GEOGRAMA - Keys to floating Car Data for traffic Control in Smart Cities

Keys to floating Car Data for traffic Control in Smart Cities

Probably, of the aspects that most value when evaluating the quality of life in a city are those related to traffic and mobility. It is not a minor issue, and it is that they are linked to health, cost savings and general satisfaction levels. Therefore, the concept of Smart City takes them very much into account. Do you know to what extent geographic data and GIS are involved in all of this? We tell you about it and provide you with some real application cases.

Smart traffic in Smart Cities

Within the universe of smart cities, aspects related to traffic are among the most relevant for their perfect operation. In fact, studies such as “Smart cities: leading platforms, segment analysis and forecasts 2019-2023”, prepared by Juniper Research, collect conclusions such as:

  • Smart solutions for traffic will generate $ 4.4 billion in revenue in 2023. In 2019 this figure was 2,000 million.
  • Pollutant emissions from vehicles will be reduced, specifically, the equivalent of more than 1,255,288.32 kilometers.

With this favorable picture, a set of essential challenges stands out for designing mobility in Smart Cities. Here we have the most relevant:

  • Sustainable mobility: Based on the intelligent combination of private and public transport, in addition to the inclusion of alternative means of transport, such as electric and non-motorized vehicles.
  • Inclusive mobility: transport accessible to the entire population, regardless of physical abilities, age or socioeconomic conditions. This prevents social polarization, making it easier for any inhabitant to go to any corner of the city.
  • Multimodal mobility: the transport infrastructure must be considered as a whole, integrating all existing means into it.
  • Reduce traffic congestion: which will have an impact on a cleaner and healthier environment, as well as on citizens with less stress and a better quality of life.

Floating Car Data (FCD) technology for traffic control in smart cities

Achieving these goals will be viable as long as we have the appropriate data, including those of a geographic nature. Hence, its study and processing with traffic platforms linked to corporate GIS, becomes essential. How is this achieved?

Historical traffic studies and FCD-based monitoring have become a powerful ally of public institutions in charge of traffic management, as well as private companies, such as specialized traffic consultancies.

With them, decisive data are obtained when making decisions related to:

  • Installation of traffic lights, junctions, roundabouts, etc.
  • Pedestrianization of streets.
  • Location of parking and loading and unloading areas.
  • Management of works on the roads.
  • Design of new infrastructures.
  • Reorganization plans.
  • Agile response to incidents, such as traffic jams and accidents.
  • Public transport services up to the demand.
  • For companies with a significant logistical load, it will be easier for them to optimize routes and choose the right types of vehicles for its activity.

GEOGRAMA - Tráfico en ciudades inteligentes
These studies allow traffic models that can be analyzed microscopically, macroscopically and mesoscopically. Depending on the degree of detail that we want to study and the resources, means and budget that are available.

The studies that form the models are based on the collection of traffic data, both historical and in real time. In addition, these are usually distributed by time zones. Among these data are those that reflect the volumes of traffic in a specific location or section, including its entry and exit routes.

All this information can come from millions of GPS devices of the vehicles, private and public (FCD data or Floating Car Data), as well as from the geolocation collected by the passengers’ own mobile phones. Of course, all this is duly anonymized and legally complying with everything related to data protection.

Another key element to work with these models are the well-known origin-destination matrices or O-D matrices. In them, the amount of trips that are made between zoning are visually reflected.

Some real cases of traffic optimization in Smart Cities

In order for you to have a clearer vision of everything we are talking about, we bring you some of the application cases in which we have worked from Geograma to optimize the traffic of smart cities.

First of all, we are going to talk about our collaboration with SyT Consultores, to know with complete fidelity the traffic behavior of the Rontegi Bridge in Bilbao, one of the most delicate points of the metropolitan area of the city and key for the Provincial Council of Bizkaia .

To shape the project, we used data from the TomTom Move platform. Specifically, 18 areas of metropolitan Bilbao were studied, in an analysis period of 2 months and in 4 time bands.

Get to know in depth the details and results of the traffic study on the Rontegi Bridge

On the other hand, we have GEOTRAFFIC Urban, the web solution for traffic control in the Swedish city of Göteborg that we developed and implemented together with Terratec. This is also based on the aforementioned floating car data (FCD) extracted from TomTom devices, of which we are strategic partners.

After reading this article, we hope you have become truly aware of how geographic data and GIS have become a fundamental element of any city that qualifies as smart.

Therefore, if you think that you have a lot of room for improvement when it comes to knowing in depth the traffic in your environment, we invite you to tell us all your concerns in this regard and that we propose the best solution applicable to your Smart City. Do we start from now?