Combined variable speed limit and lane change guidance for secondary crash prevention using distributed deep reinforcement learning
The primary objective of this paper is to develop a combined variable speed limit (VSL) and
lane change guidance (LCG) controller to prevent secondary crashes (SCs) and improve …
lane change guidance (LCG) controller to prevent secondary crashes (SCs) and improve …
Comparative study of machine learning classifiers for modelling road traffic accidents
Road traffic accidents (RTAs) are a major cause of injuries and fatalities worldwide. In recent
years, there has been a growing global interest in analysing RTAs, specifically concerned …
years, there has been a growing global interest in analysing RTAs, specifically concerned …
Prioritizing influential factors for freeway incident clearance time prediction using the gradient boosting decision trees method
Identifying and quantifying the influential factors on incident clearance time can benefit
incident management for accident causal analysis and prediction, and consequently …
incident management for accident causal analysis and prediction, and consequently …
Computational intelligence and optimization for transportation big data: challenges and opportunities
EI Vlahogianni - … and Applied Sciences Optimization: Dedicated to the …, 2015 - Springer
With the overwhelming amount of transportation data being gathered worldwide, Intelligent
Transportation Systems (ITS) are faced with several modeling challenges. New modeling …
Transportation Systems (ITS) are faced with several modeling challenges. New modeling …
Combined connected vehicles and variable speed limit strategies to reduce rear-end crash risk under fog conditions
Fog is a weather condition that reduces visibility of the driving scene, while slow traffic may
be formed due to the bottleneck on freeways. This phenomenon may lead to higher rear-end …
be formed due to the bottleneck on freeways. This phenomenon may lead to higher rear-end …
A hybrid machine learning model for predicting real-time secondary crash likelihood
Secondary crashes usually occur within the spatio-temporal impact ranges of primary
crashes, which could cause traffic disturbance and increase traffic safety problems …
crashes, which could cause traffic disturbance and increase traffic safety problems …
From data to actions in intelligent transportation systems: A prescription of functional requirements for model actionability
Advances in Data Science permeate every field of Transportation Science and Engineering,
resulting in developments in the transportation sector that are data-driven. Nowadays …
resulting in developments in the transportation sector that are data-driven. Nowadays …
Real-time estimation of secondary crash likelihood on freeways using high-resolution loop detector data
This study aimed to develop a secondary crash risk prediction model on freeways using real-
time traffic flow data. The crash and traffic data were collected on the I-880 freeway for five …
time traffic flow data. The crash and traffic data were collected on the I-880 freeway for five …
Real-time prediction of secondary incident occurrences using vehicle probe data
H Park, A Haghani - Transportation Research Part C: Emerging …, 2016 - Elsevier
Effective incident management system requires quantifying non-recurring congestion and
detecting a secondary incident under the negative influence of a primary incident. Previously …
detecting a secondary incident under the negative influence of a primary incident. Previously …
Real-time prediction and avoidance of secondary crashes under unexpected traffic congestion
Abstract According to the Federal Highway Administration, nonrecurring congestion
contributes to nearly half of the overall congestion. Temporal disruptions impact the effective …
contributes to nearly half of the overall congestion. Temporal disruptions impact the effective …