Traffic Accident Prediction Using Spatiotemporal Multi-Graph Learning
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Solution Overview
Problem
Existing traffic accident prediction systems fail to account for the non-uniform distribution of accidents and external factors, leading to inefficient dispatch of patrol vehicles and suboptimal response times.
Innovation Solution
A spatiotemporal attention-based multi-graph convolutional neural network model that integrates geographical and temporal data to predict traffic accidents, using a graph construction module, multi-graph convolution, spatial and temporal attention mechanisms, and a recurrent network to learn and visualize future accident patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If patrol vehicles are dispatched uniformly to cover as much area as possible, then the coverage area is maximized, but the response time to accidents in high-risk zones increases
Solution Approach 1:
The patent applies local quality by dynamically adjusting patrol vehicle dispatch based on localized accident risk characteristics. Different zones receive different levels of patrol attention according to their specific accident patterns, temporal trends, and external factors. High-risk zones during rush hour receive increased patrol allocation, while low-risk zones receive reduced allocation, optimizing both coverage and response time.
Solution Approach 2:
The system performs preliminary action by predicting future accident hotspots before accidents occur. Using historical accident data, temporal patterns, and external factor analysis, the system proactively identifies zones that will require patrol attention in the near future, allowing patrol vehicles to be positioned in advance rather than reacting after accidents happen.
2Loss of time
If more patrol vehicles are sent to zones with high accident rates, then the response time to accidents is improved, but the operational cost and complexity of dispatch management increases
Solution Approach 1:
The dispatch system applies self-service by automatically analyzing accident patterns, predicting hotspots, and generating optimal patrol assignments without manual intervention. The system processes historical data, identifies temporal and spatial patterns, and produces dispatch recommendations autonomously, reducing the complexity of manual dispatch management while optimizing response times.
Solution Approach 2:
The system implements feedback by continuously monitoring actual accident occurrences against predictions and using this information to refine future predictions and dispatch decisions. The closed-loop system learns from past performance, adjusting its models and recommendations based on the accuracy of predictions and the effectiveness of dispatch decisions, thereby managing complexity through adaptive learning.
3Device complexity
If independent time series models are used to predict accidents, then the model simplicity is maintained, but the prediction accuracy decreases due to ignoring spatial dependencies and external factors
Solution Approach 1:
The patent merges multiple data sources and modeling approaches by combining historical accident data, spatial information from neighboring zones, and external factors (weather, events, construction) into an integrated prediction model. This multi-factor approach captures the complex interactions between different variables, significantly improving prediction accuracy compared to simple independent time series models.
Solution Approach 2:
The system transitions from one-dimensional temporal analysis to multi-dimensional analysis by incorporating spatial dimensions (neighboring zone effects), environmental dimensions (weather conditions), and event dimensions (special events, construction). This dimensional expansion allows the model to capture comprehensive accident patterns while managing complexity through structured data integration.
Data Source
AI summary
Traffic accident prediction systems and methods are provided. The traffic accident prediction systems and methods include an accident prediction model that utilizes a spatiotemporal attention-based multi-graph convolution neural network to predict the number of traffic accidents in a predetermined region over a predetermined period of time in order to assist with the efficient dispatch of public safety resources to respond to traffic accidents.