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In this paper, we present an ensemble stacked generalization (ESG) approach for better prediction of electric vehicles (EVs) energy consumption. ESG is a weighted combination of multiple base regression models to one meta-regressor, which enhances the model prediction and decreases the model variance over a single regressor model. For the current study, we develop ESG by combining three individual base machine learning algorithms, i.e., Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbor (KNN), to predict the EVs’ energy consumption. Tackling the challenge of predicting EVs’ energy consumption, the data were collected from Aichi Prefecture, Japan, combining the digital elevation map with long-term GPS tracking data. EVs energy consumption in terms of energy efficiency (kWh/km) was estimated using several important variables such as average trip speed (km/h), trip distance, nighttime lighting, air conditioner (A/C), heater usage ratio, and road gradient. Several statistical evaluation metrics were used to evaluate the performance of the proposed methods. The prediction results show that ESG is more robust in predicting EVs’ energy consumption and outperformed other models by yielding more acceptable values for proposed evaluation metrics. The results further demonstrate that the accuracy of predictive models for EVs energy consumption can be reasonably accomplished by adopting stacking techniques. The finding of this study could provide essential guidance to decision-makers and practitioners for planned development and optimal placing of EV charging infrastructures in urban areas.
Irfan Ullah; Kai Liu; Toshiyuki Yamamoto; Muhammad Zahid; Arshad Jamal. Electric vehicle energy consumption prediction using stacked generalization: an ensemble learning approach. International Journal of Green Energy 2021, 1 -14.
AMA StyleIrfan Ullah, Kai Liu, Toshiyuki Yamamoto, Muhammad Zahid, Arshad Jamal. Electric vehicle energy consumption prediction using stacked generalization: an ensemble learning approach. International Journal of Green Energy. 2021; ():1-14.
Chicago/Turabian StyleIrfan Ullah; Kai Liu; Toshiyuki Yamamoto; Muhammad Zahid; Arshad Jamal. 2021. "Electric vehicle energy consumption prediction using stacked generalization: an ensemble learning approach." International Journal of Green Energy , no. : 1-14.
Statistical modeling of historical crash data can provide essential insights to safety managers for proactive highway safety management. While numerous studies have contributed to the advancement from the statistical methodological front, minimal research efforts have been dedicated to real-time monitoring of highway safety situations. This study advocates the use of statistical monitoring methods for real-time highway safety surveillance using three years of crash data for rural highways in Saudi Arabia. First, three well-known count data models (Poisson, negative binomial, and Conway–Maxwell–Poisson) are applied to identify the best fit model for the number of crashes. Conway–Maxwell–Poisson was identified as the best fit model, which was used to find the significant explanatory variables for the number of crashes. The results revealed that the road type and road surface conditions significantly contribute to the number of crashes. From the perspective of real-time highway safety monitoring, generalized linear model (GLM)-based exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) control charts are proposed using the randomized quantile residuals and deviance residuals of Conway–Maxwell (COM)–Poisson regression. A detailed simulation-based study is designed for predictive performance evaluation of the proposed control charts with existing counterparts (i.e., Shewhart charts) in terms of the run-length properties. The study results showed that the EWMA type control charts have better detection ability compared with the CUSUM type and Shewhart control charts under small and/or moderate shift sizes. Finally, the proposed monitoring methods are successfully implemented on actual traffic crash data to highlight the efficacy of the proposed methods. The outcome of this study could provide the analysts with insights to plan sound policy recommendations for achieving desired safety goals.
Arshad Jamal; Tahir Mahmood; Muhamad Riaz; Hassan Al-Ahmadi. GLM-Based Flexible Monitoring Methods: An Application to Real-Time Highway Safety Surveillance. Symmetry 2021, 13, 362 .
AMA StyleArshad Jamal, Tahir Mahmood, Muhamad Riaz, Hassan Al-Ahmadi. GLM-Based Flexible Monitoring Methods: An Application to Real-Time Highway Safety Surveillance. Symmetry. 2021; 13 (2):362.
Chicago/Turabian StyleArshad Jamal; Tahir Mahmood; Muhamad Riaz; Hassan Al-Ahmadi. 2021. "GLM-Based Flexible Monitoring Methods: An Application to Real-Time Highway Safety Surveillance." Symmetry 13, no. 2: 362.
Proactive management at mass gatherings is vital to ensure safe crowd evacuation during emergencies. The increasing number of crowd incidents and casualties during the Hajj season is one of the major concerns for authorities in Saudi Arabia. This study aims to explore and analyze crowd dynamics visiting the Prophet's (PBUH) tomb at the visiting (Ziara) corridor in the Holy Mosque of Madinah under continuous flow conditions. MassMotion was used to optimize the crowd flow rate with density restricted to a safe threshold value for efficient crowd management. A robust regression model has been developed to guide the authorities for the safe and efficient operation of the visiting corridor. The study results showed that the crowd flow beyond 9200 persons/h and waiting time in excess of 42 s in front of Moajha might lead to breakdown condition. The output of this study can be utilized by decision-makers and concerned authorities to take appropriate and timely remedial actions to ensure safe, smooth, and efficient crowd management.
Hassan M. Al-Ahmadi; Imran Reza; Arshad Jamal; Wael S. Alhalabi; Khaled J. Assi. Preparedness for Mass Gatherings: A Simulation-Based Framework for Flow Control and Management Using Crowd Monitoring Data. Arabian Journal for Science and Engineering 2021, 46, 4985 -4997.
AMA StyleHassan M. Al-Ahmadi, Imran Reza, Arshad Jamal, Wael S. Alhalabi, Khaled J. Assi. Preparedness for Mass Gatherings: A Simulation-Based Framework for Flow Control and Management Using Crowd Monitoring Data. Arabian Journal for Science and Engineering. 2021; 46 (5):4985-4997.
Chicago/Turabian StyleHassan M. Al-Ahmadi; Imran Reza; Arshad Jamal; Wael S. Alhalabi; Khaled J. Assi. 2021. "Preparedness for Mass Gatherings: A Simulation-Based Framework for Flow Control and Management Using Crowd Monitoring Data." Arabian Journal for Science and Engineering 46, no. 5: 4985-4997.
Predicting pillar stability in underground mines is a critical problem because the instability of the pillar can cause large-scale collapse hazards. To predict the pillar stability for underground coal and stone mines, two new models (random tree and C4.5 decision tree algorithms) are proposed in this paper. Pillar stability depends on the parameters: width of the pillar (W), height of the pillar (H), W/H ratio, uniaxial compressive strength of the rock (σucs), and pillar stress (σp). These parameters are taken as input variables, while underground mines pillar stability as output. Various performance indices, i.e., accuracy, precision, recall, F-measure, Matthews correlation coefficient (MCC) were used to evaluate the performance of the models. The performance evaluation of the established models showed that both models were able to predict pillar stability with reasonable accuracy. Results of the random tree and C4.5 decision tree were also compared with available models of support vector machine (SVM) and fishery discriminant analysis (FDA). The results show that the proposed random tree provides a reliable and feasible method of evaluating the pillar stability for underground mines.
Mahmood Ahmad; Naser A. Al-Shayea; Xiao-Wei Tang; Arshad Jamal; Hasan M. Al-Ahmadi; Feezan Ahmad. Predicting the Pillar Stability of Underground Mines with Random Trees and C4.5 Decision Trees. Applied Sciences 2020, 10, 6486 .
AMA StyleMahmood Ahmad, Naser A. Al-Shayea, Xiao-Wei Tang, Arshad Jamal, Hasan M. Al-Ahmadi, Feezan Ahmad. Predicting the Pillar Stability of Underground Mines with Random Trees and C4.5 Decision Trees. Applied Sciences. 2020; 10 (18):6486.
Chicago/Turabian StyleMahmood Ahmad; Naser A. Al-Shayea; Xiao-Wei Tang; Arshad Jamal; Hasan M. Al-Ahmadi; Feezan Ahmad. 2020. "Predicting the Pillar Stability of Underground Mines with Random Trees and C4.5 Decision Trees." Applied Sciences 10, no. 18: 6486.
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.
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 StyleMohammed 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 StyleMohammed 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.
Examining the relationships between vehicle crash patterns and urban land use is fundamental to improving crash predictions, creating guidance, and comprehensive policy recommendations to avoid crash occurrences and mitigate their severities. In the existing literature, statistical models are frequently used to quantify the association between crash outcomes and available explanatory variables. However, they are unable to capture the latent spatial heterogeneity accurately. Further, the vast majority of previous studies have focused on detailed spatial analysis of crashes from an aggregated viewpoint without considering the attributes of the built environment and land use. This study first uses geographic information systems (GIS) to examine crash hotspots based on two severity groups, seven prevailing crash causes, and three predominant crash types in the City of Dammam, Kingdom of Saudi Arabia (KSA). GIS-based geographically weighted regression (GWR) analysis technique was then utilized to uncover the spatial relationships of traffic collisions with population densities and relate it to the land use of each neighborhood. Results showed that Fatal and Injury (FI) crashes were mostly located in residential neighborhoods and near public facilities having low to medium population densities on highways with relatively higher speed limits. Distribution of hotspots and GWR-based analysis for crash causes showed that crashes due to “sudden lane deviation” accounted for the highest proportion of crashes that were concentrated mainly in the Central Business District (CBD) of the study area. Similarly, hotspots and GWR analysis for crash types revealed that “collisions between motor vehicles” constitute a significant proportion of the total crashes, with epicenters mostly stationed in high-density residential neighborhoods. The outcomes of this study could provide analysts and practitioners with crucial insights to understand the complex inter-relationships between traffic safety and land use. It can provide useful guidance to policymakers for better planning and effective management strategies to enhance safety at zonal levels.
Muhammad Tauhidur Rahman; Arshad Jamal; Hassan M. Al-Ahmadi. Examining Hotspots of Traffic Collisions and their Spatial Relationships with Land Use: A GIS-Based Geographically Weighted Regression Approach for Dammam, Saudi Arabia. ISPRS International Journal of Geo-Information 2020, 9, 540 .
AMA StyleMuhammad Tauhidur Rahman, Arshad Jamal, Hassan M. Al-Ahmadi. Examining Hotspots of Traffic Collisions and their Spatial Relationships with Land Use: A GIS-Based Geographically Weighted Regression Approach for Dammam, Saudi Arabia. ISPRS International Journal of Geo-Information. 2020; 9 (9):540.
Chicago/Turabian StyleMuhammad Tauhidur Rahman; Arshad Jamal; Hassan M. Al-Ahmadi. 2020. "Examining Hotspots of Traffic Collisions and their Spatial Relationships with Land Use: A GIS-Based Geographically Weighted Regression Approach for Dammam, Saudi Arabia." ISPRS International Journal of Geo-Information 9, no. 9: 540.
Waterborne diseases have become one of the major public health concerns worldwide. This study is aimed to investigate and develop spatial distribution mapping of the potable water quality parameters in the city of Peshawar, Pakistan. A total of 108 water samples collected across the entire study area were subjected to physio-chemical and biological analyses. Tested parameters included pH, turbidity, temperature, fluoride concentration levels, and bacterial counts (faecal coliforms). Inverse distance weighting (IDW) interpolation in geographic information systems (GIS) was used for spatial analysis. Test results revealed that 48% of water samples had faecal coliforms count (per 100 mL) greater than World Health Organization (WHO) minimum limits, while 31% of samples had fluoride concentrations in excess of the WHO maximum guide values. Spatial distribution mapping was developed for faecal coliforms count and fluoride ion concentration using ArcGIS to highlight the high-risk settlements in the study area. Results showed that around 20% area under faecal coliforms and approximately 33% area based on fluoride concentrations fall under the need for treatment category. The pH and turbidity were found in compliance with WHO desirable limits. The sanitary inspection score significantly depicted that ineffective multi-barrier approaches consequently deteriorated the water quality at the consumer’s end. Findings from the present study shall be useful to policymakers for adopting necessary remedial measures before it severely affects public health.
Mahmood Ahmad; Arshad Jamal; Xiao-Wei Tang; Mohammed A. Al-Sughaiyer; Hassan M. Al-Ahmadi; Feezan Ahmad. Assessing Potable Water Quality and Identifying Areas of Waterborne Diarrheal and Fluorosis Health Risks Using Spatial Interpolation in Peshawar, Pakistan. Water 2020, 12, 2163 .
AMA StyleMahmood Ahmad, Arshad Jamal, Xiao-Wei Tang, Mohammed A. Al-Sughaiyer, Hassan M. Al-Ahmadi, Feezan Ahmad. Assessing Potable Water Quality and Identifying Areas of Waterborne Diarrheal and Fluorosis Health Risks Using Spatial Interpolation in Peshawar, Pakistan. Water. 2020; 12 (8):2163.
Chicago/Turabian StyleMahmood Ahmad; Arshad Jamal; Xiao-Wei Tang; Mohammed A. Al-Sughaiyer; Hassan M. Al-Ahmadi; Feezan Ahmad. 2020. "Assessing Potable Water Quality and Identifying Areas of Waterborne Diarrheal and Fluorosis Health Risks Using Spatial Interpolation in Peshawar, Pakistan." Water 12, no. 8: 2163.
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.
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 StyleMuhammad 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 StyleMuhammad 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.
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.
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 StyleArshad 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 StyleArshad 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.
Road traffic crashes (RTCs) are one of the most critical public health problems worldwide. The WHO Global Status Report on Road Safety suggests that the annual fatality rate (per 100,000 people) due to RTCs in the Kingdom of Saudi Arabia (KSA) has increased from 17.4 to 27.4 over the last decade, which is an alarming situation. This paper presents an overview of RTCs in the Eastern Province, KSA, from 2009 to 2016. Key descriptive statistics for spatial and temporal distribution of crashes are presented. Statistics from the present study suggest that the year 2012 witnessed the highest number of crashes, and that the region Al-Ahsa had a significantly higher proportion of total crashes. It was concluded that the fatality rate for the province was 25.6, and the mean accident to injury ratio was 8:4. These numbers are substantially higher compared to developed countries and the neighboring Gulf states. Spatial distribution of crashes indicated that a large proportion of severe crashes occurred outside the city centers along urban highways. Logistic regression models were developed to predict crash severity. Model estimation analysis revealed that crash severity can be attributed to several significant factors including driver attributes (such as sleep, distraction, overspeeding), crash characteristics (such as sudden deviation from the lane, or collisions with other moving vehicles, road fences, pedestrians, or motorcyclists), and rainy weather conditions. After critical analysis of existing safety and infrastructure situations, various suitable crash prevention and mitigation strategies, for example, traffic enforcement, traffic calming measures, safety education programs, and coordination of key stakeholders, have been proposed.
Arshad Jamal; Muhammad Tauhidur Rahman; Hassan M. Al-Ahmadi; Umer Mansoor. The Dilemma of Road Safety in the Eastern Province of Saudi Arabia: Consequences and Prevention Strategies. International Journal of Environmental Research and Public Health 2019, 17, 157 .
AMA StyleArshad Jamal, Muhammad Tauhidur Rahman, Hassan M. Al-Ahmadi, Umer Mansoor. The Dilemma of Road Safety in the Eastern Province of Saudi Arabia: Consequences and Prevention Strategies. International Journal of Environmental Research and Public Health. 2019; 17 (1):157.
Chicago/Turabian StyleArshad Jamal; Muhammad Tauhidur Rahman; Hassan M. Al-Ahmadi; Umer Mansoor. 2019. "The Dilemma of Road Safety in the Eastern Province of Saudi Arabia: Consequences and Prevention Strategies." International Journal of Environmental Research and Public Health 17, no. 1: 157.
Sustainable transportation systems play a key role in the socio-economic development of a country. Microscopic simulation models are becoming increasingly useful tools in designing, optimizing, and evaluating the sustainability of transportation systems and concerned management strategies. VISSIM, a microscopic traffic simulation software, has gained rapid recognition in the field of traffic simulation. However, default values for different input parameters used during simulation need to be tested to ensure a realistic replication for local traffic conditions. This paper attempts to model driving behavior parameters using the microscopic simulation software VISSIM through a case study in the Khobar-Dammam metropolitan areas in Saudi Arabia. VISSIM default values for different sensitive parameters such as lane change distances, additive and multiplicative parts of desired safety distances, the number of preceding vehicles spotted, amber signal decisions, and minimum headway were identified to be most sensitive and significant parameters to be calibrated to precisely replicate field conditions. The simulation results using default values produced higher link speed, larger queue length, and shorter travel times than those observed in the field. However, measures of effectiveness (MOEs) obtained from calibrated models over desired simulation runs were comparable to those obtained from field surveys. All compared MOEs used to validate the model matched within a range of 5–10% to the field-observed values.
Hassan M. Al-Ahmadi; Arshad Jamal; Imran Reza; Khaled J. Assi; Syed Anees Ahmed. Using Microscopic Simulation-Based Analysis to Model Driving Behavior: A Case Study of Khobar-Dammam in Saudi Arabia. Sustainability 2019, 11, 3018 .
AMA StyleHassan M. Al-Ahmadi, Arshad Jamal, Imran Reza, Khaled J. Assi, Syed Anees Ahmed. Using Microscopic Simulation-Based Analysis to Model Driving Behavior: A Case Study of Khobar-Dammam in Saudi Arabia. Sustainability. 2019; 11 (11):3018.
Chicago/Turabian StyleHassan M. Al-Ahmadi; Arshad Jamal; Imran Reza; Khaled J. Assi; Syed Anees Ahmed. 2019. "Using Microscopic Simulation-Based Analysis to Model Driving Behavior: A Case Study of Khobar-Dammam in Saudi Arabia." Sustainability 11, no. 11: 3018.
In recent years, car sharing has emerged as a novel alternative to private car ownership in urban areas worldwide. Potential benefits of this system include improved mobility and reduced congestion, vehicle ownership, parking issues, and greenhouse gas (GHG) emissions. This study aimed to investigate travelers’ acceptance of car sharing systems through a stated preference survey in the city of Peshawar, Pakistan. The questionnaires were distributed online via a Google form. Questions were designed from numerous aspects of car sharing systems, such as awareness of car sharing systems, attributes related to travel modes in the choice set, and demographic characteristics. A total of 453 valid responses were received. The Multinomial and Nested Logit models were employed for evaluation and analysis of survey responses. Demographic characteristics including gender, job, and income were found to be significant. Service attributes including travel time, travel cost, registration fees, and capital cost, were also significant. The multinomial logit model based on both car-owners and non-car-owners fit a little better than the nested logit model. Our findings in the present study could be beneficial for transport planners and policy makers to timely implement car sharing systems in cities in order to mitigate increased car ownership and traffic congestion.
Irfan Ullah; Kai Liu; Tran Vanduy. Examining Travelers’ Acceptance towards Car Sharing Systems—Peshawar City, Pakistan. Sustainability 2019, 11, 808 .
AMA StyleIrfan Ullah, Kai Liu, Tran Vanduy. Examining Travelers’ Acceptance towards Car Sharing Systems—Peshawar City, Pakistan. Sustainability. 2019; 11 (3):808.
Chicago/Turabian StyleIrfan Ullah; Kai Liu; Tran Vanduy. 2019. "Examining Travelers’ Acceptance towards Car Sharing Systems—Peshawar City, Pakistan." Sustainability 11, no. 3: 808.
Purpose The purpose of this study is to analyze the crowd dynamics of the visitors at Al-Masjid al-Nabawi during the most oversaturated period to characterize the most critical conditions and suggest technical solutions to accommodate visitors and provide them safe passage. Design/methodology/approach In this study, the time of entrance from the Al-Salam Gate to the tomb and from the tomb to the exit from the Al-Baqi’ Gate has been collected in the most oversaturated period. To be precise and to model the worst case, important crowd measures of effectiveness data are collected in the two holiest times considered by Muslims, during the holy month of Ramadan and the month of Dhul-Hijjah and during the busiest hours of the day to consider safety factors while proposing future solutions. The conventional manual head-counting method has been adopted to determine the crowd density and to carry out actual counting of the visitors from the recorded videos and photos captured by the legitimate authority. Findings The analyses revealed that the crowd dynamics in the month of Ramadan (peak) are statistically different from those for other times (off peak). In general, the crowd dynamics at all times on days other than Ramadan are almost identical. Originality/value The results of crowd characterization from this study are expected to help optimize crowd management in the Masjid at the most critical location and time. The data collected in this study could be used for future research to simulate similar crowd scenes or for even different crowd management scenarios in case of emergencies such as fire hazards or evacuation process.
Hassan M. Al-Ahmadi; Wael S. Alhalabi; Rezqallah Hasan Malkawi; Imran Reza. Statistical analysis of the crowd dynamics in Al-Masjid Al-Nabawi in the city of Medina, Saudi Arabia. International Journal of Crowd Science 2018, 2, 52 -63.
AMA StyleHassan M. Al-Ahmadi, Wael S. Alhalabi, Rezqallah Hasan Malkawi, Imran Reza. Statistical analysis of the crowd dynamics in Al-Masjid Al-Nabawi in the city of Medina, Saudi Arabia. International Journal of Crowd Science. 2018; 2 (1):52-63.
Chicago/Turabian StyleHassan M. Al-Ahmadi; Wael S. Alhalabi; Rezqallah Hasan Malkawi; Imran Reza. 2018. "Statistical analysis of the crowd dynamics in Al-Masjid Al-Nabawi in the city of Medina, Saudi Arabia." International Journal of Crowd Science 2, no. 1: 52-63.
This study is focused on the problems faced by female students residing in the Kingdom of Saudi Arabia. Many of these female students live far from their colleges and they have to travel from villages to cities in order to obtain a university degree. In so doing they travel long distances everyday using various modes of transport. As they are not permitted to drive, these students depend on male drivers to take them to their colleges. The aim of this study was to investigate and identify problems associated with such modes of transport. Results show that most students experience some problems and encounter disproportionate levels of hardship. The findings were based on a survey carried out in the Kingdom and was distributed to representative sample of female students. This paper includes a detailed analysis of the data from which a number of conclusions and recommendations were made. The conclusions suggested that the travel situation encountered by female students who travel by passenger car could be improved in terms of time, cost and convenience. Thirteen percent of female students rated the vehicles they used to be in unacceptable condition for traveling while 6% marked the drivers as not violating traffic rules, and the majority (53%) was captives to their current mode of transportation.
H.M. Al-Ahmadi. Travel Characteristics of Female Students to Colleges in the Kingdom of Saudi Arabia. The Journal of Engineering Research [TJER] 2006, 3, 79 .
AMA StyleH.M. Al-Ahmadi. Travel Characteristics of Female Students to Colleges in the Kingdom of Saudi Arabia. The Journal of Engineering Research [TJER]. 2006; 3 (1):79.
Chicago/Turabian StyleH.M. Al-Ahmadi. 2006. "Travel Characteristics of Female Students to Colleges in the Kingdom of Saudi Arabia." The Journal of Engineering Research [TJER] 3, no. 1: 79.
Road construction on sabkha terrain along the coastal regions of the Arabian Gulf and the Red Sea is often faced with different types of damage due to the low bearing capacity of sabkha deposits, especially when they are wetted. Such conditions necessitate the improvement of sabkha prior to any construction. The purpose of this investigation was to upgrade the load-carrying capacity of sabkha soil using geotextiles by varying the geotextile type, base thickness, moisture condition and the magnitude of the deviatoric stress. To achieve these objectives, a special mould was fabricated to accommodate the soil-fabric-aggregate (SFA) systems. The performance of SFA systems was evaluated by measuring the permanent deformation under the applied ‘dynamic’ load. Results of this study indicate that the selection of an appropriate geotextile type can bring about significant improvement in the load-carrying capacity of the water-sensitive sabkha soils, particularly under soaked conditions. The inclusion of a geotextile layer on top of the sabkha subgrade reduced the thickness of the graded base layer by 34%. In addition, the geotextile increased the stiffness of the sabkha subgrade and reduced the permanent deformation after a certain number of load reputations. Road construction on sabkha terrain along the coastal regions of the Arabian Gulf and the Red Sea is often faced with different types of damage due to the low bearing capacity of sabkha deposits, especially when they are wetted. Such conditions necessitate the improvement of sabkha prior to any construction. The purpose of this investigation was to upgrade the load-carrying capacity of sabkha soil using geotextiles by varying the geotextile type, base thickness, moisture condition and the magnitude of the deviatoric stress. To achieve these objectives, a special mould was fabricated to accommodate the soil-fabric-aggregate (SFA) systems. The performance of SFA systems was evaluated by measuring the permanent deformation under the applied ‘dynamic’ load. Results of this study indicate that the selection of an appropriate geotextile type can bring about significant improvement in the load-carrying capacity of the water-sensitive sabkha soils, particularly under soaked conditions. The inclusion of a geotextile layer on top of the sabkha subgrade reduced the thickness of the graded base layer by 34%. In addition, the geotextile increased the stiffness of the sabkha subgrade and reduced the permanent deformation after a certain number of load reputations.
S. A. Aiban; Z. U. Siddiqi; O. S. Baghabra Al-Amoudi; H. M. Al-Ahmadi; I. M. Asi. Effect of geotextiles on the load-carrying capacity and deformation characteristics of sabkha soil. Geosynthetics International 2006, 13, 98 -110.
AMA StyleS. A. Aiban, Z. U. Siddiqi, O. S. Baghabra Al-Amoudi, H. M. Al-Ahmadi, I. M. Asi. Effect of geotextiles on the load-carrying capacity and deformation characteristics of sabkha soil. Geosynthetics International. 2006; 13 (3):98-110.
Chicago/Turabian StyleS. A. Aiban; Z. U. Siddiqi; O. S. Baghabra Al-Amoudi; H. M. Al-Ahmadi; I. M. Asi. 2006. "Effect of geotextiles on the load-carrying capacity and deformation characteristics of sabkha soil." Geosynthetics International 13, no. 3: 98-110.