Abstract
Foggy weather seriously deteriorates the performance of freeway systems, particularly regarding traffic safety and efficiency. General macroscopic traffic models have difficulty reflecting the characteristics of a freeway under foggy weather conditions. In the present study, a macroscopic traffic model using a correction factor under foggy weather conditions is therefore proposed, which is regulated according to the different levels of visibility and curve radius of the freeway using the Takagi–Sugeno (T‐S) model. Based on the proposed traffic model, a local ramp metering strategy with density correction under foggy weather conditions is proposed to improve traffic safety. The proposed local ramp metering strategy regulates the on‐ramp flow using the T‐S model according to the mainstream density, speed, and visibility. The correction factors are determined based on the parameters of the consequent part in the T‐S model, which are optimized using the particle swarm optimization algorithm. The sum of the mean absolute percentage error of the mainstream traffic density and speed is used to evaluate the proposed traffic model. The real‐time crash‐risk prediction model, which reflects the degree of traffic safety, is used to evaluate the proposed local ramp metering strategy. Simulations using VISSIM and MATLAB show that the proposed traffic model is suitable under foggy weather conditions and that the proposed local ramp metering strategy achieves a better performance in reducing fog‐related crashes.
Highlights
Foggy weather deepens the uncertainty, complexity, and randomness of freeway systems and brings about a decrease in tra c e ciency and an increase in the number of crashes [1]
According to equations (20)–(27), a real-time crash-risk prediction model reflecting traffic safety is used to evaluate the proposed local ramp metering. e data on traffic incidents are obtained through a simulation in VISSIM. e location of a traffic incident is shown in Figure 3. e time interval is Δt 10 s, the time period is ΔT 60 s, and N1 ΔT/Δt 6 is applied
Because the mainstream density of a freeway is always lower than the desired density, the flow of on-ramp R2 is not controlled by PI-Asservissement Lineaire d’Entree Autoroutiere (ALINEA)
Summary
Foggy weather deepens the uncertainty, complexity, and randomness of freeway systems and brings about a decrease in tra c e ciency and an increase in the number of crashes [1]. Tra c management strategies for improving tra c safety and e ciency under foggy weather conditions can be divided into two types: advisory strategies and control strategies [3]. Automatic visibility warning systems estimate a safe tra c speed for motorists based on the real-time visibility of the freeway as derived from visibility sensors to reduce fogrelated crashes [5]. Real-time speed recommendations derived from visibility warning systems can be conveyed using an intelligent transportation system [6, 7]. E Utah Department of Transportation used an Adverse Visibility Information System Evaluation, Complexity which provides real-time speed recommendations for motorists, to reduce fog-related crashes [8, 9]. Visibility warning systems have significant challenges in terms of cost and the application of appropriate sensor technologies
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
