Potential last-mile impacts of crowdshipping services: a simulation-based evaluation

  • Michele D. SimoniEmail author
  • Edoardo Marcucci
  • Valerio Gatta
  • Christian G. Claudel


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.


Crowdshipping Crowdsourced delivery City logistics Dynamic traffic simulation External costs 


Authors’ Contribution

The authors confirm the contribution to the paper as follows: study conception and design: MDS, EM, and VG; Data collection: MDS, EM, and VG; Analysis and interpretation of results: MDS; Manuscript preparation: MDS, EM, VG, and CGC.


  1. Allen, J., Piecyk, M., Piotrowska, M., McLeod, F., Cherrett, T., Ghali, K., Wise, S.: Understanding the impact of e-commerce on last-mile light goods vehicle activity in urban areas: the case of London. Transp. Res. Part D Transp. Environ. 61, 325 (2017)CrossRefGoogle Scholar
  2. Amer, A., Chow, J.Y.: A downtown on-street parking model with urban truck delivery behavior. Transp. Res. Part A Policy Pract. 102, 51–67 (2017)CrossRefGoogle Scholar
  3. Archetti, C., Savelsbergh, M., Speranza, M.G.: The vehicle routing problem with occasional drivers. Eur. J. Oper. Res. 254(2), 472–480 (2016)CrossRefGoogle Scholar
  4. Arslan, A.M., Agatz, N., Kroon, L., Zuidwijk, R.: Crowdsourced delivery—a dynamic pickup and delivery problem with ad hoc drivers. Transp. Sci. 53(1), 222–235 (2018)CrossRefGoogle Scholar
  5. Baindur, D., Macário, R.M.: Mumbai lunch box delivery system: A transferable benchmark in urban logistics? Res. Transp. Econ. 38, 110–121 (2013)CrossRefGoogle Scholar
  6. Barth, M., Boriboonsomsin, K.: Real-world carbon dioxide impacts of traffic congestion. Transp. Res. Rec. J. Transp. Res. Board 2058, 163–171 (2008)CrossRefGoogle Scholar
  7. Boulter, P.G., Barlow, T.J., McCrae, I.S.: Emission factors 2009: report 3-exhaust emission factors for road vehicles in the United Kingdom. TRL published project report (2009)Google Scholar
  8. Buldeo Rai, H., Verlinde, S., Macharis, C.: Shipping outside the box. Environmental impact and stakeholder analysis of a crowd logistics platform in Belgium. J. Clean. Prod. 202, 806 (2018)CrossRefGoogle Scholar
  9. Buldeo Rai, H., Verlinde, S., Merckx, J., Macharis, C.: Crowd logistics: An opportunity for more sustainable urban freight transport? Eur. Transp. Res. Rev. 9(3), 39 (2017)CrossRefGoogle Scholar
  10. Cap Gemini: Evolving E-Commerce Market Dynamics. Cap Gemini. (2013). Accessed March 2018
  11. Cheu, R.L., Jin, X., Ng, K.C., Ng, Y.L., Srinivasan, D.: Calibration of FRESIM for Singapore expressway using genetic algorithm. J. Transp. Eng. 124(6), 526–535 (1998)CrossRefGoogle Scholar
  12. Cleophas, C., Cottrill, C., Ehmke, J.F., Tierney, K.: Collaborative urban transportation: recent advances in theory and practice. Eur. J. Oper. Res. 273, 801–816 (2018)CrossRefGoogle Scholar
  13. Comune di Roma: I veicoli circolanti a Roma capitale. Anno 2016. (2016). Accessed Oct 2018
  14. Dablanc, L., Rodrigue, J.P.: The Geography of Urban Freight. The Geography of Urban Transportation. Routledge, London (2017)Google Scholar
  15. Dantzig, G., Fulkerson, R., Johnson, S.: Solution of a large-scale traveling-salesman problem. J. Oper. Res. Soc. Am. 2(4), 393–410 (1954)Google Scholar
  16. Dolan, S.: How crowdsourcing shipping through technology will make last mile delivery cheaper. Business Insider. (2018). Accessed May 2019
  17. Gatta, V., Marcucci, E., Nigro, M., Serafini, S.: Sustainable urban freight transport adopting public transport-based crowdshipping for B2C deliveries. Eur. Transp. Res. Rev. 11(1), 13 (2019a)CrossRefGoogle Scholar
  18. Gatta, V., Marcucci, E., Nigro, M., Patella, S.M., Serafini, S.: Public transport-based crowdshipping for sustainable city logistics: assessing economic and environmental impacts. Sustainability 11(1), 145 (2019b)CrossRefGoogle Scholar
  19. Gazis, D.C., Herman, R.: The moving and “phantom” bottlenecks. Transp. Sci. 26(3), 223–229 (1992)CrossRefGoogle Scholar
  20. Google Traffic: Google Traffic Map of Rome.,12.4759548,15.62z/data=!4m5!4m4!1m1!4e2!1m0!3e0!5m1!1e1 (2018). Accessed Feb 2019
  21. Hawkins, A.J.: Parking tickets: all in the cost of doing business. The bane of many companies, tickets defy solutions. (2013). Accessed Oct 2017.
  22. Highways Agency: Design Manual for Roads and Bridges: Volume 12 Traffic Appraisal of Roads Schemes, Section 2. (1996). Accessed Oct 2018
  23. Kafle, N., Zou, B., Lin, J.: Design and modeling of a crowdsource-enabled system for urban parcel relay and delivery. Transp. Res. Part B Methodol. 99, 62–82 (2017)CrossRefGoogle Scholar
  24. Le Pira, M., Marcucci, E., Gatta, V., Ignaccolo, M., Inturri, G., Pluchino, A.: Towards a decision-support procedure to foster stakeholder involvement and acceptability of urban freight transport policies. Eur. Transp. Res. Rev. 9(4), 54 (2017)CrossRefGoogle Scholar
  25. Lighthill, M.J., Whitham, G.B.: On kinematic waves. II. A theory of traffic flow on long crowded roads. In: Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, vol. 229, pp. 317–345. The Royal Society (1955)Google Scholar
  26. Lozzi, G., Gatta, V., Marcucci, E.: European urban freight transport policies and research funding: Are priorities and Horizon 2020 calls aligned? Region 5(1), 53–71 (2018)CrossRefGoogle Scholar
  27. Marcucci, E., Delle Site, P., Gatta, V., Pompetti, P.: Ex-ante acceptability of road pricing and modal shift extimation: the case of Rome. Sci. Reg. 17(3), 477–504 (2018)Google Scholar
  28. Marcucci, E., Gatta, V.: Investigating the potential for off-hour deliveries in the city of Rome: retailers’ perceptions and stated reactions. Transp. Res. Part A Policy Practice 102, 142–156 (2017)CrossRefGoogle Scholar
  29. Marcucci, E., Le Pira, M., Gatta, V., Inturri, G., Ignaccolo, M., Pluchino, A.: Simulating participatory urban freight transport policy-making: accounting for heterogeneous stakeholders’ preferences and interaction effects. Transp. Res. Part E Logist. Transp. Rev. 103, 69–86 (2017)CrossRefGoogle Scholar
  30. Marcucci, E., Le Pira, M., Carrocci, C.S., Gatta, V., Pieralice, E.: Connected shared mobility for passengers and freight: Investigating the potential of crowdshipping in urban areas. In: 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), pp. 839–843. IEEE (2017b)Google Scholar
  31. Mckinnon, A.: Crowdshipping—A communal approach to reducing urban traffic levels? Kuehne logistics university, logistics white paper 1/2016 (2016)Google Scholar
  32. Miller, J., Nie, Y., Stathopoulos, A.: Crowdsourced urban package delivery: modeling traveler willingness to work as crowdshippers. Transp. Res. Rec. J. Transp. Res. Board 2610(1), 67–75 (2017)CrossRefGoogle Scholar
  33. Munoz, J.C., Daganzo, C.F.: Moving bottlenecks: a theory grounded on experimental observation. In: Transportation and Traffic Theory in the 21st Century: Proceedings of the 15th International Symposium on Transportation and Traffic Theory, Adelaide, Australia, 16–18 July 2002, pp. 441–461. Emerald Group Publishing Limited (2002)Google Scholar
  34. Newell, G.F.: A simplified theory of kinematic waves in highway traffic, part II: queueing at freeway bottlenecks. Transp. Res. Part B 27(4), 289–303 (1993)CrossRefGoogle Scholar
  35. Paloheimo, H., Lettenmeier, M., Waris, H.: Transport reduction by crowdsourced deliveries—a library case in Finland. J. Clean. Prod. 132, 240–251 (2016)CrossRefGoogle Scholar
  36. Papageorgiou, M.: Some remarks on macroscopic traffic flow modelling. Transp. Res. Part A Policy Pract. 32(5), 323–329 (1998)CrossRefGoogle Scholar
  37. Punel, A., Ermagun, A., Stathopoulos, A.: Studying determinants of crowd-shipping use. Travel Behav. Soc. 12, 30–40 (2018)CrossRefGoogle Scholar
  38. Punel, A., Stathopoulos, A.: Modeling the acceptability of crowdsourced goods deliveries: role of context and experience effects. Transp. Res. Part E Logist. Transp. Rev. 105, 18–38 (2017)CrossRefGoogle Scholar
  39. Qi, W., Li, L., Liu, S., Shen, Z.J.M.: Shared mobility for Last-Mile delivery: design, operational prescriptions and environmental impact. Manuf. Serv. Oper. Manag. 20(4), 737–751 (2018)CrossRefGoogle Scholar
  40. Richards, P.I.: Shock waves on the highway. Oper. Res. 4(1), 42–51 (1956)CrossRefGoogle Scholar
  41. Sampaio, A., Savelsbergh, M., Veelenturf, L., Van Woensel, T.: Crowd-based city logistics. In: Sustainable Transportation and Smart Logistics, pp. 381–400. Elsevier (2019)Google Scholar
  42. Savelsbergh, M.W., Sol, M.: The general pickup and delivery problem. Transp. Sci. 29(1), 17–29 (1995)CrossRefGoogle Scholar
  43. Serafini, S., Nigro, M., Gatta, V., Marcucci, E.: Evaluating service’ scenarios for crowdshipping by public transport. Transp. Res. Procedia 30, 101–110 (2018)CrossRefGoogle Scholar
  44. Simoni, M.D., Claudel, C.G.: A simulation framework for modeling urban freight operations impacts on traffic networks. Simul. Model. Pract. Theory 86, 36–54 (2018)CrossRefGoogle Scholar
  45. Simoni, M.D., Claudel, C.G.: A Fast Lax–Hopf Formula to Solve the Lighthill–Whitham–Richards Traffic Flow Model on Networks. arXiv preprint arXiv:1802.05391 (2018)
  46. Stock, S.: Postal Service’s big delivery edge: no parking tickets. (2014). Accessed Oct 2017
  47. Taniguchi, E., Kakimoto, T.: Effects of e-commerce on urban distribution and the environment. J. East. Asia Soc. Transp. Stud. 5, 2355–2366 (2003)Google Scholar
  48. TOMTOM: Traffic Index. Full Ranking 2018. (2018)
  49. Wang, Y., Zhang, D., Liu, Q., Shen, F., Lee, L.H.: Towards enhancing the last-mile delivery: an effective crowd-tasking model with scalable solutions. Transp. Res. Part E Logist. Transp. Rev. 93, 279–293 (2016)CrossRefGoogle Scholar
  50. Wang, Y., Szeto, W.Y., Han, K., Friesz, T.L.: Dynamic traffic assignment: a review of the methodological advances for environmentally sustainable road transportation applications. Transp. Res. Part B Methodol. 111, 370–394 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Civil, Architectural, and Environmental EngineeringUniversity of Texas at AustinAustinUSA
  2. 2.Department of Public Institutions Economics and SocietyUniversity of Roma TreRomeItaly
  3. 3.Center for Transportation and Logistics, Massachusetts Institute of TechnologyCambridgeUSA

Personalised recommendations