Techniques for Smart Urban Logistics Solutions’ Simulation: A Systematic Review

  • Ioannis KarakikesEmail author
  • Eftihia Nathanail
  • Mihails Savrasovs
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 68)


Today, cities devise their own Sustainable Urban Logistics Plan (SULP) to improve the sustainability of their distribution system. Modern SULPs, following the development of technology, consider smart solutions e.g. pick-ups and deliveries by electric vehicles, bicycles or drones, city lockers, ITS systems for planning/routing, crowdsourcing services and other, which aim at mitigating the negative effects of the freight transport in the urban area. The effectiveness of these solutions, however, is not for sure, since their performance relies on particularities of cities’ urban freight transport system as well as the level of infrastructure, cooperation and policy adoption. To better understand and assess the impacts of a proposed solution in a city context, ex-ante evaluation through modeling is advised.

This study synthesizes the types of simulation techniques that are used to model the impacts of innovative smart urban freight solutions. A systematic literature review was performed in Web of Science Core Collection, SCOPUS and JSTOR databases to identify records that tackle with modeling smart urban freight solutions and present real case study results. Having gathered all relevant records through a query-based identification process, a screening process was adopted to keep only those that have an essential contribution to the topic. Eighty-two full papers met the criteria and were included in the qualitative analysis. Analysis’ key findings were that (1) the majority of studies use custom-made techniques for the evaluation of urban freight solutions, (2) there is growing tendency from 2015 onwards for such studies and, (3) “ITS for freight monitoring and planning/routing” is the most prominent solution in such studies.


City logistics Last mile distribution Evaluation Modeling PRISMA 



This work has been supported by the ALLIANCE project ( and has been funded within the European Commission’s H2020 Programme under contract number 692426. This paper expresses the opinions of the authors and not necessarily those of the European Commission. The European Commission is not liable for any use that may be made of the information contained in this paper.


  1. 1.
    Karakikes, I., Nathanail, E.: Simulation techniques for evaluating smart logistics solutions for sustainable urban distribution. Proc. Eng. 178, 569–578 (2017). Elsevier. 16th International Conference Reliability and Statistics in Transportation and Communication, RelStat 2016, Riga, Latvia (2017).
  2. 2.
    NOVELOG: Deliverable D3.2. Multi stakeholder multi criteria decision making tool (2016)Google Scholar
  3. 3.
    Tan, C.K., Blanco, E.E.: System dynamics modeling of the SmartWay transport partnership. In: Second International Symposium on Engineering Systems. MIT, Cambridge, 15–17 June 2009Google Scholar
  4. 4.
    Qiu, Y., Shi, X., Shi, C.: A system dynamics model for simulating the logistics demand dynamics of metropolitans: a case study of Beijing, China. J. Ind. Eng. Manag. JIEM 8(3), 783–803 (2015). Scholar
  5. 5.
    Schroder, S., Dabidian, P., Liedtke, G.: A conceptual proposal for an expert system to analyze smart policy options for urban CEP transports. In: 2015 Smart Cities Symposium Prague, SCSP 2015 (2015)Google Scholar
  6. 6.
    Fikar, C.: A decision support system to investigate food losses in e-grocery deliveries. Comput. Ind. Eng. 117, 282–290 (2018)Google Scholar
  7. 7.
    Alho, A., Bhavathrathan, B.K., Stinson, M., Gopalakrishnan, R., Le, D., Ben-Akiva, M.: A multi-scale agent-based modelling framework for urban freight distribution. Transp. Res. Proc. 27, 188 (2017)Google Scholar
  8. 8.
    Bean, W.L., Joubert, J.W.: A systematic evaluation of freight carrier response to receiver reordering behaviour. Comput. Ind. Eng. 124, 207–219 (2018)Google Scholar
  9. 9.
    Baindur, D., Viegas, J.M.: An agent based model concept for assessing modal share in inter-regional freight transport markets. J. Transp. Geogr. 19(6), 1093–1105 (2011)Google Scholar
  10. 10.
    Chen, P., Chankov, S.M.: Crowdsourced delivery for last-mile distribution: an agent-based modelling and simulation approach. In: IEEE International Conference on Industrial Engineering and Engineering Management, p. 1271 (2018)Google Scholar
  11. 11.
    Yang, H., Sun, L., Lan, S., Yang, C.: Freight group behavior under freight traffic restriction policy: the case of Beijing city distribution. Ind. Manag. Data Syst. 117(10), 2287–2304 (2017)Google Scholar
  12. 12.
    Boussier, J.-M., Cucu, T., Ion, L., Estraillier, P., Breuil, D.: Goods distribution with electric vans in cities: towards an agent-based simulation. In: 24th International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium and Exhibition 2009, EVS 24, p. 1851 (2009)Google Scholar
  13. 13.
    Sopha, B.M., Siagian, A., Asih, A.M.S.: Simulating dynamic vehicle routing problem using agent-based modeling and simulation. In: IEEE International Conference on Industrial Engineering and Engineering Management, p. 1335 (2016)Google Scholar
  14. 14.
    Boussier, J.-M., Cucu, T., Ion, L., Breuil, D.: Simulation of goods delivery process. Int. J. Phys. Distrib. Logist. Manag. 41(9), 913–930 (2011)Google Scholar
  15. 15.
    Kin, B., Ambra, T., Verlinde, S., Macharis, C.: Tackling fragmented last mile deliveries to nanostores by utilizing spare transportation capacity—a simulation study. Sustainability (Switzerland) 10(3), 653 (2018)Google Scholar
  16. 16.
    Wang, H., Winter, S.: Utilizing taxi empty cruise time to solve the short distance trip problem. In: 17th ITS World Congress (2010)Google Scholar
  17. 17.
    Haas, I., Friedrich, B.: An autonomous connected platoon-based system for city-logistics: development and examination of travel time aspects. Transportmetrica A: Transp. Sci. (2018).
  18. 18.
    Singhania, V.R., Marinov, M.: An event-based simulation model for analysing the utilization levels of a railway line in urban area. Promet—Traffic—Traffico 29(5), 521–528 (2017)Google Scholar
  19. 19.
    Lee, G., You, S.I., Ritchie, S.G., Saphores, J.-D., Jayakrishnan, R., Ogunseitan, O.: Assessing air quality and health benefits of the Clean Truck Program in the Alameda corridor, CA. Transp. Res. Part A: Policy Pract. 46(8), 1177–1193 (2012)Google Scholar
  20. 20.
    Marquez, L., Salim, V.: Assessing impacts of urban freight measures on air toxic emissions in Inner Sydney. Environ. Model Softw. 22(4), 515–525 (2007)Google Scholar
  21. 21.
    Lopez, C., Gonzalez-Feliu, J., Chiabaut, N., Leclercq, L.: Assessing the impacts of goods deliveries’ double line parking on the overall traffic under realistic conditions. In: 6th International Conference on Information Systems, Logistics and Supply Chain, ILS 2016 (2016)Google Scholar
  22. 22.
    Sárdi, D.L., Bóna, K.: Developing a mesoscopic simulation model for examination of freight traffic of shopping malls in Budapest. In: 2017 Smart Cities Symposium Prague, SCSP 2017—IEEE Proceedings (2017)Google Scholar
  23. 23.
    Aditjandra, P.T., Galatioto, F., Bell, M.C., Zunder, T.H.: Evaluating the impacts of urban freight traffic: application of micro-simulation at a large establishment. Eur. J. Transp. Infrastruct. Res. 16(1), 4–22 (2016)Google Scholar
  24. 24.
    Melo, S., Baptista, P.: Evaluating the impacts of using cargo cycles on urban logistics: integrating traffic, environmental and operational boundaries. Eur. Transp. Res. Rev. 9(2), 30 (2017)Google Scholar
  25. 25.
    Holguín-Veras, J., Sánchez-Díaz, I.: Freight demand management and the potential of receiver-led consolidation programs. Transp. Res. Part A: Policy Pract. 84, 109–130 (2016)Google Scholar
  26. 26.
    Alho, A.R., de Abreu e Silva, J., de Sousa, J.P., Blanco, E.: Improving mobility by optimizing the number, location and usage of loading/unloading bays for urban freight vehicles. Transp. Res. Part D: Transp. Environ. 61, 3–18 (2018)Google Scholar
  27. 27.
    Bhuiyan, M.F.H., Awasthi, A., Wang, C.: Investigating the impact of access-timing-sizing regulations on urban logistics. Int. J. Logist. Syst. Manag. 20(2), 216–238 (2015)Google Scholar
  28. 28.
    Zhang, L., Matteis, T., Thaller, C., Liedtke, G.: Simulation-based assessment of cargo bicycle and pick-up point in urban parcel delivery. Proc. Comput. Sci. 130, 18 (2018)Google Scholar
  29. 29.
    Vonolfen, S., Affenzeller, M., Beham, A., Wagner, S., Lengauer, E.: Simulation-based evolution of municipal glass-waste collection strategies utilizing electric trucks. In: Proceedings of the 3rd IEEE International Symposium on Logistics and Industrial Informatics, LINDI 2011, p. 177 (2011)Google Scholar
  30. 30.
    Marcucci, E., Danielis, R.: The potential demand for a urban freight consolidation centre. Transportation 35(2), 269–284 (2008)Google Scholar
  31. 31.
    Magniol, S., Lopez, C., Gonzalez-Feliu, J., Chiabaut, N., Leclercq, L.: The searching time to measure the freight loading zone accessibility using microscopic traffic simulation. In: Proceedings of the Information Systems, Logistics and Supply Chain, ILS 2018, p. 406 (2018)Google Scholar
  32. 32.
    Aschauer, G.J., Starkl, F.: Time, cost and carbon dioxide benefits-rescheduling urban freight operations. Proc. Inst. Civil Eng.: Transp. 167(6), 393–399 (2014)Google Scholar
  33. 33.
    Nourinejad, M., Wenneman, A., Habib, K.N., Roorda, M.J.: Truck parking in urban areas: application of choice modelling within traffic microsimulation. Transp. Res. Part A: Policy Pract. 64, 54–64 (2014)Google Scholar
  34. 34.
    Karakikes, I., Mitropoulos, L., Savrasovs, M.: Evaluating smart urban freight solutions using microsimulation. In: Kabashkin, I., Yatskiv, I., Prentkovskis, O. (eds.) Reliability and Statistics in Transportation and Communication, RelStat 2017. Lecture Notes in Networks and Systems, vol. 36. Springer, Cham (2018).
  35. 35.
    Gattuso, D., Cassone, G.C.: A statistical analysis for micro-simulation of UDC operativity. Proc. Eng. 21, 114 (2011)Google Scholar
  36. 36.
    Fatnassi, E., Chaouachi, J.: Discrete event simulation of loading unloading operations in a specific intermodal transportation context (2016)Google Scholar
  37. 37.
    Lebeau, P., Macharis, C., van Mierlo, J., Maes, G.: Implementing electric vehicles in urban distribution: a discrete event simulation. World Electr. Veh. J. 6(1), 38–47 (2013)Google Scholar
  38. 38.
    Makhloufi, R., Cattaruzza, D., Meunier, F., Absi, N., Feillet, D.: Simulation of mutualized urban logistics systems with real-time management. Transp. Res. Proc. 6, 365 (2015)Google Scholar
  39. 39.
    Behiri, W., Belmokhtar-Berraf, S., Chu, C.: Urban freight transport using passenger rail network: scientific issues and quantitative analysis. Transp. Res. Part E: Logist. Transp. Rev. 115, 227–245 (2018)Google Scholar
  40. 40.
    Costa, Y., Duarte, A., Sarache, W.: A decisional simulation-optimization framework for sustainable facility location of a biodiesel plant in Colombia. J. Clean. Prod. 167, 174–191 (2018)Google Scholar
  41. 41.
    Simoni, M.D., Claudel, C.G.: A fast simulation algorithm for multiple moving bottlenecks and applications in urban freight traffic management. Transp. Res. Part B: Methodol. 104, 238–255 (2017)Google Scholar
  42. 42.
    Simoni, M., Claudel, C.: A simulation framework for modeling urban freight operations impacts on traffic networks. Simul. Model. Pract. Theory 86, 1–204 (2018)Google Scholar
  43. 43.
    Elia, V., Gnoni, M.G., Tornese, F.: Improving logistic efficiency of WEEE collection through dynamic scheduling using simulation modeling. Waste Manag. 72, 78–86 (2018)Google Scholar
  44. 44.
    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)Google Scholar
  45. 45.
    Karakikes, I., Hofmann, W., Mitropoulos, L., Savrasovs, M.: Evaluation of freight measures by integrating simulation tools: the case of Volos Port, Greece. Transp. Telecommun. J. 19(3), 224–232 (2018). Scholar
  46. 46.
    Lin, X., Chen, Y.-H., Zhen, L., Jin, Z.-H., Bian, Z.: A crowdsourcing matching and pricing strategy in urban distribution system (2018)Google Scholar
  47. 47.
    Pan, S., Chen, C., Zhong, R.Y.: A crowdsourcing solution to collect e-commerce reverse flows in metropolitan areas. IFAC-PapersOnLine 28(3), 1984–1989 (2015)Google Scholar
  48. 48.
    Zhao, Y., Ioannou, P.A., Dessouky, M.M.: A hierarchical co-simulation optimization control system for multimodal freight routing. In: Proceedings of the IEEE Conference on Intelligent Transportation Systems, ITSC, p. 1 (2018)Google Scholar
  49. 49.
    Barceló, J., Grzybowska, H., Orozco, J.A.: A simulation based decision support system for city logistics applications. In: 15th World Congress on Intelligent Transport Systems and ITS America Annual Meeting 2008, p. 377 (2008)Google Scholar
  50. 50.
    Gonçalves, M., Jiménez-Guerrero, P., Baldasano, J.M.: Air quality management strategies in large cities: Effects of changing the vehicle fleet composition in Barcelona and Madrid greater areas (Spain) by introducing natural gas vehicles (2008)Google Scholar
  51. 51.
    Iwan, S., Kijewska, K., Johansen, B.G., Eidhammer, O., Małecki, K., Konicki, W., Thompson, R.G.: Analysis of the environmental impacts of unloading bays based on cellular automata simulation. Transp. Res. Part D: Transp. Environ. 61, 104–117 (2018)Google Scholar
  52. 52.
    Motraghi, A., Marinov, M.: Analysis of urban freight by rail using event based simulation. Simul. Model. Pract. Theory 25, 73–89 (2012)Google Scholar
  53. 53.
    Oliveira, R.L., Lima, R.S., Lima, J.P.: Arc routing using a geographic information system: application in recyclable materials selective collection (2014)Google Scholar
  54. 54.
    Hassall, K.: Bi-modal terminals—shrinking urban freight exposure through a quantum leap in freight productivity. In: 33rd Australasian Transport Research Forum, ATRF 2010 (2010)Google Scholar
  55. 55.
    van Duin, J.H.R., Kortmann, R., van den Boogaard, S.L.: City logistics through the canals? A simulation study on freight waterborne transport in the inner-city of Amsterdam. Int. J. Urban Sci. 18(2), 186–200 (2014). Scholar
  56. 56.
    Comi, A., Rosati, L.: CLASS: A DSS for the analysis and the simulation of urban freight systems. Transp. Res. Proc. 5, 132 (2015)Google Scholar
  57. 57.
    Durand, B., Mahjoub, S., Senkel, M.-P.: Delivering to urban online shoppers: the gains from “last-mile” pooling. Supply Chain Forum 14(4), 22–31 (2013)Google Scholar
  58. 58.
    Gonzalez-Feliu, J., Morana, J., Grau, J.-M.S., Ma, T.-Y.: Design and scenario assessment for collaborative logistics and freight transport systems. Int. J. Transp. Econ. 40(2), 207–240 (2013)Google Scholar
  59. 59.
    Zeimpekis, V., Giaglis, G.M., Minis, I.: Development and evaluation of an intelligent fleet management system for city logistics. In: Proceedings of the Annual Hawaii International Conference on System Sciences (2008)Google Scholar
  60. 60.
    Letnik, T., Farina, A., Mencinger, M., Lupi, M., Božičnik, S.: Dynamic management of loading bays for energy efficient urban freight deliveries. Energy 159, 916–928 (2018)Google Scholar
  61. 61.
    Schau, V., Apel, S., Gebhardt, K., Kretzschmar, J., Stolcis, C., Mauch, M., Buchholz, J.: ICT for urban area logistics with electric vehicles compared within simulated and real environments (2017)Google Scholar
  62. 62.
    Chen, D., Ignatius, J., Sun, D., Goh, M., Zhan, S.: Impact of congestion pricing schemes on emissions and temporal shift of freight transport. Transp. Res. Part E: Logist. Transp. Rev. 118, 77–105 (2018)Google Scholar
  63. 63.
    Greasley, A., Assi, A.: Improving last mile delivery performance to retailers in hub and spoke distribution systems. J. Manuf. Technol. Manag. 23(6), 794–805 (2012)Google Scholar
  64. 64.
    Muñuzuri, J., Cuberos, M., Abaurrea, F., Escudero, A.: Improving the design of urban loading zone systems. J. Transp. Geogr. 59, 1–13 (2017)Google Scholar
  65. 65.
    Schau, V., Apel, S., Gebhardt, K., Kretzschmar, J., Stolcis, C., Mauch, M., Buchholz, J.: Intelligent infrastructure for last-mile and short-distance freight transportation with electric vehicles in the domain of smart city logistic. In: Proceedings of the 2nd International Conference on Vehicle Technology and Intelligent Transport Systems, VEHITS 2016, p. 149 (2016)Google Scholar
  66. 66.
    Taniguchi, E., Shimamoto, H.: Intelligent transportation system based dynamic vehicle routing and scheduling with variable travel times. Transp. Res. Part C: Emerg. Technol. 12(3–4), 235–250 (2004). Special IssueGoogle Scholar
  67. 67.
    Pinto, R., Golini, R., Lagorio, A.: Loading/unloading lay-by areas location and sizing: a mixed analytic-Monte Carlo simulation approach. IFAC-PapersOnLine 49(12), 961–966 (2016)Google Scholar
  68. 68.
    Sárdi, D.L., Bóna, K.: Macroscopic simulation model of a multi-stage, dynamic cargo bike-based logistics system in the supply of shopping malls in Budapest. In: 2018 Smart Cities Symposium Prague, SCSP 2018, p. 1 (2018)Google Scholar
  69. 69.
    Ramsay, E.D., Bunker, J.M.: Management of competing demands on urban freight corridors. Road Transp. Res. 16(3), 3–15 (2007)Google Scholar
  70. 70.
    Crainic, T.G., Errico, F., Rei, W., Ricciardi, N.: Modeling demand uncertainty in two-tier city logistics tactical planning. Transp. Sci. 50(2), 559–578 (2016)Google Scholar
  71. 71.
    Nuzzolo, A., Comi, A.: Modelling the demand for rail in an urban context: some methodological aspects. In: European Transport—Trasporti Europei, no. 57 (2015)Google Scholar
  72. 72.
    Abadi, A., Ioannou, P., Dessouky, M.M.: Multimodal dynamic freight load balancing. IEEE Trans. Intell. Transp. Syst. 17(2), 356–366 (2016)Google Scholar
  73. 73.
    Kay, M.G., Jain, A.: Pricing a public logistics network. In: IIE Annual Conference and Exhibition 2004, p. 439 (2004)Google Scholar
  74. 74.
    Muñuzuri, J., Cortés, P., Onieva, L., Guadix, J.: Simulating the effects of pedestrianisation on urban freight deliveries. In: European Transport—Trasporti Europei, no. 54 (2013)Google Scholar
  75. 75.
    Arnold, F., Cardenas, I., Sörensen, K., Dewulf, W.: Simulation of B2C e-commerce distribution in Antwerp using cargo bikes and delivery points. Eur. Transp. Res. Rev. 10(1), 2 (2018)Google Scholar
  76. 76.
    Perboli, G., Rosano, M., Saint-Guillain, M., Rizzo, P.: Simulation-optimisation framework for City Logistics: an application on multimodal last-mile delivery. IET Intell. Transp. Syst. 12(4), 262–269 (2018)Google Scholar
  77. 77.
    Fonseca, A.G., Oliveira, R.L., Lima, R.S.: Structuring reverse logistics for waste cooking oil with geographic information systems. In: Proceedings of CUPUM 2013: 13th International Conference on Computers in Urban Planning and Urban Management—Planning Support Systems for Sustainable Urban Development, p. 1 (2013)Google Scholar
  78. 78.
    Rizet, C., Cruz, C., Vromant, M.: The constraints of vehicle range and congestion for the use of electric vehicles for urban freight in France. Transp. Res. Proc. 12, 500 (2016)Google Scholar
  79. 79.
    Moen, O.: The five-step model—procurement to increase transport efficiency for an urban distribution of goods. Transp. Res. Proc. 12, 861 (2016)Google Scholar
  80. 80.
    Holguín-Veras, J., Hodge, S., Wojtowicz, J., Singh, C., Wang, C., Jaller, M., Aros-Vera, F., Ozbay, K., Weeks, A., Replogle, M., Ukegbu, C., Ban, J., Brom, M., Campbell, S., Sanchez-Díaz, I., González-Calderón, C., Kornhauser, A., Simon, M., McSherry, S., Rahman, A., Encarnación, T., Yang, X., Ramírez-Ríos, D., Kalahashti, L., Amaya, J., Silas, M., Allen, B., Cruz, B.: The New York city off-hour delivery program: a business and community-friendly sustainability program. Interfaces 48(1), 70–86 (2018)Google Scholar
  81. 81.
    Agatz, N., Campbell, A., Fleischmann, M., Savelsbergh, M.: Time slot management in attended home delivery. Transp. Sci. 45(3), 435–449 (2011)Google Scholar
  82. 82.
    Yu, J.J.Q.: Two-stage request scheduling for autonomous vehicle logistic system. In: IEEE Transactions on Intelligent Transportation Systems (2018).
  83. 83.
    Wang, X., Shang, Y.: Urban freight service capacity dynamic coordination system. Inf. Technol. J. 12(20), 5589–5594 (2013)Google Scholar
  84. 84.
    Russo, F., Comi, A.: Urban freight transport planning towards green goals: synthetic environmental evidence from tested results. Sustainability (Switzerland) 8(4), 381 (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ioannis Karakikes
    • 1
    Email author
  • Eftihia Nathanail
    • 1
  • Mihails Savrasovs
    • 2
  1. 1.Department of Civil EngineeringUniversity of ThessalyVolosGreece
  2. 2.Transport and Telecommunication InstituteRigaLatvia

Personalised recommendations