Data integration and clustering for real time crash prediction

Paikari, Elahe; Moshirpour, Mohammad; Alhajj, Reda; Far, Behrouz H.

doi:10.1109/iri.2014.7051936

Cited by 6 publications

(2 citation statements)

References 17 publications

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“…Some studies included density, queue length, exposure to traffic(Lee et al, 2003a), hazard ratio for average volume (Abdel-Aty and, complex calculation of shockwaves(Yu and Abdel-Aty, 2005), safe stopping distance of individual vehicles(Son et al, 2008), average flow ratio calculated from the peak flow(Pande and Abdel-Aty, 2006b), congestion index(Dias et al, 2009; Muromachi, 2012, 2013a; Shi and Abdel-Aty 2015;, percentage of heavy vehicles(Pham et al, 2010;Wang et al, 2017b;Park et al, 2018), geometric mean of average flow ratios(Qu et al, 2012b), average journey time(Katrakazas et al, 2017) first order autocorrelation of count, speed and occupancy(Xu et al, 2014b), weaving volume ratio, speed difference between the beginning and end of weaving segment(Wang et al, 2015) as variables. Use of coarser data such as peak hour traffic data(Abdel-Aty et al, 2006c;Christoforou et al, 2011), 75th percentile of average, standard deviation and coefficient of variation of speed, 75th percentile of standard deviation and coefficient of variation of volume(Abdel-Aty et al, 2006c), or day of week(Xu et al, 2016b), mainly seen in conventional CPMs, were also practiced.RTCPMs built with microscopic traffic flow data also introduced traffic pressure, kinetic energy, coefficient of variation of time headway, mean velocity gradient and mean reaction time as variables(Hourdakis et al, 2006;Paikari et al, 2014). Abdel-Aty et al(2012) represented speed as both time and space mean speeds.…”

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confidence: 99%

Real-time crash prediction models: State-of-the-art, design pathways and ubiquitous requirements

Hossain

Abdel-Aty

Quddus

et al. 2019

Accident Analysis & Prevention

113

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Proactive traffic safety management systems can monitor traffic conditions in real-time, identify the formation of unsafe traffic dynamics, and implement suitable interventions to bring unsafe conditions back to normal traffic situations. Recent advancements in artificial intelligence, sensor fusion and algorithms have brought about the introduction of a proactive safety management system closer to reality. The basic prerequisite for developing such a system is to have a reliable crash prediction model that takes real-time traffic data as input and evaluates their association with crash risk. Since the early 21st century, several studies have focused on developing such models. Although the idea has considerably matured over time, the endeavours have been quite discrete and fragmented at best because the fundamental aspects of the overall modelling approach substantially vary. Therefore, a number of transitional challenges have to be identified and subsequently addressed before a ubiquitous proactive safety management system can be formulated, designed and implemented in real-world scenarios. This manuscript conducts a comprehensive review of existing real-time crash prediction models with the aim of illustrating the state-of-the-art and systematically synthesizing the thoughts presented in existing studies in order to facilitate its translation from an idea into a ready to use technology. Towards that journey, it conducts a systematic review by applying various text mining methods and topic modelling. Based on the findings, this paper ascertains the development pathways followed in various studies, formulates the ubiquitous design requirements of such models from existing studies and knowledge of similar systems. Finally, this study evaluates the universality and design compatibility of existing models. This paper is, therefore, expected to serve as a one stop knowledge source for facilitating a faster transition from the idea of real-time crash prediction models to a real-world operational proactive traffic safety management system.

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mentioning

confidence: 99%

Real-time crash prediction models: State-of-the-art, design pathways and ubiquitous requirements

Hossain

Abdel-Aty

Quddus

et al. 2019

Accident Analysis & Prevention

113

View full text Add to dashboard Cite

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“…Bayesian networks and classifiers have also been used for traffic analysis and prediction [62], [48], [63], [64], [11]. A traffic accident causality analysis using a-10 node Bayesian network is produced in [62].…”

Section: Bayesian Networkmentioning

confidence: 99%

A Framework for Traffic Collision Prediction Using Historical Accident Information and Real-Time Sensor Data: A Case Study for the City of Ottawa

Yanes¹,

Enrique²

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According to recent studies, beyond being a major worldwide problem with huge economic impact, traffic collisions are poised to become as well one of the most important leading causes of death. Proactive traffic enforcement and intervention should be based on a thorough analysis on the collision data available to identify leading causes of accidents, the most prone locations as well as to predict the conditions for collision occurrence. This thesis presents a novel framework for collision prediction that takes into consideration historical and real-time factors, such as weather, geospatial information and social event data that can be obtained with existing sensor technology. A prototype is proposed, implemented and evaluated for the city of Ottawa, Canada, to predict: (1) accident frequency (collision vs nocollisions) and (2) accident severity (in terms of fatal, injury and property damage only accidents). The best performance was achieved in both cases using gradient boosted trees.iii

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A conflict-based approach for real-time road safety analysis: Comparative evaluation with crash-based models

Orsini

Gecchele²,

Rossi

et al. 2021

Accident Analysis & Prevention

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Data integration and clustering for real time crash prediction

Cited by 6 publications

References 17 publications

Real-time crash prediction models: State-of-the-art, design pathways and ubiquitous requirements

Real-time crash prediction models: State-of-the-art, design pathways and ubiquitous requirements

A Framework for Traffic Collision Prediction Using Historical Accident Information and Real-Time Sensor Data: A Case Study for the City of Ottawa

A conflict-based approach for real-time road safety analysis: Comparative evaluation with crash-based models

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