BigSUMO: A Scalable Framework for Big Data Traffic Analytics and Parallel Simulation
🤖 AI Summary
This work addresses the challenges of efficiently integrating and analyzing heterogeneous, multi-source data in urban traffic management and the lack of scalable tools for incident detection and intervention strategy optimization. The authors propose an end-to-end, modular, and extensible open-source framework that fuses high-resolution inductive loop detector data, traffic signal states, and sparse trajectory information to identify traffic anomalies through descriptive analytics. Leveraging the SUMO microscopic simulation engine, the system enables parallel execution of hundreds of “what-if” scenarios to optimize traffic performance. By establishing a closed-loop decision-support pipeline—from big data analytics to large-scale parallel simulation—the framework significantly enhances the efficiency of evaluating intervention strategies, offering cities a cost-effective, high-performance, and easily deployable solution for intelligent traffic management.
Application Category
📝 Abstract
With growing urbanization worldwide, efficient management of traffic infrastructure is critical for transportation agencies and city planners. It is essential to have tools that help analyze large volumes of stored traffic data and make effective interventions. To address this need, we present ``BigSUMO", an end-to-end, scalable, open-source framework for analytics, interruption detection, and parallel traffic simulation. Our system ingests high-resolution loop detector and signal state data, along with sparse probe trajectory data. It first performs descriptive analytics and detects potential interruptions. It then uses the SUMO microsimulator for prescriptive analytics, testing hundreds of what-if scenarios to optimize traffic performance. The modular design allows integration of different algorithms for data processing and outlier detection. Built using open-source software and libraries, the pipeline is cost-effective, scalable, and easy to deploy. We hope BigSUMO will be a valuable aid in developing smart city mobility solutions.
Problem
Research questions and friction points this paper is trying to address.
Big Data Traffic Analytics
Traffic Simulation
Urban Traffic Management
Interruption Detection
Smart City Mobility
Innovation
Methods, ideas, or system contributions that make the work stand out.
BigSUMO
traffic analytics
parallel simulation
microsimulation
scalable framework
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