Tian Hou

Characterizing Travel Time Variability in Vehicular Traffic Networks

Modeling travel time reliability requires characterizing travel time distributions. Two key statistics commonly used to describe a distribution are the mean deviation and the standard deviation, with one depicting the central tendency and the other describing the dispersion. Although the mean travel time is easier to measure and predict, the corresponding standard deviation is usually hard to obtain because of the insufficiency of individual trip data. Building on seminal insight that goes back to Herman and Prigogines kinetic theory, this study explores a robust characterization of travel time variability that provides for a near-linear relation between the standard deviation of travel time per unit distance and the corresponding mean value. Simulation-generated vehicle trajectory data from three road networks are used to explore this relationship. On the basis of multiscale and multilevel analysis, large amounts of data show that these two quantities are highly positively correlated; that is, the dispersion of the distribution of individual travel time per unit distance increases with increasing value of the mean travel time per unit distance. Furthermore, regression models and statistical testing indicate that this relation is linear or near-linear. The relation is also validated by Global Positioning System probe data from the Seattle, Washington, area. This relation provides a robust basis for predicting the standard deviation per unit distance when the mean value is known and thus for characterizing the reliability of travel in a network in strategic and operational studies.

Connecting Networkwide Travel Time Reliability and the Network Fundamental Diagram of Traffic Flow

The existence of the network fundamental diagram (NFD) has been established at the urban network scale. It relates three traffic descriptors: speed, density, and flow. However, its deterministic nature does not convey the underlying variability within the network. In contrast, travel time reliability as a network performance descriptor is of growing concern to both the traveling public and traffic managers and policy makers. The objectives of this paper were to extend travel time reliability modeling from the link–path level to the network level and to connect overall network variability to NFD. Robust relationships between travel time variability and network density and flow rate were analytically derived, investigated, and validated with both simulated and real-world trajectory data. The distance-weighted standard deviation of travel time rate, as a measure of travel time variability, was found to increase monotonically with network density. A maximum network flow rate existed beyond which network travel time reliability deteriorated at a much faster pace. The results also suggest that these relationships are inherent network properties (signature) that are independent of demand level. The effects of en route information on the proposed relationships were also studied. The results showed that en route information reduced network travel time variability. The findings provide a strong connection between NFD and travel time variability, and this connection can be used further for modeling of network travel time reliability and assessment of measures intended to improve reliability of travel in a network.

Calibration of traffic flow models under adverse weather and application in mesoscopic network simulation

The weather-sensitive traffic estimation and prediction system (TrEPS) aims for accurate estimation and prediction of the traffic states under inclement weather conditions. Successful application of weather-sensitive TrEPS requires detailed calibration of weather effects on the traffic flow model. In this study, systematic procedures for the entire calibration process were developed, from data collection through model parameter estimation to model validation. After the development of the procedures, a dual-regime modified Greenshields model and weather adjustment factors were calibrated for four metropolitan areas across the United States (Irvine, California; Chicago, Illinois; Salt Lake City, Utah; and Baltimore, Maryland) by using freeway loop detector traffic data and weather data from automated surface-observing systems stations. Observations showed that visibility and precipitation (rain–snow) intensity have significant impacts on the value of some parameters of the traffic flow models, such as free-flow speed and maximum flow rate, while these impacts can be included in weather adjustment factors. The calibrated models were used as input in a weather-integrated simulation system for dynamic traffic assignment. The results show that the calibrated models are capable of capturing the weather effects on traffic flow more realistically than TrEPS without weather integration.

Estimating Network Fundamental Diagram Using Three-Dimensional Vehicle Trajectories: Extending Edie’s Definitions of Traffic Flow Variables to Networks

This paper evaluates measurement methods for traffic flow variables taken at the network level. Generalized Edies definitions of fundamental traffic flow variables along highways are extended for considering vehicles traveling in networks. These definitions are used to characterize traffic flow in networks and form the basis for estimating relationships among network density, flow, and speed in the form of a network fundamental diagram. The method relies on three-dimensional vehicle trajectories to provide estimates of network flow, density, and speed. Such trajectories may be routinely obtained from particle-based microscopic and mesoscopic simulation models and are increasingly available from tracking devices on vehicles. Numerical results from the simulation of two networks, in Chicago, Illinois, and Salt Lake City, Utah, are presented to illustrate and validate the estimation methodology. As part of the verification process, the study confirms that the traffic flow fundamental identity (Q = K · V) holds at the network level only when networkwide traffic flow variables are defined consistently with Edies definitions.

Implementation and Evaluation of Weather-Responsive Traffic Management Strategies

This study presents the development and application of methodologies to support weather-responsive traffic management (WRTM) strategies by building on traffic estimation and prediction system models. First, a systematic framework for implementing and evaluating WRTM strategies under severe weather conditions is developed. This framework includes activities for planning, preparing, and deploying WRTM strategies in three different time frames: long-term strategic planning, short-term tactical planning, and real-time traffic management center operations. Next, the evaluation of various strategies is demonstrated with locally calibrated network simulation-assignment model capabilities, and special-purpose key performance indicators are introduced. Three types of WRTM strategies [demand management, advisory and control variable message signs (VMSs), and incident management VMSs] are applied to multiple major U.S. areas, namely, Chicago, Illinois; Salt Lake City, Utah; and the Long Island area in New York. The analysis results illustrate the benefits of WRTM under inclement weather conditions and emphasize the importance of incorporating a predictive capability into selecting and deploying WRTM strategies.

Activity-Based Model with Dynamic Traffic Assignment and Consideration of Heterogeneous User Preferences and Reliability Valuation: Application to Toll Revenue Forecasting in Chicago, Illinois

To forecast the impact of congestion pricing schemes, it is essential to capture user responses to these schemes and the resulting dynamics of traffic flow on the network. The responses of users must include route, departure time, and mode choices. To capture the effects of these decisions, this paper lays out a framework for the integration of the relevant elements of an activity-based model (ABM) with a dynamic traffic assignment (DTA) model and a simulation framework to support the analysis and evaluation of various pricing schemes. In this paper, a multicriterion dynamic user equilibrium traffic assignment model is used; the model explicitly considers heterogeneous users who seek to minimize travel time, out-of-pocket cost, and travel time reliability in the underlying route choice framework. In addition to the methodological developments, various demand and supply parameters are estimated and calibrated for the selected application network (the Greater Chicago, Illinois, network). This paper showcases the integration of ABM components and a DTA in one coherent modeling framework for the implementation and evaluation of congestion pricing in an actual large-scale network.

Online implementation and evaluation of weather-responsive coordinated signal timing operations

This paper presents the development and application of weather-responsive traffic management strategies and tools to support coordinated signal timing operations with traffic estimation and prediction system (TREPS) models. First, a systematic framework for implementing and evaluating traffic signal operations under severe weather conditions was developed, and activities for planning, preparing, and deploying signal operations were identified in real-time traffic management center (TMC) operations. Next, weather-responsive coordinated signal plans were designed and evaluated with the TREPS method and a locally calibrated network. Online implementation and evaluation was conducted in Salt Lake City, Utah—the first documented online application of TREPS to support coordinated signal operation in inclement weather. The analysis results confirm that the deployed TREPS, which is based on DYNASMART-X, is able to help TMC operators test appropriate signal timing plans proactively under different weather forecasts before deployment and is capable of using real-time measurements to improve the quality and accuracy of the systems estimations and future predictions through detectors and roadside sensor coverage.

Development of Real-time Simulation-based Decision Support System for Weather Responsive Traffic Signal Operations

This paper presents a framework and methodology for integrating real-time Traffic Estimation and Prediction Systems (TrEPS) into weather-responsive traffic signal operations in Utah, USA. This study is motivated by the need for adjusting signal timing plans in response to changing traffic conditions during inclement weather in order to mitigate the impact of weather and maintain the network service level. This study provides a real-world application demonstrating that such a need can be effectively assisted by a TrEPS-based decision support system. Three components are introduced to form the overall decision support system: real-time TrEPS, Scenario Manager, and Scenario Library. Real-time TrEPS offers the capability to estimate and predict network states under various control scenarios; Scenario Manager provides an environment to identify and evaluate alternative signal control strategies based on TrEPS-predicted network states; and Scenario Library serves as a knowledge base defining available weather-responsive signal timing plans for Scenario Manager to access in real-time. The detailed implementation procedure and expected benefits of the proposed system are illustrated and discussed using a case study based on a historical snow event.

Incorporating Reliability Performance Measures in Operations and Planning Modeling Tools: Application Guidelines

This document provides an overview of the methodology and tools that can be applied to existing microsimulation and mesoscopic modeling software to assess travel time reliability. Specifically, the application guidelines provide: a description of the practical applications at both the policy and project level; a systematic description of the various steps involved in applying the travel time reliability methodology, including an overview of the associated tools and how they function in conjunction with the simulation models; and demonstrated evidence of how the methodology can be applied. Two case studies show how the framework and tools can be applied to potentially real-life transportation planning/engineering situations. The methodology is primarily based on research, and the tools have been developed only at the prototype stage. However, through rigorous testing at different levels of simulation resolution, the framework, the processes, and the tools have been shown to have practical applicability for use by transportation agencies and consultants for policy and project evaluation.