How to use Artificial Intelligence & Data Science to improve safety on motorways?

This paper is submitted in part of the IRF World Road Meeting


  • Yassine Alouini, Data Scientist Qucit, M.A.S.T in Applied Mathematics, Cambridge,
  • Rémi Delassus, PhD Student Deep Learning for Semantic Segmentation of Aerial Imagery,
  • Marie Quinquis, Sales & Marketing Manager Qucit, Master 2 Business & Marketing strategy EM Normandie,

Abstract: Qucit


Road safety is the main problem of motorway operators. In 2013, “1,25 millions of persons died on highways in the world! “ (Global Health Observatory (GHO), 2013)”!  Another sobering figure is the life expectancy of a pedestrian on motorways: less than 20 minutes in France! It is a matter of life or death that patrollers reach the scene of an incident as fast as possible whenever it happens. Highway operators are collecting more and more data with connected cars, sensors and machine-to-machine communication.   So, how to use these tremendous amounts of data and new technologies to improve road safety? Qucit built RoadPredict: a dedicated tool for operators relying on machine learning that predicts when and where incidents will happen to optimize their patrols and save lives!   How doest it work?   Firstly, this technology combines all contextual data available and digitizes a road:

  • Static data: surfacing, location of the roads
  • Dynamic data: calendar, events, weather forecast
  • Sensor data: connected cars, traffic
  • Calibration data: historical data of incidents (accident, animals, breakdowns, wrong ways…)

No need for a tremendous amount of historical data to get highly reliable predictions! Then, RoadPredict automatically cleans the collected data to train machine learning models that predict where and when the probability to get an incident is the highest.   A real data-driven digital tool to help road operators and safety patrols anticipate the incidents to be ready & reach the incident scene faster!



With 11,100 kilometres of expressways in 2014 (NationMaster, 2015) and an average traffic of 57,031 vehicles per day ((ASFA (Autoroutes & Ouvrages Concédés), 2017),  France is ranked 7th in terms of road network in the world. France has improved its road safety for the past years: the number of physical accident decreased by 16% between 2010 and 2015 (Observatoire national Interministériel de la sécurité routière, 2016)). However, in 2016, there were 450,000 interventions, 56,109 physical accidents ((Observatoire national Interministériel de la sécurité routière, 2016) and 100 patrollers ((Le site de la sécurité du personnel autoroutier, 2016) struck during an intervention. Roads are highly related to the French history. Since 1955 and the end of the World War II, the government have introduced a reform on highways to make them private. The breakthrough took place in 2000 when the government conceded 9,137 kilometres of motorways to private companies.  One of them is ATLANDES: a private consortium of 6 companies operating the A63. This 206-kilometre stretch of highway is located in the South-West of France, between Bordeaux and Bayonne. The motorway operator is Egis Exploitation Aquitaine (EEA).   Like every highway operator, EEA looks for an accident rate as close as possible to 0. To begin with, they wish “to optimize their responsiveness to improve the safety of customers and patrollers” (Lengrand, 2017). They have many available data in relation to their systems, and even more coming up in the future thanks to M2M and IOT. However the question is how to use this available amount of data to predict incidents and improve road safety with their existing tools? Indeed, incidents are rare and very difficult to prevent or predict based on the existing data for a human being.   This paper demonstrates the usefulness of a predictive tool to improve road safety. We present the scientific protocol used in RoadPredict. We then explain the different steps involved in the pipeline of creating an accurate predictive model. Finally, we dive deeper into how artificial intelligence helps road operators improve the overall road safety (based on the use case of EEA on the A63).



Using artificial intelligence to solve a complex issue such as predicting incidents on roads implies a strong and proven methodology. A protocol of 6 distinct phases to deal with such a complex phenomenon has been set up: data collection, data cleaning, feature interpolation, machine learning, prediction, provision and visualization. The same protocol (described in the following section) is used throughout the different products developed by Qucit (BikePredict, ParkPredict, ComfortPredict, and RoadPredict). This approach is thus formalized in many internal code libraries and allows Qucit to rapidly iterate on new products.


Data collection


Before anything meaningful can happen, data is needed. In the methodology described hereafter, data comes in different formats. To classify the collected data at Qucit, two main attributes are considered:

  • Where the data come from: is it from our clients, open source, or bought from different providers
  • How much these data vary in time: is it hourly, weekly, monthly or yearly.

Based on this observation, here is a description of the steps performed in each case:

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