Identification of freeway crash-prone traffic conditions for traffic flow at different levels of service

Transportation Research Part A: Policy and Practice

Volumn 69, Issue , 2014, Pages 58-70

  Xu, Chengcheng ^a,b   Liu, Pan ^a,b   Wang, Wei ^a,b   Li, Zhibin ^a,b  

^a SOUTHEAST UNIVERSITY   (China)

^b JIANGSU PROVINCE COLLABORATIVE INNOVATION CENTER OF MODERN URBAN TRAFFIC TECHNOLOGIES   (China)

Author keywords

Bayesian inference; Crash risk; Level of service; Markov Chain Monte Carlo (MCMC); Random forest

Indexed keywords

BAYESIAN NETWORKS; DECISION TREES; INFERENCE ENGINES; LOGISTIC REGRESSION; MARKOV CHAINS; RANDOM FORESTS; SPEED CONTROL; TRAFFIC CONTROL;

BAYESIAN INFERENCE; CRASH RISK; FREEWAY TRAFFIC FLOWS; LANE CHANGE MANEUVERS; LEVEL OF SERVICE; LOGISTIC REGRESSION MODELING; MARKOV CHAIN MONTE CARLO; PREDICTION PERFORMANCE;

HIGHWAY ACCIDENTS;

BAYESIAN ANALYSIS; MARKOV CHAIN; MONTE CARLO ANALYSIS; NUMERICAL MODEL; REGRESSION ANALYSIS; TRAFFIC CONGESTION; TRANSPORTATION PLANNING; TRANSPORTATION POLICY;

EID: 84930944172     PISSN: 09658564     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.tra.2014.08.011     Document Type: Article

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