Technology

Evaluating urban freight impacts via sustainable alternatives

urban freight impacts

Urban freight distribution has become a crucial activity for the development of cities, growing faster than other transport activities. However, its adverse effects bring consequences for people, the environment, and the liveability of cities.

Addressing this problem, this project will develop an integrated optimisation-simulation model based on two strategies:

  1. Urban consolidation centres
  2. A network of Mobile Depots (MD) that use of sustainable transport/light vehicles for last mile delivery

To evaluate the potential benefits of these strategies, the model optimises the location-routing decisions of courier vehicles, and a multi-agent system considers their interaction, communication, and the variability of stochastic parameters.

This project will use the Melbourne CBD area as a case study to probe the validity of the methodology.

Participants

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Project background

In the context of city logistics, freight transport is one of the main causes of traffic congestion, high levels of pollution, security problems, and consumption of space1.

Air pollution emissions related to urban freight transport are estimated at 16% to 50% of the total pollution generated by transport activities in a city2. In fact, the presence of trucks in urban areas increases the use of non-renewable resources and the associated levels of pollutant emissions (global and local).

Additionally, the presence of multiple stakeholders with conflicting objectives3, low load factors, empty trips, longer-times at loading and unloading points, and a large number of deliveries have exerted an enormous pressure on urban freight transport4.

To address these challenges, practitioners and academics have provided different measures classified primarily as regulatory and infrastructure. Regulatory measures are focused on forbidden movement of freight vehicles in peak hours and the entry to certain urban areas, considering different vehicle characteristics such as the type, engine size, license plate number or compliance with gas emission.

Infrastructure measures have focused mainly on the implementation of logistics platforms to increase the consolidation of products, reduce the number of freight vehicles, improve efficiency, and reduce the time and length of the route.

In this sense, the adoption of a collaborative multi-level distribution system, based mainly on the combination of logistics platforms and different modes of sustainable transport such as MDs could reduce operating costs and reduce total emissions. The MD concept is a novel measure that uses a relatively simple infrastructure combined with sustainable modes of transport.

This work proposes an urban distribution model based on the combination of two strategies: a logistics platform and a network of MDs that use light vehicles. To estimate the potential of the combination of both strategies, the integrated model will be developed as a multi-agent system.

The model will consider dynamic characteristics inherent to the urban context. These features will be integrated into the solution processes that deal with the joint problem of location and resulting routing. It is expected that these strategies will provide a sustainable solution to alleviate traffic congestion in city centres and reduce the environmental impact of urban freight transport.

Project objectives

This PhD project aims to answer the following research question:

Does a combined strategy of a network of MDs and the use of light vehicles for urban freight transport under a collaborative framework, reduce operating costs and mitigate environmental impacts, while keeping the level of service?

To answer this question, this project will:

  • Identify the system and characterise the actors in the urban freight process to determine its functions, impact, scope, and existing collaboration relationships between them.
  • Design an integrated multi-agent model aimed at reducing operating costs and mitigating the environmental impact while keeping the level of service.

This model will include:

A dynamic location model to determine the optimal number and location of MDs

  • An urban freight distribution model using light vehicles
  • Validate the proposed model using simulation methodologies

References

  1. Cleophas, C., Cottrill, C., Ehmke, J. F., & Tierney, K. (2019b). Collaborative urban transportation: Recent advances in theory and practice. European Journal of Operational Research, 273(3), 801–816.
  2. [2] Faure, L., Burlat, P., & Marquès, G. (2016). Evaluate the Viability of Urban Consolidation Centre with Regards to Urban Morphology. Transportation Research Procedia, 12(June 2015), 348–356.
  3. Rose, W. J., Bell, J. E., Autry, C. W., & Cherry, C. R. (2017). Urban logistics: Establishing key concepts and building a conceptual framework for future research. Transportation Journal, 56(4), 357–394.
  4. Cepolina, E. M., & Farina, A. (2015). A new urban freight distribution scheme and an optimization methodology for reducing its overall cost. European Transport Research Review, 7(1), 1–14

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