Trip
This section describes the trip analysis API.
Description
The overall approach behind DriveQuant’s trip analysis service is based on vehicle dynamics and powertrain modeling. Using vehicle or smartphone sensors, our services estimate the efforts applied to the powertrain enabling us, for example, to remodel the efforts between the road and the wheels or to estimate the exhaust pollutants.
DriveQuant’s data analysis service delivers a wide range of indicators describing vehicle usage and driver behavior. For a single trip, DriveQuant’s trip analysis service retrieves:
eco-driving indicators,
an estimate of the fuel consumption,
safety indicators,
tire and brake wear measurements,
pollutant emissions estimation,
a distraction score (phone use),
and a speed limit score.
The trip analysis API is automatically requested by the DriveKit SDK at the end of each trip.
This API can also be used without the DriveKit SDK if you have your own GPS data collection system (OBD dongle, black box, vehicle data).
The distraction score (phone use) is only available for SDK users.
An additional cost is required for the use of the speed limit score, which is calculated using data coming from map data providers.
This section explains how to query the trip analysis API, how driving indicators are computed and how they are structured.
DriveQuant services provide additional data by collecting all vehicle trips so you can easily retrieve statistics for each of your vehicles. We recommend to add a new vehicle before requesting the trip analysis API if you target a vehicule mantenance use case.
Trip
POST https://service.drivequant.com/v2/trip
This method returns all driving analytics calculated by DriveQuant for a trip.
Request Body
Request
Account
DriveQuant counts the number of active assets per customer. The DriveQuant API is a pay-per-active-asset API. An asset is considered active if it has performed at least one trip on a monthly basis. An asset can be a driver (identified with its driverId) or a vehicle (identified by a vehicleId).
Three main use cases can be considered:
The request includes only a driverId: This is common when the data collected comes from a mobile application installed on a driver's phone. The total number of assets per customer is equal to the number of unique driverId's.
The request includes only a vehicleId: This is common when the data collected comes from a telematics device plugged into the vehicle. The total number of assets per customer is equal to the number of unique vehicleId's.
The request includes a driverId and a vehicleId: This is common when a group of drivers can use several vehicle within a fleet. The total number of assets may be the number of unique vehicleId's or driverId's. Billing and counting will depend on your business model and the difference between the number of drivers and vehicles. Please contact DriveQuant sales department to find out the best pricing model.
The Account object must contain the account and the userId or vehicleId attributes.
Route
A request must contain all the data corresponding to a single trip. The trip data analysis cannot be cut into multiple queries. It is not recommended to merge data from several trips into a single request.
Route object must contain at least the vehDate or gpsDate and at least gpsVelocity or vehVelocity attributes.
The input variables included into Route object are arrays which must contain the same number of data points.
The sample period for all input vectors must be 1 second. The sampling frequency of 1Hz is a standard for GPS sensors. in case your telematics device does not satisfy this constraint, please contact us to determine what alternative can be applied.
Vehicle
Some parameters have a default value if not set, and a min and max limitations:
ItineraryData
Itinerary object is optional.
MetaData
Metadata can be used if you want to add some of your specific data in a trip. They can be added to the Trip API as a key/value object where the key and value have a String type
Example of JSON body request
Response
The table below summarizes the list of driving data analysis modules. The comments and itinerary statistics modules are included by default. All other modules are optional and can be combined as needed.
Comment
Possible values are described here.
ItineraryStatistics
Itinerary Statistics module provides several indicators that characterize the trip conditions: the trip distance, the vehicle movement duration and the idle duration. We also compute the number of sub-displacements. A trip can be characterized as a succession of events either dictated by the driver’s will or by external factors. These events, called breakpoints, are indicated with black dots in the figure below. Each section of a trip between two breakpoints is called sub-displacement. The figure illustrates these concepts for a short trip with one traffic light (vehicle stopped) and one intersection with priority (vehicle deceleration followed by acceleration).
ItineraryData
Eco-driving
Description
Eco-driving module performs an analysis of the entire trip, characterized by a succession of events and road segments. The driving efficiency is computed by comparing the vehicle speed recorded with an optimal speed profile, for each segment. This calculation takes into account the actual driving conditions and a physical vehicle model that captures the inertial dynamics of the vehicle and the efficiency of the powertrain components. The eco-driving score ranges from 0 and 10, and is calculated by comparing the actual energy consumed during the trip with the energy that would have been consumed using the optimal speed profile. The best eco-driving score corresponds to the highest driving efficiency.
Scores definitions
Acceleration, deceleration and speed maintain phases have a large impact on the vehicle’s fuel consumption. As a consequence, by comparing the real and the optimal speed profiles, eco-driving analysis module returns 3 key driving indicators.
The acceleration and deceleration scores range from -5 to +5. For a value of (-5), your acceleration or deceleration is too slow. For the highest value (+5) you are accelerating or decelerating too fast. A score of (0) indicates that your acceleration or deceleration perfectly matches with an eco-driving style.
The speed maintain score ranges from 0 to 5. The minimum value (0) indicates that the driver has an appropriate behavior and drives at a constant speed. On the other hand, if the driving analysis module detects an oscillating speed profile, this score increases. The highest value (+5) indicates that you can improve to keep a constant speed to reduce your fuel consumption.
These indicators will help you improve your driving efficiency and reduce your energy consumption. They can also illustrate the level of anticipation that the drivers should adopt to avoid harsh accelerations and brakings.
The numerical values of driving scores can be transformed into driving tips. The tables below give examples of content that can be returned to a driver based on the driving notes of a trip:
The eco-driving analysis is performed for vehicle displacement greater than 100 meters and speed higher than 10 km/h. This avoids providing inaccurate driving scores during vehicle maneuvers (slow driving in traffic jams or in parking lots...). In that case, eco-driving analysis module returns the following error codes:
11 for the eco-driving score
6 for acceleration phase, stabilized speed phase and deceleration phase
Energy class
The energy class « energyClass » depends on the average fuel consumption of the vehicle. It positions the trip fuel consumption with respect to the average consumption of the vehicle.
Advanced eco-driving
In order to provide fair scoring and to facilitate drivers comparison, the trip scores can be accompanied by road class scores. These scores are included in the EcoDrivingContext class into the Advanced Modules. This service differentiates 5 types of road conditions (contextId) to contextualize the driving scores:
0 - Traffic jam: This corresponds to vehicle displacements of less than 100 meters and performed with speeds below 10 km/h. In traffic jams, it is obviously not possible to improve your driving, that is why scoring are not provided for this type of road.
1 - Heavy urban traffic
2 - City
3 - Suburban
4 - Expressways
In case a road type is not included in the trip, the distance and duration percentages are equal to zero, the efficiency score is set to 11 and the driving scores (Accel/Main/Decel) are set to 6.
EcoDrivingContext
Fuel estimation
Description
The fuel consumption is obtained from a backward computation based on a mathematical modeling of the vehicle and powertrain. Fuel consumption estimation asseses the mechanical traction power from the vehicle speed and mass. A power transfer is performed between each component of the powertrain to compute the engine torque. Finally, the engine torque combined with the vehicle speed help to process the fuel mass from an engine consumption map developed by IFPEN. This fuel mapping is adapted according to the vehicle parameters and is valid for any type of vehicle or fleet of vehicles.
This approach is particularly adapted to estimate real-driving fuel consumption which can be more than 20% above the homologation data provided by car manufacturers. The main advantage lies in the accuracy of the estimate that can reach 5% when all input variables are available and the vehicle parameters set. The estimation method accounts for the physical fuel characteristics. These parameters are listed below:
Values calculated by fuel estimation module are described below:
Advanced fuel estimation
In order to provide detailed fuel consumption estimation across the vehicle trips, the advanced fuel estimation service may return the fuel consumption for each driving context of the vehicle. This service differentiates 5 types of road conditions (contextId):
0 - Traffic jam
1 - Heavy urban traffic
2 - City
3 - Suburban
4 - Expressways
In case a road type is not included in the trip, the percentages of distance and duration are equals to zero as well as the co2Mass, co2Emission, fuelVolume and fuelConsumption.
FuelEstimationContext
EnergyEstimation
For electric vehicles, DriveQuant provides energy information instead of fuel estimation.
The EnergyEstimation sub-document is only present in the response body for electric vehicles. If the configured vehicle is a conventional vehicle, the energy estimation is not added in the response body.
AdvancedEnergyEstimation
To provide more details on the energy consumption, the service may return the energy data for each driving context of the vehicle.
The AdvancedEnergyEstimation sub-document is only present in the response body for electric vehicles. If the configured vehicle is a conventional vehicle, the energy estimation is not added in the response body.
Safety
If trip is too short to be scored, safetyScore is set to 11.
Advanced safety
SafetyContext
In case a road type is no included in the trip, the distance and duration percentages are equal to zero and the safety score is set to 11.
SafetyEvents
The data provided in the following table helps to display safety events on your map system and deliver the locations of harsh braking or acceleration as well as the coordinates where a tire to road adherence threshold has been crossed.
The service provides the time, coordinate and severity level of each event. Two levels of severity are calculated using specific threshold values for each event.
Pollutants
Tire and brake wear estimates
Description
The wear analysis service has been designed to monitor the brake pads and tires wear levels. This service was designed to improve vehicle maintenance solutions and can be used to create maintenance alerts.
The wear estimation relates to the power dissipated in the tire - road or brake disc - brake contact. To compute this variable our service relies on the vehicle’s dynamic modelling that estimates the braking efforts and the efforts at the contact between the tire and the road.
For each trip, the service gives for each components (front/rear brakes/tires):
The worn mass fraction during the trip.
The total worn percentage since the installation of new tires or brakes.
Tire and brake wear indicators for a single trip
The wear analysis service measures tire wear and brake pad wear. The service allows to distinguish the front and rear axles of the vehicle. The wear is assumed to be the same for the right tire and the left tire of the vehicle. A similar assumption is made for the brake pads.
The output value corresponds to a mass fraction of the worn component (tire or brake pad). This value is expressed in thousandth part of parts-per-million (ppm) which is a unit equivalent to a parts-per-billion (ppb).
The worn mass fraction (tire or brake pad) is given for each individual trip. To obtain the total worn mass fraction, the sum of all the trip’s values must be done. The total worn mass fraction value is bounded between a minimum and a maximum value:
Minimum value = 0 ppb. If the total worn mass fraction is 0, the component (tire or brake pad) is assumed to have no wear.
The wear calculation service estimates for each trip:
The worn fraction of front tires
The worn fraction of the rear tire
The worn fraction of the front brakes
The worn fraction of the rear brakes.
Example of tire autonomy calculation:
where ω is the tire worn mass fraction for the trip i (in ppb) and di is the distance (in km) of the trip i. The wear function f(ω) is a decreasing function that goes from infinity to zero. When f(ω) equals zero, the tire has no longer autonomy and must be replaced. This function is displayed below:
Total tire and brake wear indicators
In order to facilitate the integration within a vehicle maintenance service, the DriveQuant API provides for all your requests the following set of data related to any component (tires or brakes):
The total distance traveled with a given component (Distance)
The level of wear of the considered component (TotalWear)
The remaining autonomy before a complete wear (Autonomy)
The average wear speed of the considered component (WearRate)
The total autonomy of the considered component is assumed to be equal to the total travelled distance plus the remaining autonomy (Distance + Autonomy).
The diagram below illustrates the principle and the meaning of these quantities. The figure shows the evolution of the wear as a function of the distance traveled with the component. This is a generic representation that applies to a brake pad or a tire.
The autonomy of a set of tires or brakes is calculated for each axle (front and rear). For example, the most worn tire between the left tire and the right tire on the same axle is used.
The wear rate measures the component wear (in %) as a function of distance. This variable allows to compare vehicles and to inform a vehicle fleet manager whose components are wearing out too quickly. The wear rate is related to the type of vehicle, the driving style, the type of road used and the driving condition. This is why its value can change if the typology of trips changes and if the vehicle is driven by several drivers who have different driving styles.
Driver distraction (score)
Distracted driving becomes a serious problem and is becoming a major road safety issue.
That's why we've developed a service that measures the driver's interactions with his smartphone while driving. The objective is to increase driver awareness through a distraction score. This can be used to compare drivers, to classify them or to organize driving challenges.
The DriveKit SDK is capable of measuring the two main indicators of distracted driving:
screen unlocks,
and outgoing or incoming (and answered) phone calls.
The distraction score depends on 2 parameters :
the smartphone unlocking frequency;
the total duration of the call (or calls if there are several in the same trip).
Each parameter is giving a sub-score from 0 to 10. The distraction score is the minimum between these two sub-scores.
The sensitivity function linking the number of unlocks per 100 km to the unlock score is shown below.
The sensitivity function linking the call duration to the call score is shown below.
This service is only available with the DriveKit mobile SDK.
The response body includes the trip scores as well as detailed data related to unlocks and calls. This way you can better understand the score construction and provide clear explanations to the driver.
The data returned by the service are listed in the table below:
Call
The Driver distraction score provides an array with all the calls answered or made by the driver. For each call, it contains information about its conditions.
Response body example
Example of use
The screenshot below shows how to display the phone use data on a mobile phone interface:
Driver distraction (events)
The distraction score service provides additional information related to screen unlock and screen lock events. Thus, it is possible to get the location of events related to phone use. These informations are useful if you need to display these data on your mapping system or if you want to perform advanced data analysis.
The table below lists all the data returned by the service:
Response body example
CallEvents
Thanks to the distracted driving analysis service, you will be able to obtain contextualized and geolocalized data related to the detected events. This information is of great importance for the evaluation of the road risk and to help the driver to improve (by presenting this information on a map for example).
Response body example
Speed Limit
This service measures distance and driving time when the vehicle speed exceeds the speed limit.
From this information, a speed limit score is calculated. The speed limit score is 10 if the overspeed distance is zero. The speed limit score decreases with the increase in the percentage of distance spent in overspeeding.
The sensitivity function linking the relative speeding distance (in %) with the speeding score is shown below.
This service returns a global (or trip) score for the entire trip as well as several sub-scores corresponding to each of the legal speed portions traveled during the trip.
The global (or trip) score is the average of the sub-scores weighted by distance.
SpeedLimitContexts
Response body example
SpeedingEvents
The service that computes the speeding score also returns location-based information about overspeeding events.
This type of information is used to indicate on a map the places on the trip where the speed of the vehicle exceeds the speed limit.
The start and end positions of overspeeding segments are included in the SpeedingEvents table.
Response body example
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