Crowdsourced delivery services (crowdshipping) represent a shipping alternative to traditional delivery systems, particularly suitable for e-commerce. Although some benefits in terms of reduced pollution and congestion could be obtained by replacing dedicated freight trips, the impacts of crowdshipping are unclear and depend on several factors such as the transport mode used, the match between supply and demand, length of detours, and possible induced demand. For example, private drivers could modify their existing routes or engage in new trips to pick up and drop off packages; similarly, public transport users could carry along packages on their trips and drop them off at lockers installed around the stations. In this paper, we analyze by means of a simulation-based approach the potential impacts of alternative implementation frameworks. In order to account more realistically for last-mile delivery operations, a hybrid dynamic traffic simulation is adopted such that the macroscopic features of traffic (triggering of congestion, queue spillbacks and interactions with traffic signals) are reproduced in combination with the microscopic features of delivery operations (delivery vehicles are tracked along their routes). The effects on traffic and emissions are investigated for the adoption of crowdshipping by carriers delivering parcels in the city center of Rome, Italy. Results show that not only is the mode employed by crowdshippers crucial for the sustainability of such a measure, but also operational aspects involving the length of detour, parking behavior, and daily traffic variations. Crowdsourced deliveries by car have generally higher negative impacts than corresponding deliveries by public transit. However, limiting the deviations of crowdshippers from the original trips, providing adequate parking options, and incentivizing off-peak deliveries, could significantly reduce crowdshipping externalities.
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Simoni, M.D., Marcucci, E., Gatta, V. et al. Potential last-mile impacts of crowdshipping services: a simulation-based evaluation. Transportation 47, 1933–1954 (2020). https://doi.org/10.1007/s11116-019-10028-4
- Crowdsourced delivery
- City logistics
- Dynamic traffic simulation
- External costs