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Synthetic Modeling Analytics of Bike-Transit Integration Over Auto-Dependent Infrastructural System

Zuo, Ting
http://orcid.org/0000-0003-2880-4273
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613751016160793

Abstract Details

2020, PhD, University of Cincinnati, Engineering and Applied Science: Civil Engineering.
The bike-transit integration is aimed to foster bikes as a feeder mode to mitigate the first-and-last mile (F&LM) problem while expanding the reach of transit, improving transit accessibility to activities, increasing transit use, and advocating transportation equity. However, promoting bike-transit integration has been a challenge due to the lack of continuous and safe bike routes to transit systems. Today’s highly auto-dependent infrastructures have caused cyclist-hostile environments and limited the use of (personally-owned and shared) bikes as an F&LM connector. To address the challenge, it is imperative to develop a quantity-based method to streamline the bike-transit integration analytics into the innovated transportation planning process. In the analytics, modeling capabilities of testing and measuring the effectiveness of bike-transit integration strategies against planning objectives are also needed. Existing bike network design (BND) methods often neglect intermodal connections including cyclists’ access to transit under as-built environmental constraints. Related social equity impacts have been rarely assessed in a meaningful and disaggregated manner and are often hard to be translated into transportation planning objectives. Those problems altogether make it difficult to tackle existing BND methods in compliance with the bike-transit integration. Accordingly, this dissertation research is inspired to develop a synthetic analytics framework and modeling methods for bike-transit integration over existing auto-dependent transportation networks, alongside a strategy-testing tool for developing BND-based solutions. In the framework, a transit demand model is firstly developed using the geographically weighted lasso (GWL) to reveal the community-varying relationship between F&LM bike connection and transit use. On top of the GWL method, an innovative two-stage model is developed for the transit-specific F&LM bike network planning. At the first stage, a geo-weighted multi-criteria investment area prioritization (Geo-MIAP) is set to evaluate and rank communities for prioritizing F&LM bike investments while assessing trade-offs of contradictory objectives. Local estimates of GWL are incorporated into the Geo-MIAP to assign non-uniform importance to different locations, thus pinpointing communities where enhanced bike-transit connections are most needed. At the second stage, to facilitate designing and deploying continuous bikeways between identified high-priority communities and transit, a Transit-specific Bike Network Design (TsBND) model is formulated. A bi-level multi-objective optimization is structured to solve the TsBND problem and develop the most desired bike network layouts by reallocating existing right-of-way. To prioritize the bike-transit demand for transportation disadvantaged groups that are characterized by racial-minority and low-income, improving transit accessibility for those population is explicitly interpreted as planning objectives of the Geo-MIAP and TsBND models. Besides engineering design of physical and infrastructural connection, availability of quality travel information of bike and transit modes is also a critical part of bike-transit integration. Real-time transit and bike information, once available, can be integrated and utilized to facilitate seamless transfers between the two modes. In support of that, F&LM trip planning algorithms are designed to search for the shortest low-stress bike routes to transit, estimate transit vehicle arrival times, and acquire dynamic distributions of bike sharing services. With such integrated multimodal travel information, it can efficiently accelerate full-scale bike-transit integration over auto-dependent transportation systems.
Heng Wei, Ph.D. (Committee Chair)
Na Chen, Ph.D. (Committee Member)
Hazem Elzarka, Ph.D. (Committee Member)
Jiaqi Ma, Ph.D. (Committee Member)
199 p.
Transportation
Bike-transit integration; First-and-last mile access; Bike network design; Transit accessibility; Transit equity

Recommended Citations

Citations

  • Zuo, T. (2020). Synthetic Modeling Analytics of Bike-Transit Integration Over Auto-Dependent Infrastructural System [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613751016160793

    APA Style (7th edition)

  • Zuo, Ting. Synthetic Modeling Analytics of Bike-Transit Integration Over Auto-Dependent Infrastructural System. 2020. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613751016160793.

    MLA Style (8th edition)

  • Zuo, Ting. "Synthetic Modeling Analytics of Bike-Transit Integration Over Auto-Dependent Infrastructural System." Doctoral dissertation, University of Cincinnati, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613751016160793

    Chicago Manual of Style (17th edition)

ucin1613751016160793
315
© 2020, some rights reserved.Creative Commons License Synthetic Modeling Analytics of Bike-Transit Integration Over Auto-Dependent Infrastructural System by Ting Zuo is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by University of Cincinnati and OhioLINK.