TY - JOUR
T1 - Data-driven preference-based routing and scheduling for activity-based freight transport modelling
AU - Nadi, Ali
AU - Yorke-Smith, Neil
AU - Snelder, Maaike
AU - Van Lint, J. W.C.
AU - Tavasszy, Lóránt
PY - 2023
Y1 - 2023
N2 - Understanding preferences and behaviours in road freight transport is valuable for planning and analysis. This paper proposes a data-driven vehicle routing and scheduling approach for use as a descriptive tool to study road freight transport activities. The model developed seeks to capture planners’ or drivers’ preferences in order to reproduce observed road freight activities. The model is based on a parametrized time-dependent vehicle routing problem whose parameters can be estimated from a set of observed planned tours. We propose a Bayesian optimization technique for parameter estimation of the model. Empirical results show that the model can fit real-world data accurately and synthesize tour flows close to reality.
AB - Understanding preferences and behaviours in road freight transport is valuable for planning and analysis. This paper proposes a data-driven vehicle routing and scheduling approach for use as a descriptive tool to study road freight transport activities. The model developed seeks to capture planners’ or drivers’ preferences in order to reproduce observed road freight activities. The model is based on a parametrized time-dependent vehicle routing problem whose parameters can be estimated from a set of observed planned tours. We propose a Bayesian optimization technique for parameter estimation of the model. Empirical results show that the model can fit real-world data accurately and synthesize tour flows close to reality.
KW - Activity-based tour modelling
KW - Bayesian optimization
KW - Data-driven routing and scheduling
KW - Freight transport modelling
KW - Preference-based vehicle routing
UR - http://www.scopus.com/inward/record.url?scp=85177795234&partnerID=8YFLogxK
U2 - 10.1016/j.trc.2023.104413
DO - 10.1016/j.trc.2023.104413
M3 - Article
AN - SCOPUS:85177795234
SN - 0968-090X
VL - 158
JO - Transportation Research Part C: Emerging Technologies
JF - Transportation Research Part C: Emerging Technologies
M1 - 104413
ER -