Traffic Accident Analysis using python machine learning

A dataset for traffic accident analysis in the US

A multimodal GNN framework for traffic accident prediction and causal analysis

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.

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

深圳市道路交通事故数据分析

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Data Analysis of Road Traffic Accidents to Minimize the rate of accidents

This project focuses on analyzing road traffic accident data and predicting casualty severity using machine learning techniques. It includes data preprocessing, exploratory data analysis, correlation analysis, classification models, and decision tree–based interpretation to identify key factors influencing accident severity.

An analysis of traffic accident data for the UK in 2014, using data from the UK Data Service. (Sourced from Kaggle with original data coming from UK Data Service. See wiki for complete citations.)

A comprehensive exploratory data analysis (EDA) of NYC road accidents (2021-2023). This project covers data cleaning, visualization, and in-depth analysis to uncover temporal trends, spatial hotspots, and key contributing factors behind urban traffic incidents.

This projects entails performing in-depth descriptive analysis and data visualization on United Kingdom Road Traffic and Accident dataset and also Forecasting/Predicting the rate of accidents in UK for the next 2 years.

Traffic Accident / Weather Analysis

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Final project ICT-RTA data

Repository for spatial analysis of traffic car accidents in the United States.

Exploratory Data Analysis on "US Accidents" dataset to find out and visualize different trends including impact of COVID-19 on traffic behavior and accidents, environmental stimuli on accident occurrence and accident hotspot locations in order to get control over the same.

No description available

Data visualization and prediction tool on UK traffic accidents

Data Cleaning & Exploratory Analysis of Traffic Accidents in San Antonio TX

The Road Traffic Accident Analysis project explores patterns and causes of accidents using Python. It covers driver demographics, weather, vehicle conditions, accident causes, and time trends, using EDA, statistics, outlier detection, and ML to uncover insights for improving road safety.

US-Accidents: A Countrywide Traffic Accident Dataset Analysis

Data Analysis of Road Traffic Accidents

Analysis of the Cause of Traffic Accidents

This is a traffic severity prediction model built using XGBoost, deployed on flask.

US Accidents Traffic Analysis

Data analysis performed on past traffic accidents to help Emergency Response Team prioritize their manpower on days with high probability of traffic accidents

Database for analysis of road traffic accidents

Comprehensive traffic accident analysis using Python to examine patterns across time, weather, road conditions, and geography, revealing key behavioral risk factors and actionable safety recommendations.

Nottingham Open Data Exploration and Analysis - Road Traffic Accidents