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Cooperative incident management in mixed traffic of CAVs and human-driven vehicles
Traffic incident management in metropolitan areas is crucial for the recovery of road systems
from accidents as well as the mobility and safety of the community. With the continuous …
from accidents as well as the mobility and safety of the community. With the continuous …
Prediction of the traffic incident duration using statistical and machine-learning methods: A systematic literature review
This paper aims to present a comprehensive review and analysis to demonstrate the main
papers, journals, authors, and trends significantly contributing to the scientific output in …
papers, journals, authors, and trends significantly contributing to the scientific output in …
Inferring heterogeneous treatment effects of crashes on highway traffic: A doubly robust causal machine learning approach
Accurate estimating causal effects of crashes on highway traffic is crucial for mitigating the
negative impacts of crashes. Previous studies have built up a series of methods via …
negative impacts of crashes. Previous studies have built up a series of methods via …
Traffic accident duration prediction using multi-mode data and ensemble deep learning
J Chen, W Tao, Z **g, P Wang, Y ** - Heliyon, 2024 - cell.com
Predicting the duration of traffic accidents is a critical component of traffic management and
emergency response on expressways. Traffic accident information is inherently multi-mode …
emergency response on expressways. Traffic accident information is inherently multi-mode …
Effect of feature optimization on performance of machine learning models for predicting traffic incident duration
Develo** a high-performing traffic incident-duration prediction model is considered a key
component for evaluating the impact of these incidents on the roadway network. Various …
component for evaluating the impact of these incidents on the roadway network. Various …
Automatic accident detection, segmentation and duration prediction using machine learning
Traffic accidents are often inaccurately reported, with incorrect location and disruption
duration due to various external factors. This can result in imprecise predictions and …
duration due to various external factors. This can result in imprecise predictions and …
[HTML][HTML] Prediction of duration of traffic incidents by hybrid deep learning based on multi-source incomplete data
Q Shang, T **e, Y Yu - … journal of environmental research and public …, 2022 - mdpi.com
Traffic accidents causing nonrecurrent congestion and road traffic injuries seriously affect
public safety. It is helpful for traffic operation and management to predict the duration of …
public safety. It is helpful for traffic operation and management to predict the duration of …
Bibliometric Analysis of Traffic Accident Prediction Studies from 2003 to 2023: Trends, Patterns and Future Directions
Sažetak Traffic accidents are one of the main causes of fatalities and serious injuries among
both adults and children worldwide. Due to the ongoing significant socio-economic losses …
both adults and children worldwide. Due to the ongoing significant socio-economic losses …
Prediction of Traffic Incident Locations with a Geohash-Based Model Using Machine Learning Algorithms
This paper presents a novel geohash-based approach for predicting traffic incident locations
using machine learning algorithms. The study utilized a three-stage model for predicting the …
using machine learning algorithms. The study utilized a three-stage model for predicting the …
Traffic incident duration prediction via a deep learning framework for text description encoding
Predicting the traffic incident duration is a hard problem to solve due to the stochastic nature
of incident occurrence in space and time, a lack of information at the beginning of a reported …
of incident occurrence in space and time, a lack of information at the beginning of a reported …