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SmartRoadSense is a crowdsensing system born in 2014 which basically lets users map the status of roads while they are driving on it, just activating a mobile app. But to explain better let see the app in action
The idea is not just to record when a vehicle encounters potholes and bumps but is more to give a measure how comfortable is to drive on a particular road. To explain it as quick as possible we found out that the road surface profile can be modeled as white Gaussian noise (filtered by a first order low-pass filter) so we use the accelerometers embedded in a common smartphone to sense this noise. For each wave form we then apply a noise prediction filter as well as it happens for audio noise cancelling system. In this way we are able to The prediction filter is computed with the Levinson-Durbin recursion
Data are packed and sent to the main server where they are stored in a db. A track consists of many order data points. Each point have a PPE value and geo-graphical coordinates. So if you map the data points received by a user driving on a straight road it should be something like this.
But due to gps errors more frequently is something like this.
If you try to associate each data-point to the closest road you end up doing many errors like these
So the SmartRoadSense server applies a map-matching algorithm called EMMA which is able to reconstruct the itinerary and associate each data point to the right road.
Usually you have more than just one track on a road then you have to aggregate all the tracks
And obtain something like this. SmartRoadSense does it splitting each road in multiple segments of about 20 meters. Each segment is represented by an aggregated data point which contains an averaged value of all the single data points contained in this area. SmartRoadsSense also takes care of the ageing of the data assigning more value to newer data
In the end we end up having a dataset of aggregated data points. This dataset is available as OpenData in three different forms: as a big archive updated every 6 hours which can be freely downloaded by everyone; as a map like the one I showed you few minutes ago, which is also embeddable in any website; and also as webservices which is probably the most convenient way for developers.
So we have an efficient system which is already working – you can download the SmartRoadSense app from the Android and the iOS store right now - and we have already mapped about 20 % of the Italian road network. Recently we opened the system to Romania, Greece and UK but the plan is to open it to all the others European countries by the end of 2018. What we need now is more data! But As you have seen the SmartRoadSense app is pretty simple to be used but not particularly engaging. To talk you through the tools we are planning to use to attract more users I pass the stage to Mark.
We have been designing the gamification with four key outcomes in mind.
Encourage user interaction with the app. Increase the aps user base. Increase the amount of data gathered for lesser used roads. Encourage car sharing.
C4Rs - Crowd Sensing, Gamification and Our roads
• H2020 ICT-10-2015:Collective Awareness Platforms for
Sustainability and Social Innovation
• Coordinator: University of Urbino
• 7 partners
• 4 countries (IT, UK, FR, RO)
• Budget: EUR 1,543,866.76
• Duration: 36 months
• Start date: 01-01-2016
MOTIVATIONS.1 ROAD NETWORK
Main factor of
The upper layer alone
has a value of about
1 TRILLION Euros
per EU country
is essential to
Guarantee driving safety and comfort
Reduce vehicle operating costs
Reduce CO2 emissions
Any Euro not invested in road maintenance
causes 2 Euros of extra operating costs
73.7% of intra-EU
passenger transportROAD PASSENGER TRANSPORT IN EU
MAINLY PRIVATE CARS
CAR OCCUPANCY RATE
Well below 2 people/car
About 1.1 people/car
To engage people in monitoring road surface conditions by exploiting
the accelerometers on board of their smartphones
Exploit the sharing economy and the smart specialization
to harness collective intelligence to contribute to the
sustainability of the road network
CLOUD COMPUTING SHARING ECONOMY
Environment Road network