difference between male and According to TTI, compiled by Zhang and Chen (1985) in female participants under different factors. The factors with higher 1985, participants were divided into four different temperament score were dissociative, angry and risky, and the driving behaviors types (choleric, sanguine, melancholy, phlegmatic participants) related to the three factors were anxious, angry and risky. andseveral mixedtemperaments. Liu et al. (2006) deﬁned the According to the analysis of driving videos, the dissociative factor characteristics of four temperament types in driving style. during driving was mainly caused by communication with other This experiment used above-mentioned scales to divide person, indicating that participant expected to “communicate 1 participants into several descriptions and explored speed trend and driving” model instead of focusing only on driving. The angry behavior characteristics of every description. The trend and factor was mainly caused by improper driving (e.g. jump a queue, characteristics of every description were deeply combined with frequent lane change, etc). of other vehicles and other vulnerable driving video to analyze and summarize behavior characteristics of trafﬁc participants (pedestrians, motorcycles, etc). sudden broken driving style. in, and participants mainly manifested by cursing, whistling and impatience. The main reasons for risk factor were high operating Participant speed during driving (in most cases, it is higher than the speed Taking into account the driving safety during the experiment, limit) and participant’soverconﬁdence. participants were required to have certain driving experience to The results of means and SDs of driving style measured by the avoid accidents and casualties during the experiment. According MDSI-C and self-reported information (age, driving mileage and to all the samples provided by DiDi, 30 drivers were selected as driving experience) were shown in Table 3.As a result,the mean participants, and they were asked to ﬁnish the MDSI-C and TTI. age of anxious participant was the highest, and the mean age of Based on the results of the scale, participants were classiﬁed risk participant was the lowest, and the mean driving mileage was according to demographic characteristics and driving styles. As a also the highest anxious participant, the lowest risk participant, result, the demographic characteristics were mainly based on and anxious participant were often characterized by short driving genders, and the driving style of the participant was mainly based mileage (<100,000 kilometers) and long driving mileage on the results of MDSI-C and TTI. (>500,000 kilometers). Similarly, anxious participant also showed polarization in mean driving experience. Result The means and SDs of four temperament types based on TTI were listed in Table 2. Choleric, sanguine, phlegmatic scores of Analysis of driving styles according to gender males were more discrete than those of females, and choleric, According to gender and personal information, the data selected by the MDSI-C and the TTI were listed by the relationship sanguine, melancholy mean scores of males were higher than that among different factors, gender and self-reported information, as of females. Based on Tables 1 2 and driving video, female was shown in Tables 1–3. more inclined to focus on driving (driving stability was higher), Table 1 MDSI-C Factors according to gender Factors Dissociative Anxious Angry High-velocity Risky Gender Mean SD Mean SD Mean SD Mean SD Mean SD Male(n = 21) 2.74 0.84 1.68 0.68 3.06 1.19 1.59 0.94 3.49 1.09 Female(n = 9) 2.73 0.91 1.67 0.77 2.58 0.72 1.26 0.4 3.07 0.74 Table 2 TTI Factors according to gender Factors Choleric Sanguine Melancholy Phlegmatic Gender Max Min Mean SD Max Min Mean SD Max Min Mean SD Max Min Mean SD Male(n = 21) 16 8 3.67 5.45 21 5 5.90 5.87 19 10 3 7.13 15 2 4.81 4.33 Female(n = 9) 6 4 0.89 3.38 12 5 4.44 4.95 12 11 0.78 7.52 11 3 6.33 2.40 Table 3 MDSI-C Driving style according to self-reported information Age (years) Driving mileage (10 km) Driving experience (years) Mean SD Mean SD Mean SD Risky 32.86 6.85 14.29 10.66 8.24 4.51 Anxious 40.50 7.70 32.50 23.05 11.75 7.22 Angry 34.60 5.54 20.00 5.48 12.40 4.45 30 Advanced driving assistance system Journal of Intelligent and Connected Vehicles Cunshu Pan, Jin Xu and Jinghou Fu Volume 4 · Number 1 · 2021 · 28–37 male was characterized by irritability, impatience and preference characteristics under different feature positions (Fu and Xu, 2019). for conversation, which was not conducive to safety. The author extracted the feature positions and distinguished them The 15th and 85th percentile speed were selected as according to the feature percentile positions (15th, 50th and 85th) feature percentile speed, and the feature percentile speed as the previous research, and the 85th percentile value was selected curves of the descending and ascending ramps were to analyze difference between genders. The results were showed in Figure 2.Male and female were indicated by a summarized in Table 4. solid black line and a black dotted line, respectively. The green area DPS, DEP, ASP and AEP represented decelerating start represented the operating speed male> female, and the orange area position, decelerating end position, accelerating start position represented female > male. The column represented the difference and accelerating end position, respectively. Owning to ascending between the speed difference according to gender and the minimum ramp that had two different accelerating and decelerating operating speed (green: male> female, orange: female> male). processes, the ramp was divided into two sections according to Figure 2(a) and (b) was the 15th operating speed for different the different accelerating and decelerating processes. The feature positions of the descending ramp were 380 m, genders in the descending and ascending ramps. In Figure 2(a), 520 m and 820 m, respectively. In Figure 2(a), the difference at operating speed of male was generally higher than that of female, 380 m was 5% (2 kph), 2% (1 kph) at 820 m and basically same and the differences were within 10% (generally at 2–3kph), and at 520 m. In Figure 2(c), the difference at 380 m was 2% (1 kph), thedifferences of theentranceand exit were 16% and 17%, thesameat520mand7%(5kph)at820m. respectively. In Figure 2(b), due to the good road alignment and The feature points of AR1 were 300, 480 and 740m, and that of line of sight at the entrance, operating speed of female was higher AR2 were 760, 920 and 1020m. In Figure 2(b), the difference at than male between 40 and 160m, and the differences were lower than 10% (1–4kph). At 180–960m, male ran faster than female, 300 m was 6% (3 kph), 5% (2kph) at 480m, 4% (2 kph) at 740m, and the differences were within 10% (generally at 1–4kph). 980– 3% (2 kph) at 760m, 4% (2 kph) at 920 m and 9% (4 kph) at 1020m. In Figure 2(d), the difference at 300m was basically 1100m, the section closed to the exit, female operating speed same, 5% (2 kph) at 480m, 1% (1 kph) at 740m, 2% (1 kph) at higher than male, and the differences were 14% (5kph). 760 m, 5% (2 kph) at 920m and 3% (2 kph) at 1020 m. Figure 2(c) and (d) were the 85th operating speed for different In summary, the difference between different genders was larger genders in the descending and ascending ramps. In Figure 2(c), (4–5 kph) at the AEP, while difference of other feature positions the difference was 28% (7kph) when entering the ramp, which was centralized in 1–2 kph, and the maximum was not more than 3 kph. thelargest differencein entireramp. Thedifferences between 160 and 740 m were maintained in a small region (generally at 1kph, the max was 2kph). Close to the exit (760–840m), the differences Analysis of speed behavior according to were gradually increased (5%–7%, 3–4kph). In Figure 2(d),the multidimensional driving style inventory-C and differences were relatively stable and had been maintained at 1– temperament-type inventory 3kph. At exit, the maximum difference reached 19% (7kph). Analysis of speed behavior according to multidimensional driving In the previous research, the research on the operating speed style inventory-C focused on the difference of operating speed of feature points According to the driving styles selected by MDSI-C, the (DSP, DEP, ASP and AEP) and analyzed the operating speed participants were classiﬁed according to the driving styles, and Figure 2 Speed behavior of participants according to gender Descending ramp Ascending ramp 10% 5% 40 0% –5% – 10% M> F F> M 35 Male Female 10 – 20% 30 –15% 0 100 200 300 400 500 600 700 800 0 200 400 600 800 1,000 Travel distance(m) Travel distance(m) (a) (b) Descending ramp Ascending ramp 10% 10% 0% 0% –10% M> F M> F F> M –10% 40 F> M –20% Male Male Female Female 30 –20% 20 –30% 0 200 400 600 800 1,000 0 100 200 300 400 500 600 700 800 Travel distance(m) Travel distance(m) (c) (d) Notes: a and b represented difference of 15th percentile speed, c and d represented difference of 85th percentile speed Speed(k/h) Speed(k/h) Diﬀerence(%) Diﬀerence(%) Speed(k/h) Speed(k/h) Diﬀerence(%) Diﬀerence(%) Advanced driving assistance system Journal of Intelligent and Connected Vehicles Cunshu Pan, Jin Xu and Jinghou Fu Volume 4 · Number 1 · 2021 · 28–37 Table 4 Feature points of 15th, 50th, 85th percentile distance (R)DSP (m) (Y)DEP/ASP (m) (B)AEP (m) AR 1 AR 2 DR AR 1 AR 2 DR AR 1 AR 2 DR (B)15th 0 680 180 360 820 420 600 940 600 (O)50th 60 720 200 420 860 480 720 980 680 (G)85th 300 760 380 480 920 520 740 1020 820 Note: AR1 – oval curve (ﬁrst curve) of ascending ramp, AR2 – S-shaped curve (second curve) of ascending ramp, DR – descending ramp the operating speed distribution ﬁgures of each style on the relatively stable, and the differences were also maintained at descending and ascending ramps were obtained, as shown in 23–27kph. From 740 m, the difference and operating speed Figure 3. The 15th, 50th and 85th percentile positions in Table 4 were increased. In Figure 3(f), the maximum speed of 0–160m were marked with different colors (Blue-15th, Orange-50th, showed a downward trend, and the minimum speed decreased Green-85th,Red-DSP,Yellow-DEP/ASP, Black-AEP),as ﬁrst and then increased, and the difference at the entrance shown in Figure 3 (e.g. Blue and Red-15th DSP, Orange and Black-50th AEP). Among Figure 3,the ﬁgures on left side were the descending ramp (a, c, e), and the ﬁgures on right side were Figure 3 Speed behavior of participants according to MDSI-C factors the ascending ramp (b, d, f). In Figure 3(a), the operating speed was increased from 0m to 180 m when entering the ramp, the difference at the entrance reached 30kph, and the differences were remained at 17–21kph. At 200–500m, the operating speed showed a downward trend, and the differences were maintained at 12-20kph. At 300–400m, the change of operating speed was relatively stable (difference: 16–20kph, the minimum speed difference and the maximum speed difference were not more than 2kph). After 520 m, the maximum operating speed was gradually increased and the minimum operating speed was gradually decreased, and the differences were increased (difference: 12–40kph). In Figure 3(b), the operating speed from 0 m to 200 m was decreased, and the differences were gradually decreasing (difference: 41-12kph). From 200 m to 920 m, the differences of most sections (200–500m and 740 920m) were maintained in a small interval (10–15kph), the maximum and minimum speeds were distributed in 52–64kph and 38–44kph, respectively. From 940m, the operating speed was decreased, and the difference started to increase gradually (difference: 19–33kph). In Figure 3(c), the speed from the entrance to 220m was gradually increased, the maximum speed of 300–440m was gradually decreased and the minimum speed was gradually increased. The maximum and minimum speeds of 480–640m were gradually increased, and the maximum and minimum speeds of 740–800m were gradually decreased. The differences were relatively small at 0–140m and 460–640m, and the differences at 180–320m and 660–780m were larger, while the maximum difference was lower than 29kph. In Figure 3(d),the maximum and minimum speeds of 0–100m and 120–280m had a same trend. After a period of decline (300–380m), the speed started to increase gradually from 480–740m, the minimum speed remained unchanged, the maximum speed was gradually increased, the maximum speed of 760–860m was decreased, and the minimum speed was showed without change. At 880–1100m, the speed was gradually reduced after a small increased. The speed differences were larger at 100–140m and 760–800m, and the difference from 520 to 760 m was gradually increased, and the remaining differences were around 20kph. Notes: a and b represented angry participants, c and d represented In Figure 3(e), the speed was gradually increased from 0 m to anxious participants, e and f represented risky participants 160 m, the maximum and minimum speeds of 180–640m were Desce Speed - Traveled distance Aesce Speed - Traveled distance a b 10 10 0 100 200 300 400 500 600 700 800 0 200 400 600 800 1000 Traveled distance(m) Traveled distance(m) Desce Speed - Traveled distance Aesce Speed - Traveled distance 70 70 c d 50 50 30 30 10 10 0 100 200 300 400 500 600 700 800 0 200 400 600 800 1000 Traveled distance(m) Traveled distance(m) Desce Speed - Traveled distance Aesce Speed - Traveled distance e f 10 20 0 100 200 300 400 500 600 700 800 0 200 400 600 800 1000 Traveled distance(m) Traveled distance(m) speed(kph) speed(kph) speed(kph) speed(kph) speed(kph) speed(kph) Advanced driving assistance system Journal of Intelligent and Connected Vehicles Cunshu Pan, Jin Xu and Jinghou Fu Volume 4 · Number 1 · 2021 · 28–37 reached 50kph. The maximum speed of 500–700m was In the ﬁrst curve of ascending ramp, the maximum speed of decreased, and the minimum speed was the ﬁrst to decrease DSP: Risky > Anxious > Angry (85th percentile: Anxious > and then increase. The maximum and minimum speeds of Risky > Angry), speed difference: Risky > Angry > Anxious. 740–860m were the same as the decreased trend. The The maximum speed of DEP: Risky > Anxious > Angry, speed maximum and minimum speeds of 880–960m were the same as difference: Risky > Angry > Anxious. The maximum speed of the increased trend, and then declined. AEP: Risky > Anxious > Angry, speed difference: Risky > In summary, in the descending ramp, there was a large speed Anxious > Angry. In the second curve of ascending ramp, the difference at the entrance between the angry and risky maximum speed of DSP: Risky > Angry > Anxious, speed participants, and the differences of risky participants were difference: Risky > Anxious > Angry. The maximum speed of greater. maximum speed when entering the ramp: Risky > DEP: Risky > Anxious > Angry, speed difference: Risky > Angry > Anxious, the operating speed after the accelerating Anxious> Angry. The maximum speed of AEP: Risky> Angry> process of entering the ramp: Risky = Angry > Anxious. There Anxious, speed difference: Risky> Angry = Anxious. was a signiﬁcant decelerating trend for angry participants, and According to the above analysis, the risky participants had the anxious and risky participants were more stable. When the maximum operating speed at the feature points than the driving out of the ramp, there was a speed-up phenomenon for other two types of participants, and the angry driver had the both angry and risky participants, and anxiety participants lowest. Similarly, the speed differences, that was the desired tended to move at a constant speed or slow down. speed interval, of risky participants were higher than the other In the ascending ramp, there was a large speed difference at two types of participants. the entrance of the angry and risky participants, and the difference of the risky participants were greater. The maximum Analysis of speed behavior according to temperament-type speed at the entrance: Risky > Angry > Anxious. The range of inventory speed ﬂuctuation during driving: Risky > Anxious > Angry. Based on the driving factors were selected by the TTI, the The risky and anxious participants had a signiﬁcant speed-up participants were divided into 7 categories, and the distribution phenomenon at 460–760m, and the angry participants only of the operating speed of each type of participants in the decreased with the other two types of participants after a small descending and ascending ramps were investigated, as shown increased in speed. When out of ramp, risky and angry in Figure 4. participants tended to speed up ﬁrst and then slow down, while In Figure 4(a), the operating speed was increased from 0 m Anxious participants tended to slow down until leave the ramp. (max: 28 kph, min: 11 kph) to 180 m (max: 61 kph, min: 46 kph) Based on Table 4 and Figure 3, the maximum and minimum when entering the ramp, 200–500m was slowly decreased, and operating speeds and the speed difference of participants at 520–820m was slowly increased. The speed differences between different feature points were summarized and listed in Table 5. 0 and 440 m were maintained at 14–19kph, the speed differences The speed differences at the feature points were compared between 460 and 700 m were maintained at 6-10kph, and the among different participants, and the difference of expected speed differences between 720 and 820 m were gradually speed among different types of participants at the same position increased to 22kph. and the relationship between expected speed and performance In Figure 4(b), as the maximum speed gradually decreased were discussed. and the minimum speed didn’t change, the difference of 0– According to Table 5, in descending ramp, the maximum 200m was continuously decreasing (44kph to 15 kph), and the speed at the DSP: Risky > Angry > Anxious, and the speed speed of 200 m-820m was the process of two ﬁrst rising and difference: Risky > Anxious > Angry. Maximum speed at the DEP: Risky > Anxious > Angry, and the speed difference: then falling, respectively. It had also grown from small to large Risky > Anxious > Angry (15th percentile: Anxious > Risky > then to small. When driving away from the ramp, operating Angry). Speed difference at AEP: Angry> Risky> Anxious. speed was also the trend of rising ﬁrst and then falling. Table 5 Speed of feature points according to MDSI-C driving styles Speed of DR (kph) Speed of AR 1 (kph) Speed of AR 2 (kph) Angry Anxious Risky Angry Anxious Risky Angry Anxious Risky Max Min Dis Max Min Dis Max Min Dis Max Min Dis Max Min Dis Max Min Dis Max Min Dis Max Min Dis Max Min Dis DSP 15th 64 46 17 59 37 22 65 37 27 74 33 41 65 36 29 84 33 51 63 47 15 67 40 27 71 43 28 50th 63 46 17 61 35 26 63 37 26 70 30 39 58 37 21 80 27 53 63 48 15 68 40 28 72 37 35 85th 59 39 20 58 37 21 62 37 25 56 41 15 61 35 26 60 39 21 60 47 12 69 31 38 72 35 37 DEP 15th 54 38 16 58 34 24 58 37 22 52 38 14 57 30 28 56 34 22 53 40 16 64 33 31 60 27 32 /ASP 50th 51 38 13 54 36 18 57 32 25 52 40 13 52 32 20 56 30 26 52 42 15 56 33 23 59 27 32 85th 52 38 14 56 40 16 59 33 26 53 42 12 55 32 23 58 33 25 54 40 14 56 34 21 63 30 33 AEP 15th 59 27 32 61 43 18 62 38 24 60 44 16 62 41 21 71 38 33 57 39 18 56 38 18 67 29 39 50th 57 31 26 62 35 27 59 40 19 63 48 15 48 40 28 72 37 35 41 38 18 57 39 18 68 26 43 85th 66 26 40 59 35 24 66 35 31 62 48 14 69 36 32 74 35 39 60 32 24 53 29 24 62 25 37 33 Advanced driving assistance system Journal of Intelligent and Connected Vehicles Cunshu Pan, Jin Xu and Jinghou Fu Volume 4 · Number 1 · 2021 · 28–37 In Figure 4(c), the speed of 0–160m was increased. After a Figure 4(i), (k) and (m) were the speed trends of the small decrease of 180 m-480m, the speed was gradually increased descending ramp of three mixed participants (sanguine- away from the ramp, the maximum and minimum speed melancholy, choleric-phlegmatic and melancholy-phlegmatic). 0–180m of mixed participants were on the same trend, and the differences were respectively at the entrance and exit, and the driving speeds of the two types of sanguine-melancholy and speed differences of the remaining positions were maintained at melancholy-phlegmatic participants were the same. The 20kph. In Figure 4(d), the maximum and minimum operating choleric-phlegmatic participants had large speed differences speeds of 0–180m were decreasing. After a stable operation for a between 360 m and 600 m. The speed differences between the certain distance, the maximum and minimum operating speeds sanguine-melancholy and melancholy-phlegmatic participants of 300–760m were the same as the ﬁrst rising and then were larger when driving away from the ramp. Operating speeds decreasing, and the trend of 780–1100m was the same as the of Sanguine-melancholy participants were declined as it leaved decrease-increase- decrease trend. The speed difference reached the ramp, and the other two type participants were on the rise. a maximum (42 kph) at entrance, the 0 m-140m speed Figure 4(j), (l) and (n) was the operating speed trends of three differences were maintained at a high level (>30kph), and the mixed participants (sanguine-melancholy, choleric-phlegmatic remaining positions were remained at 20–25kph. and melancholy–phlegmatic, respectively). The mixed participants In Figure 4(e), the running speed of 0 m-140m was entrance ran at a signiﬁcantly lower speed than the other four types increased, and the speed of 160–520m was maintained in a of participants. The choleric-phlegmatic and melancholy- small range (max: 58–62kph, min: 34–38kph). From 540 m, phlegmatic participants with large differences in speed were at the the speed difference showed a different trend, and the exit. The speed differences of sanguine-melancholy participants differences (> 30kph) increased signiﬁcantly. In Figure 4(f), were generally stable and did not have much ﬂuctuation. the speed differences at the entrance (0–80m) and the middle According to the data in Table 4 and Figure 4, the maximum section of the ramp to the driving ramp (560–1080m) were and minimum speeds and speed difference of different types at maintained at 30 kph. The operating speed trend was the same as different feature points were summarized in Table 6. that of the Sanguine participants, and there was a signiﬁcant According to Table 6, in the descending ramp, maximum drop-up-drop phenomenon. speed at DSP: phlegmatic > sanguine > melancholy > .. . > In Figure 4(g),the speed of 0–180m showed an upward trend, melancholy-phlegmatic, speed difference: melancholy > the maximum speed of 200 m-480m decreased, the minimum sanguine > phlegmatic > .. . > choleric-phlegmatic. Maximum speed was maintained at 40kph, the speed differences decreased speed at DEP: melancholy > sanguine > choleric-phlegmatic > gradually, the speed of 500–800m showed an upward trend, and ... > melancholy-phlegmatic, speed difference: melancholy > the speed difference began to increase gradually at 680m. In sanguine > choleric-phlegmatic > ... > melancholy-phlegmatic. Figure 4(h),0–140m, 480–600m, 1020–1060m speed differences Maximum speed at AEP: sanguine > choleric-phlegmatic > of three sections >25kph, speed differences of 180–320m and melancholy > ... > melancholy-phlegmatic, speed difference: 800–920m <15kph. The maximum and minimum operating melancholy > sanguine > phlegmatic > ... > melancholy- speeds had a same trend from 200m to 1060 m. phlegmatic. In the ﬁrst curve of ascending ramp, maximum speed at DSP: Figure 4 Speed behavior of participants according to TTI factors choleric> sanguine> phlegmatic> ... > melancholy- phlegmatic, speed difference: sanguine > choleric> melancholy > ... > melancholy-phlegmatic. Maximum speed at DEP: sanguine = melancholy = phlegmatic > ... > melancholy-phlegmatic, speed difference: melancholy > phlegmatic > sanguine > ... > melancholy-phlegmatic. Maximum speed at AEP: melancholy > sanguine > phlegmatic > ... > melancholy-phlegmatic, speed difference: melancholy> sanguine> phlegmatic> ... > sanguine- melancholy. In the second curve of ascending ramp, maximum speed at DSP: melancholy > sanguine > phlegmatic > .. . > melancholy-phlegmatic, speed difference: melancholy > sanguine> phlegmatic> .. . > sanguine-melancholy. In summary, the melancholy and sanguine participants were the two fastest types for speed, followed by the phlegmatic participants and the choleric-phlegmatic participants, and the last were the melancholy-phlegmatic participants. The biggest differences were also the melancholy participants and the sanguine participants, followed by the phlegmatic participants, the smallest being the Notes: a and b represented choleric participants, c and d r melancholy-phlegmatic participants. epresented sanguine participants, e and f represented melancholy participants, g and h represented phlegmatic participants, i and j Discussion represented sanguine-melancholy participants, k and l represented choleric-phlegmatic participants, m and n represented melancholy- This paper classiﬁed the participants according to the data phlegmatic participant selected by MDSI-C and TTI. Meanwhile, the speed behavior Desce Speed - Traveled distance Asce Speed - Traveled distance a b 10 20 0 100 200 300 400 500 600 700 800 0 200 400 600 800 1000 Traveled distance(m) Traveled distance(m) Desce Speed - Traveled distance Asce Speed - Traveled distance 70 80 c d 50 60 30 40 10 20 0 100 200 300 400 500 600 700 800 0 200 400 600 800 1000 Traveled distance(m) Traveled distance(m) Desce Speed - Traveled distance Asce Speed - Traveled distance 70 80 e f 50 60 30 40 10 20 0 100 200 300 400 500 600 700 800 0 200 400 600 800 1000 Traveled distance(m) Traveled distance(m) Desce Speed - Traveled distance Asce Speed - Traveled distance g 70 h 10 20 0 100 200 300 400 500 600 700 800 0 200 400 600 800 1000 Traveled distance(m) Traveled distance(m) Desce Speed - Traveled distance Asce Speed - Traveled distance i j 10 20 0 100 200 300 400 500 600 700 800 0 200 400 600 800 1000 Traveled distance(m) Traveled distance(m) Desce Speed - Traveled distance Asce Speed - Traveled distance k l 10 20 0 100 200 300 400 500 600 700 800 0 200 400 600 800 1000 Traveled distance(m) Traveled distance(m) Desce Speed - Traveled distance Asce Speed - Traveled distance m n 10 20 0 100 200 300 400 500 600 700 800 0 200 400 600 800 1000 Traveled distance(m) Traveled distance(m) speed(kph) speed(kph) speed(kph) speed(kph) speed(kph) speed(kph) speed(kph) speed(kph) speed(kph) speed(kph) speed(kph) speed(kph) speed(kph) speed(kph) Advanced driving assistance system Journal of Intelligent and Connected Vehicles Cunshu Pan, Jin Xu and Jinghou Fu Volume 4 · Number 1 · 2021 · 28–37 Table 6 Speed of feature points according to TTI factors Speed of DSP (kph) Speed of DEP/ASP (kph) Speed of AEP (kph) 15th 50th 85th 15th 50th 85th 15th 50th 85th DR Choleric Max 62 60 58 54 49 50 54 58 66 Min 46 43 39 38 42 43 46 50 44 Dis 16 18 19 16 7 7 8 8 22 Sanguine Max 62 62 59 58 54 56 61 62 66 Min 38 39 38 37 35 33 39 40 43 Dis 24 23 21 21 19 23 22 22 23 Melancholy Max 62 60 59 56 57 59 62 59 62 Min 37 35 37 34 36 38 27 31 26 Dis 25 25 22 22 21 21 34 28 36 Phlegmatic Max 65 63 62 58 51 53 59 58 66 Min 45 45 41 40 41 41 42 43 37 Dis 20 18 21 18 10 12 17 15 29 Sanguine-Melancholy Max 59 60 55 54 50 53 56 57 55 Min 45 49 44 44 38 39 46 45 35 Dis 14 11 11 10 12 14 10 11 20 Choleric-Phlegmatic Max 56 53 58 58 53 56 61 59 64 Min 47 48 43 42 33 37 46 47 41 Dis 9 5 15 16 20 19 16 12 24 Melancholy-Phlegmatic Max 53 51 48 46 42 47 51 50 59 Min 38 37 37 37 32 37 42 41 41 Dis 15 14 11 9 10 9 9 9 18 AR 1 Choleric Max 84 80 55 51 52 56 59 61 60 Min 39 40 40 37 35 39 41 43 44 Dis 45 40 15 13 17 17 19 18 16 Sanguine Max 76 68 61 57 52 55 62 68 69 Min 34 27 35 31 34 34 38 47 46 Dis 42 41 26 26 18 21 24 21 22 Melancholy Max 74 66 59 55 55 56 71 72 74 Min 33 30 42 30 32 32 41 40 36 Dis 41 36 17 26 23 24 30 33 37 Phlegmatic Max 73 70 59 55 56 58 65 66 66 Min 33 33 46 34 30 33 40 48 47 Dis 40 36 13 21 25 25 25 19 18 Sanguine-Melancholy Max 60 58 59 56 56 56 58 62 62 Min 44 48 48 44 42 40 50 53 50 Dis 16 10 10 12 14 16 8 9 12 Choleric-Phlegmatic Max 62 57 56 54 51 52 56 59 57 Min 43 41 46 42 40 41 39 44 45 Dis 19 16 10 12 11 11 17 15 12 Melancholy-Phlegmatic Max 65 57 44 41 39 43 47 51 52 Min 46 49 43 38 33 38 45 37 35 Dis 19 8 1 3 7 4 2 14 17 AR 2 Choleric Max 61 61 58 49 48 53 55 57 54 Min 42 43 43 40 42 43 44 39 29 Dis 19 18 16 9 6 10 11 18 24 Sanguine Max 67 68 69 64 57 62 64 63 61 Min 46 47 45 38 38 34 39 37 32 Dis 21 21 24 26 19 28 25 26 30 Melancholy Max 71 72 72 60 59 62 64 66 62 Min 40 40 31 33 33 34 35 30 25 Dis 31 33 42 27 27 27 29 35 37 Phlegmatic Max 65 66 64 55 52 54 57 61 60 Min 46 48 47 42 43 40 42 40 36 Dis 19 19 17 13 9 14 16 21 25 (continued) 35 Advanced driving assistance system Journal of Intelligent and Connected Vehicles Cunshu Pan, Jin Xu and Jinghou Fu Volume 4 · Number 1 · 2021 · 28–37 Table 6 Speed of DSP (kph) Speed of DEP/ASP (kph) Speed of AEP (kph) 15th 50th 85th 15th 50th 85th 15th 50th 85th Sanguine-Melancholy Max 60 62 62 54 50 53 54 57 55 Min 52 53 47 40 35 40 40 41 38 Dis 8 9 14 13 15 13 14 16 17 Choleric-Phlegmatic Max 57 59 58 55 55 63 67 68 56 Min 43 44 43 37 43 42 41 38 36 Dis 14 15 15 18 13 21 26 30 20 Melancholy-Phlegmatic Max 48 51 52 44 44 46 48 52 51 Min 43 37 35 27 27 30 29 26 25 Dis 6 14 17 171716 19 26 26 characteristics of different types of participants during driving difference among different participants speed and the different process and the difference between characteristics and characters in driving with the parameters such as typical descriptions of participants in operating speed were discussed. percentile speed and distance. The continuous operating speed In the past research, some conclusions of MDSI-based research during driving was selected by Mobile Eye, and the driving style studies pointed out that male reckless and angry driving style was selected by the MDSI-C and TTI. The main ﬁndings were was more obvious and prominent than female. Therefore, as follows: female was more anxious and cautious during driving Older participants were more likely to be Anxious, and (Taubman-Ben-Ari and Yehiel, 2012; Taubman-Ben-Ari and driving anxiety was more polarized in driving age and Skvirsky, 2016; Taubman-Ben-Ari et al., 2004). Based on the driving mileage. The Risky driving behavior was results of participants with different characteristics in China, characterized by low age, low mileage and low experience. The Angry driving behavior was characterized by middle- male was more irritated during driving and more stimulating by aged drivers with certain driving experience. high speed. On the contrary, female tended to be more stable in During driving, male was more motivated to drive, and driving. The risky driving behavior that pursued driving pleasure mainly existed in participants with young age, low female was more likely to pursue driving stability. driving mileage and low driving experience. Angry driving Moreover, male traits of driving (prone to anger, irritability, tended to have conversation, etc). were more behavior mainly existed in middle-aged participants with detrimental to driving safety than female. considerable driving experience. According to the analysis In descending and ascending ramps, except for the large speed results, female on some sections drove faster than the male, but differences at the entrance and exit of ramps, the differences at it was generally believed that in most cases, the speed of male other positions were small. In addition, the operating speed of was higher than that of female. Based on the operating speed of different types of male was slightly higher than female. participants and the characteristics of each type, it was found The differences between different genders at the ascending terminal were 4-5 kph, and the difference of that there were some differences between the type other feature points were mostly 1–2 kph. characteristics of participants and the speed behavior. In the The Risky participants had higher requirements for speed ascending ramp, anxious participants tended to slow down than the other two types, Anxious participants tended to from the ramp, and risky and angry participants tended to shift speed and had poor speed control. 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(2006), “Initial research on cut-in behavior based on naturalistic driving data”, Accident relationship between drivers’ temperament and travel speed”, Analysis & Prevention, Vol. 124, pp. 127-137. Journal of Beijing University of Technology, Vol. 32, pp. 27-32. Zhang, T. and Chen, H. (1985), “Report on the preparation of Musicant, O., Laufer, I. and Botzer, A. (2019), “Changes in temperament scale and its preliminary application”, Journal physiological indices and deceleration behaviour as functions of Shanxi University (Philosophy and Social Science Edition), of braking demands and driver’s physiological cluster”, Vol. 1 No. 4, pp. 73-77. Transportation Research Part F: Trafﬁc Psychology and Zhang, Q., Ge, Y., Qu, W., et al. (2019), “Effects of anger Behaviour, Vol. 62, pp. 406-415. and collision history on driver space preference”, Steinbakk, R.T., Ulleberg, P., Sagberg, F., et al. (2019), Transportation Research Part F: TrafﬁcPsychologyand “Speed preferences in work zones: the combined effect of Behaviour, Vol. 63, pp. 108-117. visible roadwork activity, personality traits, attitudes, risk perception and driving style”, Transportation Research Part F: Corresponding author Trafﬁc Psychology and Behaviour, Vol. 62, pp. 390-405. Jinghou Fu can be contacted at: 383817226@qq.com For instructions on how to order reprints of this article, please visit our website: www.emeraldgrouppublishing.com/licensing/reprints.htm Or contact us for further details: permissions@emeraldinsight.com

Journal

Journal of Intelligent and Connected Vehicles – Emerald Publishing

Published: Apr 26, 2021

Keywords: Driving style; Interchange; MDSI-C; Speed behavior; TTI

References

Dimensions of driving-related emotions and behaviors: an exploratory factor analysis of common self-report measures

Bernstein, J.P.K.; Calamia, M.
A graphical modeling method for individual driving behavior and its application in driving safety analysis using GPS data

Chen, C.; Zhao, X.; Zhang, Y.
A longitudinal investigation of the predictors of older drivers’ speeding behavior

Chevalier, A.; Coxon, K.; Rogers, K.
Traffic climate, driver behaviour, and accidents involvement in China

Chu, W.; Wu, C.; Atombo, C.
Investigating car users’ driving behaviour through speed analysis

Eboli, L.; Guido, G.; Mazzulla, G.
Analysis on the speed behavior of the helix bridge based on naturalistic driving patterns

Fu, J.; Xu, J.
Personality and professional drivers’ driving behavior

Linkov, V.; Zaoral, A.; Řezáč, P.
Initial research on relationship between drivers’ temperament and travel speed

Liu, J.; Tian, T.; Rong, J.
Changes in physiological indices and deceleration behaviour as functions of braking demands and driver's physiological cluster

Musicant, O.; Laufer, I.; Botzer, A.
Speed preferences in work zones: the combined effect of visible roadwork activity, personality traits, attitudes, risk perception and driving style

Steinbakk, R.T.; Ulleberg, P.; Sagberg, F.
Revision and preliminary application of multidimensional

Sun, L.; Yang, C.; Chang, R.
Driving styles and their associations with personality and motivation

Taubman-Ben-Ari, O.; Yehiel, D.
The multidimensional driving style inventory a decade later: review of the literature and re-evaluation of the scale

Taubman-Ben-Ari, O.; Skvirsky, V.
The multidimensional driving style inventory–scale construct and validation

Taubman-Ben-Ari, O.; Mikulincer, M.; Gillath, O.
Study on influencing factors selection of driver’s propensity

Wang, X.; Liu, Y.; Wang, J.
Analysis of cut-in behavior based on naturalistic driving data

Wang, X.; Yang, M.; Hurwitz, D.
Report on the preparation of temperament scale and its preliminary application

Zhang, T.; Chen, H.
Effects of anger and collision history on driver space preference

Zhang, Q.; Ge, Y.; Qu, W.

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Effect of gender and personality characteristics on the speed tendency based on advanced driving assistance system (ADAS) evaluation

Effect of gender and personality characteristics on the speed tendency based on advanced driving assistance system (ADAS) evaluation

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Effect of gender and personality characteristics on the speed tendency based on advanced driving assistance system (ADAS) evaluation

Effect of gender and personality characteristics on the speed tendency based on advanced driving assistance system (ADAS) evaluation

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