The Car Allowance Rebate System (CARS), otherwise known as Cash for Clunkers, was a program intended to provide economic incentives to United States residents to purchase a new and more fuel efficient vehicle when trading in a less full efficient vehicle. The program was promoted as providing stimulus to the economy by boosting auto sales, while putting safer, cleaner and more fuel efficient vehicles on the road.

The fields available include make, model, year, trim, style, fuel type, MSRP, and many more.

We have developed this file to be tied to our Consumer Demographics Database so additional demographics can be applied as needed. Each record is ranked by confidence and only the highest quality data is used. This file contains over 180 million records in addition to over 1 million+ fresh automotive intender records per day.

Note - all Consumer packages can include necessary PII (address, email, phone, DOB, etc.) for merging, linking, and activation of the data.

BIGDBM Privacy Policy: https://bigdbm.com/privacy.html

The main raw dataset within the .RAR-file contains stated choice data from consumers about MPC-enabled data marketplaces. This dataset may be used to determine the consumers' willingness to contribute automotive (GPS) data on MPC-enabled data marketplaces. Van Aalst (2021, ch. 7) draws conclusions and recommendations for future research.
Furthermore, read the README.txt file very carefully to see in which way you can use these data in Discrete Choice Modeling (DCM) in Rstudio to retrieve the data-sharing factor estimates. These data can be utilized in predicting consumers' willingness to share automotive GPS data in similar data marketplaces.

Abstract (en): The purpose of this data collection effort was to provide an accurate representation of the distribution of elements composing family balance sheets across families in the United States. To that end, the 1998 Survey of Consumer Finances was designed to gather household-level information closely comparable to that obtained in the SURVEY OF CONSUMER FINANCES, 1995 (ICPSR 2193). Detailed data were collected on the composition of household budgets, the terms of loans, and relationships with financial institutions. Information was also obtained on the employment history and pension rights of the survey respondent and the spouse or partner of the respondent. Detailed data were also gathered on characteristics of the survey respondent's housing and vehicle(s). In addition to recording data on the economic assets and liabilities of families, the survey examined the attitudes of consumers toward credit use and their reactions to consumer credit regulations, and lines of credit. Demographic variables include age, sex, marital status, housing, and financial independence. Households within the 48 contiguous United States. The Survey of Consumer Finances (SCF) is based on a dual-frame sample design (see Arthur B. Kennickell and R. Louise Woodburn, "Consistent Weight Design for the 1989, 1992, and 1995 SCFs, and the Distribution of Wealth," August 1997, http://www.federalreserve.gov/pubs/oss/oss2/method.html for more details). One set of the survey cases was selected from a standard multistage area probability design. This part of the sample, which contributed 2,813 cases to the final set of interviews, is intended to provide good coverage of characteristics, such as home ownership, that are broadly distributed in the population. The other set of the survey cases was selected as a list sample from statistical records (the Individual Tax File) derived from tax data by the Statistics of Income Division of the Internal Revenue Service. These records were made available under strict rules governing confidentiality, the rights of potential respondents to refuse participation in the survey, and the types of information that can be made available. This second sample was designed to disproportionately select families that were likely to be relatively wealthy (see Arthur B. Kennickell, "List Sample Design for the 1998 Survey of Consumer Finances," April 1998, http://www.federalreserve.gov/pubs/oss/oss2/method.html, for a more extended discussion of the design of the list sample). The list sample contributed 1,496 cases to the final set of interviews. Because of the complexity of the SCF design, users cannot apply some of the standard procedures for variance estimation. A set of sample replicates has been created with bootstrap techniques and analysis weights have been computed independently for each replicate. Analysts may use these weights to make approximate estimates of sampling variance. Replicate weights corresponding to both X42000 and X42001 are available. See the codebook for more details. 2006-03-30 File QU3155.ALL.PDF was removed from any previous datasets and flagged as a study-level file, so that it will accompany all downloads.2006-03-30 File CB3155.ALL.PDF was removed from any previous datasets and flagged as a study-level file, so that it will accompany all downloads.2006-03-30 File AP3155.ALL.PDF was removed from any previous datasets and flagged as a study-level file, so that it will accompany all downloads. Funding insitution(s): Board of Governors of the Federal Reserve System. United States Department of the Treasury. Office of the Comptroller of the Currency. United States Department of Health and Human Services. National Institutes of Health. National Institute on Aging. United States Small Business Administration. Government Accountability Office. United States Congress. Joint Committee on Taxation. The data have been optimized and thus have a different record length from the original provided by the principal investigators.The SAS transport files were created using the SAS XPORT engine.

Automotive USB C Market size is growing at a moderate pace with substantial growth rates over the last few years and is estimated that the market will grow significantly in the forecasted period i.e. 2024 to 2030.

Global Automotive USB C Market Drivers

The market drivers for the Automotive USB C Market can be influenced by various factors. These may include:

Demand for Fast Charging Has Increased: In comparison to earlier standards, USB-C offers better power supply and faster charging. USB-C is becoming more widely used as car manufacturers and consumers look for more energy-efficient charging options. Increasing Integration of Consumer gadgets: Modern cars are fitted with more and more sophisticated consumer gadgets, such laptops, tablets, and smartphones, which need for fast data transfer and charging. The adaptability of USB-C efficiently meets these requirements. Standardization and Compatibility: The widespread acceptance of USB-C in cars is fueled by the fact that it is quickly becoming a standard for numerous electronic devices. Users will benefit from this standardization as it helps to reduce the number of cords and adapters required. Improved Data Transfer Rates: When compared to earlier USB standards, USB-C provides faster data transfer rates. For cars with sophisticated infotainment systems that have to manage a lot of data, such multimedia files or software upgrades, this is especially helpful. Trends in the Automotive Industry: Autonomous driving technology and sophisticated infotainment systems are only two examples of the advanced and linked car systems that the industry is headed toward. Because of its powerful data and power capacities, USB-C is ideally suited to accommodate these advancements. Regulatory and Compliance Factors: Standardized charging methods are being pushed for by some authorities in an effort to cut down on electronic waste and increase consumer convenience. Global market trends are influenced, for example, by the European Union's promotion of USB-C as a common charging standard. Consumer Experience Improvement: The reversible connector that USB-C offers reduces aggravation when attempting to plug in the connector correctly, hence improving the user experience. Its increasing popularity is partly due to its simplicity of usage. Automaker Innovation and Differentiation: To set their cars apart from the competition and provide a more contemporary, high-end experience, automakers are adding USB-C connectors. The necessity to remain competitive and appeal to tech-savvy consumers is what's driving this adoption. Manufacturing Cost Reductions: As USB-C is used more frequently, the price of producing USB-C connectors and parts has dropped, making it more accessible for use in automobiles. Growth of Electric Vehicles (EVs): As the market for EVs expands, there's a desire to incorporate cutting-edge technology into these cars. Many modern electric vehicles have a tech-oriented design, thus USB-C is a perfect fit for meeting their data and charging needs.

Abstract

This article details the methodology and the approach used to extract and decode the data obtained from the Controller Area Network (CAN) buses in three personal vehicles and four commercial trucks. The dataset is composed of two complementary parts, namely the raw data and the decoded ones. Along with the description of the data, this article also reports both hardware and software requirements to firstly extract the data from the vehicles and secondly decode the binary data frames to obtain the actual sensors' data. Finally, necessary code snippets have been described in pseudo-code and will be publicly available in a code repository. Preliminary results suggest that motivated enough actors may intercept, interact and recognize the vehicle data with consumer-grade technology, ultimately refuting, once-again, the security-through-obscurity paradigm used by automotive manufacturer as main defensive countermeasure.

Keywords

Automotive; Controller Area Network (CAN); Reverse Engineering; Dataset

Type of data

• RAW: CSV files with timestamp, CANline, ECU identifier, binary data
• Decoded: CSV files with timestamp, CANline, ECU identifier, variable, value

How data where acquired

Controller Area Network (CAN) buses have been accessed using a standard CAN connector and a CANtact board. The CAN Utils library, publicly available in the Linux Kernel, has been used to intercept the network traffic of the vehicle. Sensors data have been decoded using state-of-the-art algorithm. Source code for each step of the analysis is publicly available in the repository, as specified below. Parameters: - Cars: 500k baudrate, connected o the OBD-II port of each vehicle. - Trucks: 500k baudrate, connected both to the OBD-II port and to a second wire into a second CAN bus.

Source code

• Repository: ReCAN Source - Reverse engineering of Controller Area Networks
• Provider: Github
• Identification number: 10.5281/zenodo.3625715
• URL: https://github.com/Cyberdefence-Lab-Murcia/ReCAN

Acknowledgments

This study was founded by a predoctoral grant from the Spanish National Cybersecurity Institute (INCIBE) within the program "Ayudas para la Excelencia de los Equipos de Investigación Avanzada en Ciberseguridad" ("Grants for the Excellence of Advanced Cybersecurity Research Teams"), with code INCIBEI-2015-27353; a predoctoral travel grant within the program "Ayudas para estancias en el estranjero de alumnos de doctorado en las líneas de actuación de Campus Mare Nostrum" ("Grants for stays abroad of Ph.D. students within the lines of action of Campus Mare Nostrum'').

These data are from a survey of participants in the California's Clean Vehicle Rebate Project, a statewide consumer cash rebate for the purchase or lease of a qualifying clean vehicle. The survey data were used in an analysis of "EV Converts" or rebate recipients who had little or no knowledge or interest in an EV at the start of their new car search. The abstract for that work follows here: To expand markets for plug-in electric vehicles (EVs) beyond enthusiastic early adopters, investments must be strategic. This research characterizes a segment of EV adoption that points the way toward the mainstream: EV consumers with low or no initial interest in EVs, or ���EV Converts.��� Logistic regression is utilized to profile EV Convert demographic, household, and regional characteristics; vehicle-transaction details; and purchase motivations���based on 2016���2017 survey data characterizing 5,447 rebated California EV consumers. Explanatory factors are rank-ordered���separately for battery EVs (BEVs) and plug-in hybrid EVs (PHEVs), to inform targeted outreach and incentive design. EV Converts tend to have relatively ���lower��� values on factors that might have otherwise ���pre-converted��� them to EV interest: hours researching EVs online; motivation from environmental impacts and carpool-lane access; and solar ownership. PHEV Converts more closely resemble new-car buyers, but BEV Converts ���go beyond��� mainstream markets on two measures: they are younger and less-frequently white/Caucasian than new-car buyers. BEV Converts also tend to: lack workplace charging, be moderately motivated by energy independence, and reside in Southern California or the Central Valley. Predictors that not only help target, but also help convert, consumers include rebates for BEV consumers and, modestly, fuel-cost savings for PHEV consumers. Version 2: corrected a typo in the title and description on Mendeley Data page and in the 2020-10_CVRP-Data-EV-Converts.xlsx file (now 2020-10_CVRP-Data-EV-Converts_v2.xlsx).

This dataset supports the study titled "The Impact of Consumer Perception and Behavior on the Growth of the New Energy Vehicle Market in China." The data includes: Survey responses from 405 participants, selected using Krejcie and Morgan’s sample size table. Quantitative data was analyzed using SMART PLSsoftware. This replication package contains raw data files, coding schemes, and analysis outputs used to test hypotheses grounded in the Diffusion of Innovations Theory and the Theory of Planned Behavior.

This data set is the final sample used in the analysis which is at country-model-year level. It contains information on vehicle attributes, charging infrastructure measures, policy variables, and country demographics etc. Note that the proprietary sales data is excluded. The code is Stata do-file for producing regression tables in the paper.

The data in the file correspond to responses from potential electric vehicle customers in Ecuador. The Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM) served as the theoretical basis for this study. The participants ranged in age from 18 to 65 years and resided in the cities of Quito and Guayaquil. The first variables in the file represent the demographic information of the individuals. The constructions utilized in the model are shortened and assigned a numerical identifier.

Abstract (en): This data collection is one in a series of financial surveys of consumers conducted annually since 1946. In a nationally representative sample, the head of each family unit was interviewed. Starting in 1966, in order to examine the effect that increased car ownership was having on American families, the data collected in this series were organized so that they could be analyzed by both family unit and car unit. The 1969 data are based on car unit. Survey questions regarding automobiles included number of drivers and car owners in the family, make and model of each car, purchase method, car financing and installment debt, and expectations of car purchases in the coming year. Other questions in the 1969 survey covered the respondent's attitudes toward national economic conditions (e.g., the effect of income tax, Vietnam War involvement, and relations with other communist countries on United States business) and price activity, as well as the respondent's own financial situation. Other questions examined the family unit head's occupation, and the nature and amount of the family's income, debts, liquid assets, changes in liquid assets, savings, investment preferences, and actual and expected purchases of major durables. In addition, the survey explored in detail the subject of housing, e.g., previous and present home ownership, value of respondent's dwelling, and mortgage information. Personal data include age and education of head, household composition, and occupation. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Performed consistency checks.; Standardized missing values.. The population of the United States. The sample was made up of a national cross-section of family units. Data on a family unit basis have a record for each family, with car information only for the first (usually the newest) car owned. Data on a car unit basis have a record for every car owned by the family. By using a global filter, data on a car unit basis can also be analyzed on a family unit basis. The frequencies in the codebook have been filtered to a family unit basis.

The North American market for automotive cybersecurity is experiencing robust growth, driven by the increasing connectivity of vehicles and the rising threat landscape. The market, estimated at $X billion in 2025 (assuming a proportional share of the global market based on regional GDP and vehicle production), is projected to expand significantly over the forecast period (2025-2033). This expansion is fueled by several key factors. Firstly, the proliferation of advanced driver-assistance systems (ADAS) and autonomous driving technologies introduces numerous potential attack vectors, necessitating sophisticated cybersecurity solutions. Secondly, stringent government regulations and industry standards, mandating robust cybersecurity measures in vehicles, are driving adoption. Thirdly, consumer awareness of data breaches and the potential for malicious attacks on connected cars is increasing, leading to greater demand for enhanced security features. The market segmentation reveals a strong demand for software-based solutions due to their flexibility and cost-effectiveness, while hardware-based solutions are crucial for core security functionalities. Network security remains a dominant segment, addressing vulnerabilities arising from vehicle-to-everything (V2X) communication, while cloud security is gaining traction with the increasing reliance on cloud-based services for data management and software updates. The competitive landscape is characterized by a mix of established automotive suppliers, technology giants, and cybersecurity specialists. Key players such as Harman International, IBM, and Continental are leveraging their expertise in automotive systems and cybersecurity to capture a significant market share. However, smaller, specialized cybersecurity firms are also emerging, offering innovative solutions and creating a dynamic ecosystem. While challenges remain, such as the high cost of implementing comprehensive cybersecurity measures and the complexity of integrating security solutions into existing vehicle architectures, the North American market is poised for substantial growth due to the increasing sophistication of vehicle technology and the heightened awareness of cybersecurity risks. The projected CAGR of 9.5% for the global market suggests a similar, if not higher, growth rate for North America considering its advanced automotive sector and stringent regulatory environment. This implies that the market could reach a significant size by 2033. Recent developments include: January 2020 - HARMAN launched the HARMAN Ignite Marketplace, an extensive network of cloud-based applications and services available on the HARMAN Ignite Cloud Platform. The HARMAN Ignite platform provides a built-in Over-the-Air (OTA) functionality, which helps manage potential risks like network problems, file tampering, and cybersecurity attacks, due to which automakers are equipped with a secure and efficient way to deliver and frequently update a robust service ecosystem while still mitigating risk.. Key drivers for this market are: Rising Security Threats as More Technologies Get Integrated Into Cars, Government Regulations. Potential restraints include: Rising Security Threats as More Technologies Get Integrated Into Cars, Government Regulations. Notable trends are: Application Security Expected to Witness Significant Market Share.

Market Overview:

The Data Converter market is projected to grow at a CAGR of 5.5% from 2017 to 2030. The growth of the data converter market can be attributed to the increasing demand for Data Converters in various applications, such as communications, automotive, consumer electronics, industrial, medical, and test and measurement.

Product Definition:

A data converter is a type of software used to convert data from one format to another. It is used to convert data from one type of file to another, such as from text to a spreadsheet, or from a text file to an image file. The data converter can also be used to convert data from one type of application to another, for example, from a spreadsheet to a word processor. This type of software is used to make sure data is compatible with different applications and can be used in different ways.

Analog-to-Digital Converters:

Analog-to-Digital Converters (ADCs) are devices that convert analog signals into digital signals. They are commonly used in digital systems to enable the conversion of analog data such as sound, temperature, pressure, and voltage into a format that can be used by digital systems. ADCs can be used to sample and convert analog signals into digital signals for processing in a computer or other digital system.

Digital-to-Analog Converters:

The digital-to-analog converters (DAC) are electronic devices that perform the conversion of a digital signal into an analog one. The DACs are used in data converters where they help convert the digital signals received from microprocessors or computers into an audio signal that can be played through speakers, headphones, and other audio equipment.

Application Insights:

The data converters sales market is segmented by application into communications, automotive, consumer electronics, industrial and medical. The communications segment dominated the overall industry in 2015 owing to the increasing demand for high-speed internet access and the rising number of users across the globe. The growing need for data transmission over long distances has led to an increase in demand for data converters in this sector. The automotive sector is projected to emerge as one of the fastest-growing segments over the forecast period owing to a rise in demand from emerging economies.

Regional Analysis:

The Asia Pacific regional market accounted for over 40% of the global revenue share in 2015 and is projected to continue its dominance over the forecast period. The growth can be attributed to rising demand from emerging countries such as China, India, and Japan. Increasing disposable income coupled with the growing consumer electronics industry is anticipated to drive demand further.

Growth Factors:

• Increasing demand for data converters in the industrial sector.
• The growing popularity of data converters in the telecommunications sector.
• Rising demand for miniaturized and high-performance data converters.
• The proliferation of digital signal processing (DSP) technology.
• The emergence of new applications that require data conversion.

Report Scope

The size of the United Kingdom Auto Loan Market was valued at USD 90.30 Million in 2023 and is projected to reach USD 122.64 Million by 2032, with an expected CAGR of 4.47% during the forecast period. The auto loan market refers to the financial services sector that provides loans for purchasing vehicles. It enables consumers to finance the cost of new or used cars, spreading payments over a set period through monthly installments. Auto loans are typically offered by banks, credit unions, and specialized financial institutions, with loan terms and interest rates depending on factors like the borrower’s creditworthiness, the type of vehicle, and market conditions. The demand for auto loans is driven by increasing vehicle ownership, rising consumer incomes, and the need for flexible financing options. Additionally, the growth of the automotive industry, particularly electric vehicles (EVs), has further boosted the market as more consumers seek loans for environmentally friendly cars. Digitalization and the rise of online banking have simplified the loan application process, allowing for quicker approvals and enhanced customer convenience. Recent developments include: December 2023: Blue Motor Finance Limited (Blue), an FCA-regulated UK-based car finance provider, prides itself on its ability to use technology to enhance its customer service. Customers can now request all of their agreement-related documentation for the life of their loan in one simple file at the touch of a button. Customers can also request to receive a settlement quote in real-time at a time convenient to them., August 2023: Santander Consumer Finance extended its partnership with MG Motor to provide dealers with an EV benefits scheme for customers.. Key drivers for this market are: Quick Processing of Loan through Digital Banking. Potential restraints include: Rising Interest Rates Affecting New Auto Buyers Demand for Loan. Notable trends are: Increasing Registrations of Electric Vehicle in United Kingdom.

NOR Flash for Cars Market Outlook

The global NOR flash for cars market size was valued at approximately USD 2.1 billion in 2023 and is projected to reach around USD 4.7 billion by 2032, growing at a CAGR of 9.2% during the forecast period. The robust growth of this market can be attributed to the increasing demand for advanced automotive electronics and the rising adoption of electric vehicles, which necessitate sophisticated memory solutions for their various systems.

One of the primary growth factors driving the NOR flash for cars market is the rapid technological advancements in automotive electronics. Modern vehicles are increasingly becoming more complex with a higher degree of automation and connectivity, leading to a surge in the demand for reliable and high-performance memory solutions. NOR flash memory, known for its fast read speeds and high reliability, is an essential component in various automotive applications such as infotainment systems, ADAS (Advanced Driver Assistance Systems), and instrument clusters.

Another significant driver for the market is the growing trend of electric and hybrid vehicles. These vehicles rely heavily on electronic systems for efficient operation, including battery management, powertrain control, and in-car communications. The need for robust and reliable memory solutions in these environments has led to increased adoption of NOR flash memory. Furthermore, the push for autonomous driving technology further amplifies the demand for high-quality memory solutions capable of supporting complex algorithms and real-time data processing.

The increasing consumer demand for enhanced in-car experience through advanced infotainment systems is also propelling the market. Infotainment systems require substantial memory for storing maps, software updates, multimedia files, and other data. NOR flash memory, with its quick access time and durability, ensures seamless operation of these systems, thereby enhancing the overall driving experience.

From a regional perspective, Asia Pacific dominates the NOR flash for cars market, primarily due to the presence of major automotive manufacturing hubs in countries like China, Japan, and South Korea. These regions are not only significant producers of automobiles but also lead in the adoption of cutting-edge automotive technologies. Coupled with the growing middle-class population and increasing disposable incomes, the demand for advanced automotive electronics in this region is set to rise, driving the growth of the NOR flash market.

Type Analysis

The NOR flash for cars market is segmented by type into Serial NOR Flash and Parallel NOR Flash. Serial NOR Flash is expected to witness substantial growth owing to its cost-effectiveness and smaller footprint, which makes it suitable for various automotive applications. Its ability to operate efficiently at lower power levels is particularly advantageous for electric and hybrid vehicles where energy efficiency is crucial. Additionally, the simplicity in circuit design and ease of integration with other components make Serial NOR Flash a preferred choice among automotive manufacturers.

Parallel NOR Flash, on the other hand, offers higher speed and performance attributes, making it ideal for applications requiring rapid data access and high throughput. This type of NOR Flash is typically used in high-end automotive systems such as advanced infotainment systems and ADAS. Despite being more expensive than Serial NOR Flash, its performance benefits justify its use in scenarios where speed and reliability are paramount.

The growing complexity of modern vehicles, which necessitates the use of both Serial and Parallel NOR Flash in different systems, ensures that both segments will continue to thrive. Automotive manufacturers are increasingly looking for versatile memory solutions that can cater to various performance and cost requirements, thereby driving the demand for both types of NOR Flash.

Recent advancements in NOR Flash technology, such as the development of multi-level cell (MLC) NOR Flash, are further enhancing the performance and capacity of these memory solutions. Such innovations are making NOR Flash more adaptable to the evolving needs of the automotive sector, ensuring sustained growth for both Serial and Parallel NOR Flash segments.

Moreover, regulatory norms aimed at improving vehicle safety and efficiency are pushing manufacturers towards the adoption of advanced memory solutions like NOR Flash. With the increasing integration of safety-critical

Context

Our client is an Insurance company that has provided Health Insurance to its customers now they need your help in building a model to predict whether the policyholders (customers) from past year will also be interested in Vehicle Insurance provided by the company.

An insurance policy is an arrangement by which a company undertakes to provide a guarantee of compensation for specified loss, damage, illness, or death in return for the payment of a specified premium. A premium is a sum of money that the customer needs to pay regularly to an insurance company for this guarantee.

For example, you may pay a premium of Rs. 5000 each year for a health insurance cover of Rs. 200,000/- so that if, God forbid, you fall ill and need to be hospitalised in that year, the insurance provider company will bear the cost of hospitalisation etc. for upto Rs. 200,000. Now if you are wondering how can company bear such high hospitalisation cost when it charges a premium of only Rs. 5000/-, that is where the concept of probabilities comes in picture. For example, like you, there may be 100 customers who would be paying a premium of Rs. 5000 every year, but only a few of them (say 2-3) would get hospitalised that year and not everyone. This way everyone shares the risk of everyone else.

Just like medical insurance, there is vehicle insurance where every year customer needs to pay a premium of certain amount to insurance provider company so that in case of unfortunate accident by the vehicle, the insurance provider company will provide a compensation (called ‘sum assured’) to the customer.

Building a model to predict whether a customer would be interested in Vehicle Insurance is extremely helpful for the company because it can then accordingly plan its communication strategy to reach out to those customers and optimise its business model and revenue.

Now, in order to predict, whether the customer would be interested in Vehicle insurance, you have information about demographics (gender, age, region code type), Vehicles (Vehicle Age, Damage), Policy (Premium, sourcing channel) etc.

Data Description

• Train Data

• Test Data

• Submission

Evaluation Metric

The evaluation metric for this hackathon is ROC_AUC score.

Public and Private split

The public leaderboard is based on 40% of test data, while final rank would be decided on remaining 60% of test data (which is private leaderboard)

Guidelines for Final Submission

Please ensure that your final submission includes the following:

1. Solution file containing the predicted response of the customer (Probability of response 1)
2. Code file for reproducing the submission, note that it is mandatory to submit your code for a valid final submission

This dataset contains 4350 samples with 11 attributes. There are some missing values in this dataset. Here are the columns in this dataset-

1. Brand: This indicates the Automotive Brand for which the product i.e. Tyres are suitable.
2. Model: This is the Car model for which the Tyres are a good match.
3. Submodel: This indicates the type of vehicle model i.e. Diesel, Petrol etc.
4. Tyre Brand: This column has the names of major Tyres manufacturers for this particular Brand - Maruti.
5. Serial No.: This is the Tyre model number.
6. Type: This indicates the type of Tyres i.e. Tube or Tubeless.
7. Load Index: This is the maximum weight that each tyre of the vehicle can carry at the maximum speed limit as specified by the manufacturer.
8. Size: This is the standard tyre size indicating the width, profile, radial construction and rim size.
9. Selling Price: This is the selling price of the tyre.
10. Original Price: This is the actual/ original price of the tyre.
11. Rating: Average customer ratings on a scale of 5.

AUCTION RULES AND REGULATIONS :

In order to ensure that order is maintained, acts of theft are eliminated, and to keep the risk of any loss to a minimum on auction days, auction customers must: Be licensed with the State of Missouri, as a salvage dealer in order to purchase salvage vehicles. If the buyer present at an auction is not listed on the state salvage license, a letter from the owner of the company must be on file, authorizing the buyer to purchase vehicles under the companies state salvage license. If the buyer is a salvage dealer operating inside the city limits of the City of Kansas City, they must have been issued a State of Missouri salvage license and a KCMO Salvage Yard permit; OR be a KCMO licensed automotive repair business with a Non-Salvage Yard determination. - All buyers must register with the auctioneer and be issued a buyer number. Buyer numbers must be worn at all times. - All buyers must have valid state ID to register. - All buyers must be at least 18 years of age.

Penalties may be imposed on auction customers for failure to comply with all established protocols and procedures. A buyer may be suspended or banned for a period of 3 years or permanently banned for violation of established auction protocols and procedures. Buyer may be removed immediately and given a 3 year suspension for: - Disruptive, disrespectful, obscene or abusive language toward other customers, the auctioneer or City staff members. - Refusal to follow legal direction of City staff or auctioneers. - Bidders may enter the driver’s side door of a vehicle to access the hood release only. Entering vehicles to access trunks, glove boxes, or interior areas will result in immediate removal and permanent suspension. - Dumping trash from purchased vehicles on the lot before taking the purchased vehicle. - Failing to take all vehicle parts when taking the purchased vehicle from the lot. - Failure to pay for vehicles purchased at an auction. (Upon completion of the three year suspension, a $500.00 deposit will be required for all purchases made) - Failure to pay security deposit for vehicles purchased at auction, when required. - 2nd violation of any offense listed as a 6 month suspension

Buyers may be banned permanently for: - Any form of theft. - Any 2nd failure to pay for vehicles purchased. - Assault on another customer, city staff member, the auctioneer and their staff or anyone on Tow Service property. - Any misrepresentation, as a buyer for a company, when not authorized or without written approval, from that company to act as a buyer for said company. - Being found in violation of any city code pertaining to inoperable vehicles on property owned or controlled by the buying company or individual buyer. - Attempting to purchase vehicles while under a 6-month or 12-month suspension.

All sales must be final and paid in full by 4:30 PM on the day of the sale. The auctioneer will process all final sales and provide documentation of the sale to the City outlining each transaction by the close of business on the day of the auction. Additionally, the auctioneer will provide a check for the proceeds of the auction to the City by the end of business on the day of the auction as outlined in the contract. No exceptions. The City does not guarantee a title to unclaimed vehicles sold at auction. All unclaimed vehicles are sold on a Missouri Department of Revenue form #4579, “Abandoned Property Bill of Sale”. Buyers will receive a bill of sale within 14 days of the auction date. Buyers that lose their original bill of sale will be charged $10.00 for a duplicate bill of sale. The bill of sale can be used to obtain a title in the state of Missouri following procedures established by the Missouri Department of Revenue. It may not be accepted by other states. The purchaser must make application within 30 days of purchase for an original title, salvage title, or junking certificate. It is the responsibility of the buyer to obtain information on titling vehicles outside Missouri. Auction vehicles are not presumed safe for operation on streets and cannot be driven off the lot. All purchased vehicles must be towed off city property by authorized tow equipment. All parts and contents of purchased vehicles must be removed with vehicles. Buyers cannot “clean out” a vehicle before removing it. Purchased vehicles cannot be unloaded or stored in the parking lot, along the access road, or on Front Street. Repairs of auction vehicles cannot be performed on the lot or in the parking area. Auction vehicles left in the parking lot or on the entrance road will be towed back on to the lot and a $240.00 tow fee per vehicle will be assessed. Parking space is limited. Please arrive early and try to carpool. No parking is allowed on the grass and violators will be ticketed. The Tow Services Division makes every attempt to identify stolen vehicles before they are sold, but if the Missouri Highway Patrol later discovers a vehicle purchased at the auction to be stolen, the buyer will only be reimbursed for the cost of the vehicle and the buyer premium. Auction vehicles may be withdrawn from the sale at any time prior to payment. Buyers can register in-person at the lot from 8-11 a.m. on the day of the sale. All bidders must sign-in at the auction window before entering the lot. The auction begins at 10:00 a.m.; no one arriving after 11:00 a.m. is allowed to enter the auction. No guests, children, or pets are allowed entry. Buyers must pay for their cars in cash or cashier’s check by 4:30 p.m. on the day of the auction. Please make cashier’s checks payable to Official Auctions. Buyers have until 4 P.M. on the Friday following the auction to pick up their vehicle(s). Vehicles may be picked up between 8:00 AM and 6:00 PM during that period of time. Drivers will have 10 minutes on the lot to retrieve their vehicle.

City of Kansas City,
Missouri Neighborhoods and Housing Services Department - Tow Services Section
7750 E Front Street Kansas City, MO 64120

These are the 36 instances used in the article "The Periodic Vehicle Routing Problem with Driver Consistency and Service Time Optimization", written by Inmaculada Rodríguez-Martín and Hande Yaman, and published in …………….. These data files have the same format as the classical PVRP instances from the literature. We have kept this format, but in for the PVPR-DC-SO we skip some of the information in the files. The infomation we do not consider is: - The maximum duration of a route (denoted by D bellow) - The vehicles’capacity (denoted by Q bellow) - The service duration for each customer (denoter by d bellow)

Taking this into account, the files are read in the following way: The first line contains the following information: type m n t where type = 1 (PVRP), m = number of vehicles, n = number of customers, t = number of days. The next t lines contain, for each day ,the following information: D Q where D = maximum duration of a route (0 means 'unbounded'), Q = maximum load of a vehicle. The next lines contain, for the depot and each customer, the following information: i x y d q f a list where i = customer number (0 corresponds to the depot), x = x coordinate, y = y coordinate, d = service duration, q = demand, f = frequency of visit, a = number of possible visit combinations, list = list of all possible visit combinations.

Each visit combination is coded with the decimal equivalent of the corresponding binary bit string. For example, in a 5-day period, the code 10 which is equivalent to the bit string 01010 means that a customer is visited on days 2 and 4. (Days are numbered from left to right.)

This dataset involves the normal distribution parameters, calculated using used to create simulation instances for the experimental study of the paper "Dynamic Multi-Period Vehicle Routing with Touting". These distributions are used to generate random demand on each planning day. The dataset also includes 3-months historical collections data for a waste collection company, which are used to compare the heuristic solutions with the ones obtained by an exact solver. There are two sets of customers, belonging to two drivers, who cover different geographical areas.

The information for two drivers are stored in separate excel files and each file has 2 sheets, as explained below:

In "Orders" sheet, The historical collections information is presented. The amount of demand is given in Column A, while the day the demand is generated is provided in Column C. Column B has an index value for each customer, indicating its location. The depot has the index of "1".

In "Customer Information" sheet, The list of customers is presented in Column A. They are named starting from C1. Columns B and C present the mean and variance values of the customers' demand distributions. Column D provides the index value for each customer, indicating its location. Finally, Column E presents the tank capacity values for the customers.

In "Distance-Time Matrices" sheet, The distance (in Column C) and travel time (in Column D) values for each pair of nodes are presented. Distances and travel times are given in kilometres and minutes, respectively. "from" and "to" entries are associated with the index values of the customers. For example, to find the distance and travel time from C1 to C2 for Driver 1, one needs to look at the values from index 167 to index 168, which corresponds to a 28.7 kilometres of distance and 21.45 minutes of travel time.

Driver 1 and 2 have 142 and 125 unique customers, respectively. However, the historical data includes total of 273 and 260 customers for Driver 1 and Driver 2, respectively, which means that some customers have multiple orders within that 3-month period.