Leveraging Neo4j and deep learning for traffic congestion simulation & optimization

TL;DR

This paper combines knowledge graph modeling in Neo4j with deep learning to improve traffic congestion simulation and optimization, providing more accurate and insightful analysis of urban traffic patterns.

Contribution

It introduces a novel approach integrating graph databases and deep learning for traffic analysis, enhancing congestion prediction and network optimization capabilities.

Findings

Graph-based simulation improves congestion estimation accuracy.

Deep learning models effectively predict real-time traffic conditions.

Optimization algorithms identify congestion-free routes.

Abstract

Traffic congestion has been a major challenge in many urban road networks. Extensive research studies have been conducted to highlight traffic-related congestion and address the issue using data-driven approaches. Currently, most traffic congestion analyses are done using simulation software that offers limited insight due to the limitations in the tools and utilities being used to render various traffic congestion scenarios. All that impacts the formulation of custom business problems which vary from place to place and country to country. By exploiting the power of the knowledge graph, we model a traffic congestion problem into the Neo4j graph and then use the load balancing, optimization algorithm to identify congestion-free road networks. We also show how traffic propagates backward in case of congestion or accident scenarios and its overall impact on other segments of the roads. We also train a sequential RNN-LSTM (Long Short-Term Memory) deep learning model on the real-time traffic data to assess the accuracy of simulation results based on a road-specific congestion. Our results show that graph-based traffic simulation, supplemented by AI ML-based traffic prediction can be more effective in estimating the congestion level in a road network.