Transferability and robustness of real-time freeway crash risk assessment

Shew, Cameron; Pande, Anurag; Nuworsoo, Cornelius

doi:10.1016/j.jsr.2013.04.005

Cited by 44 publications

(13 citation statements)

References 13 publications

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“…For instance, Abdel-Aty et al (2008). Later, transferability issues were studied by Shew et al (2013), Xu et al…”

Section: Transferability Adaptability and Timeliness (Tat)mentioning

confidence: 99%

“…2013a,b,c;Yu and Abdel-Aty, 2013a,b;Sun and Sun, 2016;Katrakazas et al, 2016Katrakazas et al, , 2017Yang et al, 2018a,b;Roy et al, 2018b), identifying their types (Golob et al, 2004;Pande and Abdel-Aty, 2006a,b;Christoforou et al, 2011), understanding crash mechanism (Lee et al, 2003a,b,c;Luo and Garber, 2006, Hossain and Muromachi, 2011Xu et al, 2012;Yeo et al, 2013), evaluating countermeasures through variable speed limits (Abdel-Aty et al, 2006a,b, 2008aAbdel-Aty, 2008, Lee et al, 2004), ramp metering (Abdel-Aty and Gayah, 2010; Lee et al, 2006b), and variable message signs (Al-Ghamdi, 2007;Lee and Abdel-Aty, 2008). The recent trend has been focused on addressing the issues of transferability (Shew et al, 2013;Roy et al, 2018a), building them for specific road sections (e.g., weaving areas as shown by Wang et al, 2015), optimizing real-time safety and congestion in tandem (Park and Haghani, 2015), considering severity (Xu et al, 2013a) or simply, using more sophisticated modeling methods to improve accuracy (Xu et al, 2013b;Park and Haghani, 2015;Xu et al, 2016aXu et al, , 2016b.…”

Section: Introductionmentioning

confidence: 99%

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

Hossain

Abdel-Aty

Quddus

et al. 2019

Accident Analysis & Prevention

124

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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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“…For instance, Abdel-Aty et al (2008). Later, transferability issues were studied by Shew et al (2013), Xu et al…”

Section: Transferability Adaptability and Timeliness (Tat)mentioning

confidence: 99%

Section: Introductionmentioning

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

124

View full text Add to dashboard Cite

show abstract

“…Lee et al [1] proposed the concept 'crash precursor' and developed an aggregate log-linear model with crash data of the upstream detectors, showing that high-speed variation and high traffic density play a key role in the crash occurrence. Various traffic datasets from different traffic surveillance systems were obtained to estimate the crash likelihood, such as dual loop detectors [1][2][3][4][5], single loop detector [6][7][8] and automatic vehicle identification system [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…Despite the convincing results by utilizing SVMs to evaluate real-time crash risk [17], the SVMs have difficulty in dealing with the imbalanced dataset as well, and additional efforts should be made to optimize the function parameters of the model and preprocess the raw data to achieve a higher prediction accuracy. In general, with the requirement of high-quality traffic flow data, majority of existing models cannot be applied in other regions where limited detectors or surveillance devices are installed on the freeways, though transferability of the models has been validated [8]. Moreover, without consideration of the human factors and traffic patterns, the validity of the models cannot be verified.…”

Section: Introductionmentioning

confidence: 99%

Real-time crash prediction on freeways using data mining and emerging techniques

2017

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Recent advances in intelligent transportation system allow traffic safety studies to extend from historic data-based analyses to real-time applications. The study presents a new method to predict crash likelihood with traffic data collected by discrete loop detectors as well as the web-crawl weather data. Matched case-control method and support vector machines (SVMs) technique were employed to identify the risk status. The adaptive synthetic over-sampling technique was applied to solve the imbalanced dataset issues. Random forest technique was applied to select the contributing factors and avoid the over-fitting issues. The results indicate that the SVMs classifier could successfully classify 76.32% of the crashes on the test dataset and 87.52% of the crashes on the overall dataset, which were relatively satisfactory compared with the results of the previous studies. Compared with the SVMs classifier without the data, the SVMs classifier with the web-crawl weather data increased the crash prediction accuracy by 1.32% and decreased the false alarm rate by 1.72%, showing the potential the potential value of the massive web weather data. Mean impact value method was employed to evaluate the variable effects, and the results are identical with the results of most of previous studies. The emerging technique based on the discrete traffic data and web weather data proves to be more applicable on realtime safety management on freeways.

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“…Meanwhile, transferability and robustness of real-time freeway crash risk assessment were emphasized in recent works (Shew et al, 2013). Xu et al (2014) developed a Bayesian updating approach with data from the I-880N freeway in 2002 and 2009 and from the I-5N freeway in 2009 to improve transferability, and found it effective.…”

Section: Introductionmentioning

confidence: 99%

Utilizing the eigenvectors of freeway loop data spatiotemporal schematic for real time crash prediction

Fang

Xie

Wang

et al. 2016

Accident Analysis & Prevention

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Transferability and robustness of real-time freeway crash risk assessment

Cited by 44 publications

References 13 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

Real-time crash prediction on freeways using data mining and emerging techniques

Utilizing the eigenvectors of freeway loop data spatiotemporal schematic for real time crash prediction

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