A validated multi-agent simulation test bed to evaluate congestion pricing policies on population segments by time-of-day in New York City. (February 2021)

• Record Type: Journal Article
• Authors: He, Brian Yueshuai; Zhou, Jinkai; Ma, Ziyi; Wang, Ding; Sha, Di; Lee, Mina; Chow, Joseph Y.J.; Ozbay, Kaan
• Is Part Of: Transport policy. Volume 101(2021)
• Page Start: 145
• Page End: 161
• Publication Date: 2021-02
• Subjects: Travel demand forecasting -- Emerging mobility -- Multi-agent simulation -- Congestion pricing -- New York city; Transportation and state -- Periodicals; Transportation -- Rates -- Periodicals; 388
• DOI: 10.1016/j.tranpol.2020.12.011
• Languages: English
• ISSNs: 0967-070X

Abstract

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. Highlights: Developed first calibrated MATSim model of NYC includes bikeshare and ride-hail trips. Model validation using transit stations and road links is comparable to NYPBM. RPA congestion pricing policy would have net increase in consumer surplus. Pricing increase from $9 to $14 benefits Manhattan at expense of the city. Results suggest toll revenue redistribution should focus on outer boroughs.