Hani S. Mahmassani

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

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. This paper presents a simulation-assignment model, DYNASMART (Dynamic Network Assignment Simulation Model for Advanced Road Telematics) specificallydeveloped for studyg 2. responsiveness to dynamic O-D information available to the controller as reported by the ATIS and other sources; 3. ability to track the locations of the drivers, both those who receive advice from the control center, and those who do not; 4. ability to predict the time-dependent impedance (travel time) based on the assignment results, and provide feedback to the control center that may be used in the assignment of vehicles;

Dynamic Network Traffic Assignment and Simulation Methodology for Advanced System Management Applications

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.

A transportation network evacuation model

This paper describes NETVACl, a model for simulating the traffic pattern during an emergency evacuation. The development of the model has been motivated by the need to estimate network clearance time for areas surrounding nuclear power plant sites, and the model has been applied in this context. NETVACl is a macro traffic simulation model sensitive to network topology, intersection design and control, and a wide array of evacuation management strategies. The model can handle large networks at modest computational costs and includes many reporting options. The paper includes a review of other approaches used to model evacuations and estimate network clearance times, a description of the structure and logic of the model and some computational experience.

Hybrid route generation heuristic algorithm for the design of transit networks

Abstract In this paper we present a Lisp-implemented route generation algorithm (RGA) for the design of transit networks. Along with an analysis procedure and an improvement algorithm, this algorithm constitutes one of the three major components of an AI-based hybrid solution approach to solving the transit network design problem. Such a hybrid approach incorporates the knowledge and expertise of transit network planners and implements efficient search techniques using AI tools, algorithmic procedures developed by others, and modules for tools implemented in conventional languages. RGA is a design algorithm that 1. (a) is heavily guided by the demand matrix, 2. (b) allows the designers knowledge to be implemented so as to reduce the search space, and 3. (c) generates different sets of routes corresponding to different trade-offs among conflicting objectives (user and operator costs). We explain in detail the major components of RGA, illustrate it on data generated for the transit network of the city of Austin, TX, and report on the numerical experiments conducted to test the performance of RGA.

ON BOUNDEDLY RATIONAL USER EQUILIBRIUM IN TRANSPORTATION SYSTEMS.

A boundedly rational user equilibrium (BRUE) is achieved in a transportation system when all users are satisfied with their current travel choices. The theoretical and behavioral background for such a state is given in this paper. The properties of a BRUE in an idealized commuting system with a single bottleneck are investigated, and conditions for the existence of a BRUE are given. More general situations with multiple bottlenecks are also addressed. In general, BRUE flows are not unique, raising methodological and practical issues in flow prediction.

Multiple user classes real-time traffic assignment for online operations: A rolling horizon solution framework

Abstract Existing dynamic traffic assignment formulations predominantly assume the timedependent O-D trip matrix and the time-dependent network configuration to be known a priori for the entire planning horizon. However, there is also a need to provide real-time path information to network users under ATIS/ATMS when unpredicted variations in O-D desires and/or network characteristics (e.g. capacity reduction on certain links due to incidents) occur. This paper presents a rolling horizon framework for addressing the real-time traffic assignment problem, where an ATIS/ATMS controller is assumed to have O-D desires up to the current time interval, and short-term and mediumerm forecasts of future O-D desires. The assignment problem is solved in quasi-real time for a near-term future duration (or stage) to determine an optimal path assignment scheme for users entering the network in real-time for the short-term roll period. The resulting model is intricate due to the intertemporal dependencies characterizing this problem. Two formulations are discussed based on whether a capability to reroute vehicles en route exists. A rolling horizon solution procedure amenable to a quasi-real time implementation of a multiple user classes (MUC) time-dependent traffic assignment solution algorithm developed previously by the authors is described. Implementation issues are discussed from the perspective of ATIS/ATMS applications.

Dynamic User Equilibrium Departure Time and Route Choice on Idealized Traffic Arterials

An extension of a recent framework for analyzing the time-dependent departure pattern arising in an idealized situation of a pool of commuters going from a single origin to a single destination along a unique route is presented. Congestion along this route is represented using elementary traffic flow theoretic relationships; time-varying patterns of basic traffic variables are derived under user equilibrium conditions, along with the corresponding time-dependent departure pattern of system users. After demonstrating the basic model in the single-route context, an additional dimension of choice is introduced by considering the joint departure time and route choice decisions of users. Results are derived in a simplified two-route context and numerical illustrations provided.

Optimal Scheduling of Evacuation Operations

Evacuations necessitated by extreme events are usually envisioned as taking place with all people evacuating simultaneously; this leads to premature congestion on the surface streets and excessive delays. With the evacuating load onto the network staggered, the onset of congestion may be delayed, and people can evacuate more quickly. In this study, the problem of scheduling evacuation trips between a selected set of origin nodes and (safety) destinations was considered, with the objective of minimizing network clearance time. A modified system-optimal dynamic traffic assignment formulation is proposed; in it the total system evacuation time, as opposed to the total system trip time, is minimized. An iterative heuristic procedure is used to solve this problem: the method of successive averages is used to find the flow assignments for the next iteration; a traffic simulator, DYNASMART-P, is used to propagate the vehicles on their prescribed paths and determine the state of the system. Therefore, the simulator serves as a tool to satisfy the dynamic traffic assignment constraints implicitly while evaluating the objective function. The output of this model will be the departure time, route, and destination choices for each evacuee. The output is then aggregated to produce a time-dependent staging policy for each selected origin.

Using gap sequences to estimate gap acceptance functions

Traditional gap acceptance functions have been estimated based on the first gap observed. In this paper we show that the critical gap of drivers is decreasing on the average, as they are waiting for an acceptable gap. Our gap acceptance function is based on a probit model which assumes a normal distribution of gaps across gaps and drivers.

Dynamic origin-destination demand estimation using automatic vehicle identification data

This paper proposes a dynamic origin-destination (OD) estimation method to extract valuable point-to-point split-fraction information from automatic vehicle identification (AVI) counts without estimating market-penetration rates and identification rates of AVI tags. A nonlinear ordinary least-squares estimation model is presented to combine AVI counts, link counts, and historical demand information into a multiobjective optimization framework. A joint estimation formulation and a one-sided linear-penalty formulation are further developed to take into account possible identification and representativeness errors, and the resulting optimization problems are solved by using an iterative bilevel estimation procedure. Based on a synthetic data set, this study shows the effectiveness of the proposed estimation models under different market-penetration rates and identification rates