Time Efficiency Model for Identification of Development Potentials in Urban Logistics

  • Asad Karišik University of Ljubljana, Faculty of Maritime Studies and Transport
  • Sebastjan Škerlič University of Ljubljana, Faculty of Maritime Studies and Transport
  • Robert Muha University of Ljubljana, Faculty of Maritime Studies and Transport
Keywords: urban logistics, intermodal loading unit, models, development potential, case study


The aim of this paper is to develop a model for estimating the urban logistics improvements potential based on success factors of intermodal urban transport. There were two aspects considered for building the urban logistics time efficiency model: achieving an improved transport capacity without purchasing new vehicles, and transferring responsibility of poor shipment planning to its owners by implementing the intermodal transport success factors. The model is to establish functional relationship among the shipment distribution requests (urbanization) and urban logistics inefficiencies management (market inconsistencies), and their impact on business operations. The applicability of the proposed model was tested on urban population growth data and time inefficiencies in urban distribution. The results provide both theoretical and practical confirmation of time efficiency importance of urban logistics and potential for introduction of new intermodal solutions in urban logistics. Different case scenarios for Sarajevo prove that reducing inefficiencies in urban logistics could reduce the number of delivery vehicles by less than a half. Since the delivery vehicles are sources of pollution, the subsequent conclusion is valid for externalities levels. The model, therefore, complements the existing knowledge and represents a practical tool for urban planners and logistics professionals for creating an efficient, innovative, and integrative approach to the development of urban logistics services.


European Environment Agency. The European environment - state and outlook 2020: Knowledge for transition to a sustainable Europe. European Environment Agency; 2019. 496 p. Available from: https://www.eea.europa.eu/publications/soer-2020

ALICE / ERTRAC Urban mobility WG. Urban freight research roadmap. Ertrac; 2014. Available from: https://www.ertrac.org/uploads/documentsearch/id36/ERTRAC_Alice_Urban_Freight.pdf

Aljohani K, Thompson RG. A stakeholder-based evaluation of the most suitable and sustainable delivery fleet for freight consolidation policies in the inner-city area. Sustain. 2018;11(1).

United Nations. World Urbanization Prospects. Vol. 12, Demographic Research; 2018. p. 197-236.

Robert J. Urban Europe. The European Territory. 2018. p. 3-69.

European Transport Commission. Roadmap to a Single European Transport Area - Towards a competitive and resource efficient transport system. White paper 2011; 2011. Available from: http://ec.europa.eu/transport/themes/strategies/2011_white_paper_en.htm

Anderluh A, Hemmelmayr VC, Rüdiger D. Analytic hierarchy process for city hub location selection-The Viennese case. Transp Res Procedia. 2020;46: 77-84. DOI: 10.1016/j.trpro.2020.03.166

CIVITAS. Making urban freight logistics more sustainable. Civ Policy Note. 2015;1-63. Available from: http://www.eltis.org/resources/tools/civitas-policy-note-making-urban-freight-logistics-more-sustainable

He Z, Haasis HD. A theoretical research framework of future sustainable urban freight transport for smart cities. Sustain. 2020;12(5): 1-28.

Strale M. Sustainable urban logistics: What are we talking about? Transp Res Part A Policy Pract. 2019;130(October): 745-51. DOI: 10.1016/j.tra.2019.10.002

Schoemaker J, Allen J, Huschebeck M, Monigl J. Quantification of urban freight transport effects I -BESTUFS deliverable report 5.1. 2006; Available from: www.bestufs.net

European Comission. DG MOVE European Commission: Study on Urban Freight Transport; 2012. 156 p.

UNCTAD. Reveiw of Maritime Transport. United Nations Conference on Trade and Development; 2018. 29 p. Available from: https://unctad.org/en/PublicationsLibrary/rmt2018_en.pdf

Toilier F, Gardrat M, Routhier JL, Bonnafous A. Freight transport modelling in urban areas: The French case of the FRETURB model. Vol. 6. Case Studies on Transport Policy. 2018. p. 753-64. Available from: http://www.sciencedirect.com/science/article/pii/S2213624X18302748

Winkenbach M, Janjevic M. Classification of Last-Mile Delivery Models for e-Commerce Distribution: A Global Perspective. City Logist 1. 2018; 209-29.

Taniguchi E, Thompson RG, Qureshi AG. Modelling city logistics using recent innovative technologies. Transp Res Procedia. 2020;46(2019): 3-12. DOI: 10.1016/j.trpro.2020.03.157

Bjerkan KY, Bjørgen A, Hjelkrem OA. E-commerce and prevalence of last mile practices. Transp Res Procedia. 2020;46(2019): 293-300. DOI: 10.1016/j.trpro.2020.03.193

Alho A, Bhavathrathan BK, Stinson M, Gopalakrishnan R, Le DT, Ben-Akiva M. A multi-scale agent-based modelling framework for urban freight distribution. Transp Res Procedia. 2017;27: 188-96. DOI: 10.1016/j.trpro.2017.12.138

Sternberg H, Germann T, Klaas-Wissing T. Who controls the fleet? Initial insights into road freight transport planning and control from an industrial network perspective. Int J Logist Res Appl. 2013;16(6): 493-505.

Buldeo Rai H, van Lier T, Meers D, Macharis C. An indicator approach to sustainable urban freight transport. J Urban. 2018;11(1): 81-102.

Gevaers R, Van de Voorde E, Vanelslander T. Cost Modelling and Simulation of Last-mile Characteristics in an Innovative B2C Supply Chain Environment with Implications on Urban Areas and Cities. Procedia - Soc Behav Sci. 2014;125: 398-411. DOI: 10.1016/j.sbspro.2014.01.1483

Kordnejad B. Intermodal Transport Cost Model and Intermodal Distribution in Urban Freight. Procedia - Soc Behav Sci. 2014;125: 358-72. DOI: 10.1016/j.sbspro.2014.01.1480

Cepolina EM, Farina A. A new urban freight distribution scheme and an optimization methodology for reducing its overall cost. Eur Transp Res Rev. 2015;7(1): 1-14.

Muñoz-Villamizar A, Santos J, Montoya-Torres J, Velázquez-Martínez J. Measuring environmental performance of urban freight transport systems: A case study. Sustain Cities Soc. 2020;52(February 2019): 101844. DOI: 10.1016/j.scs.2019.101844

Letnik T, Farina A, Mencinger M, Lupi M, Božičnik S. Dynamic management of loading bays for energy efficient urban freight deliveries. Energy. 2018;159: 916-28.

Calabrò G, Torrisi V, Inturri G, Ignaccolo M. Improving inbound logistic planning for large-scale real-world routing problems: A novel ant-colony simulation-based optimization. Eur Transp Res Rev. 2020;12(1).

Qiu F, Zhang G, Chen PK, Wang C, Pan Y, Sheng X, et al. A novel multi-objective model for the cold chain logistics considering multiple effects. Sustain. 2020;12(19): 1-28.

Taniguchi E, Thompson RG, Yamada T. Recent Trends and Innovations in Modelling City Logistics. Procedia - Soc Behav Sci. 2014;125: 4-14. DOI: 10.1016/j.sbspro.2014.01.1451

Pinto R, Lagorio A. Supporting the decision making process in the urban freight fleet composition problem. Int J Prod Res. 2020; p. 1-19.

Nuzzolo A, Comi A, Polimeni A. Urban Freight Vehicle Flows: An Analysis of Freight Delivery Patterns through Floating Car Data. Transp Res Procedia. 2020;47(2019): 409-16. DOI: 10.1016/j.trpro.2020.03.116

He Z, Haasis HD. Integration of urban freight innovations: Sustainable inner-urban intermodal transportation in the retail/postal industry. Sustain. 2019;11(6).

United Nations, Department of Economic and Social Affairs PD. Population 2030: Demographic challenges and opportunities for sustainable development planning (ST/ESA/SER.A/389). New York: United Nations; 2015. Available from: http://www.un.org/en/development/desa/population/publications/pdf/trends/Population2030.pdf [Accessed 27th August 2017].

United Nations Department of Economic and Soical Affairs. World Urbanization Prospects 2018. Webpage; 2018. Available from: https://population.un.org/wup/

Firdausiyah N, Taniguchi E, Qureshi AG. Multi-agent simulation-Adaptive dynamic programming based reinforcement learning for evaluating joint delivery systems in relation to the different locations of urban consolidation centres. Transp Res Procedia. 2020;46(2019): 125-32. DOI: 10.1016/j.trpro.2020.03.172

Cherrett T, Allen J, McLeod F, Maynard S, Hickford A, Browne M. Understanding urban freight activity - key issues for freight planning. J Transp Geogr. 2012;24: 22-32. DOI: 10.1016/j.jtrangeo.2012.05.008

TomTom. Traffic congestion ranking. Available from: https://www.tomtom.com/en_gb/traffic-index/ranking/ [Accessed 28th Nov 2020].

How to Cite
Karišik A, Škerlič S, Muha R. Time Efficiency Model for Identification of Development Potentials in Urban Logistics. Promet - Traffic&Transportation. 2021;33(3):437-50. DOI: 10.7307/ptt.v33i3.3662