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A validated multi-agent simulation test bed to evaluate congestion pricing policies on population segments by time-of-day in New York City

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  • He, Brian Yueshuai
  • Zhou, Jinkai
  • Ma, Ziyi
  • Wang, Ding
  • Sha, Di
  • Lee, Mina
  • Chow, Joseph Y.J.
  • Ozbay, Kaan

Abstract

Evaluation of the demand for emerging transportation technologies and policies can vary by time of day due to spillbacks on roadways, rescheduling of travelers’ activity patterns, and shifting to other modes that affect the level of congestion. These effects are not well-captured with static travel demand models. We calibrate and validate the first open-source multi-agent simulation model for New York City, called MATSim-NYC, to support agencies in evaluating policies such as congestion pricing. The simulation-based virtual test bed is loaded with an 8M + synthetic 2016 population calibrated in a prior study. The road network is calibrated to INRIX speed data and average annual daily traffic for a screenline along the East River crossings, resulting in average speed differences of 7.2% on freeways and 17.1% on arterials, leading to average difference of +1.8% from the East River screenline. Validation against transit stations shows an 8% difference from observed counts and median difference of 29% for select road link counts. The model is used to evaluate a congestion pricing plan proposed by the Regional Plan Association and suggests a much higher (127K) car trip reduction compared to their report (59K). The pricing policy would impact the population segment making trips within Manhattan differently from the population segment of trips outside Manhattan: benefits from congestion reduction benefit the former by about 110%+ more than the latter. The multiagent simulation can show that 37.3% of the Manhattan segment would be negatively impacted by the pricing compared to 39.9% of the non-Manhattan segment, which has implications for redistribution of congestion pricing revenues. The citywide travel consumer surplus decreases when the congestion pricing goes up from $9.18 to $14 both ways even as it increases for the Charging-related population segment. This implies that increasing pricing from $9.18 to $14 benefits Manhattanites at the expense of the rest of the city.

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  • He, Brian Yueshuai & Zhou, Jinkai & Ma, Ziyi & Wang, Ding & Sha, Di & Lee, Mina & Chow, Joseph Y.J. & Ozbay, Kaan, 2021. "A validated multi-agent simulation test bed to evaluate congestion pricing policies on population segments by time-of-day in New York City," Transport Policy, Elsevier, vol. 101(C), pages 145-161.
    • Handle: RePEc:eee:trapol:v:101:y:2021:i:c:p:145-161
    DOI: 10.1016/j.tranpol.2020.12.011
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    1. Brownstone, David & Ghosh, Arindam & Kazimi, Camilla & Van Amelsfort, Dirk, 2002. "Drivers' Willingness-to-Pay to Reduce Travel Time: Evidence from the San Diego I-15 Congestion Pricing Project," University of California Transportation Center, Working Papers qt8q7331mz, University of California Transportation Center.
    2. Kay Axhausen & Andrea Zimmermann & Stefan Schönfelder & Guido Rindsfüser & Thomas Haupt, 2002. "Observing the rhythms of daily life: A six-week travel diary," Transportation, Springer, vol. 29(2), pages 95-124, May.
    3. Brownstone, David & Ghosh, Arindam & Golob, Thomas F & Kazimi, Camilla & Van Amelsfort, Dirk, 2002. "Drivers' Willingness-to-Pay to Reduce Travel Time: Evidence from the San Diego I-15 Congestion Pricing Project," University of California Transportation Center, Working Papers qt3mx1d1k4, University of California Transportation Center.
    4. Brownstone, David & Ghosh, Arindam & Golob, Thomas F. & Kazimi, Camilla & Van Amelsfort, Dirk, 2003. "Drivers' willingness-to-pay to reduce travel time: evidence from the San Diego I-15 congestion pricing project," Transportation Research Part A: Policy and Practice, Elsevier, vol. 37(4), pages 373-387, May.
    5. He, Brian Y. & Zhou, Jinkai & Ma, Ziyi & Chow, Joseph Y.J. & Ozbay, Kaan, 2020. "Evaluation of city-scale built environment policies in New York City with an emerging-mobility-accessible synthetic population," Transportation Research Part A: Policy and Practice, Elsevier, vol. 141(C), pages 444-467.
    6. Small, Kenneth A., 1992. "Using the Revenues from Congestion Pricing," University of California Transportation Center, Working Papers qt32p9m3mm, University of California Transportation Center.
    7. Zhang, Xiaoning & Yang, Hai, 2004. "The optimal cordon-based network congestion pricing problem," Transportation Research Part B: Methodological, Elsevier, vol. 38(6), pages 517-537, July.
    8. Kaddoura, Ihab & Nagel, Kai, 2019. "Congestion pricing in a real-world oriented agent-based simulation context," Research in Transportation Economics, Elsevier, vol. 74(C), pages 40-51.
    9. Wong, Yale Z. & Hensher, David A. & Mulley, Corinne, 2020. "Mobility as a service (MaaS): Charting a future context," Transportation Research Part A: Policy and Practice, Elsevier, vol. 131(C), pages 5-19.
    10. Yang, Hai & Huang, Hai-Jun, 1998. "Principle of marginal-cost pricing: how does it work in a general road network?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 32(1), pages 45-54, January.
    11. Djavadian, Shadi & Chow, Joseph Y.J., 2017. "An agent-based day-to-day adjustment process for modeling ‘Mobility as a Service’ with a two-sided flexible transport market," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 36-57.
    12. Chow, Joseph Y.J. & Recker, Will W., 2012. "Inverse optimization with endogenous arrival time constraints to calibrate the household activity pattern problem," Transportation Research Part B: Methodological, Elsevier, vol. 46(3), pages 463-479.
    13. Small, Kenneth A., 2001. "Using the Revenues from Congestion Pricing," University of California Transportation Center, Working Papers qt7170x9b0, University of California Transportation Center.
    14. Small, Kenneth A, 1982. "The Scheduling of Consumer Activities: Work Trips," American Economic Review, American Economic Association, vol. 72(3), pages 467-479, June.
    15. Amirhossein Baghestani & Mohammad Tayarani & Mahdieh Allahviranloo & H. Oliver Gao, 2020. "Evaluating the Traffic and Emissions Impacts of Congestion Pricing in New York City," Sustainability, MDPI, vol. 12(9), pages 1-16, May.
    16. Becker, Henrik & Balac, Milos & Ciari, Francesco & Axhausen, Kay W., 2020. "Assessing the welfare impacts of Shared Mobility and Mobility as a Service (MaaS)," Transportation Research Part A: Policy and Practice, Elsevier, vol. 131(C), pages 228-243.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Pablo González-Aliste & Iván Derpich & Mario López, 2023. "Reducing Urban Traffic Congestion via Charging Price," Sustainability, MDPI, vol. 15(3), pages 1-21, January.
    2. Thi Ngoc Nguyen & Felix Muesgens, 2024. "Fuel tax loss in a world of electric mobility: A window of opportunity for congestion pricing," Papers 2409.20033, arXiv.org.
    3. Geng, Kexin & Wang, Yacan & Cherchi, Elisabetta & Guarda, Pablo, 2023. "Commuter departure time choice behavior under congestion charge: Analysis based on cumulative prospect theory," Transportation Research Part A: Policy and Practice, Elsevier, vol. 168(C).
    4. Daoge Wang & Jianhong Ye & Bin Yu & Peng Jing & Lei Gao, 2024. "Simulating one-way electric carsharing systems with a multi-agent model," Transportation, Springer, vol. 51(6), pages 2277-2300, December.
    5. Tan, Yu & Sun, Zhanbo & Zhu, Baichuan & Qin, Ziye & Zhao, Yu & Wang, Xuting, 2024. "Minimize population exposure to vehicle-generated emissions by road pricing," Transport Policy, Elsevier, vol. 148(C), pages 15-30.
    6. Wang, Ding & Tayarani, Mohammad & Yueshuai He, Brian & Gao, Jingqin & Chow, Joseph Y.J. & Oliver Gao, H. & Ozbay, Kaan, 2021. "Mobility in post-pandemic economic reopening under social distancing guidelines: Congestion, emissions, and contact exposure in public transit," Transportation Research Part A: Policy and Practice, Elsevier, vol. 153(C), pages 151-170.
    7. Davis, Haggai & Landes, Hector & Namdarpour, Farnoosh & Yang, Hai & Y. J. Chow, Joseph & Ozbay, Kaan, 2024. "Aggregate urban truck tour synthesis from public data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 185(C).
    8. Jiang, Qinhua & Zhang, Ning & Yueshuai He, Brian & Lee, Changju & Ma, Jiaqi, 2024. "Large-scale public charging demand prediction with a scenario- and activity-based approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 179(C).
    9. Peiyu Jing & Ravi Seshadri & Takanori Sakai & Ali Shamshiripour & Andre Romano Alho & Antonios Lentzakis & Moshe E. Ben-Akiva, 2023. "Evaluating congestion pricing schemes using agent-based passenger and freight microsimulation," Papers 2305.07318, arXiv.org.

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