An Integrated Choice and Latent Variable Framework to Incorporate the Influence of Travel Time Variability on Truck Route Choice
Abstract
Route choice models (or path choice models) are useful for quantifying travellers’ preferences for or sensitivity to route attributes, predicting network-level traffic flows, examining the influence of information provided to travellers, and studying travellers’ adaptation to uncertainty in travel conditions. Among the various factors influencing route choice, variability in travel conditions is an influential one. Day-to-day and within-day variations in travel conditions influence route choice decisions in many geographical contexts. Empirical studies on values of time and reliability have concluded that travellers, besides being interested in minimizing their travel times, also wish to minimize their travel time variability. The influence of travel time variability on route choice becomes more important in the context of freight transportation and logistics where delays due to uncertainty translate to large financial losses. Therefore, it is useful to quantify variability in travel conditions and to understand the influence of such variability on freight route choice decisions.
This thesis proposes an Integrated Choice and Latent Variable (ICLV) modelling framework that allows simultaneous estimation of route-level travel time variability and incorporation of the influence of such variability on travel route choice of freight-trucks. The proposed framework considers the travel time on a route as a latent (unobserved) variable and uses GPS data measurements of route-level travel time to identify the parameters of its statistical distribution. Since such measurements are not always available for all routes, the latent variable component of the ICLV framework helps impute or inform the travel time distribution for routes without travel time measurements. In this regard, simultaneous estimation of the measurement and choice components of the proposed model allows the use of partial
measurement data for estimation of travel time variability as well as incorporation of the influence of travel time variability on route choice. Further, route-level travel time variability is viewed as a result of variability in travel conditions (e.g., variability of travel speeds on links, etc.) and is captured through random coefficients on the route attributes specified in the latent variable model.
The proposed model is applied to an empirical data set on truck route choice using truck-GPS data collected in the Tampa Bay region of Florida, USA. The empirical parameter estimates suggest that the variability of travel time on a route depends on the network structure along the route, such as the lengths of different roadway types, largely due to differences in variability of travel speeds among different types of roadways. The empirical findings indicate a superior statistical model fit of the proposed ICLV model than the traditional choice models that do not consider the influence of travel time variability on route choice.
Although the ICLV model in this study was applied to the empirical context of freight-truck route choice, the proposed framework is applicable to accommodate the influence of variability in travel conditions on other travel choices such as transit route choice and travel mode choice; thanks to the increasing ubiquity of passively collected data on travel time (such as GPS data).
Collections
- Civil Engineering (CiE) [352]
Related items
Showing items related by title, author, creator and subject.
-
Mate Choice, Mate Sampling And Baffling Behaviour In The Tree Cricket Oecanthus henryi
Deb, Rittik (2017-07-12)Among the different sensory modalities that play a role in sexual selection, acoustic communication plays an important one. Acoustic communication has been known to be used for male-male competition (territory maintenance, ... -
Ranking from Pairwise Comparisons : The Role of the Pairwise Preference Matrix
Rajkumar, Arun (2018-07-05)Ranking a set of candidates or items from pair-wise comparisons is a fundamental problem that arises in many settings such as elections, recommendation systems, sports team rankings, document rankings and so on. Indeed it ... -
Resolving the Complexity of Some Fundamental Problems in Computational Social Choice
Dey, Palash (2017-12-16)In many real world situations, especially involving multiagent systems and artificial intelligence, participating agents often need to agree upon a common alternative even if they have differing preferences over the available ...