Traffic congestion is a huge problem in urban areas. In Stockholm, car owners spend about 16 workdays stuck in traffic each year. Public transport is also affected by this, and during rush hours, bus services can suffer huge delays.

The objective of the Spatial Modelling Analytics & Real-time Tracking (SMART) Mobility Project is to mitigate growing urban traffic congestion challenges and associated issues of environmental degradation, economic inefficiency and negative impacts to the quality of life of citizens.

SMART Mobility will revolutionise the efficiency of traffic and commuting in cities by leveraging the capabilities of new 4D spatial technology and analysis platforms using real-time vehicle location and various movement data.

The project will use data from many different sources, including:

• Wifi data from Bumbee Labs. Position data is collected when smartphone searches for WiFi-connectivity. Bumbee Labs technologies utilizes this for crowd measurements and pedestrian flow, which is compliant with GDPR regulations.
• Vehicle data from Ramboll, Inrix and Tomtom. Through GPS, data such as speed and route can be obtained.
• Crowd data from Tre. Anonymize crowd data obtained from different sources, like cellular data and WiFi.

The platform will be open and transparent for information and data exchange. The data sets can then be used for new types of services and applications, such as new routes for public transport and analyzing the size of a bus fleet. Through visualisation, this can be used to plan for a more effective public transport. In the end, this will benefit the end users – the travellers.

The project aims to:

1. Conduct three use cases in the Kista-Järva area
2. Build the SMART-platform
3. Integrate data sets into the platform

The anticipated result of the project is to completely integrate siloed data using 4D location. A real-time, shared and collaborative application platform will give access to multiple stakeholders. Furthermore, SMART will optimize mobility in the existing transportation network on a massive scale

Kistagången and Urban ICT Arena’s testbed provides a great spot for testing new products and cases for a sustainable city. One big challenge for the cities of today is how to manage traffic, avoid congestions and provide a good urban environment.

Planning new traffic solutions and evaluating the effectiveness of changes made to the current environment requires data. In order to provide this, continuous measurements of traffic and the direct and indirect effects of traffic on the urban environment are necessary. By gathering relevant data and analysing them, city authorities may receive better support and bases for decisions, in turn leading to efforts that may lower pollution and reduce noise in the urban environment.

In this project, Edeva shows how combining several data sources at a fixed position in the test bed on Kistagången can contribute to more informed decisions for traffic management. Mounted on a light pole, the sensors measure traffic flow through radar and multi-sensor data, air quality in the form of particles, and noise. Analysing the data provided also makes it possible to draw conclusions about the effects of the traffic.

The goal is to supply a comprehensive solution to provide municipalities and city authorities with data. This in turn will help with a more robust decision-making process. Measuring both traffic and the direct and indirect effects of that traffic provides better data which makes it possible to measure the effects of introduced solutions and measures to improve air quality and the general quality of the urban environment.

Air quality is a matter of life and death affecting the daily lives in our cities. When we collect real-time data of air quality in our connected cities, we could contribute to new solutions and prevent illness. Imagine a future where the bus automatically avoids the most polluted streets or if factories limit their production on certain days, thanks to connected sensors.

In Kista, new sensors for local air quality measurement are tested in a number of projects. In the project Vehicle as a Sensor: Air Quality, Edeva and Smart Sensor Devices explored the possibilities of air quality sensors attached to moving vehicles. The goal was to see how mobile sensors can provide data in ways that complements fixed sensors, and how these two types of measurements interact to provide a better picture of the situation.

The project collected real-time air quality data and use software to visualize and analyze the data. This would test the robustness of the sensors and systems, and how this would work in a real urban setting. The sensors were attached to the vehicles used in the Delta project, taking place between October and December of 2020.

The air quality sensors measured two types of air quality. The sensors from Smart Sensor Devices measured air quality inside the vehicles, while the sensors from Edeva measured air quality in the urban environment around the vehicles. The Edeva readings used GPS positioning to keep track of where those readings were taken, and given a time stamp.

Measurements of air quality are important for different decision-making processes regarding traffic and planning of the urban environments. Many measurements are, however, bound to fixed positions, which will give a snapshot rather than a comprehensive picture of the city. Proving the concept of mobile, GPS-positioned measurements taken continuously will assist municipalities and city authorities in complementing their data in order to have a better foundation for future decisions. Indoor measurements of air quality also helps with this process, as it may give data on how indoor environments are affected by the surrounding environment. It also gives important data about the work environment.

One important takeback from the trials performed within the scope of the project was that it proved to be valuable to measure random, on-demand voyages rather than regular public transports moving on a time table. This enabled measurements on several different locations and times that might otherwise have been missed. It also gives a better picture of “where people are” rather than only the main thoroughfares in an area.