Abstract
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.
Highlights
The Variable speed limit (VSL) control method was designed to keep the credibility of speed limits even under adverse conditions such as congestion and bottleneck road segments, so that the speed limits still work to maintain traffic safety and the highest possible traffic throughput
In the case of the stochastic cell transmission model (SCTM) in this paper, the stochastic elements are added in two ways: (1) demand stochasticity, the inflow disturbance term added to the cell transmission model (CTM); (2) supply stochasticity fundamental diagram (FD) stochasticity, each of the disturbance terms containing stochastic FD parameters extracted by statistical technics
The original METANET was already implemented in the VSL field test, and the predicted traffic variables were stored, so the collected field data were considered as ground truth data
Summary
The VSL control method was designed to keep the credibility of speed limits even under adverse conditions such as congestion and bottleneck road segments, so that the speed limits still work to maintain traffic safety and the highest possible traffic throughput. To achieve the highest traffic prediction accuracy, in this paper, a second-order traffic flow model named METANET [17, 18] is chosen It is a discretized and enhanced version of the Lighthill–Whitham–Richards model combined with the Payne model. As it is a second-order traffic flow model, it is able to predict traffic density, average vehicle speed, and traffic flow by three dynamic functions in the model. The most straightforward method to model the stochasticity is to apply a sequential Monte Carlo simulation method to the CTM to mimic the change from free flow traffic conditions to congested conditions [20], but this method has a high computation cost Another way to incorporate the stochastic element is to set the sending and receiving flow as random variables [21]. The following sections of this paper include a literature review of VSL and the original SCTM model; an explanation of the model predictive control- (MPC-) based VSL algorithm; a description of the modified SCTM that is used in this study; presentation of the case study; and, a discussion of the results and conclusion
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