On the public citation graph

A Dynamic Bayesian Network Model for Real-Time Risk Propagation of Secondary Rear-End Collision Accident Using Driving Risk Field

0 signed · 49 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/s11432-019-2983-0openalex-snapshot
A probabilistic risk assessment framework considering lane-changing behavior interaction
inferredcites →https://doi.org/10.1007/s12205-021-1585-5openalex-snapshot
A Dynamic Control Method for Cavs Platoon Based on the MPC Framework and Safety Potential Field Model
inferredcites →https://doi.org/10.1016/j.aap.2015.07.007openalex-snapshot
Driving risk assessment using near-crash database through data mining of tree-based model
inferredcites →https://doi.org/10.1016/j.aap.2018.02.024openalex-snapshot
Assessing rear-end crash potential in urban locations based on vehicle-by-vehicle interactions, geometric characteristics and operational conditions
inferredcites →https://doi.org/10.1016/j.aap.2019.01.029openalex-snapshot
A new integrated collision risk assessment methodology for autonomous vehicles
inferredcites →https://doi.org/10.1016/j.aap.2019.05.005openalex-snapshot
A feature learning approach based on XGBoost for driving assessment and risk prediction
inferredcites →https://doi.org/10.1016/j.aap.2019.05.017openalex-snapshot
Key feature selection and risk prediction for lane-changing behaviors based on vehicles’ trajectory data
inferredcites →https://doi.org/10.1016/j.aap.2020.105660openalex-snapshot
Real-time conflict-based Bayesian Tobit models for safety evaluation of signalized intersections
inferredcites →https://doi.org/10.1016/j.aap.2020.105680openalex-snapshot
Driving risk assessment based on naturalistic driving study and driver attitude questionnaire analysis
inferredcites →https://doi.org/10.1016/j.aap.2020.105805openalex-snapshot
Risk perception and the warning strategy based on safety potential field theory
inferredcites →https://doi.org/10.1016/j.aap.2020.105933openalex-snapshot
Left-turn conflict identification at signal intersections based on vehicle trajectory reconstruction under real-time communication conditions
inferredcites →https://doi.org/10.1016/j.aap.2021.106007openalex-snapshot
Prediction of rear-end conflict frequency using multiple-location traffic parameters
inferredcites →https://doi.org/10.1016/j.aap.2021.106035openalex-snapshot
Incorporating accident liability into crash risk analysis: A multidimensional risk source approach
inferredcites →https://doi.org/10.1016/j.aap.2021.106051openalex-snapshot
Comparison of threshold determination methods for the deceleration rate to avoid a crash (DRAC)-based crash estimation
inferredcites →https://doi.org/10.1016/j.aap.2021.106191openalex-snapshot
Using empirical traffic trajectory data for crash risk evaluation under three-phase traffic theory framework
inferredcites →https://doi.org/10.1016/j.eswa.2021.115041openalex-snapshot
Cost-sensitive semi-supervised deep learning to assess driving risk by application of naturalistic vehicle trajectories
inferredcites →https://doi.org/10.1016/j.physa.2020.125039openalex-snapshot
A novel lane-changing model of connected and automated vehicles: Using the safety potential field theory
inferredcites →https://doi.org/10.1016/j.ress.2020.106959openalex-snapshot
Dynamic risk assessment with bayesian network and clustering analysis
inferredcites →https://doi.org/10.1016/j.trc.2016.10.003openalex-snapshot
Driving safety field theory modeling and its application in pre-collision warning system
inferredcites →https://doi.org/10.1016/j.trc.2017.12.012openalex-snapshot
Developing an algorithm to assess the rear-end collision risk under fog conditions using real-time data
inferredcites →https://doi.org/10.1016/j.trc.2019.04.015openalex-snapshot
From univariate to bivariate extreme value models: Approaches to integrate traffic conflict indicators for crash estimation
inferredcites →https://doi.org/10.1016/j.trc.2019.07.004openalex-snapshot
Mining automatically extracted vehicle trajectory data for proactive safety analytics
inferredcites →https://doi.org/10.1016/j.trc.2020.102716openalex-snapshot
Probabilistic field approach for motorway driving risk assessment
inferredcites →https://doi.org/10.1016/j.trc.2020.102820openalex-snapshot
Risk assessment based collision avoidance decision-making for autonomous vehicles in multi-scenarios
inferredcites →https://doi.org/10.1016/j.trf.2020.09.003openalex-snapshot
Risk prediction model using eye movements during simulated driving with logistic regressions and neural networks
inferredcites →https://doi.org/10.1038/s41467-020-18353-4openalex-snapshot
Human-like driving behaviour emerges from a risk-based driver model
inferredcites →https://doi.org/10.1049/iet-its.2016.0207openalex-snapshot
Driving risk status prediction using Bayesian networks and logistic regression
inferredcites →https://doi.org/10.1109/access.2019.2941959openalex-snapshot
Dynamic Bayesian Network Approach to Evaluate Vehicle Driving Risk Based on On-Road Experiment Driving Data
inferredcites →https://doi.org/10.1109/access.2020.3044578openalex-snapshot
Multi-Link Scheduling Algorithm of LLC Protocol in Heterogeneous Vehicle Networks Based on Environment and Vehicle-Risk-Field Model
inferredcites →https://doi.org/10.1109/robot.2008.4543783openalex-snapshot
Artificial potential functions for highway driving with collision avoidance
inferredcites →https://doi.org/10.1109/tits.2015.2401837openalex-snapshot
The Driving Safety Field Based on Driver–Vehicle–Road Interactions
inferredcites →https://doi.org/10.1109/tits.2017.2768318openalex-snapshot
Collision Risk Assessment Algorithm via Lane-Based Probabilistic Motion Prediction of Surrounding Vehicles
inferredcites →https://doi.org/10.1109/tits.2020.3008284openalex-snapshot
Dynamic Driving Risk Potential Field Model Under the Connected and Automated Vehicles Environment and Its Application in Car-Following Modeling
inferredcites →https://doi.org/10.3141/2659-14openalex-snapshot
Comparative Assessment of Safety Indicators for Vehicle Trajectories on Highways

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4511909openalex-extracted
Use the Risk Propagation Chain (Rpc) to Graphical Model the Risk Propagation Process in the Urban Rail Transit System
inferred← citeshttps://doi.org/10.1016/j.aap.2025.108080openalex-snapshot
Composite safety potential field for highway driving risk assessment
inferred← citeshttps://doi.org/10.1016/j.aap.2026.108588openalex-snapshot
Traffic risk spatial-temporal evolution mechanism and dynamic prediction approach from the vehicle-to-vehicle interaction perspective
inferred← citeshttps://doi.org/10.1016/j.aap.2026.108674openalex-snapshot
A human-centered risk pulse field framework for real-time car-following risk control in connected driving environments
inferred← citeshttps://doi.org/10.1016/j.cie.2024.110683openalex-snapshot
Modelling for resource risk propagation in dynamic heterogeneous project portfolio network
inferred← citeshttps://doi.org/10.1016/j.physa.2025.130584openalex-snapshot
The connected vehicle microscopic behavior modeling base on risk field theory: Theoretical developments, methodological overview and future trends
inferred← citeshttps://doi.org/10.1016/j.ress.2025.111280openalex-snapshot
Quantitative analysis of risk propagation in urban rail transit: A novel ensemble learning method based on the structure of Bayesian Network
inferred← citeshttps://doi.org/10.1016/j.tra.2024.104268openalex-snapshot
A flight risk field model for advanced low-altitude transportation system using field theory
inferred← citeshttps://doi.org/10.1063/5.0293293openalex-snapshot
Deep reinforcement learning-based real-time data analytics of automatic industrial robot for digital transformation
inferred← citeshttps://doi.org/10.1080/15389588.2025.2469112openalex-snapshot
Risk characteristics analysis of road segments: Considering multiple scales and temporal stages
inferred← citeshttps://doi.org/10.1109/access.2025.3534787openalex-snapshot
Assessment of Rear-End Collision Risk Based on a Deep Reinforcement Learning Technique: A Break Reaction Assessment Approach
inferred← citeshttps://doi.org/10.1109/tgcn.2026.3736582openalex-snapshot
Network-Based Secure Sampled-Data Consensus Control for Autonomous Vehicles Motorcade Systems Under Stochastic Cyber–Physical Attacks
inferred← citeshttps://doi.org/10.1109/tits.2023.3335110openalex-snapshot
Memory-Event-Triggered Tracking Control for Intelligent Vehicle Transportation Systems: A Leader-Following Approach
inferred← citeshttps://doi.org/10.15837/ijccc.2023.5.5317openalex-snapshot
Intelligent Model for Avoiding Road Accidents Using Artificial Neural Network
inferred← citeshttps://doi.org/10.3390/jmse12122113openalex-snapshot
Integrating Bayesian Network and Cloud Model to Probabilistic Risk Assessment of Maritime Collision Accidents in China’s Coastal Port Waters
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.