Route Choice Behavior: Understanding the Impact of Asymmetric Preference on Travelers’ Decision Making

This paper investigated the impact of asymmetric preference on travelers’ route choices. Firstly, a status quo-dependent route choice mode was developed to describe travelers’ route choices. Then, based on that model, a route choice experiment was conducted, and during the experiment, participants were requested to choose a route from two arbitrary non-dominated routes. Finally, according to the observation data, data analysis and model parameter estimation were conducted. The results show that participants used different measures to trade off travel cost and travel time. Additionally, there was a gap between most participants’ willingness to pay (WTP) and willingness to accept (WTA). Moreover, participants’ WTP greater than their own WTA was the key reason resulting in the inertial route choices. The empirical results in this paper can help the traffic manager to understand travelers’ inertial route choice behavior from a different perspective.

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Context-dependent influence of road attributes and pricing policies on route choice behavior of truck drivers: results of a conjoint choice experiment

Transportation ◽

10.1007/s11116-012-9391-z ◽

2012 ◽

Vol 39 (6) ◽

pp. 1173-1188 ◽

Cited By ~ 21

Author(s):

Theo Arentze ◽

Tao Feng ◽

Harry Timmermans ◽

Jops Robroeks

Keyword(s):

Choice Experiment ◽

Choice Behavior ◽

Route Choice ◽

Truck Drivers ◽

Pricing Policies ◽

Route Choice Behavior ◽

Conjoint Choice ◽

Context Dependent ◽

Conjoint Choice Experiment

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The Impact of Traffic Information on Drivers’ Day-to-Day Route Choice Behavior

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.97-98.925 ◽

2011 ◽

Vol 97-98 ◽

pp. 925-930

Author(s):

Shi Xu Liu ◽

Hong Zhi Guan

Keyword(s):

Travel Time ◽

Road Network ◽

Choice Behavior ◽

Choice Model ◽

Route Choice ◽

User Equilibrium ◽

Statistical Characteristics ◽

Traffic Information ◽

Route Choice Behavior ◽

The Impact

The influence of different traffic information on drivers’ day-to-day route choice behavior based on microscopic simulation is investigated. Firstly, it is assumed that drivers select routes in terms of drivers’ perceived travel time on routes. Consequently, the route choice model is developed. Then, updating the drivers’ perceived travel time on routes is modeled in three kinds of traffic information conditions respectively, which no information, releasing historical information and releasing predictive information. Finally, by setting a simple road network with two parallel paths, the drivers’ day-to-day route choice is simulated. The statistical characteristics of drivers’ behavior are computed. Considering user equilibrium as a yardstick, the effects of three kinds of traffic information are compared. The results show that the impacts of traffic information on drivers are related to the random level of driver’s route choice and reliance on the information. In addition, the road network cannot reach user equilibrium in three kinds of information. This research results can provide a useful reference for the application of traffic information system.

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Route-Choice Modeling for Pedestrian Evacuation Based on Infrastructure Knowledge and Personal Preferences

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/2623-09 ◽

2017 ◽

Vol 2623 (1) ◽

pp. 82-89 ◽

Cited By ~ 4

Author(s):

Martin Stubenschrott ◽

Thomas Matyus ◽

Helmut Schrom-Feiertag ◽

Christian Kogler ◽

Stefan Seer

Keyword(s):

Choice Behavior ◽

Choice Model ◽

Route Choice ◽

Behavioral Model ◽

Emergency Evacuation ◽

Global Knowledge ◽

Evacuation Simulation ◽

Route Choice Behavior ◽

Crowd Density ◽

The Impact

In recent years, pedestrian simulation has been a valuable tool for the quantitative assessment of egress performance in various environments during emergency evacuation. For a high level of realism, an evacuation simulation requires a behavioral model that takes into account behavioral aspects of real pedestrians. In many studies, however, it is assumed that simulated pedestrians have a global knowledge of the infrastructure and choose either a predefined or the shortest route. It is questionable whether this simplification provides realistic results. This study addresses the problem of human-like route-choice behavior for microscopic pedestrian simulations. A route-choice model is presented that considers two concepts: first, the modeling of infrastructure knowledge to represent the variations in the decision-making processes of pedestrians with different degrees of familiarity with the infrastructure (e.g., regular commuters versus first-time visitors). Second, for each pedestrian the internal preference for selecting a certain path can be calibrated to allow the choice for the fastest routes or the ones that are most convenient for the agent (e.g., by avoiding stairs). The approach here uses a hybrid route-choice behavior model composed of a graph-based macrolevel representation of the environment, which is augmented with local information to avoid obstacles and dense crowds in the vicinity. This method was applied with different parameter sets in an evacuation study of a multilevel subway station. The results show the impact of these parameters on evacuation times, use of infrastructure elements, and crowd density at specific locations.

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Time Pressure Impact on En-Route Choice Behavior under Guidance Information

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.614.539 ◽

2014 ◽

Vol 614 ◽

pp. 539-542

Author(s):

Feng Gao

Keyword(s):

Choice Behavior ◽

Time Pressure ◽

Route Choice ◽

Traffic Information ◽

Preference Reversals ◽

Route Choice Behavior ◽

Pressure Impact ◽

The Impact ◽

Guidance Information ◽

Choice Probabilities

This paper investigates time pressure impact on en-route choice behavior under guidance information. The impact of time pressure constraints is quite evident in en-route choice decisions. Understanding en-route choice behavior under time pressure and predicting route choices are important components in the overall goal of building a more reliable and efficient advanced traffic information systems (ATIS). A hybrid model to predict en-route driver routing decisions under guidance information is proposed. The model accounts for the observed changes in choice probabilities, including preference reversals as a function of time limit.

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