Integrating an Activity-Based Travel Demand Model with Dynamic Traffic Assignment and Emission Models

Abstract

Microsimulation is becoming more popular in transportation research. This research explores the potential of microsimulation by integrating an existing activity-based travel demand model, TASHA, with a dynamic agent-based traffic simulation model, MATSim. Differences in model precisions from the two models are resolved through a series of data conversions, and the models are able to form an iterative process similar to previous modeling frameworks using TASHA and static assignment using Emme/2. The resulting model is then used for light-duty vehicle emission modeling where the traditional average-speed modeling approach is improved by exploiting agent-based traffic simulation results. This improved method of emission modeling is more sensitive to the effect of congestion, and the linkage between individual vehicles and link emissions is preserved. The results have demonstrated the advantages of the microsimulation approach over conventional methodologies that rely heavily on temporal or spatial aggregation. The framework can be improved by further enhancing the sensitivity of TASHA to travel time.