Mapping Multimodality of Cities - A Data Driven Approach for Improvement

July 11, 2019

Research colloquium held by Luis Natera – 2nd year PhD Student at DNDS.

Singapore, Budapest and Jakarta: situated in different geopolitical positions, subject to different economic constraints, inhabited by different societies. Can we nonetheless compare them meaningfully, in a way flexible enough to accommodate very divergent situations? Luis Natera, a second year PhD student at DNDS with a background in architecture and urban planning, is working to come up with new techniques and methodologies for the task of capturing the multimodality of transportation in cities. He aims to find ways to improve urban transportation systems in a socially engaged, ecologically and financially sustainable way.

By defining fingerprints of cities based on their multiplex transportation profiling taking advantage of OpenStreetMap data, Luis is comparing different cities from all over the world across geopolitical regions. A fingerprint is constructed as follows: each city can be understood as a network in which nodes are the junction points and ties are the routes connecting them. To take it further, a city may be understood as a multiplex network, where each layer stands for a separate mode of transportation built up from the junction points of the city which are active on that layer. In the current case, four modes of transportation are taken into account; bicycles, cars/buses, trams/trolleys and the pedestrian layer. As shown in the image below, the Octogon in Budapest is a junction point which has activity in all four layers. 

Having mapped out their multiplex networks, cities can then be compared, for example, based on the distribution of their junction points across the layers to see how multimodal the city actually is; which ones the leading layers and most multimodal junctions are, as well as their geographical scatteredness in the city across different districts.

Analyzing the structure of these networks one may see the rupture points on each layer; i.e. where a bicycle path interrupts, and at which points it would be the most efficient to connect to disconnected paths to enhance the overall connectedness of the city. Efficiency, of course, may be defined in multiple ways. Does it mean being the cheapest in a direct financial sense? Does it mean it enhances social inclusivity and decreases the differences between the various parts the most? Does it stand for facilitating the most ecologically sustainable modes of transportation in an overall structural sense? These approaches may not necessarily coincide and choosing one principle over the others in case of each city may develop them in very divergent ways.

Urban (mobility) studies have gained considerably from the extensive and increasing availability of big data and from the approaches of network and data science. But how can these complex and interdependent theoretical understandings be incorporated also into network analytical approach while building algorithms to analyze cities? Constructing such tools is truly a non-trivial task. It is precisely what this ambitious doctoral research is set out to do.

Blog post by Júlia Perczel