Abstract
Travel time reliability (TTR) is one of the important indexes for effectively evaluating the performance of road network, and TTR can effectively be improved using the real-time traffic guidance information. Compared with traditional traffic guidance, connected vehicle (CV) guidance can provide travelers with more timely and accurate travel information, which can further improve the travel efficiency of road network. Five CV characteristics indexes are selected as explanatory variables including the Congestion Level (CL), Penetration Rate (PR), Compliance Rate (CR), release Delay Time (DT), and Following Rate (FR). Based on the five explanatory variables, a TTR model is proposed using the multilogistic regression method, and the prediction accuracy and the impact of characteristics indexes on TTR are analyzed using a CV guidance scenario. The simulation results indicate that 80% of the RMSE is concentrated within the interval of 0 to 0.0412. The correlation analysis of characteristics indexes shows that the influence of CL, PR, CR, and DT on the TTR is significant. PR and CR have a positive effect on TTR, and the average improvement rate is about 77.03% and 73.20% with the increase of PR and CR, respectively, while CL and DT have a negative effect on TTR, and TTR decreases by 31.21% with the increase of DT from 0 to 180 s.
Highlights
Travel time reliability (TTR) is an important concern issue for traveler in daily travel and is influenced by various factors such as traffic accidents and weather and flow states
Lee and Park [9] analyzed the performance of connected vehicle (CV) guidance under accident conditions using simulation, and the results indicated that Penetration Rate of the CV equipment has a significant impact on the guidance effect
Traffic jam occurs in Route 1 to cause the increasing of the impedance of Route 1 when a broken vehicle (BV) is set on Route 1
Summary
Travel time reliability (TTR) is an important concern issue for traveler in daily travel and is influenced by various factors such as traffic accidents and weather and flow states. Traffic guidance information can help travelers make better travel plans and improve their travel efficiency and TTR. Previous studies on TTR and CV guidance have mainly focused on analyzing the impact of traditional factors such as weather [4,5,6,7,8] and the impact of CV guidance on the travel time, fuel consumption, and average delay [9,10,11,12]. Few studies were conducted on the impact of CV guidance on TTR considering the CV characteristics indexes. Based on the current research status, a TTR model in a CV environment was presented using the multilogistic regression method
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