Types Of Cars Research Articles

Predicting Carbon Dioxide Emissions with the Orange Application: An Empirical Analysis

The effects of climate change, such as droughts, storms, and extreme weather, are increasingly being felt around the world. Greenhouse gases are the primary contributors to climate change, with carbon dioxide (CO2) being the most significant. In fact, CO2 accounts for a significant percentage of all greenhouse gas emissions. As a result, reducing CO2 emissions has become a critical priority for mitigating the impacts of climate change and preserving our planet for future generations. Based on simulation and data mining technologies that use historical data, CO2 is expected to continue to rise. Around the world, 80% of CO2 emissions come from burning fossil fuels, mostly in the automotive or manufacturing industries. Governments have created policies to control CO2 emissions by focusing them on either consumers or manufacturers, in both developed and developing nations. Within the scope of this project, an investigation of vehicle emissions will be carried out using various attributes included within the vehicle dataset, as well as the use of many data mining techniques via the utilization of an orange application. The practical program is an example of organization, and the example will be about cars, exploring data, and figuring out how much gas will be needed. CO2 is taken away from cars, and we will use the CARS.csv file, which has data for a group of car types. It has a table with 36 records that shows the model, weight, and amount of carbon dioxide based on the car's size and weight.

Our MapAI approach: focusing on data pipeline and loss functions

Precision building detection is a difficult challenge because resolution, lighting conditions, and image quality greatly influence the performance of machine learning models. Additionally, the building types, settlement structure, road structure, soil color and texture, vegetation, and car types can also affect image segmentation, making the solutions local or regional. In this paper, we describe the solution for the MapAI challenge submitted by the ATELIER team. We focused on two primary parts: data processing and loss functions. Our main insights were that the data can be effectively resampled by exploiting its structure and that the boundary intersection over union metric is very forgiving, not giving the network enough incentive to refine the borders. The segmentation was performed with a standard 5-level deep U-Net with an additional conditional random fields (CRF) denoiser.

Analysis of Teslas Advertising and Marketing Strategy

Recently, Tesla has become the most popular product, which is a new type of electric car. And the ultimate reason for winning people's attention is his propaganda method - advertising. Tesla's high-quality advertising also answers why Tesla's product was able to succeed. Therefore, this paper explores Tesla's marketing strategy, through Tesla's advertising. It can be concluded that setting multiple target audiences and implementing policies one by one are the main strategies of Tesla.

Bezpieczeństwo podczas wymiany pasażerów w tramwajach

Safety during exchange of passengers in trams Based on three real–life accidents the impact of tram platform design on the safety of passengers during their exchange is discussed. In one of the examples a tram stop which did not meet the public use requirements despite having been built following applicable building regulations is described. On this stop the tram driver was not able to observe the entire side of the tram cars and passenger exchange taking place due to the radius of the curve on which the stop was placed. The other two cases illustrate events which happened after the height of the platforms was increased relative to the track level in order to adapt them to new types of tram cars while the older ones were still being used. On these stops the lower edge of doors of the new low-floor cars was close to the edge of the platform while in the case of the older types of the cars the distance between these edges threatened the safety of boarding or disembarking passengers.

Sorokh-Poth: A Balanced Bangladeshi Road Vehicle Image Dataset Integrated With a Detection System Based On Deep Convolutional Neural Networks

When it comes to the administration of highways, the identification of intelligent vehicles is becoming an increasingly important component. Applications for vehicle detection are essential for both military and civilian usage, including the monitoring of highway traffic, administration, and the control of urban traffic. The vast amount of vehicle image data available online has the potential to encourage the development of increasingly sophisticated object recognition and classification models and algorithms. But finding an organized, balanced, and meaningful dataset continues to be a major challenge. This study proposes Sorokh-Poth, a new complete balanced image dataset based on Bangladeshi road travel that is compatible with a number of CNN-based architectures. The majority of the photographs in the collection were taken with a smartphone. The dataset includes 9,809 classified and annotated images of ten different types of vehicles, including autorickshaws, bicycles, buses, cars, CNG-powered vehicles, lagoon rickshaws, trucks, and vans. This research work utilizes the residual network ResNet-50 model, a CNN-based architecture. Here, features specific to the type of vehicle were automatically retrieved and grouped. Accuracy, precision, recall, and f1-score were just a few of the metrics used for evaluation during the research. The proposed model exhibited an increasing accuracy despite the vehicles shifting physical characteristics. The purpose of this work is to implement various CNN-based residual networks ResNet-50 and ResNet-150V2. Our proposed ResNet-50 model achieves an accuracy of 98.00% in the detection of native Bangladeshi vehicles, according to result comparisons and evaluations.

T cells engineered with full-length NKG2D linked to signaling domains of 4-1BB and CD3ζ show enhanced antitumor activity.

Since NKG2D ligands (NKG2DLs) are primarily overexpressed on multiple types of solid tumors but absent on most normal tissues, NKG2DLs could be optimal antigens for CAR-T cells. To date, there have been two types of NKG2DL CARs: (i) the extracellular domain of NKG2D fused to the CD8a transmembrane domain, signaling domains of 4-1BB and CD3ζ (NKBz) and (ii) full-length NKG2D fused to the CD3ζ signaling domain (chNKz). Although NKBz- and chNKz-engineered T cells both showed antitumor activities, a comparison of their functions has not been reported. In addition, use of the 4-1BB signaling domain into the CAR construct could prolong the persistence and resistance to antitumor activities of CAR-T cells, we designed a new NKG2DL CAR, full-length NKG2D fused to the signaling domains of 4-1BB and CD3ζ (chNKBz). Among the two types of NKG2DL CAR-T cells reported in previous studies, we found that chNKz T cells had stronger antitumor ability than NKBz T cells in vitro, but their antitumor activity in vivo is similar. The chNKBz T cells showed antitumor activity superior to that of chNKz T cells and NKBz T cells in vitro and in vivo, providing a new option for the immunotherapy of NKG2DL-positive tumor patients.

Automated design of freeform imaging systems for automotive heads-up display applications.

The freeform imaging system is playing a significant role in developing an optical system for the automotive heads-up display (HUD), which is a typical application of augmented reality (AR) technology. There exists a strong necessity to develop automated design algorithms for automotive HUDs due to its high complexity of multi-configuration caused by movable eyeballs as well as various drivers' heights, correcting additional aberrations introduced by the windshield, variable structure constraints originated from automobile types, which, however, is lacking in current research community. In this paper, we propose an automated design method for the automotive AR-HUD optical systems with two freeform surfaces as well as an arbitrary type of windshield. With optical specifications of sagittal and tangential focal lengths, and required structure constraints, our given design method can generate initial structures with different optical structures with high image quality automatically for adjusting the mechanical constructions of different types of cars. And then the final system can be realized by our proposed iterative optimization algorithms with superior performances due to the extraordinary starting point. We first present the design of a common two-mirror HUD system with longitudinal and lateral structures with high optical performances. Moreover, several typical double mirror off-axis layouts for HUDs were analyzed from the aspects of imaging performances and volumes. The most suitable layout scheme for a future two-mirror HUD is selected. The optical performance of all the proposed AR-HUD designs for an eye-box of 130 mm × 50 mm and a field of view of 13° × 5° is superior, demonstrating the feasibility and superiority of the proposed design framework. The flexibility of the proposed work for generating different optical configurations can largely reduce the efforts for the HUD design of different automotive types.

Product Line Optimization for Car-Sharing Platforms in the Sustainable Transportation

As the sharing economy develops in sustainable transportation, car-sharing platforms improve the allocation efficiency of idle cars to customers. However, as the travel demand increases, and the vehicle type becomes various, the sharing market faces a new challenge to satisfy the customers’ diverse needs for quality-differentiated cars. Each customer would like to choose the right car type, while the manufacturing firm and the e-commerce platform should set the right car price for profit maximization. How to match the platform’s product line with the customer’s choice behavior becomes a problem for all the stakeholders in the sharing economy. From the perspective of customer heterogeneity, we establish a game-theoretic framework to study product line optimization in business-to-consumer (B2C) and consumer-to-consumer (C2C) sharing. We solve the optimal quality decision and the optimal pricing strategy for the manufacturing firm and the e-commerce platform in the sharing economy. In numerical experiments, we investigate how the sensitivity of the quality and the cost can jointly influence the profits of sustainable transportation. Our findings show that within the product line, the firm’s selling profit in the C2C sharing is negatively influenced by the sharer’s valuation, while the platform’s sharing profit in the B2C sharing is independent of either buyer’s valuation. We give some policy implications for scholars, practitioners, and policymakers in the sharing economy and point out some limitations and recommendations for the product line design.

Identification of objects based on the data of tenzometrical systems with using methods of machine learning

Objective. To increase the quality of work of tensometric systems, using data obtained from the strain gauge system in the form of maps of the passage of moving objects, it is necessary to conduct research on the data of the strain gauge system and develop a set of algorithms, thanks to which it is possible to obtain the characteristics of railway cars in rolling stock and carry out identification of railway objects objects with a minimum error for further use of the obtained results in the construction of a system of identification and weighing of moving objects through tensometric systems. Research methods. To build a system for identifying different types of moving objects through single-platform railway scales in motion, it is proposed to use machine learning methods, namely neural networks and clustering algorithms. The software implemented as part of scientific research is written in the Python programming language using numPy, sklearn, statistics, and other libraries. Findings. Using such methods of machine learning as convolutional neural networks, clustering, perceptron and relying on the reference data of railway objects that can be used on the territory of Ukraine, a number of algorithmic solutions were obtained and implemented in the form of software, which identify the type of car by such characteristics such as the axle of the cart, the axle of the wagon, the ratio of the base of the wagon to the length of the wagon between the couplings, the weight of the axles. Using the weight coefitient for a specific tensometric system, during the calibration of the scales, the dependence of the weight of the car on its type and the mass of each of the axles was obtained. The originality. After conducting a study of the data on the passage of railway carriages and auto couplings through single-platform scales, it was established that the types of wagons can be categorized by such characteristics as the ratio of the base of the wagon to the length of the wagon between the auto couplings, the axle, weight. To obtain the ratio of the wagon base to the length of the wagon between autocouplings, it is necessary to perform data segmentation and clustering as follows - the wagon base is found as the distance between the middle of two bogies, and the length of the wagon between autocouplings as the middle of the distance between the bogies to the middle of the autocoupling. Practical implications. Using such methods of machine learning as convolutional neural networks, clustering, perceptron and others, an algorithmic solution for data processing of strain gauge systems was obtained, which allows to increase the accuracy of the identification of wagons, while reducing the dependence of the results on the speed of the wagons, which allows to increase the capacity of weighing systems of enterprises

Impact of Information Parameters of Online Auto Dealership Website Towards Consumer Purchase Intention

Purpose: Information plays a significant role in the online business space where consumers perform information search before making the final purchase decision. Information is the source for the word of mouth marketing which becomes critical in achieving the marketing goals of the organization. The study aims to understand the role of information presented to the customers in determining the purchase intention. Theoretical Framework: Consumers make their purchase decision in various stages and the role of information search plays a vital role in making the decision. Theory of planned behavior and theory of reasoned action are the base theories in studying consumer behavior. However the theories take into account the usage behavior and present lesser relevance to the information presented. This study focus on the nature of the information based on the existing literature. Methodology: The scale to measure the Purchase Intention based on the intention attributes weretaken from (Javier A. Sánchez Torres et al,2018).Data were analysed using the MS Excel and structural equation modelling is carried out using SmartPLS3. Purposive sampling was adopted and the respondents who have used at least one Online auto dealership website were considered for the study Findings: Credibility of the information has a higher influence towards the Purchase intention among consumers. The second highest factor that influence the purchase intention of consumers is found to be the need for the information. Research Implications: Affordable and ease of access towards internet has opened the path towards information overload through various mobile and social media applications. In this era. Online autodealership business owners have to create methods to emphasize on authenticity of the information presented in their website. The need analysis of the consumers based using the online tools and algorithms have to be improved to suggest the type of cars. Originality/Value: The research is one among the few studies in understanding the information parameters for an online auto dealership website and it would play a vital role in designing the content and marketing campaigns.

Non-Linear Control of Fuel Cell/Ultra-Capacitor Hybrid Electric Vehicle Using Comprehensive Function Algorithm Based on IDA-PBC

Hybrid electric vehicles (HEVs) have become increasingly popular due to their high fuel economy performance and low greenhouse gas emissions. HEVs that use fuel cells produce fewer emissions and are more efficient than many other types of cars. Accordingly, many researchers are significantly interested in applying a combination of the fuel cell (FC) and the ultra-capacitor (UC) for HEVs. This study proposes a method for improving the control of an FC/UC hybrid electric vehicle, also analyzing its performance. In the HEV used, the fuel cell provides the main power, but in transient situations where the FC cannot support the vehicle alone, the UC gives the stored energy to the system to solve the energy deficiency. In this study, the interconnection and damping assignment passivity-based control (IDA-PBC) method is applied and regulates the system performance under nonlinear operation. In addition, a comprehensive energy management strategy proposes to cover all driving cycle situations. The main control objective is to keep the system voltage within an acceptable range despite the appropriate dynamic behavior. To increase the accuracy of the results, in system structure modeling, energy losses are considered. In the case study part, a standard Japanese driving cycle (SJDC) uses, which comprises different practical conditions such as off-load, overload, uphill, and downhill. The simulation results using MATLAB/Simulink show the effectiveness of the proposed algorithm and control method.

“Is Energy That Different from Labor?” Similarity in Determinants of Intensity for Auto Assembly Plants

This paper addresses the question “Is energy that different from labor?” from the perspective of efficiency. It presents a novel statistical analysis for the auto assembly industry in North America to examine the determinants of relative energy intensity, and contrasts this with a similar analysis of the determinants of another important factor of production, labor intensity. The data used combine two non-public sources of data previously used to separately study key performance indicators (KPIs) for energy and labor intensity. The study found these two KPIs are statistically correlated (the correlation coefficient is 0.67) and the relationship is one-to-one. The paper identifies 11 factors that may influence both energy and labor intensity KPIs. The study then contrasts which of the empirical factors the two KPIs’ share and how they differ. Two novel statistical methods, Huber estimators and Multiple M-estimators, combined with regularized algorithms, are identified as the preferred methods for robust statistical models to estimate energy intensity. Based on our analysis, the underlying determinants of energy efficiency and labor productivity are quite similar. This implies that strategies to improve energy may have spillover benefits to labor, and vice versa. The study shows vehicle variety, car model types, and launch of a new vehicle penalize both energy and labor intensity, while flexible manufacturing, production volume, and year of production improve both energy and labor intensity. In addition, the study found that the plants that produce small cars are more energy-efficient and productive compared to plants that produce large vehicles. Moreover, in a given functional unit, i.e., on a per-unit basis, Japanese plants are more energy-efficient and productive compared to American plants. Plant managers can use the proposed data-driven approach to make the right decisions about the energy efficiency targets and improve plants’ energy efficiency up to 38% using hybrid regression methods, mathematical modeling, plants’ resources, and constraints.

Effect of spatial masking release on perception of vehicle's approaching sound in headphone music listening

Since music listening with headphones or earphones disturbs auditory information necessary for safety, pedestrians, for example, have a high risk of causing a severe accident. When listening to music and walking on the road with earphones or headphones, a person may fail to notice approaching vehicles from behind. It is because of the masking by the music played through headphones, which makes it difficult to perceive the sound of approaching vehicles. To improve such situations, the authors aim to actualize a listening environment where pedestrians enjoy listening to music with headphones with perceiving their ambient sounds. As a process, this report deals with a possible methodology to preserve auditory information for safety using sound localization of music. Authors investigate the effect of the music localization played through headphones on the perceptibility of the vehicle's approaching sound. The approaching sound of several types of cars are presented to listeners, who are asked to answer when the cars arrive at their places. The timing when the car reach is measured. The author will demonstrate the result of the investigation at the conference.

A teacher-student based attention network for fine-grained image recognition

Fine-grained image recognition (FGIR) task is dedicated to distinguishing similar sub-categories that belong to the same super-category, such as bird species and car types. In order to highlight visual differences, existing FGIR works often follow two steps: discriminative sub-region localization and local feature representation. However, these works pay less attention on global context information. They neglect a fact that the subtle visual difference in challenging scenarios can be highlighted through exploiting the spatial relationship among different sub-regions from a global view point. Therefore, in this paper, we consider both global and local information for FGIR, and propose a collaborative teacher-student strategy to reinforce and unity the two types of information. Our framework is implemented mainly by convolutional neural network, referred to Teacher-Student Based Attention Convolutional Neural Network (T-S-ACNN). For fine-grained local information, we choose the classic multi-attention network (MA-Net) as our baseline, and propose a type of boundary constraint to further reduce background noises in the local attention maps. In this way, the discriminative sub-regions tend to appear in the area occupied by fine-grained objects, leading to more accurate sub-region localization. For fine-grained global information, we design a graph convolution based global attention network (GA-Net), which can combine extracted local attention maps from MA-Net with non-local techniques to explore spatial relationship among sub-regions. At last, we develop a collaborative teacher-student strategy to adaptively determine the attended roles and optimization modes, so as to the cooperative reinforcement of MA-Net and GA-Net. Extensive experiments on CUB-200-2011, Stanford Cars and FGVC Aircraft datasets illustrate the promising performance of our framework.© 2022 Published by Elsevier Ltd.

Is the Transition to Electric Passenger Cars Sustainable? A Life Cycle Perspective

Compared to conventional passenger cars, the hybrid and electric alternatives include electric motors and large batteries; the use of clean energy, reduced operation emissions, and decreasing purchase prices can represent solid reasons for their market adoption. The feasibility of the transition to electric cars is analyzed herein in terms of the costs, main pollutants, and energy consumption of compact to large-sized cars. In this regard, the proposed life cycle assessment methodology evaluates the pollution and energetic impacts of the current passenger car models with a weight varying from 1.6 to 1.8 tons, depending on the car type, for a complete life cycle. The life cycle emissions and energy consumption are also determined through simulation in order to validate the estimated values for the considered powertrains. This study has shown that a transition to current full-electric passenger cars, based on a European and United States energy mix, is not currently sustainable in terms of energy consumption. The complete life cycle values are similar for the tested conventional and full-electric passenger cars, ranging from 5 to 5.2 MJ/km. By comparison, the hybrid alternatives and full-electric cars based solely on renewable energy present lower energy consumption, ranging from 3.32 to 4.62 MJ/km. At the same time, the hybrid alternatives and conventional cars provide relevant benefits in life cycle costs: 20–25% lower than full-electric cars. In terms of life cycle emissions, the tested full-electric cars based on renewables show a noticeable reduction in greenhouse gases and in other relevant pollutants: 37% and 62%, respectively, lower than that of conventional cars.

Identifying vehicles as green cars using Q methodology: Viewpoints of Korean transport policy experts

The recent trend of transitioning from cars with internal combustion engines to green cars has accelerated amidst growing concerns of a climate crisis and the COVID-19 pandemic. South Korea is a particularly good test bed for exploring green mobility alternatives and setting priorities for green car policies, owing to its fast-growing green car market and its considerable share in the worldwide purchases of fuel cell electric vehicles. Accordingly, this study explores the Korean conceptualization of green cars and the prioritization of different aspects of future green car policy using qualitative Q methodology and additional interviews. A total of 31 transport policy experts participated in our survey, resulting in three distinct perspectives: a hybrid vehicle supporter with comparative pragmatism, an electric vehicle supporter from multiple aspects, and a radical ecologist searching for new alternatives. Based on these expert insights, we determine the most appropriate green car type and provide important recommendations for future green car policy.

CAR-T Cells with Phytohemagglutinin (PHA) Provide Anti-Cancer Capacity with Better Proliferation, Rejuvenated Effector Memory, and Reduced Exhausted T Cell Frequencies.

The development of genetic modification techniques has led to a new era in cancer treatments that have been limited to conventional treatments such as chemotherapy. intensive efforts are being performed to develop cancer-targeted therapies to avoid the elimination of non-cancerous cells. One of the most promising approaches is genetically modified CAR-T cell therapy. The high central memory T cell (Tcm) and stem cell-like memory T cell (Tscm) ratios in the CAR-T cell population increase the effectiveness of immunotherapy. Therefore, it is important to increase the populations of CAR-expressing Tcm and Tscm cells to ensure that CAR-T cells remain long-term and have cytotoxic (anti-tumor) efficacy. In this study, we aimed to improve CAR-T cell therapy's time-dependent efficacy and stability, increasing the survival time and reducing the probability of cancer cell growth. To increase the sub-population of Tcm and Tscm in CAR-T cells, we investigated the production of a long-term stable and efficient cytotoxic CAR-T cell by modifications in the cell activation-dependent production using Phytohemagglutinin (PHA). PHA, a lectin that binds to the membranes of T cells and increases metabolic activity and cell division, is studied to increase the Tcm and Tscm population. Although it is known that PHA significantly increases Tcm cells, B-lymphocyte antigen CD19-specific CAR-T cell expansion, its anti-cancer and memory capacity has not yet been tested compared with aCD3/aCD28-amplified CAR-T cells. Two different types of CARs (aCD19 scFv CD8-(CD28 or 4-1BB)-CD3z-EGFRt)-expressing T cells were generated and their immunogenic phenotype, exhausted phenotype, Tcm-Tscm populations, and cytotoxic activities were determined in this study. The proportion of T cell memory phenotype in the CAR-T cell populations generated by PHA was observed to be higher than that of aCD3/aCD28-amplified CAR-T cells with similar and higher proliferation capacity. Here, we show that PHA provides long-term and efficient CAR-T cell production, suggesting a potential alternative to aCD3/aCD28-amplified CAR-T cells.

Noise Assessment during Motor Race Events: New Approach and Innovative Indicators

Motorsport races significantly affect, on a local scale, noise pollution even if they do not represent the majority of its contribution, which is a prerogative of road transportation, railways, airports, and industries. Nevertheless, such noise emissions surely affect the well-being of inhabitants in the surrounding area of the circuit. In fact, during a motor race event, vehicles produce high noise emissions while on tests, qualifying, and race sessions. Since noise indicators commonly used in national regulations are computed over fixed times, it is challenging to properly assess the total noise emission and immission at the receivers during such events. Moreover, in literature, only a few works can be found assessing this specific issue, and consequently, there’s also a lack of appropriate methods to properly measure the global noise emission of each event. In this contribution, the authors report the characterization of noise emission during motor race events by using two new acoustic indicators, namely LEL (Lap Equivalent Level) and REL (Race Equivalent Level) starting from noise data collected on different points along a racing circuit. Measurements show that the REL tends to stabilize its value during a race, suggesting that its modelling can be achieved only based on the average LEL and the number of vehicles participating in a race. These indicators will allow predicting the total noise emission at a certain receiver of a motor race event by knowing the number and type of cars involved, without using the duration of the race itself.

When epidemiological databases inform injury mechanisms: biomechanical analysis of injury associations

BackgroundVehicle accidents are still a heavy social burden despite improvements due the latest technologies and policies. To pursue the trend of decrease, having a more detailed view and understanding of the injury patterns would contribute to inform both the rescue team to optimize victim’s management and policymakers in order for them to tackle at best this issue.MethodsTwo complementary analyses of injury associations were performed, one using a biomechanical classification and the other an anatomic one, computed on data stratified by car accident type (lateral or frontal). Our objective is to understand whether these two categories of crash lead to similar or heterogeneous injury association patterns, and analyze these findings from an impact mechanics point of view. Indeed, having an improved understanding of the injury mechanisms would facilitate their diagnosis and prevention.ResultsWhile each type of accident possesses its own injury profile, most injury associations are found for both types. Injuries such as clavicle and rib fractures were identified as involved in a high number of associations. Several associations between fractures and blood vessel injuries were found.ConclusionsThe results suggests three main conclusions: (i) Injury associations are rather independent from crash characteristics, (ii) Clavicle and rib fractures are typical of poly-traumatized victims, (iii) Certain fractures can be used to early detect victims at higher risk of hemorrhage. Overall, this study provide paramedics and doctors with data to orientate them toward a faster and more appropriate decision. Moreover, this exploratory work revealed the potential that injury association analyses have to inform policy-making and issue recommendations to decrease road accident mortality and morbidity.

Effects of life events and attitudes on vehicle transactions: A dynamic Bayesian network approach

Individual and household life events are interdependent and influence mobility-related decisions at different levels over time. This paper developed an integrated dynamic model to capture the interdependences among life events, with a special focus on vehicle transactions. Particular attention was paid to the inclusion of vehicles’ characteristics such as the age, fuel type, and size of cars, which are pertinent to emission forecast. A dynamic Bayesian network (DBN), containing individual and household characteristics and latent attitudes toward car ownership and use alongside life events, was employed to study the interdependences. The temporal relationships among life events and lead-lag effects were also captured in the DBN. The longitudinal survey data “the Netherlands Mobility Panel (MPN)” from 2013 to 2018 was used to train and test the DBN. The analysis results confirm the dynamic interdependences between vehicle transactions and other life events and reveal noticeable associations between attitudes and purchase decisions. It is found that several life events (e.g., “Birth of a baby”, “Marital status change”) have concurrent or varied lag-effects on vehicle transaction decisions. The validation indicates that the proposed DBN approach has a high predictive accuracy of vehicle transaction decisions and other life events.