Assessing the potential impacts of public transport-based crowdshipping: A case study in a central district of Copenhagen

Abstract

The expansion of e-commerce and the sharing economy has paved the way for crowdshipping as an innovative approach to addressing last-mile delivery challenges. Previous studies and implementations have predominantly concentrated on private vehicle-based crowdshipping, which may lead to increased traffic congestion and emissions due to additional trips made specifically for deliveries. To circumvent these possible adverse effects, this paper explores a public transport (PT)-based crowdshipping concept as a complementary solution to the traditional parcel delivery systems. In this model, PT users leverage their routine journeys to perform delivery tasks. We propose a methodology that includes a parcel locker location model and a vehicle routing model to analyze the effect of PT-based crowdshipping. Notably, the parcel locker location model aids in planning a PT-based crowdshipping network and identifying obstacles to its development. A case study conducted in the central district of Copenhagen utilizing real-world data assesses the effects of PT-based crowdshipping. The findings suggest that PT-based crowdshipping can decrease the total kilometers traveled by vehicles, the overall working hours of drivers, and the number of vans required for last-mile deliveries, thereby alleviating urban traffic congestion and environmental pollution. Nevertheless, the growth of PT-based crowdshipping may be limited by the availability of crowdshippers, indicating that initiatives to increase the number of crowdshippers are essential.

Graphical abstract

Keywords

last-mile delivery / crowdshipping / public transport-based crowdshipping / integrated passenger and freight transportation / impact assessment

References

[1] Allahviranloo M, Baghestani A, (2019). A dynamic crowdshipping model and daily travel behavior. Transportation Research Part E, Logistics and Transportation Review, 128: 175–190

[2] Alnaggar A, Gzara F, Bookbinder J H, (2021). Crowdsourced delivery: A review of platforms and academic literature. Omega, 98: 102139

[3] Boysen N, Fedtke S, Schwerdfeger S, (2021). Last-mile delivery concepts: a survey from an operational research perspective. OR-Spektrum, 43( 1): 1–58

[4] Buldeo Rai H, Verlinde S, Macharis C, (2018). Shipping outside the box. Environmental impact and stakeholder analysis of a crowd logistics platform in Belgium. Journal of Cleaner Production, 202: 806–816

[5] Cheng R, Jiang Y, Nielsen O A, (2023a). Integrated people-and-goods transportation systems: From a literature review to a general framework for future research. Transport Reviews, 43( 5): 997–1020

[6] Cheng R, Jiang Y, Nielsen O A, Pisinger D, (2023b). An adaptive large neighborhood search metaheuristic for a passenger and parcel share-a-ride problem with drones. Transportation Research Part C, Emerging Technologies, 153: 104203

[7] Curtale R, Liao F, (2023). Travel preferences for electric sharing mobility services: Results from stated preference experiments in four European countries. Transportation Research Part C, Emerging Technologies, 155: 104321

[8] European Commission (2007). Green paper, towards a new culture for urban mobility. European Union, Brussels

[9] European Regulators Group for Postal Services (2022). ERGP PL II (22) 12 ERGP report on core indicators 2021 for monitoring the European postal market

[10] Fessler A, Cash P, Thorhauge M, Haustein S, (2023). A public transport based crowdshipping concept: Results of a field test in Denmark. Transport Policy, 134: 106–118

[11] Fessler A, Thorhauge M, Mabit S, Haustein S, (2022). A public transport-based crowdshipping concept as a sustainable last-mile solution: Assessing user preferences with a stated choice experiment. Transportation Research Part A, Policy and Practice, 158: 210–223

[12] Gatta V, Marcucci E, Nigro M, Serafini S, (2019). Sustainable urban freight transport adopting public transport-based crowdshipping for B2C deliveries. European Transport Research Review, 11( 1): 13–26

[13] GevaersRVan de VoordeEVanelslanderT (2011). Characteristics and typology of last-mile logistics from an innovation perspective in an urban context. In: City Distribution and Urban Freight Transport. Edward Elgar Publishing

[14] Iannaccone G, Marcucci E, Gatta V, (2021). What young e-consumers want? Forecasting parcel lockers choice in Rome. Logistics, 5( 3): 57–72

[15] Karakikes I, Nathanail E, (2022). Assessing the impacts of crowdshipping using public transport: A case study in a middle-sized Greek city. Future Transportation, 2( 1): 55–83

[16] Kızıl K U, Yıldız B, (2023). Public transport-based crowd-shipping with backup transfers. Transportation Science, 57( 1): 174–196

[17] Li B, Krushinsky D, Van Woensel T, Reijers H A, (2016). An adaptive large neighborhood search heuristic for the share-a-ride problem. Computers & Operations Research, 66: 170–180

[18] Lim S F W, Jin X, Srai J S, (2018). Consumer-driven e-commerce: A literature review, design framework, and research agenda on last-mile logistics models. International Journal of Physical Distribution & Logistics Management, 48( 3): 308–332

[19] PausE (2018). Confronting Dystopia: The New Technological Revolution and the Future of Work. Cornell University Press

[20] Punel A, Stathopoulos A, (2017). Modeling the acceptability of crowdsourced goods deliveries: Role of context and experience effects. Transportation Research Part E, Logistics and Transportation Review, 105: 18–38

[21] Ropke S, Pisinger D, (2006). An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows. Transportation Science, 40( 4): 455–472

[22] Statista 2024. Global retail e-commerce sales 2014–2027. Statista

[23] Wang D, Liao F, (2023). Incentivized user-based relocation strategies for moderating supply–demand dynamics in one-way car-sharing services. Transportation Research Part E, Logistics and Transportation Review, 171: 103017

[24] Zhang M, Cheah L, (2024). Prioritizing outlier parcels for public transport-based crowdshipping in urban logistics. Transportation Research Record: Journal of the Transportation Research Board, 2678( 3): 601–612

[25] Zhang M, Cheah L, Courcoubetis C, (2023). Exploring the potential impact of crowdshipping using public transport in Singapore. Transportation Research Record: Journal of the Transportation Research Board, 2677( 2): 173–189

Acknowledgments

We thank the anonymous logistics services provider and Rejsekort & Rejseplanen A/S for providing the data.

Competing Interests

The authors declare that they have no competing interests.

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2024 The Author(s). This article is published with open access at link.springer.com and journal.hep.com.cn