Route Choice Process Research Articles

Behaviours in a dynamical model of traffic assignment with elastic demand

This paper investigates the dynamical behaviour of network traffic flow. Assume that trip rates may be influenced by the level of service on the network and travellers are willing to take a faster route. A discrete dynamical model for the day-to-day adjustment process of route choice is presented. The model is then applied to a simple network for analysing the day-to-day behaviours of network flow. It finds that equilibrium is arrived if network flow consists of travellers not very sensitive to the differences of travel cost. Oscillations and chaos of network traffic flow are also found when travellers are sensitive to the travel cost and travel demand in a simple network.

Handling uncertainty in route choice models: From probabilistic to possibilistic approaches

In order to design effective advanced traffic information systems (ATIS) suitable mathematical models have to be defined to simulate the effects of information on users route choice behaviour and then to incorporate it into traffic assignment models to estimate how traffic demand loads the roads network. To face this problem it is necessary to deal with uncertainty that plays a crucial role in the users decision-making processes. To this purpose this paper first analyses how uncertainty affects users’ route choice process and how traffic assignment models may take it into account. In literature route choice behaviour modelling is widely solved within the random utility theory framework but, we show in this paper that such an approach only considers one type of uncertainty. More precisely, the consideration of randomness of traffic by drivers is, for example, hardly ever represented in classical models in spite of its importance in the management of information by drivers. Starting from the presented analysis a new route choice model is also proposed to represent explicitly the uncertainty lying in users’ route choice behaviour. It is based on recent developments in possibility theory which is an alternate way to probability theory in order to represent or measure uncertainty.

Qu’est-ce qu’une régression logistique ?

The aim of the present study was to better understand how public transport users make their choice of route, in order to favor the use of public transport (henceforth PT) in large cities. Based on decision-making theories, a classical choice paradigm (Slovic, 1975), and recent findings in psychology (Chowdhury & Ceder, 2013; Grison et al., 2016) we developed a new method to investigate the effect of contextual and individual factors on PT route choices. We proposed to sixty PT users realistic forced choices between two PT routes that differed in affective (level of physical comfort) and instrumental (number of transport modes) attributes. We also varied the context of the decision (long or short route; route to go to work, to a leisure activity, etc.), and we recorded various individual characteristics (age, sex, attitude towards PT, habits, etc.). Our results highlight that: the comfort of the route is preferred to the number of transport modes, especially for long trips; the choice of the comfortable alternative or the one with only one transport mode depends on user characteristics; the length of the trip and habits are the most important variables in the decision, but attitudes also seem to play a major role. Our study furthers current knowledge about the psychological process of PT route choice in light with multi-attribute choices theories, and provides new insights that can contribute to improving current large city route-planning aids.

면역 알고리즘의 개선된 클론선택에 의한 퍼지 뉴로 네트워크와 교통경로선택으로의 응용

본 논문에서는 복잡하고 비선형적인 시스템을 위하여 최적 면역 알고리즘의 개선된 클론선택에 기반을 둔 최적FNN 설계방법을 제안한다. FNN은 퍼지추론의 간략 추론과 학습방법으로는 오류역전파 알고리즘을 하였고 멤버쉽함수의 파라미터, 학습률 및 모멘텀 계수들을 선정하기 위하여 개선된 클론 선택을 사용하는 방법을 도입하였다. 제안한 알고리즘은 생체의 면역반응에 기초를 둔 면역알고리즘의 클론선택을 기본으로 분화율을 조절하여 성능을 개선하였다. 그 과정을 통하여 다양한 항체들을 생성하고 목적함수나 제한조건과 같은 항원들에 대하여 가장 높은 친화도를 가지는 항체를 최적 항체로 선택하였다. 제안된 알고리즘의 성능을 평가하기 위하여 가스로공정과 교통경로선택 공정을 사용한다. In this paper, an optimal design method of clonal selection based Fuzzy-Neural Networks (FNN) model for complex and nonlinear systems is presented. The FNNs use the simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. Also Advanced Clonal Selection (ACS) is proposed to find the parameters such as parameters of membership functions, learning rates and momentum coefficients. The proposed method is based on an Immune Algorithm (IA) using biological Immune System and The performance is improved by control of differentiation rate. Through that procedure, the antibodies are producted variously and the parameter of FNN are optimized by selecting method of antibody with the best affinity against antigens such as object function and limitation condition. To evaluate the performance of the proposed method, we use the time series data for gas furnace and traffic route choice process.

A rational reasoning method from fuzzy perceptions in route choice

The purpose of this paper is to develop a psychometric approach for making possible a more proper description of route choice behavior in transportation systems. A hybrid model that uses concepts from fuzzy logic and analytical hierarchy process (AHP) is proposed. Based on Weber's psycho-physical law of 1834, a set of fuzzy ‘if–then’ rules is developed to represent a typical driver's psychology for capturing essential preferences, pairwise, among the alternatives that a driver may consider. The AHP is then incorporated in this model to represent the underlying decision mechanism and to estimate drivers’ preference allotment among the alternatives. This new procedure is applied in a real-world sample based on stated values of subjects. Findings show that this method provides intuitively promising results and that can be used for explaining route choice process of drivers.

The Model Analysis on Route choice behavior based on Probe-Car Data

Analysis on route choice behavior is the key to analyzing travel behavior and traffic demand on road networks. The data from the probe-car systems are very useful for making it clear. In this study we analyze the route choice behaviors, especially the accuracy of the travel time information drivers use for route choicc and the fits of several route choice models The result indicates that drivers use rather rough information on expected travel conditions for route choice, and that necessity of analysis on dynamic route choice behavior and framework of route choice process.

Formulation and Solution Algorithm for Fuzzy Dynamic Traffic Assignment Model

An issue that is always important in the development of traffic assignment models is how travelers' perceptions of travel time should be modeled. Because travelers rarely have perfect knowledge of the road network or of the travel conditions, they choose routes on the basis of their perceived travel times. Traditionally, travelers' perceived travel times are treated as random variables, leading to the stochastic traffic assignment problem. However, uncertain factors are also observed in the subjective recognition of travel times by travelers, and these can be illustrated as fuzzy variables. Therefore, a fuzzy dynamic traffic assignment model that takes into account the imprecision and the uncertainties in the route choice process is proposed. By modeling the expressions of perceived travel times as fuzzy variables, this model makes possible the description of a traveler's process of choosing a route that is more accurate and realistic than those from its deterministic or stochastic counter parts. The fuzzy perceived link travel time and fuzzy perceived path travel time are defined, and a fuzzy shortest path algorithm is used to find the group of fuzzy shortest paths and to assign traffic to each of them by using the so-called C-logit method. The results of the proposed model are also compared with those from the stochastic dynamic traffic assignment model, and it is demonstrated that the impact of advanced traveler information systems on the traveler's route choice process can be readily incorporated into the proposed model.

A Fuzzy route choice model for air transportation networks

The paper develops a route choice model for air transportation networks. Since the route choice process is characterized by uncertainty and subjectivity, the model is based on Fuzzy Set Theory, which utilises the principles of fuzzy logic. The approximate reasoning model developed is tested using real data. Excellent correspondence was achieved between the results obtained from using the model and the real data.

The influence of route guidance advice on route choice in urban networks

The paper begins by reviewing what is known about route choice processes and notes the mismatch between this knowledge and the route choice assumptions embedded in the most widely used assignment models. Empirical evidence on the influence of route guidance advice on route choice is reviewed and, despite its limited nature, is seen to suggest that users are reluctant to follow advice unless they find it convincing and that, the more familiar they are with the network, the less likely they are to accept advice. Typically only a small minority of journeys are made in total compliance with advice. Results from an interactive route choice simulator (IGOR) are summarised and are seen to reveal that compliance depends on the extent to which the advice is corroborated by other factors, on the drivers' familiarity with the network and on the quality of advice previously received. It is noted that the IGOR results are in a form which would enable response models to be calibrated. Recent approaches to the modelling of route choice in the context of guidance are discussed. Some are seen to make simplifying assumptions which must limit the relevance of their results; most make no allowance for the fact that drivers are unlikely to comply with all advice and several are not able to represent the benefits which guidance might bring in the context of sporadic congestion or incidents. As an alternative, a two phase model comprising a medium term strategic equilibrium and a day-specific simulation with explicit representation of driver response is proposed.

THE INFLUENCE OF ROUTE GUIDANCE ADVICE ON ROUTE CHOICE IN URBAN NETWORKS

The paper begins by reviewing what is known about route choice processes and notes the mismatch between this knowledge and the route choice assumptions embedded in the most widely used assignment models. Empirical evidence on the influence of route guidance advice on route choice is reviewed and, despite its limited nature, is seen to suggest that users are reluctant to follow advice unless they find it convincing and that, the more familiar they are with the network, the less likely they are to accept advice. Typically only a small minority of journeys are made in total compliance with advice. Results from an interactive route choice simulator (IGOR) are summarised and are seen to reveal that compliance depends on the extent to which the advice is corroborated by other factors, on the drivers' familiarity with the network and on the quality of advice previously received. It is noted that the IGOR results are in a form which would enable response models to be calibrated. Recent approaches to the modelling of route choice in the context of guidance are discussed. Some are seen to make simplifying assumptions which must limit the relevance of their results; most make no allowance for the fact that drivers are unlikely to comply with all advice and several are not able to represent the benefits which guidance might bring in the context of sporadic congestion or incidents. As an alternative, a two phase model comprising a medium term strategic equilibrium and a day-specific simulation with explicit representation of driver response is proposed.