Telematics Data Research Articles

Auto Insurance Pricing Using Telematics Data: Application of a Hidden Markov Model

This study develops a hidden Markov model (HMM)-based clustering framework to predict auto insurance losses using driving characteristics extracted from telematics data. Through a simulation experiment based on a proprietary telematics dataset, we show that HMM can effectively classify driving trips using model-implied hidden states, and HMM-based pricing methods provide better predictive power measured by deviance statistics. Importantly, the proposed framework not only enables us to price usage-based insurances at a granular level but is also viable for estimating long-term insurance losses utilizing the limiting properties of HMM.

Drivers’ behavior confronting fixed and point-to-point speed enforcement camera: agent-based simulation and translation to crash relative risk change

Utilizing a novel microsimulation approach, this study evaluates the impact of fixed and average point-to-point Speed Enforcement Cameras (SEC) on driving safety. Using the SUMO software, agent-based models for a 6-km highway without exits or obstacles were created. Telematics data from 93,160 trips were used to determine the desired free-flow speed. A total of 13,860 scenarios were simulated with 30 random seeds. The ratio of unsafe driving (RUD) is the spatial division of the total distance travelled at an unsafe speed by the total travel distance. The study compared different SEC implementations under different road traffic and community behaviours using the Power Model and calculated crash risk changes. Results showed that adding one or two fixed SECs reduced RUD by 0.20% (0.18–0.23) and 0.57% (0.54–0.59), respectively. However, average SECs significantly lowered RUD by 10.97% (10.95–10.99). Furthermore, a 1% increase in telematics enforcement decreased RUD by 0.22% (0.21–0.22). Point-to-point cameras effectively reduced crash risk in all implementation scenarios, with reductions ranging from − 3.44 to − 11.27%, pointing to their superiority as speed enforcement across various scenarios. Our cost-conscious and replicable approach can provide interim assessments of SEC effectiveness, even in low-income countries.

NFORMATION MODEL OF VEHICLE TELEMATICS DATA CLUSTER COLLECTION USING UAV

Telematics is a set of basic systems that provide services in accordance with user needs. Telematics systems can be classified according to the areas of tasks performed: monitoring the condition of the road, managing the flow of traffic, providing information services to road users, etc. Telematics also allows you to monitor the condition of vehicles or their cargo using remote monitoring methods and technologies. The use of telematic systems and technologies allows for real-time management of transport systems. The article is devoted to the development of an information model for collecting motor transport telematics data using unmanned aerial vehicles. A model of the V2R network type is considered in which one car in the cluster is defined as a central node that receives telematic information from other cars in the cluster and then transmits the collected information to the base station. It is proposed to replace the central vehicle with a specialized UAV that collects telematic information from all vehicles in the cluster and transmits the collected information to the base station. A formula has been obtained to calculate the maximum amount of information that can be collected and transmitted to the base station.

Predicting Damage Incidents, Fines, and Fuel Consumption from Truck Driver Data: A Study from the Netherlands

Trucks are disproportionately involved in fatal traffic accidents and contribute significantly to CO2 emissions. Gathering data from trucks presents a unique opportunity for estimating driver-specific costs associated with truck operation. Although research has been published on the predictive validity of driver data, such as in the contexts of pay-how-you-drive insurance and naturalistic driving studies, the investigation into how telematics data relate to the negative consequences of truck driving remains limited. In the present study, driving data from 180 truck drivers, collected over a 2-year period, were examined to predict damage incidents, traffic fines, and fuel consumption. Correlation analysis revealed that the number of fines and damage incidents could be predicted based on the number of harsh braking events per hour of driving, whereas fuel consumption was predicted by engine torque exceedances. Our analysis also sheds light on the impact of covariates, including the engine capacity of the truck operated and time of day, among others. We conclude that the damage incidents and fines incurred by truck drivers can be predicted not only from their number of harsh decelerations but also through driving demands that extend beyond the driver’s immediate control. It is recommended that transportation companies adopt a systemic approach to mitigating truck-driving-related expenses.

In-vehicle Sensing and Data Analysis for Older Drivers with Mild Cognitive Impairment.

Driving is a complex daily activity indicating age and disease-related cognitive declines. Therefore, deficits in driving performance compared with ones without mild cognitive impairment (MCI) can reflect changes in cognitive functioning. There is increasing evidence that unobtrusive monitoring of older adults' driving performance in a daily-life setting may allow us to detect subtle early changes in cognition. The objectives of this paper include designing low-cost in-vehicle sensing hardware capable of obtaining high-precision positioning and telematics data, identifying important indicators for early changes in cognition, and detecting early-warning signs of cognitive impairment in a truly normal, day-to-day driving condition with machine learning approaches. Our statistical analysis comparing drivers with MCI to those without reveals that those with MCI exhibit smoother and safer driving patterns. This suggests that drivers with MCI are cognizant of their condition and tend to avoid erratic driving behaviors. Furthermore, our Random Forest models identified the number of night trips, number of trips, and education as the most influential factors in our data evaluation.

Context-aware driver risk prediction with telematics data

Driving risk prediction is crucial for safety and risk mitigation. While traditional methods rely on demographic information for insurance pricing, they may not fully capture actual driving behavior. To address this, telematics data has gained popularity. This study focuses on using telematics data and contextual information (e.g., road type, daylight) to represent a driver’s style through tensor representations. Drivers with similar behaviors are identified by clustering their representations, forming risk cohorts. Past at-fault traffic accidents and citations serve as partial risk labels. The relative magnitude of average records (per driver) for each cohort indicates their risk label, such as low or high risk, which can be transferred to drivers in a cohort. A classifier is then constructed using augmented risk labels and driving style representations to predict driving risk for new drivers. Real-world data from major US cities validates the effectiveness of this framework. The approach is practical for large-scale scenarios as the data can be obtained at scale. Its focus on driver-based risk prediction makes it applicable to industries like auto-insurance. Beyond personalized premiums, the framework empowers drivers to assess their driving behavior in various contexts, facilitating skill improvement over time.

Bayesian-based Component Lifetime Prediction Model Using Workshop and Telematics Data

This paper presents a Bayesian approach to predicting brake pad and battery life based on field service data from a fleet management system(FMS). The data includes vehicle driving data collected via telematics and maintenance record data managed by the workshop. The proposed approach consists of three modules: component health diagnosis, workshop data analysis and driving pattern analysis. The health diagnosis module detects domain-based transformed feature, from the driving data, changes using KL divergence. The maintenance record data from workshop analysis module estimates the prior probability of maintenance cycles. The censored nature of workshop data is validated by updating the posterior probability using driving patterns from driving data. The driving pattern analysis module classifies driving patterns for lifetime prediction. This study develops a predictive maintenance model for brakes and batteries without additional sensors using the data required for fleet operation. The mileagebased cycle maintenance approach commonly used for fleet management is improved by this model. Future FMS systems are expected to make extensive use of this concept.

A novel approach for analysing environmental sustainability aspects of combine harvesters through telematics data. Part II: an IT tool for comparative analysis

A novel approach for analysing environmental sustainability aspects of combine harvesters through telematics data. Part II: an IT tool for comparative analysis

A novel approach for analysing environmental sustainability aspects of combine harvester through telematics data. Part I: evaluation and analysis of field tests

A novel approach for analysing environmental sustainability aspects of combine harvester through telematics data. Part I: evaluation and analysis of field tests

Traffic Noise Assessment Using Intelligent Acoustic Sensors (Traffic Ear) and Vehicle Telematics Data.

Here, we introduce Traffic Ear, an acoustic sensor pack that determines the engine noise of each passing vehicle without interrupting traffic flow. The device consists of an array of microphones combined with a computer vision camera. The class and speed of passing vehicles were estimated using sound wave analysis, image processing, and machine learning algorithms. We compared the traffic composition estimated with the Traffic Ear sensor with that recorded using an automatic number plate recognition (ANPR) camera and found a high level of agreement between the two approaches for determining the vehicle type and fuel, with uncertainties of 1-4%. We also developed a new bottom-up assessment approach that used the noise analysis provided by the Traffic Ear sensor along with the extensively detailed urban mobility maps that were produced using the geospatial and temporal mapping of urban mobility (GeoSTMUM) approach. It was applied to vehicles travelling on roads in the West Midlands region of the UK. The results showed that the reduction in traffic engine noise over the whole of the study road was over 8% during rush hours, while the weekday-weekend effect had a deterioration effect of almost half. Traffic noise factors (dB/m) on a per-vehicle basis were almost always higher on motorways compared the other roads studied.

Telematics data for geospatial and temporal mapping of urban mobility: Fuel consumption, and air pollutant and climate-forcing emissions of passenger cars

In this study, we use the approach of geospatial and temporal (GeoST) mapping of urban mobility to evaluate the speed-time-acceleration profile (dynamic status) of passenger cars. We then use a pre-developed model, fleet composition and real-world emission factor (EF) datasets to translate vehicles dynamics status into real-urban fuel consumption (FC) and exhaustive (CO2 and NOx) emissions with high spatial (15 m) and temporal (2 h) resolutions. Road transport in the West Midlands, UK, for 2016 and 2018 is the spatial and temporal scope of this study. Our approach enables the analysis of the influence of factors such as road slope, non-rush/rush hour and weed days/weekends effects on the characteristics of the transport environment. The results show that real-urban NOx EFs reduced by more than 14 % for 2016–18. This can be attributed to the increasing contribution of Euro 6 vehicles by 63 %, and the increasing contribution of diesel vehicles by 13 %. However, the variations in the real-urban FC and CO2 EFs are less significant (±2 %). We found that the FC estimated for driving under the NEDC (National European Driving Cycle) is a qualified benchmark for evaluating real-urban FCs. Considering the role of road slope increases the estimated real-urban FC, and NOx, and CO2 EFs by a weighted average of 4.8 %, 3.9 %, and 3.0 %, respectively. Time of travel (non-rush/rush hour or weed days/weekends) has a profound effect on vehicle fuel consumption and related emissions, with EFs increasing in more free-flowing conditions.

Driving risk prevention in usage-based insurance services based on interpretable machine learning and telematics data

Usage-based insurance (UBI) adjusts premiums based on an individual policyholder’s dynamic risk evaluation, incentivizing policyholders to maintain safe driving behavior in pursuit of a lower insurance premium. Although post-trip interventions can improve driving behavior significantly, most interventions do not provide risk mitigation strategies tailored to long-term driving behavior. This study proposes a novel approach for driving risk prevention in UBI services, providing risk analysis services and tailoring driving suggestions for policyholders. Advanced prediction models and model interpretation methods were adopted to assess and analyze personal driving risks, respectively. A multi-objective counterfactual model was developed to generate risk mitigation strategies for policyholders. The significance of driving behavior in risk mitigation and its actionability were assessed to provide realistic and actionable suggestions for policyholders. The case study, based on a real dataset from an insurance company in China, showed that the proposed approach can be directly embedded in the existing UBI service framework to provide personalized feedback and services to policyholders.

Development of a Heavy-Duty Electric Vehicle Integration and Implementation (HEVII) Tool

<div class="section abstract"><div class="htmlview paragraph">As demand for consumer electric vehicles (EVs) has drastically increased in recent years, manufacturers have been working to bring heavy-duty EVs to market to compete with Class 6-8 diesel-powered trucks. Many high-profile companies have committed to begin electrifying their fleet operations, but have yet to implement EVs at scale due to their limited range, long charging times, sparse charging infrastructure, and lack of data from in-use operation. Thus far, EVs have been disproportionately implemented by larger fleets with more resources. To aid fleet operators, it is imperative to develop tools to evaluate the electrification potential of heavy-duty fleets. However, commercially available tools, designed mostly for light-duty vehicles, are inadequate for making electrification recommendations tailored to a fleet of heavy-duty vehicles. The main challenge is that light-duty tools do not estimate real-time vehicle mass, a factor that has a disproportionate impact on the energy consumption of large commercial vehicles. The Heavy-Duty Electric Vehicle Integration and Implementation (HEVII) tool advances the state of the art in evaluating electrification potential and infrastructure requirements for fleets of commercial vehicles. In this work, the HEVII tool is demonstrated with non-uniformly sampled telematics data from an existing fleet to assess the suitability for electrification of each individual vehicle, determine optimal locations for charging infrastructure to support a fleet of EVs and analyze associated costs. Payload mass is predicted using sparse ground-truth data for all input drive cycles and an initial data analysis is conducted to assess the characteristics driving behaviors and energy consumption of the fleet using an adaptable vehicle model. Battery size requirements are determined by applying a novel charger placement algorithm to maximize routes that are viable for EVs and balance time delays with infrastructure development costs. This work details and demonstrates the different aspects of the HEVII tool, presenting preliminary results from an example use case.</div></div>

A New Framework for Regional Traffic Volumes Estimation with Large-Scale Connected Vehicle Data and Deep Learning Method

Connected vehicle (CV) data in this paper refer to the in-vehicle telematic data, including trajectories and driving events (e.g., hard braking) collected by vehicle manufacturers when vehicles are moving. Recently manufactured vehicles are equipped with cellular modems and Internet of Things (IoT) devices to collect vehicle data. Such data, after removing personal information, are being redistributed to third-party organizations. Compared to other probe vehicle data, the CV data has a higher penetration rate, ubiquitous coverage, and almost lane-level positioning accuracy. These features pave the road for novel transportation applications in transportation planning and traffic operations. In this paper, we represent a novel framework to estimate the regional link volumes based on the CV data and a deep neural network (DNN) model. The training data are generated according to the link volumes (targeted model output) and the corresponding CV counts (input features) at the same locations. The DNN model’s performance was compared with other estimation methods like linear regression and random forest and showed superior performance. The trained DNN model takes ubiquitous CV counts from other locations to estimate the corresponding link volumes. As a case study, the proposed DNN model was trained with a large training data set derived from CV data and time-dependent link counts collected at over 1,200 locations on freeways in the Dallas Fort Worth, Texas, area. The results reveal good accuracy and robustness.

Weather Conditions and Telematics Panel Data in Monthly Motor Insurance Claim Frequency Models

Risk analysis in motor insurance aims to identify factors that increase the frequency of accidents. Telematics data is used to measure behavioural information of drivers. Contextual variables include temperature, rain, wind and traffic conditions that are external to the driver, but may also influence the probability of having an accident, as well as vehicle and personal characteristics. This paper uses a monthly panel data structure and the Poisson model to predict the expected frequency of claims over time. Some meteorological information is included. Two types of claims are considered separately: only those related to at-fault third-party liability accidents, and all types of claims including assistance on the road. A sample of drivers in Spain in 2018–2019 is analysed with information on claiming frequency per month. Drivers were observed for seven months. Our analysis is novel because monthly summaries of telematics information are combined with weather data in a panel structure, revealing that external factors affect the expected claims frequencies. Reckless speeding behaviours and intense urban circulation increase the risk of an accident, which also increases with windy conditions.

Integration of Traditional and Telematics Data for Efficient Insurance Claims Prediction

Integration of Traditional and Telematics Data for Efficient Insurance Claims Prediction

A Data-driven Survival Modelling Approach for Predictive Maintenance of Battery Electric Trucks

Predictive Maintenance (PdM) aims to estimate the optimal moment when the maintenance of an industrial asset should be performed according to its actual health status. The goal is to minimize the costs, by finding the optimal point where the sum of the prevention and repair cost is at the lowest. Data-driven model may predict whether an asset is close to a real breakdown, therefore helping to build more cost-efficient maintenance strategies. This paper focuses on survival analysis based predictive maintenance applied to the operation of Battery Electric Trucks (BET). Cox Proportional Hazards and Random Survival Forests methods are adopted for modelling time-to-failure and the associated survival functions. Detailed telematics data from BET vehicles in real operations are used for modelling and analysis. The model performance is further improved by the feature selection and hyperparameter tuning processes.

Possibilities for the Development of an Electric Hybrid Skidder Based on Energy Consumption Measurement in Real Terrain Conditions

Growing demand for forest machines that cost less to operate than current compared to traditional hydraulic and mechanical ones, along with regulatory pressures for lower emissions, is increasing manufacturers’ interest in developing electric and hybrid drives. While purely electric drives of forest machines meet a lot of bottlenecks (costs of the electric components, battery durability, duration of charging, access to the electrical grid, size of batteries that can ensure enough energy for 8 h working time), electric hybrid drives offer a favorable solution for the propulsion of forestry machinery in terms of lower fuel consumption and improved efficiency. Among all forest vehicles, specialized forest tractors (skidders), so far, have not been considered for forest vehicles with hybrid drive capabilities. A skidder is a forest-articulated self-propelled vehicle for pulling trees or parts of trees. In most countries in southern Europe, the use of skidders equipped with forest winches is the most common technique for timber extraction. The first goal of the research is to develop methods for measuring the energy consumption of skidders at different operating tasks and under different field conditions. Research was performed on the skidder Ecotrac 140V (from Croatian producer Hittner Ltd.) during timber extraction in mountainous terrains in Lika–Senj County. The skidder was equipped with a measuring device WIGO-E (Telematic Data collector) gateway with an integrated GPS system, which ensured data were collected from sensors and motor and stored in a computer via CANBUS and data transfer with GSM to Web platforms. Additionally, a fuel-flow meter was installed on the skidder. Data on fuel consumption (mL), position (traveling route), detection of winch work, engine rpm (min−1), engine torque (% of max), throttle position (%), and engine temperature were measured with a sampling frequency of 5 s. Furthermore, skidder load volumes per cycles and slopes of tractor paths were constantly measured. The paper shows the skidder’s energy consumption per day, work cycle, and individual work procedure with regard to the size of the load, the slope of the tractor path, and the direction of movement based on overlapping and merging all measurement data. Using mathematical and simulation models of the drive with defined operating cycles obtained by measurement, the possibilities of the hybrid drive and the dimensions of the elements of the hybrid drive (internal combustion engine, electric motor, batteries, control unit) were determined and are presented in this paper.

Driver behavior indices from large-scale fleet telematics data as surrogate safety measures

Large-scale telematics data enable a high-resolution inference of road network’s safety conditions and driver behavior. Although many researchers have investigated how to define meaningful safety surrogates and crash predictors from telematics, no comprehensive study analyzes the driver behavior derived from large-scale telematics data and relates them to crash data and the road networks in metropolitan cities. This study extracts driver behavior indices (e.g., speed, speed variation, hard braking rate, and hard acceleration rate) from large-scale telematics data, collected from 4000 vehicles in New York City five boroughs. These indices are compared to collision frequencies and collision rates at the street level. Moderate correlations were found between the safety surrogate measures and collision rates, summarized as follows: (i) When normalizing crash frequencies with traffic volume, using a traffic AADT model, safety-critical regions almost remain the same. (ii) The correlation magnitude of hard braking and hard acceleration varies by road types: hard braking clusters are more indicative of higher collision rates on highways, whereas hard acceleration is a stronger hazard indicator on non-highway urban roads. (iii) Locations with higher travel times coincide with locations of high crash incidence on non-highway roads. (iv) However, speeding on highways is indicative of collision risks. After establishing the spatial correlation between the driver behavior indices and crash data, two prototype safety metrics are proposed: speed corridor maps and hard braking and hard acceleration hot-spots. Overall, this paper shows that data-driven network screening enabled by telematics has great potential to advance our understanding of road safety assessment.

Data analytics for improved closest hospital suggestion for EMS operations in New York City

In addition to fires and various other emergencies, the Fire Department of New York City (FDNY) responds to medical emergencies throughout New York City. When a patient requires hospital care, the FDNY uses a hospital suggestion pattern to recommend the closest hospital that can appropriately treat the patient, given the geographic location of the incident. In a city like New York with a high density of hospitals, determining the closest hospital (in terms of travel time) is not trivial. This paper presents the results of a collaboration between the FDNY and Columbia University which integrates data analytics in the derivation, implementation and monitoring of this hospital suggestion pattern. Both telematics data of city-owned vehicles and historical ambulance travel times to hospitals are utilized to estimate the order of closest hospitals for a given incident location. Implementation constraints (coarse spatial and temporal resolutions of the pattern) and sparsity of the historical ambulance dataset generate large aleatory and epistemic uncertainties that are accounted for using a probabilistic data fusion procedure. The result of this analysis, hospital suggestion pattern N, was implemented in December 2020. A statistical analysis shows an overall decrease in travel times to hospitals as an effect of this pattern. • A data-driven methodology is used to optimize hospital transports in New York City. • The methodology leverages a road network and origin–destination ambulance data. • Uncertainties in travel times are accounted for in the data fusion procedure. • The proposed solution was successfully deployed in December 2020 in New York City.