The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Fadel M Megahed - One of the best experts on this subject based on the ideXlab platform.
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a review of data analytic applications in Road Traffic Safety part 1 descriptive and predictive modeling
Sensors, 2020Co-Authors: Amir Mehdizadeh, Nasrin Mohabbatikalejahi, Mohammad Ali Alamdar Yazdi, Alexander Vinel, Steven E Rigdon, Karen C Davis, Qiong Hu, Fadel M MegahedAbstract:This part of the review aims to reduce the start-up burden of data collection and descriptive analytics for statistical modeling and route optimization of risk associated with motor vehicles. From a data-driven bibliometric analysis, we show that the literature is divided into two disparate research streams: (a) predictive or explanatory models that attempt to understand and quantify crash risk based on different driving conditions, and (b) optimization techniques that focus on minimizing crash risk through route/path-selection and rest-break scheduling. Translation of research outcomes between these two streams is limited. To overcome this issue, we present publicly available high-quality data sources (different study designs, outcome variables, and predictor variables) and descriptive analytic techniques (data summarization, visualization, and dimension reduction) that can be used to achieve safer-routing and provide code to facilitate data collection/exploration by practitioners/researchers. Then, we review the statistical and machine learning models used for crash risk modeling. We show that (near) real-time crash risk is rarely considered, which might explain why the optimization models (reviewed in Part 2) have not capitalized on the research outcomes from the first stream.
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a review of data analytic applications in Road Traffic Safety part 2 prescriptive modeling
Sensors, 2020Co-Authors: Miao Cai, Nasrin Mohabbatikalejahi, Amir Mehdizadeh, Mohammad Ali Alamdar Yazdi, Alexander Vinel, Steven E Rigdon, Karen C Davis, Fadel M MegahedAbstract:In the first part of the review, we observed that there exists a significant gap between the predictive and prescriptive models pertaining to crash risk prediction and minimization, respectively. In this part, we review and categorize the optimization/ prescriptive analytic models that focus on minimizing crash risk. Although the majority of works in this segment of the literature are related to the hazardous materials (hazmat) trucking problems, we show that (with some exceptions) many can also be utilized in non-hazmat scenarios. In an effort to highlight the effect of crash risk prediction model on the accumulated risk obtained from the prescriptive model, we present a simulated example where we utilize four risk indicators (obtained from logistic regression, Poisson regression, XGBoost, and neural network) in the k-shortest path algorithm. From our example, we demonstrate two major designed takeaways: (a) the shortest path may not always result in the lowest crash risk, and (b) a similarity in overall predictive performance may not always translate to similar outcomes from the prescriptive models. Based on the review and example, we highlight several avenues for future research.
Mohsin Iftikhar - One of the best experts on this subject based on the ideXlab platform.
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enhancing quality of service conditions using a cross layer paradigm for ad hoc vehicular communication
IEEE Access, 2017Co-Authors: Sabih Ur Rehman, Arif M Khan, Muhammad Imran, Tanveer A Zia, Mohsin IftikharAbstract:The Internet of Vehicles (IoVs) is an emerging paradigm aiming to introduce a plethora of innovative applications and services that impose a certain quality of service (QoS) requirements. The IoV mainly relies on vehicular ad-hoc networks (VANETs) for autonomous inter-vehicle communication and Road-Traffic Safety management. With the ever-increasing demand to design new and emerging applications for VANETs, one challenge that continues to stand out is the provision of acceptable QoS requirements to particular user applications. Most existing solutions to this challenge rely on a single layer of the protocol stack. This paper presents a cross-layer decision-based routing protocol that necessitates choosing the best multi-hop path for packet delivery to meet acceptable QoS requirements. The proposed protocol acquires the information about the channel rate from the physical layer and incorporates this information in decision making, while directing Traffic at the network layer level. Key performance metrics for the system design are analyzed using extensive experimental simulation scenarios. In addition, three data rate variant solutions are proposed to cater for various application-specific requirements in highways and urban environments.
E. Kuliczkowska - One of the best experts on this subject based on the ideXlab platform.
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An Analysis of Road Pavement Collapses and Traffic Safety Hazards Resulting From Leaky Sewers
Baltic Journal of Road and Bridge Engineering, 2016Co-Authors: E. KuliczkowskaAbstract:In this paper, Road pavement collapses resulting from sewer leakage are divided into six categories: negligible, marginal, considerable, serious, very serious and catastrophic, with the categorization being based on two criteria, both related to Traffic Safety, i.e., the number of fatalities caused by sinkholes, and the extent of the Road pavement damage. The causes of Road pavement collapses are also discussed. The study involved analyzing the deterioration of sewer pipes with long service lives, focusing on the most common materials, i.e., concrete and vitrified clay. The results of the sewer inspections performed by the Kielce University of Technology suggest that the spot and linear defects detected in sewers of this type can be divided into three groups. The findings were used to formulate some recommendations on how to improve Road Traffic Safety by preventing Road pavement collapses.
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the interaction between Road Traffic Safety and the condition of sewers laid under Roads
Transportation Research Part D-transport and Environment, 2016Co-Authors: E. KuliczkowskaAbstract:Abstract The main objective of this paper is to show how the service condition of the underground pipeline infrastructure, especially sewage systems, may contribute to Road surface failures such as subsidence, bulging and particularly collapse. The analysis was based on CCTV surveys conducted in Poland to investigate the causes of more than a hundred Road surface collapse incidents. The method proposed in this paper enables us to determine the risk of Road surface collapse as the product of the category of probability of Road collapse caused by damaged sewer pipelines and the weighted arithmetic mean category of their consequences. The key conclusions highlight the causes of Road collapse incidents, the scale of the hazards, and the benefits resulting from the application of the proposed method to prioritize Roads according to the Road collapse risk.
Amir Mehdizadeh - One of the best experts on this subject based on the ideXlab platform.
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a review of data analytic applications in Road Traffic Safety part 1 descriptive and predictive modeling
Sensors, 2020Co-Authors: Amir Mehdizadeh, Nasrin Mohabbatikalejahi, Mohammad Ali Alamdar Yazdi, Alexander Vinel, Steven E Rigdon, Karen C Davis, Qiong Hu, Fadel M MegahedAbstract:This part of the review aims to reduce the start-up burden of data collection and descriptive analytics for statistical modeling and route optimization of risk associated with motor vehicles. From a data-driven bibliometric analysis, we show that the literature is divided into two disparate research streams: (a) predictive or explanatory models that attempt to understand and quantify crash risk based on different driving conditions, and (b) optimization techniques that focus on minimizing crash risk through route/path-selection and rest-break scheduling. Translation of research outcomes between these two streams is limited. To overcome this issue, we present publicly available high-quality data sources (different study designs, outcome variables, and predictor variables) and descriptive analytic techniques (data summarization, visualization, and dimension reduction) that can be used to achieve safer-routing and provide code to facilitate data collection/exploration by practitioners/researchers. Then, we review the statistical and machine learning models used for crash risk modeling. We show that (near) real-time crash risk is rarely considered, which might explain why the optimization models (reviewed in Part 2) have not capitalized on the research outcomes from the first stream.
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a review of data analytic applications in Road Traffic Safety part 2 prescriptive modeling
Sensors, 2020Co-Authors: Miao Cai, Nasrin Mohabbatikalejahi, Amir Mehdizadeh, Mohammad Ali Alamdar Yazdi, Alexander Vinel, Steven E Rigdon, Karen C Davis, Fadel M MegahedAbstract:In the first part of the review, we observed that there exists a significant gap between the predictive and prescriptive models pertaining to crash risk prediction and minimization, respectively. In this part, we review and categorize the optimization/ prescriptive analytic models that focus on minimizing crash risk. Although the majority of works in this segment of the literature are related to the hazardous materials (hazmat) trucking problems, we show that (with some exceptions) many can also be utilized in non-hazmat scenarios. In an effort to highlight the effect of crash risk prediction model on the accumulated risk obtained from the prescriptive model, we present a simulated example where we utilize four risk indicators (obtained from logistic regression, Poisson regression, XGBoost, and neural network) in the k-shortest path algorithm. From our example, we demonstrate two major designed takeaways: (a) the shortest path may not always result in the lowest crash risk, and (b) a similarity in overall predictive performance may not always translate to similar outcomes from the prescriptive models. Based on the review and example, we highlight several avenues for future research.
Luke B Connelly - One of the best experts on this subject based on the ideXlab platform.
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determinants of Road Traffic Safety new evidence from australia using state space analysis
Accident Analysis & Prevention, 2016Co-Authors: Son Nghiem, Jacques J F Commandeur, Luke B ConnellyAbstract:This paper examines the determinants of Road Traffic crash fatalities in Queensland for the period 1958-2007 using a state-space time-series model. In particular, we investigate the effects of policies that aimed to reduce drink-driving on Traffic fatalities, as well as indicators of the economic environment that may affect exposure to Traffic, and hence affect the number of accidents and fatalities. The results show that the introduction of a random breath testing program in 1988 was associated with a 11.3% reduction in Traffic fatalities; its expansion in 1998 was associated with a 26.2% reduction in Traffic fatalities; and the effect of the "Safe4life" program, which was introduced in 2004, was a 14.3% reduction in Traffic fatalities. Reductions in economic activity are also associated with reductions in Road fatalities: we estimate that a one percent increase in the unemployment rate is associated with a 0.2% reduction in Traffic fatalities.
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benchmarking Road Traffic Safety across oecd countries a distance function approach
Journal of Transport Economics and Policy, 2015Co-Authors: Son Nghiem, Luke B ConnellyAbstract:This study combines an economic production framework with a latent risk theory framework to examine improvements in Road Safety performance of thirteen Organisation for Economic Cooperation and Development (OECD) countries for the period 1975-2004. We find that, on average, total factor productivity in Road Safety increased by 2 per cent per annum over the past 30 years. We also find that technological progress has been the main contributor to improvements in Road Safety. Indicators of economic activity, including the employment rate and CO 2 emissions per capita as well as population density, are positively associated with improvements in Traffic Safety. © 2015 LSE and the University of Bath
