Mobility providers these days offer a wide range of mobility solutions. Companies like TIER, Felyx or Miles offer fleets of free floating sharing vehicles ranging from scooters to large transport vehicles. Ride hailing services like UBER, Bolt or FreeNow have disrupted the taxi markets around the world offering better quality, more convenience and lower prices than classical taxi companies.
All of these services depend on localization technology to identify the right chauffeur and notifying the customer about its approach, to locate sharing vehicles and letting the customers know where they can be found, and to keep track of position, movement and utilization of the fleet as a whole to have a data basis to improve operations.
The localization technologies are provided by the smartphones which are at the core of all of these services. Modern day smartphones use multi band GNSS(Global Navigation Satellite System) receivers to communicate with GPS satellites to locate the device within the global coordinate system.
Mobility companies are thriving, those that suffered from a dip by the COVID-19 pandemic have recovered while for others the past years have brought a major boom as people were avoiding public transit. Unit economics however are still tight. Many providers are still losing money on their fleets and market consolidations as well as investor expectations are raising the pressure. Considering the size of the fleets and the number of rides, even slight adjustments of cost, up or down, have a significant impact on the operator’s bottom line.
The services depend on the built-in GNSS technologies of smartphones and vehicle IOT-devices. While convenient, the technology has its downsides. Low frequency, poor data quality and high energy consumption of the technology have a significant negative impact on operational cost and customer satisfaction.
By using improved location and motion tracking technologies, providers are able to locate and track their vehicles more accurately while reducing battery consumption on the devices. This will increase customer satisfaction, reduce costs in maintenance and service and improve fleet utilization.
truemetrics offers precise location and motion data as a service. Our customers can implement our technology into their services with a simple SDK or using our API. Based on the data sent by their users’ devices we provide them with accurate location and motion data and offer the detection of motion patterns of interest, e.g. a customer getting off the e-bike or reckless driving behavior.
This happens in three simple steps
While GPS relies solely on the quality of the data from the satellite navigation system we also use the data of all the other motion sensors which are available in modern smartphones. Accelerometers record the device’s acceleration, gyroscopes measure its rotation, barometers track the air pressure and magnetometers sense changes in the magnetic field. Data from all these sensors is recorded and sent to the backend via a plug and play SDK (or via an API endpoint).
Additionally, if available, data from IOT devices within the vehicles can be used to further improve the accuracy of the estimation.
The data is processed by truemetrics’ algorithm engine. It consists of two core components.
Sensor fusion and filtering for state estimation: We use the data of multiple sensors which track the devices motion, each type of sensor has its own advantages and disadvantages. By fusing the data from different sensors we can improve the overall accuracy, reliability and frequency of the estimated state of the device which consists of position, orientation, velocity and acceleration.
Pattern recognition with machine learning: The original sensor data as well as the results of the sensor fusion are the input for machine learning models which detect motion patterns of interest. These motion patterns are linked to events which are of interest to our customers for their specific use case. Examples for such events in delivery use cases are a rider getting of their bike for the final drop off or reckless driving.
The result data provided by the truemetrics is twofold: