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Muhammad Zahid
College of Metropolitan Transportation, Beijing University of Technology, Beijing, 100124, China

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Journal article
Published: 21 March 2021 in Accident Analysis & Prevention
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Motorcycles and motorcyclists have a variety of attributes that have been found to be a potential contributor to the high liability of vulnerable road users (VRUs). Vulnerable Road Users (VRUs) that include pedestrians, bicyclists, cycle-rickshaw occupants, and motorcyclists constitute by far the highest share of road traffic accidents in developing countries. Motorized three-wheeled Rickshaws (3W-MR) is a popular public transport mode in almost all Pakistani cities and is used primarily for short trips to carry passengers and small-scale goods movement. Despite being an important mode of public transport in the developing world, little work has been done to understand the factors affecting the injury severity of three-wheeled motorized vehicles. Crash injury severity prediction is a promising research target in traffic safety. Traditional statistical models have underlying assumptions and predefined associations, which can yield misleading results if flouted. Machine learning(ML) is an emerging non-parametric method that can effectively capture the non-linear effects of both continuous and discrete variables without prior assumptions and achieve better prediction accuracy. This research analyzed injury severity of three-wheeled motorized rickshaws (3W-MR) using various machine learning-based identification algorithms, i.e., Decision jungle (DJ), Random Forest (RF), and Decision Tree (DT). Three years of crash data (from 2017 to 2019) was collected from Provincial Emergency Response Service RESCUE 1122 for Rawalpindi city, Pakistan. A total of 2,743 3W-MR crashes were reported during the study period that resulted in 258 fatalities. The predictive performance of proposed ML models was assessed using several evaluation metrics such as overall accuracy, macro-average precision, macro-average recall, and geometric means of individual class accuracies. Results revealed that DJ with an overall accuracy of 83.7 % outperformed the DT and RF-based on a stratified 10-fold cross-validation approach. Finally, Spearman correlation analysis showed that factors such as the lighting condition, crashes involving young drivers (aged 20–30 years), facilities with high-speed limits (over 60 mph), weekday, off-peak, and shiny weather conditions were more likely to worsen injury severity of 3W-MR crashes. The outcomes of this study could provide necessary and essential guidance to road safety agencies, particularly in the study area, for proactive implementation of appropriate countermeasures to curb road safety issues pertaining to three-wheeled motorized vehicles.

ACS Style

Muhammad Ijaz; Liu Lan; Muhammad Zahid; Arshad Jamal. A comparative study of machine learning classifiers for injury severity prediction of crashes involving three-wheeled motorized rickshaw. Accident Analysis & Prevention 2021, 154, 106094 .

AMA Style

Muhammad Ijaz, Liu Lan, Muhammad Zahid, Arshad Jamal. A comparative study of machine learning classifiers for injury severity prediction of crashes involving three-wheeled motorized rickshaw. Accident Analysis & Prevention. 2021; 154 ():106094.

Chicago/Turabian Style

Muhammad Ijaz; Liu Lan; Muhammad Zahid; Arshad Jamal. 2021. "A comparative study of machine learning classifiers for injury severity prediction of crashes involving three-wheeled motorized rickshaw." Accident Analysis & Prevention 154, no. : 106094.

Journal article
Published: 09 September 2020 in Sustainability
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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.

ACS Style

Mohammed Al-Turki; Arshad Jamal; Hassan M. Al-Ahmadi; Mohammed A. Al-Sughaiyer; Muhammad Zahid. 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. Sustainability 2020, 12, 7394 .

AMA Style

Mohammed Al-Turki, Arshad Jamal, Hassan M. Al-Ahmadi, Mohammed A. Al-Sughaiyer, Muhammad Zahid. 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. Sustainability. 2020; 12 (18):7394.

Chicago/Turabian Style

Mohammed Al-Turki; Arshad Jamal; Hassan M. Al-Ahmadi; Mohammed A. Al-Sughaiyer; Muhammad Zahid. 2020. "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." Sustainability 12, no. 18: 7394.

Journal article
Published: 18 July 2020 in International Journal of Environmental Research and Public Health
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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.

ACS Style

Muhammad Zahid; Yangzhou Chen; Arshad Jamal; Khalaf A. Al-Ofi; Hassan M. Al-Ahmadi. Adopting Machine Learning and Spatial Analysis Techniques for Driver Risk Assessment: Insights from a Case Study. International Journal of Environmental Research and Public Health 2020, 17, 5193 .

AMA Style

Muhammad Zahid, Yangzhou Chen, Arshad Jamal, Khalaf A. Al-Ofi, Hassan M. Al-Ahmadi. Adopting Machine Learning and Spatial Analysis Techniques for Driver Risk Assessment: Insights from a Case Study. International Journal of Environmental Research and Public Health. 2020; 17 (14):5193.

Chicago/Turabian Style

Muhammad Zahid; Yangzhou Chen; Arshad Jamal; Khalaf A. Al-Ofi; Hassan M. Al-Ahmadi. 2020. "Adopting Machine Learning and Spatial Analysis Techniques for Driver Risk Assessment: Insights from a Case Study." International Journal of Environmental Research and Public Health 17, no. 14: 5193.

Journal article
Published: 02 June 2020 in International Journal of Environmental Research and Public Health
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Risky and aggressive driving maneuvers are considered a significant indicator for traffic accident occurrence as well as they aggravate their severity. Traffic violations caused by such uncivilized driving behavior is a global issue. Studies in existing literature have used statistical analysis methods to explore key contributing factors toward aggressive driving and traffic violations. However, such methods are unable to capture latent correlations among predictor variables, and they also suffer from low prediction accuracies. This study aimed to comprehensively investigate different traffic violations using spatial analysis and machine learning methods in the city of Luzhou, China. Violations committed by taxi drivers are the focus of the current study since they constitute a significant proportion of total violations reported in the city. Georeferenced violation data for the year 2016 was obtained from the traffic police department. Detailed descriptive analysis is presented to summarize key statistics about various violation types. Results revealed that over-speeding was the most prevalent violation type observed in the study area. Frequency-based nearest neighborhood cluster methods in Arc map Geographic Information System (GIS) were used to develop hotspot maps for different violation types that are vital for prioritizing and conducting treatment alternatives efficiently. Finally, different machine learning (ML) methods, including decision tree, AdaBoost with a base estimator decision tree, and stack model, were employed to predict and classify each violation type. The proposed methods were compared based on different evaluation metrics like accuracy, F-1 measure, specificity, and log loss. Prediction results demonstrated the adequacy and robustness of proposed machine learning (ML) methods. However, a detailed comparative analysis showed that the stack model outperformed other models in terms of proposed evaluation metrics.

ACS Style

Muhammad Zahid; Yangzhou Chen; Sikandar Khan; Arshad Jamal; Muhammad Ijaz; Tufail Ahmed. Predicting Risky and Aggressive Driving Behavior among Taxi Drivers: Do Spatio-Temporal Attributes Matter? International Journal of Environmental Research and Public Health 2020, 17, 3937 .

AMA Style

Muhammad Zahid, Yangzhou Chen, Sikandar Khan, Arshad Jamal, Muhammad Ijaz, Tufail Ahmed. Predicting Risky and Aggressive Driving Behavior among Taxi Drivers: Do Spatio-Temporal Attributes Matter? International Journal of Environmental Research and Public Health. 2020; 17 (11):3937.

Chicago/Turabian Style

Muhammad Zahid; Yangzhou Chen; Sikandar Khan; Arshad Jamal; Muhammad Ijaz; Tufail Ahmed. 2020. "Predicting Risky and Aggressive Driving Behavior among Taxi Drivers: Do Spatio-Temporal Attributes Matter?" International Journal of Environmental Research and Public Health 17, no. 11: 3937.

Journal article
Published: 02 March 2020 in Sustainability
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Traffic signal control is an integral component of an intelligent transportation system (ITS) that play a vital role in alleviating traffic congestion. Poor traffic management and inefficient operations at signalized intersections cause numerous problems as excessive vehicle delays, increased fuel consumption, and vehicular emissions. Operational performance at signalized intersections could be significantly enhanced by optimizing phasing and signal timing plans using intelligent traffic control methods. Previous studies in this regard have mostly focused on lane-based homogenous traffic conditions. However, traffic patterns are usually non-linear and highly stochastic, particularly during rush hours, which limits the adoption of such methods. Hence, this study aims to develop metaheuristic-based methods for intelligent traffic control at isolated signalized intersections, in the city of Dhahran, Saudi Arabia. Genetic algorithm (GA) and differential evolution (DE) were employed to enhance the intersection’s level of service (LOS) by optimizing the signal timings plan. Average vehicle delay through the intersection was selected as the primary performance index and algorithms objective function. The study results indicated that both GA and DE produced a systematic signal timings plan and significantly reduced travel time delay ranging from 15 to 35% compared to existing conditions. Although DE converged much faster to the objective function, GA outperforms DE in terms of solution quality i.e., minimum vehicle delay. To validate the performance of proposed methods, cycle length-delay curves from GA and DE were compared with optimization outputs from TRANSYT 7F, a state-of-the-art traffic signal simulation, and optimization tool. Validation results demonstrated the adequacy and robustness of proposed methods.

ACS Style

Arshad Jamal; Muhammad Tauhidur Rahman; Hassan M. Al-Ahmadi; Irfan Ullah; Muhammad Zahid. Intelligent Intersection Control for Delay Optimization: Using Meta-Heuristic Search Algorithms. Sustainability 2020, 12, 1896 .

AMA Style

Arshad Jamal, Muhammad Tauhidur Rahman, Hassan M. Al-Ahmadi, Irfan Ullah, Muhammad Zahid. Intelligent Intersection Control for Delay Optimization: Using Meta-Heuristic Search Algorithms. Sustainability. 2020; 12 (5):1896.

Chicago/Turabian Style

Arshad Jamal; Muhammad Tauhidur Rahman; Hassan M. Al-Ahmadi; Irfan Ullah; Muhammad Zahid. 2020. "Intelligent Intersection Control for Delay Optimization: Using Meta-Heuristic Search Algorithms." Sustainability 12, no. 5: 1896.

Journal article
Published: 27 January 2020 in Sensors
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Short-term traffic state prediction has become an integral component of an advanced traveler information system (ATIS) in intelligent transportation systems (ITS). Accurate modeling and short-term traffic prediction are quite challenging due to its intricate characteristics, stochastic, and dynamic traffic processes. Existing works in this area follow different modeling approaches that are focused to fit speed, density, or the volume data. However, the accuracy of such modeling approaches has been frequently questioned, thereby traffic state prediction over the short-term from such methods inflicts an overfitting issue. We address this issue to accurately model short-term future traffic state prediction using state-of-the-art models via hyperparameter optimization. To do so, we focused on different machine learning classifiers such as local deep support vector machine (LD-SVM), decision jungles, multi-layers perceptron (MLP), and CN2 rule induction. Moreover, traffic states are evaluated using traffic attributes such as level of service (LOS) horizons and simple if–then rules at different time intervals. Our findings show that hyperparameter optimization via random sweep yielded superior results. The overall prediction performances obtained an average improvement by over 95%, such that the decision jungle and LD-SVM achieved an accuracy of 0.982 and 0.975, respectively. The experimental results show the robustness and superior performances of decision jungles (DJ) over other methods.

ACS Style

Muhammad Zahid; Yangzhou Chen; Arshad Jamal; Muhammad Qasim Memon. Short Term Traffic State Prediction via Hyperparameter Optimization Based Classifiers. Sensors 2020, 20, 685 .

AMA Style

Muhammad Zahid, Yangzhou Chen, Arshad Jamal, Muhammad Qasim Memon. Short Term Traffic State Prediction via Hyperparameter Optimization Based Classifiers. Sensors. 2020; 20 (3):685.

Chicago/Turabian Style

Muhammad Zahid; Yangzhou Chen; Arshad Jamal; Muhammad Qasim Memon. 2020. "Short Term Traffic State Prediction via Hyperparameter Optimization Based Classifiers." Sensors 20, no. 3: 685.

Journal article
Published: 16 January 2020 in Sustainability
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Short-term traffic speed prediction is vital for proactive traffic control, and is one of the integral components of an intelligent transportation system (ITS). Accurate prediction of short-term travel speed has numerous applications for traffic monitoring, route planning, as well as helping to relieve traffic congestion. Previous studies have attempted to approach this problem using statistical and conventional artificial intelligence (AI) methods without accounting for influence of data collection time-horizons. However, statistical methods have received widespread criticism concerning prediction accuracy performance, while traditional AI approaches have too shallow architecture to capture non-linear stochastics variations in traffic flow. Hence, this study aims to explore prediction of short-term traffic speed at multiple time-ahead intervals using data collected from loop detectors. A fast forest quantile regression (FFQR) via hyperparameters optimization was introduced for predicting short-term traffic speed prediction. FFQR is an ensemble machine learning model that combines several regression trees to improve speed prediction accuracy. The accuracy of short-term traffic speed prediction was compared using the FFQR model at different data collection time-horizons. Prediction results demonstrated the adequacy and robustness of the proposed approach under different scenarios. It was concluded that prediction performance of FFQR was significantly enhanced and robust, particularly at time intervals larger than 5 min. The findings also revealed that speed prediction error (in terms of quantiles loss) ranged between 0.58 and 1.18.

ACS Style

Muhammad Zahid; Yangzhou Chen; Arshad Jamal; Coulibaly Zie Mamadou. Freeway Short-Term Travel Speed Prediction Based on Data Collection Time-Horizons: A Fast Forest Quantile Regression Approach. Sustainability 2020, 12, 646 .

AMA Style

Muhammad Zahid, Yangzhou Chen, Arshad Jamal, Coulibaly Zie Mamadou. Freeway Short-Term Travel Speed Prediction Based on Data Collection Time-Horizons: A Fast Forest Quantile Regression Approach. Sustainability. 2020; 12 (2):646.

Chicago/Turabian Style

Muhammad Zahid; Yangzhou Chen; Arshad Jamal; Coulibaly Zie Mamadou. 2020. "Freeway Short-Term Travel Speed Prediction Based on Data Collection Time-Horizons: A Fast Forest Quantile Regression Approach." Sustainability 12, no. 2: 646.