Estimation and Control of Traffic Relying on Vehicular Connectivity

Vehicular traffic flow is essential, yet complicated to analyze. It describes the interplay among vehicles and with the infrastructure. A better understanding of traf-fic would benefit both individuals and the whole society in terms of improving safety, energy efficiency, and reducing environmental impacts. A large body of research ex-ists on estimation and control of vehicular traffic in which, however, vehicles were assumed not to be able to share information due to the limits of technology. With the development of wireless communication and various sensor devices, Connected Vehicles(CV) are emerging which are able to detect, access, and share information with each other and with the infrastructure in real time. Connected Vehicle Technology (CVT) has been attracting more and more attentions from different fields. The goal of this dissertation is to develop approaches to estimate and control vehicular traffic as well as individual vehicles relying on CVT. On one hand, CVT sig-nificantly enriches the data from individuals and the traffic, which contributes to the accuracy of traffic estimation algorithms. On the other hand, CVT enables commu-nication and information sharing between vehicles and infrastructure, and therefore allows vehicles to achieve better control and/or coordination among themselves and with smart infrastructure. The first part of this dissertation focused on estimation of traffic on freeways and city streets. We use data available from on road sensors and also from probe One of the most important traffic performance measures is travel time. How-ever it is affected by various factors, and freeways and arterials have different travel time characteristics. In this dissertation we first propose a stochastic model-based approach to freeway travel-time prediction. The approach uses the Link-Node Cell Transmission Model (LN-CTM) to model traffic and provides a probability distribu-tion for travel time. The probability distribution is generated using a Monte Carlo simulation and an Online Expectation Maximization clustering algorithm. Results show that the approach is able to generate a reasonable multimodal distribution for travel-time. For arterials, this dissertation presents methods for estimating statistics of travel time by utilizing sparse vehicular probe data. A public data feed from transit buses in the City of San Francisco is used. We divide each link into shorter segments, and propose iterative methods for allocating travel time statistics to each segment. Inspired by K-mean and Expectation Maximization (EM) algorithms, we iteratively update the mean and variance of travel time for each segment based on historical probe data until convergence. Based on segment travel time statistics, we then pro-pose a method to estimate the maximum likelihood trajectory (MLT) of a probe vehicle in between two data updates on arterial roads. The results are compared to high frequency ground truth data in multiple scenarios, which demonstrate the effectiveness of the proposed approach. The second part of this dissertation emphasize on control approaches enabled by vehicular connectivity. Estimation and prediction of surrounding vehicle behaviors and upcoming traffic makes it possible to improve driving performance. We first propose a Speed Advisory System for arterial roads, which utilizes upcoming traffi

Freeway traffic incident detection using large scale traffic data and cameras

Automatic incident detection (AID) is crucial for reducing non-recurrent congestion caused by traffic incidents. In this paper, a data-driven AID framework is proposed that can leverage large-scale historical traffic data along with the inherent topology of the traffic networks to obtain robust traffic patterns. Such traffic patterns can be compared with the real-time traffic data to detect traffic incidents in the road network. Our AID framework consists of two basic steps for traffic pattern estimation. First, we estimate a robust univariate speed threshold using historical traffic information from individual sensors. This step can be parallelized using MapReduce framework thereby making it feasible to implement the framework over large networks. Our study shows that such robust thresholds can improve incident detection performance significantly compared to traditional threshold determination. Second, we leverage the knowledge of the topology of the road network to construct threshold heatmaps and perform image denoising to obtain spatio-temporally denoised thresholds. We used two image denoising techniques, bilateral filtering and total variation for this purpose. Our study shows that overall AID performance can be improved significantly using bilateral filter denoising compared to the noisy thresholds or thresholds obtained using total variation denoising. The second research objective involved detecting traffic congestion from camera images. Two modern deep learning techniques, the traditional deep convolutional neural network (DCNN) and you only look once (YOLO) models, were used to detect traffic congestion from camera images. A shallow model, support vector machine (SVM) was also used for comparison and to determine the improvements that might be obtained using costly GPU techniques. The YOLO model achieved the highest accuracy of 91.2%, followed by the DCNN model with an accuracy of 90.2%; 85% of images were correctly classified by the SVM model. Congestion regions located far away from the camera, single-lane blockages, and glare issues were found to affect the accuracy of the models. Sensitivity analysis showed that all of the algorithms were found to perform well in daytime conditions, but nighttime conditions were found to affect the accuracy of the vision system. However, for all conditions, the areas under the curve (AUCs) were found to be greater than 0.9 for the deep models. This result shows that the models performed well in challenging conditions as well. The third and final part of this study aimed at detecting traffic incidents from CCTV videos. We approached the incident detection problem using trajectory-based approach for non-congested conditions and pixel-based approach for congested conditions. Typically, incident detection from cameras has been approached using either supervised or unsupervised algorithms. A major hindrance in the application of supervised techniques for incident detection is the lack of a sufficient number of incident videos and the labor-intensive, costly annotation tasks involved in the preparation of a labeled dataset. In this study, we approached the incident detection problem using semi-supervised techniques. Maximum likelihood estimation-based contrastive pessimistic likelihood estimation (CPLE) was used for trajectory classification and identification of incident trajectories. Vehicle detection was performed using state-of-the-art deep learning-based YOLOv3, and simple online real-time tracking (SORT) was used for tracking. Results showed that CPLE-based trajectory classification outperformed the traditional semi-supervised techniques (self learning and label spreading) and its supervised counterpart by a significant margin. For pixel-based incident detection, we used a novel Histogram of Optical Flow Magnitude (HOFM) feature descriptor to detect incident vehicles using SVM classifier based on all vehicles detected by YOLOv3 object detector. We show in this study that this approach can handle both congested and non-congested conditions. However, trajectory-based approach works considerably faster (45 fps compared to 1.4 fps) and also achieves better accuracy compared to pixel-based approach for non-congested conditions. Therefore, for optimal resource usage, trajectory-based approach can be used for non-congested traffic conditions while for congested conditions, pixel-based approach can be used

Correlating sparse sensing for large-scale traffic speed estimation: A Laplacian-enhanced low-rank tensor kriging approach

Traffic speed is central to characterizing the fluidity of the road network. Many transportation applications rely on it, such as real-time navigation, dynamic route planning, and congestion management. Rapid advances in sensing and communication techniques make traffic speed detection easier than ever. However, due to sparse deployment of static sensors or low penetration of mobile sensors, speeds detected are incomplete and far from network-wide use. In addition, sensors are prone to error or missing data due to various kinds of reasons, speeds from these sensors can become highly noisy. These drawbacks call for effective techniques to recover credible estimates from the incomplete data. In this work, we first identify the issue as a spatiotemporal kriging problem and propose a Laplacian enhanced low-rank tensor completion (LETC) framework featuring both lowrankness and multi-dimensional correlations for large-scale traffic speed kriging under limited observations. To be specific, three types of speed correlation including temporal continuity, temporal periodicity, and spatial proximity are carefully chosen and simultaneously modeled by three different forms of graph Laplacian, named temporal graph Fourier transform, generalized temporal consistency regularization, and diffusion graph regularization. We then design an efficient solution algorithm via several effective numeric techniques to scale up the proposed model to network-wide kriging. By performing experiments on two public million-level traffic speed datasets, we finally draw the conclusion and find our proposed LETC achieves the state-of-the-art kriging performance even under low observation rates, while at the same time saving more than half computing time compared with baseline methods. Some insights into spatiotemporal traffic data modeling and kriging at the network level are provided as well