Date of Award
Doctor of Philosophy (PhD)
Dr. Ardalan Vahidi, Committee Chair
Dr. Mohammed Daqaq
Dr. John R. Wagner
Dr. Mashrur Chowdhury
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 traﬃc
Wan, Nianfeng, "Estimation and Control of Traffic Relying on Vehicular Connectivity" (2016). All Dissertations. 1691.