Structure and dynamics of urban freight truck movements: A complex network theory perspective

Knowledge of the core structure and inherent dynamics of urban freight transport systems is important for the development of policies, aimed at improving the livability and sustainability of cities. The past decade has witnessed a great deal of efforts into analyzing the geographic structure of urban freight transport systems. However, in...

Empirical analysis and modeling of the allometric scaling of urban freight systems

Heavy trucks which undertake the majority of freight volume play an important role in urban freight systems. By analyzing heavy truck trip data, we find a superlinear scaling relationship for heavy truck trips and a sublinear scaling relationship for heavy truck numbers relative to urban population size. Although these allometric scaling...

A multisource transportation network model explaining allometric scaling

The universal scaling relationship between an attribute and the size of a system is widespread in nature and society and is known as allometric growth. Previous studies have explained that the allometric growth exponent of single-source systems is uniquely determined by the dimension. However, the phenomenon that the exponent shows diversity...

Uncovering and modeling the hierarchical organization of urban heavy truck flows

Knowledge of the hierarchical organization of urban heavy truck flows is important for understanding the structure of urban freight system and underlying interactions dynamics, providing insights to assess and develop freight policies. The complexity and dynamic nature of urban freight system pose significant challenges in comprehensively...

Estimating intercity heavy truck mobility flows using the deep gravity framework

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...

A time series forecasting based on cloud model similarity measurement

In this paper, a local cloud model similarity measurement (CMSM) is proposed as a novel method to measure the similarity of time series. Time series similarity measurement is an indispensable part for improving the efficiency and accuracy of prediction. The randomness and uncertainty of series data are critical problems in the processing of...