Choice Set Formation Behavior in Selecting Travel Routes: Application of an Interactive Online Survey Platform

2017 ◽  
Vol 2669 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Kiran Shakeel ◽  
Taha Hossein Rashidi ◽  
Travis S. Waller
Keyword(s):  
Online Survey ◽  
Route Choice ◽  
Revealed Preference ◽  
Discrete Choice Models ◽  
Survey Tool ◽  
Factors Affecting ◽  
Route Choice Behavior ◽  
Formation Behavior ◽  
The Difference ◽  
Choice Set

One of the major challenges associated with the analysis of route choice modeling is the formulation of the choice set of alternatives that may allow a relatively accurate prediction of demand for travel routes. The subset of route alternatives in the choice set should be relevant and feasible and include the attributes considered most by travelers when they choose a route. This research investigated the role and significance of route choice set formations with a focus on the perspectives of the modeler and of travelers. Revealed preference data were collected from Sydney, Australia, residents about their choice of route for their most recent commuting trip. The survey tool was programmed to use the Google Maps application programming interfaces to collect the route choice information, including the selected route and the set of routes that were considered. Three discrete choice models were used to investigate the traveler’s inclination toward certain attributes of routes, considering both car and public transit routes with the master choice set. The effect of possible bias generated because of the formation of route choice from the perspective of the modeler was also analyzed and presented with the results. The results show the intuitive signs of various attributes, with travel time being the significant factor for route choice. The difference between the choice sets considered by the traveler and by the modeler also suggests that those considered by the modeler possess enough variation to offer the possibility of better capturing important factors affecting route choice behavior.

Analysis of Route Choice Decisions by Long-Haul Truck Drivers

2005 ◽  
Vol 1923 (1) ◽  
pp. 46-60 ◽  
Author(s):  
John H. Knorring ◽  
Rong He ◽  
Alain L. Kornhauser
Keyword(s):  
Decision Making ◽  
Logistic Model ◽  
Route Choice ◽  
Revealed Preference ◽  
Truck Drivers ◽  
Data Set ◽  
Route Choice Behavior ◽  
Highway Network ◽  
Multiple Routes ◽  
Haul Truck

This study has done an empirical analysis of long-haul truck drivers’ route choice decision making as they navigate the U.S. highway network. The most important factor that has been analyzed is how long-haul truck drivers trade off between distance and time when faced with multiple routes. From information gathered from a revealed preference data set consisting of about 250,000 trucks over a 13-day period, a logistic model was constructed to describe route choice behavior when truck drivers are faced with alternate routes. The logistic model predicted the percentage of trucks that used the bypass route as a function of the perceived speed on the downtown route. The results of this study show that time is a significant factor in the decision-making process.

scholarly journals Exploring Route Choice Behaviours Accommodating Stochastic Choice Set Generations

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Shin-Hyung Cho ◽  
Seung-Young Kho
Keyword(s):  
Radio Frequency Identification ◽  
Choice Model ◽  
Route Choice ◽  
Revealed Preference ◽  
Guidance System ◽  
Model Parameters ◽  
Generation Model ◽  
Choice Behaviour ◽  
Choice Set Generation ◽  
Choice Set

Modelling route choice behaviours are essential in traffic operation and transportation planning. Many studies have focused on route choice behaviour using the stochastic model, and they have tried to construct the heterogeneous route choice model with various types of data. This study aims to develop the route choice model incorporating travellers’ heterogeneity according to the stochastic route choice set. The model is evaluated from the empirical travel data based on a radio frequency identification device (RFID) called dedicated short-range communication (DSRC). The reliability level is defined to explore the travellers’ heterogeneity in the choice set generation model. The heterogeneous K-reliable shortest path- (HK α RSP-) based route choice model is established to incorporate travellers’ heterogeneity in route choice behaviour. The model parameters are estimated for the mixed path-size correction logit (MPSCL) model, considering the overlapping paths and the heterogeneous behaviour in the route choice model. The different behaviours concerning the chosen routes are analysed to interpret the route choice behaviour from revealed preference data by comparing the different coefficients’ magnitude. There are model validation processes to confirm the prediction accuracy according to travel distance. This study discusses the policy implication to introduce the traveller specified route travel guidance system.

scholarly journals Empirical Study of Effect of Dynamic Travel Time Information on Driver Route Choice Behavior

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3257 ◽  
Author(s):  
Jinghui Wang ◽  
Hesham Rakha
Keyword(s):  
Travel Time ◽  
Choice Behavior ◽  
Route Choice ◽  
Time Variability ◽  
Route Choice Behavior ◽  
Increase Risk ◽  
The Difference ◽  
Alternative Routes ◽  
Time Information ◽  
Travel Time Variability

The objective of this paper is to study the effect of travel time information on day-to-day driver route choice behavior. A real-world experimental study is designed to have participants repeatedly choose between two alternative routes for five origin-destination pairs over multiple days after providing them with dynamically updated travel time information (average travel time and travel time variability). The results demonstrate that historical travel time information enhances behavioral rationality by 10% on average and reduces inertial tendencies to increase risk seeking in the gain domain. Furthermore, expected travel time information is demonstrated to be more effective than travel time variability information in enhancing rational behavior when drivers have limited experiences. After drivers gain sufficient knowledge of routes, however, the difference in behavior associated with the two information types becomes insignificant. The results also demonstrate that, when drivers lack experience, the faster less reliable route is more attractive than the slower more reliable route. However, with cumulative experiences, drivers become more willing to take the more reliable route given that they are reluctant to become risk seekers once experience is gained. Furthermore, the effect of information on driver behavior differs significantly by participant and trip, which is, to a large extent, dependent on personal traits and trip characteristics.

Drivers’ Route Choice Behavior: Analysis by Data Mining Algorithms

2002 ◽  
Vol 1807 (1) ◽  
pp. 59-66 ◽  
Author(s):  
Toshiyuki Yamamoto ◽  
Ryuichi Kitamura ◽  
Junichiro Fujii
Keyword(s):  
Data Mining ◽  
Travel Behavior ◽  
Choice Behavior ◽  
Route Choice ◽  
Discrete Choice Models ◽  
Travel Times ◽  
Production Rules ◽  
Route Choice Behavior ◽  
Data Mining Algorithms ◽  
C4.5 Algorithm

Decision trees and production rules, which are among the methods used in knowledge discovery and data mining, are applied to investigate drivers’ route choice behavior. These methods have an advantage over artificial neural networks, another data mining method often used in analysis of travel behavior: they facilitate determination of the relationships between the explanatory variables and the choice. Specifically, the C4.5 algorithm, which produces a decision tree and a set of production rules from the tree, is applied here. Two surveys were carried out to collect data on drivers’ route choice behavior between two alternative routes on expressway networks. The two data sets include the expected minimum, maximum, and average travel times along each alternative route, as indicated by the respondent as well as his or her sociodemographic attributes. The results of the analyses suggest that different expected travel times influence route choice in different cases and that a maximum or average travel time determines route choice in some cases regardless of other attributes. The results of a comparison analysis between the C4.5 algorithm and discrete choice models indicate the superior ability offered by the former in representing drivers’ route choice.

scholarly journals What Do Young Adults Think about the Safety of Over-the-Counter Analgesics? Findings from a Cross-Sectional Survey

Pharmacy ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 54
Author(s):  
Tahlia Duyster ◽  
Sara S. McMillan ◽  
Ella Whately ◽  
Fiona S. Kelly
Keyword(s):  
Young Adults ◽  
University Students ◽  
Online Survey ◽  
Parental Influence ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Over The Counter ◽  
Factors Affecting ◽  
The Difference ◽  
Analgesic Use

Analgesics are commonly used over-the-counter (OTC) medicines readily available for purchase, sometimes without advice of a health professional. However, analgesics can cause harm even when taken according to dosing recommendations. Young adults may be more vulnerable to harm if they perceive low risk with OTC analgesic use, or struggle to interpret dosing instructions. This study aimed to explore factors affecting how young adults use OTC analgesics and associated perceptions of safety. An online survey was distributed to school-leavers and university students (17 to 25 years), in South-East Queensland, Australia, in the period November–December 2019. Most of the 302 respondents (school-leavers n = 147, university students n = 155) did not use analgesics frequently. School-leavers deferred to parents for analgesic information, while university students preferred the internet. The majority of respondents appeared safety conscious and did not take outside indicated use or instructions. However, a small proportion reported taking analgesics for an inappropriate indication. The difference in preferred source of analgesic information may reflect experience with analgesic use, increasing autonomy or decreased parental influence. Whilst it is encouraging that the majority of young adults appeared safety conscious, greater insight is needed into factors influencing decision making on OTC use, e.g., medicines knowledge, and changes with increasing age.

Influence of Changes in Level on Passenger Route Choice in Railway Stations

2005 ◽  
Vol 1930 (1) ◽  
pp. 12-20 ◽  
Author(s):  
Winnie Daamen ◽  
Piet H. L. Bovy ◽  
Serge P. Hoogendoorn
Keyword(s):  
Choice Behavior ◽  
Choice Model ◽  
Route Choice ◽  
Choice Models ◽  
Walking Distance ◽  
Choice Data ◽  
Factors Affecting ◽  
Simulation Tools ◽  
Route Choice Behavior ◽  
Transfer Station

In assessing the design of a public transfer station, it is important to be able to predict the routes taken by passengers. Most simulation tools use simple route choice models that take into account only the shortest walking distance or walking time between a passenger's origin and destination. To improve this type of route choice model, other factors affecting passenger route choice need to be identified. Also, the way these factors influence route choice behavior needs to be determined to indicate how each factor is valued. In this research, route choice data have been collected in two Dutch train stations by following passengers through the facility from their origins to their destinations. These data have been used to estimate extended route choice models. The focus is on the influences of level changes in walking routes on passenger route choice behavior. It appears that ways of bridging level changes (ramps, stairs, escalators) each have a significant and different impact on the attractiveness of a route to a traveler.

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