Services and expertise

Research group Transport and Regional Economics has developed advanced instruments to predict the flows of logistics and warehousing in urban, interurban and port environments, and calculate the associated costs and the added value of investments at both operational and societal levels. Costs of transport operations are the vital criterion for logistics decision making. And yet, calculation of costs related to chain costs, traffic and congestion remain mostly based on intuition. Companies looking to innovate for sustainability and competitiveness are testing or implementing technologies like AI, blockchain, etc., requiring a great deal of effort and financial investments, and thus involving significant risks. Transport and Regional Economics offers five bespoke instruments to help your business limit risk and counter your most pressing challenges: Chain Cost Model, Last Mile Cost Model, Transport and Traffic Forecasting Model, Congestion Cost Model, and Innovation ROI Tool.

The Chain Cost Model calculates the generalized cost of transporting containers from origin to destination by integrating both maritime and hinterland transportation. It defines a container loop, which represents a circular maritime route connecting multiple ports, and incorporates hinterland connections through road, rail, or waterways to inland geographic regions. Each port consists of several terminals with specific characteristics, and these terminals are linked to the hinterland areas, allowing the model to calculate the transport costs across the entire chain, from a hinterland region in one aggregated area to another. The model also accounts for port costs, such as port dues and container handling, and maritime costs between the loading and unloading ports. In addition to transport costs, the model incorporates transport time as an essential factor in the overall logistics chain, taking into account dwell times at terminals, sea travel, and hinterland transport times. The model provides a comprehensive calculation of the chain cost, which is impacted by changes in the maritime loop structure and the transport modes used. The model is adaptable to various scenarios, including land-based transport, making it applicable to regions in Europe, the US, and China.

The Last Mile Logistics Model was developed to simulate the dynamic nature of the final segment of the logistics chain, focusing on optimizing efficiency and reducing costs. The model incorporates several key areas that influence last mile operations, including consumer service levels, security and delivery types, geographic region, market penetration, vehicle fleet, technology, and environmental factors. For each of these areas, two to four proxy variables are defined, allowing the model to capture the varying impacts based on the value of goods being transported and other relevant factors. The simulation cost model functions as a tool to evaluate the cost effects of changes in last mile characteristics or policy decisions. By adjusting inputs such as delivery density, fleet technology, or service levels, the model calculates how these changes affect the overall cost structure of the last mile. It allows businesses to predict the cost outcomes of different strategies and make informed decisions to enhance last mile delivery, which is often the most cost-sensitive and complex part of the chain.

​Transport and Traffic Forecasting Model. The decision to provide new or additional transport capacities is challenging since it should be supported by a growing demand, which requires modelling and forecasting transport demand. TPR applies a specialized type of models (link) that allows predictions of the growth of freight transport, as freight transport is closely linked to the evolution of economic activity. The model can take into account the volatility and uncertainty in global economic activity and the complexity of different segments of supply chains.  

The Congestion Cost Model introduces a tool designed to calculate the monetary impact of road congestion, providing a globally accepted approach for both operational and societal costs. This tool breaks down congestion costs into private and societal components, and considers various trip types, including trucks, vans, work-related, and private trips, each with distinct cost characteristics. By standardizing the calculation of these costs, the tool offers uniform insights into the extent and effects of congestion in specific areas or on specific roads. The model’s output has practical applications across multiple sectors. Trucking companies can use the resulting congestion cost values to negotiate transparent surcharges with customers, while businesses with transport needs can assess potential locations with lower congestion impacts. Additionally, producers can use the tool to evaluate alternative transport modes, such as rail or barge, based on actual cost and time impacts rather than theoretical estimates. Policymakers can leverage the results to identify areas most affected by congestion and test the effectiveness of mitigation strategies, such as road pricing, making the tool a versatile instrument for both commercial and public decision-making.

The Innovation ROI tool is a modular model designed to help decision-makers calculate key economic indicators for evaluating the implementation of new technologies in logistics and port operations. The tool starts by gathering general details about the operations and technologies to be compared, then breaks down logistics processes impacted by the technologies. It uses individual operational cost calculation modules to assess various cost elements, such as employee costs, fuel/energy, delays, and accidents. The tool then calculates the total cost of implementing each technology, including initial deployment, third-party costs, and additional premium features or risks. Yearly costs are also projected, factoring in volumes handled and time in use, to provide a comprehensive analysis of the technology's financial impact. The main outputs of the TPR ROI tool include average operational costs per process, a detailed cost structure of the implementation, the break-even period, and the return on investment (ROI) for each technology. These results enable decision-makers to compare different technologies by analyzing the variations in cost elements, making it easier to identify the most cost-effective solution. Designed for use by managers in logistics operations, the tool is also applicable in research projects aimed at calculating the cost-effectiveness of technology adoption in areas such as planning, loading/unloading, and predictive operations. Through its systematic approach, the ROI tool offers a clear, data-driven basis for making informed decisions about technology investments.

About the Department of Transport and Regional Economics

We are committed to be an international centre of excellence for fundamental and applied academic research in transport economics, logistics and regional economics. Our mission is to improve transport and logistics for society and the business community. Our research results in theories, applications and instruments to enhance existing academic knowledge, transport policy and supply chain environments. Therefore, we conduct innovative and multi-disciplinary research within an international context and organizes educational programs from Bachelor to PhD level. We pursue results that are academically sound, economically viable and supporting sustainable development. We value a critical and independent approach and open communication.