A data-driven traffic modeling for analyzing the impacts of a freight departure time shift policy
article
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
Topics
TNO Identifier
980726
ISSN
09658564
Source
Transportation Research Part A: Policy and Practice, 161, pp. 130-150.
Publisher
Elsevier Ltd
Pages
130-150