Bayesian random effect models incorporating real-time weather and traffic data to investigate mountainous freeway hazardous factors

Yu, Rongjie; Abdel-Aty, Mohamed; Ahmed, Mohamed M.

doi:10.1016/j.aap.2012.05.011

Cited by 152 publications

(78 citation statements)

References 18 publications

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“…Table 3 shows the results of Pearson correlation tests. From the results, we find that the two variable pairs, Veh (2) , and Veh (5) , and Veh (4) , and Veh (5) , , are significantly correlated with coefficients over 0.6 or below −0.6. To avoid the adverse effect of significant correlation Veh (5) , is excluded from the models.…”

Section: Data Preparation and Preliminary Analysismentioning

confidence: 73%

“…, + 1.5 (2) , + 2 Table 2 illustrates the definitions and descriptive statistics of the variables used in the model development. Correlation tests and multicollinearity diagnoses for the risk factors are conducted.…”

Section: Data Preparation and Preliminary Analysismentioning

confidence: 99%

“…To account for important issues related to crash data such as overdispersion, underdispersion, excess zero observations, spatiotemporal correlation, multilevel structures, and unobserved heterogeneity, a great many Poisson model variations have been proposed, which significantly improve model fit and predictive performance [3,6]. With more recent advances in crash prediction modeling, Bayesian inference has been extensively applied to traffic safety analysis because of its ability to deal with complex models (often without closed-form likelihood functions) such as the hierarchical model [1,2], spatiotemporal model [7,8], random parameters model [9], and multivariate model [10] and through these [8].…”

Section: Introductionmentioning

confidence: 99%

“…This diversity in freeway traffic composition may result in numerous vehicle interactions, and the high speeds result in shorter driver response times when encountering emergencies. Freeways in mountainous areas may also suffer from poor geometrical design and adverse weather conditions [1,2], which increase the crash risk. Developing a crash prediction model (or safety performance function) that provides a good understanding of the crash occurrence mechanism on a freeway is thus vital [1] when ranking sites for safety improvement and for accurately evaluating the effectiveness of countermeasures.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Bayesian Hierarchical Modeling Monthly Crash Counts on Freeway Segments with Temporal Correlation

Zeng

Sun

Wen

2017

Journal of Advanced Transportation

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As the basis of traffic safety management, crash prediction models have long been a prominent focus in the field of freeway safety research. Studies usually take years or seasons as the observed time units, which may result in heterogeneity in crash frequency. To eliminate that heterogeneity, this study analyzes monthly crash counts and develops Bayesian hierarchical models with random effects, lag-1 autoregression (AR-1), and both (REAR-1) to accommodate the multilevel structure and temporal correlation in crash data. The candidate models are estimated and evaluated in the freeware WinBUGS using a crash dataset obtained from the Kaiyang Freeway in Guangdong Province, China. Significant temporal effects are found in the three models, and Deviance Information Criteria (DIC) results show that taking temporal correlation into account considerably improves the model fit compared with the Poisson model. The hierarchical models also avoid any misidentification of the factors with significant safety effects, because their variances are greater than in the Poisson model. The DIC value of the AR-1 model is substantially lower than that of the random effect model and equivalent to that of the REAR-1 model, which indicates the superiority of the lag-1 autoregressive structure in accounting for the temporal effects in crash frequency.

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Section: Data Preparation and Preliminary Analysismentioning

confidence: 73%

Section: Data Preparation and Preliminary Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Bayesian Hierarchical Modeling Monthly Crash Counts on Freeway Segments with Temporal Correlation

Zeng

Sun

Wen

2017

Journal of Advanced Transportation

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“…The most important feature of this system, adapted for use in different parts of the world and with different and relatively contradictory traffic cultures, is the momentary and [12].…”

Section: Sydney Coordinated Adaptive Traffic Systemmentioning

confidence: 99%

Modeling and Evaluation of Intelligent Monitoring Systems at Urban Intersections to Improve Real-Time Monitoring of Congestion and Traffic Safety

Fatholahzadeh¹,

Akbarizadeh²,

Romoozi³

2018

IJCATR

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Abstract:In recent decades, with the advent of technology and information technology, transportation systems have also been moving in this direction, and one of the challenges of the transportation system is due to the multiplicity and increase of vehicle management and control. Many techniques and methods have been proposed in this field that in today's world, the use of intelligent systems has a higher efficiency. In fact, safety has the ability to reduce the number of vehicles with minimal delay and create discomfort for the users depends directly in order and arrangement to facilitate the immediate and direct involvement of the vehicle in traffic flow. One of the ways to demonstrate the efficiency of intelligent monitoring systems is create model before the implementation phase. This article aims at modeling and evaluating an intelligent surveillance system at urban intersections, which is carried out using colored petri nets. In the proposed method, first, the urban intersection architecture is described with the benefit of traffic rules then, the inconsistency and coincidence states are determined and finally, reliability and response time are measured by the CpnTools with the help of Petri Nets. The results show that the intelligent monitoring equipment related to the intelligent surveillance system increases the safety of the road network and, finally, has secured and fluent traffic.

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Cooperative Intelligent Transport Systems as a Policy Tool for Mitigating the Impacts of Climate Change on Road Transport

Mitsakis

Kotsi

2018

Data Analytics: Paving the Way to Sustainable Urban Mobility

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Bayesian random effect models incorporating real-time weather and traffic data to investigate mountainous freeway hazardous factors

Cited by 152 publications

References 18 publications

Bayesian Hierarchical Modeling Monthly Crash Counts on Freeway Segments with Temporal Correlation

Bayesian Hierarchical Modeling Monthly Crash Counts on Freeway Segments with Temporal Correlation

Modeling and Evaluation of Intelligent Monitoring Systems at Urban Intersections to Improve Real-Time Monitoring of Congestion and Traffic Safety

Cooperative Intelligent Transport Systems as a Policy Tool for Mitigating the Impacts of Climate Change on Road Transport

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