Car Data Research Articles

Modeling of Car Insurance Premiums Using the Bayes Method with Poisson and Exponential Distributions

Car insurance is included in the general insurance category. Determination of general insurance premiums can be done through various approaches, one of which is the Bayes method used in this study. The Poisson distribution is chosen to model the frequency of claims, while the Exponential distribution is used to model the amount of claims. Insurance premiums are calculated by multiplying the expected frequency of claims by the expected amount of claims. Based on the results of the analysis of car insurance data using the Bayes method, it was found that the highest premium rates were for Mercedes brand vehicles, while the lowest rates were for Saab brand vehicles. The results of this calculation can be used by insurance companies as a reference in managing car insurance reserve funds.

Error and Traffic State Analysis of Floating Car Data

Abstract. Floating car technology is widely applied in traffic information collection and has become a common method for real-time and dynamic monitoring of urban traffic conditions. Using floating car data from Shanghai as the research object, this paper analyzes the positioning error of floating cars and finds that most errors are within 15 meters. The error is significantly affected by vehicle speed but less by time, and the error decreases as the road grade increases. Speed and travel time ratio are used as parameters to assess the spatiotemporal traffic state, exploring the spatial distribution and temporal evolution of speed, as well as the spatiotemporal distribution of traffic congestion. In the spatial distribution of speed, clustering analysis is used to deduce the temporal distribution pattern of speed on the same road segment. Based on this, corresponding traffic management measures can be applied to different road segments and at different times.

Study protocol of the safe driving program for the prevention of car accidents: A randomized controlled trial protocol.

Driving cessation is strongly associated with adverse health outcomes in older adults. Although numerous studies have focused on driving skill interventions for safe driving, the effects of interventions on car accidents remain unclear. We designed a randomized controlled trial to examine the effects of driving skill training on the prevention of car accidents among community-dwelling older adults using on-road training. A total of 1408 community-dwelling older drivers aged ≥65 years participated in the randomized controlled trial with blinded endpoint assessment. Participants randomized to the intervention group (n = 697) underwent four sessions, with 200 min re-training focused on the problem of driving in the older people. Controls (n = 697) received one classroom education session. On-road driving performance was assessed by certified driving school instructors in a driving school. The primary endpoint is an incident car accident, which will be detected based on the Japanese national car accident report data, after 24 months. This study has the potential to provide the first evidence of the effectiveness of on-road driving skill training regarding the prevention of car accidents. If our trial results show a lower number of car accidents due to driving skill training, this kind of intervention will provide an effective method for maintaining safe driving. These results will be disseminated to the appropriate national transportation agencies to improve or modify safe driving policies and possibly extend the validity of driver's licenses for older adults. UMIN-CTR (identification number: UMIN000034709). Registered 31 October 2018. Geriatr Gerontol Int 2024; ••: ••-••.

Railway Track Irregularity Estimation Using Car Body Vibration: A Data-Driven Approach for Regional Railway

Track and preventive maintenance are necessary for the safe and comfortable operation of railways. Track displacement measured by track inspection vehicles or trolleys has been primarily used for track management. Thus, vibration data measured in in-service vehicles have not been extensively used for track management. In this study, we propose a new technique for estimating track irregularities from measured car body vibration for track management. The correlation between track irregularity and car body vibration was analysed using a multibody dynamics simulation of travelling rail vehicles. Gaussian process regression (GPR) was applied to the track irregularity and car body vibration data obtained from the simulation, and a method was proposed to estimate the track irregularities from the constructed regression model. The longitudinal-level, alignment, and cross-level irregularities were estimated from the measured car body vibrations and travelling speeds on a regional railway, and the results were compared with the actual track irregularity data. The results showed that the proposed method is applicable for track irregularity management in regional railways.

METHOD OF MONITORING TRAFFIC FLOWS USING THE BRIEFCAM SYSTEM

Urban logistics forms the backbone of modern cities, ensuring the efficient movement of goods and services. With growing urbanisation and traffic congestion, optimising traffic flows has become crucial. The study monitors traffic flows using BriefCam software to enhance sustainable urban logistics. Cost-effective surveillance systems like CCTV generate vast data, opening new opportunities for improving traffic flow monitoring. Traditional methods, while reliable, are limited by labour intensity, cost, and accuracy. BriefCam software optimises vehicle monitoring through structured methodologies, including collection of data, filter application, result validation, and data generation. Utilising this data provides insights to improve traffic management, urban transport speed, and sustainable logistics practices. Modern cities use affordable CCTV systems, operating 24/7 and accumulating vast data suitable for automated traffic monitoring. Traditional methods require significant labour and costs, whereas floating car data (FCD) offers real-time traffic data collection, despite some reliability issues. BriefCam provides an innovative approach with a structured methodology for monitoring vehicles, aiding in sustainable urban logistics by optimising traffic flows and improving urban transport speed. The article provides a detailed look at the methods used in the study, the process of using BriefCam to monitor vehicles, the filters applied for detailed analysis, techniques for verifying the results, and general conclusions drawn from the study. Efficient traffic management based on this analysis can lead to additional measures during peak hours, optimising traffic lights, and increasing lanes. Knowing the time frames when freight vehicle numbers rise helps businesses plan deliveries to avoid peak congestion, enhancing logistics efficiency. BriefCam software offers immediate insights for reducing congestion, enhancing safety, and improving urban quality of life. Integrating digital approaches with traditional methods provides a comprehensive view of traffic dynamics, which aids urban planners and policymakers in optimising strategies, reducing emissions, and creating better living conditions in cities. Keywords: traffic monitoring, urban traffic management, intelligent transport systems, freight traffic flow.

From Radar Sensor to Floating Car Data: Evaluating Speed Distribution Heterogeneity on Rural Road Segments Using Non-Parametric Similarity Measures

Rural roads, often characterized by winding paths and nearby settlements, feature frequent curvature changes, junctions, and closely spaced private accesses that lead to significant speed variations. These variations are typically represented by average speed or v85 profiles. This paper examines complete speed distributions along rural two-lane roads using Floating Car Data (FCD). The Wasserstein distance, a non-parametric similarity measure, is employed to compare speed distributions recorded by a radar Control Unit (CU) and a selected FCD sample. Initially, FCD speeds were validated against CU speeds. Subsequently, differences in speed distributions between the CU location and specific sections identified by sharp curves, intersections, or accesses have been assessed. The Wasserstein Distance is proposed as the most effective synthetic indicator of speed distribution variability along roadways, attributed to its metric properties. This measure offers a more concise and immediate assessment compared to an extensive array of statistical metrics, such as mean, median, mode, variance, percentiles, v85, interquartile range, kurtosis, and symmetry, as well as qualitative assessments derived from box plot trends.

Predictive Models for Aggregate Available Capacity Prediction in Vehicle-to-Grid Applications

The integration of vehicle-to-grid (V2G) technology into smart energy management systems represents a significant advancement in the field of energy suppliers for Industry 4.0. V2G systems enable a bidirectional flow of energy between electric vehicles and the power grid and can provide ancillary services to the grid, such as peak shaving, load balancing, and emergency power supply during power outages, grid faults, or periods of high demand. In this context, reliable prediction of the availability of V2G as an energy source in the grid is fundamental in order to optimize both grid stability and economic returns. This requires both an accurate modeling framework that includes the integration and pre-processing of readily accessible data and a prediction phase over different time horizons for the provision of different time-scale ancillary services. In this research, we propose and compare two data-driven predictive modeling approaches to demonstrate their suitability for dealing with quasi-periodic time series, including those dealing with mobility data, meteorological and calendrical information, and renewable energy generation. These approaches utilize publicly available vehicle tracking data within the floating car data paradigm, information about meteorological conditions, and fuzzy weekend and holiday information to predict the available aggregate capacity with high precision over different time horizons. Two data-driven predictive modeling approaches are then applied to the selected data, and the performance is compared. The first approach is Hankel dynamic mode decomposition with control (HDMDc), a linear state-space representation technique, and the second is long short-term memory (LSTM), a deep learning method based on recurrent nonlinear neural networks. In particular, HDMDc performs well on predictions up to a time horizon of 4 h, demonstrating its effectiveness in capturing global dynamics over an entire year of data, including weekends, holidays, and different meteorological conditions. This capability, along with its state-space representation, enables the extraction of relationships among exogenous inputs and target variables. Consequently, HDMDc is applicable to V2G integration in complex environments such as smart grids, which include various energy suppliers, renewable energy sources, buildings, and mobility data.

Accounting for taxi service conditions in estimating route travel time from floating car data using Markov chain model

Recognising the variations in driving behaviour between taxis in the empty and carry conditions is pivotal for enhancing the accuracy of route travel time estimations using floating car data. However, existing methods largely overlook this distinction. In light of this, this study aims to harness these variations for more precise estimations. Utilising taxi data, we segmented the information by service conditions and executed distinct estimations for each segment. The route travel time was deduced through convolutional operation, complemented by a Markov chain model to discern correlations between travel times across various links. Our innovative approach realised a substantial enhancement in accuracy. Notably, when accounting for distinct service conditions, there was a reduction of 51.44% in mean absolute error and a 46.83% decline in maximum percentage error. By providing more accurate and reliable travel time predictions, our methodology enables better-informed traffic management decisions. Accurate travel time estimations are essential for optimising traffic signal timings, planning efficient routing strategies, and managing road network usage. These improvements in traffic management can lead to smoother traffic flow, reduced travel times, and ultimately, diminished congestion in urban areas.

Implementation of Density-Based Spatial Clustering of Applications with Noise and Fuzzy C – Means for Clustering Car Sales

This study compares the performance of two clustering algorithms, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Fuzzy C-Means (FCM), in clustering car sales data at PT. XYZ. The dataset, comprising sales transactions from 2020 to 2023, includes information about vehicles, customers, and transactions. Preprocessing methods such as data transformation and normalization were applied to prepare the data. The results indicate that DBSCAN produces clusters with better validity, measured using the Silhouette Score, compared to FCM. Specifically, DBSCAN achieves the highest Silhouette Score of 0.7874 in cluster 2, while FCM reaches a maximum score of 0.3666 in cluster 3. Thus, DBSCAN proves to be more optimal for clustering car sales data at PT. XYZ, highlighting its superior performance in terms of cluster validity.

Implementasi Model Prototye Dalam Pembuatan Aplikasi Pemesanan dan Perawatan Taxi Berbasis Website

Travel Janyakng Group is a travel company that operates in the car rental sector, where currently the company is facing several problems such as consumer inconvenience in ordering, apart from that, there are also difficulties in managing car maintenance data at the company. Seeing these problems, with the era of information technology Currently, companies can take steps, namely creating a website-based information system with the aim of improving the taxi ordering and maintenance process, as well as improving service to consumers. There are several methods used, namely the software development method using a prototype model and the data collection method using observation, interviews and literature study, while for building website-based applications the PHP and MySQL languages ​​are used, while for the application prototype the Balsamiq Wireframes application is used. In this application there will be three activities involved, namely user, admin and taxi driver. By using this application, it is hoped that the company can improve the quality of service to customers, make the ordering and maintenance process more efficient and the company can make better strategic decisions

Macroscopic fundamental diagram with volume–delay relationship: Model derivation, empirical validation and invariance property

This paper presents a macroscopic fundamental diagram model with volume–delay relationship (MFD-VD) for road traffic networks, by exploring two new data sources: license plate cameras (LPCs) and road congestion indices (RCIs). We derive a first-order, nonlinear and implicit ordinary differential equation involving the network accumulation (the volume) and average congestion index (the delay), and use empirical data from a 266 km2 urban network to fit an accumulation-based MFD with R2>0.9. The issue of incomplete traffic volume observed by the LPCs is addressed with a theoretical derivation of the observability-invariant property: The ratio of traffic volume to the critical value (corresponding to the peak of the MFD) is independent of the (unknown) proportion of those detected vehicles. Conditions for such a property to hold are discussed in theory and verified empirically. This offers a practical way to estimate the ratio-to-critical-value, which is an important indicator of network saturation and efficiency, by simply working with a finite set of LPCs. The significance of our work is the introduction of two new data sources widely available to study empirical MFDs, as well as the removal of the assumptions of full observability, known detection rates, and spatially uniform sensors, which are typically required in conventional approaches based on loop detector and floating car data.

Handling highly imbalanced data for classifying fatality of auto collisions using machine learning techniques

Accurate prediction of fatal events in car accidents has significant health management implications. This research article explores the application of imbalanced data handling techniques in machine learning to enhance prediction performance. By implementing these techniques on car accident data, health organizations can identify and forecast a fatal event, enabling more efficient and effective allocation of limited health resources. Concurrently, enhancing the performance of machine learning models through imbalanced data handling techniques can impact health management decisions. Our findings highlight the significance of imbalanced data handling techniques in predicting fatality in car accidents, ultimately contributing to improved road safety and better management of health resources. Moreover, the effective use of imbalanced data demonstrates a substantial improvement in the specificity of the prediction. Addressing the impact of machine learning techniques on imbalanced car accident data can significantly improve overall health outcomes.

Design and Research of an Elevator Electric Brake Release Device with a Safety Detection Function

Abstract In response to the issues and defects associated with the current method of using electrical devices to open brakes for emergency rescue, a new elevator electric brake release device applicable to any elevator employing an electromechanical braking system has been designed. This device mainly consists of a car data collection unit and a brake release control unit, utilizing the STM32F103C8T6 microcontroller as the control core for elevator emergency rescue operations. The car data collection unit is responsible for gathering and transmitting data on the car’s operational state and position, while the brake release control unit evaluates the conditions for emergency rescue operations and performs real-time analysis, storage, and display of the collected data. It also decides whether to output a brake release signal. Experimental results show that when conditions for rescue operations are met, the electric brake release device can ensure the safe operation of the elevator; when abnormalities are detected, it outputs a signal to stop operations, alongside fault alarms and status displays.

UTILIZATION OF CAR PARKING ULTRASONIC SENSORS AS HYBRID TECHNOLOGY FOR MONITORING PEAT WATER LEVEL BASED ON INTERNET OF THINGS (IoT) AND DATA LOGGER WITH FIREBASE WEB

Peatland water level monitoring systems have been developed to facilitate water level readings. Currently, car parking sensors are rarely used for this purpose. The tool used in this research is a hybrid technology of iot-based car parking sensors and data loggers. Water level (TMA) data is analyzed using a data logger and Firebase web. The data logger records data which is then integrated on the Firebase web to facilitate remote monitoring. Data (TMA) is sent every hour, so in one day the tool will send data 24 times. The status that can be seen from the Firebase web dashboard includes water level (TMA), water condition indicators (safe, caution, danger), last data update status, tool placement map, and a collection of data history. This research uses a pre-experiment method this tool requires a current of 200 mAh, a 10 watt peak solar panel, and a 12.5 ampere hour battery to turn on constantly. The utilization of ultrasonic sensor with esp 32 microcontroller is effective in recording Real-Time water level data. Iot plays an important role because it allows monitoring (TMA) from anywhere with internet availability. The maximum water level reading data occurred on November 31, 2023 at 40 cm from the peat soil surface and is comparable to the rainfall data on November 29, 2023 at 2.2 mm / day (Light Rain). The minimum water level reading occurred on December 16, 2023 at -5 cm (flooding) from the peat soil surface and was proportional to the rainfall data on December 16, 2023 of 53 mm/day (Heavy Rain). So it can be concluded that the minimum and maximum water level distance data fall into the safe category for peatland water levels that are between 0-40 cm (PP No. 57 of 2016).

The Impact of Scale on Extracting Individual Mobility Patterns from Location-Based Social Media.

Understanding human movement patterns is crucial for comprehending how a city functions. It is also important for city planners and policymakers to create more efficient plans and policies for urban areas. Traditionally, human movement patterns were analyzed using origin-destination surveys, travel diaries, and other methods. Now, these patterns can be identified from various geospatial big data sources, such as mobile phone data, floating car data, and location-based social media (LBSM) data. These extensive datasets primarily identify individual or collective human movement patterns. However, the impact of spatial scale on the analysis of human movement patterns from these large geospatial data sources has not been sufficiently studied. Changes in spatial scale can significantly affect the results when calculating human movement patterns from these data. In this study, we utilized Weibo datasets for three different cities in China including Beijing, Guangzhou, and Shanghai. We aimed to identify the effect of different spatial scales on individual human movement patterns as calculated from LBSM data. For our analysis, we employed two indicators as follows: an external activity space indicator, the radius of gyration (ROG), and an internal activity space indicator, entropy. These indicators were chosen based on previous studies demonstrating their efficiency in analyzing sparse datasets like LBSM data. Additionally, we used two different ranges of spatial scales-10-100 m and 100-3000 m-to illustrate changes in individual activity space at both fine and coarse spatial scales. Our results indicate that although the ROG values show an overall increasing trend and the entropy values show an overall decreasing trend with the increase in spatial scale size, different local factors influence the ROG and entropy values at both finer and coarser scales. These findings will help to comprehend the dynamics of human movement across different scales. Such insights are invaluable for enhancing overall urban mobility and optimizing transportation systems.

Research on Intelligent Signal Timing Optimization of Signalized Intersection Based on Deep Reinforcement Learning Using Floating Car Data

Research on Intelligent Signal Timing Optimization of Signalized Intersection Based on Deep Reinforcement Learning Using Floating Car Data

Sistem Informasi Manajemen dan Monitoring (Simamo) Rental Mobil Berbasis Iot (Sub Sistem Manajemen)

Car rental is a business that provides car rental services to parties in need, both individuals and companies, usually ranging from a few hours to a few days. However, many car rental service providers experience several problems, such as a lack of vehicle monitoring, difficulty for renters to obtain information on car availability and data archives that are still conventional. The research method used was the waterfall system development method with UML system modeling. The programming language used is the PHP programming language and MySQL as the database. The system built has been tested using blackbox testing and whitebox testing and can run well. The features contained in the IoT-based car rental management and monitoring information system are car ordering, monitoring, managing car data and managing transactions. The aim of creating an IoT-based car rental management and monitoring information system is expected to be able to help renters and car rental providers in business processes.

Transfer Learning for Transportation Demand Resilience Pattern Prediction Using Floating Car Data

Understanding the response of a transportation system to disruptive events is significant for evaluating the resilience of the system. However, data collection during such events is always challenging, and the data volume is insufficient for building a robust model. Transfer learning provides an effective solution to this problem. In this study, we propose a floating car data (FCD) driven transfer learning framework for predicting the resilience of target transportation systems to similar disruptive events to those that have ever occurred in the source systems. The core of the framework is an unsupervised pattern extractor that combines the k-Shape clustering and Bayes inference methods for extracting resilience patterns from the FCD collected in the source systems during the disruption period. The extracted patterns can then be used to assist in the prediction of the resilience of the target systems. We examine the effectiveness of the proposed framework by conducting a case study under the context of the COVID-19 pandemic, in which the source domain cities include Antwerp and Bangkok, and the target domain city is Barcelona. Results show that the extracted resilience patterns can improve the prediction performance of transfer learning neural networks with less pre-event information and limited data volume.

PENGARUH PEMBERIAN INSENTIF PAJAK PPNBM KENDARAAN BERMOTOR DI TENGAH PANDEMI

The COVID-19 pandemic has had a negative influence on economic growth in Indonesia, to overcome this, one of the efforts of the Indonesian government is by providing incentives in the form of reducing taxation, in this case Luxury Goods Sales Tax (PPnBM) on the sale of motor vehicles. This study aims to measure the effectiveness of the policy by comparing car sales data in the period before and after the Covid-19 pandemic. The method used is quantitative descriptive with snowball sampling techniques with the population being all automotive companies in Indonesia, then the data is tested using SPSS tools with data normality test techniques to t-test tests. The results of this study show that the Indonesian government's policy by providing PPnBM incentives to motor vehicles has proven to have a significant positive effect on the sales of motor vehicles (cars) in Indonesia, both in the categories that get incentives and those that do not get incentives are also encouraged in terms of sales. Key words: Tax Incentive; Tax Policy; Luxury Sales Tax

Improving the system of protection of the rights of buyers of vehicles

The subject of the study is the existing system of protection of the rights of buyers of vehicles, which includes the prevention of offenses involving liability for misleading consumers about consumer properties or quality of goods (Article 14.7 of the Administrative Code of the Russian Federation) and for causing property damage by deception or abuse of trust (Article 7.27.1. of the Administrative Code of the Russian Federation), the commission of which involves, including for example, with the correction or reduction of the recorded mileage of the car. The connection of these actions with the technical condition of vehicles and road safety is considered, since any unauthorized actions with the technical condition of the vehicle are associated with a decrease in the level of safety of road users. The prevalence of this phenomenon requires an adequate response from the state's law enforcement system and the establishment of legislative barriers for this activity. The research methodology is based on general scientific (logical, systematic, analysis, generalization) and private scientific (comparative, formal legal) methods. The scientific novelty of the study lies in an integrated approach to studying the existing procedure for protecting the rights of buyers of vehicles at risk of being misled about the quality of the goods. The article substantiates the need to create a legal mechanism at the state level and on its basis an open mileage accounting system for used cars for citizens, which allows them to track their history from the moment they go on sale. It is proposed to give a legal assessment of the actions and establish liability for persons who, through the provision of services for correcting car mileage data, contribute to deceiving consumers, causing property damage to buyers, and are in direct connection with the decisions of vehicle owners on their maintenance and repair, which affects road safety.