Spatio-temporal traffic flow estimation and optimum control in sensor-equipped road networks

University of Technology Sydney. Faculty of Engineering and Information Technology.With rapid urbanization, ITS (Intelligent Transportation Systems) has been deployed in some metropolitan areas to relieve traffic congestion and traffic accidents by traffic flow prediction and optimum traffic control. Due to temporary deployment of sensors, sensor malfunction and lossy communication systems, data missing problems has drawn significant attention from both academia and industry. Missing traffic data problem has negative impact on traffic flow prediction and optimum traffic control because ATIS (Advance Traveler Information Systems) and ATMS (Advance Traffic Management Systems) both rely on reliable, accurate and consistent traffic data measurements. Furthermore, adaptive traffic control is the most effective method to relieve traffic congestion and maximize road capacity. In this thesis, an Optimum Closed Cut (OCC) based spatio-temporal imputation technique was proposed, which can fully exploit the spatial-temporal correlation and road topological information in urban traffic network. The road topological information and flow conservation law can be explored to further improve the estimation performance while reducing the number of sensors involved in the data imputation, hence improving the computational efficiency. Besides, this thesis investigated the fundamental limits of missing traffic data estimation accuracy in urban networks using the spatio-temporal random effects (STRE) model. Furthermore, a hybrid dynamical system was investigated, which incorporates flow swap process, green-time proportion swap process and flow divergence for a general network with multiple OD pairs and multiple routes. A novel control policy was proposed to fill the gap by only adjusting the green-time proportion vector, and a sufficient condition was derived for the existence of equilibrium of the dynamical system under the mild constraints that (1) the travel cost function and stage pressure function should be continuous functions; (2) the flow and green-time proportion swap processes project all flow and green-time proportion vectors on the boundary of the feasible region onto itself. The condition of unique equilibrium was derived for fixed green-time proportion vector and it is shown that with varying green-time proportion vector, the set of equilibria is a compact, non-convex set, and with the same partial derivative of travel cost function with respect to the flow and green-time proportion vectors. Finally, the stability of the proposed dynamical system was proved by using Lyapunov stability analysis

Assessing the Impact of Game Day Schedule and Opponents on Travel Patterns and Route Choice using Big Data Analytics

The transportation system is crucial for transferring people and goods from point A to point B. However, its reliability can be decreased by unanticipated congestion resulting from planned special events. For example, sporting events collect large crowds of people at specific venues on game days and disrupt normal traffic patterns. The goal of this study was to understand issues related to road traffic management during major sporting events by using widely available INRIX data to compare travel patterns and behaviors on game days against those on normal days. A comprehensive analysis was conducted on the impact of all Nebraska Cornhuskers football games over five years on traffic congestion on five major routes in Nebraska. We attempted to identify hotspots, the unusually high-risk zones in a spatiotemporal space containing traffic congestion that occur on almost all game days. For hotspot detection, we utilized a method called Multi-EigenSpot, which is able to detect multiple hotspots in a spatiotemporal space. With this algorithm, we were able to detect traffic hotspot clusters on the five chosen routes in Nebraska. After detecting the hotspots, we identified the factors affecting the sizes of hotspots and other parameters. The start time of the game and the Cornhuskers’ opponent for a given game are two important factors affecting the number of people coming to Lincoln, Nebraska, on game days. Finally, the Dynamic Bayesian Networks (DBN) approach was applied to forecast the start times and locations of hotspot clusters in 2018 with a weighted mean absolute percentage error (WMAPE) of 13.8%

Decentralized Data Fusion and Active Sensing with Mobile Sensors for Modeling and Predicting Spatiotemporal Traffic Phenomena

The problem of modeling and predicting spatiotemporal traffic phenomena over an urban road network is important to many traffic applications such as detecting and forecasting congestion hotspots. This paper presents a decentralized data fusion and active sensing (D2FAS) algorithm for mobile sensors to actively explore the road network to gather and assimilate the most informative data for predicting the traffic phenomenon. We analyze the time and communication complexity of D2FAS and demonstrate that it can scale well with a large number of observations and sensors. We provide a theoretical guarantee on its predictive performance to be equivalent to that of a sophisticated centralized sparse approximation for the Gaussian process (GP) model: The computation of such a sparse approximate GP model can thus be parallelized and distributed among the mobile sensors (in a Google-like MapReduce paradigm), thereby achieving efficient and scalable prediction. We also theoretically guarantee its active sensing performance that improves under various practical environmental conditions. Empirical evaluation on real-world urban road network data shows that our D2FAS algorithm is significantly more time-efficient and scalable than state-of-the-art centralized algorithms while achieving comparable predictive performance.Comment: 28th Conference on Uncertainty in Artificial Intelligence (UAI 2012), Extended version with proofs, 13 page