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Traffic Assignment (traffic + assignment)

Distribution by Scientific Domains

Engineering	63%

Terms modified by Traffic Assignment

traffic assignment problem

Selected Abstracts

A Path-Based Algorithm for the Cross-Nested Logit Stochastic User Equilibrium Traffic Assignment

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 1 2009
Shlomo Bekhor
A SUE assignment based on the Cross-Nested Logit (CNL) route choice model is presented. The CNL model can better represent route choice behavior compared to the Multinomial Logit (MNL) model, while keeping a closed form equation. The article uses a specific optimization formulation developed for the CNL model, and develops a path-based algorithm for the solution of the CNL-SUE problem based on adaptation of the disaggregate simplicial decomposition (DSD) method. The article illustrates the algorithmic implementation on a real size network and discusses the trade-offs between MNL-SUE and CNL-SUE assignment. [source]

Dynamic Optimal Traffic Assignment and Signal Time Optimization Using Genetic Algorithms

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 4 2004
H. R. Varia
A simulation-based approach is employed for the case of multiple-origin-multiple-destination traffic flows. The artificial intelligence technique of genetic algorithms (GAs) is used to minimize the overall travel cost in the network with fixed signal timings and optimization of signal timings. The proposed method is applied to the example network and results are discussed. It is concluded that GAs allow the relaxation of many of the assumptions that may be needed to solve the problem analytically by traditional methods. [source]

A Methodology for Assessing Transportation Network Terrorism Risk with Attacker and Defender Interactions

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 6 2010
Pamela M. Murray-Tuite
Decision makers need a methodology that can capture the complex attacker,defender interactions and help them understand the overall effects on the transportation system, as well as the consequences of asset failure. This article presents such a methodology, which uses probabilities of target,attack method combinations that are degree of belief based and updated using Bayes' Theorem after evidence of the attack is obtained. Monte Carlo simulation generates the probability of link capacity effects by sampling from distributions of capacity reductions due to preevent security measures, substitutions, target failure, and postevent security measures. The average capacity reduction for a particular target,attack method combination serves as an input to the traffic assignment,simulation package DYNASMART-P to determine travel time effects. The methodology is applied to a sample network based on the northern Virginia area. Results based on notional data indicated that preevent security measures reduced attack probabilities, but in some cases increased the mobility consequences. Thus, decision makers must carefully evaluate the effects of their decisions. [source]

Bi-level Programming Formulation and Heuristic Solution Approach for Dynamic Traffic Signal Optimization

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 5 2006
Dazhi Sun
Conventional methods of signal timing optimization assume given traffic flow pattern, whereas traffic assignment is performed with the assumption of fixed signal timing. This study develops a bi-level programming formulation and heuristic solution approach (HSA) for dynamic traffic signal optimization in networks with time-dependent demand and stochastic route choice. In the bi-level programming model, the upper level problem represents the decision-making behavior (signal control) of the system manager, while the user travel behavior is represented at the lower level. The HSA consists of a Genetic Algorithm (GA) and a Cell Transmission Simulation (CTS) based Incremental Logit Assignment (ILA) procedure. GA is used to seek the upper level signal control variables. ILA is developed to find user optimal flow pattern at the lower level, and CTS is implemented to propagate traffic and collect real-time traffic information. The performance of the HSA is investigated in numerical applications in a sample network. These applications compare the efficiency and quality of the global optima achieved by Elitist GA and Micro GA. Furthermore, the impact of different frequencies of updating information and different population sizes of GA on system performance is analyzed. [source]

A Linear Model for the Continuous Network Design Problem

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 5 2006
S. Travis Waller
A linear programming formulation is introduced based on a dynamic traffic assignment (DTA) model that propagates traffic according to the cell transmission model. The introduced approach is limited to continuous link improvements and does not provide for new link additions. The main contribution of the article is to provide an analytical formulation for network design that accounts for DTA conditions that can be used for further analysis and extensions. The model is tested on a single destination example network, resembling a freeway corridor, for various congestion levels, loading patterns and budget sizes, to demonstrate the simplicity and effectiveness of the approach. [source]

Case,Based Reasoning for Assessing Intelligent Transportation Systems Benefits

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 3 2003
Adel Sadek
Existing transportation planning modeling tools have critical limitations with respect to assessing the benefits of intelligent transportation systems (ITS) deployment. In this article, we present a novel framework for developing modeling tools for quantifying ITS deployments benefits. This approach is based on using case,based reasoning (CBR), an artificial intelligence paradigm, to capture and organize the insights gained from running a dynamic traffic assignment (DTA) model. To demonstrate the feasibility of the approach, the study develops a prototype system for evaluating the benefits of diverting traffic away from incident locations using variable message signs. A real,world network from the Hartford area in Connecticut is used in developing the system. The performance of the prototype is evaluated by comparing its predictions to those obtained using a detailed DTA model. The prototype system is shown to yield solutions comparable to those obtained from the DTA model, thus demonstrating the feasibility of the approach. [source]

A risk-averse user equilibrium model for route choice problem in signal-controlled networks

JOURNAL OF ADVANCED TRANSPORTATION, Issue 4 2010
William H.K. Lam
Abstract This paper proposes a new risk-averse user equilibrium (RAUE) model to estimate the distribution of traffic flows over road networks with taking account the effects of accident risks due to the conflicting traffic flows (left- and right-turning and through traffic flows) at signalized intersections. It is assumed in the proposed model that drivers consider simultaneously both the travel time and accident risk in their route choices. The accident risk of a route is measured by the potential accident rate on that route. The RAUE conditions are formulated as an equivalent path-based variational inequality problem which can be solved by a path-based solution algorithm. It is shown that the traditional user equilibrium (UE) model is in fact a special case of the proposed model. A numerical example on a grid network is used to illustrate the application of the proposed model and to compare the results with the conventional UE traffic assignment. Numerical results show that the traditional UE model may underestimate the total system travel time and overestimate the system accident rate. Sensitivity tests are also carried out to assess the effects of drivers' preferences, signal control parameters (i.e., green time proportions), and various network demand levels on the route choice problem. Copyright © 2010 John Wiley & Sons, Ltd. [source]

Efficiency and fairness of system-optimal routing with user constraints,

NETWORKS: AN INTERNATIONAL JOURNAL, Issue 4 2006
Andreas S. Schulz
Abstract We study the route-guidance system proposed by Jahn, Möhring, Schulz, and Stier-Moses Operations Research 53 (2005), 600,616 from a theoretical perspective. As system-optimal guidance is known to be problematic, this approach computes a traffic pattern that minimizes the total travel time subject to user constraints. These constraints are designed to ensure that routes suggested to users are not much longer than shortest paths for the prevailing network conditions. To calibrate the system, a certain measure,called normal length,must be selected. We show that when this length is defined as the travel time at equilibrium, the resulting traffic assignment is provably efficient and close to fair. To measure efficiency, we compare the output to the best solution without guidance and to user equilibria. To measure unfairness, we compare travel times of different users, and show that they do not differ too much. Inefficient or unfair traffic assignments cause users to travel too long or discourage people from accepting the system; either consequence would jeopardize the potential impact of a route-guidance system. © 2006 Wiley Periodicals, Inc. NETWORKS, Vol. 48(4), 223,234 2006 [source]

Toward faster algorithms for dynamic traffic assignment.

NETWORKS: AN INTERNATIONAL JOURNAL, Issue 1 2003

Abstract Being first in a three-part series promising a practical solution to the user-equilibrium dynamic traffic assignment problem, this paper devises a parametric quickest-path tree algorithm, whose model makes three practical assumptions: (i) the traversal time of an arc i , j is a piecewise linear function of the arrival time at its i -node; (ii) the traversal time of a path is the sum of its arcs' traversal times; and (iii) the FIFO constraint holds, that is, later departure implies later arrival. The algorithm finds a quickest path, and its associated earliest arrival time, to every node for every desired departure time from the origin. Its parametric approach transforms a min-path tree for one departure-time interval into another for the next adjacent interval, whose shared boundary the algorithm determines on the fly. By building relatively few trees, it provides the topology explicitly and the arrival times implicitly of all min-path trees. Tests show the algorithm running upward of 10 times faster than the conventional brute-force approach, which explicitly builds a min-path tree for every departure time. Besides dynamic traffic assignment, other applications for which these findings have utility include traffic control planning, vehicle routing and scheduling, real-time highway route guidance, etc. © 2002 Wiley Periodicals, Inc. [source]