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

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. For example, congestion pricing in New York City would impact travelers' departure times and mode choices, among other decisions. 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 to fit ride-hail services and bike-share. 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 was used to evaluate a congestion pricing plan proposed by the Regional Plan Association and suggested 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 50% more than the latter, which has implications for redistribution of congestion pricing revenues. These results and open-source tool will help policymakers in New York City and support the need for multi-agent simulation in travel demand modeling of emerging transportation technologies and policies.