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On-Time Delivery in Crowdshipping Systems: An Agent-Based Approach Using Streaming Data

Jeremias Dötterl, Ralf Bruns, Jürgen Dunkel, Sascha Ossowski

TL;DR

The paper tackles the challenge of reliable on-time delivery in crowdshipping, where autonomous, self-interested couriers complicate last-mile performance. It introduces an agent-based architecture that streams sensor data from smartphones to monitor local and global courier states, predict delays with Hoeffding Trees, and proactively negotiate transfers to substitutes to maintain timeliness. Key contributions include a full monitoring/prediction pipeline, a Transfer Negotiation framework, and evaluations on real GPS data plus crowdshipping simulations demonstrating meaningful delay reductions. The findings suggest that accurate delay prediction combined with structured transfer agreements can substantially improve reliability in crowd-based last-mile delivery, though achieving optimal performance depends on courier autonomy, incentive design, and operating area density. The work lays groundwork for practical, scalable delay prevention in autonomous crowds, with potential extensions to multi-tasking, advanced payment schemes, and larger-scale deployments.

Abstract

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.

On-Time Delivery in Crowdshipping Systems: An Agent-Based Approach Using Streaming Data

TL;DR

The paper tackles the challenge of reliable on-time delivery in crowdshipping, where autonomous, self-interested couriers complicate last-mile performance. It introduces an agent-based architecture that streams sensor data from smartphones to monitor local and global courier states, predict delays with Hoeffding Trees, and proactively negotiate transfers to substitutes to maintain timeliness. Key contributions include a full monitoring/prediction pipeline, a Transfer Negotiation framework, and evaluations on real GPS data plus crowdshipping simulations demonstrating meaningful delay reductions. The findings suggest that accurate delay prediction combined with structured transfer agreements can substantially improve reliability in crowd-based last-mile delivery, though achieving optimal performance depends on courier autonomy, incentive design, and operating area density. The work lays groundwork for practical, scalable delay prevention in autonomous crowds, with potential extensions to multi-tasking, advanced payment schemes, and larger-scale deployments.

Abstract

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.
Paper Structure (35 sections, 3 equations, 5 figures, 1 table)

This paper contains 35 sections, 3 equations, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Parcel Delivery with Autonomous Couriers
  4. Delay Prevention through Proactive Task Transfers
  5. General Idea
  6. Agent-Based Crowdshipping Architecture
  7. Situation Monitoring and Delay Prediction
  8. Situation Monitoring
  9. Local situation monitoring
  10. Global situation monitoring
  11. Trigger Policy
  12. Delivery Delay Prediction
  13. Feature vector
  14. Prediction
  15. Learning
  16. ...and 20 more sections

Figures (5)

  • Figure 1: Agent-based crowdshipping architecture
  • Figure 2: Delay prediction based on smartphone sensor data
  • Figure 3: Scenario 1: Delivery delays under different transfer strategies with incident probability 5% and 50 delivery tasks per hour
  • Figure 4: Scenario 2: Delivery delays under different transfer strategies with incident probability 10% and 50 delivery tasks per hour
  • Figure 5: Scenario 3: Delivery delays under different transfer strategies with incident probability 5% and 100 delivery tasks per hour