Decision Support Tool
Basic Information
Language
English
Latest update
Price
Free
Assistance data
General mobility data (vehicle sharing data for bicycles, scooters and cars)
Tool type
Indicator set Mobile app
Application area
- Analysis, scenarios and measure selection
- Appraisal and assessment
Target Audience
- Small cities
- Medium-sized cities
- Large cities
- Metropolitan regions
Summary
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.
Good Example
First use case
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).
Second use case
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.
Third use case
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.
