Joseph N. Prashker

Route Choice Models Used in the Stochastic User Equilibrium Problem: A Review

Several route choice models are reviewed in the context of the stochastic user equilibrium problem. The traffic assignment problem has been extensively studied in the literature. Several models were developed focusing mainly on the solution of the link flow pattern for congested urban areas. The behavioural assumption governing route choice, which is the essential part of any traffic assignment model, received relatively much less attention. The core of any traffic assignment method is the route choice model. In the wellknown deterministic case, a simple choice model is assumed in which drivers choose their best route. The assumption of perfect knowledge of travel costs has been long considered inadequate to explain travel behaviour. Consequently, probabilistic route choice models were developed in which drivers were assumed to minimize their perceived costs given a set of routes. The objective of the paper is to review the different route choice models used to solve the traffic assignment problem. Focus is on the different model structures. The paper connects some of the route choice models proposed long ago, such as the logit and probit models, with recently developed models. It discusses several extensions to the simple logit model, as well as the choice set generation problem and the incorporation of the models in the assignment problem.

EFFECTS OF CHOICE SET SIZE AND ROUTE CHOICE MODELS ON PATH-BASED TRAFFIC ASSIGNMENT

Few of the recently developed route choice models have actually been applied in traffic assignment problems. This paper discusses the implementation of selected route choice models in stochastic user equilibrium algorithms. The focus of the paper is on path-based assignment, which is essential in the implementation of route choice models. The paper analyzes the effect of choice set size and selected choice models on problem convergence, running time and selected results. The results presented in the paper indicate that for real-size networks, generation of a large number of alternative routes is needed. Furthermore, convergence properties greatly improve if the generated routes are sufficiently disjointed.

Transportation projects selection process using fuzzy sets theory

Government transportation agencies are faced with the problem of efficiently selecting a subset of transportation projects for implementation. This selection process is based on multiple objectives which are often measured in incommensurable units. Usually, the problem is treated by neglecting or biasing the qualitative characteristics of the various projects. Moreover, the usual selection methods cannot deal effectively with the decision makers’ preferences or vagueness. Fuzzy sets theory is able to cope with inexact information, and therefore is believed to be an appropriate tool for use in the projects’ selection process. This work presents an efficient technique for the selection of transportation projects using fuzzy sets theory. The selection procedure is a multiple objectives process, and projects are rated both on a quantitative and qualitative basis, using linguistic variables. In order to describe appropriately a given transportation policy, both fuzzy weighted average and noncompensatory fuzzy decision rules are used in the proposed approach. In addition, this work contains a case study of a selection process of interurban road projects in Israel. The results of the proposed method, obtained by a fuzzy expert system, are compared with the results obtained by an ordinary crisp process.

Evaluation of activity constrained choice sets to shopping destination choice modelling

Abstract Recent research indicates that the constraints on an individuals freedom to move through space and time, have an important influence on his likelihood to participate in various activities, and on the amount of time he budgets to them. To date, commonly used transportation planning processes have failed to incorporate the time budget concept into the various planning and policy evaluation stages. It seems as well, that one of the drawbacks of the disaggregate modelling process is its neglect of the effect of temporal constraints on an individuals choice process. The temporal constraints affect the size of choice sets of individuals and thus influence the probabilities of selection of the available alternatives. The core of this paper is to demonstrate how individual specific constraint choice sets can be defined in a destination choice model, and how they can be applied for improving accuracy in prediction. Emphasis is placed on the additional capabilities provided by the incorporation of the choice set definitions, both in terms of improved prediction accuracy, and in terms of evaluating policies, which affect the constraints on activity scheduling. A model is developed which uses knowledge concerning the constraints on shopping travel to generate choice sets of retail locations for consumers. This is done by calculating the maximum amounts of time which these constraints allow an individual to be at various retail outlets. Locations which cannot be reached while open are removed from the choice set. This model was applied to data from the Tel Aviv Metropolitan Area. The results show that applying choice set restrictions yields a modest improvement in the accuracy of prediction of shopping destination choice. Exploratory analyses indicate that by further restricting choice sets in an appropriate amnner, it may be possible to achieve substantial improvement in prediction accuracy.

DIRECT ANALYSIS OF THE PERCEIVED IMPORTANCE OF ATTRIBUTES OF RELIABILITY OF TRAVEL MODES IN URBAN TRAVEL

Reliability of travel modes was found to be the most important characteristic of transportation systems in several attitudinal investigations of individual travel behavior. This paper represents the first part of a research effort aimed at gaining a better understanding of the characteristics of reliability of transportation modes in urban travel. In this research, reliability characteristics are identified; their importance relative to each other is assessed, and an insight into possible structure of an objective reliability index is discussed. The research is based on perceived values of reliability, which were identified through a large attitudinal survey conducted in the Chicago metropolitan area.

Congestion, Stochastic, and Similarity Effects in Stochastic: User-Equilibrium Models

Different effects in stochastic user-equilibrium formulations are compared. The starting point is the logit assignment formulation for the stochastic user-equilibrium model. Recently, extended logit-based models were developed as solutions of equivalent stochastic user-equilibrium problems. These extended logit models are theoretically superior to the simple logit model, because they take into account the similarity among routes. The similarity indexes were defined based on physical parameters of the network, such as link lengths, and therefore did not depend on congestion. The assumption that similarity coefficients are independent of congestion means that the similarity effect and the congestion effect are treated separately. However, both similarity and congestion effects are taken into account in the equilibrium formulations. Simple network examples are presented to illustrate the sensitivity of the link flow pattern (and path flow pattern) to the three different effects in stochastic assignment models: the congestion effect, the stochastic effect, and the similarity effect. The relative influence of those effects on the flow patterns is discussed.

A SYSTEM FOR THE ALLOCATION OF SAFETY RESOURCES ON A ROAD NETWORK

Abstract This paper presents a complete allocation methodology of resources for safety improvements of a road network. In the first part of the paper, a set of models is estimated to identify so-called “black spots” in the road network. In the second part of the paper, a Linear Programming (L.P.) model is defined for the allocation of a total budget to those projects which ensure the most cost-effective results in reducing road accidents. The specific solution method for the allocation L.P. problem is defined, and its superiority to a cost-benefit analysis for budget allocation is shown through a real world example.

PROBABILISTIC AND ECONOMIC FACTORS IN HIGHWAY GEOMETRIC DESIGN

This paper presents an integrated approach to geometric highway design which incorporates probabilistic and economic factors. It assumes that the characteristics of the traffic flow on the road are stochastic in nature. These traffic flow variables are inputs into physical and behavioral relationships that determine the properties of alternative designs. The choice of a specific design among the many possibilities is based on the minimization of an expected cost function that includes an explicit tradeoff between road users cost on the one hand and construction and maintenance costs on the other. The approach is demonstrated by an application to the design of a climbing lane. We postulate distributions of car and truck speeds on upgrades, formulate an expected cost function, and obtain a closed form solution for the optimal location of the climbing lane. It is shown that the current AASHO procedure, as an example, is a special case of the optimal solution based on simplified truck performance assumptions ...

An algorithm to evaluate public transportation stops for minimizing passenger walking distance

Abstract The problem under consideration is to find the smallest number and location of public transit (buses, P.R.T., etc.) stops, so that no passenger is further away than a preselected distance from a stop. An optimum algorithm is proposed and discussed for a general network in which the nodes are community locations (and therefore it is just an evaluation procedure) and the stops are to be located along the arcs or on nodes.

On negative correlation: a comparison between Multinomial Probit and GEV-based discrete choice models

ABSTRACT General extreme value (GEV)-type models such as Nested Logit (NL) and Cross-Nested Logit (CNL) have gained popularity for their closed-form formulation of the choice probabilities. A key assumption in GEV estimation process is that any correlation between the error terms is necessarily non-negative. No fundamental reason indicates that negative correlations should not occur from a behavioral perspective in the real world. In this paper, we investigate models’ outcomes when alternatives exhibit negative correlation. In experiments using synthetic databases, we estimate and validate Multinomial Probit (MNP) models that correctly handle negative correlations and we compare coefficients’ estimates and correlations to those obtained with GEV models. A real case study in which choices reveal the presence of negative correlations is also used to assess the performances of the proposed models. Results are obtained with NL, CNL and Mixed Logit models and compared to MNP. The implications for further practices are discussed.