  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. 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.
