The design of a Decision Support Tool for transport planning and management: the Torino Metropolitan Area case study

The challenges of the cities of tomorrow imply more and more an innovative approach in transport planning, based on the new technologies to collect and analyse the data needed to describe the mobility patterns. To this end, the big data are becoming the new paradigm for feeding the decision support systems and the use of business intelligent and dashboard are fundamental to properly visualise data. The paper aims at contributing to the design of the Decision Support Tool (DST) MOMA, for the benefit of decision makers, technicians working in the cities and the transport operators. MOMA is a multi-layer web viewer allowing to: a) give all the information of the multimodal transport network related to the Torino metropolitan area; b) calculate the Key Performance Indicators (KPIs) of mobility visualised through a dashboard; c) analyse the territorial information collected by all stakeholders, crossing them with mobility patterns. To this end a quali-quantitative approach was adopted, administering a questionnaire and organising 4 focus groups prior to the actual testing of the DST. It has involved a sample of 48 people (17 decision makers, 25 technicians and 6 transport operators) answering the questionnaire while a subsample of 31 respondents participated also to the focus groups. The survey was carried out to individuate the key features of MOMA (ex-ante survey) before its test in the Torino Metropolitan Area and, likewise, to gather data on: • the current access to the information of the different transport modes in the metropolitan area; • the current knowledge of planned and unplanned events influencing the mobility; • the current update and quality of mobility data; • the current data exchange with other bodies; • the main needs (data typology and data analysis) and what the users expect from the tool; • the attributes that the DST should have to support both the transport planning and management; • the use and calculation of the KPIs; • the users’ willingness to pay (WTP) for the DST services. To study the collected data, a descriptive analysis of the questionnaires through the divergent stacked bar charts, consistency analysis (Cronbach alpha) and X2 test of Pearson were used as statistical approaches. The textual analysis was performed to go in depth in the focus groups content. The results showed a significant lack of data in the cities around Torino . Moreover, transport operators were not able to access to the information related to the transport services offered by the other companies with whom important synergies could be developed. Furthermore, the potential users of the DST have showed a broad diversity of requirements and hence, the dashboard should have different interfaces according to the user typology in addition to a public interface mainly devoted to the citizens. Finally, a set of KPIs has been selected to be included in the DST.