Estimating intercity heavy truck mobility flows using the deep gravity framework

Abstract

Accurate estimation of intercity heavy truck mobility flows is of vital importance to urban planning, transportation management and logistics operations. The inaccessibility of big data related to intercity transport systems and the heterogeneity of trucking activities pose challenges for the reliable estimation. Recently, the advance of Artificial Intelligence (AI) provides a potential solution to this problem. However, most previous studies focused on the estimation of inter-regional passenger mobility. In-depth studies of estimating intercity heavy truck mobility flows by using deep learning techniques are still scarce. To fill in the gaps, we construct a deep neural network based on the Deep Gravity framework, an advanced predictive model for human mobility. We collect a wide range of data related to heavy truck movements, freight locations, road networks and land uses to train the model, and validate its high performance by comparing to traditional gravity model. Furthermore, we use an explainable AI technique to interpret how the city features contribute to the determination of intercity heavy truck movements, and the results can provide valuable policy implications for logistics operations, businesses and urban planning.