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 between 7am and 7pm.
This is a great example of how people can use open source data to help develop data supporting sustainable transport. In this case it is clear that better enforcement and protected bike lanes are needed. Residents can take this data to government agencies and demand change.
Transit Alliance Miami Metrorail frequency dashboard created with open source 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 quality of public transport service as part of an advocacy campaign to improve public transport.
According to the website the graphic presents: A real-time audit of Miami’s Metrorail system. It measures the time between each train at Government Center. Each dot represents a train arrival. Every color corresponds to a time. Hover over a dot for more information.
Screenshot Metroquest application for Toronto 2016.
MetroQuest is an application that provides a suite of tools that can be used to improve the public participation process. The tools support all three types of collaboration: engagement, education and process. It’s been used for many transport projects including Toronto’s Big Move 25-year transportation plan.
Transit App uses crowdsourced information to make real-time information available for NY Subway (2017).
The Transit App can now collect tracking data from users to help them predict real time arrival information. This is an excellent tool especially in cities where there is no current real time data available. It’s also quite helpful because it can be more accurate than vehicle GPS signals since these signals may only be sent every several minutes or so.
The crowdsourced data is being rolled out slowly. It started in Montreal (which had no real time data) and has now been extended to New York.
The Transit App continues to develop neat features and is quickly becoming a favorite in cities where it it is deployed. Some references from their blog: https://medium.com/transit-app
Real-time data is now available for ALL New York City subways — thanks to crowdsourcing – 19 January 2017
Better real-time transit data is coming to your city (finally) – 20 December 2016 – How real time transit arrivals works with GPS based systems and crowdsourced systems.
How Boston’s Changing the Way People Experience Transit – 6 September 2016 – The MBTA in Boston held an app contest and Transit was the winner. It’s now the MBTA’s “Recommended” App.
Sensors developed and tested in EU Making Sense Project. Source: Waag Society.
The Making Sense project is funded by the European Commission and has the mission to make advances and experiments in participatory sensing. According to making-sense.eu
Making Sense aims to explore how open source software, open source hardware, digital maker practices and open design can be effectively used by local communities to fabricate their own sensing tools, make sense of their environments and address pressing environmental problems in air, water, soil and sound pollution.
The project runs between 2015 and 2017.
The website provides an excellent Making Sense Toolkit of materials and methods for planning and implementing participatory sensing campaigns.