A Gradient Projection Algorithm for Side-constrained Traffic Assignment

Standard static traffic assignment models do not take into account the direct effects of capacities on network flows. Separable link performance functions cannot represent bottleneck and intersection delays, and thus might load links with traffic volumes, which far exceed their capacity. This work focuses on the side-constrained traffic assignment problem (SCTAP), which incorporates explicit capacity constraints into the traffic assignment framework to create a model that deals with capacities and queues. Assigned volumes are bounded by capacities, and queues are formed when capacity is reached. Delay values at these queues are closely related to Lagrange multipliers values, which are readily found in the solution. The equilibrium state is defined by total path travel times, which combine link travel times and delays at bottlenecks and intersections for which explicit capacity constraints have been introduced. This paper presents a new solution procedure for the SCTAP based on the inner penalty function method combined with a path-based adaptation of the gradient projection algorithm. This procedure finds a solution at the path level as well as at the link level. All intermediate solutions produced by the algorithm are strictly feasible. The procedure used to ensure that side-constraints are not violated is efficient since it is only performed on constrained links that belong to the shortest path

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 roles 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

Multipath Capacity Limited Transit Assignment Using UTPS Package

At present most patronage predictions of transit systems are performed using UMTA's UTPS package or some adaptation of it. The transit assignment produced by a typical UTPS system can be classified as an All-or-Nothing limited equilibrium assignment. However, passenger loads assigned to a transit line can far exceed the line capacity. In such a case, line headway has to be reduced to provide enough capacity to accommodate transit demand. If the increase in frequency is not accounted for by iterating again through the mode choice and assignment models, the equilibrium assumptions are violated. If equilibrium between demand and supply is achieved it might occur at a point which requires transit capacity much beyond the economically feasible or engineering practical level. Thus the present transit assignment procedure suffers from two problems. First, trips are assigned to transit lines with disregard to their actual capacity. Second, while some lines are assigned passenger loads beyond capacity, there might be other lines with just slightly longer travel times which are greatly underutilized. A realistic assignment should take into account and not exceed the actual capacity of every transit line. Furthermore, it should consider lines capacities while rationally simulating people's travel behavior. In this paper a transit assignment algorithm is presented which takes into account the actual capacity of transit lines and assigns trips to more than a single path when the shortest path reaches its capacity. This procedure produces a practical Multipath Capacity Limited Transit Assignment (McLAT). The procedure was implemented on an IBM mainframe computer using standard UMTA's UTPS package with the addition of only one Fortran program

Sensitivity to Uncertainty: Need for a Paradigm Shift

Existing common route choice models are based on random utility theory, which follows the maximum utility assumption. Recent intelligent transportation system applications have highlighted the need for better models of the behavioral processes involved in route choice decisions. Therefore, prediction of travelers' responses to uncertainty was analyzed. Route choice experiments were conducted to evaluate the effect of the feedback mechanism on decision making under uncertainty. The experimental results were compared with those from a model based on cumulative prospect theory and models based on learning approaches. It is shown that a traveler's sensitivity to travel time differences is lower when variances in travel times are higher. This better understanding of route choice behavior predicted by learning models may improve traffic predictions, as well as the design of traffic control mechanisms

Sensitivity to travel time variability: Travelers learning perspective

This paper discusses the effect of the feedback mechanism on route-choice decision-making under uncertainty. Recent ITS (intelligent transportation systems) applications have highlighted the need for better models of the behavioral processes involved in travel decisions. However, travel behavior, and specifically route-choice decision-making, is usually modeled using normative models instead of descriptive models. Common route-choice models are based on the assumption of utility maximization. In this work, route-choice laboratory experiments and computer simulations were conducted in order to analyze route-choice behavior in iterative tasks with immediate feedback. The experimental results were compared to the predictions of two static models (random utility maximization and cumulative prospect theory) and two dynamic models (stochastic fictitious play and reinforcement learning). Based on the experimental results, it is showed that the higher the variance in travel times, the lower is the travelers' sensitivity to travel time differences. These results are in conflict with the paradigm about travel time variability and risk-taking behavior. The empirical results may be explained by the payoff variability effect: high payoff variability seems to move choice behavior toward random choice. © 2005 Elsevier Ltd. All rights reserved

The Relationship Between an Option Space and Drivers' Indecision

A traffic signal is a substantially different traffic sign compared with othe traffic devices. The uniqueness of traffic signals is manifested in their displaying an alternate message and not a constant one. The transition period from one message to another creates a decision problem for drivers. An inappropriate decision might create a risk of a rear-end collision. This article presents a disaggregate behavioral model for drivers' decision when the green light ends. It is demans trated, and supported by field data, that a large option zone increases the indecision of drivers. The increase in indecision creates a greater risk of rear-end collisions, as experienced at many intersections. The influence of distance from the intersection and of approach speed on drivers' decision is examined through the model

GEV-based destination choice models that account for unobserved similarities among alternatives

This paper investigates the destination choice problem in transportation planning processes. Most models assume a Multinomial Logit (MNL) form for the problem. The MNL cannot account for unobserved similarities which exist among choice alternatives. The purpose of this paper is to investigate alternative destination choice model structures, focusing on closed-form models. The paper reviews recent GEV formulations and discusses the adaptation of these models to destination choice situation. In addition the paper presents a new model structure composed of three hierarchical levels: it assumes a choice process composed of a broad selection of zones based on a specific land use characteristic (in this case, presence of shopping center) and then a finer selection of zones based on a geographical characteristic (in this case, adjacent zones). To illustrate the similarity measures of selected GEV formulations and the new model structure the paper specifies, estimates and compares destination choice models for weekday shopping trips based on a revealed preference survey. The paper discusses the structure of the proposed choice models, similarity measures and implementation issues related to the GEV destination choice models.

Changes in Travel Demand Characteristics During the 1984 Los Angeles Olympics

This paper presents results from a travel survey of downtown area employees conducted during the 1984 Los Angeles Summer Olympics. The Olympics provided a unique opportunity to observe travel demand responses to major anticipated changes in the level of service of the transportation system. The survey examined all aspects of work trip travel including travel times, mode choice, work schedules, absences from work, and route choice. Nearly 2,000 surveys from four different downtown area employers were analyzed. Results showed that many different changes in work trip travel behavior occurred. These changes contributed to the reduced congestion experienced during the Olympics. The most frequent changes include shifts in the work schedule and higher than usual absences from work. Modal shifts and change in route choice were much less common. Results also show that employers had a significant influence on the strategies chosen by employees. The paper concludes with a discussion of the significance of the research findings