A Data-Driven Quasi-Dynamic Traffic Assignment Model Integrating Multi-Source Traffic Sensor Data on the Expressway Network

Xing Zeng,Xuefeng Guan,Huayi Wu,Heping Xiao

doi:10.3390/ijgi10030113

Abstract

Static traffic assignment (STA) models have been widely utilized in the field of strategic transport planning. However, STA models cannot fully represent the dynamic road conditions and suffer from inaccurate assignment during traffic congestion. At the same time, an increasing number of installed sensors have become an important means of detecting dynamic road conditions. To address the shortcomings of STA models, we integrate multi-source traffic sensor datasets and propose a novel data-driven quasi-dynamic traffic assignment model, named DQ-DTA. In this model, records of toll stations are used for time-varying travel demand estimation. GPS trajectory datasets of vehicles are further used to calculate the dynamic link costs of the road network, replacing the imprecise Bureau of Public Roads (BPR) function. Moreover, license plate recognition (LPR) data are used to design a statistical probability-based multipath assignment method to capture travelers’ route choices. The expressway network in the Hunan province is selected as the study area, and several classic STA models are also chosen for performance comparison. Experimental results demonstrate that the accuracy of the proposed DQ-DTA model is about 6% higher than that of the chosen STA models.

Highlights

The use of the expressway has become the favorite choice for inter-city travel due to its high capacity and low time cost
License plate recognition (LPR) data are used to constrain the path assignment for each OD pair, while a multipath assignment method based on statistical probability is used to capture the travelers’ multipath choices in order to improve the logit-related models
Utilizing massive amounts of travel history data provided by OD pairs and license plate recognition (LPR) records, a multipath assignment method based on statistical probability (MSP) can be defined to capture user route choices

Summary

Introduction

The use of the expressway has become the favorite choice for inter-city travel due to its high capacity and low time cost. A quasi-dynamic traffic assignment model (DQ-DTA) based on multisource data is proposed in this paper with the goal of achieving better assignment accuracy on large-scale expressway networks. This approach can exploit multi-source data fusion to address the shortcomings of STA models, and can approximate the effect of DTA models by discretizing the time dimension into coarse intervals to represent the traffic dynamics. We propose a data-driven quasi-dynamic traffic assignment model (DQ-DTA) This approach is capable of realizing traffic assignment with low computational efficiency, in the same way as traditional STA models, but can achieve higher assignment accuracy in the large-scale expressway network context. The second case is when the entry and exit times are on different days separately; under these circumstances, records in which less than half of the total travel time elapses on this day will be discarded

Real-Time Surveillance Data

The GPS Trajectory Dataset

Multipath Assignment Based on Statistical Probability

Results and Discussion

Performance Comparison with Classical STA Models

Conclusions