Why do you take that route?

The purpose of this paper is to determine whether a particular context factor among the variables that a researcher is interested in causally affects the route choice behavior of drivers. To our knowledge, there is limited literature that consider the effects of various factors on route choice based on causal inference.Yet, collecting data sets that are sensitive to the aforementioned factors are challenging and the existing approaches usually take into account only the general factors motivating drivers route choice behavior. To fill these gaps, we carried out a study using Immersive Virtual Environment (IVE) tools to elicit drivers' route choice behavioral data, covering drivers' network familiarity, educationlevel, financial concern, etc, apart from conventional measurement variables. Having context-aware, high-fidelity properties, IVE data affords the opportunity to incorporate the impacts of human related factors into the route choice causal analysis and advance a more customizable research tool for investigating causal factors on path selection in network routing. This causal analysis provides quantitative evidence to support drivers' diversion decision.Comment: 7 pages, 3 figure

Pedestrian route choice: an empirical study

There has been relatively little work done on route choice for pedestrians. The present paper addresses this issue by using a sample survey of daily walks in a UK urban area. The walks undertaken are reconstructed using a geographical information system and compared with the shortest available route. It was found that about 75 per cent of walkers in the sample chose the shortest available route. Two strategies were used to synthesise sets from which pedestrians could have chosen their routes. These choice sets can then be used in discrete choice modelling to study route choice and to determine which factors are important to pedestrians in this. At the time of writing, it is proposed to proceed with this modelling. The structure of the paper is as follows. Section 2 describes the various sources of data used in this work, section 3 discusses the choice set generation strategies that were developed, section 4 briefly compares the walks with the corresponding shortest routes, while section 5 presents the conclusions that were drawn from this

Hellmann and Feynman theorem versus diffusion Monte Carlo experiment

In a computer experiment the choice of suitable estimators to measure a physical quantity plays an important role. We propose a new direct route to determine estimators for observables which do not commute with the Hamiltonian. Our new route makes use of the Hellmann and Feynman theorem and in a diffusion Monte Carlo simulation it introduces a new bias to the measure due to the choice of the auxiliary function. This bias is independent from the usual one due to the choice of the trial wave function. We used our route to measure the radial distribution function of a spin one half Fermion fluid.Comment: 7 pages, 1 figure, 1 tabl

A Portfolio Theory of Route Choice

Although many individual route choice models have been proposed to incorporate travel time variability as a decision factor, they are typically still deterministic in the sense that the optimal strategy requires choosing one particular route that maximizes utility. In contrast, this study introduces an individual route choice model where choos- ing a portfolio of routes instead of a single route is the best strategy for a rational traveler who cares about both journey time and lateness when facing stochastic net- work conditions. The model is then tested with GPS data collected in metropolitan Minneapolis-St. Paul, Minnesota. Our data suggest strong correlation among link speed when analyzing morning commute trips. There is no single dominant route (de- fined here as a route with the shortest travel time for a 15 day period) in 18% of cases when links travel times are correlated. This paper demonstrates that choosing a port- folio of routes could be the rational choice of a traveler who wants to optimize route decisions under variability.Transportation planning, route choice, travel behavior, link performance

An Example of Complex Pedestrian Route Choice

Pedestrian route choice is a complex, situation- and population-dependent issue. In this contribution an example is presented, where pedestrians can choose among two seemingly similar alternatives. The choice ratio is not even close to being balanced, but almost all pedestrians choose the same alternative. A number of possible causes for this are given.Comment: Extended version of a contribution to "Pedestrian and Evacuation Dynamics 2010" conference (accepted for publication) in Gaithersburg, M

A tutorial on recursive models for analyzing and predicting path choice behavior

The problem at the heart of this tutorial consists in modeling the path choice behavior of network users. This problem has been extensively studied in transportation science, where it is known as the route choice problem. In this literature, individuals' choice of paths are typically predicted using discrete choice models. This article is a tutorial on a specific category of discrete choice models called recursive, and it makes three main contributions: First, for the purpose of assisting future research on route choice, we provide a comprehensive background on the problem, linking it to different fields including inverse optimization and inverse reinforcement learning. Second, we formally introduce the problem and the recursive modeling idea along with an overview of existing models, their properties and applications. Third, we extensively analyze illustrative examples from different angles so that a novice reader can gain intuition on the problem and the advantages provided by recursive models in comparison to path-based ones

Pedestrian Route Choice by Iterated Equilibrium Search

In vehicular traffic planning it is a long standing problem how to assign demand such on the available model of a road network that an equilibrium with regard to travel time or generalized costs is realized. For pedestrian traffic this question can be asked as well. However, as the infrastructure of pedestrian dynamics is not a network (a graph), but two-dimensional, there is in principle an infinitely large set of routes. As a consequence none of the iterating assignment methods developed for road traffic can be applied for pedestrians. In this contribution a method to overcome this problem is briefly summarized and applied with an example geometry which as a result is enhanced with routes with intermediate destination areas of certain shape. The enhanced geometry is used in some exemplary assignment calculations.Comment: contribution to proceedings of Traffic and Granular Flow 2013 (TGF13

An Agent-based Route Choice Model

Travel demand emerges from individual decisions. These decisions, depending on individual objectives, preferences, experiences and spatial knowledge about travel, are both heterogeneous and evolutionary. Research emerging from fields such as road pricing and ATIS requires travel demand models that are able to consider travelers with distinct attributes (value of time (VOT), willingness to pay, travel budgets, etc.) and behavioral preferences (e.g. willingness to switch routes with potential savings) in a differentiated market (by tolls and the level of service). Traditional trip-based models have difficulty in dealing with the aforementioned heterogeneity and issues such as equity. Moreover, the role of spatial information, which has significant influence on decision-making and travel behavior, has not been fully addressed in existing models. To bridge the gap, this paper proposes to explicitly model the formation and spread- ing of spatial knowledge among travelers. An Agent-based Route Choice (ARC) model was developed to track choices of each decision-maker on a road network over time and map individual choices into macroscopic flow pattern. ARC has been applied on both SiouxFalls network and Chicago sketch network. Comparison between ARC and existing models (UE and SUE) on both networks shows ARC is valid and computationally tractable. To be brief, this paper specifically focuses on the route choice behavior, while the proposed model can be extended to other modules of travel demand under an integrated framework.Agent-based model, route choice, traffic assignment, travel demand modeling

A Model of Risk-Sensitive Route-Choice Behavior and the Potential Benefit of Route Guidance

In this paper, we present a simulation-based investigation of the potential benefit of route-guidance information in the context of risk-sensitive travelers. We set up a simple two-route scenario where travelers are repeatedly faced with risky route-choice decisions. The risk averseness of the travelers is implicitly controlled through a generic utility function. We vary both the travelers' sensitivity toward risk and the equipment fraction with route-guidance devices and show that the benefits of guided travelers increase with their sensitivity toward risk

An Analysis of Motorists’ Route Choice Using Stated Preference Techniques

This paper presents some results of an analysis of motorists' route choice based on stated preference responses. This is done for both an inter-urban and urban route choice context. The nature of the study is exploratory; the analysis being based upon a pilot survey of some 79 motorists undertaken in March/April 1984. The quality and nature of the responses are assessed in terms of a 'rationality' test and also through a consideration of lexicographical forms of decision making. The formal quantitative analysis examines the ranked preferences of motorists by means of an ordered multinomial logit model. Detailed results are presented for various formulations of the representative utility function to assess the influence of various relevant variables upon mute choice and to identify the best explanation of motorists' stated route preferences in both route choice contexts. Values of time are derived for a variety of rodel specifications as part of this consideration of the usefullness of the ranking approach to an analysis of motorists route choice