Technology

Urban freight: Optimising loading/unloading bays

Optimising loading/unloading bays
Thanks to Ivan Bandura for sharing their work on Unsplash.

Over the last decade social and economic trends have increased the pressure on transport networks in cities, demanding them to be fast, reliable, and at the same time, to reduce any adverse impacts on the deterioration of mobility, safety and the environment.

However, the available space in cities is limited and the ability to reconfigure the infrastructure and expand parking areas will not be enough to meet the growing demand for urban freight and the need for efficient and affordable solutions.

To tackle these problems practitioners and academics have proposed solutions like off-peak deliveries, Urban Consolidation Centres (UCC) and on-street Loading/Unloading (L/U) bays. This last parking infrastructure has been recognised as an important strategy for the management of freight traffic.

Participants

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This project focuses on one type of on-street, freight-dedicated, parking infrastructure: The L/U bays. The project will develop a simulation/optimisation framework to generate the operational and environmental trade-offs derived from the interaction of the optimal number and location of the L/U bays with varying traffic conditions, illegal parking and the simulation of the decision-making process of freight carriers.

As validation of the proposed methodology, the model will be applied as a case study to the Melbourne CBD. This area gathers social, economic, and topological characteristics that clearly represent modern urban logistics challenges.

Project background

Transportation networks are the backbone of modern urban societies. The concept of urbanisation implies relying on an efficient transportation network to provide the goods and much-needed services necessary to sustain urban life1.

Over the last decade social and economic trends have increased the pressure on freight transportation networks in cities. First, the decline of agriculture and old manufacturing as primary drivers of global economic prosperity is shifting the population from rural to urban areas [6]. Second, the shifting in the economy from high product lifespan to declining product durability, faster replacement and increasing consumption2 and 3. Third, advances in information and communication technologies have fostered a boom in e-commerce companies that offer ever-declining lead times4.

These factors are demanding transportation systems to be fast, reliable, and at the same time, to reduce any adverse impacts on the deterioration of mobility, safety and the environment. However, the available space in most cities is limited and the ability to reconfigure the current infrastructure, to enhance the transportation network, and expand parking areas will not be enough to meet the growing demand for urban freight and the need for efficient, environmentally sound and affordable solutions5.

To tackle these problems practitioners and academics have proposed a variety of solutions including:

  • Off-peak deliveries6
  • Urban Consolidation Centres (UCC)7
  • Crossdocking platforms8
  • Mini-hubs9
  • Mobile Depots (MD)10
  • On-street Loading/Unloading (L/U) bays11

This last, a relatively simple parking infrastructure has been recognised as an important strategy for the management of freight traffic12. The limited supply and inadequate location of freight parking areas is one of the major causes of double and/or illegal parking and is a main driver for controlling freight-related externalities13 and 14.

This work proposes the use of a simulation/optimisation framework to find the operational (routing time and length, walking distance) and environmental (congestion, emissions and noise) trade-offs generated by the optimal number and location of L/U bays. These trade-offs will be derived by analysing the interaction between the decisions made by the optimisation model and the variability of stochastic parameters like traffic conditions, L/U bay occupation, illegal parking, and the decision-making process of freight carriers.

By using a micro simulation model, we will evaluate the optimal decisions under a wide range of scenarios, and at the same time, we will provide feedback to improve these decisions and to guarantee the generation of robust solutions.

Project objectives

This project aims to answer the following research question:

What are the main operational (routing time and length, walking distance) and environmental (congestion and emissions) trade-offs derived from the application of a combined simulation/optimisation framework for the location and number of freight L/U bays?

To answer this question, this project will:

  1. Design a micro simulation model with variable traffic conditions, L/U bay occupation probabilities and illegal parking.
  2. Develop an optimisation model to determine the optimal number and location of L/U bays.
  3. Find the operational (routing time and length, walking distance) and environmental (congestion and emissions) trade-offs generated by the interaction of the optimisation and simulation models.
  4. Apply the methodology as a real case study to the Melbourne CBD.

References

  1. K. W. Ogden, “Urban Goods Movement: A Guide to Policy and Planning”, 1st ed. Aldershot, Hants, England, Routledge, 1992.
  2. C. Bakker, F. Wang, J. Huisman, and M. Den Hollander, “Products that go round: exploring product life extension through design”, Journal of Cleaner Production, vol. 69, pp. 10–16, 2014.
  3. F. Echegaray, “Consumers’ reactions to product obsolescence in emerging markets: the case of Brazil”, Journal of Cleaner Production, vol. 134, no. A, pp. 191–203, 2016.
  4. M. Savelsbergh and T. Van Woensel, “City logistics: Challenges and opportunities”, Transportation Science, vol. 50, no. 2, pp. 579–590, 2016.
  5. S. Marcia and S. Anderka, “Improving Freight Movement in Delaware Central Business”, University of Delaware, 2009. Available:
  6. I. Sánchez-díaz, P. Georén, and M. Brolinson, “Shifting urban freight deliveries to the off-peak hours: a review of theory and practice”, Transport Reviews, 37:4, pp. 521-543, 2017.
  7. A. Lagorio, R. Pinto, and R. Golini, “Urban distribution centers: Doomed to fail or optimal solutions for last mile deliveries?”, Proceedings of the 21st Summer School Francesco Turco, vol. 13-15-Sept, pp. 220–224, 2016.
  8. N. Boysen and M. Fliedner, “Cross dock scheduling: Classification, literature review and research agenda”, Omega, vol. 38, no. 6, pp. 413–422, 2010.
  9. J. Muñuzuri, P. Cortés, R. Grosso, and J. Guadix, “Selecting the location of minihubs for freight delivery in congested downtown areas”, Journal of Computational Science, vol. 3, no. 4, pp. 228–237, 2012.
  10. S. Verlinde, C. Macharis, L. Milan, and B. Kin, “Does a mobile depot make urban deliveries faster, more sustainable and more economically viable: results of a pilot test in Brussels”, Transportation Research Procedia, vol. 4, pp. 361–373, 2014.
  11. M. Jaller, J. Holguín-veras, and S. D. Hodge, “Parking in the City Challenges for Freight Traffic”, Transportation Research Record. No. 2379, pp. 46-56, 2013.
  12. Y. Shiftan and R. Burd-eden, “Modeling Response to Parking Policy”, Transportation Research Record, vol. 1765, no. 01, pp. 27–34, 2001.
  13. L. Delaitre, “A new approach to diagnose urban delivery areas plans”, in International Conference on Computers and Industrial Engineering, 2009, pp. 991–998.
  14. A. R. Alho, J. De Abreu e Silva, J. P. De Sousa, and E. Blanco, “Improving mobility by optimizing the number, location and usage of loading/unloading bays for urban freight vehicles”, Transportation Research Part D: Transport and Environment, vol. 61, pp. 3–18, 2018.

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