Abstract
Shared automated vehicles (AVs) are expected to become commercially available within the next decade and hold great promise for improving traffic safety, accessibility, and energy consumption (due to improved operational efficiencies). However, shared AVs could also dramatically increase congestion, and implementing fair and equitable traffic demand management strategies will become more valuable along congested corridors in urban regions. 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 different congestion pricing policies (e.g., facility-based tolls vs. mileage-based tolls) could affect energy use, mode choice, AV operator profit, and mobility across different population groups.
Description
With automated vehicle (AV) technology progressing fast, questions arise regarding how to price the roads and the impact of these regulations on congestion, profitability, and travel times and mode choice for different demographic populations. On one hand, driverless vehicles could improve transportation network congestion through smaller headways and a reduction in vehicle crashes. However, driverless vehicles will present a new passenger mode and trips made by traditional modes of travel could be replaced by shared AVs, potentially exacerbating congestion conditions. 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 congestion pricing policies for shared AVs could affect mobility for different population groups as well as system level congestion and energy use
• Conduct proof-of-concept simulations to help cities make informed policy decisions regarding congestion pricing in a world of self-driving vehicles
Our hypothesis is that congestion pricing policies will have disproportionate economic and travel time impacts on low-income and minority travelers. Currently, there are not many tools to help state and local agencies assess how congestion pricing policies in a world of shared AVs 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 congestion pricing 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 the daily utility scores for travelers in equity emphasis and non-equity emphasis areas before and after the implementation of congestion pricing policy. 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. Mode choice will be modeled for travelers based on income (or value of travel time) and travel cost and distance (Chen & Kockelman, 2016).
The proposed work will focus on several congestion pricing scenarios that include: 1) a baseline policy scenario with no policy interventions, 2) facility-based tolls where road users are charged higher prices to travel on congested links, 3) cordon charges where vehicles are charged to enter the central business district during peak hour, 4) distance based fees where road users are charged based on their distance traveled, and 5) dynamic marginal cost pricing where road users are charged for the extra cost that their trip causes to other travelers.
Finally, we apply our model to a case study of Pittsburgh, PA and make policy recommendations that consider: 1) those communities and populations that benefit the most and are negatively impacted under each scenario and 2) which congestion demand management strategies 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, both of which will be obtained from the Southwestern Pennsylvania Commission. Value of travel time estimates for individuals at different income levels will be derived from the United States Department of Transportation’s (USDOT) value of travel time guidelines (White, 2016) and other literature (e.g., Gao et al., 2019). 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.
This work pushes forward Secretary Buttigieg’s priorities of improving mobility for disadvantaged communities and addressing racial inequity and climate change mitigation by evaluating how road pricing policies in a world of self-driving vehicles could affect emissions as well as mode choice and mobility for travelers with different socioeconomic backgrounds. This work also pushes forward the City of Pittsburgh's 2070 mobility plan, which highlights a need for developing equitable road pricing strategies to support a sustainable shift to future transportation modes (City of Pittsburgh, 2021).
References
Chen, T. D., & Kockelman, K. M. (2016). Management of a Shared Autonomous Electric Vehicle Fleet: Implications of Pricing Schemes. Transportation Research Record, 2572(1), 37–46. https://doi.org/10.3141/2572-05
City of Pittsburgh. (2021). Envision 2070: Mobility in a Sustainable Pittsburgh. https://apps.pittsburghpa.gov/redtail/images/16135_PGH_2070_2021-10-15-compressed.pdf
Gao, J., Ranjbari, A., & MacKenzie, D. (2019). Would being driven by others affect the value of travel time? Ridehailing as an analogy for automated vehicles. Transportation, 46(6), 2103–2116. https://doi.org/10.1007/s11116-019-10031-9
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. Wa?niewski (Eds.), Parallel Processing and Applied Mathematics (pp. 551–560). Springer. https://doi.org/10.1007/978-3-642-31500-8_57
White, V. (2016). Revised Departmental Guidance on Valuation of Travel Time in Economic Analysis. USDOT. https://www.transportation.gov/sites/dot.gov/files/docs/2016%20Revised%20Value%20of%20Travel%20Time%20Guidance.pdf
Timeline
Task 1: Analyze and Evaluate Socioeconomic Characteristics of Travelers in Equity Emphasis and Non-Equity Emphasis Areas (July 2022-August 2022)
Task 2: Develop Agent-Based Model to Simulate Evening Peak Hour Travel with Shared AVs Under Different Road Pricing Futures (September 2022-Feburary 2023)
Task 3: Apply Model to a Pittsburgh Case Study and Propose Potential Suggestions to the City and Public Utility Commission on Equitable and Fair Congestion Pricing Strategies in a World with Self-Driving Vehicles (March 2023-June 2023)
Strategic Description / RD&T
Deployment Plan
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 Argo AI and the Pennsylvania Public Utility Commission to share the implications of our research and receive feedback. These agencies will help us to guide this research from the beginning to ensure that this project addresses real-world problems. Our tool will be made open source for public agencies interested in conducting their own analysis to develop fair congestion pricing policies.
Expected Outcomes/Impacts
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.
Expected Outputs
TRID
Individuals Involved
| Email |
Name |
Affiliation |
Role |
Position |
| cdharper@andrew.cmu.edu |
Harper, Corey |
CEE |
PI |
Faculty - Untenured, Tenure Track |
| bethannh@andrew.cmu.edu |
Hockenberry, Beth |
CEE |
Other |
Other |
| haomingy@andrew.cmu.edu |
Yang, Haoming |
CEE |
Other |
Student - PhD |
Budget
Amount of UTC Funds Awarded
$100000.00
Total Project Budget (from all funding sources)
$200000.00
Documents
| Type |
Name |
Uploaded |
| Data Management Plan |
DMP_Equitable_Congestion_Pricing_Harper.pdf |
Feb. 27, 2022, 7:58 p.m. |
| Progress Report |
393_Progress_Report_2022-09-30 |
Sept. 28, 2022, 11:53 a.m. |
| Publication |
COVID-19 public transit precautions: Trade-offs between risk reduction and costs |
March 17, 2023, 1:47 p.m. |
| Presentation |
Taking a Multimodal Approach to Equitable Biekshare Station Siting |
March 17, 2023, 1:47 p.m. |
| Progress Report |
393_Progress_Report_2023-03-30 |
March 17, 2023, 1:48 p.m. |
| Publication |
What Stay-At-Home Orders Reveal About Dependence on Transportation Network Companies |
March 30, 2023, 6 a.m. |
| Publication |
Coalitional Fairness of Autonomous Vehicles at a T-Intersection |
March 30, 2023, 6:01 a.m. |
| Publication |
Congestion and environmental impacts of short car trip replacement with micromobility modes |
March 30, 2023, 6:01 a.m. |
| Publication |
Equity and Transportation System Implications of Shared Autonomous Vehicle Deployment |
March 30, 2023, 6:02 a.m. |
| Publication |
Congestion and Emission Impacts of Switching from In-person to Online Grocery Delivery: A Seattle Case Study |
March 30, 2023, 6:02 a.m. |
| Publication |
Societal Impacts of a Complete Street Project in a Mixed Urban Corridor: Case Study in Pittsburgh |
March 30, 2023, 6:03 a.m. |
| Publication |
Travel impacts of a complete street project in a mixed urban corridor |
March 30, 2023, 6:04 a.m. |
| Publication |
Socioeconomic and usage characteristics of transportation network company (TNC) riders |
March 30, 2023, 6:04 a.m. |
| Publication |
Economic and Behavioral Dimensions of Urban Transport Policy |
March 30, 2023, 6:05 a.m. |
| Final Report |
Final_Report_-_Harper_393_gnsTuai.pdf |
Aug. 3, 2023, 9:43 a.m. |
| Progress Report |
393_Progress_Report_2023-09-30 |
Oct. 18, 2023, 6:54 a.m. |
Match Sources
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Partners
| Name |
Type |
| City of Pittsburgh |
Deployment & Equity Partner Deployment & Equity Partner |
| Pennsylvania Public Utility Commission |
Deployment Partner Deployment Partner |
| Argo AI |
Deployment Partner Deployment Partner |
