Evaluating sustainability of truck weight regulations: A system dynamics view

Abstract

Purpose: Targeting the problem of overload trucking in Highway Transportation of iron ore from Caofeidian to Tangshan (HTCT), this paper aims to assess long-term effects of alternative Truck Weight Regulation (TWR) policies on sustainability of HTCT.

Design/methodology/approach: A system dynamics model was established for policy evaluation. The model, composed of six interrelating modules, is able to simulate policies effects on trucking issues such as freight flow, truck traffic flow, pavement performance, highway transport capacity and trucking time, and further on the Cumulative Economic Cost (CEC) including transport cost and time cost of freight owners and the Cumulative Social Cost (CSC) including pavement maintenance cost, green house gas emission cost, air pollutants emission cost and traffic accidents cost, so the effects of TWR policies on sustainability of HTCT could be evaluated.

Findings: According to different values of overload ratio which a TWR policy allows, alternative TWR policies are classified into three types, which are The Rigid Policy (TRP), The Moderate Policy (TMP) and The Tolerant Policy (TTP). Results show that the best policy for sustainability of HTCT depends on the importance of CSC which is expected by the local government. To be specific, (1) if CSC is considered much less important than CEC, the local government should continue implementing the current TTP with the maximum overload ratio; (2) if CSC is considered much more important than CEC, then TRP is recommended; and (3) if CSC is considered slightly more important than CES, TMP with overload ratio of 80% is the best.

Practical implications: Conclusions of this paper may help the local government design appropriate TWR policies to achieve sustainability of HTCT.

Originality/value: To the best of our knowledge, this is the first effort to evaluate TWR policies on sustainability of regional freight transportation based on system dynamics modeling.