Data-driven Estimation of Origin-Destination Demand and User Cost Functions for the Optimization of Transportation Networks

In earlier work (Zhang et al., 2016) we used actual traffic data from the Eastern Massachusetts transportation network in the form of spatial average speeds and road segment flow capacities in order to estimate Origin-Destination (OD) flow demand matrices for the network. Based on a Traffic Assignment Problem (TAP) formulation (termed "forward problem"), in this paper we use a scheme similar to our earlier work to estimate initial OD demand matrices and then propose a new inverse problem formulation in order to estimate user cost functions. This new formulation allows us to efficiently overcome numerical difficulties that limited our prior work to relatively small subnetworks and, assuming the travel latency cost functions are available, to adjust the values of the OD demands accordingly so that the flow observations are as close as possible to the solutions of the forward problem. We also derive sensitivity analysis results for the total user latency cost with respect to important parameters such as road capacities and minimum travel times. Finally, using the same actual traffic data from the Eastern Massachusetts transportation network, we quantify the Price of Anarchy (POA) for a much larger network than that in Zhang et al. (2016).Comment: Preprint submitted to The 20th World Congress of the International Federation of Automatic Control, July 9-14, 2017, Toulouse, Franc

Detection and optimization problems with applications in smart cities

This dissertation proposes solutions to a selected set of detection and optimization problems, whose applications are focused on transportation systems. The goal is to help build smarter and more efficient transportation systems, hence smarter cities. Problems with dynamics evolving in two different time-scales are considered: (1) In a fast time-scale, the dissertation considers the problem of detection, especially statistical anomaly detection in real-time. From a theoretical perspective and under Markovian assumptions, novel threshold estimators are derived for the widely used Hoeffding test. This results in a test with a much better ability to control false alarms while maintaining a high detection rate. From a practical perspective, the improved test is applied to detecting non-typical traffic jams in the Boston road network using real traffic data reported by the Waze smartphone navigation application. The detection results can alert the drivers to reroute so as to avoid the corresponding areas and provide the most urgent "targets" to the Transportation department and/or emergency services to intervene and remedy the underlying cause resulting in these jams, thus, improving transportation systems and contributing to the smart city agenda. (2) In a slower time-scale, the dissertation investigates a host of optimization problems, including estimation and adjustment of Origin-Destination (OD) demand, traffic assignment, recovery of travel cost functions, and joint recovery of travel cost functions and OD demand (joint problem). Integrating these problems leads to a data-driven predictive model which serves to diagnose/control/optimize the transportation network. To ensure good accuracy of the predictive model and increase its robustness and consistency, several novel formulations for the travel cost function recovery problem and the joint problem are proposed. A data-driven framework is proposed to evaluate the Price-of-Anarchy (PoA; a metric assessing the degree of congestion under selfish user-centric routing vs. socially-optimal system-centric routing). For the case where the PoA is larger than expected, three viable strategies are proposed to reduce it. To demonstrate the effectiveness and efficiency of the proposed approaches, case-studies are conducted on three benchmark transportation networks using synthetic data and an actual road network (from Eastern Massachusetts (EMA)) using real traffic data. Moreover, to facilitate research in the transportation community, the largest highway subnetwork of EMA has been released as a new benchmark network

Joint Estimation of OD Demands and Cost Functions in Transportation Networks from Data

Existing work has tackled the problem of estimating Origin-Destination (OD) demands and recovering travel latency functions in transportation networks under the Wardropian assumption. The ultimate objective is to derive an accurate predictive model of the network to enable optimization and control. However, these two problems are typically treated separately and estimation is based on parametric models. In this paper, we propose a method to jointly recover nonparametric travel latency cost functions and estimate OD demands using traffic flow data. We formulate the problem as a bilevel optimization problem and develop an iterative first-order optimization algorithm to solve it. A numerical example using the Braess Network is presented to demonstrate the effectiveness of our method.Comment: To appear at the Proceedings of the 58th IEEE Conference on Decision and Contro

Estimation and optimization methods for transportation networks

While the traditional approach to ease traffic congestion has focused on building infrastructure, the recent emergence of Connected and Automated Vehicles (CAVs) and urban mobility services (e.g., Autonomous Mobility-on-Demand (AMoD) systems) has opened a new set of alternatives for reducing travel times. This thesis seeks to exploit these advances to improve the operation and efficiency of Intelligent Transportation Systems using a network optimization perspective. It proposes novel methods to evaluate the prospective benefits of adopting socially optimal routing schemes, intermodal mobility, and contraflow lane reversals in transportation networks. This dissertation makes methodological and empirical contributions to the transportation domain. From a methodological standpoint, it devises a fast solver for the Traffic Assignment Problem with Side Constraints which supports arbitrary linear constraints on the flows. Instead of using standard column-generation methods, it introduces affine approximations of the travel latency function to reformulate the problem as a quadratic (or linear) programming problem. This framework is applied to two problems related to urban planning and mobility policy: social routing with rebalancing in intermodal mobility systems and planning lane reversals in transportation networks. Moreover, it proposes a novel method to jointly estimate the Origin-Destination demand and travel latency functions of the Traffic Assignment Problem. Finally, it develops a model to jointly optimize the pricing, rebalancing and fleet sizing decisions of a Mobility-on-Demand service. Empirically, it validates all the methods by testing them with real transportation topologies and real traffic data from Eastern Massachusetts and New York City showing the achievable benefits obtained when compared to benchmarks

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig