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This paper proposes a possible methodology for detecting and mitigating traffic congestion. This method is carried out using a custom-designed traffic scenario model. The model is fully developed in lieu of abundant data support from actual traffic events, which is applicable to localized traffic surveillance conditions, where massive data collection from surveilling devices is infeasible or unviable. This approach includes two parts: model construction and re-routing strategy. The model construction part focuses on the development of a traffic driving scenario, which takes various criteria such as traffic volume and traffic signal into consideration. The goal of this setup is to create a realistic-possible environment, where the proposed methods can be tested. The re-routing strategy is implemented based on the model simulation result of a medium-scale drive-able road map. The idea of the adaptive vehicle re-routing strategy is inspired by the k-shortest path algorithm, adapted with the dynamic congestion re-routing strategy. It will be shown that the model is able to automatically identify congestion patterns that are happening on any road segments, and then initiates a proper re-routing strategy to alleviate such congestion in a timely manner. Although the methodology is realized and validated within a simulated model, the concept is transparent to any transportation system under study without extra complexity. In addition, the proposed modeling and simulation technique can be used for real-time implementation in intelligent transportation management systems.

Real-time traffic signal acquisition and network transmission are essential components of intelligent transportation systems, facilitating real-time traffic monitoring, management, and analysis in urban environments. In this paper, we introduce a comprehensive system that incorporates live traffic signal acquisition, real-time data processing, and secure network transmission through a combination of hardware and software modules, called LIDATS. LIDATS stands for Live Intersection Data Acquisition for Traffic Simulators. The design and implementation of our system are detailed, encompassing signal acquisition hardware as well as a software platform that is used specifically for real-time data processing. The performance evaluation of our system was conducted by simulation in the lab, demonstrating its capability to reliably capture and transmit data in real time, and to effectively extract the relevant information from noisy and complex traffic data. Supporting a variety of intelligent transportation applications, such as real-time traffic flow management, intelligent traffic signal control, and predictive traffic analysis, our system enables remote data analysis and decisionmaking, providing valuable insights and enhancing the traffic efficiency while reducing the congestion in urban environments.