Computational-Based Approach to Estimating Travel Demand in Large-Scale Microscopic Traffic Simulation Models

摘要

The increased interest in the development and application of large-scale or regional microsimulation transportation models has brought to the forefront the challenges associated with estimating the dynamic demand information needed to run such models. This paper develops a computational-based approach for estimating or adjusting dynamic origin-destination matrices for regional microsimulation models on the basis of hourly traffic counts. The proposed approach, while based on genetic algorithms (GA), includes a special module, called Plan Analyzer, to guide the search process in an intelligent way. This results in a customized algorithm for the problem that can be regarded as an example of a guided genetic algorithm (GGA). To cut down on execution time, a distributed implementation of the algorithm is adopted, and several software design procedures are developed to deal with the demanding memory requirements of the problem. To demonstrate the effectiveness of the algorithm, the Transportation Analysis and Simulation System (TRANSIMS) model, a microsimulation platform designed for regional simulations, is used to model two test networks, a synthetic grid network and a realistic regional model of Chittenden County, Vermont. The GGA is then utilized to estimate the dynamic demand for those two models on the basis of hourly traffic count information. The results clearly demonstrate the effectiveness of the GGA in dramatically reducing the average absolute error (AAE) between the simulated and field counts, and in closely estimating the "true" demand, which was known in this research by virtue of how the case studies were designed. The results also show that the developed GGA significantly outperforms standard GAs.