Investigating Potential Transit Ridership by Fusing Smartcard and Global System for Mobile Communications Data

Author:

de Regt Karin1,Cats Oded2,Van Oort Niels2,van Lint Hans2

Affiliation:

1. Transport, Infrastructure, and Logistics, Faculty of Civil Engineering and Geosciences, Delft University of Technology, P.O. Box 5048, 2600 GA Delft, Netherlands

2. Transport and Planning, Faculty of Civil Engineering and Geosciences, Delft University of Technology, P.O. Box 5048, 2600 GA Delft, Netherlands

Abstract

The public transport industry faces challenges in catering to the variety of mobility patterns and corresponding needs and preferences of passengers. Travel habit surveys provide information on overall travel demand as well as its spatial variation. However, that information often does not include information on temporal variations. By applying data fusion to smartcard and Global System for Mobile Communications (GSM) data, researchers were able to examine spatial and temporal patterns of public transport usage versus overall travel demand. The analysis was performed by contrasting different spatial and temporal levels of smartcard and GSM data. The methodology was applied to a case study in Rotterdam, Netherlands, to analyze whether the current service span is adequate. The results suggested that there is potential demand for extending public transport service on both ends. In the early mornings, right before transit operations are resumed, an hourly increase in visitor occupancy of 33% to 88% was observed in several zones, showing potential demand for additional public transport services. The proposed data fusion method was shown to be valuable in supporting tactical transit planning and decision making and can easily be applied to other origin-destination transport data.

Publisher

SAGE Publications

Subject

Mechanical Engineering,Civil and Structural Engineering

Cited by 12 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Effectiveness of trip planner data in predicting short-term bus ridership;Transportation Research Part C: Emerging Technologies;2022-09

2. A congested schedule-based dynamic transit passenger flow estimator using stop count data;Transportmetrica B: Transport Dynamics;2022-04-08

3. Zone prioritisation for transit improvement using potential demand estimated from smartcard data;Transportmetrica A: Transport Science;2022-02-15

4. A Data Driven Approach to Match Demand and Supply for Public Transport Planning;IEEE Transactions on Intelligent Transportation Systems;2021-10

5. Transit OD matrix estimation using smartcard data: Recent developments and future research challenges;Transportation Research Part C: Emerging Technologies;2021-04

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3