Evaluating Transportation Automation in Megacities: The Dallas-Fort Worth Case Study

Source abstract

This article presents the results of an evaluation of the impact of transportation automation on the performance of large-scale regional transportation systems. The methodology integrates capacity improvement estimates from connected and autonomous vehicle (CAV) penetration in the traffic mix, estimated using microscopic traffic simulation, into a regional travel demand model (TDM). Thus, it takes advantage of the accuracy of microscopic traffic simulation models in capturing CAV interactions at a high resolution and the scalability of regional TDMs enabling the evaluation of the impact of CAVs at the regional level. Three scenarios are considered in this analysis, assuming the gradual adoption of CAVs in the Dallas-Fort Worth (DFW) region. The first scenario evaluates the CAVs’ impact on roadway network performance, assuming that CAVs will enhance network capacity and reduce the value of time (VOT) for drivers. The second scenario investigates the benefits of the connected vehicle-intersection systems. The last scenario investigates the effect of CAV adoption on population and employment distribution. In this scenario, the increased travel convenience and reduced travel delays might cause travelers to make longer trips, affecting their residence and job location choices. The analysis shows that adopting CAVs generally increases the vehicle-miles traveled, reduces the vehicle hour (VH) traveled, increases the average speed, and reduces the total daily delay.