Vehicle Data Research Articles

Bottom-up Analysis for Carbon Emissions from Passenger and Freight Transport Sectors in Xi’an: A City-Level Study Using MOVES and LMDI Models

Abstract Road transportation is identified as one of the primary contributors to urban carbon emissions. This study employed a localized MOVES model, integrating high-resolution urban vehicle data, along with a categorical LMDI decomposition model, to conduct a detailed bottom-up analysis of carbon emissions from passenger and freight vehicles in Xi'an. The MOVES model was utilized to evaluate the inter annual variability of carbon emissions from road transportation sources. The LMDI models assessed the impact of six factors, focusing on microscopic traffic activities such as passenger/freight volume and turnover, vehicle ownership, mileage, carbon emission rate, vehicle types, and fuel types. The research indicates that carbon emissions in Xi'an increased significantly between 2012 and 2021, rising from 21.95 to 51.15 million tons, with an average annual growth rate of 9.9%. Passenger vehicles are the primary contributors to this increase. The emission patterns of passenger and freight vehicles differ notably due to various factors. For passenger transport, which includes passenger cars and school buses accounting for 97% of emissions, per capita vehicle turnover is the primary influencing factor. In contrast, for freight transport, combination long-haul trucks dominate emissions, comprising 65% of total freight emissions, with freight volume being the key influencing factor. By analyzing the factors driving carbon emissions from passenger and freight vehicles, this study provides valuable insights for reducing road traffic emissions in Xi'an and similar regions.

Multi-modal framework for battery state of health evaluation using open-source electric vehicle data

Accurate, practical, and robust evaluation of the battery state of health is crucial to the efficient and reliable operation of electric vehicles. However, the limited availability of large-scale, high-quality field data hinders the development of the battery management system for state of health estimation, lifetime prediction, and fault detection in various applications. In this work, to gain insights into underlying factors limiting battery management system performance in real-world vehicles, we analyze the operational data of 300 diverse electric vehicles over three years to understand the disparities between field data and laboratory battery test data and their effect on state of health estimation. Furthermore, we propose a deep learning-based multi-modal framework to effectively leverage historical vehicle data for efficient, accurate, and cost-effective state of health estimation. The proposed paradigm exhibits considerable potential for numerous applications in state estimation and diagnostics in multi-sensor systems. Furthermore, we make the field data of these electric vehicles publicly available aiming to promote further research on the development of effective and reliable battery management systems for real-world vehicles.

Innovative Methodology for Generating Representative Driving Profiles for Heavy-Duty Trucks from Measured Vehicle Data

The imperative for electrification of road transport, driven by global climate targets, underscores the need for innovative powertrain systems in heavy-duty vehicles. When developing new electric drive modules, individual operational requirements need to be considered instead of generalized usage profiles, as heavy-duty vehicles experience significantly differing loads depending on their field of operation. Real driving data, representing the demands of different application scenarios, offers great potential for digital replication of driving conditions at different stages of simulation and physical validation. Application- and vehicle-specific longitudinal requirements during operation are particularly relevant for the dimensioning of powertrain components. Road gradient and mass estimation assist in the description of these operating conditions, allowing for detailed modeling of the real load conditions. An incorporation of real driving data instead of solely relying on standardized cycles has the potential of tailoring components to the target lead users and applications. While some operating conditions can be recorded by vehicle manufacturers, these are usually not accessible by third parties. In this paper, the authors present an innovative methodology of estimating vehicle parameters for the generation of representative driving profiles for implementation into a consecutive powertrain design process. The approach combines the measurement of real driving data with state estimation. The authors show that the presented methodology enables the generation of driving profiles with less than 25% deviation from the original data set.

Based on the New Dynamic Network DEA and Malmquist Index of High-Quality Innovation in the New Energy Vehicle Economy Development Research of the Internet of Things

This paper takes the enterprise economic data of new energy vehicles (NEVs) in 2018–2023 as the research background, combined with the performance evaluation theory of the NEV industry, and systematically discusses how to make the high-quality innovative development of NEVs as the research purpose. We use the new dynamic network data envelope analysis (DEA) model, Marmquist Index and other and the Internet of Things internationally recognized, scientific and effective data models as research methods to make an in-depth analysis of the business performance of five representative enterprises on multi-quantitative input-output issues. The research results show that the NEV industry has a good development prospect and economic foundation on the whole, but it still faces challenges such as limited space for core technology progress, insufficient carbon emission reduction and environmental protection planning. At the end, this paper puts forward innovation suggestions such as accelerating technological innovation, optimizing production process and responding to preferential policies and exploring international market.

Affordable mobile application camera system to monitor residential societies vehicle activity

Residents of the community are faced with increasing challenges in managing traffic operations, ensuring safety, parking and improving traffic flow. Traditional methods, mostly manual or relying on expensive monitoring equipment, have difficulty meeting these needs. This study investigates investments in the use of video surveillance systems designed to monitor activities in residential areas, providing high-quality, efficient and cost-effective results. Integrated vehicle data using technology certification license. Provides residents and community management with a mobile application interface that provides remote access to live video, detailed traffic information and instant notifications about entry, illegal or unusual activities. The system eliminates the need for physical infrastructure by utilizing cloud- based storage, providing scalability and secure data management. Automatic entry simplifies long management and reduces manual errors for efficient operation. Keywords- Traffic operations management, video surveillance systems, residential traffic monitoring, cloud-based storage, traffic flow optimization, remote access application, vehicle performance data, automated entry systems, real-time notifications, scalable traffic management, smart parking solutions, technology certification license, paperless operations.

Cyclist safety assessment using autonomous vehicles.

Proactive and holistic safety management approaches should consider multi-modal crash risk. Cyclist crash risk should be prioritized given the high-severity of vehicle-cyclist crashes. Cyclist crash risk is difficult to quantify given the sparse nature of cyclist collisions and collisions in general. There is thus a need to develop a more proactive approach for multi-modal road-safety management by leveraging new technologies. This study proposes a conflict-based methodology to estimate cyclist crash risk using autonomous vehicle data, extrapolating from observed conflicts to real-time dynamic crash risk. Using 87hours of data from an autonomous vehicle dataset from downtown Boston (nuPlan), traffic conflicts were identified. A Bayesian Hierarchical Extreme Value model was created representing driver and cyclist crash risk over short time intervals. This allows for identifying the real-time crash risk of various intersections and mid-blocks, enabling route-level safety metrics. The spatiotemporal characteristics of crash risk were examined in this study. Routes with cyclist facilities were found to be safer for cyclists, on average, than those with shared facilities. However, substantial fluctuations in crash risk were observed at different time intervals, with the shared facilities sometimes being safer than those with painted or buffered bicycle lanes. This highlights the need for real-time safety monitoring. At the user-level, a safest route application was also proposed, allowing for an impedance function to be developed based on real-time crash risk and the comparison of any number of nodes and links along a particular route.

Autonomous Van and Robot Last-Mile Logistics Platform: A Reference Architecture and Proof of Concept Implementation

Background: With urban logistics facing challenges such as high delivery volumes and driver shortages, autonomous driving emerges as a promising solution. However, the integration of autonomous vans and robots into existing fulfillment processes and platforms remains largely unexplored. Method: This paper addresses this gap by developing and piloting a comprehensive blueprint architecture tailored for autonomous mobility in urban last-mile delivery. The proposed framework integrates autonomous vehicle operations, data processing, and stakeholder collaboration. Results: Through initial implementation and piloting, we demonstrate the practical applicability and advantages of this architecture. Conclusions: This study contributes to the understanding of essential data, services, and tools, providing a valuable guideline for Logistics Service Providers aiming to implement autonomous last-mile delivery solutions.

Comprehensive Monitoring of Construction Spoil Disposal Areas in High-Speed Railways Utilizing Integrated 3S Techniques

High-speed railways are critical infrastructure in many countries, but their construction generates substantial spoil, particularly in mountainous regions dominated by tunnels and slopes, necessitating the establishment and monitoring of spoil disposal areas. Inadequate monitoring of spoil disposal areas can lead to significant environmental issues, including soil erosion and geological hazards such as landslides and debris flows, while also hindering the recycling and reuse of construction spoil, thereby impeding the achievement of circular economy and sustainable development goals for high-speed railways. Although the potential of geographic information systems, remote sensing, and global positioning systems in waste monitoring is increasingly recognized, there remains a critical research gap in their application to spoil disposal areas monitoring within high-speed railway projects. This study proposes an innovative framework integrating geographic information systems, remote sensing, and global positioning systems for monitoring spoil disposal areas during high-speed railway construction across three key scenarios: identification of disturbance boundaries (scenario 1), extraction of soil and water conservation measures (scenario 2), and estimation of spoil volume changes (scenario 3). In scenario 1, disturbance boundaries were identified using Gaofen-1 satellite data through processes such as imagery fusion, unsupervised classification, and spatial analysis. In scenario 2, unmanned aerial vehicle data were employed to extract soil and water conservation measures via visual interpretation and overlay analysis. In scenario 3, Sentinel-1 data were used to analyze elevation changes through the differential interferometric synthetic aperture radar method, followed by the estimation of spoil volume changes. The effectiveness of this integrated framework was validated through a case study. The results demonstrate that the framework can accurately delineate disturbance boundaries, efficiently extract soil and water conservation measures, and estimate dynamic changes in spoil volume with an acceptable error margin (15.5%). These findings highlight the framework’s capability to enhance monitoring accuracy and efficiency. By integrating multi-source data, this framework provides robust support for sustainable resource management, reduces the environmental impact, and advances circular economy practices. This study contributes to the efficient utilization of construction spoil and the sustainable development of high-speed railway projects.

Vehicle log automation and accessibility in cloud computings

Traditionally, the so-called Automotive Embedded Computing has been applied for safety purposes. Subsequently, with the advent of entertainment technologies and the Internet, Automotive Embedded Computing started to be applied in a wide range of functionalities. Among the advances in this area is the On-Board Diagnostic Interface – OBD, whose main functionality is to take the information from the Electronic Control Units – ECU to the external environment. Initially, this interface allowed the identification of problems, based on standardized error codes, only for external devices. Later, with the advancement of other technologies, such as Smartphones and Cloud Computing, new applications for data collected through the OBD interface emerged. In this context, the present research was developed considering the current trends of Automotive Embedded Computing and the importance and advances in the use of On-Board Diagnosis. The objectives of this research are to obtain codes of anomalies, collected in the Electronic Control Units – ECU of the vehicle, through the OBD interface, its storage in a cloud database, and eventual consultations and subsequent analyses. Thus, a solution consisting of four applications and a database was developed to test the feasibility of exploring a large set of operational vehicle data, storing them in the cloud, and which could be used for different purposes, but above all, would allow a fast and consistent evolution of new vehicle designs. After the development of the software suite, several tests were carried out, and the solution proved viable for the fulfillment of the proposed objective.

VSPNet: a vehicle speed prediction model incorporating transformer and BiLSTM

Abstract In recent years, with the increasing adoption of hybrid vehicles, energy management strategies 
have become a prominent research focus. Accurate Vehicle Speed Prediction (VSP) is a critical 
prerequisite for achieving optimal results in predictive energy management strategies. However, 
existing speed prediction algorithms fail to fully leverage vehicle data to enhance prediction 
accuracy. Therefore, a novel Vehicle Speed Prediction Net (VSPNet) is proposed in this study. 
Firstly, we constructed a combined cycle condition for model training through comprehensive 
analysis and analysed the vehicle feature parameters through the Random Forest (RF) algorithm 
and Pearson correlation analysis to select the best input feature parameters. Then a VSPNet 
speed prediction model is proposed based on the Transformer model. In the encoder part, firstly, 
by assigning weights to the input feature parameters and incorporating the temporal attention 
mechanism, the model is made to make better use of the input features from two dimensions, 
and at the same time the Transformer model's encoder based on positional coding combined 
with Bi-directional Long Short-Term Memory (BiLSTM) belonging to Recurrent Neural 
Networks(RNN), which is used as a decoder to better catch and handle long-term dependencies 
in sequence data. Finally, a comparative experiment between VSPNet and the classical speed 
prediction models was carried out. The proposed VSPNet model reduces the RMSE by 37%, 
22%, and 20% and MAE by 39%, 25, and 24% compared to the LSTM model for the prediction 
time horizons of 3s, 5s, and 8s. The RMSE is reduced by 47%, 28%, and 7%, and the MAE is 
reduced by 47%, 30, and 9% compared to the Transformer model for the prediction time 
horizons of 3s, 5s, and 8s. The experimental results demonstrate the superiority of this speed 
prediction model.

Research on Road Traffic Sign Detection and Recognition Technology

With the development of intelligent transportation systems (ITS) and autonomous driving technologies, road traffic sign detection and recognition have become increasingly important in the fields of traffic safety, road management, and vehicle automation. Road traffic signs not only provide crucial information for drivers but also offer essential perception data for autonomous vehicles within intelligent transportation systems. This paper systematically reviews the current research progress in road traffic sign detection and recognition technology, analyzing the classification, visual features of signs, and the challenges faced in complex traffic environments. By comparing traditional image processing methods with advanced deep learning-based technologies, this paper discusses the advantages and disadvantages of sign detection and recognition in different application scenarios, and explores future technological development trends. Finally, the paper provides an outlook on the research directions in this field, aiming to provide theoretical support for future technological improvements and application promotion.

Ensuring Reliable Network Communication and Data Processing in Internet of Things Systems with Prediction-Based Resource Allocation

The distributed nature of IoT systems and new trends focusing on fog computing enforce the need for reliable communication that ensures the required quality of service for various scenarios. Due to the direct interaction with the real world, failure to deliver the required QoS level can introduce system failures and lead to further negative consequences for users. This paper introduces a prediction-based resource allocation method for Multi-Access Edge Computing-capable networks, aimed at assurance of the required QoS and optimization of resource utilization for various types of IoT use cases featuring adaptability to changes in users’ requests. The method considers the current resource load and predicted changes in resource utilization based on historical request data, which are then utilized to adjust the resource allocation optimization criteria for upcoming requests. The proposed method was developed for scenarios utilizing edge computing, e.g., autonomous vehicle data exchange, which can be susceptible to periodic resource demand fluctuations related to typical rush hours, predictable with the proposed approach. The results indicate that the proposed approach can increase the reliability of processes conducted in IoT systems.

Exploring spatiotemporal heterogeneity of urban green freight delivery parking based on new energy vehicle GPS data

To enhance the efficiency and sustainability of urban freight operations, China has initiated the Urban Green Freight Delivery (UGFD) project, which involves optimizing access control policies and introducing new energy vehicles. Identifying the parking trips of new energy vehicles and exploring the spatiotemporal patterns is crucial to actively promoting the optimal layout of temporary stops and the formulation of parking policies in the UGFD project. In this study, we aim to comprehend the spatiotemporal heterogeneity of parking for new energy vehicles both on roads (on-street) and within urban communities (off-street) for promoting the UGFD project. Its specific content includes: (1) proposing a method for identifying valid parking trips for the loading and unloading of goods based on trajectory data of UGFD new energy vehicles; and (2) mapping the identification results of valid parking trips onto communities and roads to analyze the spatiotemporal heterogeneity. Taking Suzhou, Jiangsu Province, China as an example, the identification results show that the established valid parking trips identification method can outperform state-of-the-art methods. The accuracy, precision, recall, and F1 value were found to be 0.957, 0.908, 0.937, and 0.922, respectively. Further examination of parking patterns indicates a bimodal temporal distribution of delivery demand, with peak activity occurring between 08:00–09:00 and between 14:00–17:00, with a higher delivery demand in the morning. Spatially, delivery demand was aggregated, while the parking time distribution of most delivery activities was normal. Additionally, the parking characteristics of communities and roads conformed to the ‘Rank–size rule’, suggesting that most delivery parking activities were concentrated in a few communities and roads. These findings can also be used in UGFD stop station utilization, travel time, arrival time prediction, and other related fields, all of which can further support relevant management departments in discovering abnormal delivery behaviors and reduce their negative impacts.

Numerical and experimental investigation of vibration of a car chassis and its effect with road gradient on noise and response optimization with factorial design method

This paper delves in to a numerical and experimental investigation of vibration of a car chassis and its effect with road gradient on noise and response optimization. Peak acceleration, peak amplitude as vibration response parameters in vertical motion, internal and pass by noise as a noise response parameters. Vibration parameters obtained numerically by developing a mathematical model of car and equation of motion. Newmark Beta method programmed in MATLAB software used to solve the equation of motion. Experimental investigation of the vibration were carried on a car consist of monocoque chassis, three cylinder engine, Macpherson front suspension and twist beam rear suspension. FFT analyzer and digital sound level meter were used for the data collection. Pass by noise data of vehicle were collected at two locations with gradient 1.15° and 11.31° on state highway (MH SH-44) near Konchi Maharashtra state, India. Comparison between numerical and experimental investigated values of vibration parameters shows minimum error of 0.6035% and average error of 2.81% which validates the car model. Factorial design method was used to optimize the index of response parameters which shows reduction in peak acceleration, peak amplitude, internal noise and pass by noise level by 65.61%, 17.70%, 7.13% and 19.41% respectively. The methodology proposed in this study provides reference to improve the performance of car suspension and passenger comfort.

Methodological approach to analyze vehicle data in electric road systems: case study – ELISA, the eHighway project

Methodological approach to analyze vehicle data in electric road systems: case study – ELISA, the eHighway project

Time-dependent effect of advanced driver assistance systems on driver behavior based on connected vehicle data

Time-dependent effect of advanced driver assistance systems on driver behavior based on connected vehicle data

Unveiling Sybil Attacks Using AI‐Driven Techniques in Software‐Defined Vehicular Networks

ABSTRACTThe centralized nature of software‐defined networks (SDN) makes them a suitable choice for vehicular networks. This enables numerous vehicles to communicate within an SD‐vehicular network (SDVN) through vehicle‐to‐vehicle (V2V) and with road‐side units (RSUs) via vehicle‐to‐infrastructure (V2I) connections. The increased traffic volume necessitates robust security solutions, particularly for Sybil attacks. Here, the attacker aims to undermine network trust by gaining unauthorized access or manipulating network communication. While traditional cryptography‐based security methods are effective, their encryption and decryption processes may cause excess delays in vehicular scenarios. Previous studies have suggested machine learning (ML) like AI‐driven approaches for Sybil attack detection in vehicular networks. However, the primary drawbacks are high detection time and feature engineering of network data. To overcome these issues, we propose a two‐phase detection framework, in which the first phase utilizes cosine similarity and weighting factors to identify attack misbehavior in vehicles. These metrics contribute to the calculation of effective node trust (ENT), which helps in further attack detection. In the second phase, deep learning (DL) models such as CNN and LSTM are employed for further granular classification of misbehaving vehicles into normal, fault, or Sybil attack vehicles. Due to the time series nature of vehicle data, CNN and LSTM are used. The methodology deployed at the controller provides a comprehensive analysis, offering a single‐ to multi‐stage classification scheme. The classifier identifies six distinct vehicle types associated with such attacks. The proposed schemes demonstrate superior accuracy with an average of 94.49% to 99.94%, surpassing the performance of existing methods.

Desain dan Implementasi Aplikasi Kasir Rental Mobil Menggunakan Visual Studio

This study presents an analysis of the development of a Visual Studio-based Car Rental application that aims to simplify the online car rental process. By increasing the need for more efficient transportation solutions, the app is designed to overcome obstacles in car rental, such as distance and information limitations. The methods used in this study include interviews with users and service providers, observation of existing rental practices, and literature related to information technology. The development results show that this application has a simple and user-friendly interface, is able to manage customer and vehicle data in a structured manner, and improves operational efficiency for service providers. In conclusion, the Car Rental application not only provides convenience for users but also supports the growth of the car rental business in the digital era.

AVChain: Trusted Sharing of Autonomous Vehicle Crash Incident Data using Interoperating HyperLedger Fabric Networks and IPFS

Autonomous vehicles (AVs) are gaining in popularity over the years as a viable cab service apps as well as for personal use. However, incidents of crashes involving AVs continue to occur, adversely affecting their prospects for widespread acceptance by both end users and regulatory authorities. While such cases are routinely investigated, in the absence of a human to testify on what caused the crash, one has to rely solely on available data. It is therefore imperative that the data logged by AVs is accessible to the concerned parties in a trustworthy manner. In this paper, we present AVChain - a novel framework for using a permissioned blockchain like HyperLedger Fabric (HLF) to record and share AV data comprised of sensors, actuators, maps, planning algorithms and machine learning models so that the data stays immutable even in the face of cross blaming among involved parties. Since the data volume is extremely large, we appropriately compress and down sample the same before storing in a distributed file system, namely, IPFS (Inter-Planetary File System). The hashes of such IPFS data called Content Ids (CIDs) are committed to the HLF network for making them tamper proof. The HLF ledger can later be queried to obtain the CIDs, which are then further used to retrieve and un-compress the original data from IPFS. Effectiveness and usability of AVChain is demonstrated by generating autonomous vehicle data from CARLA, which is a widely used open source AV simulator. For sharing AV data across organizations like sensor and actuator suppliers, map service providers, machine learning model developers and law enforcement authorities, the Weaver tool has been used to make multiple HLF networks interoperate. We have also developed a web application to demonstrate the working of AVChain. Results of an extensive set of experiments establish the efficacy of our approach.

Portable Vehicle Outer Contour Dimension Measurement Algorithm Using Unmanned Aerial Vehicle for Overlimit Mobile Detection

In the realm of highway traffic management, the detection of overlimit vehicles holds paramount importance. Presently, the predominant methods for overlimit detection rely heavily on stationary detection equipment, thus constraining the ability to conduct mobile detection. To tackle this challenge, the current study introduced an unmanned aerial vehicle (UAV)-based portable methodology aimed at measuring vehicle outer contour dimensions. By employing a binocular camera affixed to a UAV, the method captured point cloud data of vehicles, from which a specialized algorithm extracted the point cloud of target vehicles. The dimensions of the vehicle profile were then determined through a combination of line-scanning method and rotation of axis. We carried out experiments on trucks of different sizes and colors, with the following results: the average error of length measurement was 2.35%, of width measurement was 1.85%, and the average error of height measurement was 1.18%. This indicated the method’s high accuracy. Beyond offering a novel technical solution for identifying overlimit vehicles, this study expanded UAV applications into a new domain. The proposed method stands out for its portability, speed, and efficiency, facilitating immediate detection of highway overlimit infractions and significantly contributing to highway traffic management.