DESCRIPTION
How to design and put in place measures that concretely reduce the use of private vehicles and CO2 emissions in the home-work route?
How to support Mobility Managers (of companies and Public entities) in their tasks focused on solutions for a sustainable local mobility?
DATMAN will provide a SAAS to Area (Public Entities) and Company’s Mobility Managers who need to monitor travel habits of employees with tailor-made strategies aimed at reducing CO2 emissions.
Current state of the art :
- available standard tools supporting the survey of home-to-work travel habits: MISSING;
- available data format that companies are required to use when communicating their initiatives: MISSING
Expected future scenario with DATMAN:
- Company MM conduct mobility surveys in a homogeneous manner, report and evaluate impact;
- Area MMs get an overview of their territory, assess the effect of measures to put in place and leverage on them.
DATMAN’s overall goal is to monitor and analyse local/area mobility habits of employees in their home-work route using data driven strategies.
DATMAN is an innovative data driven tool that allows each Area MM to interface with multiple Company MMs; it is a key to enable them all to “speak the same language” & share data analysis results and strategies in a coordinated way.
The main innovation of DATMAN experiment is the predictive function that complements the data analysis and makes a link between potential actions and the consequent impact.
The experiment generates an Innovative data driven Software as a Service Solution that supports not only MMs but can also be an asset for Urban planners to implement new strategies for smarter commuting and greener cities.
Pilot results will be focused on the area of Venice (Italy).
Main results:
- Data analysis of employees home-work routes
- Predictive function to simulate impact of strategy decision
- Collaborative tool for Area and Company MMs design of strategies
It will be then possible to adapt DATMAN to any other geographical area and/or company interested.
- Data collection (from forms filled out about mobility habits)
- Data correlation to generate forecast
- Mobility prediction model
