Hani S. Mahmassani

Bio: Hani S. Mahmassani is an academic researcher from Northwestern University. The author has contributed to research in topics: Traffic flow & Travel behavior. The author has an hindex of 75, co-authored 610 publications receiving 20000 citations. Previous affiliations of Hani S. Mahmassani include University of California, Irvine & University of Texas at El Paso.

Influence of connected and autonomous vehicles on traffic flow stability and throughput

Abstract: The introduction of connected and autonomous vehicles will bring changes to the highway driving environment. Connected vehicle technology provides real-time information about the surrounding traffic condition and the traffic management center’s decisions. Such information is expected to improve drivers’ efficiency, response, and comfort while enhancing safety and mobility. Connected vehicle technology can also further increase efficiency and reliability of autonomous vehicles, though these vehicles could be operated solely with their on-board sensors, without communication. While several studies have examined the possible effects of connected and autonomous vehicles on the driving environment, most of the modeling approaches in the literature do not distinguish between connectivity and automation, leaving many questions unanswered regarding the implications of different contemplated deployment scenarios. There is need for a comprehensive acceleration framework that distinguishes between these two technologies while modeling the new connected environment. This study presents a framework that utilizes different models with technology-appropriate assumptions to simulate different vehicle types with distinct communication capabilities. The stability analysis of the resulting traffic stream behavior using this framework is presented for different market penetration rates of connected and autonomous vehicles. The analysis reveals that connected and autonomous vehicles can improve string stability. Moreover, automation is found to be more effective in preventing shockwave formation and propagation under the model’s assumptions. In addition to stability, the effects of these technologies on throughput are explored, suggesting substantial potential throughput increases under certain penetration scenarios.

An evaluation tool for advanced traffic information and management systems in urban networks

Abstract: Tools for evaluating traffic networks under information supply are a crucial necessity in view of the ATMS/ATIS systems being proposed and implemented around the world as part of Intelligent Vehicle-Highway Systems of the future. This paper presents an evaluation model that incorporates the driver response to information, the traffic flow behavior, and the resulting changes in the characteristics of network paths, into an integrated simulation framework. The model is based on simulating individual vehicle movements according to macroscopic flow principles, the driver path selection behavior under information being explicitly modelled. Detailed modelling of intersection delays as well as a variety of traffic control options for both freeways and arterials are performed. The path-processing component is designed for efficient application of the framework to large and realistic networks. The model can be effectively used for studying alternative information supply and traffic control strategies under various levels of market penetration of in-vehicle ATIS hardware. The paper also discusses its application to candidate networks.

Dynamic network traffic assignment and simulation methodology for advanced system management applications

Abstract: Evaluation and operation of intelligent transportation system technologies in transportation networks give rise to methodological capabilities that require description of the dynamics of network traffic flows over time and space. Both descriptive and normative dynamic traffic assignment capabilities are required in this environment. Several dynamic network flow modeling problem formulations that arise in this context are discussed, and simulation-assignment procedures are described for these problems. A dynamic traffic assignment (DTA) system for advanced traffic network management is described. It is built around a traffic simulation-assignment modeling framework, which describes the evolution of traffic patterns in the network for given traffic loading under particular control measures and route guidance information supply strategies to individual motorists. The simulator is also embedded in an interactive search algorithm to determine optimal route guidance instructions to motorists. Numerical experiments with the model illustrate the relative effectiveness of different information supply strategies under different user behavior response rules.

Least Expected Time Paths in Stochastic, Time-Varying Transportation Networks

Abstract: We consider stochastic, time-varying transportation networks, where the arc weights (arc travel times) are random variables with probability distribution functions that vary with time. Efficient procedures are widely available for determining least time paths in deterministic networks. In stochastic but time-invariant networks, least expected time paths can be determined by setting each random arc weight to its expected value and solving an equivalent deterministic problem. This paper addresses the problem of determining least expected time paths in stochastic, time-varying networks. Two procedures are presented. The first procedure determines the a priori least expected time paths from all origins to a single destination for each departure time in the peak period. The second procedure determines lower bounds on the expected times of these a priori least expected time paths. This procedure determines an exact solution for the problem where the driver is permitted to react to revealed travel times on traveled links en route, i.e., in a time-adaptive route choice framework. Modifications to each of these procedures for determining least expected cost (where cost is not necessarily travel time) paths and lower bounds on the expected costs of these paths are given. Extensive numerical tests are conducted to illustrate the algorithms' computational performance as well as the properties of the solution.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Forecasting, Structural Time Series Models and the Kalman Filter

Abstract: In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Traffic and related self-driven many-particle systems

Abstract: Since the subject of traffic dynamics has captured the interest of physicists, many surprising effects have been revealed and explained. Some of the questions now understood are the following: Why are vehicles sometimes stopped by ``phantom traffic jams'' even though drivers all like to drive fast? What are the mechanisms behind stop-and-go traffic? Why are there several different kinds of congestion, and how are they related? Why do most traffic jams occur considerably before the road capacity is reached? Can a temporary reduction in the volume of traffic cause a lasting traffic jam? Under which conditions can speed limits speed up traffic? Why do pedestrians moving in opposite directions normally organize into lanes, while similar systems ``freeze by heating''? All of these questions have been answered by applying and extending methods from statistical physics and nonlinear dynamics to self-driven many-particle systems. This article considers the empirical data and then reviews the main approaches to modeling pedestrian and vehicle traffic. These include microscopic (particle-based), mesoscopic (gas-kinetic), and macroscopic (fluid-dynamic) models. Attention is also paid to the formulation of a micro-macro link, to aspects of universality, and to other unifying concepts, such as a general modeling framework for self-driven many-particle systems, including spin systems. While the primary focus is upon vehicle and pedestrian traffic, applications to biological or socio-economic systems such as bacterial colonies, flocks of birds, panics, and stock market dynamics are touched upon as well.