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Government and governance in intelligent cities, smart transportation study case in Bogotá Colombia
Ain Shams Engineering Journal xxx (xxxx) xxx
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Electrical Engineering
Government and governance in intelligent cities, smart transportation study case in Bogotá Colombia Ricardo Alirio Gonzalez a, Roberto Escobar Ferro a, Daríoo Liberona b a b
Department of Electronic Engineering, University District José de Caldas, Bogotá, Colombia Department of Electronic Engineering, University Technique Federico Santa-María, Chile
a r t i c l e
i n f o
Article history: Received 15 February 2019 Revised 25 March 2019 Accepted 1 May 2019 Available online xxxx Keywords: Governement Governance Artificial intelligence Smart city Transportation Traffic
a b s t r a c t This article presents an analysis of some smart cities worldwide, similarly shows in detail the concepts of artificial intelligence, governance and governance with the continuous support of the population, through various methodologies that try to meet the needs of the community in various fields such as the economic, mobility and the environment where they live. The city of Bogotá located in Colombia will be exposed as a specific case, where the respective technological achievements obtained in recent years will be shown to approach what should be an intelligent city, the faults that it has at the transportation level that is a great challenge for generated traffic jams and their possible solution through supervised neural networks, generating a simulation in the Road traffic simulator application for the data, likewise taking advantage of the approach that is being generated to make a transition in the traffic light of the city from incandescent bulbs to LED luminaire, which generates a lower consumption of electricity and a wide range of possibilities with respect to the internal control that can be given, to optimize traffic in the city and thus provide a solution to people who want a shorter time for your daily trips, the main motivation for the creation of this document is the proposal of a method with artificial intelligence that helps to improve the traffic flow of the city, since Bogotá is one of the cities with the most traffic in the world, as a result a rigorous evaluation of different neuronal architectures will be shown to alleviate the congestion of some streets of the city. Ó 2019 Ain Shams University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
1. Introduction Cities bring together a large part of the population of a region, however, with this great challenges arise in the various economic, cultural and social sectors of a country, an example of this is transport, a determining factor for citizen mobility, and that a considerable amount of time is usually lost due to the routine displacement from one place to another, Bogota capital city of Colombia is a good experiment to take into account, because it is one of the most congested cities in the world [1], in fact as a curious fact The 2017 world traffic meter of the specialized firm Inrix, left the city very badly stopped, because when analyzing the traffic congestion of
E-mail addresses: [email protected] (R.A. Gonzalez), udistrital.edu.co (R.E. Ferro), [email protected] (D. Liberona) Peer review under responsibility of Ain Shams University.
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1360 cities in 38 countries around the world. Colombia is the third most congested country since, on average, people spend 48 h a year in the middle of maximum congestion [2], the motivation and inspiration of this article arises from the traffic problem of the city of Bogotá, it is desired to reduce the time of displacement of streets 45, 46 and 47 of Caracas Avenue as a first step to review the estimated duration of a point to another of the city. Analyzing the literature on the subject, it was observed that all the recent technologies through IOT try to interconnect the world, from this arise smart cities, which are the vision that a researcher grants when trying to develop multiple technologies that serve to improve, the transport, health and economy with the purpose of making sustainable developments with the planet and with ourselves [3]. The fundamental contribution of this document is the analysis that is generated with the neural network on the possible positive impact that would be created by implementing a multilayer perceptron for the relief of thousands of citizens who daily live in Bogotá. The article is organized as follows, section 2 summarizes the basic concepts of smart cities, governance and governance with the respective most advanced models of cities currently, section
https://doi.org/10.1016/j.asej.2019.05.002 2090-4479/Ó 2019 Ain Shams University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Please cite this article as: R. A. Gonzalez, R. E. Ferro and D. Liberona, Government and governance in intelligent cities, smart transportation study case in Bogotá Colombia, Ain Shams Engineering Journal, https://doi.org/10.1016/j.asej.2019.05.002
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3 shows the concept of artificial intelligence with neural networks, the section 4 shows some examples of cities with artificial intelligence, section 5 talks about the Colombian government and technologies, section 6 analyzes Bogotá as an intelligent city, section 7 shows the proposal of the traffic model analyzed through a supervised neural network, section 8 talks about results and discussions, section 9 talks about the conclusions obtained from the analysis. 2. Smart city The concept of smart city has been present since the late twentieth century, addressing key issues that emerged continuously by people who moved from rural areas to large cities, generating a massification of urban and demographic processes with a substantial increase in the inconveniences originated in each of the social, economic, political, technological and environmental [4], since in the beginning cities with little sustainability were generated, creating negative effects in the surrounding habitat due to exaggerated consumption models that they were reflected in the resources acquired, causing a greater energy expenditure. In the social and economic field there was an increase in inequality, unemployment and deprivation of opportunities. However, over the years it was reflected in the technological field, the development of information and communication technologies (ICTs), which allowed the evolution of the internet and later a revolution of interconnection with mobile devices such as topic of the Internet of Things (IoT), which significantly reduced the gaps caused by the problems mentioned above [5]. Smart cities aim to be at the forefront of technology with the purpose of helping in the evaluation and selection of different criteria that allow optimizing the continuous flow of these and facilitate urban development, improving significantly the quality of life of citizens [6], by implementing terms such as governance and government, which allow for appropriate policies for the implementation and proper management of ICT in relation to public regulations that are generated around an adequate behavior of the digital environment with citizens [7], enabling technologies to exchange between public organizations (government), with private companies that have their own political context that generate impacts derived to concepts such as ‘‘e-government”, which had the foundations to induce effects positive results within public administrations dependent on the use of ICT in the different contexts of public action [8]. According to the above, the concept of ‘‘intelligent government” is discussed, which is subject to good governance, which takes transparent, equitable, participatory measures that are linked to electronic government, in order to generate a progressive technological advance [9]. However, these ideas are also based on citizens, which are the main nucleus of certain projects, since electronic governance is aimed at providing services desired by the inhabitants living in a country, carried out through the ICTs that define a desired architecture in a specified area [10], for this reason the interests of citizens must be considered and safeguarded, preventing smart cities from becoming innocuous zones, limiting the daily life of a community. On the other hand, some cities such as Rio de Janeiro, Barcelona and Helsinki are used as test banks that seek to find models of satisfaction for the needs of its inhabitants, based on an empirical experimentation based on the scientific method (trial and error), making it possible to find some desired models that allow the integration and implementation of good services for citizens [11,12]. With reference to the above, there is an idea called: ‘‘Intelligent government”, which is subject to good governance, which takes transparent, equitable, participatory measures that are linked to electronic government, in order to generate a progressive
technological advance [9]. However, these ideas are also based on citizens, which are the main nucleus of certain projects, since electronic governance is aimed at providing services desired by the inhabitants living in a country, carried out through the (ICT) that define a desired architecture in a specified area [10], for this reason should be considered and protect the interests of citizens, preventing smart cities from becoming innocuous, limiting the daily life of a community. On the other hand, some cities such as Rio de Janeiro, Barcelona and Helsinki are used as test banks that seek to find satisfaction models for for the needs of its inhabitants, from an empirical experimentation (trial and error), allowing to find some desired models that allow an integration and implementation of good services [11,12]. However, to achieve a correct analysis of citizen needs, the results and data storage of the systems mentioned above, a concept called cloud computing is used, which allows a continuous storage and analysis of the information and starts new fields that affect the progressive development of designed algorithms, providing a spatial intelligence with a continuous innovation [13], that adding the digital inclusion directed by governments, implement new technologies such as Blockchain, taken as a database Distributed decentralized transaction that has allowed building a society increasingly connected by its growing use as digital identity, obtaining greater control of security [14] and therefore better management of governance that is carried through the provision of public services, under the monitoring and regulation of this, by an organization, which is elected to meet the needs of a collective. If these services are designated through ICT, they are considered electronic public services [15]. It is appropriate to highlight the concept of IoT due to its constant use in smart cities, since it provides high replicability, scalability, interoperability and sustainability, which give the opportunity to unite and experience solutions required by any community in their respective area [16], this is possible thanks to ICT, which allows the emergence of new applications such as Industry 4.0, where they implement the advantages of the IoT and also add important factors such as: modularity, virtualization, decentralization, real time capacity and Orientation to the service, providing solutions to isolated and poorly connected sectors in cities [17], another application of smart cities is aimed at health and welfare services, an example of this is the Smart Genetics for Smarter Health proposal, which seeks to improve medical care in the cities, providing health service providers, governments and citizens to ma of smarter medical decisions [18]; it must be taken into account that the most valuable resource of a city is probably its residents and it is for this reason that in some articles such as [19], they consult with the population to have theoretical bases and substantiated studies of what cities should be of the future with technologies applied to the services required by the citizen. 2.1. Highlighted models of emerging smart cities It is important to bear in mind that most cities currently digitized, had an adequate infrastructure, making the most prominent and advanced models around emerging cities and developing countries: Model Boyd Cohen: It is a prototype of the Smart Cities Wheel that identifies six key dimensions that are: – Intelligent economy: Economic competitiveness factors such as entrepreneurship, innovation, productivity and interaction in the national and international market are immersed. – Intelligent environment: At this point plays an important role the intelligent environment, which aims to establish a responsible environmental protection through buildings, energy and green urban planning.
Please cite this article as: R. A. Gonzalez, R. E. Ferro and D. Liberona, Government and governance in intelligent cities, smart transportation study case in Bogotá Colombia, Ain Shams Engineering Journal, https://doi.org/10.1016/j.asej.2019.05.002
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– Intelligent Government: Includes the enabling aspects of the political supply and demand side, transparency and open data, ICT and electronic government. – Intelligent life: It is the construction of a culturally vibrant community by increasing the quality of life of citizens in the aspects of health, safety and happiness conceived through technology. – Intelligent mobility: It is the availability of information and communication integrated to the technologies, which together with transport systems promote mixed and clean modal access. – Intelligent people: These are people who, empowered by 21st century education, acquired a wide degree of creativity, thanks to the training obtained. When carrying out these six components, the model introduced a series of indicators and controllers that regulate and analyze the obtained performance [20]. Here are some models implemented during these years: IBM Smarter Cities: IBM is a leading provider of intelligent solutions, which observed three basic services to be supplied, which are: human services, infrastructure services and those related to the planning and management of the city, consequently, the company perceives a city as a tripod, including the following pillars: o People o The infrastructure o Operations Hitachi’s Smart City Solutions: Hitachi’s concept of an intelligent city is: ‘‘an environmentally conscious city that uses information technology to use energy and other resources efficiently.” In his vision, a smart city seeks to satisfy desires and values of its residents, with the use of advanced information technology improving energy efficiency and concern for the global environment as a prerequisite, maintaining a ‘‘well-balanced relationship between people and the Earth” [20]. 3. Artificial Intelligence, the brain of the intelligent city Artificial intelligence is the science that allows computers to emulate human behavior mainly in processes related to the mind, which lead to intelligent agent designs. The first person to give a formal definition of the subject was John McCarthy, a computer scientist in 1979, who coined the term Artificial Intelligence (AI) to define it as ‘‘the science and engineering of making intelligent machines” [21]. Artificial intelligence supports electronic governance and citizen participation fulfilling the desired objectives in the smart city in fields such as: Public Transport: In this field, the AI can perform a control of coordinated devices that show the active traffic of the entire system on a dashboard, thus helping the visualization of the entire network by determining the possible problems to be solved. Electricity Supply: The AI helps the production, distribution and electronic control of electricity networks in smart cities, generating a boost to the concept known as smart grid. Waste and paper management: With the AI, feasible programs can be made that result in the formation of intelligent robots, an example of this is the company ZEN Robotics that built an intelligent system called SITA Finland which orders construction and demolition waste through an AI that makes its own decisions and predicts the course of action of the given task.
Uses in health: It has been used in the health sector for the correct diagnosis, analysis of reports and treatment strategies, some specific cases are: o Surveillance systems that track the evolution and spread of germs capable of causing hospitalization. o Personnel administration solution, based on the respective records obtained previously. o Electronic systems based on the cloud, to improve the efficiency in the decision making of these algorithms. Security: Used in the continuous monitoring of some sites such as prisons, nuclear facilities and government offices, supported by neural networks that try to emulate the human brain [21]. Digitization: This area is about the digitization of all data, to increase the predictive capacity and intelligence of the system [22]. 3.1. Artificial neural networks In 1943 McCulloch and Pitts designed the model of an artificial neuron, describing the physiological system followed by a biological neuron in the synapse, this abstract and simple model of a neuron as shown in Fig. 1, is the basic element of processing in a artificial neural network. The model is composed of a vector of weights W = (W1, W2, . . ., Wd), where W0 is the threshold of action or activation. Fig. 1 shows the Y output of the neuron, the activation function U, the inputs X = (X1, X2, . . ., Xm) and the vector of weights W, the mathematical model for a single neuron. shows below [23]:
Y¼U
m X
!
XiW i W 0
ð1Þ
i¼1
The perceptron, was exposed by Rosenblat in 1958 and later by Widrow and Hoff in 1960, as the simplest architecture of artificial neural networks, however it had several limitations that were shown in the work of Minksy and Papert in 1969, where shows that the perceptrón does not allow to obtain solutions to the XOR gate, since this model is exclusive for linearly separable problems, to solve this inconvenience it was thought of the inclusion of hidden layers that were between the output and input layer, a first step it was Rumerhart’s work in 1986 [24], which established the bases that allowed a later study of the multilayer perceptron, shown in Fig. 2: Observing Fig. 2 we have that the output equation for the neurons hj of the hidden layer is:
hj ¼ f
n X
!
W ij X i bj
ð2Þ
i¼1
Fig. 1. Elements of an artificial neuron and analogy with a biliary neuron [23].
Please cite this article as: R. A. Gonzalez, R. E. Ferro and D. Liberona, Government and governance in intelligent cities, smart transportation study case in Bogotá Colombia, Ain Shams Engineering Journal, https://doi.org/10.1016/j.asej.2019.05.002
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Santander (Spain): In 2010, this city initiated the smart city project, using IoT, by implementing three types of sensors that are: o Static Sensors: Located at fixed points in the city, they are used to measure temperature, humidity, precipitation and luminosity. o Dynamic Sensors: They are installed in vehicles that are in motion in order to keep track of traffic. o Participatory sensors: Interactive applications created for users, in which current events on the road are reported [26]. 5. The Colombian government and technologies
Fig. 2. Architecture of a multilayer perceptron. Source: [own elaboration].
where Wij is the synaptic weight between each input neuron i with the jth neuron of the hidden layer, f is the activation function and bj is the trigger threshold of the hidden neuron layer j. On the other hand, the output for the neuron Yk of the output layer is preceded by the following equation:
hk ¼ g
n X i¼1
!
W ij hj bj
ð3Þ
where W- ij is the synaptic weight between each neuron j of the hidden layer with each neuron and k of the output layer and g is the activation function [24]. 4. Intelligent cities featured worldwide At present, when naming a city as intelligent, at least one welldefined structure of cloud computing, big data and information and communication technologies (ICT) must be fulfilled, for the performance of diverse objectives, such as a sustainable architecture based on the environment, significantly increasing the quality of life with the proper use of existing resources. Based on this fact, we proceed to show some cities in the world that present these characteristics at least: Tokyo (Japan): It is considered a smart city because: o Intelligent energy saving that maximizes the use of technologies. o Low consumption of carbon and distributed energy resources. o Optimal control of the supply and demand of urban energy through the intelligent management of energy [25].
Developing countries like Colombia, face daily new challenges around the economy, public health and problems linked to poverty, violence and war, which requires the construction of new programs such as artificial intelligence in some areas that facilitate the solution of problems [30], however there is some uncertainty in different organizations when implementing new projects that integrate technologies related to AI, as is the case of digital universities that provide quality services to people and transform economies from different processes, addressing cultural and/or manufacturing difficulties, however thanks to the government of ICTs, a balance is assured where these practices can be strengthened to improve the common good [28], there are other fields such as traffic accident control, which counts with few tools for analysis and possible solutions requested by experts, but that in the Colombian country studies have been initiated that involve an improvement in group decision making in a descriptive way through the analysis of interviews and modeling of business processes with BPM1, in which software tools were constructed from the union of the iterative life cycle and a model of construction of expert systems adopting the Integrated Production System in Language C (CLIPS) [31], there are other Colombian projects in which they are working, such as data mining and computer intelligence that Based on applied research, systems have been created that recommend various tourist attractions through the CRISP-DM Cross Industry Standard Process for Data Mining methodologies, where their operation is based on the interaction of the client with the platform, providing and summarizing their information through a user profile in which the travel experience is specified, through the RecomCaribe mobile application, which allows the system to filter information through the ratings located in a database, queries, searches and other functionalities. The foundation of the system is the eclectic recommender, which combines three information filtering algorithms such as: collaborative filtering, knowledge-based filtering and content-based filtering, allowing a search for the best places thanks to the recommendations of the people [33].
New York (United States): This city carried out an analysis strategy through a set of information, solving problems of urban violence, mobility and traffic management from a data acquisition and surveillance service that makes use of various sensors and cameras in certain places, and then send them to the respective competent authorities [26,27].
According to the study ’Smart City Playbook’, carried out by Machina Research Consulting and commissioned by Nokia in 2016, Bogotá is a smart city, like Medellín [31]. To understand Bogotá as a smart city, we have the following items:
Paris (France): This city carries out sustainability strategies to find a balance between transport, green areas, business teams and other issues that allow a varied functionality addressing multiple issues integrating governance, through a scalable and participatory platform with the vision of citizens, allowing data acquisition and storage [28]. Amsterdan (Holland): This city has a focus on smart citizens, for smart cities, it proposes an agile hybrid methodology (HAM) that can be used to include citizens in the different phases of the project [29].
Mobility: An important feature of Bogotá is mobility, which over time has been evolving, as during the 1990 s a data collection was made, which allowed it to be for a long time, as one of the pioneer cities of Colombia in Regarding data storage technology [26], however, the current situation is very different and at present, Bogotá has an excessive traffic congestion that has a negative impact on the quality of life of citizens and the productivity of the city. According to a study done by an external consultant of the National Planning Department (DNP), transfers from home to work in a normal range are 30 min,
6. Why Bogotá is an intelligent city?
Please cite this article as: R. A. Gonzalez, R. E. Ferro and D. Liberona, Government and governance in intelligent cities, smart transportation study case in Bogotá Colombia, Ain Shams Engineering Journal, https://doi.org/10.1016/j.asej.2019.05.002
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but in Bogotá ‘‘the average transfer is 67 min, however, in the city, an important advance has been observed in this field and technology is being incorporated throughout the Integrated Transportation System, with the design of bike paths and the lending of shared bicycles. The capital of the country is an example of good practices thanks to the development of synergies between the Government and different academic institutions to counteract aspects such as traffic congestion, at the same time the city has created a traffic management center through a monitoring platform examining the movements vials of the city and integrating captured data from cameras, traffic lights and bike paths; At this time the city, has started the tenders and changes in the intelligent signaling system and electronic tickets, trying to generate different alternatives to improve mobility and consolidate a safe and efficient transport for the city. This Center works hand in hand with the District Department of Mobility (SDM), which in coordination with the TransMilenio, the Transportation Terminal, the Ideam and the Environment Secretariat, make decisions to manage and reduce response times in the incidents of mobility or eventualities that are presented, in addition an app called Moovit is granted that allows the population to plan the route they will make during the day, combining the TransMilenio with the integrated routes ”(Bouskela et al., 2016, p.80) [34,37]. People: Considering education as an aspect of great impact for smart cities, we have the following stages: o The Central Government has promoted educational platforms such as Colombia Learn that has recreational contents of basic, primary, secondary and media. o Live Digital Plan for People promoted by the Ministry of Information and Communication Technologies (MinTIC). Quality of Life: Bogota has a ‘‘District Plan for science, technology and innovation for health 2012-202200 , which is focused on the exchange and use of knowledge for technological development and innovation at the service of equity in health. In turn, the District Department of Health (SDS) has the Center DisInstitute of Urban Studies of Education and Health Research (CDEIS) that advises and accompanies the SDS in the strengthening of education, research and development processes, Bogota also has the Command, Control, Communications and Computing Center -C4, which is a security and emergency attention center, which integrates thousands of cameras to contribute to the monitoring of the city, in addition the city has the application ” Civic ‘‘that informs citizens of the various options they can find in terms of recreational and cultural activities [34,37]. Environment: Bogotá, through the Bogotá Air Quality Monitoring Network (RMCAB), collects information on the concentration of anthropogenic and natural pollutants and the behavior of meteorological variables. Government: The Bogotá Abierta platform was awarded as the most successful initiative of the Digital Government in Latin America, recognized by the Inter-American Association of Telecommunications Companies (ASIET). The platform is recognized as a generator of innovation in the exercise of digital citizenship and empowers Bogotanos to participate in solving the challenges of the city through technology. This digital platform has received 953, 362 visits to the page and a total of 35,000 contributions and solutions to the different challenges that the city has. Economy: ‘‘Club de Trading” is the only online interactive platform specialized in financial education with the mission of creating accessibility to financial markets. Like ‘‘ePayco”, an app product of the Bogotano enterprise, which is a digital platform that promotes financial inclusion [34,37]. Below is a general outline (Fig. 3) of smart cities in the world:
5
Fig. 3. Results of the Smart City Playbook study regarding Bogotá as a smart city [36].
In the image you can see an IoT development scale from 1 to 5, with 1 being the lowest development level and 5 the highest development level. In the same way, the following items are observed: o Smart: Improves the quality of life of citizens, observes the economic and social developments of the city. o Safe: Examines the security of a city, observing its level of protection, regarding crimes, crimes, etc . . . o Sustainable: Reduction of the environmental impact, example: renewable energies. 6.1. Parallel between Bogotá and the city of New York From Fig. 4 the following content is generated (see Table 1): It is indicated that New York has the maximum development in the three evaluation criteria of Nokia, however Bogotá is regularly in the sustainability and intelligence and the security it implements is null, Bogota needs to generate a good security system, which give confidence to citizens, however, to improve these factors the government secretariat raised the following strategic objectives:
Fig. 4. It is shown that the largest traffic to be controlled is that of Bogotá that reaches 80,000 vehicles per day [34,37].
Please cite this article as: R. A. Gonzalez, R. E. Ferro and D. Liberona, Government and governance in intelligent cities, smart transportation study case in Bogotá Colombia, Ain Shams Engineering Journal, https://doi.org/10.1016/j.asej.2019.05.002
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Table 1 Parallel between cities.
SMART SAFE SUSTANAIBLE
Bogotá
New York
3 0 3
5 5 5
Strengthen the institutional capacity for the exercise of the police function by the local authorities in charge of the District Department of Government. Effectively articulate the institutional and social participation scheme for the formulation, implementation and evaluation of policies and strategies aimed at the promotion, prevention and protection of Human Rights in the Capital District and respect for human dignity. Articulate the formulation and execution of guidelines for the use of public space. Strengthen the strategic relations of the District Administration with social political actors. Increase the capacity of attention and response to situations of social conflict in the Capital District. Integrate the planning, management and control tools, with a focus on innovation, continuous improvement, social responsibility, integral development of human talent, sector articulation and transparency. Ensure citizens’ access to information and institutional offerings. The mission of the government secretariat is: Lead the efficient and effective articulation of the district authorities to improve the quality of life of all Bogota citizens. Guarantee peaceful coexistence and compliance with the law in the Capital District, protecting rights and promoting the duties of citizens. Serve all Bogotans and promote active and responsible citizenship. Likewise, their vision for 2020 is to be a leading entity in the articulation of a democratic, effective and reliable government for citizenship, recognized by its model of good governance, management for its results, institutional innovation.
Fig. 5. The number of traffic lights found in each location of the capital is shown. Source: Ministry of mobility [40].
that does not allow synchrony with other devices, which means that they do not respond to possible variations in vehicular, pedestrian, etc. flow, creating large road congestions, significant increase due to polluting emissions, considerable loss of time and therefore money (Mayor’s Office of Bogota, 2017); For some time now the mayor of Bogotá has been analyzing the possibility of implementing intelligent traffic lights, which offer the following benefits (see Fig. 6): 7. Proposal for an intelligent smoothing model We proceeded to design a neural network to see if this network can be coupled with the selected input parameters for a good performance of the smart semaphores that are thought to be installed in Bogotá, by implementing neural networks as a form of control
6.2. Some bogota mobility data From the set of ‘‘Open Data”, which contains a certain amount of information related to motor vehicles used as transport, found on the mobility secretariat page, a histogram is made referring to vehicular traffic through the mobility page, where it is allowed to place variables in different graphic models: It is shown that the largest traffic to be controlled is that of Bogotá that reaches 80,000 vehicles per day, for the trips made continuously. The open data of the mobility secretariat also allows observing the number of traffic lights that exist in the different locations of the capital, then Fig. 5 is shown, which illustrates the situation: In Fig. 5 you can see the town with the largest number of traffic lights which is Chapinero, followed by Teusaquillo, it is important to note that the city of Bogotá has 1384 traffic lights, according to the Mayor of Bogotá. 6.3. Smart traffic lights It must be taken into account that the traffic lights of the capital are independent, that is, they have a fixed and individual control
Fig. 6. Benefits of the new traffic light system that the city will have [38].
Please cite this article as: R. A. Gonzalez, R. E. Ferro and D. Liberona, Government and governance in intelligent cities, smart transportation study case in Bogotá Colombia, Ain Shams Engineering Journal, https://doi.org/10.1016/j.asej.2019.05.002
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and prediction of what should be done around the continuous variation of the vehicular flow, at the same time the existing traffic on a main thoroughfare of Bogotá such as Caracas Avenue was examined as a reference through online traffic simulation software called ‘‘Road” traffic simulator ‘‘, which allowed us to obtain some input data for the neural network designed in Matlab, in the same way we proceeded to perform an analysis with the article [35,38]; In the year 2017 Bogotá had 2,180,000 private vehicles transiting through its streets, then proceed to show a graph of growth of private vehicles in the capital (see Fig. 7): It should be taken into account that vehicular traffic is variable at all times and that is why the general structure of a main road is observed (Fig. 8). This document analyzes the traffic of streets 45, 46 and 47 with 20 and 40 vehicles respectively where the time factor, the time interval of the traffic lights management are the inputs of the system and the number of vehicles that Each street is the exit of the system. Fig. 9 shows the map of the avenue and its respective simulation in the application. It was obtained 40 data were obtained for streets 45, 46 and 47 of Caracas Avenue respectively with the simulation and proceeded to normalize taking into account the following equation:
Vn ¼
V V min V max V min
7
Fig. 9. General structure of the road to be analyzed [40,41].
ð4Þ
Obtaining the normalized data and knowing the entries, we proceeded to load the file and generate the partition of the data to evaluate one part as training and another part as validation (see Figs. 10–18). A training was chosen for the 87% and for validation data 13% of the information. Observing this, we proceeded to create and train the network having the following: The Levenberg Marquardt method was used to observe different architectures that fit the traffic simulation, obtaining the following
Fig. 10. File upload and data partition in Matlab.
Fig. 11. Creation and training of the network in Matlab.
Fig. 7. Number of vehicles per year in Bogotá [39].
Fig. 8. General structure of the road to be analyzed [38].
results for a vehicle flow of 20 (Table 2) and 40 vehicles (Table 3) respectively: From Table 2 it could be observed that the architecture with the lowest mean squared error (MSE) for the training was (10 10 10 3), from this the following can be observed: Now we proceed to observe the data obtained with a vehicular traffic of 40 vehicles having: From Table 3 it can be inferred that the architecture with the lowest mean squared error (MSE) was (10 10 10 3), from this we can observe the following: However, the validation error in both cases was quite high exceeding 50% and sometimes reaching 100%, so it is inferred that this neural network is not the most appropriate to implement with the input data selected from the application. Taking into account the previous results and observing that there is no data on density, volume of traffic in the city of Bogotá, we proceeded to study the article [32], which made an algorithm based on fuzzy inference systems with adaptive networks. account the following parameters:
Please cite this article as: R. A. Gonzalez, R. E. Ferro and D. Liberona, Government and governance in intelligent cities, smart transportation study case in Bogotá Colombia, Ain Shams Engineering Journal, https://doi.org/10.1016/j.asej.2019.05.002
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Fig. 15. Mean Square Error realized in MATLAB with Levenberg-Marquadt architecture for traffic with 40 vehicles. Fig. 12. Mean Square Error realized in MATLAB with Levenberg-Marquadt architecture for traffic with 20 vehicles.
Fig. 16. Maximum and minimum validation errors obtained during the learning of the neural network realized in Matlab.
Fig. 13. Linear Regression realized in MATLAB with Levenberg-Marquadt architecture for traffic with 20 vehicles.
Fig. 17. Best Neuronal architecture for the case of 20 vehicles and 40 vehicles realized in MATLAB.
A separation Ln between the n and (n + 1) semaphore, where the n light is green if sin (xnt + un) > 0 and red otherwise, where xn is the semaphore frequency and un is the semaphore offset. In this paper it is explained that a car can have the following sequence: A positive acceleration a +, until the speed it reaches is the maximum on the track. A constant speed with zero acceleration. A negative acceleration a- until the vehicle stops. Also in article [35,38] the average speed, the spacing between the respective cars and the separation of vehicles at maximun flow are taken into account.
Fig. 14. Mean Quadratic Error realized in MATLAB with Levenberg-Marquadt architecture for traffic with 40 vehicles.
From [35,38] the following data was obtained for 45th and 47th streets: Table 4 shows the times, average lengths between the junctions and the time lag of the semaphore when it is green, however, these
Please cite this article as: R. A. Gonzalez, R. E. Ferro and D. Liberona, Government and governance in intelligent cities, smart transportation study case in Bogotá Colombia, Ain Shams Engineering Journal, https://doi.org/10.1016/j.asej.2019.05.002
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networks were also reviewed, but they did this by observing images of cameras and data obtained already installed in the city.
8. Results and discussions The closest network with the data generated through the Road Traffic Simulator simulation platform for 20 vehicles and 40 vehicles was the architecture (10 10 10 3). You can see how you can increase the shape of the hidden parts of the system, you can increase the possibility of having a significant amount of time limits, erroneous responses in the system and the cause of traffic congestion at the moment to reduce. For more information, you can use the base for the entries with the base of the entries. It is important to try several architectures to find the best solution of all. It is the activation function. Likewise, the flow diagram generated for the creation of the network was the following:
9. Conclusions
Fig. 18. Proposed flow diagram.
Table 2 Architecture with Levenberg Marquardt for traffic with 20 vehicles. Size
Number of neurons
MSE
Regression
(2 3) (5 3) (1 0 3) (5 5 3) (15 15 3) (20 20 3) (5 5 5 3) (10 10 10 3)
5 8 13 13 33 43 18 33
0.012 0.009 0.002 0.002 0.001 0.002 0.002 0.001
0.901 0.933 0.982 0.983 0.988 0.989 0.984 0.989
Table 3 Architecture with Levenberg Marquadt for traffic with 40 vehicles. Size
Number of neurons
MSE
Regression
(2 3) (5 3) (1 0 3) (5 5 3) (15 15 3) (20 20 3) (5 5 5 3) (10 10 10 3)
5 8 13 13 33 43 18 33
0.007 0.005 0.002 0.003 0.001 0.001 0.001 0.001
0.905 0.93 0.969 0.958 0.982 0.982 0.977 0.983
Table 4 Crossings data of Caracas Avenue [35]. Avenida Caracas
Longitud entre cruces (m)
Trojo(s)
Tverde(s)
Temporary offset of green
No 45 No 47
281 212
49 47
66 68
– 4.5
times are fixed and do not vary so they can not be taken as inputs for the neural network. We proceeded to look for a current database on mobility in Bogotá, however, only databases of the number of semaphores installed per area were found in the different localities. Other papers that analyzed the vehicular flow through neural
It was observed that the use of a supervised neural network with a good amount of data and appropriate selection of the inputs that allows a good architecture simulation with a low error. Smart cities must be connected through ICT in order to help in the evaluation and selection of different criteria that allow optimizing the continuous flow of these and facilitate urban development, improving significantly the quality of life of citizens, by implementing terms such as governance and government. For future work the use of other parameters such as temperature, climate, air quality, peak and plate that can contribute more weight to the investigation is left, as well as the analysis in other congested points of the city to observe the possible tendencies generate on the road.
References [1] Revista Semana ‘‘Cosas que no sabía sobre la velocidad en Bogotá”. Avaliable:,2019.. [2] Inrix ‘‘TRAFFIC CONGESTION COST UK MOTORIST OVER £37.7 BILLION IN 2017”. Avaliable, 2017.
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[15] Silvia E, Ariel P, Marcos B, Julieta C, Rocıo M, Preisegger Santiago PP, Gobernanza digital. Mejora de procesos de gestión y calidad de software 2018; p. 6. [16] Rhee S, Catalyzing the internet of things and smart cities: Global city teams challenge. p. 4, 2015. [17] Postranecky M, Sviıtek M, Smart city near to 4.0 – an adoption of industry 4.0 conceptual model, 2017; p. 5. [18] von Son FW, Ceballos GR, Rivera HM, Larios VM, Beltran NE, Beltran R, Maciel R, Ascencio JA, Smart genetics for smarter health – an innovation proposal to improve wellness and health care in the cities of the future, 2017; p. 4. [19] Ceballos GR, Larios VM, A model to promote citizen driven government in a smart city, 2016; p. 6. [20] Lekamge S, Marasinghe A, Developing a smart city model that ensures the optimum utilization of existing resources in cities of all sizes, 2013; p. 6. [21] Srivastava S, Bisht A, Safety and security in smart cities using artificial intelligence- a review., 2017; p. 4. [22] Mathur S, Modani US, Smart city- a gateway for artificial intelligence in India, 2016; p. 3. [23] Hernández D, Susana, Córdova, Teodoro F, Simulación de una red neuronal con mapas autor-´ ganizados (som) de kohonen, vol. 3, 2017; p. 4. [24] Muñoz E, Seijas C, Análisis comparativo de ajuste en entrenamiento de redes neuronales artificiales a partir de las librerías open nn y alglib, 2015; p. 12. [25] Fietkiewicz KJ, Stock WG, How ‘‘smart” are japanese cities? an empirical investigation of infrastructures and governmental programs in Tokyo, Yokohama, Osaka and Kyoto, 2015; p. 10 [26] Mart´in SFG., ‘‘Smart cities, la evolución de las ciudades, 2017; p. 18. [27] Cavallera G, Salgado C, Gil AF, Sánchez A, Peralta M, Movilidad y gestión del tráfico: Automatización y aplicación de un modelo de calidad para flotas dinámicas en una ciudad inteligente, 2018; p. 5. [28] Pierre-Emmanuel, Elsa Negre C, Knowledge and decision for smart cities initiatives cases of Paris and nice, 2016; p. 5. [29] Vácha T, Pribyl O, Lom M., Bacúrová M, Involving citizens in smart city projects: Systems engineering meets participation, 2016; p. 6. [30] Segura O., Biomédica instituto nacional de salud, vol. 38, 2018; p. 3. [31] Largo FL., el reto de alinear las ti a la estrategia institucional de las universidades, p. 6, 2017 [32] Puetamána IA, Pantojaa GH, Toledoa RJ, Navarroa AM, Producción de conocimiento con clips para el apoyo a la toma de decisiones en el gobierno municipal colombiano, 2015; p. 6. [33] Garrido LCT, Campo JDM, Gómez RJM, Sistema ecléctico de filtrado de información basado en inteligencia computacional para recomendación de atractivos turísticos del caribe colombiano, vol. 1, 2015; p. 12. [34] Villa CAP, Pantoja FZ, Mora DG, Debates de gobierno urbano es una publicación seriada del instituto de estudios urbanos de la universidad nacional de Colombia, sede Bogotá, 2017; p. 32. [35] Salcedo O, Pedraza LF, Hernández CA, Modelo de semaforización inteligente para la ciudad de Bogotá, vol. 11, 2007; p. 9. [36] Nokia, ‘‘World Map of Smart, Safe, Sustaneible cities”, Avaliable: https:// onestore.nokia.com/asset/200703. [37] Datos abierto del gobierno, Avaliable: http://www.movilidadbogota.gov.co/ web/node/1654 [38] Alcaldía Mayor de Bogotá, ‘‘Bondades del nuevo sistema de semaforización que tendrá la ciudad”. Avaliable: ‘‘http://www.bogota.gov.co/temas-de-ciudad/ movili dad / asi-funcionaran-los-semaforos-inteligente -in Bogota” [39] Ministerio de ambiente, ‘‘Número de vehículos por año”. Avaliable: http:// oab2.ambientebogota.gov.co.
[40] Simulador de tráfico vehicular. Avaliable: http://volkhin.com/ RoadTrafficSimulat or/ [41] Google maps, ‘‘Avenida Caracas”. Avaliable: https://www.google.com.co/ maps?q=Avenida+caracas&um=1&ie=UTF8&sa=X&ved= 0ahUKEwjA5sXxqoneAhVHj1kKHXcvDawQ_AUIDigB
Ricardo Alirio Gonzalez He finished his career in electronic engineering at the Francisco José de Caldas District University (1993), later he specialized in the Pontifical University Javeriana (1998), then proceeded to do a master’s degree at the Francisco José de Caldas District University (2012), currently Doctorate at the same university.
Roberto Escobar Ferro: He finished his career in electronic engineering at the Francisco José de Caldas District University (1998), later he completed a master’s degree in teleinformatics at the Francisco José de Caldas District University (2006), and later he obtained his doctorate in computer engineering at the Pontifical University of Salamanca (2013).
Darío Liberona de la Fuente: He completed an electronic engineering career focused on telecommunications at the Federico de Santa María Technical University, attended the MBA of the University of Lleida and the USACH and is a doctor in the general management of companies at the University of Lleida.
Please cite this article as: R. A. Gonzalez, R. E. Ferro and D. Liberona, Government and governance in intelligent cities, smart transportation study case in Bogotá Colombia, Ain Shams Engineering Journal, https://doi.org/10.1016/j.asej.2019.05.002
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Electrical Engineering
Government and governance in intelligent cities, smart transportation study case in Bogotá Colombia Ricardo Alirio Gonzalez a, Roberto Escobar Ferro a, Daríoo Liberona b a b
Department of Electronic Engineering, University District José de Caldas, Bogotá, Colombia Department of Electronic Engineering, University Technique Federico Santa-María, Chile
a r t i c l e
i n f o
Article history: Received 15 February 2019 Revised 25 March 2019 Accepted 1 May 2019 Available online xxxx Keywords: Governement Governance Artificial intelligence Smart city Transportation Traffic
a b s t r a c t This article presents an analysis of some smart cities worldwide, similarly shows in detail the concepts of artificial intelligence, governance and governance with the continuous support of the population, through various methodologies that try to meet the needs of the community in various fields such as the economic, mobility and the environment where they live. The city of Bogotá located in Colombia will be exposed as a specific case, where the respective technological achievements obtained in recent years will be shown to approach what should be an intelligent city, the faults that it has at the transportation level that is a great challenge for generated traffic jams and their possible solution through supervised neural networks, generating a simulation in the Road traffic simulator application for the data, likewise taking advantage of the approach that is being generated to make a transition in the traffic light of the city from incandescent bulbs to LED luminaire, which generates a lower consumption of electricity and a wide range of possibilities with respect to the internal control that can be given, to optimize traffic in the city and thus provide a solution to people who want a shorter time for your daily trips, the main motivation for the creation of this document is the proposal of a method with artificial intelligence that helps to improve the traffic flow of the city, since Bogotá is one of the cities with the most traffic in the world, as a result a rigorous evaluation of different neuronal architectures will be shown to alleviate the congestion of some streets of the city. Ó 2019 Ain Shams University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
1. Introduction Cities bring together a large part of the population of a region, however, with this great challenges arise in the various economic, cultural and social sectors of a country, an example of this is transport, a determining factor for citizen mobility, and that a considerable amount of time is usually lost due to the routine displacement from one place to another, Bogota capital city of Colombia is a good experiment to take into account, because it is one of the most congested cities in the world [1], in fact as a curious fact The 2017 world traffic meter of the specialized firm Inrix, left the city very badly stopped, because when analyzing the traffic congestion of
E-mail addresses: [email protected] (R.A. Gonzalez), udistrital.edu.co (R.E. Ferro), [email protected] (D. Liberona) Peer review under responsibility of Ain Shams University.
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rferro@
1360 cities in 38 countries around the world. Colombia is the third most congested country since, on average, people spend 48 h a year in the middle of maximum congestion [2], the motivation and inspiration of this article arises from the traffic problem of the city of Bogotá, it is desired to reduce the time of displacement of streets 45, 46 and 47 of Caracas Avenue as a first step to review the estimated duration of a point to another of the city. Analyzing the literature on the subject, it was observed that all the recent technologies through IOT try to interconnect the world, from this arise smart cities, which are the vision that a researcher grants when trying to develop multiple technologies that serve to improve, the transport, health and economy with the purpose of making sustainable developments with the planet and with ourselves [3]. The fundamental contribution of this document is the analysis that is generated with the neural network on the possible positive impact that would be created by implementing a multilayer perceptron for the relief of thousands of citizens who daily live in Bogotá. The article is organized as follows, section 2 summarizes the basic concepts of smart cities, governance and governance with the respective most advanced models of cities currently, section
https://doi.org/10.1016/j.asej.2019.05.002 2090-4479/Ó 2019 Ain Shams University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Please cite this article as: R. A. Gonzalez, R. E. Ferro and D. Liberona, Government and governance in intelligent cities, smart transportation study case in Bogotá Colombia, Ain Shams Engineering Journal, https://doi.org/10.1016/j.asej.2019.05.002
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3 shows the concept of artificial intelligence with neural networks, the section 4 shows some examples of cities with artificial intelligence, section 5 talks about the Colombian government and technologies, section 6 analyzes Bogotá as an intelligent city, section 7 shows the proposal of the traffic model analyzed through a supervised neural network, section 8 talks about results and discussions, section 9 talks about the conclusions obtained from the analysis. 2. Smart city The concept of smart city has been present since the late twentieth century, addressing key issues that emerged continuously by people who moved from rural areas to large cities, generating a massification of urban and demographic processes with a substantial increase in the inconveniences originated in each of the social, economic, political, technological and environmental [4], since in the beginning cities with little sustainability were generated, creating negative effects in the surrounding habitat due to exaggerated consumption models that they were reflected in the resources acquired, causing a greater energy expenditure. In the social and economic field there was an increase in inequality, unemployment and deprivation of opportunities. However, over the years it was reflected in the technological field, the development of information and communication technologies (ICTs), which allowed the evolution of the internet and later a revolution of interconnection with mobile devices such as topic of the Internet of Things (IoT), which significantly reduced the gaps caused by the problems mentioned above [5]. Smart cities aim to be at the forefront of technology with the purpose of helping in the evaluation and selection of different criteria that allow optimizing the continuous flow of these and facilitate urban development, improving significantly the quality of life of citizens [6], by implementing terms such as governance and government, which allow for appropriate policies for the implementation and proper management of ICT in relation to public regulations that are generated around an adequate behavior of the digital environment with citizens [7], enabling technologies to exchange between public organizations (government), with private companies that have their own political context that generate impacts derived to concepts such as ‘‘e-government”, which had the foundations to induce effects positive results within public administrations dependent on the use of ICT in the different contexts of public action [8]. According to the above, the concept of ‘‘intelligent government” is discussed, which is subject to good governance, which takes transparent, equitable, participatory measures that are linked to electronic government, in order to generate a progressive technological advance [9]. However, these ideas are also based on citizens, which are the main nucleus of certain projects, since electronic governance is aimed at providing services desired by the inhabitants living in a country, carried out through the ICTs that define a desired architecture in a specified area [10], for this reason the interests of citizens must be considered and safeguarded, preventing smart cities from becoming innocuous zones, limiting the daily life of a community. On the other hand, some cities such as Rio de Janeiro, Barcelona and Helsinki are used as test banks that seek to find models of satisfaction for the needs of its inhabitants, based on an empirical experimentation based on the scientific method (trial and error), making it possible to find some desired models that allow the integration and implementation of good services for citizens [11,12]. With reference to the above, there is an idea called: ‘‘Intelligent government”, which is subject to good governance, which takes transparent, equitable, participatory measures that are linked to electronic government, in order to generate a progressive
technological advance [9]. However, these ideas are also based on citizens, which are the main nucleus of certain projects, since electronic governance is aimed at providing services desired by the inhabitants living in a country, carried out through the (ICT) that define a desired architecture in a specified area [10], for this reason should be considered and protect the interests of citizens, preventing smart cities from becoming innocuous, limiting the daily life of a community. On the other hand, some cities such as Rio de Janeiro, Barcelona and Helsinki are used as test banks that seek to find satisfaction models for for the needs of its inhabitants, from an empirical experimentation (trial and error), allowing to find some desired models that allow an integration and implementation of good services [11,12]. However, to achieve a correct analysis of citizen needs, the results and data storage of the systems mentioned above, a concept called cloud computing is used, which allows a continuous storage and analysis of the information and starts new fields that affect the progressive development of designed algorithms, providing a spatial intelligence with a continuous innovation [13], that adding the digital inclusion directed by governments, implement new technologies such as Blockchain, taken as a database Distributed decentralized transaction that has allowed building a society increasingly connected by its growing use as digital identity, obtaining greater control of security [14] and therefore better management of governance that is carried through the provision of public services, under the monitoring and regulation of this, by an organization, which is elected to meet the needs of a collective. If these services are designated through ICT, they are considered electronic public services [15]. It is appropriate to highlight the concept of IoT due to its constant use in smart cities, since it provides high replicability, scalability, interoperability and sustainability, which give the opportunity to unite and experience solutions required by any community in their respective area [16], this is possible thanks to ICT, which allows the emergence of new applications such as Industry 4.0, where they implement the advantages of the IoT and also add important factors such as: modularity, virtualization, decentralization, real time capacity and Orientation to the service, providing solutions to isolated and poorly connected sectors in cities [17], another application of smart cities is aimed at health and welfare services, an example of this is the Smart Genetics for Smarter Health proposal, which seeks to improve medical care in the cities, providing health service providers, governments and citizens to ma of smarter medical decisions [18]; it must be taken into account that the most valuable resource of a city is probably its residents and it is for this reason that in some articles such as [19], they consult with the population to have theoretical bases and substantiated studies of what cities should be of the future with technologies applied to the services required by the citizen. 2.1. Highlighted models of emerging smart cities It is important to bear in mind that most cities currently digitized, had an adequate infrastructure, making the most prominent and advanced models around emerging cities and developing countries: Model Boyd Cohen: It is a prototype of the Smart Cities Wheel that identifies six key dimensions that are: – Intelligent economy: Economic competitiveness factors such as entrepreneurship, innovation, productivity and interaction in the national and international market are immersed. – Intelligent environment: At this point plays an important role the intelligent environment, which aims to establish a responsible environmental protection through buildings, energy and green urban planning.
Please cite this article as: R. A. Gonzalez, R. E. Ferro and D. Liberona, Government and governance in intelligent cities, smart transportation study case in Bogotá Colombia, Ain Shams Engineering Journal, https://doi.org/10.1016/j.asej.2019.05.002
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– Intelligent Government: Includes the enabling aspects of the political supply and demand side, transparency and open data, ICT and electronic government. – Intelligent life: It is the construction of a culturally vibrant community by increasing the quality of life of citizens in the aspects of health, safety and happiness conceived through technology. – Intelligent mobility: It is the availability of information and communication integrated to the technologies, which together with transport systems promote mixed and clean modal access. – Intelligent people: These are people who, empowered by 21st century education, acquired a wide degree of creativity, thanks to the training obtained. When carrying out these six components, the model introduced a series of indicators and controllers that regulate and analyze the obtained performance [20]. Here are some models implemented during these years: IBM Smarter Cities: IBM is a leading provider of intelligent solutions, which observed three basic services to be supplied, which are: human services, infrastructure services and those related to the planning and management of the city, consequently, the company perceives a city as a tripod, including the following pillars: o People o The infrastructure o Operations Hitachi’s Smart City Solutions: Hitachi’s concept of an intelligent city is: ‘‘an environmentally conscious city that uses information technology to use energy and other resources efficiently.” In his vision, a smart city seeks to satisfy desires and values of its residents, with the use of advanced information technology improving energy efficiency and concern for the global environment as a prerequisite, maintaining a ‘‘well-balanced relationship between people and the Earth” [20]. 3. Artificial Intelligence, the brain of the intelligent city Artificial intelligence is the science that allows computers to emulate human behavior mainly in processes related to the mind, which lead to intelligent agent designs. The first person to give a formal definition of the subject was John McCarthy, a computer scientist in 1979, who coined the term Artificial Intelligence (AI) to define it as ‘‘the science and engineering of making intelligent machines” [21]. Artificial intelligence supports electronic governance and citizen participation fulfilling the desired objectives in the smart city in fields such as: Public Transport: In this field, the AI can perform a control of coordinated devices that show the active traffic of the entire system on a dashboard, thus helping the visualization of the entire network by determining the possible problems to be solved. Electricity Supply: The AI helps the production, distribution and electronic control of electricity networks in smart cities, generating a boost to the concept known as smart grid. Waste and paper management: With the AI, feasible programs can be made that result in the formation of intelligent robots, an example of this is the company ZEN Robotics that built an intelligent system called SITA Finland which orders construction and demolition waste through an AI that makes its own decisions and predicts the course of action of the given task.
Uses in health: It has been used in the health sector for the correct diagnosis, analysis of reports and treatment strategies, some specific cases are: o Surveillance systems that track the evolution and spread of germs capable of causing hospitalization. o Personnel administration solution, based on the respective records obtained previously. o Electronic systems based on the cloud, to improve the efficiency in the decision making of these algorithms. Security: Used in the continuous monitoring of some sites such as prisons, nuclear facilities and government offices, supported by neural networks that try to emulate the human brain [21]. Digitization: This area is about the digitization of all data, to increase the predictive capacity and intelligence of the system [22]. 3.1. Artificial neural networks In 1943 McCulloch and Pitts designed the model of an artificial neuron, describing the physiological system followed by a biological neuron in the synapse, this abstract and simple model of a neuron as shown in Fig. 1, is the basic element of processing in a artificial neural network. The model is composed of a vector of weights W = (W1, W2, . . ., Wd), where W0 is the threshold of action or activation. Fig. 1 shows the Y output of the neuron, the activation function U, the inputs X = (X1, X2, . . ., Xm) and the vector of weights W, the mathematical model for a single neuron. shows below [23]:
Y¼U
m X
!
XiW i W 0
ð1Þ
i¼1
The perceptron, was exposed by Rosenblat in 1958 and later by Widrow and Hoff in 1960, as the simplest architecture of artificial neural networks, however it had several limitations that were shown in the work of Minksy and Papert in 1969, where shows that the perceptrón does not allow to obtain solutions to the XOR gate, since this model is exclusive for linearly separable problems, to solve this inconvenience it was thought of the inclusion of hidden layers that were between the output and input layer, a first step it was Rumerhart’s work in 1986 [24], which established the bases that allowed a later study of the multilayer perceptron, shown in Fig. 2: Observing Fig. 2 we have that the output equation for the neurons hj of the hidden layer is:
hj ¼ f
n X
!
W ij X i bj
ð2Þ
i¼1
Fig. 1. Elements of an artificial neuron and analogy with a biliary neuron [23].
Please cite this article as: R. A. Gonzalez, R. E. Ferro and D. Liberona, Government and governance in intelligent cities, smart transportation study case in Bogotá Colombia, Ain Shams Engineering Journal, https://doi.org/10.1016/j.asej.2019.05.002
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Santander (Spain): In 2010, this city initiated the smart city project, using IoT, by implementing three types of sensors that are: o Static Sensors: Located at fixed points in the city, they are used to measure temperature, humidity, precipitation and luminosity. o Dynamic Sensors: They are installed in vehicles that are in motion in order to keep track of traffic. o Participatory sensors: Interactive applications created for users, in which current events on the road are reported [26]. 5. The Colombian government and technologies
Fig. 2. Architecture of a multilayer perceptron. Source: [own elaboration].
where Wij is the synaptic weight between each input neuron i with the jth neuron of the hidden layer, f is the activation function and bj is the trigger threshold of the hidden neuron layer j. On the other hand, the output for the neuron Yk of the output layer is preceded by the following equation:
hk ¼ g
n X i¼1
!
W ij hj bj
ð3Þ
where W- ij is the synaptic weight between each neuron j of the hidden layer with each neuron and k of the output layer and g is the activation function [24]. 4. Intelligent cities featured worldwide At present, when naming a city as intelligent, at least one welldefined structure of cloud computing, big data and information and communication technologies (ICT) must be fulfilled, for the performance of diverse objectives, such as a sustainable architecture based on the environment, significantly increasing the quality of life with the proper use of existing resources. Based on this fact, we proceed to show some cities in the world that present these characteristics at least: Tokyo (Japan): It is considered a smart city because: o Intelligent energy saving that maximizes the use of technologies. o Low consumption of carbon and distributed energy resources. o Optimal control of the supply and demand of urban energy through the intelligent management of energy [25].
Developing countries like Colombia, face daily new challenges around the economy, public health and problems linked to poverty, violence and war, which requires the construction of new programs such as artificial intelligence in some areas that facilitate the solution of problems [30], however there is some uncertainty in different organizations when implementing new projects that integrate technologies related to AI, as is the case of digital universities that provide quality services to people and transform economies from different processes, addressing cultural and/or manufacturing difficulties, however thanks to the government of ICTs, a balance is assured where these practices can be strengthened to improve the common good [28], there are other fields such as traffic accident control, which counts with few tools for analysis and possible solutions requested by experts, but that in the Colombian country studies have been initiated that involve an improvement in group decision making in a descriptive way through the analysis of interviews and modeling of business processes with BPM1, in which software tools were constructed from the union of the iterative life cycle and a model of construction of expert systems adopting the Integrated Production System in Language C (CLIPS) [31], there are other Colombian projects in which they are working, such as data mining and computer intelligence that Based on applied research, systems have been created that recommend various tourist attractions through the CRISP-DM Cross Industry Standard Process for Data Mining methodologies, where their operation is based on the interaction of the client with the platform, providing and summarizing their information through a user profile in which the travel experience is specified, through the RecomCaribe mobile application, which allows the system to filter information through the ratings located in a database, queries, searches and other functionalities. The foundation of the system is the eclectic recommender, which combines three information filtering algorithms such as: collaborative filtering, knowledge-based filtering and content-based filtering, allowing a search for the best places thanks to the recommendations of the people [33].
New York (United States): This city carried out an analysis strategy through a set of information, solving problems of urban violence, mobility and traffic management from a data acquisition and surveillance service that makes use of various sensors and cameras in certain places, and then send them to the respective competent authorities [26,27].
According to the study ’Smart City Playbook’, carried out by Machina Research Consulting and commissioned by Nokia in 2016, Bogotá is a smart city, like Medellín [31]. To understand Bogotá as a smart city, we have the following items:
Paris (France): This city carries out sustainability strategies to find a balance between transport, green areas, business teams and other issues that allow a varied functionality addressing multiple issues integrating governance, through a scalable and participatory platform with the vision of citizens, allowing data acquisition and storage [28]. Amsterdan (Holland): This city has a focus on smart citizens, for smart cities, it proposes an agile hybrid methodology (HAM) that can be used to include citizens in the different phases of the project [29].
Mobility: An important feature of Bogotá is mobility, which over time has been evolving, as during the 1990 s a data collection was made, which allowed it to be for a long time, as one of the pioneer cities of Colombia in Regarding data storage technology [26], however, the current situation is very different and at present, Bogotá has an excessive traffic congestion that has a negative impact on the quality of life of citizens and the productivity of the city. According to a study done by an external consultant of the National Planning Department (DNP), transfers from home to work in a normal range are 30 min,
6. Why Bogotá is an intelligent city?
Please cite this article as: R. A. Gonzalez, R. E. Ferro and D. Liberona, Government and governance in intelligent cities, smart transportation study case in Bogotá Colombia, Ain Shams Engineering Journal, https://doi.org/10.1016/j.asej.2019.05.002
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but in Bogotá ‘‘the average transfer is 67 min, however, in the city, an important advance has been observed in this field and technology is being incorporated throughout the Integrated Transportation System, with the design of bike paths and the lending of shared bicycles. The capital of the country is an example of good practices thanks to the development of synergies between the Government and different academic institutions to counteract aspects such as traffic congestion, at the same time the city has created a traffic management center through a monitoring platform examining the movements vials of the city and integrating captured data from cameras, traffic lights and bike paths; At this time the city, has started the tenders and changes in the intelligent signaling system and electronic tickets, trying to generate different alternatives to improve mobility and consolidate a safe and efficient transport for the city. This Center works hand in hand with the District Department of Mobility (SDM), which in coordination with the TransMilenio, the Transportation Terminal, the Ideam and the Environment Secretariat, make decisions to manage and reduce response times in the incidents of mobility or eventualities that are presented, in addition an app called Moovit is granted that allows the population to plan the route they will make during the day, combining the TransMilenio with the integrated routes ”(Bouskela et al., 2016, p.80) [34,37]. People: Considering education as an aspect of great impact for smart cities, we have the following stages: o The Central Government has promoted educational platforms such as Colombia Learn that has recreational contents of basic, primary, secondary and media. o Live Digital Plan for People promoted by the Ministry of Information and Communication Technologies (MinTIC). Quality of Life: Bogota has a ‘‘District Plan for science, technology and innovation for health 2012-202200 , which is focused on the exchange and use of knowledge for technological development and innovation at the service of equity in health. In turn, the District Department of Health (SDS) has the Center DisInstitute of Urban Studies of Education and Health Research (CDEIS) that advises and accompanies the SDS in the strengthening of education, research and development processes, Bogota also has the Command, Control, Communications and Computing Center -C4, which is a security and emergency attention center, which integrates thousands of cameras to contribute to the monitoring of the city, in addition the city has the application ” Civic ‘‘that informs citizens of the various options they can find in terms of recreational and cultural activities [34,37]. Environment: Bogotá, through the Bogotá Air Quality Monitoring Network (RMCAB), collects information on the concentration of anthropogenic and natural pollutants and the behavior of meteorological variables. Government: The Bogotá Abierta platform was awarded as the most successful initiative of the Digital Government in Latin America, recognized by the Inter-American Association of Telecommunications Companies (ASIET). The platform is recognized as a generator of innovation in the exercise of digital citizenship and empowers Bogotanos to participate in solving the challenges of the city through technology. This digital platform has received 953, 362 visits to the page and a total of 35,000 contributions and solutions to the different challenges that the city has. Economy: ‘‘Club de Trading” is the only online interactive platform specialized in financial education with the mission of creating accessibility to financial markets. Like ‘‘ePayco”, an app product of the Bogotano enterprise, which is a digital platform that promotes financial inclusion [34,37]. Below is a general outline (Fig. 3) of smart cities in the world:
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Fig. 3. Results of the Smart City Playbook study regarding Bogotá as a smart city [36].
In the image you can see an IoT development scale from 1 to 5, with 1 being the lowest development level and 5 the highest development level. In the same way, the following items are observed: o Smart: Improves the quality of life of citizens, observes the economic and social developments of the city. o Safe: Examines the security of a city, observing its level of protection, regarding crimes, crimes, etc . . . o Sustainable: Reduction of the environmental impact, example: renewable energies. 6.1. Parallel between Bogotá and the city of New York From Fig. 4 the following content is generated (see Table 1): It is indicated that New York has the maximum development in the three evaluation criteria of Nokia, however Bogotá is regularly in the sustainability and intelligence and the security it implements is null, Bogota needs to generate a good security system, which give confidence to citizens, however, to improve these factors the government secretariat raised the following strategic objectives:
Fig. 4. It is shown that the largest traffic to be controlled is that of Bogotá that reaches 80,000 vehicles per day [34,37].
Please cite this article as: R. A. Gonzalez, R. E. Ferro and D. Liberona, Government and governance in intelligent cities, smart transportation study case in Bogotá Colombia, Ain Shams Engineering Journal, https://doi.org/10.1016/j.asej.2019.05.002
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Table 1 Parallel between cities.
SMART SAFE SUSTANAIBLE
Bogotá
New York
3 0 3
5 5 5
Strengthen the institutional capacity for the exercise of the police function by the local authorities in charge of the District Department of Government. Effectively articulate the institutional and social participation scheme for the formulation, implementation and evaluation of policies and strategies aimed at the promotion, prevention and protection of Human Rights in the Capital District and respect for human dignity. Articulate the formulation and execution of guidelines for the use of public space. Strengthen the strategic relations of the District Administration with social political actors. Increase the capacity of attention and response to situations of social conflict in the Capital District. Integrate the planning, management and control tools, with a focus on innovation, continuous improvement, social responsibility, integral development of human talent, sector articulation and transparency. Ensure citizens’ access to information and institutional offerings. The mission of the government secretariat is: Lead the efficient and effective articulation of the district authorities to improve the quality of life of all Bogota citizens. Guarantee peaceful coexistence and compliance with the law in the Capital District, protecting rights and promoting the duties of citizens. Serve all Bogotans and promote active and responsible citizenship. Likewise, their vision for 2020 is to be a leading entity in the articulation of a democratic, effective and reliable government for citizenship, recognized by its model of good governance, management for its results, institutional innovation.
Fig. 5. The number of traffic lights found in each location of the capital is shown. Source: Ministry of mobility [40].
that does not allow synchrony with other devices, which means that they do not respond to possible variations in vehicular, pedestrian, etc. flow, creating large road congestions, significant increase due to polluting emissions, considerable loss of time and therefore money (Mayor’s Office of Bogota, 2017); For some time now the mayor of Bogotá has been analyzing the possibility of implementing intelligent traffic lights, which offer the following benefits (see Fig. 6): 7. Proposal for an intelligent smoothing model We proceeded to design a neural network to see if this network can be coupled with the selected input parameters for a good performance of the smart semaphores that are thought to be installed in Bogotá, by implementing neural networks as a form of control
6.2. Some bogota mobility data From the set of ‘‘Open Data”, which contains a certain amount of information related to motor vehicles used as transport, found on the mobility secretariat page, a histogram is made referring to vehicular traffic through the mobility page, where it is allowed to place variables in different graphic models: It is shown that the largest traffic to be controlled is that of Bogotá that reaches 80,000 vehicles per day, for the trips made continuously. The open data of the mobility secretariat also allows observing the number of traffic lights that exist in the different locations of the capital, then Fig. 5 is shown, which illustrates the situation: In Fig. 5 you can see the town with the largest number of traffic lights which is Chapinero, followed by Teusaquillo, it is important to note that the city of Bogotá has 1384 traffic lights, according to the Mayor of Bogotá. 6.3. Smart traffic lights It must be taken into account that the traffic lights of the capital are independent, that is, they have a fixed and individual control
Fig. 6. Benefits of the new traffic light system that the city will have [38].
Please cite this article as: R. A. Gonzalez, R. E. Ferro and D. Liberona, Government and governance in intelligent cities, smart transportation study case in Bogotá Colombia, Ain Shams Engineering Journal, https://doi.org/10.1016/j.asej.2019.05.002
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and prediction of what should be done around the continuous variation of the vehicular flow, at the same time the existing traffic on a main thoroughfare of Bogotá such as Caracas Avenue was examined as a reference through online traffic simulation software called ‘‘Road” traffic simulator ‘‘, which allowed us to obtain some input data for the neural network designed in Matlab, in the same way we proceeded to perform an analysis with the article [35,38]; In the year 2017 Bogotá had 2,180,000 private vehicles transiting through its streets, then proceed to show a graph of growth of private vehicles in the capital (see Fig. 7): It should be taken into account that vehicular traffic is variable at all times and that is why the general structure of a main road is observed (Fig. 8). This document analyzes the traffic of streets 45, 46 and 47 with 20 and 40 vehicles respectively where the time factor, the time interval of the traffic lights management are the inputs of the system and the number of vehicles that Each street is the exit of the system. Fig. 9 shows the map of the avenue and its respective simulation in the application. It was obtained 40 data were obtained for streets 45, 46 and 47 of Caracas Avenue respectively with the simulation and proceeded to normalize taking into account the following equation:
Vn ¼
V V min V max V min
7
Fig. 9. General structure of the road to be analyzed [40,41].
ð4Þ
Obtaining the normalized data and knowing the entries, we proceeded to load the file and generate the partition of the data to evaluate one part as training and another part as validation (see Figs. 10–18). A training was chosen for the 87% and for validation data 13% of the information. Observing this, we proceeded to create and train the network having the following: The Levenberg Marquardt method was used to observe different architectures that fit the traffic simulation, obtaining the following
Fig. 10. File upload and data partition in Matlab.
Fig. 11. Creation and training of the network in Matlab.
Fig. 7. Number of vehicles per year in Bogotá [39].
Fig. 8. General structure of the road to be analyzed [38].
results for a vehicle flow of 20 (Table 2) and 40 vehicles (Table 3) respectively: From Table 2 it could be observed that the architecture with the lowest mean squared error (MSE) for the training was (10 10 10 3), from this the following can be observed: Now we proceed to observe the data obtained with a vehicular traffic of 40 vehicles having: From Table 3 it can be inferred that the architecture with the lowest mean squared error (MSE) was (10 10 10 3), from this we can observe the following: However, the validation error in both cases was quite high exceeding 50% and sometimes reaching 100%, so it is inferred that this neural network is not the most appropriate to implement with the input data selected from the application. Taking into account the previous results and observing that there is no data on density, volume of traffic in the city of Bogotá, we proceeded to study the article [32], which made an algorithm based on fuzzy inference systems with adaptive networks. account the following parameters:
Please cite this article as: R. A. Gonzalez, R. E. Ferro and D. Liberona, Government and governance in intelligent cities, smart transportation study case in Bogotá Colombia, Ain Shams Engineering Journal, https://doi.org/10.1016/j.asej.2019.05.002
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Fig. 15. Mean Square Error realized in MATLAB with Levenberg-Marquadt architecture for traffic with 40 vehicles. Fig. 12. Mean Square Error realized in MATLAB with Levenberg-Marquadt architecture for traffic with 20 vehicles.
Fig. 16. Maximum and minimum validation errors obtained during the learning of the neural network realized in Matlab.
Fig. 13. Linear Regression realized in MATLAB with Levenberg-Marquadt architecture for traffic with 20 vehicles.
Fig. 17. Best Neuronal architecture for the case of 20 vehicles and 40 vehicles realized in MATLAB.
A separation Ln between the n and (n + 1) semaphore, where the n light is green if sin (xnt + un) > 0 and red otherwise, where xn is the semaphore frequency and un is the semaphore offset. In this paper it is explained that a car can have the following sequence: A positive acceleration a +, until the speed it reaches is the maximum on the track. A constant speed with zero acceleration. A negative acceleration a- until the vehicle stops. Also in article [35,38] the average speed, the spacing between the respective cars and the separation of vehicles at maximun flow are taken into account.
Fig. 14. Mean Quadratic Error realized in MATLAB with Levenberg-Marquadt architecture for traffic with 40 vehicles.
From [35,38] the following data was obtained for 45th and 47th streets: Table 4 shows the times, average lengths between the junctions and the time lag of the semaphore when it is green, however, these
Please cite this article as: R. A. Gonzalez, R. E. Ferro and D. Liberona, Government and governance in intelligent cities, smart transportation study case in Bogotá Colombia, Ain Shams Engineering Journal, https://doi.org/10.1016/j.asej.2019.05.002
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networks were also reviewed, but they did this by observing images of cameras and data obtained already installed in the city.
8. Results and discussions The closest network with the data generated through the Road Traffic Simulator simulation platform for 20 vehicles and 40 vehicles was the architecture (10 10 10 3). You can see how you can increase the shape of the hidden parts of the system, you can increase the possibility of having a significant amount of time limits, erroneous responses in the system and the cause of traffic congestion at the moment to reduce. For more information, you can use the base for the entries with the base of the entries. It is important to try several architectures to find the best solution of all. It is the activation function. Likewise, the flow diagram generated for the creation of the network was the following:
9. Conclusions
Fig. 18. Proposed flow diagram.
Table 2 Architecture with Levenberg Marquardt for traffic with 20 vehicles. Size
Number of neurons
MSE
Regression
(2 3) (5 3) (1 0 3) (5 5 3) (15 15 3) (20 20 3) (5 5 5 3) (10 10 10 3)
5 8 13 13 33 43 18 33
0.012 0.009 0.002 0.002 0.001 0.002 0.002 0.001
0.901 0.933 0.982 0.983 0.988 0.989 0.984 0.989
Table 3 Architecture with Levenberg Marquadt for traffic with 40 vehicles. Size
Number of neurons
MSE
Regression
(2 3) (5 3) (1 0 3) (5 5 3) (15 15 3) (20 20 3) (5 5 5 3) (10 10 10 3)
5 8 13 13 33 43 18 33
0.007 0.005 0.002 0.003 0.001 0.001 0.001 0.001
0.905 0.93 0.969 0.958 0.982 0.982 0.977 0.983
Table 4 Crossings data of Caracas Avenue [35]. Avenida Caracas
Longitud entre cruces (m)
Trojo(s)
Tverde(s)
Temporary offset of green
No 45 No 47
281 212
49 47
66 68
– 4.5
times are fixed and do not vary so they can not be taken as inputs for the neural network. We proceeded to look for a current database on mobility in Bogotá, however, only databases of the number of semaphores installed per area were found in the different localities. Other papers that analyzed the vehicular flow through neural
It was observed that the use of a supervised neural network with a good amount of data and appropriate selection of the inputs that allows a good architecture simulation with a low error. Smart cities must be connected through ICT in order to help in the evaluation and selection of different criteria that allow optimizing the continuous flow of these and facilitate urban development, improving significantly the quality of life of citizens, by implementing terms such as governance and government. For future work the use of other parameters such as temperature, climate, air quality, peak and plate that can contribute more weight to the investigation is left, as well as the analysis in other congested points of the city to observe the possible tendencies generate on the road.
References [1] Revista Semana ‘‘Cosas que no sabía sobre la velocidad en Bogotá”. Avaliable:,2019.
Please cite this article as: R. A. Gonzalez, R. E. Ferro and D. Liberona, Government and governance in intelligent cities, smart transportation study case in Bogotá Colombia, Ain Shams Engineering Journal, https://doi.org/10.1016/j.asej.2019.05.002
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Ricardo Alirio Gonzalez He finished his career in electronic engineering at the Francisco José de Caldas District University (1993), later he specialized in the Pontifical University Javeriana (1998), then proceeded to do a master’s degree at the Francisco José de Caldas District University (2012), currently Doctorate at the same university.
Roberto Escobar Ferro: He finished his career in electronic engineering at the Francisco José de Caldas District University (1998), later he completed a master’s degree in teleinformatics at the Francisco José de Caldas District University (2006), and later he obtained his doctorate in computer engineering at the Pontifical University of Salamanca (2013).
Darío Liberona de la Fuente: He completed an electronic engineering career focused on telecommunications at the Federico de Santa María Technical University, attended the MBA of the University of Lleida and the USACH and is a doctor in the general management of companies at the University of Lleida.
Please cite this article as: R. A. Gonzalez, R. E. Ferro and D. Liberona, Government and governance in intelligent cities, smart transportation study case in Bogotá Colombia, Ain Shams Engineering Journal, https://doi.org/10.1016/j.asej.2019.05.002