Intelligent Intersection Control for Delay Optimization: Using Meta-Heuristic Search Algorithms

Jamal, Arshad; Rahman, Muhammad Tauhidur; Al-Ahmadi, Hassan M.; Ullah, Irfan; Zahid, Muhammad

doi:10.3390/su12051896

Cited by 66 publications

(40 citation statements)

References 65 publications

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“…Studies conducted in the past have focused on optimizing the signal cycle length, intersection phasing sequence, and offset to enhance certain MOEs, such as average vehicle delay, travel time, number of stops, queue length, fuel consumption, emissions, etc. Delay at intersections is the most common optimization objective addressed by previous studies [31,50,51]. Regression and probability-based methods were initially proposed to approach the problem of traffic light optimization [52,53].…”

Section: Related Workmentioning

confidence: 99%

“…Consequently, approaches based on adaptive traffic control were proposed to mitigate the issues of congested and transient traffic flow [54,55]. Among the different models used for traffic light setting, Transport and Road Research Laboratory (TRRL), Australian Road Research Board (ARRB), and Highway Capacity Manual (HCM) are widely used [31]. With rapid advances in information and communication technologies (such as 5G, Internet of Things (IoT), Dedicated Short Range Communication (DRSC), etc.…”

Section: Related Workmentioning

confidence: 99%

“…Some of the commonly used MOEs for evaluating the intersection level of service (LOS) include average vehicle delays, number of stops, travel time, throughput and traffic capacity, queue lengths, average fuel consumption, and total vehicular emissions (such as CO, NO x , HC), etc. Recently, numerous studies have been conducted to optimize these MOEs at intersections as well as at network levels [30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

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On the Potential Impacts of Smart Traffic Control for Delay, Fuel Energy Consumption, and Emissions: An NSGA-II-Based Optimization Case Study from Dhahran, Saudi Arabia

et al. 2020

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Intelligent traffic control at urban intersections is vital to ensure efficient and sustainable traffic operations. Urban road intersections are hotspots of congestion and traffic accidents. Poor traffic management at these locations could cause numerous issues, such as longer travel time, low travel speed, long vehicle queues, delays, increased fuel consumption, and environmental emissions, and so forth. Previous studies have shown that the mentioned traffic performance measures or measures of effectiveness (MOEs) could be significantly improved by adopting intelligent traffic control protocols. The majority of studies in this regard have focused on mono or bi-objective optimization with homogenous and lane-based traffic conditions. However, decision-makers often have to deal with multiple conflicting objectives to find an optimal solution under heterogeneous stochastic traffic conditions. Therefore, it is essential to determine the optimum decision plan that offers the least conflict among several objectives. Hence, the current study aimed to develop a multi-objective intelligent traffic control protocol based on the non-dominated sorting genetic algorithm II (NSGA-II) at isolated signalized intersections in the city of Dhahran, Kingdom of Saudi Arabia. The MOEs (optimization objectives) that were considered included average vehicle delay, the total number of vehicle stops, average fuel consumption, and vehicular emissions. NSGA-II simulations were run with different initial populations. The study results showed that the proposed method was effective in optimizing considered performance measures along the optimal Pareto front. MOEs were improved in the range of 16% to 23% compared to existing conditions. To assess the efficacy of the proposed approach, an optimization analysis was performed using a Synchro traffic light simulation and optimization tool. Although the Synchro optimization resulted in a relatively lower signal timing plan than NSGA-II, the proposed algorithm outperformed the Synchro optimization results in terms of percentage reduction in MOE values.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On the Potential Impacts of Smart Traffic Control for Delay, Fuel Energy Consumption, and Emissions: An NSGA-II-Based Optimization Case Study from Dhahran, Saudi Arabia

et al. 2020

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“…It is established that strict enforcement could discourage the drivers from committing traffic violations, and significantly reduce the number of crashes, as well as their severity [ 16 ]. Similarly, studies have shown that pro-active traffic control and forecasting could be very beneficial to monitor dynamic drivers maneuvers, thus ensuring strict compliance traffic regulations and mitigate congestion in urban areas [ 23 , 24 , 25 ].…”

Section: Introductionmentioning

confidence: 99%

Adopting Machine Learning and Spatial Analysis Techniques for Driver Risk Assessment: Insights from a Case Study

Zahid

Chen

Jamal

et al. 2020

IJERPH

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Traffic violations usually caused by aggressive driving behavior are often seen as a primary contributor to traffic crashes. Violations are either caused by an unintentional or deliberate act of drivers that jeopardize the lives of fellow drivers, pedestrians, and property. This study is aimed to investigate different traffic violations (overspeeding, wrong-way driving, illegal parking, non-compliance traffic control devices, etc.) using spatial analysis and different machine learning methods. Georeferenced violation data along two expressways (S308 and S219) for the year 2016 was obtained from the traffic police department, in the city of Luzhou, China. Detailed descriptive analysis of the data showed that wrong-way driving was the most common violation type observed. Inverse Distance Weighted (IDW) interpolation in the ArcMap Geographic Information System (GIS) was used to develop violation hotspots zones to guide on efficient use of limited resources during the treatment of high-risk sites. Lastly, a systematic Machine Learning (ML) framework, such as K Nearest Neighbors (KNN) models (using k = 3, 5, 7, 10, and 12), support vector machine (SVM), and CN2 Rule Inducer, was utilized for classification and prediction of each violation type as a function of several explanatory variables. The predictive performance of proposed ML models was examined using different evaluation metrics, such as Area Under the Curve (AUC), F-score, precision, recall, specificity, and run time. The results also showed that the KNN model with k = 7 using manhattan evaluation had an accuracy of 99% and outperformed the SVM and CN2 Rule Inducer. The outcome of this study could provide the practitioners and decision-makers with essential insights for appropriate engineering and traffic control measures to improve the safety of road-users.

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“…Globally, various strategies have been successfully implemented to alleviate the burden of RTCs [ 3 , 4 , 5 , 6 , 7 , 8 ]. Intelligent traffic control and vehicle automation in urban areas are also aimed to ensure safe and sustainable traffic operation [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Exploring the Injury Severity Risk Factors in Fatal Crashes with Neural Network

Jamal

Umer

2020

IJERPH

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A better understanding of circumstances contributing to the severity outcome of traffic crashes is an important goal of road safety studies. An in-depth crash injury severity analysis is vital for the proactive implementation of appropriate mitigation strategies. This study proposes an improved feed-forward neural network (FFNN) model for predicting injury severity associated with individual crashes using three years (2017–2019) of crash data collected along 15 rural highways in the Kingdom of Saudi Arabia (KSA). A total of 12,566 crashes were recorded during the study period with a binary injury severity outcome (fatal or non-fatal injury) for the variable to be predicted. FFNN architecture with back-propagation (BP) as a training algorithm, logistic as activation function, and six number of hidden neurons in the hidden layer yielded the best model performance. Results of model prediction for the test data were analyzed using different evaluation metrics such as overall accuracy, sensitivity, and specificity. Prediction results showed the adequacy and robust performance of the proposed method. A detailed sensitivity analysis of the optimized NN was also performed to show the impact and relative influence of different predictor variables on resulting crash injury severity. The sensitivity analysis results indicated that factors such as traffic volume, average travel speeds, weather conditions, on-site damage conditions, road and vehicle type, and involvement of pedestrians are the most sensitive variables. The methods applied in this study could be used in big data analysis of crash data, which can serve as a rapid-useful tool for policymakers to improve highway safety.

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Intelligent Intersection Control for Delay Optimization: Using Meta-Heuristic Search Algorithms

Cited by 66 publications

References 65 publications

On the Potential Impacts of Smart Traffic Control for Delay, Fuel Energy Consumption, and Emissions: An NSGA-II-Based Optimization Case Study from Dhahran, Saudi Arabia

On the Potential Impacts of Smart Traffic Control for Delay, Fuel Energy Consumption, and Emissions: An NSGA-II-Based Optimization Case Study from Dhahran, Saudi Arabia

Adopting Machine Learning and Spatial Analysis Techniques for Driver Risk Assessment: Insights from a Case Study

Exploring the Injury Severity Risk Factors in Fatal Crashes with Neural Network

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