Abstract
Variable speed limit (VSL) control dynamically adjusts the displayed speed limit to harmonize traffic speed, prevent congestions, and reduce crash risks based on prevailing traffic stream and weather conditions. Previous research studies examine the impacts of VSL control on reducing corridor-level crash risks and improving bottleneck throughput. However, less attention focuses on utilizing real-world data to see how compliant the drivers are under different VSL values and how the aggregated driving behavior changes. This study aims to fill the gap. With the high-resolution lane-by-lane traffic big data collected from a European motorway, this study performs statistical analysis to measure the difference in driving behavior under different VSL values and analyze the safety impacts of VSL controls on aggregate driving behaviors (mean speed, average speed difference, and the percentage of small space headway). The data analytics show that VSL control can effectively decrease the mean speed, the speed difference, and the percentage of small space headways. The safety impacts of VSL control on aggregated driving behavior are also discussed. The aggregated driving behavior variables follow a trend of first decreasing and then increasing with the continuous decrease in VSL values, indicating that potential traffic safety benefits can be achieved by adopting suitable VSL values that match with prevailing traffic conditions.
Highlights
Variable speed limit (VSL) control dynamically adjusts the displayed speed limit on the variable message signs to harmonize traffic speed, prevent congestions, and reduce crash risks based on prevailing traffic stream and weather conditions, which is an essential control strategy for Active Traffic Management (ATM) system
Based on the quantitative analysis results, this study measures the difference in driving behavior under different VSL values and discusses the safety impacts of VSL control on aggregated driving behaviors and potential improvement on the motorway rear-end collisions. e results of this study revealed the mechanism leading to the safety benefits of VSL control and provided more realistic assumptions for modeling traffic flow operations under VSL control
In order to compare the effect of different VSL values on the mean speed, the arithmetic average of all the sample’s mean speed within different density intervals is calculated under different speed limits on different lanes. e results are summarized in Table 2. e two-sample Student’s t-test is used to compare the mean speeds’ difference with and without VSL controls, and the associated p values for t-tests are provided in parenthesis to infer the impact significance of the VSL control. e null hypothesis (H0) is that the index is the same for the conditions with and without control
Summary
Received 2 July 2020; Revised 9 August 2020; Accepted 19 March 2021; Published 26 March 2021. Variable speed limit (VSL) control dynamically adjusts the displayed speed limit to harmonize traffic speed, prevent congestions, and reduce crash risks based on prevailing traffic stream and weather conditions. With the high-resolution lane-by-lane traffic big data collected from a European motorway, this study performs statistical analysis to measure the difference in driving behavior under different VSL values and analyze the safety impacts of VSL controls on aggregate driving behaviors (mean speed, average speed difference, and the percentage of small space headway). E data analytics show that VSL control can effectively decrease the mean speed, the speed difference, and the percentage of small space headways. E aggregated driving behavior variables follow a trend of first decreasing and increasing with the continuous decrease in VSL values, indicating that potential traffic safety benefits can be achieved by adopting suitable VSL values that match with prevailing traffic conditions With the high-resolution lane-by-lane traffic big data collected from a European motorway, this study performs statistical analysis to measure the difference in driving behavior under different VSL values and analyze the safety impacts of VSL controls on aggregate driving behaviors (mean speed, average speed difference, and the percentage of small space headway). e data analytics show that VSL control can effectively decrease the mean speed, the speed difference, and the percentage of small space headways. e safety impacts of VSL control on aggregated driving behavior are discussed. e aggregated driving behavior variables follow a trend of first decreasing and increasing with the continuous decrease in VSL values, indicating that potential traffic safety benefits can be achieved by adopting suitable VSL values that match with prevailing traffic conditions
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