What Drives Success in Public Bikeshare Programs?
Funding Entity: UC ITS from the State of California through the Public Transportation Account and the Road Repair and Accountability Act of 2017 (Senate Bill 1)
Many cities across the US and the world have implemented bikeshare programs. Some programs have been successful and others less so, but despite the experience accumulated, we could not find practical guidelines outlining the conditions for successful bikeshare programs based on the characteristics of the target population and community. To unlock the potential for growth within communities that already have bikeshare systems and to foster the adoption of bikeshare systems in communities currently without bikeshare, the purpose of this research project is to assess the ingredients for and obstacles to successful bikeshare systems. In our analysis of bikeshare programs (not limited to California or the US), we will account for community characteristics, land-use, bicycle infrastructure, bikeshare system design, other transportation infrastructure, existing modal shares, weather factors, and relevant regulations. To meet this objective, we will perform a meta-analysis of relevant bikeshare studies. However, because most of the studies in the literature focus on bikeshare systems in large cities, to supplement our meta-analysis, we plan to also analyze data from bikeshare systems in small and medium size cities after interviewing their bikeshare system managers.
Addressing Unprecedented Community-Centered Transportation Infrastructure Needs and Policies for the Mobility Revolution
Funding Entity: NSF Smart and Connected Communities Planning Grant
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1952241&HistoricalAwards=false
Project Description: This Smart and Connected Communities Planning Grant (SCC-PG) will support the development of next-generation transportation planning models that incorporate mobility service providers (e.g. Uber and Lyft) and connected automated vehicle technology.
The overarching goal of this research is to improve sustainability, livability, and mobility throughout entire metropolitan regions via supporting transport planning, specifically infrastructure upgrades and transport policies, to capture the potential benefits of automated vehicles and mobility services. To meet this goal, the project will prototype multi-resolution agent-based regional transport system modeling tools that are sensitive to transport policies (e.g. congestion pricing) and infrastructure investments (e.g. protected left-turns, lane striping) and also explicitly capture the behavior and system impacts of mobility service providers and connected automated vehicle technology. The project will also involve prototyping optimization models to support proactive infrastructure investments that maximize the community benefits of new mobility/vehicle technologies such as connected and automated vehicles, rather than reactively upgrading infrastructure. Additionally, the planning phase of the project involves: working closely with community partners to identify their specific modeling needs; forming the best team of interdisciplinary researchers; and further refining the methodological approach. The research team will work closely with its main community stakeholder, the San Diego Association of Governments, other regional planning agencies, and cities who will implement infrastructure upgrades.
Development of Modeling Framework to Assess Impacts of Congestion Pricing Policies in Southern California with Consideration of New Mobility Options
Funding Entity: UC ITS from the State of California through the Public Transportation Account and the Road Repair and Accountability Act of 2017 (Senate Bill 1)
https://www.ucits.org/research-project/2020-51/
Project Description: This research project aims to develop an integrated transportation supply-and-demand modeling framework that captures new mobility options and dynamic congestion pricing policies. The model will capture relevant aspects of traveler behavior (i.e. activity choices, activity location choices, mode choices, willingness-to-share, and route choices) and transportation network performance by modeling new mobility fleet operators (i.e. decision-makers at companies like Uber and Lyft), whose profit maximizing motives are likely to make them even more sensitive to pricing changes than individual travelers. After operationalizing the modeling framework, the research team will apply it to Southern California to assess the potential impacts of dynamic congestion pricing policies on the region’s transportation system.
Non-myopic path-finding for shared-ride vehicles: A bi-criterion best-path approach considering travel time and proximity to demand
Funding Entity: Caltrans via Pacific Southwest Region University Transportation Center
Project Description: Shared-ride mobility-on-demand (MOD) services offered by transit agencies (e.g. flexible, demand-adaptive, and demand-responsive transit) and private companies (e.g. Uber Pool, Lyft Line, microtransit) have the potential to provide high-quality, convenient, and affordable on-demand mobility service to individual travelers, while simultaneously obtaining the societal benefits of decreased vehicle miles traveled, congestion, and vehicle emissions through increased vehicle occupancies. However, for shared-ride MOD services to capture these societal and individual mobility benefits, they need to be operated efficiently. Hence, this research project focuses on the efficient operation of shared-ride MOD services. Although several research studies, including work by the PI, address shared-ride MOD operational problems, this research project addresses a severely overlooked shared-ride MOD operational subproblem, namely, the assignment of individual shared-ride vehicles to network paths as they move between user pickup and drop-off locations.
In practice, and in the academic literature, fleet controllers assign shared-ride vehicles (like non-shared-ride vehicles) to the shortest network path, in terms of travel time, between pickup and drop-off locations in their schedules. While this strategy/policy is intuitive, it is also myopic given the nature of shared-ride on-demand service and the (high) likelihood new users will request service as vehicles traverse network paths between pickup and drop-off locations. A non-myopic approach would anticipate the possibility of new requests and consider the proximity of network paths to future user requests (i.e. demand) when assigning shared-ride vehicles to network paths.
Modeling the Supply and Demand Effects of Transportation Network Companies with an Autonomous Fleet
Funding Entity: Subcontract from Argonne National Laboratory
This project involved integrating Dr. Hyland’s optimization framework for assigning (automated) vehicles to TNC traveler requests into Argonne’s POLARIS software system. POLARIS is an integrated activity-based travel demand and dynamic transportation network simulation platform developed at Argonne National Laboratory. Prior to the contract with Dr. Hyland, the TNC fleet simulation module in POLARIS employed simplistic strategies to assign vehicles to TNC requests. After Dr. Hyland’s optimization-based vehicle-traveler assignment approach was integrated into POLARIS, there was a significant increase in the operational efficiency of the vehicle fleet (more requests served, decreases in wait times, fewer deadhead miles, etc.) without a significant increase in computational time. The optimization-based TNC fleet module was tested on medium (Bloomington IN) and large (Chicago IL) transportation networks.
Assessment of the Employment Accessibility Benefits of Shared Autonomous Mobility Services
Funding Entity: UC ITS from the State of California through the Public Transportation Account and the Road Repair and Accountability Act of 2017 (Senate Bill 1)
https://www.ucits.org/research-project/2019-32/
Project Description: This study quantifies the potential impact of SAMS modes on access to employment opportunities in the Southern California region. The results indicate: (i) the accessibility benefit differences across latent classes are modest but young workers and low-income workers do see higher benefits than high- and middle-income workers; (ii) there are substantial spatial differences in accessibility benefits with workers living in lower density areas benefiting more than workers living in high-density areas; (iii) nearly all the accessibility benefits come from the SAMS-only mode as opposed to the SAMS+Transit mode (i.e., SAMS used in coordination with transit to complete a trip); and (iv) the SAMS cost per mile assumption significantly impacts the magnitude of the overall employment accessibility benefits.