2021

Yixue Zhang (yixue.zhang@mail.utoronto.ca), University of Toronto
Steven Farber, University of Toronto, Scarborough
Mischa Young, University of California, Davis

New mobility technologies are rapidly changing the transportation landscape. The City of Belleville, Canada, has recently replaced its evening and nighttime bus routes by an on-demand transit service. Preliminary reports have found a 300% increase in nighttime bus ridership since the implementation of this service, yet to fully understand the social return of thon-demand services, we must also quantify whether these new services have enabled increased levels of activity participation. We further believe that it is important to consider whether riders are satisfied with this service, and whether satisfaction levels have an impact on activity participation. As such, this study explores the determinants of activity participation of on-demand transit users using a custom ridership survey instrument. We collect information on travel experience, satisfaction, and perceived changes in activity participation due to on-demand transit services. We then perform a factor analysis and estimate structural equation models to identify the relationships between aspects of the on-demand transit service and activity participation. Our results show that reliability and service quality are the most important aspects of on-demand services in terms of positively influencing activity participation, and that shorter wait times are most likely to lead to an incrase in activity participation. Our findings further suggest that Belleville can eliminate barriers to nighttime activity participation by providing reliable and affordable on-demand transit service, and that on-demand services are an effective solution to provide transit services in low demand areas or during low demand periods of the day.

Giovanni Calabrò, University of Catania
Andrea Araldo, Institut Polytechnique de Paris
Simon Oh, Singapore-MIT Alliance for Research and Technology
Ravi Seshadri, Singapore-MIT Alliance for Research and Technology
Giuseppe Inturri, Universita degli Studi di Catania
Moshe Ben-Akiva, Massachusetts Institute of Technology (MIT)

In cities around the world, transit is currently provided with fixed route transportation only, whence the inherent limited Quality of Service (QoS) for travelers in sub-urban areas and during off-peak. On the other hand, it has been shown that completely replacing fixed-route with demand-responsive transit fails to serve the high transportation demand during peak hours. Therefore, it is still unclear how we can maximize the potential of demand-responsive transit by satisfying the complicated demand pattern varying with time and space. In this paper we propose Flexible Transit, a transit system design which gets the best from fixed-route and demand-responsive transit, depending on the demand observed in each sub-region of the urban conurbation and time-of-day. The goal is to provide high transportation capacity while guaranteeing high QoS, two objectives that are instead conflicting with classic fixed-schedule transportation. To this end, we first resort to microsimulation to show the limits of using either only fixed-route buses or only demand-responsive buses. This motivates the need of alternating between them instead, which we do in Flexible Transit. We then resort to Continuous Approximation to find the optimal design of flexible transit. We show that the flexible transit can significantly improve the user costs, in particular in suburban areas, while also reducing the overall cost of user and operator. We believe our findings suggest important policy insights in designing and planning of future transit systems, to take full advantage of demand-adaptive transportation modes.

Hossain Mohiuddin, University of California, Davis

A transit trip consists of “first-mile” trips from an origin to a transit station and “last-mile” trips from a transit station to a destination. Recently, some transit agencies have produced distinct planning documents on first and last-mile connection strategies. As this is a relatively new practice by transit agencies, a review of these plans can inform other transit agencies and assist them in preparing their own. Four first and last-mile strategies plans were selected for review:  three prepared by transit agencies namely, Los Angeles County Metropolitan Transportation Authority (LA Metro), Riverside Transit Agency (RTA), and Denver Regional Transit District (RTD), and one from the City of Richmond. We developed a framework to examine these plans holistically. We found that both RTA and RTD developed different station typologies based on locational characteristics to inform transit connectivity improvement strategies. LA Metro and RTA produced detailed design elements for transit stations. The plans heavily focused on multimodality and emphasized incorporating emerging mobility services as first and last mile modes. All the plans gave attention to the development of pedestrian and bicycle infrastructures and connected them with transit stations. The plans sourced innovative funding options for implementing projects. As these plans are still relatively new, it will take time to measure the impact on ridership and overall transit experience.

Todd Hansen, Texas A&M Transportation Institute
Michael Walk, Texas A&M Transportation Institute
Shuman Tan, Texas A&M Transportation Institute
Ahmadreza Mahmoudzadeh, Texas A&M Transportation Institute

This research project developed an evaluation framework and service standards for microtransit service at a metropolitan transportation authority. Microtransit provides an elevated level of service from traditional demand response, enabling more spontaneous travel and better connectivity to the fixed-route system, meaning microtransit service performance standards fit in between traditional demand response and fixed route. Microtransit neighborhood zones also serve distinct purposes depending on the operational and financial characteristics of a given zone. The research team gathered information from agency staff on current goals and objectives for microtransit, conducted an industry scan, and looked at measurable metrics from available data sources. The project developed a typology of microtransit service zones, then created the evaluation framework using the performance measures applicable to agency goals. The research team conducted tests of the framework at the three-month and six-month periods of service to refine the measures as needed. Minimum standards were set for each performance measure based on system performance and agency goals for innovative service. The framework uses minimum service standards that are applicable to given types of neighborhood zones and distinct from standards for fixed-route or other demand responsive service. The evaluation framework can be integrated into transit agency service standards to measure the success of microtransit neighborhood zones and planning future service.

PRIYAMVADA NATARAJAN (natarajan.78@osu.edu), Ohio State University
Cathy Xia, Ohio State University
Andre Carrel, Ohio State University

While there currently are many excellent models for transit services in the literature, there is little consideration of the fact that passengers are independent actors who are influenced by supply-side variables. This oversight can lead to low utilization and other poor performance metrics post implementation. In this paper, we attempt to bridge that gap with a framework for modeling this behavior by presenting an example of a Last Mile Transit System (LMTS). We examine the interdependence of fleet size (a decision variable) and passenger demand (an input parameter) by way of three models: a queuing model, a mode choice model, and a fixed-point map. The LMTS is analysed as a queuing model and upper bounds on mean wait time are established. This information is passed to a mode choice model where longer wait times result in lower demand. This interplay between the queuing model and the mode choice model is captured with a fixed-point map, providing additional constraints to the overall optimization problem.