Variable Speed Limit Control Strategy with Collision Probability Assessments Based on Traffic State Prediction

2014 ◽  
Vol 2435 (1) ◽  
pp. 11-18 ◽  
Author(s):  
Jie Fang ◽  
M. Hadiuzzaman ◽  
Ahsanul Karim ◽  
Ying Luo ◽  
Tony Z. Qiu
Keyword(s):  
Control Strategy ◽  
Collision Probability ◽  
Speed Limit ◽  
Variable Speed ◽  
Variable Speed Limit ◽  
Traffic State ◽  
State Prediction ◽  
Variable Speed Limit Control

scholarly journals Improving Traffic State Prediction Model for Variable Speed Limit Control by Introducing Stochastic Supply and Demand

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Yuwei Bie ◽  
Mudasser Seraj ◽  
Can Zhang ◽  
Tony Z. Qiu
Keyword(s):  
Prediction Model ◽  
Prediction Error ◽  
Supply And Demand ◽  
Transmission Model ◽  
Speed Limit ◽  
Cell Transmission Model ◽  
Variable Speed ◽  
Variable Speed Limit ◽  
Traffic State ◽  
State Prediction

Variable speed limit (VSL) is becoming recognized as an effective way to improve traffic throughput and road safety. In particular, methods based on traffic state prediction exhibit promising potential to prevent future traffic congestion and collisions. However, field observations indicate that the traffic state prediction model results in nonnegligible error that impacts the next step decision making of VSL. Thus, this paper investigates how to eliminate this prediction error within a VSL environment. In this study, the traffic state prediction model is a second-order traffic flow model named METANET, while the VSL control is model predictive control (MPC) based, and the VSL decision is discrete optimized choice. A simplified version of the switching mode stochastic cell transmission model (SCTM) is integrated with the METANET model to eliminate the prediction error. The performance of the proposed method is assessed using field data from a VSL pilot test in Edmonton, Canada, and is compared with the prediction results of the baseline METANET model during the road test. The results show that during the most congested period the proposed SCTM-METANET model significantly improves the prediction accuracy of regular METANET model.

Assessing the mobility benefits of proactive optimal variable speed limit control during recurrent and non-recurrent congestion

2015 ◽  
Vol 42 (7) ◽  
pp. 477-489 ◽  
Author(s):  
Ying Luo ◽  
M. Hadiuzzaman ◽  
Jie Fang ◽  
Tony Z. Qiu
Keyword(s):  
Traffic Control ◽  
Propagation Speed ◽  
Speed Limit ◽  
Variable Speed ◽  
Variable Speed Limit ◽  
Detection Delay ◽  
Traffic Performance ◽  
Total Travel Time ◽  
Variable Speed Limit Control ◽  
The Impact

Over the past few decades, several active traffic control methods have been proposed to improve freeway efficiency at bottleneck locations. Variable speed limit (VSL) is one of these effective controls. Previous studies have evaluated VSL control, but primarily during recurrent congestion only. This study focuses on evaluating the performance of VSL control for both recurrent and non-recurrent congestion. To assess the effectiveness of a previously proposed VSL control in a real-world situation, this study has three evaluation objectives: (1) examine the control performance when recurrent and (or) non-recurrent congestion occurs; (2) assess the effectiveness of the control when a queue encounters the VSL sign; and (3) consider the impact of system detection delay in VSL control. Comparative experiments for Whitemud Drive in Edmonton, Alberta, Canada, are simulated in the VISSIM platform, and traffic performance is compared among scenarios with and without control. The simulation results show that VSL improves mobility for both recurrent and non-recurrent congestion. The VSL control reduces total travel time, and improves total travel distance and total flow. Furthermore, it slows down the shockwave propagation speed, improves the average speed on most of the freeway segments, and reduces the duration of traffic recovery.

scholarly journals Predicting Traffic Dynamics with Driver Response Model for Proactive Variable Speed Limit Control Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Huixuan Ye ◽  
Lili Tu ◽  
Jie Fang
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Control Algorithm ◽  
Field Data ◽  
Speed Limit ◽  
Response Model ◽  
Variable Speed ◽  
Variable Speed Limit ◽  
Driver Response ◽  
Traffic State

Variable Speed Limit (VSL) control contributes to potential crash risk reduction by suggesting a suitable dynamic speed limit to achieve more stable and uniform traffic flow. In recent studies, researchers adopted macroscopic traffic flow models and perform prediction-based optimal VSL control. The response of drivers to the advised VSL is one of the most critical parameters in VSL-controlled speed dynamics modeling, which significantly affects the accuracy of traffic state prediction as well as the control reliability and performance. Nevertheless, the variations of driver responses were not explicitly modeled. Thus, in this research, the authors proposed a dynamic driver response model to formulate how the drivers respond to the advised VSL during various traffic conditions. The model was established and calibrated using field data to quantitatively analyze the dynamics of drivers’ desired speed regarding the advised VSL and current traffic state variables. A proactive VSL control algorithm incorporating the established driver response model was designed and implemented in field-data-based simulation study. The design proactive control algorithm modifies VSL in real-time according to the traffic state prediction results, aiming to reduce potential crash risks over the experiment site. By taking into account the real-time driver response variations, the VSL-controlled traffic state dynamics was more accurately predicted. The experimental results illustrated that the proposed control algorithm effectively reduces the crash probabilities in the traffic network.

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