Assessment of new needs and knowledge analysis gaps, defining requirements for analysis methods and data
This report has the objective of identifying trends, knowledge needs, policy analysis needs and the potential roles and possibilities for new modelling for freight transport in the EU. A literature review of developments in freight transport was conducted, together with an online survey and expert interviews. In terms of the outputs and insight of freight transport modelling, this review has found some important areas where information and insights are lacking:
- Plausible projections of how the different aspects of change in logistics will drive structural change in logistics
- Scenario simulations that are based on the interlinked system changes of new digitalised logistics structures and zero-carbon energy in freight transport.
- Policy package simulations that will deliver sustainability: since freight transport is facing non-marginal change, models that can represent processes of structural change will be needed to assess potential points of influence on transport system changes.
Reflecting the changes in freight transport and new data structures, models of freight transport are changing too. ABMs of transport decision making and movements are an active field of development. Models of new market structures in logistics and of low carbon freight transport systems are being developed, using GPS/AIS data and ‘big data’ analytics.
The modelling approaches to sustainability transitions offer general concepts for addressing structural and system change. This implies that what is required are clearer ideas of possible changes ahead in freight transport, to enable problem definitions that can be addressed by the approaches discussed here. These views of future changes can be developed through qualitative techniques of foresight and scenario development with stakeholders, with many opportunities for using quantitative models as a part of such processes to develop combined qualitative and quantitative analyses.
Define requirements for data and advanced analysis methods
These three case studies are intended to provide examples to show how some of the main analysis needs identified in D2.1 might be addressed. In terms of data analysis, they cover different combinations of data. Case study 1 on electric goods vehicle charging combines operational patterns of HGVs with electric charging infrastructure system design. Case study 2 on city distribution combines city morphology and household data with logistic networks and transport activity data. Case study three combines qualitative scenario assessment of digitalised logistics systems with aggregated digitalised logistic systems characteristics. The cases cover city region and EU geographical levels.
The specifications of the analyses and development of the analysis methods will be developed in WP4 of the STORM project. The methodologies will be applied in the case studies in WP5.
This report builds on D2.1 ‘Assessment of new needs and knowledge analysis gaps, defining requirements for analysis methods and data’. It has two objectives:
- Use the results of D2.1 ‘Assessment of new needs and knowledge analysis gaps, defining requirements for analysis methods and data’ to specify the requirements for case studies. Knowledge gaps in current analysis methods and policy assessment will be identified and based on the knowledge gaps, case studies will be defined.
The research questions for the cases are defined and the logic of how they are derived from D2.1 explained. The requirements for the analyses are specified.
- Initial proposals for analysis methods and data requirements for the cases are given.
This report provides the basis for further work in WP4, in which the analysis methods and data are described and WP5 in which the case studies are performed. This current report is therefore an intermediate step in the flow of the work programme of the STORM project, to identify needs, propose model and analysis developments and undertake some illustrative, small scale pilot studies for such analyses.
Status report on the review of new data sources and methods
This deliverable is part of WP3 within the STORM project. The deliverable provides a literature review of the state-of-the-art and the current knowledge gaps for Big Data analytics, methods and algorithms in the freight sector. The deliverable also reviews relevant applications and concludes with a summary of challenges and opportunities of big data in the freight sector. The main attributes of Big Data include the “5V” concept consisting of volume, velocity, variety, veracity, and value. Big Data, however, remains big bulks of unstructured data that is of no real value unless it is converted into useful information. That is where big data analytics have a big role to apply advanced analytic techniques including data mining, statistical analysis, predictive analytics, etc. on big datasets as new business intelligence practice. It enables the analysis of huge amounts of complex data while harnessing traditional data and tools. It gives promises for exploring the hidden structures of each subpopulation of the data, which is traditionally not feasible and might even be treated as ‘outliers’ when the sample size is small. There are a variety of challenges hindering access and utilization of Big Data for freight transport applications due to its nature and unique characteristics, including, but not limited to, data collection, data ownership and accessibility, heterogeneity and standardization, storage, privacy and legal constraints, technical challenges and expertise, quality, validation and representativeness of the data. The lack of awareness or interest in data and data-driven decision-making by senior managers can be a major organizational challenge. Privacy issues often forbid the usage for purposes other than explicitly mentioned in the agreement or contract with the users. Likewise, to share data collected by companies with third parties, individual non-disclosure agreements need to be negotiated which forms a major obstacle in data collaboration and exchange. Finally, we identify few areas for further research including data collection and preparation, data analytics and utilization, and applications to support decision-making categories.
Analysis framework for decision-support/assessment and intervention tools at the operational level
The deliverable presents a methodology to apply new synthesized data for heavy-duty or long-haul truck (LHT) movements to allocate an alternative fuel vehicle technology, i.e., fast and slow stationary charging, infrastructure network for heavy-duty vehicles in Europe. Many recent studies reflect on long-haul battery-powered electric trucks (LBET) charging needs but are mainly limited to a small geographical scale, e.g., nationally, or do not identify significant charging station requirements, i.e., locations of charging stations, the characteristics and number of installed charging points, and the daily energy requirements, due to lack of details from LHT travel patterns. The detailed charging requirements also impact (or are constrained by) other significant systems connected to the charging stations, such as the power grid. Other studies utilize detailed datasets from the original equipment manufacturers (OEM)s, which cannot represent the whole region and ignore the impact of passing trucks from neighboring countries. Other methodologies utilize more representative data, e.g., traffic counts of all passing trucks, to identify charging needs. However, such methodologies cannot distinguish between the truck travel pattern heterogeneity, which fails to capture the charging point differences, e.g., the power rate.
In this report, we present a model that evaluates the LBET’s charging requirements in the year 2030 for the European continent. Following the EU truck driver regulations, the research converts an origin-destination (OD) matrix resulting from a four-step transport model into long-haul or heavy-duty truck (LHT) trip chains. A trip chain denotes a set of connected trips between “significant” locations (e.g., depos and shops) before a vehicle ends its journey. A complete trip chain determines the travel patterns of a higher spatial and temporal resolution table of the OD matrix. The detailed trip chain identifies each LBET’s multiple stop location and duration, energy consumption, and the amount of energy that had to be charged at each stop. This report shows that the suggested methodology could provide charging requirement infrastructure insights (i.e., charging types, station capacity, and energy supply) for all moving trucks of the EU member state to serve electrified LHTs. The identified information could also be used for emission analysis from all moving trucks.
We show that in our main case scenario of 15 % LBET share, a minimum of 28,800 slow (≤ 100 kW) and 9,800 fast (≥ 1 mW) charging points are required to meet the energy demand corresponding to the daily energy requirements of 112 GWh. On average, the ratio of slow to fast charging points is 3:1. The fast and slow charging points serve 12 and 2 LBETs daily, respectively
Proposals for integrated strategic assessment methods and models
Current freight transport modelling approaches might be able to represent pathways of low carbon technologies adoption according to Paris goals.However, they are not able to represent the impact of structural system change in achieving decarbonization goals. This report first provides a literature review on freight transport models and strategic assessment models and tools. Then, we review and discuss the application of agent-based models (ABMs)in the future assessment models. Next, we review and discuss two of the most important digital technologydisruptions and trends in future decarbonization: synchromodality and supply chain synchronization and the impact of blockchain technology on decarbonization in logistics and supply chain.The discussion and conclusions provide general comments on model development for strategic assessments and the current generation of integrated strategic assessment models currently used for EU level transport policy analysis. We make proposals for requirements that future model development could address and have a few suggestions for new modelling directions. Requirements for future logistics and freight transport models include life cycle analysis, improved consideration of total cost of ownership, further development of agent-based models(ABMs), modelling the impact of digitalisation,and improvement in data structures and data standardisation. New types of models could include models of information flows as well as physical goods flows, ‘Big models’ for ‘Big Data’ or aggregate-disaggregate-aggregate(ADA)models and rapid response models for stakeholder processes.Finally, we identify policy areas,especially around digitalisation, that may require new types of assessment. The policy areas include data sharing and data standards,low carbon infrastructure with combined energy sources / hybrid vehicles,scenarios of low conventional demand growth and scenarios of more complex urban distribution structures.