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

Ali Nadi, Salil Sharma, J.W.C. van Lint, Lóránt Tavasszy and Maaike Snelder

Transportation Research Part A: Policy and Practice, 2022, vol. 161, issue C, 130-150

Abstract: 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.

Keywords: Freight departure time shifts; Freight transport policy; Predictive departure time advice; Data-driven traffic modelling; Graph convolutional deep neural network (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1016/j.tra.2022.05.008

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