Abstract
A smart city is an environment that uses innovative technologies to make networks and services more flexible, effective, and sustainable with the use of information, digital, and telecommunication technologies, improving the city’s operations for the benefit of its citizens. Most cities incorporate data acquisition elements from their own systems or those managed by subcontracted companies that can be used to optimise their resources: energy consumption, smart meters, lighting, irrigation water consumption, traffic data, camera images, waste collection, security systems, pollution meters, climate data, etc. The city-as-a-platform concept is becoming popular and it is increasingly evident that cities must have efficient management systems capable of deploying, for instance, IoT platforms, open data, etc., and of using artificial intelligence intensively. For many cities, data collection is not a problem, but managing and analysing data with the aim of optimising resources and improving the lives of citizens is. This article presents deepint.net, a platform for capturing, integrating, analysing, and creating dashboards, alert systems, optimisation models, etc. This article shows how deepint.net has been used to estimate pedestrian traffic on the streets of Melbourne (Australia) using the XGBoost algorithm. Given the current situation, it is advisable not to transit urban roads when overcrowded, thus, the model proposed in this paper (and implemented with deepint.net) facilitates the identification of areas with less pedestrian traffic. This use case is an example of an efficient crowd management system, implemented and operated via a platform that offers many possibilities for the management of the data collected in smart territories and cities.
Highlights
Statistics from the Department of Economic and Social Affairs of the United Nations, DESAP, indicate that 68% of the world’s population will live in cities or urban areas by 2050 [1], which means rapid and even uncontrolled growth with consequent challenges for governments, for example: pollution, problems of travel due to traffic and congestion, high costs of housing, food, and basic services, as well as security problems [2]
To address the abovementioned problems, the smart city (SC) concept emerged over the last years, which refers to the integration of the urban environment with the information and communication technologies (ICTs)
Deepint.net allows users to create different roles, structure user projects in such a way that with one account the tool can be used in different environments or for different clients, exploit results for the creation of reports, etc., and deploy the system in a commercial cloud environment (i.e., AWS) that allows all users to be served in a way that is adapted to their needs, on demand, with high performance and high availability
Summary
Many cities have cameras to guarantee security and/or facilitate decision-making with respect to, for example, traffic, frequency of cleaning, etc In this case, we show how a model with a facial recognition algorithm has been implemented on images captured in real-time and another regressive one. The facility that deepint.net has to incorporate sensor data, in this case from camera images, and to implement these algorithms, makes it very simple to build mechanisms for automated decision-making processes With this information, citizens will be able to plan their walks, know the density of pedestrians on a street at a given time, and know what is likely to happen in the future.
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