nuMIDAS: The New Mobility Data and Solutions Toolkit

To successfully support transportation planners, researchers, and policy makers, emphasis was put on the harmonisation of the development of the toolkit. With this objective, a series of parameters was defined: extensibility, interoperability, minimisation of development and operational costs, reliability, and usefulness and user-friendliness of the toolkit. nuMIDAS provided high-level descriptions of the use cases, along with descriptions of problems which need to be addressed in the specific city(ies) and the technical approach to be adopted. In addition, we also provided UML mock-ups giving the visual representation of each use case approach, and capturing the entire process of a tool in combination with the interrelations with the involved actors. In order to make the scope of each use case more understandable and tangible, they set the boundaries of the tool, the steps to be followed each time, and the requirements needed to move forward (see also Fig. 1). Various use cases were implemented in the toolkit, some fully, others partially, thus providing researchers, planners, and policy makers with visualisations of the results derived from the methods and tools used in the case studies (Fig. 2).

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Thanks to the requirements that emerged from the discussion and the interaction with the project pilot cities and relevant stakeholders, we identified the following 6 use cases (UC) to address the problems and provide the technical support to solve them by creating specific methods and tools: Use case 1: Pre-planning of shared mobility services (Milan and Leuven): This use case designed a tool for optimising the fleet size of shared mobility services, like bikes and e-scooters, by considering various factors to minimise both the generalised trip costs and the system's operating costs. Use case 2: Operative areas analysis (Milan): This use case sought to allocate shared mobility service fleets across metropolitan sub-areas to minimise service providers’ economic losses and maintain equitable, high-quality service for citizens. Use case 3: Air quality and vehicle emissions analysis based on multi-source data (Barcelona and Leuven): This use case involved integrating diverse data sources, such as traffic, emissions, and weather, to evaluate traffic’s impact on air quality and support the forecasting and policy-making process for air quality improvement. Use case 4: Planning for parking (Leuven): This use case analysed the impact of decreasing on-street parking in urban centers on traffic flow, assisting in crafting and applying policies that address longer parking search times and the shift of parking demand to adjacent areas. Use case 5: Inflows and outflows in a metropolitan area (Barcelona): This use case focused on using data from ANPR-systems and census information to estimate vehicle movements between metropolitan districts, aiding in the enhancement and enforcement of environmental policies like low-emission zones. Use case 6: Assessment of traffic management scenarios (Thessaloniki and Leuven): This use case evaluated traffic management scenarios through simulation and data analytics, integrating data from connected vehicles and traditional counting systems to support decision-making.

We implemented all of these use case, some more as a proof-of-concept, others more as prototype that could (and was) already be put to use by the cities. As such, our nuMIDAS toolkit now supports answering the following questions from policy makers:

As an example, in the city of Milan, the policy maker required a tool for the preplanning of shared mobility services. The nuMIDAS team designed and developed a tool that processes available data to produce the output required by Milan city mobility planners. The aim was to produce a tool replicable and scalable in different cities/regions in Europe. In the case of station-based car-sharing, as an example, the dashboard suggests the proper location of new car-sharing stations and a well-balanced car fleet. This outcome is produced by back-end algorithms that elaborate data provided by the municipality. New car-sharing services, or extensions of the existing ones, can then be planned based on the outcomes of the nuMIDAS tool. The dashboard allows tuning parameters of the algorithms to compare different scenarios, depending on the weight given to conflicting objectives. Examples of the latter are the increase of shared mobility options to citizens in low-demand areas, and the economic sustainability of car-sharing operators. A policy maker can use the dashboard to link data sources and input parameters, and to visualise the results of the computation in a user-friendly environment. This specific example was tested in the case of Milan, but can be extended to be used by any European city willing to make use of a tool that supports car-sharing planning. For Milan, we modelled and simulated various scenarios through the dashboard, using a suitable set of input parameters: scenario 1: high demand/low fleet; scenario 2: low demand/high fleet; scenario 3: weight factor significantly in favour of the society; scenario 4: weight significantly in favour of the service provider. For example, scenario 1’s outcome showed the optimal fleet to be 84 free-floating bicycles. In addition, demand was about 97% covered, outlining a very good coverage of the service for the user segment. The average walking time was within an acceptable value for which the user may choose to use the bicycle for his or her commute. Scenario 3 on the other hand, considered a net imbalance for society. In fact, looking at the results, the optimal fleet value of 93 was deemed very close to the optimal fleet value for the end user (which was 94), indicating how the algorithm took into account that the preponderant objective for this simulation is to satisfy demand at the expense of the operator’s profit. As a result, compared to scenario 1, demand coverage was greater while profits were slightly lower (the same reasoning applies to profits as for scenario 1).

The main objective was to assess the potential impact of on-street parking restriction policies in the city of Leuven. The city is especially concerned with possible spill-over effects of hyperlocal policy changes, such as substantively reducing the available parking spaces in one street or neighbourhood. Operationally, the algorithm, relies on the discretisation of a road network using a grid comprised of cells of appropriate size and the use of parameters, such as the demand for parking and parking capacity (number of parking places) corresponding to each cell. By that means, the tool will enable the simulation of enforcing a parking restriction policy in one or more grid cells and the assessment of its impacts on the remaining cells. Through the simulation of enforcing a parking restriction policy in one or more roads of the network, both the policy maker as well as the transport planner are able to evaluate the effects, on the basis of the KPIs provided by the tool. Specifically, the evaluation of an on-street parking restriction policy includes the calculation of parking pressure, the calculation of searching time, and the examination of the extent of the effect. For the city of Leuven, we used the tool to evaluate a number of scenario’s, varying in complexity. Here, several parking places were suspended, leading to a redistribution of the parking demand. Some areas indicated a drop in demand (as expected), with some redistribution to the neighbouring areas, mostly to the southern region. This redistribution had an effect on the parking searching times (Fig. 3).

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Our nuMIDAS project provided insights into what methodological tools, databases, and models are required, and how existing ones need to be adapted or augmented with new data. To this end, we started from insights obtained through (market) research and stakeholders, as well as quantitative modelling. A wider applicability of the project’s results across the whole EU was guaranteed as all the research was validated within a selection of case studies in pilot cities, with varying characteristics. Finally, through an iterative approach, nuMIDAS created a tangible and readily available toolkit that can now be deployed elsewhere, including a set of transferability guidelines, thus thereby contributing to the further adoption and exploitation of the project’s results.

nuMIDAS’s project results represented a reference for any other future scientific work in Europe and provided to the whole scientific community more advanced bases for the theoretical elaboration on mobility. This outcome of nuMIDAS can be quite beneficial for city administrations and local policy makers, as they not always have a clear view of the real role and potential of data in cities. This work can help them in making more informed and evidence-based decisions, and in getting the mobility system “ready” through land use planning and zoning, transport system design, information services and processes for bringing together the different interested actors (city departments, public transport and sharing-mobility operators, developers, etc.).

In order to maximise the exploitation of project results, and in particular a widespread use of our new mobility toolkit, we drew up transferability guidelines. These describe how new cities can implement new instances of the tools within the nuMIDAS architecture framework, and use them for new mobility solutions analysis, assessing, and monitoring. From an operational point of view, due to the city-specific and location specific parameters and constraints (air quality, origin-destination matrix, etc.), the guidelines transferred not the models themselves, but rather provided a useful methodology to adapt the model to each city.

The state-of-the-art analysis provided early on in our project the basis for the work on business models for particular services. As the mobility field is rapidly changing due to, among others, digitalisation, as well as the uprise of small and large electric vehicles, business models and stakeholder roles change. Furthermore, more and more services combine many disciplines, which often means that services require multiple types of stakeholders to become effective. For this reason, a stakeholder analysis was performed in the context of nuMIDAS, in combination with a business model analysis. The analysis was performed using a Service Dominant Business Model Radar (SDBM/R). The SDBM/R gives insight into which stakeholders can be found within the business model, as well as the way these stakeholders add value to this service. In addition to the SDBM/R, the analysis contains descriptions of the change in business models and stakeholder roles within the business models over time. All SDBM/Rs were created per service, based on the achievement of shared goals and value co-creation by a group of actors (businesses, firms, and costumers) which interact to achieve that shared goal. This activity required to identify the values-in-use as the focal points of the services, and further the value propositions (part of the central values contributed by the particular actors), coproduction activities (i.e. those that actors perform to achieve the co-creation of the values), and costs and benefits for particular actors. The radars were complemented with the description of the corresponding business model scenarios, descriptions of actors and their roles and activities, and the discussions of the transitions of business models influenced by new methods and tools. The summary resulted in a short list of the most common stakeholders within the SDBM/Rs. These are: municipalities, travellers, (mobility) service providers, intermediaries, and MaaS providers. The first three stakeholders within this list are most important and most common. These three stakeholders respectively provide policies, demand, and supply for transportation. The last two of these stakeholder types, intermediaries and MaaS providers, are not yet so common in the actual urban mobility field. These are categorised as future partners and are expected to become more prevalent. As transport systems become more and more integrated, while mobility service providers are competing, intermediaries stay independent and are able to handle data of competing partners with care to provide services which would otherwise not have been possible due to said competition. When the apps of MaaS providers finally really take-off, many services are expected to change, as mobility will be even more accessible from within our pockets. As such, our nuMIDAS toolkit is excellently positioned in light of further commercial exploitation.