On-Time Delivery in Crowdshipping Systems: An Agent-Based Approach Using
  Streaming Data
Jeremias Dötterl Ralf Bruns Jürgen Dunkel Sascha Ossowski
In parcel delivery, the "last mile" from the parcel hub to the customer is
costly, especially for time-sensitive delivery tasks that have to be completed
within hours after arrival. Recently, crowdshipping has attracted increased
attention as a new alternative to traditional delivery modes. In crowdshipping,
private citizens ("the crowd") perform short detours in their daily lives to
contribute to parcel delivery in exchange for small incentives. However,
achieving desirable crowd behavior is challenging as the crowd is highly
dynamic and consists of autonomous, self-interested individuals. Leveraging
crowdshipping for time-sensitive deliveries remains an open challenge. In this
paper, we present an agent-based approach to on-time parcel delivery with
crowds. Our system performs data stream processing on the couriers' smartphone
sensor data to predict delivery delays. Whenever a delay is predicted, the
system attempts to forge an agreement for transferring the parcel from the
current deliverer to a more promising courier nearby. Our experiments show that
through accurate delay predictions and purposeful task transfers many delays
can be prevented that would occur without our approach.