An optimal control theory approach for freight structure path evolution post-COVID-19 pandemic.

Area of Science:

• Logistics and Transportation Science
• Environmental Economics
• Operations Research

Background:

• The COVID-19 pandemic caused initial disruptions in freight demand, followed by an increase in trucking's share, leading to higher energy consumption and pollution.
• Optimizing freight structure is crucial for sustainable economic activity post-pandemic.

Purpose of the Study:

• To develop an evolution model using Markov's theory to forecast freight structure post-COVID-19.
• To establish a freight structural adjustment path optimization model using economic cybernetics.
• To determine optimal annual freight transportation growth and timelines for achieving a rational freight structure.

Main Methods:

• Application of Markov's theory for freight structure evolution modeling.
• Utilizing economic cybernetics for freight structural adjustment path optimization.
• Case study analysis of China's freight transport structure.

Main Results:

• China's freight transport structure is currently in an adjustment period and projected to stabilize by 2035.
• The COVID-19 pandemic has complicated the adjustment of freight transport structures.
• The proposed optimization model can advance the rationalization of freight structures.

Conclusions:

• Increasing the growth rate of railway and waterway freight is key to optimizing the freight structure.
• The developed freight structure path optimization method enables advanced rationalization.
• Sustainable freight transport strategies are essential for mitigating environmental impact.