Overview
The planning, observation and steering of urban mobility is growing ever more complex due to increasing urban density, varied mobility offers and rising expectations of city inhabitants and visitors. Integrated information about capacity bottlenecks in the transport infrastructure combined with current and future events is only available in a limited manner. The consequence of this is mobility constraints, capacity overload in current transportation services, and negative social and environmental consequences for the city inhabitants and visitors.
In order to address these challenges, the EcoMove project will develop new knowledge-based solutions for the efficient and environmentally sustainable movement of users in cities. This will be achieved by providing customised information about available mobility options in real time. Recommendations for delaying, avoiding or taking alternative mobility options will be presented visually to the users - city inhabitants, visitors, and professional stakeholders - for the purpose of prioritizing “necessary” mobility.
Vienna will be our city test case; the developed methods will be generic and the data collection will cover the entirety of Austria so covering the entirety of Austria so that an extended application of the solutions to other regions as part of future projects will also be possible.
EcoMove will integrate real time data of anonymized person-movements through public WiFi hotspots with online communications extracted from news, social media and public events. Furthermore, Open Data sources such as DBPedia and the real-time data of Wiener Linien will be used. Based on this data integration, combined with predictive modelling, knowledge about mobility bottlenecks (exceeding the nominal capacity of mobility options or capacity restrictions due to a disruption in a well-defined geographic region, e.g. crowding due to a demonstration) can be extracted, and combined with concrete recommendations for individual mobility decisions. Data visualisations will communicate how personal mobility behaviour can be shaped more efficiently and environmentally friendly through the minimisation of waiting time and prioritisation of individual and necessary mobility.
The EcoMove solution will include drawing from detected trends across various data sources and the possibility to predict future mobility bottlenecks. Innovative recommendation algorithms will base predictions of future events on the combination of historical data and automatically extracted knowledge from public (online) communication. The integrated data will be made available via data services and a mobility dashboard for supporting professional stakeholders in their planning. With respect to individual behaviour, analysis of WiFi and interaction data will help identify factors and stimuli which lead city inhabitants and tourists to modify their mobility behaviour - for example to deviate from a planned destination to avoid known overcrowding.