Predicting Real-time Crash Risks during Hurricane Evacuation Using Connected Vehicle Data

• URL: http://arxiv.org/abs/2306.08682v1
• Date: Wed, 14 Jun 2023 18:04:07 GMT
• Title: Predicting Real-time Crash Risks during Hurricane Evacuation Using Connected Vehicle Data
• Authors: Zaheen E Muktadi Syed and Samiul Hasan
• Abstract summary: We present methods to determine potential crash risks during hurricane evacuation from an emerging alternative data source known as connected vehicle data. Multiple machine learning models were trained considering weather features and different traffic characteristics extracted from the connected vehicle data.
• Score: 1.3706331473063877
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hurricane evacuation, ordered to save lives of people of coastal regions, generates high traffic demand with increased crash risk. To mitigate such risk, transportation agencies need to anticipate highway locations with high crash risks to deploy appropriate countermeasures. With ubiquitous sensors and communication technologies, it is now possible to retrieve micro-level vehicular data containing individual vehicle trajectory and speed information. Such high-resolution vehicle data, potentially available in real time, can be used to assess prevailing traffic safety conditions. Using vehicle speed and acceleration profiles, potential crash risks can be predicted in real time. Previous studies on real-time crash risk prediction mainly used data from infrastructure-based sensors which may not cover many road segments. In this paper, we present methods to determine potential crash risks during hurricane evacuation from an emerging alternative data source known as connected vehicle data. Such data contain vehicle location, speed, and acceleration information collected at a very high frequency (less than 30 seconds). To predict potential crash risks, we utilized a dataset collected during the evacuation period of Hurricane Ida on Interstate-10 (I-10) in the state of Louisiana. Multiple machine learning models were trained considering weather features and different traffic characteristics extracted from the connected vehicle data in 5-minute intervals. The results indicate that the Gaussian Process Boosting (GPBoost) and Extreme Gradient Boosting (XGBoost) models perform better (recall = 0.91) than other models. The real-time connected vehicle data for crash risks assessment will allow traffic managers to efficiently utilize resources to proactively take safety measures.

Related papers

• Advance Real-time Detection of Traffic Incidents in Highways using Vehicle Trajectory Data [3.061662434597097]
  This study uses vehicle trajectory data and traffic incident data on I-10, one of the most crash-prone highways in Louisiana. Various machine learning algorithms are used to detect a trajectory that is likely to face an incident in the downstream road section. Results suggest that the Random Forest model achieves the best performance for predicting an incident with reasonable recall value and discrimination capability.
  arXiv  Detail & Related papers  (2024-08-15T00:51:48Z)
• Vehicle-group-based Crash Risk Formation and Propagation Analysis for Expressways [9.337163968120915]
  This study focuses on vehicle groups as the subject of analysis and explored risk formation and propagation mechanisms. Key factors contributing to crash risks were identified, including past high-risk vehicle-group states, complex vehicle behaviors, high percentage of large vehicles, frequent lane changes within a vehicle group, and specific road geometries. The results indicated that extended periods of high-risk states, increase in vehicle-group size, and frequent lane changes are associated with adverse risk propagation patterns.
  arXiv  Detail & Related papers  (2024-02-19T07:47:23Z)
• CAT: Closed-loop Adversarial Training for Safe End-to-End Driving [54.60865656161679]
  Adversarial Training (CAT) is a framework for safe end-to-end driving in autonomous vehicles. Cat aims to continuously improve the safety of driving agents by training the agent on safety-critical scenarios. Cat can effectively generate adversarial scenarios countering the agent being trained.
  arXiv  Detail & Related papers  (2023-10-19T02:49:31Z)
• An Open Case-based Reasoning Framework for Personalized On-board Driving Assistance in Risk Scenarios [0.46408356903366527]
  We propose an open evolving framework for generating personalized on-board driving assistance. A tailored CBR-based method is then proposed to retrieve, reuse and revise the existing cases to generate the assistance. We take the 100-Car Naturalistic Driving Study dataset as an example to build and test our framework.
  arXiv  Detail & Related papers  (2022-11-23T09:28:28Z)
• Reinforcement Learning based Cyberattack Model for Adaptive Traffic Signal Controller in Connected Transportation Systems [61.39400591328625]
  In a connected transportation system, adaptive traffic signal controllers (ATSC) utilize real-time vehicle trajectory data received from vehicles to regulate green time. This wirelessly connected ATSC increases cyber-attack surfaces and increases their vulnerability to various cyber-attack modes. One such mode is a'sybil' attack in which an attacker creates fake vehicles in the network. An RL agent is trained to learn an optimal rate of sybil vehicle injection to create congestion for an approach(s)
  arXiv  Detail & Related papers  (2022-10-31T20:12:17Z)
• Safety-aware Motion Prediction with Unseen Vehicles for Autonomous Driving [104.32241082170044]
  We study a new task, safety-aware motion prediction with unseen vehicles for autonomous driving. Unlike the existing trajectory prediction task for seen vehicles, we aim at predicting an occupancy map. Our approach is the first one that can predict the existence of unseen vehicles in most cases.
  arXiv  Detail & Related papers  (2021-09-03T13:33:33Z)
• Deep Representation of Imbalanced Spatio-temporal Traffic Flow Data for Traffic Accident Detection [0.3670422696827526]
  This paper studies deep representation of loop detector data using Long-Short Term Memory (LSTM) network for automatic detection of freeway accidents. Experiments on real accident and loop detector data collected from the Twin Cities Metro freeways of Minnesota demonstrate that deep representation of traffic flow data using LSTM network has the potential to detect freeway accidents in less than 18 minutes.
  arXiv  Detail & Related papers  (2021-08-21T13:18:04Z)
• R4Dyn: Exploring Radar for Self-Supervised Monocular Depth Estimation of Dynamic Scenes [69.6715406227469]
  Self-supervised monocular depth estimation in driving scenarios has achieved comparable performance to supervised approaches. We present R4Dyn, a novel set of techniques to use cost-efficient radar data on top of a self-supervised depth estimation framework.
  arXiv  Detail & Related papers  (2021-08-10T17:57:03Z)
• Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detection [60.89616629421904]
  Lidar-based object detectors are critical parts of the 3D perception pipeline in autonomous navigation systems such as self-driving cars. They are sensitive to adverse weather conditions such as rain, snow and fog due to reduced signal-to-noise ratio (SNR) and signal-to-background ratio (SBR)
  arXiv  Detail & Related papers  (2021-07-14T21:10:47Z)
• Impact of risk factors on work zone crashes using logistic models and Random Forest [9.5148976460603]
  This study focuses on the 2016 severe crashes that occurred in the State of Michigan (USA) in work zones along highway I-94. The study identified the risk factors from a wide range of crash variables characterizing environmental, driver, crash and road-related variables.
  arXiv  Detail & Related papers  (2021-04-14T00:27:11Z)
• A model for traffic incident prediction using emergency braking data [77.34726150561087]
  We address the fundamental problem of data scarcity in road traffic accident prediction by training our model on emergency braking events instead of accidents. We present a prototype implementing a traffic incident prediction model for Germany based on emergency braking data from Mercedes-Benz vehicles.
  arXiv  Detail & Related papers  (2021-02-12T18:17:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.