Spatio-Temporal Forecasting for Traffic Simulation Framework

Chengxin Wang, Gary Tan

Abstract: The recent expansion in sensor deployment throughout cities has produced extensive traffic data, empowering the traffic management center to closely monitor traffic conditions. These sensors generate massive spatio-temporal (ST) data, serving as the resource that can be harnessed to forecast traffic conditions within the city. Accurate forecasting of these conditions is crucial for traffic management centers, which can facilitate a wide range of urban applications, such as intelligent transportation, traffic simulation, and infrastructure planning. In this summary, we focus on leveraging ST traffic data through AI techniques for forecasting applications. We design novel methodologies to extract knowledge from the traffic data, providing support for a wide variety of real-world applications, including traffic simulation. To achieve it, we introduce the ST forecasting algorithms that operate at two distinct levels: the region-based level and the road-based level. These predictions can be then utilized as input to a traffic simulation framework, enabling the generation of proactive and advantageous planning strategies for urban transportation management. At the region-based level, a city is divided into multiple regions, i.e., even grid cells. Our goal is to generate accurate predictions concurrently for all regions within a city. We first investigate the inherent characteristics of traffic data, i.e., periodicity and ST dependency. We then devise a novel periodic residual learning network (PRNet) to better model the periodicity in traffic ST data. PRNet frames traffic condition forecasting as a periodic residual learning problem by modeling the variation between past and future conditions. Compared to directly predicting traffic conditions that are highly dynamic, learning more stationary variation is much easier, which facilitates model training. Also, the learned variation enables the network to produce residuals between future conditions and its corresponding weekly observations at each time interval and thus contributes to substantially more accurate predictions. At the road-based level, we focus on modeling traffic conditions of individual roads within a region, with the aim of accurately predicting traffic conditions for each road. Among various traffic conditions, traffic incidents are important to traffic management centers, as they can cause significant disruptions and delays. Thus, we introduce an incident forecasting framework, utilizing ST forecasting algorithms, to infer the incident in advance, allowing for more proactive and responsive traffic management strategies. Specifically, we adopt the ST forecasting algorithm to generate each road's speed condition. Considering that speed conditions do not change dramatically within short intervals, we use the predicted speed to infer if a traffic incident has taken place on a road. If there is a significant deviation in the predicted speed conditions from the previous speed behavior, we generate a severity level of the incident based on the deviations. This severity level can be then integrated into simulations, allowing the system to generate response plans in advance.