Estimating Route Choice Characteristics of Truck Drivers from Sparse Automated Vehicle Identification Data through Data Fusion and Bi-Objective Optimization

Estimating Route Choice Characteristics of Truck Drivers from Sparse Automated Vehicle Identification Data through Data Fusion and Bi-Objective Optimization

Sharma, Salil (TU Delft Transport and Planning)
van Lint, J.W.C. (TU Delft Transport and Planning)
Tavasszy, Lorant (TU Delft Transport and Logistics)
Snelder, M. (TU Delft Transport and Planning; TU Delft BUS/TNO STAFF)

2022

Optimizing route choices for truck drivers is a key element in achieving reliable road freight operations. For commercial reasons, it is often difficult to collect freight activity data through traditional surveys. Automated vehicle identification (AVI) data on fixed locations (e.g., Bluetooth or camera) are low-cost alternatives that may have the potential to estimate route choice models. However, in cases where these AVI sensors are sparsely located, the resulting data lack actual route choices (or labels), which limits their application estimating route choice models. This paper overcomes this limitation with a new two-step approach based on fusing AVI and loop-detector data. First, a sparse Bluetooth data set is fused with travel times estimated from densely spaced loop-detector data. Second, the combined data set is fed into a bi-objective optimization method which simultaneously infers the actual route choices of truck drivers between an origin–destination pair and estimates the parameters of a route choice (discrete choice-based) model. We apply this approach to investigate the route choice behavior of truck drivers operating to and from the port of Rotterdam in the Netherlands. The proposed model can distinguish between peak and off-peak periods and identify different segments of truck drivers based on a latent classes choice analysis. Our results indicate the potential of traffic and logistics interventions in improving the route choices of truck drivers during peak hours. Overall, this paper demonstrates that it might be possible to estimate route choice characteristics from readily available data that can be retrieved from traffic management agencies.

data and data science
driver
freight movement data
freight systems
freight traffic
general
road freight vehicles (trucks)

https://doi.org/10.4233/uuid:12a308c5-c2df-40a7-82c4-1932ae425134

https://doi.org/10.1177/03611981221095089

SAGE Publishing

2023-07-01

Transportation Research Record, 2676 (12)

Transportation Research Record, 0361-1981, 2676 (12)

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

book chapter

© 2022 Salil Sharma, J.W.C. van Lint, Lorant Tavasszy, M. Snelder