  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.
