A data-driven traffic modeling for analyzing the impacts of a freight departure time shift policy

A data-driven traffic modeling for analyzing the impacts of a freight departure time shift policy

Nadi Najafabadi, A. (TU Delft Transport and Planning)
Sharma, Salil (TU Delft Transport and Planning)
van Lint, J.W.C. (TU Delft Transport and Planning)
Tavasszy, Lorant (TU Delft Transport and Planning; TU Delft Transport and Logistics)
Snelder, M. (TU Delft Transport and Planning; TNO)

2022

This paper proposes a data-driven transport modeling framework to assess the impact of freight departure time shift policies. We develop and apply the framework around the case of the port of Rotterdam. Container transport demand data and traffic data from the surrounding network are used as inputs. The model is based on a graph convolutional deep neural network that predicts traffic volume, speed, and vehicle loss hours in the system with high accuracy. The model allows us to quantify the benefits of different degrees of adjustment of truck departure times towards the off-peak hours. In our case, travel time reductions over the network are possible up to 10%. Freight demand management can build on the model to design departure time advisory schemes or incentive schemes for peak avoidance by freight traffic. These measures may improve the reliability of road freight operations as well as overall traffic conditions on the network.

Data-driven traffic modelling
Freight departure time shifts
Freight transport policy
Graph convolutional deep neural network
Predictive departure time advice

http://resolver.tudelft.nl/uuid:f7118e85-2214-4533-b6c8-fc8ee16e927d

https://doi.org/10.1016/j.tra.2022.05.008

0965-8564

Transportation Research. Part A: Policy & Practice, 161, 130-150

Institutional Repository

journal article

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