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Ensemble-Based Methodology to Identify Optimal Personal Mobility Service Areas Using Public Data

Ensemble-Based Methodology to Identify Optimal Personal Mobility Service Areas Using Public Data Public transportation networks are well established in main cities, but there are some inconveniences in using public transportation in some cities. Public transportation is less accessible and walking distance of getting to public transportation is too long in some cities. Compared to other cities, Seoul has a higher satisfaction rate with public transportation. There are many cases, however, where short-distance taxis are used because walking to destinations after using public transportation is inconvenient; instead, Personal mobility (PM) devices can be used for these short-distances trip. This study aims to find the optimal PM service area using GIS(Geographic Information System)-based public transportation big data analyses. Variables were generated by collecting socio-economic factors, public transportation data, and geographic data and Extreme gradient boosting and Random forest, which are representative ensemble methods, were used for evaluation. We divided Seoul into a hexagonal grid and developed the optimal PM location service model by creating hexagonal cell data units and analyzing the areas with the models. We found that residential complexes, parks, and near subway stations (all areas with high foot traffic) are best suited for optimal placement. We also determined deployment should be in lower sloped areas. We expect this work to help determine public transportation stop and shared mobility station locations as well as contribute to public transportation demand surveys and accessibility analyses. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png KSCE Journal of Civil Engineering Springer Journals

Ensemble-Based Methodology to Identify Optimal Personal Mobility Service Areas Using Public Data

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References (37)

Publisher
Springer Journals
Copyright
Copyright © Korean Society of Civil Engineers 2022
ISSN
1226-7988
eISSN
1976-3808
DOI
10.1007/s12205-022-1356-y
Publisher site
See Article on Publisher Site

Abstract

Public transportation networks are well established in main cities, but there are some inconveniences in using public transportation in some cities. Public transportation is less accessible and walking distance of getting to public transportation is too long in some cities. Compared to other cities, Seoul has a higher satisfaction rate with public transportation. There are many cases, however, where short-distance taxis are used because walking to destinations after using public transportation is inconvenient; instead, Personal mobility (PM) devices can be used for these short-distances trip. This study aims to find the optimal PM service area using GIS(Geographic Information System)-based public transportation big data analyses. Variables were generated by collecting socio-economic factors, public transportation data, and geographic data and Extreme gradient boosting and Random forest, which are representative ensemble methods, were used for evaluation. We divided Seoul into a hexagonal grid and developed the optimal PM location service model by creating hexagonal cell data units and analyzing the areas with the models. We found that residential complexes, parks, and near subway stations (all areas with high foot traffic) are best suited for optimal placement. We also determined deployment should be in lower sloped areas. We expect this work to help determine public transportation stop and shared mobility station locations as well as contribute to public transportation demand surveys and accessibility analyses.

Journal

KSCE Journal of Civil EngineeringSpringer Journals

Published: Jul 1, 2022

Keywords: Big data; Ensemble model; Random forest; Extreme gradient boosting; Hexagonal grid; Personal mobility; Public transportation

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