Data Collection and Analysis
The widespread availability of computers and inexpensive sensors makes it possible for residents to collect and analyse transport data in ways that could hardly be imagined just a few years ago. Residents can use purpose-built or smartphone sensors to monitor air quality, count traffic or track their trips. They can combine this data with open data sets (see: Transit Center: Data seeking open-minded transit agencies – on the benefits of open data) to create new applications or analyses. This page summarises crowdsourced data collection and analysis.
Sensor Based Data Collection
The ability of residents to collect data means that agencies can no longer hide behind data. It’s possible for neighbourhood residents to measure air quality or traffic volumes themselves. They can use this information to check official figures and/or develop their own ideas for solving transport problems.
For example, Japanese citizens were the first to publicly describe radiation impacts of the Fukushima earthquake, the government was forced to take action after citizens publicised the data. The European Commission’s Making Sense Project is developing a toolkit to help residents develop and use sensors to improve their environment.
In the transport-related sector sensors have been developed for:
GPS Tracking Data Collection
The GPS function in mobile devices enables applications to track users as they travel. These data are an excellent source of planning and transport information. They show paths actually taken by users (more accurate than questionnaires) and also enable users to add information to maps (for example: problem locations). It’s also possible to track users in real time to obtain actual speed information (e.g., WAZE tracks users to understand roadway congestion).
Tracking applications have been developed for all transport modes. Many bicycling and walking apps have been developed commercially as “fitness” apps. They represent a goldmine of information for planners and agencies, but require making agreements to obtain the data.
Open Source Data
Open source data is information provided by agencies and organisations (e.g., geographic and socio-economic data). Agencies make this data available for residents to use and analyse.
The great benefit of open source data is that residents often think outside of traditional silos. They combine data sets from different agencies and in different ways. This makes it possible to draw new conclusions and identify potential improvements, especially more comprehensive ideas.
Many agencies organise Hackathons to develop new apps using open source data, an excellent resource is: New York’s Pursuit of a More Useful App Contest from the Atlantic’s CityLab.
Public agencies are increasingly offering their data via open source. Many agencies require users to sign an agreement to access and use the data. It’s important that agencies offer data in machine readable formats rather than scanned copies of data tables via pdf.
Data Networks
The real value of resident-collected data is created when residents share their data so that others can analyse it and use it in their own applications (in other words, it’s good for residents to provide open access to their data, just like it’s good for government to do so).
There are a variety of networks developed to share user collected data. Often sensor builders create their own data sharing network on the Internet as part of the project. For example you can go to the Smart Citizen site to see all the data collected with the Smart Citizen environmental data sensors worldwide.
More information about data networks is available from the following resources:
Blog Posts: Data
2019 Reading List
Three interesting articles about Crowdsourcing that appeared in late 2019:2020 and beyond: 11 predictions at the intersection of technology and citizen engagement in the DemocracySpot blog by Tiago Peixoto and Tom Steinberg. Lots of food for thought.Why Crowdsourcing Often Leads to Bad Ideas by Oguz A. Acar in the Harvard Business Review, outlining some of […]
Using Open Source Data to Identify Blocked Bus and Bike Lanes
Alan Bell has used machine learning to develop a program that analyses data from traffic cameras to identify blocked bus and bike lanes. He analysed a section of St. Nicholas Avenue in Manhattan and found that the bike lane was blocked 55% of the time and the bus stop was blocked 57% of the time […]
Transit Alliance Miami – Metrorail Arrival Data
The Transit Alliance Miami has created a simple graphic display illustrating the time between Miami Metrorail trains (frequency) at the Government Center station. They have taken Metrorail data and displayed it in an easy to understand format. It is an excellent example of how city residents can use open data to analyse and publicise the […]
2018 Updates
Over the holidays I had a chance to update crowdsourced-transport.com with new information. Here are the highlights: Crowdsourced Public Transport page – added: WikiRoutes – site where users can add information about public transport routes and suggest improvements (PT Mapping). Digital Matatus – an application for using smartphones to map public transport routes (PT Mapping). […]