Traffic Flow Modeling of Freeway Variable Speed Limit Control Based on the Big Data of Driving Behavior

Abstract

Variable speed limit (VSL) control is a flexible restriction on the rate at which motorists can drive on a given stretch of road. Effective VSL control can increase safety and provide clear guidance for motorists. Previous traffic flow models of VSL control were mostly based on the influence of VSL on average speed (macro) or driver’s expected speed (micro). Few models considered the influence of VSL on driver’s actual driving behavior. In this paper, we first briefly introduce the big traffic data involved in this study and explain the mapping relationship between the data and driving behavior. Then, we analyze the driver’s actual driving behavior under the VSL control. Then, an improved single-lane cellular automaton model is established based on the driving behavior characteristics under VSL control. After that, we calibrate the parameters of the single-lane cellular automaton model with the left lane as the calibration object. Finally, this paper uses the proposed single-lane cellular automaton model to simulate the traffic flow characteristics under VSL control. The numerical simulation results show that the simulation of the variable speed limit in different density intervals presents different results, but these results are consistent with the actual situation of variable speed limit control, which verifies the validity of the proposed model.

Analyzing the Safety Impacts of Variable Speed Limit Control on Aggregated Driving Behavior Based on Traffic Big Data

“…For instance, under the low VSL values, the mean speed, average speed difference, and the percentage of small headways are greater than the same measurements under high VSL values. e reason for this phenomenon is the change in the driver's compliance with the variable speed limit control [8].…”

“…However, both questionnaire surveys and driving simulators are biased towards respondents' subjective factors, which might lead to the overestimation of VSL systems' safety benefits. Traffic simulation methods have been widely used to examine both the operation and safety benefits of different ATM control strategies [6][7][8][9]. To better reproduce the real-world effect of VSL control, different traffic simulation methods have also been applied to evaluated VSL benefits by simulating different driver groups' behavioral responses to the VSL [7][8][9].…”

“…Traffic simulation methods have been widely used to examine both the operation and safety benefits of different ATM control strategies [6][7][8][9]. To better reproduce the real-world effect of VSL control, different traffic simulation methods have also been applied to evaluated VSL benefits by simulating different driver groups' behavioral responses to the VSL [7][8][9]. Some researchers integrated a real-time crash potential prediction model that utilizes the output of the microscopic simulation [10][11][12][13].…”

Safety Effects of Connected and Automated Vehicle-Based Variable Speed Limit Control near Freeway Bottlenecks considering Driver’s Heterogeneity

“…Freeway bottlenecks have attracted considerable attention from transportation researchers since the bottlenecks drastically reduce both efficiency and safety [1][2][3][4][5]. e formation of traffic bottlenecks is caused by a variety of reasons, such as lane capacity reduction, temporary lane closure, and crashes.…”

Q-Learning-Based Coordinated Variable Speed Limit and Hard Shoulder Running Control Strategy to Reduce Travel Time at Freeway Corridor

“…VSL strategies release bottleneck congestion by regulating the inflow volume from upstream through speed limits. Posted speed limits are set dynamically in response to different travel conditions to meet the objectives of safety, efficiency, and environmental consistency (4)(5)(6)(7)(8)(9). Early VSL studies tend to use tree-based strategies that take actions based on the preclassified traffic conditions.…”