Monitoring Movement in the Smart City: Opportunities and Challenges of Measuring Urban Bustle

Abstract

Abstract. One of the promises of the smart city concept is using real-time data to enhance policy making. In practice, such promises can turn out to be either very limited in what is actually possible or quickly trigger dystopian scenarios of tracking and monitoring. Today, many cities around the world already measure forms of urban bustle, i.e. how busy it is during specific periods of time. They do this for all kinds of purposes like optimising mobility flows, attracting tourism, monitoring safety during events or stimulating the local economy, and they employ divergent technologies: from analogue counting, over surveys, to more advanced near real-time tracking using mobile operator data. This fragmentation of approaches to measuring urban bustle creates some challenges for cities related to privacy, vendor lock-in, comparability of data, data quality and accuracy, historical and predictive analysis of data and so on. To tackle these challenges and formulate a standardised approach to measuring urban bustle, the thirteen largest cities in Flanders (Belgium), together with local technology vendors, co-created a “definition manual”; a document outlining indicators and relevant technologies for measuring urban bustle, as well as shared profile descriptions of residents and visitors of the city. This paper outlines the process and presents the results, an agreed-upon framework of standard profiles and indicators, which are useful to academics, public servants and technology companies involved in this topic.

Using ANPR data to create an anonymized linked open dataset on urban bustle

“…We argue that, although anonymized quarterly time bins can be generated above a certain threshold, a coverage issue still exists with ANPR cameras to measure around specific locations. To date, research 7 , 8 is undertaken on how urban bustle can be measured by combining high-level measurements from, among others, ANPR data with low-level measurements [ 42 ] originating from, for example, mobile traffic analysis devices. The generated visualizations of the ANPR Metrics tool are aligned with related work: the hourly and daily traffic behaviour are similar to Telraam [ 10 ] and Mechelen’s ANPR visualizations [ 7 ].…”

“…In future investigations, it might be possible to cluster travel times for more in depth analysis, such as clustering peak hours or holidays. Lastly, this study provides new insights into visualizing unique versus in transit vehicles to demonstrate profiling vehicles according to a smart city definition manual [ 7 , 42 ].…”

“…Unique versus in transit vehicles An important visualization for city administrations is the passersby that are being made through the city. In the smart cities “definition manual” for urban bustle [ 42 ], an “in transit” profile is proposed for determining whether a person is passing by and is not lingering or staying in the city. We applied this for vehicles instead of people, and added a second criterion to decide when a vehicle is in transit.…”

“…This way, anonymized ANPR data does not only overcome technical barriers, but also organizational barriers. This allows answering policy questions, such as how global traffic volume in the city is evolving or how many vehicles are transiting the city center [ 42 ]. In light of COVID-19, questions were regularly asked to compare traffic volume before and during COVID-19, for example between September 2019 and September 2020, to analyze the impact of measurements against the spread of the coronavirus.…”