Real-Time multi-objective optimization of safety and mobility at signalized intersections

Reyad, Passant; Sayed, Tarek

doi:10.1080/21680566.2022.2141911

Cited by 5 publications

(2 citation statements)

References 72 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This can be achieved by minimizing the total time spent by vehicles and minimizing the time spent in conflict. A multi-objective optimization framework can be developed for real-time optimization of safety and mobility ( 82 ).…”

Section: Practical Implications Of the Studymentioning

confidence: 99%

Novel Traffic Conflict-Based Framework for Real-Time Traffic Safety Evaluation Under Heterogeneous and Weak Lane-Discipline Traffic

Patel

Gore

Easa

et al. 2023

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

The present study proposed a real-time traffic safety evaluation framework using macroscopic flow variables. To this end, open-access extended vehicle trajectories were employed. Rear-end traffic conflicts and macroscopic traffic flow variables were derived from the trajectory data and were integrated for real-time safety evaluation. The Proportion of Stopping distance ( PSD) accounts for all types of interactions (both safe and unsafe) in the traffic stream; therefore, the same was adopted to analyze the rear-end traffic conflicts. A macroscopic indicator termed “time spent in conflict ( TSC)” was derived to evaluate the rear-end traffic conflicts. Machine learning models, namely, Random Forest (RF), Support Vector Machines (SVM), and eXtreme Gradient Boosting (XGB), were employed to predict TSCs using macroscopic traffic flow variables. The results revealed that the TSC computed based on PSD exhibits a reliable and explainable relationship with the macroscopic traffic flow variables. TSC computed based on PSD revealed that intermediately congested traffic flow conditions are critical in traffic safety and can be attributed to complex traffic phenomena such as traffic hysteresis, traffic oscillations, and increased speed variance. Moreover, a stable relation between traffic safety and traffic flow was suggested for varying threshold values. Among different machine learning models, the RF model was observed as the best-fitted model to predict TSC based on macroscopic traffic variables. TSC quantifies the safety status of a given traffic flow condition, where a higher value of TSC for a particular traffic flow condition indicates that vehicles prevail in the conflicting scenario for a longer time and, therefore, reflect higher operational risk. The developed machine learning model can be employed to predict TSC (operational risk) in real time using the macroscopic traffic flow variables and, therefore, facilitate traffic safety monitoring.

show abstract

Section: Practical Implications Of the Studymentioning

confidence: 99%

Novel Traffic Conflict-Based Framework for Real-Time Traffic Safety Evaluation Under Heterogeneous and Weak Lane-Discipline Traffic

Patel

Gore

Easa

et al. 2023

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

show abstract

“…Several studies have the introduction of a recommended approach for multi-objective optimization approaches to address traffic congestion at signalized intersections [7,8]. The approaches aimed to optimize more than two effective objectives, such as throughput vehicle, the average duration of delays, fuel usage, traffic flow consistency, and the count of vehicle stops, queue ratio, and traffic capacity.…”

Section: Introductionmentioning

confidence: 99%

Enhanced-GA supports traffic signal optimizations and protects the urban environment

Do Van,

Tran Quang,

Le Khanh

et al. 2024

View full text Add to dashboard Cite

Much current research on traffic signal optimization often neglects the impact of environmental factors in urban areas. This can result in suboptimal solutions that do not consider the effects of traffic on air pollution and the overall urban environment. To address this issue, this article proposes a solution that combines Enhanced GA with a comprehensive framework for considering environmental factors in traffic signal optimization. By optimizing traffic signal timings and minimizing emissions, the proposed solution aims to reduce congestion and improve urban transportation networks' efficiency while protecting the environment. The proposed approach uses a set of optimization algorithms and assumptions to generate a comprehensive framework for traffic signal optimization. These algorithms and assumptions consider environmental factors such as air quality and the impact of traffic on the local ecosystem. Moreover, this article provided the enhanced genetic algorithm operators and suggested model formulation that could be applied in other research on traffic signal optimization directly to reduce calculation times and increase the efficiency of the novel suggested models

show abstract

The Integrated optimization of intermittent lane intersection design and dynamic signal control: efficiency, safety, and fuel consumption

Zheng,

Liu,

Liang

et al. 2023

Transportation Letters

View full text Add to dashboard Cite

Real-Time multi-objective optimization of safety and mobility at signalized intersections

Cited by 5 publications

References 72 publications

Novel Traffic Conflict-Based Framework for Real-Time Traffic Safety Evaluation Under Heterogeneous and Weak Lane-Discipline Traffic

Novel Traffic Conflict-Based Framework for Real-Time Traffic Safety Evaluation Under Heterogeneous and Weak Lane-Discipline Traffic

Enhanced-GA supports traffic signal optimizations and protects the urban environment

The Integrated optimization of intermittent lane intersection design and dynamic signal control: efficiency, safety, and fuel consumption

Contact Info

Product

Resources

About