Big Data-enabled transport policies for our cities
It’s never easy to let go of your old ways. This applies to almost everything: from the first time your grandparents used a smartphone, to the reaction of public administrations when they first heard of Big Data. Let’s take a concrete example: car accidents. Traditionally, policymakers would receive complaints from residents or civil society, and then investigate. With Big Data however, they have an opportunity to access interactive maps using police data to provide a dynamic overview of road accidents across the city. Here lies the difference between slow, out-of-touch policymaking and timely and effective decisions. “Many decision makers are still rooted in the traditional way of doing things. They make policy decisions based on static models of consultation and closed planning meetings over the time frame of a year or more. Their action is often siloed and slow, providing outdated solutions by the time it is ready to be implemented,” says Lieven Raes, coordinator of PoliVisu (Policy Development based on Advanced Geospatial Data Analytics and Visualisation).
From interactive maps to better policy
PoliVisu aims to challenge these old ways by improving data literacy and providing access to advanced technology. Its policy visualisation tools rely on interactive maps to display data and enable exploration down to the tiniest detail. The project specifically focused on transport and mobility. As Raes notes: “It’s an area with lots of potential datasets to use. Mobility forms the backbone of any city, and it’s a great topic to experiment with in locations of different sizes and with different states of advancement with regards to the use of Big Data.” Besides road accidents, the project team focused on a traffic modeller and a tool to increase safety on streets with a school. Whilst the former provides real-time data on traffic flows and allows for simulating the likes of crises or road maintenance, the latter provides sensors for neighbouring citizens to place on their windows. The sensors measure traffic and provide collected data to administrations so they can decide, for instance, which road should be closed in the morning. “We initially ran three pilots in three cities,” Raes explains. “In Issy-les-Moulineaux (France), we looked for a solution to high congestion. In Ghent (Belgium), authorities didn’t know where the student population was concentrated and could therefore not deliver relevant services in the optimal locations. We experimented with multiple data sources to find the information they needed. Finally, we provided traffic predictions for road constructions foreseen in Pilsen (Czechia), to avoid any negative impact.” The project team created a total of 15 policy visualisations. A book and an online course on data for policy will also be launched soon to educate people on the benefits of data visualisations for better policymaking. Access to data wasn’t always easy, but it was definitely worth the team’s while. The three initial pilot cities keep using PoliVisu’s tools to this day. “Often the data needed didn’t exist or it may be owned by someone else who would like a large sum of money for you to use it. In Ghent for instance, there wasn’t a single source of data available. We had to gather it from multiple sources like anonymised university records and Proximus telecoms. Now, they have a good idea of where their students are throughout the week and weekends,” adds Raes. By the time the project comes to an end, the team will provide a toolbox of data processing and visualisation tools to interested cities. The toolbox will be coupled with a structured and tailorable methodology, as well as real-life case studies as sources of inspiration to introduce open (geo) data into the policymaking life cycle.
Keywords
PoliVisu, transport, big data, traffic, congestion, school, streets, policymaking, car accidents