Automatic Calibration of Mesoscopic Traffic Simulation Using Vehicle Trajectory Data

TL;DR

This paper introduces an automatic calibration framework for mesoscopic traffic simulation using vehicle trajectory data, improving efficiency and accuracy in traffic modeling.

Contribution

It presents a novel vehicle trajectory-based calibration method combining demand, route choice, capacity, and behavior models with optimization and dimensionality reduction techniques.

Findings

Effective calibration of demand and route choice models.

Accurate estimation of capacity and driver behavior parameters.

Validated framework on Birmingham, Michigan network.

Abstract

Traffic simulation models have long been popular in modern traffic planning and operation applications. Efficient calibration of simulation models is usually a crucial step in a simulation study. However, traditional calibration procedures are often resource-intensive and time-consuming, limiting the broader adoption of simulation models. In this study, a vehicle trajectory-based automatic calibration framework for mesoscopic traffic simulation is proposed. The framework incorporates behavior models from both the demand and the supply sides of a traffic network. An optimization-based network flow estimation model is designed for demand and route choice calibration. Dimensionality reduction techniques are incorporated to define the zoning system and the path choice set. A stochastic approximation model is established for capacity and driving behavior parameter calibration. The applicability and performance of the calibration framework are demonstrated through a case study for the City of Birmingham network in Michigan.