Vehicle Ownership Research Articles

Kurangnya Kesadaran Masyarakat Untuk Uji Kendaraan Bermotor : Penyebab dan Akibat

The public's lack of understanding of the benefits of vehicle testing and driving safety can have serious impacts on road safety and the environment. Many vehicle owners do not realize that vehicle testing is not only a legal obligation, but is also a preventive step that can reduce the risk of accidents and extend the service life of the vehicle. Efforts to increase public awareness need serious attention from the government, educational institutions and non-governmental organizations. The aim of this writing is to raise public awareness about motor vehicle testing. The method used in this research is Literature Review. The literature review method is a description of theories, findings and other research materials obtained from reference materials to serve as a basis for research activities. Literature reviews contain reviews, summaries and the author's thoughts about several library sources (articles, books, slides, information from the internet, etc.) about the topic being discussed. The research results show that by exploring the causes and consequences of this lack of awareness, strategic steps can be taken to increase public understanding and compliance with vehicle testing procedures. The government, educational institutions and other related parties need to work together to create a safer and more sustainable environment on Indonesian roads. In this context, education and outreach are the main keys to changing society's paradigm regarding the importance of motor vehicle testing.

The impact of financial incentives on the total cost of ownership of electric light commercial vehicles in EU countries

Many EU countries have used financial incentives to promote electric light commercial vehicles and decarbonise urban freight transport. However, the high costs of electric light commercial vehicles still pose a significant adoption barrier. This paper evaluates the impact of financial incentives on the total cost of ownership of electric light commercial vehicles in the European Union and their role in achieving the cost-competitiveness of electric vehicles. A pairwise comparison is performed for three weight categories of electric and diesel vehicles in twelve countries. Our analysis shows that financial incentives have a high impact on the total cost of ownership and have proven necessary to achieve electric vehicles' cost-competitiveness in most cases. However, in specific cases, financial incentives may no longer be needed as the cost of electric vehicles' ownership would have been lower than diesel vehicles even without them. This paper demonstrates the importance of adjusting government incentives to avoid unnecessary funding allocations while promoting the decarbonisation of light commercial vehicles, particularly as the number of incentives does not directly correlate with electric vehicles penetration.

Electric vehicle usage, pollution damages, and the electricity price elasticity of driving

I study battery electric vehicle (BEV) usage and ownership characteristics with fundamental implications for the electrification of passenger transportation. Using data covering the entire BEV population in New York, I quantify BEV mileage and electricity consumption and highlight the important role of vehicle utilization in contributing to real-world pollution damages and their spatial variation. I then study the factors influencing how much BEVs are driven with a focus on estimating the electricity price elasticity of BEV mileage. Understanding how drivers respond to these changes in operating costs may help align the social and private costs of BEV driving and illustrates how electric utilities may affect transportation outcomes in the future. I find a 10% increase in residential electricity prices reduces mileage by 1%, but responsiveness falls as public charging stations—where prices are often decoupled from electricity costs—become available.

Introducing shared, electric, autonomous vehicles (SAEVs) in sub-urban zones: Simulating the case of Vienna

Shared, autonomous electric vehicles (SAEVs) are expected to enter the market in the coming decades. Using MATSim, we simulate a use case where SAEVs are introduced in multiple suburban zones at the outskirts of Vienna (Austria), which are characterized by relatively low population density, but have access to at least one rail-based public transport stop. For all combinations of different fleet sizes and fare levels, we find that a relatively small share of car trips by residents of these zones (7%–14%) are replaced by SAEVs, generating CO2 emissions reductions of 5%–11%. Moreover, 23%–35% of trips previously undertaken by foot or bicycle are replaced by SAEVs, as well as 10%–20% of public transport trips. The potential of SAEVs to reduce the use and ownership of private vehicles in suburban areas therefore seems to be rather limited, which is also reflected in our finding that one SAEV usually replaces only 2–4 private vehicles. The potential becomes somewhat larger when the usage and ownership of private cars is assumed to become more expensive, leading to 17%–20% of car trips being replaced by SAEVs and generating CO2 emissions reductions of up to 32%.

Federated Learning for Intrusion Detection Systems in Internet of Vehicles: A General Taxonomy, Applications, and Future Directions

In recent years, the Internet of Vehicles (IoV) has garnered significant attention from researchers and automotive industry professionals due to its expanding range of applications and services aimed at enhancing road safety and driver/passenger comfort. However, the massive amount of data spread across this network makes securing it challenging. The IoV network generates, collects, and processes vast amounts of valuable and sensitive data that intruders can manipulate. An intrusion detection system (IDS) is the most typical method to protect such networks. An IDS monitors activity on the road to detect any sign of a security threat and generates an alert if a security anomaly is detected. Applying machine learning methods to large datasets helps detect anomalies, which can be utilized to discover potential intrusions. However, traditional centralized learning algorithms require gathering data from end devices and centralizing it for training on a single device. Vehicle makers and owners may not readily share the sensitive data necessary for training the models. Granting a single device access to enormous volumes of personal information raises significant privacy concerns, as any system-related problems could result in massive data leaks. To alleviate these problems, more secure options, such as Federated Learning (FL), must be explored. A decentralized machine learning technique, FL allows model training on client devices while maintaining user data privacy. Although FL for IDS has made significant progress, to our knowledge, there has been no comprehensive survey specifically dedicated to exploring the applications of FL for IDS in the IoV environment, similar to successful systems research in deep learning. To address this gap, we undertake a well-organized literature review on IDSs based on FL in an IoV environment. We introduce a general taxonomy to describe the FL systems to ensure a coherent structure and guide future research. Additionally, we identify the relevant state of the art in FL-based intrusion detection within the IoV domain, covering the years from FL’s inception in 2016 through 2023. Finally, we identify challenges and future research directions based on the existing literature.

Orderly Charging and Discharging Group Scheduling Strategy for Electric Vehicles

To address the challenge of optimizing the real-time scheduling for electric vehicles on a large scale, a day-ahead–intraday multi-timescale electric vehicle cluster division strategy is proposed based on the different expected charging completion times of the accessed electric vehicles. In the pre-day phase, historical travel statistics are used to model and determine the moments when the electric vehicles are on-grid and off-grid. In the intraday phase, the EV clusters are carefully divided by real-time data collection, taking into full consideration the response willingness and ability of vehicle owners. For each scheduling period, a real-time optimal scheduling model for EV clusters based on the V2G mode is established by taking into account the constraints of the power grid, vehicle owners, batteries, and other parties. The model is divided into two layers to find the charging and discharging plans: the upper model aims to determine the aggregate charging and discharging power of the cluster during the current time period by targeting the distribution grid’s minimum variance load curve within the scheduling interval; the lower model takes the lowest cost to the EV owner as the goal to find the charging and discharging plan of a single EV and, at the same time, introduces the scheduling penalty factor to adjust the difference with the cluster charging and discharging plan. The simulation outcomes indicate that the suggested approach successfully mitigates load fluctuations and has a good optimization effect and fast solution speed for dealing with large-scale EV access problems. The simulation results show that the proposed strategy can effectively smooth load fluctuations and can significantly reduce the difficulty of optimal real-time scheduling for electric vehicles on a large scale, and it has a better optimization effect and faster solution speed for dealing with large-scale electric vehicle access problems.

Mobile-Based Car Diagnostic Application Using Onboard Diagnostic-II Scanner

Mobile applications today serve as versatile tools across diverse sectors, enhancing human productivity through specialized software on electronic devices. Implementation of the mobile application can also be applied to vehicles, with inspection and checking functions assisted by the Onboard Diagnostic-II (OBD-II) scanner. The research aimed to develop an integrated mobile application that utilized the OBD-II scanner and Data Acquisition System (DAS) to monitor vehicle health and provide timely service reminders. Vehicle information was taken by the DAS process into a Diagnostic Trouble Code (DTC) from the vehicle itself. The method applied the waterfall model, which consisted of communication, planning, modeling, construction, and evaluation. The problem analysis and requirements gathering for developing the application involves the interview method and Google Forms-generated questionnaires with 101 responses. Then, the research used OBD-II series ELM327 and ELM 327 IC devices for testing. The research results in an application developed for vehicle diagnostics using a recommendation system through notifications that provide vehicle health information and service time reminders to users. This application consists of eight modules, with the main module being able to provide recommendations for vehicle owners. These recommendations are helpful for users to maintain the health of their vehicles regularly. Further research is recommended to enhance the development of the application, aiming to create a more comprehensive user interface.

THE INFLUENCE OF SOCIALIZATION, APPLICATION SYSTEM QUALITY, APPLICATION USAGE KNOWLEDGE, AND THE USE OF THE E-SAMSAT APPLICATION AS A MEDIATION VARIABLE ON MOTOR VEHICLE TAXPAYER COMPLIANCE (CASE STUDY IN BEKASI CITY)

This research was conducted as a case study in Bekasi City because revenue receipts, especially in vehicle taxes and the number of motorized vehicle owners, have increased significantly in 2018-2022. The government made an innovation in the form of an online application called "E-SAMSAT," which aims to reduce the number of taxpayers who do not comply with their motorized vehicle obligations and make it easier for taxpayers to carry out their obligations without the need to come to the SAMSAT office. This study examines the effect of socialization, the quality of the application system, and the knowledge of using the application on motor vehicle taxpayer compliance mediated by using the E-Samsat application. This research uses quantitative methods. The population in this study is the number of registered motor vehicle taxpayers in Bekasi City. Sampling using the random sampling method and the number of samples determined as many as 99 samples with the Slovin formula. We collected data using an e-survey with a questionnaire. This study used linear regression analysis and PROCESS by Hayes with the IBM SPSS 26 application. These results showed that socialization, application system quality, and application usage knowledge significantly affected motor vehicle taxpayer compliance mediated by the use of the E-Samsat application.

Calibrating walkability indicators for commute walk trips: A structural equation modeling approach

Walking plays a significant role in promoting public health, decreasing air pollution, and vitalizing cities. Walkability is an important notion that transportation planners and policymakers often use to quantify the suitability of and to promote travelers’ propensity for walking in an urban area. This paper adopts a data-driven approach using structural equation modeling for simultaneous estimation of 1) a latent construct of walkability using different walkability indicators (thus estimation of weights of the indicators), and 2) the effect of walkability on commute walking behavior for an urban area. Doing so accounts for the effects of various indicators of walkability as well as socio-economic and security characteristics. A Partial Least Square Structural Equation Model is developed to estimate both direct and indirect effects of walkability on the share of commute walk trips in total commute trips for the city of Chicago using census tract-level data. We find that walkability is the most important factor affecting the share of commute walk trips. Among the indicators of walkability, transit- and job density-related indicators are the most important ones. Some socio-economic factors, especially travel time, vehicle ownership, and college student share, also critically influence the propensity for walking to commute. We further find that our calibrated walkability score results in a different hotspot distribution in Chicago than using the uncalibrated walkability score, as is currently practiced by the Chicago Metropolitan Agency for Planning, but is more consistent with hotspot distribution of the actual commute walk trips in the city. These findings advance the understanding and characterization of walkability and actual walking behavior in urban areas, which in turn helps inform pedestrian infrastructure investment and policy making.

Impact of COVID-19 on Student’s Travel Behavior in Bangalore City, Karnataka

The COVID-19 tragedy had a significant impact on travel and demand for transportation in India before everything returned to normal. The current study is focused on students’ travel behavior in Bangalore City because commutes to college and university were most adversely affected during the pandemic. The modeling of travel demand and transportation planning in and around educational regions depend heavily on the investigation of student travel behavior in large cities like Bengaluru. An online questionnaire survey was used to gather information about changes in travel behavior before and after the COVID-19 outbreak. Gender, age, vehicle ownership, household income, travel expenditure, and travel distance all have a substantial impact on the primary trip taken according to the findings. It is also observed that the frequency of student trips reduced from 6 days per week to 3 to 4 days per week. The analysis also shows that many students chose to stay at home than PG or hostel post-COVID-19 due to concerns about the virus’s spread. To simulate the behavior change, multiple linear regression, and artificial neural networks were employed. The ANN model showed the best fit for forecasting travel behavior in terms of travel duration before COVID-19 and after the COVID-19 outbreak, according to the AUC values. The finding of the work can be used for better planning of their operations and services, especially, near colleges and universities.

Dynamic Characteristics of Three-Body Braking System Considering Tire-Road Friction

Abstract With the significant increase in vehicle ownership in our country, urban traffic conditions have become increasingly congested. Low-speed driving has become more prevalent, leading to more frequent instances of starting and braking. Consequently, the issue of low-speed braking flutter has become increasingly prominent. While the brake is in an open environment, the dust and particles in the air and the debris generated by the brake itself will have an impact on the braking behavior. In addition, according to the theory of modal coupling, the braking stability of the vehicle is also affected by other components. In this paper, different dynamic torsional models of braking system are established according to different braking conditions. Through numerical calculation, the influence of tire parameters, road conditions, and particles on the friction dynamics characteristics of braking system pairs is explored. The results show that the instability of the brake pair system often occurs at low speed. Different tire slip ratios, tire offset factors, and road conditions will lead to different relative motions of the braking system, but the existence of particles in the brake lining-disk interface can enhance the motion stability of the system.

Modelling commuters’ travel time perception on an urban corridor using the fuzzy logic technique: A case study of Surat, India

On the one hand, travel time is important in planning and managing urban roads; on the other hand, it is important in commuters' route and departure time decisions. Often, mode and route choices and a sense of departure time are mainly based on one’s perceived travel time (PTT). This study focuses on modelling travellers’ PTT considering socio-economic and trip attributes for an urban corridor, taking Surat as a case study. An ANOVA test has been conducted to identify factors governing PTT. Analysis shows that trip attributes (e.g., traffic conditions, traffic environment, and travel distance) influence PTT more than travellers' socio-economic characteristics (e.g., income, occupation, age, and vehicle ownership). Commuters overestimate PTT (min/km) nearly two-fold, and the overestimation decreases with increasing trip distance. A PTT Estimation Model has been developed using Fuzzy Logic, considering the epistemic uncertainty associated with attributes. The developed model can help inform necessary improvement measures to bring travel times to a reasonable level.

Exploring the endogenous effects among car dependency, work arrangement choice, and daily travel using the 2017 NHTS data

The paper presents an effort in investigating the cause-effect relationships among telecommuting, car dependency, and trip making behavior. Data from the 2017 National Household Travel Survey (NHTS) was used to develop a structural equations model (with latent constructs) to examine the endogenous effects between the decision to telework, the degree of car dependency, and the number of daily trips. Using confirmatory factor analysis, car dependency was measured by vehicle ownership, annual mileage, and car use frequency. The model provided interesting insights. Car dependency had a negative (both direct and total) impact on telework, an inference that confirms the role of car dependency as a resistant factor against alternative work arrangements and potentially any other new tech-based policies. In the opposite direction and surprisingly, telework encouraged car dependency, which might indicate that teleworkers tended to utilize the relaxed temporal-spatial constraints for non-work trips, and they were unlikely to give up private vehicle ownership simply because of their option to telecommute. Second, number of daily trips tended to increase the probability to telework, confirming the positive endogenous role of daily activity patterns on work arrangement decisions in a daily framework. Lastly, the significant negative impact of telework on daily trips was positively mediated through car dependency, leading to a smaller negative total impact on daily trips. Our study confirms that the association between telework and trip generation is quite a complex and multi-faceted relationship, leaving the argument regarding whether or not telework increases the number of daily trips as a debatable topic that calls for further exploration.

Development of a GSM based Vehicle Demobilizer and Tracking System

Vehicle hijacking remains a pervasive global issue, posing heightened risks, especially when owners face armed hijackers. Concurrently, the disposal of old mobile phones contributes significantly to the escalating electronic waste (e-waste) challenge, emphasizing the imperative of responsible recycling practices. Addressing these concerns, this paper introduces a GSM-based vehicle demobilizer and tracking system, utilizing a cost-effective mobile phone connected to the device through a SIM card and operating with an NE 555 timer IC in bistable mode. In the event of a hijacking, a call to the mobile phone triggers the device, halting the vehicle. In the prototype design presented in this paper, the result of the output is represented by an LED to show that the circuitry actually works. When the device was tested, the LED illuminated when a call was placed to the mobile phone connected to the device which signifies that the circuit works and can achieve its purpose. The resulting design also shows that the vehicle owner can prevent his car from being stolen away even after it has been hijacked without self-endangerment and within a short time frame.

Self-driving vehicles’ impacts on Americans’ long-distance domestic travel choices

ABSTRACT This research estimated models for long-distance domestic passenger trips before and after the introduction of autonomous vehicles (AVs) and their application to a 10% synthetic US population. The authors synthesized 12.1M households and 28.1M individuals across 73,056 US census tracts. To generate disaggregated passenger trips, travel demand models, including trip frequency, season, purpose, party size, mode choice, and destination choice, and vehicle ownership models were estimated. Different datasets, including a 2021 long-distance AV survey, 2016/17 National Household Travel Survey (NHTS) survey, EPA Smart Location data, and FHWA rJourney dataset were used for model estimation. The model applications indicated AV ownership to be 0.33 per capita after the introduction of AVs within the marketplace with a $3500 AV technology cost premium in the year 2040. Total person-miles traveled per capita in long-distance trips was also estimated to rise 35% (from 280 to 379 miles per month).

Finding the Spot: IoT enabled Smart Parking Technologies for Occupancy Monitoring – A Comprehensive Review

Major cities in India have a significant number of vehicles, and the rate of ownership is increasing every day. However, the lack of proper parking infrastructure in these cities causes problems such as difficulty in finding parking spaces. According to the Urban Mobility Survey 2023 by Times Network, nearly 74% of vehicle owners in metropolitan cities struggle to find a parking slot. Various measures have been implemented to address this issue. One of the most promising measures is a smart parking management system. This system can use technologies like Radio Frequency Identification (RFID) and Automatic License Plate Recognition (ALPR) to make check-in and check-out easier. It can also include Wireless Sensor Networks (WSN), wired sensors, or visual occupancy detection to provide real-time occupancy status. The smart parking management system can offer useful services through mobile or web applications such as parking occupancy monitoring, reservation, payment gateway, occupancy prediction, automated check-in and check-out, and parking record management. The purpose of this review paper is to summarize the works undertaken in the field of smart IoT parking systems and educate the technological community about the technologies, features, and procedures for implementing the smart parking management system. In the paper, we aim to summarise the works on occupancy monitoring in smart IoT parking systems addressing the advantages and issues of the present occupancy monitoring methods, also suggesting future inclusions for smart IoT parking systems.

A Hardware Vehicle Security and Accident Notification System using the Haar Cascade Algorithm

Low-cost vehicle security and accident notification system is essential in this modern society as vehicle theft and accidents that occur are increasing daily. In this proposed system, hardware for vehicle security is implemented, with authentication, and accident notification. Authentication is achieved by face detection and recognition using an open CV. The defaulter information is forwarded to the authenticated mobile number of the person by whom the vehicle is owned, using the GSM module and a notification email. The face authentication module was developed using the discrete wavelet transform-based Haar cascade algorithm. In addition to the face authentication, accident notifications will be sent to family members of the vehicle owner. Micro electrical mechanical system sensor was utilized to detect accident incidents using the accelerometer concept.

Equation Modelling of Automotive Textiles for Car Seat Covers in the Ghanaian Upholstery Industry

Among the many materials utilized in the production of automotive components are textile materials. Long-term use of vehicle seats causes the textile material used to cover the seats to degrade. Because of this, drivers and vehicle owners critically inspect such deteriorated car seat covers before getting a new one. This study is aimed at identifying the various factors that influence drivers and vehicle owners’ decisions about the choice of seat covers. In addition, to determine the factors that influence customers’ perception of locally manufactured seat covers by upholstery artisans. Structured questionnaire and online google form were used to collect data from 1042 respondents across the 16 regions of Ghana. The five main factors that customers focused on were identified using Principal Component Analysis (PCA) with Varimax rotation. Structural Equation Modelling (SEM) was used to predict the perception factors of local upholstery artisans and customers using the Smart PLS software. Textile resistance and accessibility, durability, quality, perception and aesthetics were the selection factors. The findings showed that consumers of automotive textile materials take into account the aforementioned aspects before making purchases, therefore manufacturers should pay attention to these factors to increase efficiency. The study concludes that local upholstery artisans need to concentrate on these factors in their operations.

Smart Parking System using Image Detection Algorithms & Optical Character Recognition (OCR)

Abstract: The Smart Parking System is an innovative and effective solution designed to make parking more efficient in urban areas, using advanced technology to help both vehicle owners and parking operators automate and optimize the parking process. When a vehicle enters the parking lot, it is successfully identified using an advanced sensor and camera system, with a highresolution camera that records the license plate number and optical character recognition (OCR) technology. Get accurate license plate numbers. The data is then stored in the MongoDB database along with the input timestamp and when the vehicle moves, the license plate is re-identified using the same technique and the system stores the input timestamp associated with the detected license plate in the database. he does. Reading from Parking time is calculated from the time difference between entering and leaving the parking lot, and the parking fee is calculated per hour. To facilitate payment, a QR code is generated and displayed at the exit. Vehicle owners can use their mobile device to scan this QR code and connect to the secure payment gateway. After the payment is completed, the exit door will open automatically and the car can leave the parking lot. This smart parking system offers many benefits, including fewer numbers, increased accuracy, reduced waiting time, and efficient payment processing. It reduces congestion in urban areas and improves traffic flow while providing users with convenient and troublefree parking

Vehicle Model and Owner Registration, Detection & Car Price Prediction

Abstract: The key algorithm in autonomous driving is vehicle recognition based computer vision, which tries to recognize whether vehicles are being located by digital photos or videos. Identifying "blocks," which represent the position of the vehicle in photos or videos, is the fundamental concept behind vehicle detection. Additionally, this work explores 3D stereo-based vehicle identification methods, which emerged from sophisticated planar vehicle detection perception. In terms of the differences between the feature extraction approach and the perceived results, this research concludes by summarizing the vehicle detection algorithms used in recent years. It offers theories for more thorough investigation of the vehicle detection systems. With the rapid advancement of machine learning techniques, this tool is now even more essential for object detection. Compared to manually developed features, machine learning-based picture features are more representative. This review paper focuses on object recognition methods based on machine learning and other deep convolutional neural networks however the traditional object detection techniques will also be briefly reviewed.