Evidence-based policymaking requires that the technical developments concerning the indicators and assessments are attainable by policymakers. MOMENTUM took this as a guiding principle and developed a Decision Support Tool (DST) that offers a user-friendly interface for the data analysis and modelling algorithms provided by the project. The DST supports cities in the implementation of new mobility services in line with their policy objectives and Sustainable Urban Mobility Plans (SUMPs).
The overall goal of the MOMENTUM project is to develop a set of mobility data analysis and exploitation methods, transport models, planning and decision support tools, able to capture the impact of new transport options and ICT-driven behavioral changes on urban mobility environment. The multilevel Decision Support Toolset consists of three levels and its primary goal, is to develop a conceptual framework for assessing the impacts of new mobility options by collecting and analyzing heterogeneous data sources and develop mobility patterns. The developed Decision support toolset integrates mobility data from different sources and modelling improvements in to one online platform, where cities can virtually test and asses the performance in order to support local authorities in the task of designing the right policy mix of emerging mobility solutions.
For this case study, we will assume that a city do not have mobility data nor a transportation model. In this example, cities can only test level 1. In this level, users need to add average values or highly aggregated socioeconomic, operational and functional variables. Based on the KPIs available for Level 1, users can identify the layout of their proposed solution, through produced dashboards, charts and values of the parameters (such as number of stations, docks, number of bicycles and scooters)
For this case study, we will assume that a city have mobility data, but not a transportation model. In that case users can test till level 2 of the DST. It goes without saying that users can test level 1 but not level 3, as a transport model is needed. In level 2, users can use algorithms available in the DST in order to convert data into the applicable format. Mobility data to be used as input data, can be used floating car data or OD matrices can be used as input data. Data such as bike lane network, public transport lines and road network are used as constrains in order to provide more accurate results. Based on the KPIs available for Level 2, users can identify more precise decisions compared to Level 1. Users can have access to a set of results like the actual location of stops of the service, the fleet size needed, the capacity of stops and units (vehicles, bicycles and scooters). Those values are critical parameters for the overall performance of the system under various scenarios.
For this case study, we will assume that the city have a transportation model. In that case, a more analytical procedure need to be followed, including modal split of the available means of transport and optionally a synthetic population investigation. Due to the amount of calculation needed, these actions need to take place off line and then using the results produced to be imported on the online version of the tool. Based on the KPIs available for Level 2, users can identify more precise decisions compared to previous levels. The mobility service simulator provides KPIs related to the users of each service. These include, waiting times for each user to be served, travel times to complete their trip, number of served and unserved requests. Moreover, model produced, provide indicators regarding traffic emissions, car-ownership as well as induced demand due to the introduction of new shared mobility services.
The decision support toolset is a scientific framework for assessing and evaluating emerging mobility services in a city. The aim of the DST is to provide a set of mobility data analysis options and exploitation methods, to capture the layout of new transport options and ICT-driven behavioral changes on urban mobility. The aim of the project is to support local authorities in the task of designing and adopt the efficient policy mix for each city, to exploit the full potential of emerging mobility solutions in a more sustainable and resilient way
The Decision Support Toolset encompasses various developments with a user friendly environment to test and assess mobility services, with data visualization dashboards, providing an interactive validation of results of testing different mobility services in the examined area. User can easily modify input values and test different scenarios. Interactive dashboards and KPIs produced, provide insights for the tested services in a city or an area of the city.
DST is a powerful tool for both decision makers and stakeholders. Using the Decision Support Toolset, solutions can be suggested in a centralized and secure manner from governance approach while operator using the results from the tool, can manage and assess their contribution to the system of urban mobility investments in a city.
The multilevel Decision Support toolset is developed in order to be accessible to every city. Despite the availability of data each city has, they can derive layouts useful information of the tools. The first level of the decision tool, requires a small amount of data such as geospatial socio-economic data of the studied area and the available operating fleets of mobility services.
Anyone with basic or comprehensive mobility data information. The Decision Support toolset is designed to apply to every city, despite the amount of transportation data a city has.
The decision support toolset consists of 3 levels. Each level provides a different level of analysis to the user based on the availability of input data. Level 1 provides a preliminary analysis of the examined area, while level 2 needs data driven transportation data. The last level is more complex than the previous ones. Level 3 needs a transportation model in order to extract useful KPIs for the examined area.
In each level of the Decision Support Toolset, user can find a manual with information about the how they can use each level of the tool and a recorded video, demonstrating the workflow of each level and the insights deriving.
Once each level is tested, user can export the results produced for further exploitation and consultation. The aim of the Decision Support Tool is to provide all the necessary means for stakeholders and decision makers to support urban emerging solutions in the cities.
The Decision Support Toolset, provide users the ability to test various emerging mobility services. Those services include bike-sharing system, ride-sharing systems, scooter floating systems and Demand Responsive Transport (DRT) services. All these services are available in every level of the DST.
