Shared automated vehicles (AVs) hold great promise for improving transportation access in urban centers (due to improved operational efficiency) while drastically reducing transportation-related energy consumption and air pollution. This project develops a replicable, open, deployable model that can (1) identify areas with high concentrations of low-income or minority populations, or both within a region and (2) conduct a scenario analysis to help public agencies assess how regulatory requirements for shared AV services (e.g., number of handicap accessible vehicles and fleet size) could impact energy use, travel demand (i.e., vehicle miles traveled), average wait times across different population groups, and AV operator profit.
Low-income communities and communities of color have experienced inferior access to ride sharing services when compared to their white and more affluent counterparts (Ge et al., 2016). Specifically, these communities experience longer wait times and more frequent ride cancellations. In addition to equity issues, ride-sharing services have also exacerbated congestion and emissions in many urban areas (Hawkins, 2019). While current ride sharing services have issues around equity and environmental impacts, autonomous vehicles offer a way to improve ridesharing programs. Shared automated vehicles (AVs) have the potential to improve transportation access (due to improved operational efficiency) while drastically reducing transportation-related energy consumption and air pollution. However, shared AVs, as with any emerging technology, could also exacerbate existing social inequalities if government agencies do not implement the appropriate policies to encourage shared AV providers to deploy systems that consider these traditionally underserved populations. The aim of this project is to develop a replicable, open, deployable model that can:
• Identify the equity emphasis areas in a metropolitan area and compile a sociodemographic profile of these census block groups
• Evaluate how regulatory requirements for shared AV could affect access, congestion, and environmental sustainability
• Conduct proof-of-concept simulations to help cities make informed policy decisions regarding shared AV technology deployment
Our hypothesis is that without the appropriate policies and regulations in place, minority populations will continue to be underserved by ridesharing systems even as we transition to automated transportation. Currently, there are not many tools to help state and local agencies assess how regulatory requirements for shared AV services could affect how different population groups are served by the transportation system.
In the first part of this project, we will utilize spatial analysis tools and census tract data to identify areas in the study region with high concentrations of minority and low-income populations. Because these populations have historically been underserved by our transportation system, these census block groups will be labeled as our priority areas or equity emphasis areas.
The second part of the project focuses on developing an agent-based model to simulate evening peak hour travel under different shared AV future scenarios. The simulation of shared AVs will be performed in MATSim using its dynamic vehicle routing problems (DVRP) extension (Maciejewski & Nagel, 2012). Shared AVs will be coordinated by a dispatching service that reacts to incoming events (e.g., new requests and vehicle arrivals and departures) and dynamically re-optimizes shared AV routes and schedules in order to ensure the efficient processing of taxi demand. Agents in each scenario will be travelers and a central AV dispatcher matching vehicles to customer requests. To assess distributional equity, the authors will use the gini coefficient; this will provide a metric to assess how average wait times vary for travelers in equity emphasis and non-equity emphasis areas and how policy affects this metric. Congestion and emissions system-level impacts will also be assessed using link travel times and a combination of trip distance and emissions factors, respectively. AV operator profit will be evaluated using mode share and distance-based average load, which is an operational cost metric that provides an estimate of the number of travelers on board for each mile traveled.
The proposed work will focus on three scenarios: 1) Private company operating a fleet and pursuing profit with no regulatory requirements, 2) Private company operating a fleet with limited regulatory oversight, and 3) Private company operating a fleet with full regulatory oversight (similar to taxicab companies). In the first scenario, AVs can make decisions that maximize AV operator profit and without any policy constraints. In the second scenario, there will be limited regulatory oversight of shared AV operations such as requiring no refusal of service (i.e., AVs must serve customers on a first-come, first-served basis) and a portion of the shared AV fleet to be handicap accessible. In the third scenario, shared AVs are under complete regulatory oversight (similar to taxicabs) where shared AV operators must meet the regulatory requirements outlined in scenario 2, in addition to, fleet size requirements, fixed fare rates regardless of supply and demand, and minimum trip fees.
Finally, we apply our model to a case study of Pittsburgh, PA and make policy recommendations that consider: 1) fleet size and number of handicap accessible vehicles that are needed to satisfy peak hour trip demand, 2) those communities and populations that benefit the most under each scenario, and 3) which regulatory structure(s) promote a balance between equity, environmental sustainability, and profitability.
For this project, we plan to utilize several readily available datasets such as the Make My Trip Count Survey from the Green Building Alliance, census tract data, and Pittsburgh’s transportation network model and origin, destination matrix. Pittsburgh was chosen as a case study for proof of concept due to the existing relationship with city transportation officials, which provides an avenue to promote policy recommendations. The methodology and results from this analysis can be applied to other metropolitan areas.
Ge, Y., Knittel, C. R., MacKenzie, D., & Zoepf, S. (2016). Racial and Gender Discrimination in Transportation Network Companies (No. w22776). National Bureau of Economic Research. https://doi.org/10.3386/w22776
Hawkins, A. J. (2019, August 6). Uber and Lyft finally admit they’re making traffic congestion worse in cities. The Verge. https://www.theverge.com/2019/8/6/20756945/uber-lyft-tnc-vmt-traffic-congestion-study-fehr-peers
Maciejewski, M., & Nagel, K. (2012). Towards Multi-Agent Simulation of the Dynamic Vehicle Routing Problem in MATSim. In R. Wyrzykowski, J. Dongarra, K. Karczewski, & J. Wasniewski (Eds.), Parallel Processing and Applied Mathematics (pp. 551–560). Springer. https://doi.org/10.1007/978-3-642-31500-8_57
Task 1: Analyze and Evaluate Socioeconomic Characteristics of Travelers in Equity Emphasis and Non-Equity Emphasis Areas (July 2021-August 2021)
Task 2: Develop Agent-Based Model to Simulate Evening Peak Hour Travel Under Different Shared AV Futures (September 2021-Feburary 2022)
Task 3: Apply Model to a Pittsburgh Case Study and Propose Potential Suggestions to the City and Public Utility Commission on Policies for Equitable AV Deployment (March 2022-June 2022)
A policy brief of findings and recommendations from the tasks outlined in the previous section will be compiled to help cities make more informed policy decisions that will lead to a more sustainable, equitable, and profitable connected and automated transportation system. We will travel to the Pittsburgh Department of Mobility and Infrastructure to share with stakeholders the implications our research. We will also arrange a meeting with the Pennsylvania Public Utility Commission to share the implications of our research and receive feedback.
Expected Accomplishments and Metrics
Deliverables from this proposal include:
1. Novel models to develop equitable, profitable, and sustainable shared AV deployment strategies.
2. Data sets, simulation results, and online/offline tools to deploy and validate the proposed methods.
3. Memorandum to the City of Pittsburgh and Pennsylvania Public Utility Commission and the development of a policy brief to inform future policy-making.
||Faculty - Adjunct
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|City of Pittsburgh
||Deployment & Equity Partner Deployment & Equity Partner
|Pennsylvania Public Utility Commission
||Deployment Partner Deployment Partner
||Deployment Partner Deployment Partner