The aim of the measure 5.2 in Stockholm is to implement in the city environment, if possible on existing infrastructure, sensors for data collection, analysis, visualization.
Two types of sensors have been implemented in the Slakthus-area and have been operating for one year. The 10 sensors for measuring vehicle traffic on a real-time base have been functioning well and have provided accurate data. Some of the sensors were installed in existing infrastructure (a bridge, existing traffic sign structures), whereas others were installed on poles specifically set up for that purpose, as neither the traffic or light poles could bear their weight. As it is costly to install the poles, bring electricity and connectivity to them, it is important that the sensors can operate for a long period of time. Therefore we are currently working on extending the measurement time beyond the project time.
The wifi-nodes were installed in buildings owned by the city and could use the existing connectivity (broadband) there. No additional cabling for electricity was needed as the wifi-nodes were connected with power over Ethernet (PoE). They were supposed to detect passing people in a very accurate way, but in reality did not do so. The issues were the sensitivity of the sensors, that regularly stopped working and the connection to people’s mobile device which was too long (between 20 seconds and 3 minutes) to determine if people were pedestrians, cyclers, or passing in a car.
We are currently going through all wifi-nodes not providing data to re-start and/or replace them. We also add new wifi-nodes to provide additional data. And as a third step IBM is installing multisensors to gather additional data about pedestrians and bicyclists in the Slakthus-area, as part of M8.1.
The data from the sensors are analysed and visualised in the IOT platform provided by IBM, which also has a map. IBM, who is responsible for the open consolidated big data platform , has built up a multiuse data platform where real-time data can be analysed, but also were the data can be turned into practical usecases on reducing transport emissions and increasing the quality of life for citizens. Data flows from the sensor vendors’ systems via two separate entry points. The data is then immediately stored in the data lake for later processing but also fed online via the IBM Event Streams system for online processing, e.g. real time counting of unique visitors, or passages through the system.
Another concrete example of smart connected city tool is the MetroLIVE application whose purpose is to help people (arena visitor), to plan their trip based on the current traffic situation in the area. The application is based on the existing SL (Stockholm Lokaltrafik) API but differentiates in the detection of traffic congestion, traffic congestion alerts, heatmap of number of visitors on a map and estimated walk time based on the traffic situation. The application’s main focus is to provide the visitor with different trip alternatives.
Implementation of a Big Data platform often impose a higher start-up cost for the first use case. Adding additional cases or increasing the usage of the same use case can lower the cost per unit of a use case. This effect is basically a result of services or labour costs. The IT related platform cost (IBM Cloud) is consumption based and will have a low cost for the low volumes also at the start.
The measure is economically sustainable when we assume that the foundation would be used for more cases or at a larger scale. The measure is installed in a limited geographical area with few sensors connected which makes the relative cost per sensor higher.
By Mika Hakosalo