The rising demand for public transport services and infrastructure in urban areas [1] requires institutional setups to effectively design sustainable and inclusive mobility strategies ultimately aimed to improve the quality of life [2]. Facing this trend, one of the most promising approach for transport planning is based on the integration of multidisciplinary knowledge and skills derived from urban studies, social sciences and computer science [3].
Thanks to the recent development of advanced Information and Communication Technologies (ICTs) and the increasing availability of digitally wide spread data sources, the Big Data is becoming a valuable support to the activity of decision makers by unveiling hidden movement patterns within the cities.
Big Data for unveiling urban transport patterns
In this regard, Systematica joined the European project DIAMOND, aimed at turning data from different sources into actionable knowledge for addressing gender-specific needs in current and future transport systems and ensuring women’s inclusion in the transport sector. According to the recent development of international policies and best practices, the project aims at contrasting the inequality of opportunities for women in transport sector as users and employees.
DIAMOND project aims to collect large-scale structured data to support the design of more inclusive transport systems
As the leader of Work Package 3 – Data Collection, Systematica supervises the Data Collection process, through the design and the application of the overall methodological of the DIAMOND project (see Figure 2) and leads innovative research activities aiming at collecting large-scale structured data to support the design of gender-equitable and inclusive transport systems.
Figure 2. The overall methodological approach of the DIAMOND project.
Within the objectives of the Use Case 1 ‘Public Transport Infrastructures – Railway’, which is aimed at investigating women’s needs as users of metro and urban railway public transports to support the development of gender-equitable transport planning policies and to increase the percentage of women using public transport services, Systematica carried out extensive research based on GIS-Geographic Information Systems for the analysis of structured open data. This was aimed at identifying a short list of relevant metro and urban railway stations managed by Ferrocarrils de la Generalitat de Catalunya (FGC) – the Railway Agency in Catalunya in the Province of Barcelona (Spain) (see Fig. 1).
A series of open geospatial datasets were selected, sorted and filtered through combining several indicators (see Tab. 1):
- Preliminary data (boundaries of the Province of Barcelona, localisation of the stations)
- Territorial data (urban fabric on land use, localization of points of interest);
- Socio-demographics data (census data about total population, gender, age and nationality of the population
- Mobility data (localization of public transports, road infrastructures, parking services).
Table 1 List of open data used for the execution of GIS-bases suitability analysis.
From a general point of view, the analysis was based on different attributes and characteristics of the land, socio-demographics and mobility of the urban area surrounding each station. To do so, raw data related to the urban scale were extracted in surrounding areas around each station within a catchment area of 400 meter, commonly known to a comfortable walkable distance [2].
Figure 3 Examples of datasets extraction on catchment areas surrounding each station.
Data were post-processed through density-based calculation of buffer areas, normalization of values and weighted summations. This allowed the identification of a short list of positively and negatively relevant stations based on (Figure 4), statistics that describe various subdivisions of a frequency distribution into equal proportions: highest quintile containing the 80% of the total sample (≥ 80th percentile) and the lowest quintile containing the 20% of the total sample (≤ 20th percentile). The selection was finalized through the analysis of travel demand data provided by FGC (Figure 5) focused on the number of passengers per station per year.
Figure 4 Quintile frequency distribution of normalized values for selecting the stations.
Figure 5 Correlation between open data and travel demand data.
To further characterize the selected stations, additional data collection activities based on universal design indicators including observations, user-generated data from social media (namely Twitter) and end-users’ needs and expectations collected through focus groups and surveys will be carried out.
Data analytics based on Neural Networks, Machine Learning techniques and Geographic Information Systems (GIS) will be aimed to define a hierarchical model for the design parameters, influencing the inclusion of women, combatting the barriers that are intrinsic to the public transport infrastructure, supporting the development of an interoperable and user-friendly toolbox for fairness self-diagnosis and decision support in transport planning.
Rawad Choubassi
Director and Board member of Systematica where he leads planning and research projects on mobility in urban environments and complex buildings.
Andrea Gorrini, PhD
Psychologist with experience in human behaviour in transport systems. Since 2019, he collaborates with Systematica as Transport Research Consultant.
Anahita Rezaallah, PhD
Experienced architect working with Systematica since 2015 as urban planner and sustainability adviser.
Gregorio Olivetti
Joined Systematica in 2017 to support the company business development strategies, to strengthen the relationships with clients and to foster research initiatives.
About Systematica Srl
Established in 1989, Systematica is a Transport Planning and Mobility Engineering consultancy with its main office in Milan (Italy).
Within the DIAMOND project, Systematica is responsible for the Work Package 3 – Data collection, leading innovative research activities aiming at collecting large-scale disaggregated data.
References
[1] United Nations (2014). World urbanization prospects, the 2011 revision. Department of Economic and Social Affairs, Population Division, United Nations Secretariat, New York.
[2] Buhrmann, S., Wefering, F., Rupprecht, S. (2019). Guidelines for Developing and implementing a sustainable urban mobility plan – 2nd edition. Rupprecht Consult-Forschung und Beratung GmbH.
[3] Batty, M. (2013). The new science of cities. Mit Press
