Sustainability analysis framework for on-demand public transit systems

被引:0
|
作者
Nael Alsaleh
Bilal Farooq
机构
[1] Toronto Metropolitan University,Laboratory of Innovations in Transportation (LiTrans), Department of Civil Engineering
来源
关键词
D O I
暂无
中图分类号
学科分类号
摘要
There is an increased interest from transit agencies to replace fixed-route transit services with on-demand public transits (ODT). However, it is still unclear when and where such a service is efficient and sustainable. To this end, we provide a comprehensive framework for assessing the sustainability of ODT systems from the perspective of overall efficiency, environmental footprint, and social equity and inclusion. The proposed framework is illustrated by applying it to the Town of Innisfil, Ontario, where an ODT system has been implemented since 2017. It can be concluded that when there is adequate supply and no surge pricing, crowdsourced ODTs are the most cost-effective transit system when the demand is below 3.37 riders/km2/day. With surge pricing applied to crowdsourced ODTs, hybrid systems become the most cost-effective transit solution when demand ranges between 1.18 and 3.37 riders/km2/day. The use of private vehicles is more environmentally sustainable than providing public transit service at all demand levels below 3.37 riders/km2/day. However, the electrification of the public transit fleet along with optimized charging strategies can reduce total yearly GHG emissions by more than 98%. Furthermore, transit systems have similar equity distributions for waiting and in-vehicle travel times.
引用
收藏
相关论文
共 50 条
  • [1] Sustainability analysis framework for on-demand public transit systems
    Alsaleh, Nael
    Farooq, Bilal
    [J]. SCIENTIFIC REPORTS, 2023, 13 (01)
  • [2] Bilevel Optimization for On-Demand Multimodal Transit Systems
    Basciftci, Beste
    Van Hentenryck, Pascal
    [J]. INTEGRATION OF CONSTRAINT PROGRAMMING, ARTIFICIAL INTELLIGENCE, AND OPERATIONS RESEARCH, CPAIOR 2020, 2020, 12296 : 52 - 68
  • [3] On-Demand Public Transit: A Markovian Continuous Approximation Model
    Silva, Daniel F.
    Vinel, Alexander
    Kirkici, Bekircan
    [J]. TRANSPORTATION SCIENCE, 2022, 56 (03) : 704 - 724
  • [4] Directionality-centric bus transit network segmentation for on-demand public transit
    Perera, Thilina
    Wijesundera, Deshya
    Wijerathna, Lahiru
    Srikanthan, Thambipillai
    [J]. IET INTELLIGENT TRANSPORT SYSTEMS, 2020, 14 (13) : 1871 - 1881
  • [5] Resiliency of on-demand multimodal transit systems during a pandemic
    Auad, Ramon
    Dalmeijer, Kevin
    Riley, Connor
    Santanam, Tejas
    Trasatti, Anthony
    Van Hentenryck, Pascal
    Zhang, Hanyu
    [J]. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2021, 133
  • [6] Ridesharing and fleet sizing for On-Demand Multimodal Transit Systems
    Auad-Perez, Ramon
    Van Hentenryck, Pascal
    [J]. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2022, 138
  • [7] Ridesharing and fleet sizing for On-Demand Multimodal Transit Systems
    Auad-Perez, Ramon
    Van Hentenryck, Pascal
    [J]. Transportation Research Part C: Emerging Technologies, 2022, 138
  • [8] A Framework for the Sustainability Assessment of (Micro)transit Systems
    Buenk, Reinhart
    Grobbelaar, Sara S.
    Meyer, Isabel
    [J]. SUSTAINABILITY, 2019, 11 (21)
  • [9] Transfer-Expanded Graphs for On-Demand Multimodal Transit Systems
    Dalmeijer, Kevin
    Van Hentenryck, Pascal
    [J]. INTEGRATION OF CONSTRAINT PROGRAMMING, ARTIFICIAL INTELLIGENCE, AND OPERATIONS RESEARCH, CPAIOR 2020, 2020, 12296 : 167 - 175
  • [10] The impact of congestion and dedicated lanes on on-demand multimodal transit systems
    Lu, Jason
    Trasatti, Anthony
    Guan, Hongzhao
    Dalmeijer, Kevin
    Van Hentenryck, Pascal
    [J]. TRAVEL BEHAVIOUR AND SOCIETY, 2024, 36