Abstract for: Dynamics of supply and demand for competing shared mobility services

Travel demand models typically include transportation supply options (e.g., drive, transit, walk/bike) whose characteristics are largely exogenous. Today, options like on-demand shared mobility services respond, sometimes in near-real time, to market cues. Often, they compete for shared resources, such as drivers or vehicles. Travel demand models struggle to account for the behavior of these modes, which can account for a substantial portion of traffic, particularly in central business districts. A stock-flow model represents several shared mobility services, and how they might respond to market cues. The model includes a common pool of regional shared mobility resources (e.g., drivers and vehicles); multiple service providers with their own characteristics (fare, cost structure, target utilization); service attributes that matter to the rider (fare, wait time); and competition among vendors. The model is calibrated with New York City data. Automated driving systems (ADS) may bring lower fares, so we test lower-fare scenarios. Results indicates a significant increase in overall car travel, which in New York City primarily means a shift away from transit. Aside from implications for transit-agency revenue, this suggests that even if ADS leads to a lower shared mobility fare, there may be increases in the time cost of trips, as vehicle congestion is likely to rise. By integrating demand-side responses, driver behavior, and operator strategies, the model provides a long-term planning framework for shared-mobility markets. It addresses strategic fleet sizing while responding to demand fluctuations and competitive interactions. It can help cities estimate how vehicle miles travelled attributable to shared mobility may change if ADS-based services are deployed. Finally, it discusses how an SD and agent-based model are linked to take advantage of strengths of each.