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Crash Data and Mobility Data: A Winning Mix for Road Safety


  • Crash Data: Essential yet Insufficient
  • Driving Behavior Data: Additional Mobility Datasets to Prevent Accidents
  • Embracing Intelligent Mobility Data Analysis
  • Michelin DDI’s Services Catalog: Preventing Accidents by Identifying High-Risk Areas

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Every year, more than one million people worldwide die in road accidents. They are the eighth leading cause of death in the world and the No. 1 cause for children and young people under 30. A better understanding of the causes and context of accidents is therefore essential for public authorities and communities to improve road safety and driver behavior. Until now, data analysis has been based mainly on road accident data. However, there is a growing amount of mobility data sources and driving behavior data sets now available. They can play a much larger and more comprehensive role in informing decision-making for infrastructure and safety managers, in addition to crash data.

Crash Data: Essential yet Insufficient

In the United States, crash data is aggregated by the National Highway Traffic Safety Administration (NHTSA). Founded in 1970, the NHTSA is a US federal government agency, belonging to the Department of Transportation. Operational since 1975, its Fatality Analysis Reporting System (FARS) contains data on a census of fatal traffic crashes within the 50 States*.  

What kind of crash data is available via FARS and where does it come from?

FARS data is obtained from documentation already held by the State:
  • Police accident reports
  • State vehicle registration files
  • State driver licensing files
  • State highway department data
  • Vital statistics
  • Death certificates
  • Coroner/medical examiner reports
  • Emergency medical service reports
  • Other state records
Information on over 989,451 motor vehicle fatalities has been collected, based on over 100 different coded data elements. FARS data sets already provide a crucial understanding of characteristics that are part of a crash, including the environment, road, vehicles, motorists, and other vulnerable users.  

Once crash data has been collected, what are the limits of crash data analysis?

FARS road accident data is very useful for identifying accident-prone areas, analyzing the circumstances and determining the causes of crashes. However, they are based exclusively on the analysis of accidents involving a motor vehicle traveling on a roadway normally open to the public, and that results in the death of a person (vehicle occupant or non-occupant) within 30 days. This crash data does not take into account other types of accident: material damage, non-fatal injuries or “near misses”.    

Driving Behavior Data: Additional Mobility Datasets to Prevent Accidents

To analyze road accident statistics more precisely and comprehensively, an array of previously untapped mobility data is now available. Once transformed and contextualized, driving behavior data makes it possible to evaluate risk areas with great precision. Near miss accident : a cyclist is stopped near a car after a harsh braking.  

The challenge

To move from a post-accident vision to a preventive approach based on the analysis of real data on driving behavior.  

The objective

To reduce accidents by identifying “near misses” that involve atypical driving behavior and that, until now, have not been taken into account.  

The principle

Capture data via an on-board GPS unit or smartphone application, which is then enriched, contextualized and analyzed to identify risky behavior:
  • Harsh acceleration
  • Harsh braking
  • Abnormal or risky speed level
  • Excessive or inappropriate speeds for the context

The benefit

Analyzing driving behavior data aggregated across road users reveals near misses (occurrence and intensity of atypical behaviors) and identifies and characterizes risk areas. These insights enable maintenance operations to be anticipated and thus improve road safety. With detailed analysis of aggregated driving behavior, it becomes possible to detect risks even before an accident occurs. And this can save road users’ lives.      

Embracing Intelligent Mobility Data Analysis

As more and more mobility data sources are available, the challenge is to capitalize on its potential to create effective decision-making tools. Simply providing raw data is of limited interest and does not immediately lead to a concrete action plan. The solution lies in intelligent data analysis to better understand and contextualize behavior in order to identify and secure risky zones.  

But how do you make mobility data smart?

Data researchers change the game and add value by transforming raw data into directly actionable insights. For decision-makers and infrastructure managers, this means access to levering maintenance plans and safety operations on their road network.   The challenge today is to develop a service that covers the entire data processing and analysis chain:
  • Continually capture raw data to obtain detailed information on driving behavior
  • Enrich via context data (e.g. weather data, day/night etc..) and transform with algorithms and models
  • Supply interpreted and actionable insights in operational decision support tools

Michelin DDI’s Services Catalog: Preventing Accidents by Identifying High-Risk Areas

Specializing in actionable intelligent mobility data, Michelin DDI has designed a platform of services based on driving behavior analysis. The goal? To simplify and optimize the evaluation and management of road networks for decision-makers and infrastructure managers.  


Prevent accidents by accurately detecting and locating hotspots of driving events that happened on your road network.  


Based on millions of US connected drivers & vehicles and our driving data behavior expertise, we capture 5 main events: Harsh Braking, Harsh Acceleration, Phone Handling, Suspected Collision, Excessive Speeding, all gathered on our services catalog. We combine and analyze driving events data including: Identification and severity grading, Details about events (counts of events, intensity indicator…), Contextualized data (weather, time of day, day of the week…).


  • Simplified digital diagnostics to analyze driving behavior data, which can be integrated into a local authority’s GIS system
  • Operational decision support to prioritize maintenance investments based on real data
  • Increased autonomy and savings with a reliable and easy-to-use tool
  • Ability to measure the impact of actions through comparative analysis of before/after indicators
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