Evaluation of Load Factor Control and Urban Freight Road Pricing Joint Schemes with Multi-agent Systems Learning Models

This paper describes the use of the multi-agent systems (MAS) modelling approach, incorporated with reinforcement learning models, that is experimented on a part of Osaka road network with ITS-based travel time information. The MAS model included an exact solution method to solve the vehicle routing problem of carriers’ delivery jobs and is used to evaluate the short-term impact of distance-based road pricing on the major stakeholders including the carriers, shippers, administrators and customers. The evaluation is extended to consider the load factor control scheme implemented as a joint scheme with the urban freight road pricing. The results from our experiment show that the city logistics joint scheme has the potential of improving average daily load factors and reduce emissions in comparison with no schemes implemented.