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SMART CITY

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Urban Planning Application

UPA is a solution for city and transportation planning that integrates citizen experience with state-of-the-art data handling. It helps to see the reasons behind more or less obvious problems and bottlenecks in the urban space.

Redefining mobility

Cities grow - fast and often uncontrollably. The population of urban residents is estimated to increase 85 per cent by 2050. This convergence of people, creation of city conglomerates and suburbanization causes complex movement of people in and between cities as well as excessive commuting. Are city governments and planners prepared for this turbulence in mobility and problems it could cause for transportation and logistics? Do they foresee the future of cities? One might argue if such a complicated system as evolution of urban areas is predictable in the first place. Leaving aside theories and paradigms then from the practical point of view there is an urgent need for adequate predictions and assessments. 

The data and knowledge which is nowadays widely used for strategic decisions is bulky, inadequate, expires quickly and might lead to misinterpretation. We believe that mobile technology is the best solution to handle mobility data and to make cities smarter by using citizen and visitor experience. This knowledge should be available for city planners, decision makers and citizens.

While analysing mobility it is rather usual to ask questions like “How many” and “How often”. The answers to those questions tend to be simple but at the same time clearly too primitive to draw any conclusions. Instead we should be curious where do these people come from, where are they heading to and what is most important – why are they there? These are questions that can´t be answered by barely counting people. While defining and explaining mobility, we need to take into account indications such as aggregation, trajectory, intensity, circadian and seasonal variability, relation to topography and infrastructure etc. The knowledge obtained from such combined analyses is especially valuable in terms of planning and managing urban space.

Mobile phones as sensors

From single user to location semantics – how (do) we do it?

There is no need to build additional surveillance infrastructure to carry out this ambition. In digital world we all leave behind footprints which are stored and can be processed. Mobile radio access network produces huge amount of location data. With existing technology it is possible to use all mobile-phone owners as sensors and that could be done almost in real-time, if necessary. A single cell phone, even when it’s idle, registers its location to the network at least once in every hour. What is important is that this function cannot be turned off as long as mobile phone is working. In addition, every data transmission or calling event registers the location of the user. For example a city with one million inhabitants can generate quarter billion location updates per day. No static sensor could provide us so adequate and up-to-date information about citizens’ movement. To make this huge amount of location data valuable and safe asset for city governments and citizens, it has to be generalized and combined with other spatial datasets.

The raw data that is registered in cell phone network reflects single user behaviour and the location of user is determined by the closest mobile antenna. To turn single location event into general pattern of movement, we use probabilistic methods to evaluate users’ trajectories and anchor points (home, work etc.). Mobile location data is often not good enough to calculate accurate single trajectories but its spatial resolution is sufficient for urban planning.   

Data mining and different algorithms enable to find correlations between different phenomena, evaluate citizens’ mobility in various aspects and spatial resolutions. One outcome of mobile data enrichment process is origin-destination (O/D) matrix which illustrates people’s movement from one area to other through different aspects of time and speed. To put it simple, you can pick any place – for example problematic traffic junction, and answer to the questions like where all these people, at specific time, come from and are heading to. 

Statistical methods estimate hypothetical approximate locations for mobile phones, which deviate from real locations in physical world. Deviations can be reduced by statistical detection of “unreasonable” movements that are most probably wrong. The final analysis is usually conducted by dividing the area of interest into smaller homogeneous regions (e.g. residential area vs city centre vs suburban vs rural) and aggregating the data per region. The aggregation reduces effects of spatial uncertainty and also facilitates privacy preservation. 

O/D matrix is excellent tool for assessing the effectiveness of changes made in urban space. Comparing “before” and “after” situation we get reliable feedback to our planning actions and decisions.  

Analysing mobile network data alone is not enough to deduct the reasons for mobility. Perhaps the most important question “Why?” remains, but it can be addressed when combining location data with other spatial datasets. Adding semantics to raw location data broadens our view of how urban areas evolve in practise.