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Travelers always perform some preference during the decision-making process. The preference will affect the decision results and can be improved by continuously learning. In order to understand the influence of individual preference on travel behavior choice , two individual preferences, including indifference preference and compulsive preference are considered in the paper. Two updating mechanisms of compulsive preference are proposed to obtain the choosing probability of all alternatives. Reinforcement learning models are established integrating the gain stimulating and loss stimulating considering expected utility. Nguyen Dupuis network is adopted for numerical simulation to study the updating process. Simulation results denote that the equilibrium state is much more efficient when preference learning mechanism is considered comparing with the traditional stochastic user equilibrium model, and can decrease the total travel time greatly, which can be applied for urban traffic management. Personalized traffic guidance is the effective solution to traffic congestion in the future [ABSTRACT FROM AUTHOR]

This paper evaluates how the route overlapping feature affects the stochastic assignment paradox by comparing the multinomial logit (MNL) model and two path-size logit (PSL) models. Two circumstances, (1) improving an existing link and (2) adding/removing a new link, are studied. The paradoxical features of three models are illustrated and compared mathematically and graphically in four networks with route overlapping links: (1) a single origin-destination (O-D) pair network with four links; (2) a single O-D pair network with five links; (3) a multiple O-D pair network with seven links; and (4) an application in a road sub-network of Harbin City. The general conclusions are significant for traffic planners and engineers when those kinds of route choice models are used to evaluate traffic planning, management, and control policies/measures. [ABSTRACT FROM AUTHOR]

The advanced travel information system is a prominent intelligent transportation system for which variable message signs (VMSs) are the major components. In Iran, more attention has been paid to sign placement than to other components which are complementary to placement and combine to create relevant and effective messages for the driver. The current study used a stated preference survey to investigate the attitudes and reactions of drivers to VMS information that then were used to build models to reveal the reasons behind driver reactions to a VMS request to change route. The survey included variables affecting driver decisions, such as driver characteristics, attitude and perceptions of current VMS information and traffic in general. The influence of personality traits as subjective factors for driver behavior regarding route choice also was investigated. The results of the estimation of sequential hybrid binary logistic regression model showed that visual health had a significant positive effect on the driver decisions to divert to an alternate route when the VMS messages related to an accident or traffic congestion. Provision of expected delay information about the current route was found to be the most significant factor causing drivers to change route when a VMS indicated traffic congestion ahead. Personality traits such as Agreeableness, neuroticism, extraversion and openness significantly affected driver decisions about changing route when the VMS indicated traffic congestion or adverse weather conditions. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive framework for estimating passengers' transfer times and extracting their distribution and related transfer routes using WIFI probe data. The departure time of preceding station, arrival time of subsequent station, and train running time are selected to obtain transfer times. Then, the collected data is analyzed using kernel density estimation to obtain candidate distribution. Gaussian mixture models are adopted to extract the distribution of each possible transfer route at both peak hours and off-peak hours. This method is tested at two transfer stations of Xi'an metro system with the comparison of results from automatic fare collection data and manual sampling survey data. The results indicate that the proposed approach can collect the transfer time with a sampling ratio greater than 30% and a deviation less than 5%. The route choice behaviors and distribution of transfer time under various conditions can be identified using the proposed methods. [ABSTRACT FROM AUTHOR]

Comparison of routes recommended by online trip planners, such as Google Maps and MapQuest, with routes traveled by cyclists allows estimation of whether the routing criteria used in such trip planners are the same as those used by cyclists. This study uses GPS tracking data from the Endomondo fitness tracker app from trips in Miami-Dade (Florida) and North Holland and compares their characteristics to cycling routes suggested by Google Maps, MapQuest and the shortest path. Results highlight which trip attributes differ between Endomondo and its alternative trips, and how consistent those differences are across the two study areas. For example, Endomondo cyclists in both regions go through fewer traffic signals, and use more cycleways, footways, or bike lanes than suggested routes from Google and MapQuest. A multinomial logit model on observed Endomondo trips provides insight into the routing preferences of Endomondo commute and sport cyclists in both study regions. [ABSTRACT FROM AUTHOR]

Several systems have been proposed in recent years for Advanced Traveler Information Systems (ATIS), ranging from personal (mobile application, on-board) navigation to VMS (Variable Message Signs). All these applications are essentially aimed at assisting the travelers in their travel choices by means of technologically dispatched information about traffic network structure and traffic conditions. It is well known that in ATIS applications one of the key issues to be addressed concerns the accuracy of the dispatched information, here intended as the ability of the system in supplying information that can be verified by the travelers to be correct (affected by a small error). This paper aims at proposing some analytical models able to simulate the effects of the (in)accuracy on travelers' compliance with information. [ABSTRACT FROM AUTHOR]

The environmental traffic assignment problem in the bimodal network with electric vehicles (EVs) and gasoline vehicles (GVs) has become a hot topic recently. However, few previous works consider the psychological difference between EV users and GV users in terms of environmental awareness , in order to fill in this research gap, we formulate an environment-oriented mixed-behavior user equilibrium (EMUE) model to distinguish the route choice criterion between EV users and GV users by considering the Pro-environmental behavior (PEB) of EV users.. We formulate a bi-objective optimization model and propose a differentiable road pricing scheme for EV and GV users to manage the congestion and emissions simultaneously. We investigate whether the differentiable road pricing can decentralize the Pareto-efficient flow as a unique mixed-behavior equilibrium. Moreover, we conduct numerical experiments based on the Sioux Falls network and the existing urban network in Qianhai Zone to evaluate the effectiveness of the proposed pricing scheme, models and algorithms. [ABSTRACT FROM AUTHOR]

Interchange provides more flexibility in route choice, a key travel behaviour in urban rail transit, but its influence is usually simplified. This paper investigates how interchange affects route choice with passenger perception considered. At single-interchange level, perceived interchange time was proposed and modelled under three resolutions to capture passenger perception and its sensitivity. At route level, the influence of interchange was modeled by first comparing eight quantifications of interchange. Mixed logit models with the best interchange proxy were further developed to address the correlation among alternative routes and reveal the potential taste variations among passengers. Results based on Shanghai Metro data showed perceived interchange time, including passenger perception and interchange environment, better represents the influence of interchange in route choice, meanwhile the weights of interchanges on one route rise sequentially and non-linearly. The results can improve route choice prediction in demand modelling and route recommendation in advanced traveller information systems. [ABSTRACT FROM AUTHOR]

Travel time information assists road users in making informed travel decisions such as mode choice, route choice and/or time of travel. This study explores the use of GPS data from buses and Wi-Fi and Bluetooth data from a sample of vehicles, for accurate estimation of the travel time of all vehicles on the roadway. A 5.5 km road stretch in Chennai city was selected as study stretch and data were collected for a week's period. The present study develops models using linear regression and artificial neural network (ANN) techniquesFto estimate stream travel time using bus travel time obtained from GPS. ANN performed better compared to the linear regression for all sizes of segments. Most of the Indian cities have an integrated network of buses traveling on most of the road segments with on-board tracking devices, making this a useful development for real-time travel time estimation. [ABSTRACT FROM AUTHOR]

In this paper, we revisit the concept of Prospect-based User Equilibrium in a dynamic context. We consider a mesoscopic Lighthill-Whitham-Richards (LWR) traffic model to determine time-dependent route costs. We propose a solution algorithm to determine the network equilibria. Monte Carlo simulations are used to account for the travel time distributions. We analyze the dynamic Prospect-based User Equilibrium compared to the benchmarks Deterministic and Stochastic User Equilibrium, on a synthetic Manhattan network. We set four endogenous reference points. We show that the setting of the reference point plays a very important role in the route flow patterns and on the network performance at an aggregated level, i.e. in terms of vehicles mean speed as well as internal and outflow capacities. Our results also enhance that the Prospect-based User Equilibrium is more sensitive to a change in the reference point than in the calibration of the users' risk-aversion and risk-seeking parameters. [ABSTRACT FROM AUTHOR]

Predicting benefits of advanced traveler information systems before implementation is one of the challenges in the area of transport modeling. Taking into consideration the differences in commuting behavior of unequipped and would be equipped drivers, as well as their different level of perception error are the key factor. Accordingly, it seems that the multi-class approach of traffic assignment (TA) can be regarded as a possible solution to the problem. However, dealing with the challenge of lack of observed data before system installation is still a major challenge. To deal with this problem, a double-class stochastic TA approach is proposed in this work. The network loading procedure follows a paired combinatorial logit (PCL) model, which addresses the classical problem of path overlapping. In addition, the model is origin-destination (OD)-specific parameter, which enables the modeler to represent different levels of uncertainty and stochasticity involved in route decision-making between different OD pairs. A heuristic practical estimation method is also proposed, which exempts the modeler from resorting to route choice data and facilitates the challenges involved in estimation of route choice models to a considerable extent. Furthermore, in the approximate proposed method of estimation, the new perspective from which the estimation parameter is considered provides a more tangible interpretation than that of the classical approach. It allows manipulation of data to obtain some sort of synthesized information as to the route choice behavior of prospective equipped travelers. The estimation method is applied to an experimental data set and the TA method is tested on an illustrative network. Authors demonstrate that, given the market penetration of the system, how the analyst would be able to provide quantitative forecasts as to the expected improvements in the network performance as a result of being introduced to advanced travelers information systems. [ABSTRACT FROM AUTHOR]

This article identifies the truck routing priorities of freight companies through a survey of Washington state shippers, carriers, and receivers. To elicit these priorities, the survey prompted the respondents to rate 15 items believed to affect route choice decision making with respect to each item's influence on route choice. Item response theory (IRT) and latent class analysis (LCA) highlights priorities that were common among all survey respondents and priorities that were different among the sample. Minimizing cost and meeting customer requirements were priorities for all. The influence of other items such as road grade, hours of service limits, and driver availability depended on whether the respondent was best described as a long-haul, local-regional, or urban trucking provider. These three classes of companies were derived from the LCA, and each class has a distinct response pattern to the 15 routing items. This result suggests that truck routing priorities are not constant and uniform across a state's trucking industry but rather variable and largely dependent on trip length. The paper concludes with practical recommendations as to how these priorities can be implemented within a truck routing model. [ABSTRACT FROM AUTHOR]