Traffic Flow Theory and Characteristics with Applications for Intelligent Transportation System Technologies

TRANSPORTATION RESEARCH RECORD No. 1510 Highway Operations, Capacity, and Traffic Control Traffic Flow Theory and Characteristics with Applications for Intelligent Transportation System Technologies A peer-reviewed publiCation of the Transportation Research Board TRANSPORTATION RESEARCH BOARD NATIONAL RESEARCH COUNCIL NATIONAL ACADEMY PRESS WASH_INGTON, D.C. 1995 Transportation Research Record 1510 Sponsorship of Transportation Research Record 1510 ISSN 0361-1981 ISBN 0-309-06203-9 GROUP 3--0PERATION, SAFETY, AND MAINTENANCE OF Price: $ 23.00 TRANSPORTATION FACILITIES Chairman: Jerome W. Hall, University of New Mexico Subscriber Category IV A highway operations, capacity, and traffic control Facilities and Operation Section Chairman: Jack L. Kay, JHK & Associates Printed in the United States of America Committee on Traffic Flow Theory and Characteristics Chairman: Hani S. Mahmassani, University of Texas at Austin Secretary: James C. Williams, University of Texas Siamak (Sia) A. Ardekani, Rahim F. Benekohal, Gang-Len Chang, Gary A. Davis, Richard W. Denney, Jr., Nathan H. Gartner, Fred L. Hall, Benjami G. Heydecker, R. Jayakrishnan, Masao Kuwahara, Michael Kyte, John Leonard, Henry C. Lieu, David Maha/el, Carroll J. Messer, Abbas Mohaddes, Kyriacos C. Mouskos, Markos Papageorgiou, Ajay K. Rathi, Nagui M. Rouphail, Michael A. P. Taylor, Michel Van Aerde Transportation Research Board Staff Robert E. Spicher, Director, Technical Activities Richard A. Cunard, Engineer of Traffic and Operations Nancy A. Ackerman, Director, Reports and Editorial Services Sponsorship is indicated by a footnote at the end of each paper. The organizational units, officers, and members are as of December 31, 1994. Transportation Research Record 1510 Contents Foreword v Another Look at A Priori Relationships Among Traffic Flow Characteristics 1 James H. Banks DISCUSSION, Michael Cassidy, 9 AUTHOR'S CLOSURE, 9 DISCUSSION, Matti Pursula, 10 AUTHOR'S CLOSURE, 10 Description of Macroscopic Relationships Among Traffic Flow Variables 11 Using Neural Network Models Takashi Nakatsuji, Mitsuru Tanaka, Pourmoallem Nasser, and Toru Hagiwara Microscopic Modeling of Traffic Within Freeway Lanes 19 Jonathan M. Bunker and Rod J. Troutbeck I Statistical Analysis of Day-to-Day Variations in Real-Time Traffic 26 1 Flow Data H. Rakha and M. Van Aerde Statistical Analysis and Validation of Multipopulation Traffic 35 Simulation Experiments Shirish S. Joshi and Ajay K. Rathi Event-Based Short-Term Traffic Flow Prediction Model 45 K. Larry Head Estimating Intersection Turning Movement Proportions from 53 Less-Than-Complete Sets of Traffic Counts Gary A. Davis and Chang-Jen Lan Arterial Incident Detection Integrating Data from Multiple Sources 60 Nikhil Bhandari, Frank S. Koppelman, Joseph L. Schafer, Vaneet Sethi, and John N. Ivan Driver Deceleration Behavior on a Freeway in New Zealand 70 Christopher R. Bennett and Roger C. M. Dunn Foreword he papers in this volume are from the 1995 Annual Meeting of the Transportation Research Board nd were peer reviewed by the TRB Committee on Traffic Flow Theory and Characteristics. The study of the relationships among traffic flow characteristics and flow models is fundamental to raffic operations and is the subject of considerable interest as a result of the development of intelli ent transportation systems. Readers with an interest in traffic flow models and characteristics will find papers pertaining to re ationships among traffic speed, flow, and concentration; neural network modeling of the macroscopic elationships between traffic flow variables; microscopic models for traffic operations at the individ al vehicle level; the analysis of day-to-day variations in real-time traffic flow data for providing in ehicle real-time information on traffic conditions; and the validation of traffic simulation networks. The use of traffic flow theory for control applications in real-time. advanced traffic management ystems is the focus of the last series of papers. Included are papers on an event-based traffic flow rediction model for real-time traffic-adaptive signal control, estimation of intersection turning ovements, arterial incident detection, and deceleration behavior and prediction models. v TRANSPORTATION RESEARCH RECORD 1510 Another Look at A Priori Relationships Among Traffic Flow Characteristics JAMES H. BANKS Past derivations of a priori relationships among speed, flow, and con where centration (such as the fundamental relationship and the speed-flow occupancy relationship) have involved unrealistic assumptions of uni q =flow, formity in at least one traffic flow characteristic. Several relationships u = speed, and are derived for which these assumptions of uniformity are relaxed. For k =density. relationships involving both time- and distance-based variables, this requires that the relationship be understood in probabilistic terms; A similar relationship exists among speed, flow, vehicle length, and where all variables are time-based, deterministic relationships are also occupancy: possible. The fundamental relationship can be shown to be strictly true in the limit where the time and distance intervals over which measure u = qL (2) ments are taken approach 0. Where the order of arrival of vehicles with H particular speeds is random, the fundamental relationship is found to hold for average values of the variables in question; this is also true if the section over which density is measured is empty at the beginning where L represents vehicle length and H occupancy, defined as the and end of the time interval used for averaging. Relationships derived fraction of time that vehicles are present at a point. The classical under various other assumptions involve covariance terms, so that if derivation of Equation I is that of Wardrop (5). Equation 2, which particular variables are not correlated, simple relationships continue to most commonly has been used to estimate speeds from flow and hold. Where these variables are correlated, biases may be expected. occupancy data, was proposed by Athol (6). Under certain conditions, these biases may be quite serious. Compari Since the use of these relationships (especially Equation 1) has son of the relationships derived here with those of past empirical stud ies results in good agreement for the relationship between density, as been pervasive in traffic flow theory, confirming major inaccuracies estimated from the fundamental relationship, and occupancy. On the in them could have far-reaching consequences. The purpose of this other hand, previously reported discrepancies between measured speeds paper is to reexamine the validity of these relationships, extend their and speeds calculated from flows and occupancies cannot be explained derivations to address certain oversimplifications, and consider the fully by the covariance terms in the relationships derived here. possible reasons for apparent discrepancies between them and actual data, particularly those reported by Hall and Persaud (J). The study of relationships among the traffic flow characteristics speed, flow, and concentration (either density or occupancy) has long been a fundamental part of traffic research. In general, two FUNDAMENTAL RELATIONSHIP types of relationships among these variables are possible: a priori relationships, which proceed from the definitions of the various Theoretical objections to the fundamental relationship (Equation 1) measures, and empirical relationships, which can be discovered arise from the distinction between relationships that hold true for only by observing actual traffic flow. uniform traffic streams (those with constant, identical speed and Not surprisingly, the bulk of the literature focuses on empirical spacing for all vehicles) and those that hold for averages of the char elationships. The major a priori relationships were worked out acteristics of nonuniform traffic streams. In addition, the funda arly in the history of traffic flow research and have been little mental relationship involves both time- and distance-based vari xamined since. They have often been taken for granted and used ables, which may be incompatible with one another in nonuniform reely to transform data from one form to another or to move back traffic streams. nd forth among the three possible bivariate relationships involving peed, flow, and concentration. Nevertheless, several recent studies y Hall et al. have raised questions about the accuracy and applica .Point Relationships Among Traffic Flow ility of these relationships (J-4). These studies have also presented Characteristics ata that appear to contradict them to some extent and have sug ested using three-dimensional empirical models that are indepen The version of Equation 1 presented here implicitly assumes a uni ent of them. form traffic stream and under that assumption can be derived easily The relationships in question include the so-calledfundamental by means of dimensional analysis. It can also be shown to be true at elationship: a point, if all measures are regarded as continuous variables. This approach to its derivation makes use of a three-dimensional surface = uk (1) proposed by Makigami et al. (7). If vehicle trajectories are plotted and numbered (with some adjustments in cases in which vehicles epartment of Civil Engineering, San Diego State University, San Diego, pass one another), the trajectories may be considered as the contour alif. 92182-1324. lines of a surface of cumulative flow versus time and distance. For 2 TRANSPORTATION RESEARCH

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