An integrated crowdshipping framework for green last mile delivery

Sustainable urban freight transport is a key element of the smart city concept. In this context, the availability of large number of citizens connected with mobile devices has created numerous opportunities for performing the last mile delivery (LMD) in more environmentally friendly forms. However, due to several logistical challenges, the assignment of delivery jobs to the crowd is a complex and multifaceted process. Motivated by the research gap for development of practical crowdshipping methods, this study proposes an integrated framework for the assignment of LMD to a set of registered crowdshippers. In contrast to the dominant crowdshipping model in which individuals perform dedicated delivery trips, the framework proposed in this research employs people going about their daily travels. The proposed framework is composed of two components: (i) trajectory analytics for profiling the crowd to identify the list of suitable crowdshippers and (ii) the optimisation module that aims at maximising platform's profitability. We validated the framework using real-world crowdshipping operations data, which allows us to employ the mobility patterns extracted from geo-location data of mobile devices. The experimental results demonstrated satisfactory performance in terms of profit maximisation, computational time and environmental performance.

Automated summary

Sustainable urban freight transport is a crucial aspect of the smart city concept, and the use of crowdshipping offers environmentally friendly last mile delivery options. This study proposes an integrated framework that utilizes trajectory analytics and an optimization module to assign delivery tasks to registered crowdshippers. The framework demonstrates satisfactory performance in terms of profit maximization, computational time, and environmental impact, as validated by real-world crowdshipping operations data.