Investigation of carriers’ ability to transfer toll increases: an empirical analysis of freight agents’ relative market power

Zhang, David; Wang, Xiaokun; Holguín‐Veras, José; Zou, Wei

doi:10.1007/s11116-018-9930-3

Cited by 2 publications

(1 citation statement)

References 30 publications

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“…In addition, Holguin-Veras et al [1] argues that many freight activities are results of the freight agents' interactions, and that these interactions determine the supply chain's response to freight policies. For example, delivery rates [25], sizes, and frequency are impacted jointly by suppliers, carriers, and receivers. Disregarding interactions among agents may prevent the research community from fully understanding the decision mechanism, leading to misleading assessments of policy effects on each individual decision maker and, consequently, poor predictive power.…”

Section: Freight Demand Modelsmentioning

confidence: 99%

Investigation of Freight Agents’ Interaction Considering Partner Selection and Joint Decision Making

Zhang

Wang

2020

Sustainability

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Freight transportation plays an increasingly important role in sustainable development. However, freight travel demand has not been understood comprehensively, due to its unique features: freight activities are the result of collaboration among freight agents. It distinguishes freight transportation from passenger transportation, in which travel decisions are made mostly by individuals. Specifically, two processes in the collaboration can be observed: partner selection and joint decision making. Using the supplier-customer collaboration as an example, partner selection is a process for suppliers and customers to evaluate their potential partners and select the best one. Joint decision making allows suppliers and customers to seek common interests and make compromises. As a traditional travel demand model cannot model the two processes effectively, this research develops an innovative econometric model, spatial matching model, to bridge the gap. The proposed model is specified based on freight agents’ behavioral, estimated by Bayesian MCMC methods, and demonstrated by numerical examples. The proposed model and estimation methods can recover the coefficient values in the econometric models, and establish the relationship between the influential factors and the observed matching behavior. The analysis improves the understanding of freight travel demand in a behavioral-consistent manner and enriches the body of freight demand modeling literature.

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Section: Freight Demand Modelsmentioning

confidence: 99%