Code of the Unsupervised Traffic Accident Detection paper in Pytorch.

Code for CVPR 2022 paper "Generating Useful Accident-Prone Driving Scenarios via a Learned Traffic Prior"

A dataset of traffic, fire and accident images for training deep learning models.

[ACM MM 2020] CCD dataset for traffic accident anticipation.

[ACM MM 2020] Uncertainty-based Traffic Accident Anticipation

codes of Deep Spatio-Temporal Graph Convolutional Network for Traffic Accident Prediction

[IEEE T-ITS] MG-TAR: Multi-view Graph Convolutional Networks for Traffic Accident Risk Prediction

Traffic accident prediction using graph neural networks "TAP: A Comprehensive Data Repository for Traffic Accident Prediction in Road Networks" (ACM SIGSPATIAL 2023)

Deep model for accident risk prediction over sparse traffic data

An accurate traffic accident predictor can potentially save lives. I hereby described the creation and deployment of such a model using scikit-learn, Google Maps API, Flask and PythonAnywhere.

AAAI 2021. GSNet: Learning Spatial-Temporal Correlations from Geographical and Semantic Aspects for Traffic Accident Risk Forecasting

This is the implementation code for the paper, "A Dynamic Spatial-temporal Attention Network for Early Anticipation of Traffic Accidents"

Repository for Traffic Accident Benchmark for Causality Recognition (ECCV 2020)

Traffic Accident Analysis using python machine learning

[WWW-21] DF-TAR: A Deep Fusion Network for Citywide Traffic Accident Risk Prediction with Dangerous Driving Behavior

No description available

A dataset for traffic accident analysis in the US

Real-Time Accident Detection in Traffic Surveillance Using Deep Learning

A multimodal GNN framework for traffic accident prediction and causal analysis

IDL course project: Traffic Accident Detection via Deep Learning.

IV 2020 "CSG: Critical Scenario Generation from Real Traffic Accidents"

An SQL analysis of traffic accidents within the City of Chicago limits and under the jurisdiction of Chicago Police Department (CPD).

Accident detection system for traffic footage. Using computer vision and ML to detect and analyze accidents in a CCTV footage in real-time.

Official codebase for "TAU-106K: A New Dataset for Comprehensive Understanding of Traffic Accident"

Problem Statement A smart city needs smart mobility, and to achieve this objective, the travel should be made convenient through sustainable transport solutions. Transportation system all over the world is facing unprecedented challenges in the current scenario of increased population, urbanization and motorization. Farewell to all difficulties as reinforcement learning along with deep learning can now make it simpler for consumers. In this paper we have applied reinforcement learning techniques for a self-driving agent in a simplified world to aid it in effectively reaching its destinations in the allotted time. We have first investigated the environment, the agent operates in, by constructing a very basic driving implementation. Once the agent is successful at operating within the environment, we can then identify each possible state the agent can be in when considering such things as traffic lights and oncoming traffic at each intersection. With states identified, we can implement a Q-Learning algorithm for the self-driving agent to guide the agent towards its destination within the allotted time. Finally, we can improve upon the Q-Learning algorithm to find the best configuration of learning and exploration factors to ensure the self-driving agent is reaching its destinations with consistently positive results. Our aim is also to find optimum values of parameters of the fitting function alpha, gamma and epsilon, so that the agent can work in an optimized way with the most optimum parameter values. Hence, a comparative analysis has also been conducted. Methodology used The solution to the smart cab objective is deep reinforcement learning in a simulated environment. The smart cab operates in an ideal, grid-like city (similar to New York City), with roads going in the North-South and East-West directions. Other vehicles will certainly be present on the road, but there will be no pedestrians to be concerned with. At each intersection there is a traffic light that either allows traffic in the North-South direction or the East-West direction. We have assumed that the smart cab is assigned a route plan based on the passengers' starting location and destination. The route is split at each intersection into waypoints, and the smart cab, at any instant, is at some intersection in the world. Therefore, the next waypoint to the destination, assuming the destination has not already been reached, is one intersection away in one direction (North, South, East, or West). The smart cab has only an egocentric view of the intersection it is at: It can determine the state of the traffic light for its direction of movement, and whether there is a vehicle at the intersection for each of the oncoming directions. For each action, the smart cab may either stay idle at the intersection, or drive to the next intersection to the left, right, or ahead of it. Finally, each trip has a time to reach the destination which decreases for each action taken (the passengers want to get there quickly). If the allotted time becomes zero before reaching the destination, the trip has failed. The smart cab will receive positive or negative rewards based on the action it has taken. Expectedly, the smart cab will receive a small positive reward when making a good action, and a varying amount of negative reward dependent on the severity of the traffic violation it would have committed. Based on the rewards and penalties the smart cab receives, the self-driving agent implementation should learn an optimal policy for driving on the city roads while obeying traffic rules, avoiding accidents, and reaching passengers' destinations in the allotted time. Environment: The smartcab operates in an ideal, grid-like city (similar to New York City), with roads going in the North-South and East-West directions. Other vehicles will certainly be present on the road, but there will be no pedestrians to be concerned with. At each intersection there is a traffic light that either allows traffic in the North-South direction or the East-West direction. U.S. Right-of-Way rules apply:  On a green light, a left turn is permitted if there is no oncoming traffic making a right turn or coming straight through the intersection.  On a red light, a right turn is permitted if no oncoming traffic is approaching from your left through the intersection. To understand how to correctly yield to oncoming traffic when turning left.

A Data Visualization project on the French traffic accidents database

深圳市道路交通事故数据分析

This is a capstone project that entails building an end-to-end ETL (Extract-Transform-Load) Data pipeline which extracts UK accident and traffic datasets from Amazon S3, clean and transform with Pyspark, transfer it back to S3 and finally load to Amazon Redshift (Distributed Database), from where the data can be queried for ad-hoc analyses.

AI-based Traffic Management System that utilizes IoT and AI technologies to enhance traffic signal optimization, prioritize pedestrian safety, and reduce accidents at crossings. This system aims to improve overall traffic efficiency, promote sustainability, and facilitate emergency service response times.

No description available