Access the full text.
Sign up today, get DeepDyve free for 14 days.
R Huang, Z-R Peng (2002)
An object-oriented GIS data model for transit trip planning systemsTransp Res Rec J Transp Res Board, 1804
C Cherry, N Hickman, A Garg (2006)
Design of a map-based transit itinerary plannerJ Public Transp, 9
R Huang, Z-R Peng (2002)
Schedule-based path-finding algorithms for transit trip-planning systemsTransp Res Rec J Transp Res Board, 1783
B Ferris, K Watkins, A Borning (2010)
Location-aware tools for improving public transit usabilityIEEE Pervasive Comput, 9
UG Acer, P Giaccone, D Hay, G Neglia, S Tarapiah (2012)
Timely data delivery in a realistic bus networkIEEE Trans Veh Technol, 61
SC Wong, CO Tong (1998)
Estimation of time-dependent origin–destination matrices for transit networksTransp Res Part B Methodol, 32
W Hoffman, R Pavley (1959)
A method for the solution of the nth best path problemJ ACM, 6
F Li, T Wu, A Badiru, M Hu, S Soni (2013)
A single-loop deterministic method for reliability-based design optimizationEng Optim, 45
JY Yen (1971)
Finding the K shortest loopless paths in a networkManag Sci, 17
A Chen, Z Ji (2005)
Path finding under uncertaintyJ Adv Transp, 39
C Brakewood, S Barbeau, K Watkins (2014)
An experiment evaluating the impacts of real-time transit information on bus riders in Tampa, FloridaTransp Res Part A Policy Pract, 69
L Liu, J Yang, H Mu, X Li, F Wu (2014)
Exact algorithms for multi-criteria multi-modal shortest path with transfer delaying and arriving time-window in urban transit networkAppl Math Model, 38
M Friedrich, I Hofsaess, S Wekeck (2001)
Timetable-based transit assignment using branch and bound techniquesTransp Res Rec J Transp Res Board, 1752
L Fu, LR Rilett (1998)
Expected shortest paths in dynamic and stochastic traffic networksTransp Res Part B Methodol, 32
Q Fu, R Liu, S Hess (2012)
A review on transit assignment modelling approaches to congested networks: a new perspectiveProc Soc Behav Sci, 54
J Hershberger, M Maxel, S Sur (2007)
Finding the k shortest simple paths: a new algorithm and its implementationACM Trans Algorithms (TALG), 3
D Sun, Z-R Peng, X Shan, W Chen, X Zeng (2011)
Development of web-based transit trip-planning system based on service-oriented architectureTransp Res Rec J Transp Res Board, 2217
S Yang, A Malik, Y Wu (2014)
Travel time reliability using Hasofer Lind-Rackwitz Fiessler algorithm and kernel density estimationTransp Res Rec J Transp Res Board, 2442
C Brakewood, GS Macfarlane, KE Watkins (2015)
The impact of real-time information on bus ridership in New York CityTransp Res Part C Emerg Technol, 53
BY Chen, WHK Lam, A Sumalee, Q Li, H Shao, Z Fang (2013)
Finding reliable shortest paths in road networks under uncertaintyNetw Spat Econ, 13
S Yang, Y Wu (2016)
Moving ahead to mixture models for fitting freeway travel time distributions and measuring travel time reliabilityTransp Res Rec J Transp Res Board
S Chandra, AK Bharti (2013)
Speed distribution curves for pedestrians during walking and crossingProc Soc Behav Sci, 104
Z Ji, YS Kim, A Chen (2011)
Multi-objective α-reliable path finding in stochastic networks with correlated link costs: a simulation-based multi-objective genetic algorithm approach (SMOGA)Expert Syst Appl, 38
David L Olson, D Wu (2010)
Enterprise risk management models
A Nuzzolo, U Crisalli (2004)
The schedule-based approach in dynamic transit modelling: a general overviewOper Res Comput Sci Interfaces Ser, 28
Transit, although an important public transportation mode, is not thoroughly utilized in the United States. To encourage the public to take transit, agencies have developed systems and tools that assist travelers in accessing and using information. Transit data modeling and trip planner system architecture developments have helped advance these systems, and the recent emergence of transit trip planning algorithms promises further enhancement. Conventional transit trip planning algorithms are usually developed based on graph theory. In order to utilize these algorithms, certain assumptions must be made to support these algorithms (e.g. buses always run on time). However, these assumptions may not be realistic. To overcome these limitations, our study develops an innovative transit trip planning model using chance constrained programming. Unlike previous studies, which only minimized passenger-experienced travel time, our study also considers transit service reliability. Additionally, in-vehicle travel time, transfer time, and walking time are all included as elements of passenger-experienced travel time. Our transit trip planning model avoids the assumptions of previous studies by incorporating transit service reliability and is capable of finding reliable transit paths with minimized passenger-experienced travel time. The algorithm can also suggest a buffer time before departure to ensure on-time arrivals at a given confidence level. General Transit Feed Specification data, collected around Tucson, Arizona, was used to model the transit network using a “node-link” scheme and estimate link-level travel time and travel time reliability. Three groups of experiments were developed to test the performance of the proposed model. The experiment results suggested that the optimal anticipated travel time increased with increasing on-time arrival confidence level and walking was preferred over direct bus transfers that involved out of direction travel. The proposed model can also include additional travel modes and can easily be extended to include intercity trip planning.
Public Transport – Springer Journals
Published: Nov 4, 2016
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.