A domain‐of‐influence based pricing strategy for task assignment in crowdsourcing package delivery

Crowdsourced package delivery has gained great interest from the logistics industry and academe due to its significant economic and environmental impact. However, there are few research achievements about incentive mechanism to motivate people to participate. A novel domain‐of‐influence based pricing strategy for crowdsourced delivery is proposed. The three‐stage package delivery framework is extended with the proposed pricing algorithm, which can iteratively figure out the price that represents a state of balance between the package demand and driver supply. To create better matching, even hyperbolic temporal discounting function is employed to estimate the driver's perceived reward to accept the package. The performance is evaluated using the Jinan dataset and real delivery data. Results show that economic utility and stable assignment rate have been increased by over 9% and over 6%, respectively, while the average delivery time and average delivery price have also been improved.