The Impact of Big Data on Smart Parking

Introduction:

In the data age, Big data may make everything become convenient. Big data helps us understand customers, meet customer service needs, optimize business processes, big data is good for our lives, improve cities, improve medical and R&D, improve athletic performance, optimize machine and equipment performance, improve safety and law enforcement, improve Financial operations. However, today’s big data does not solve the problem of parking difficulties. What hinders the extent of the big data on smart parking? Looking for the road can be used for GPS positioning services. Why do we need to use the naked eye to find the parking facility? In the face of parking problems, can we analyze the time and location through big data, and realize convenient parking by judging the accuracy and consistency of data, thereby improving people’s living efficiency.

Due to the limited space resources in the city, the construction cost of the new parking lot is high, and the shortage of parking spaces is considered to be a major problem in urban traffic management (Shin & Jun 2014). Many cars on the road should spend unnecessary time and consume extraneous energy during searching for parking spaces. The other study (Soup, 2007) found that the wandering of cars in order to find a parking facility is responsible for about 30% of the entire traffic in a city.

The smart parking is an ignored project but this project very good. it can improve people’s living efficiency, so that people will not worry about finding a parking space, and even effectively prevent overtime parking problems. However, there are very few organizations doing research in this area. The research on sharing parking spaces based on big data is basically zero. This research is not impossible, but other research institutions do not have more complete and systematic research on this area. Therefore, I write this research proposal that I hope the funding agency can pay attention to my research and funding us, this research can extend the application of big data to the smart parking systems, even share parking spaces. The following is my background knowledge and put forward statement of the problem and sub-problems about the application of big data on smart parking, and assumptions, finally, using relevant research to prove the feasibility of my proposal.

Background:

With the development of various portable devices, the Internet of Things and cloud computing, cloud storage and other technologies, data content and data formats are diversified, and data granularity is becoming more and more fine. Distributed storage, distributed computing, and streaming have emerged. To deal with such big data technologies, various industries explore multiple application scenarios based on multiple or even cross-industry data sources and pay more attention to the timeliness of individual-oriented decision-making and application. Therefore, the data form, processing technology, and application form of big data constitute a big data application that is different from traditional data applications. From government agencies to enterprises, to organizations, the application of big data has penetrated into various industries and brought significant results in these industries. For example, the role of big data in the news media is to quickly and accurately track and collect thousands of online media information, expand news leads, and increase the speed of collection; support the effective capture of tens of thousands of news every day. Realize the integration of Internet information content collection, browsing, editing, management and distribution.

Motivation:

The relationship between big data and e-commerce shows me the feasibility of smart parking. Compared to the traditional business model, the explosive growth of data has become a very commercially valuable resource for e-commerce. Because e-commerce almost has the comprehensive data information, including browsing information of all registered users, purchasing consumption records, user evaluation of products, sellers’ sales records on their platforms, product transaction volume, inventory, and merchants. Credit information and more. Therefore, big data runs through the entire e-commerce process and has become the real core competitiveness of e-commerce. We can turn the parking process into e-commerce, let big data run through the business process of the parking industry, registered users are people who looking for parking spaces, product is parking space, purchase consumption records is the record of parking, the evaluation of the product can be the customer parking experience in the parking space, The sales record of the seller on the platform can be the used record of the parking space. The product trading volume is the total parking time and the total trading amount of all parking spaces. The inventory is the available parking space. The merchant is the parking space owner and parking lot. Smart parking is an industry that urgently needs big data support has not been discovered. Therefore, this proposal has great research potential. Giving full play to the role of each parking space, improve the efficiency of life, and create employment.

Research problem:

‘The Impact of Big Data on The Smart Parking’ is my main research problem. Can we use an application to find all the parking spaces near the destination based on big data?  Helping people to find the right parking space according to the time of arrival at the destination, the parking spaces mentioned here include parking lots, street parking spaces and even private parking spaces. Whether the private car owner can share his private parking space, whether the access control system of the apartment parking lot can be effectively managed by big data and open to other people who looking for a parking space is a sub-question, Put together the information of all the parking spaces in a certain area, it is another sub-question for me to take the mobility of the parking space to the most perfect position.

My hypothesis is that all future parking space information will be integrated into the central database through big data, combined with the GPS system, and cooperate with Google map or other companies analyze parking space data. The types of parking spaces are free public parking, paid parking, off-street parking, private parking for apartments, and house parking. The available time of each parking space can be realistically seen by the parking seeker. The customer can reserve the above parking space and set his own estimated parking time. According to the time and route to the destination, the system finally selects the most suitable parking space for him. People who share their private parking spaces can earn extra income, such as sharing their parking spaces to useful people when he is at work or not at home.

The parking space is fixed, the state of the car is flowing, and the problem between fixing and flowing needs to be solved. The hypothesis I made was to convert the parking space from fixed to ‘flowing’ base on big data. The topic I have research smart parking is how to turn fixed to flowing through big data. based on the literature review that relatively few studies have been devoted to my topic.

The delimitations about my topic, some specific things that my own research will not address. Regarding the sharing of their own parking spaces and the security of the open access control system, information security issues are temporarily out of my research. Although the implementation of smart parking must be an in-depth discussion of information security and private information issues, in my research these are not the focus, this problem is left in the future research and then in-depth discussion.

The definition of terms

Big data:

Big data suddenly became a hot topic in recent times. Big data is an abstract term that is somewhat different for each researcher. According to McKinsey, big data is used to refer to data sets, which are large in scale and beyond the ability of existing data analysis tools to mine, collect, store, process, and analyze at specific times (James et al., 2011).  Gantz and Reinsel (2011) define big data as a big data technology that describes next-generation technologies and architectures, designed to economically extract value from a wide variety of data by enabling high-speed capture, discovery, and analysis. Pawar (2016) states that Big data is usually a large amount of data, which is difficult to mine, collect, maintain, and manage through analysis tools or techniques.

Big data is used to describe an unprecedented amount of data, including a large amount of unstructured data and semi-structured data that requires more time analysis (Chen et al., 2014). It is illustrated here that the pattern of data includes semi-structured data and unstructured data in addition to the traditional data we are familiar with. The semi-structured data and unstructured data including pictures, audio, video, and web pages. The data we are currently facing is basically semi-structured or unstructured data, which is often closer to our lives than traditional data. So research on big data is crucial. Big data will also create value for consumers and businesses. The billions of dollars in companies such as Yelp, Zagat, TripAdvisor, Uber, eBay, Netflix and Amazon contain a lot of data, including ratings from service providers and sellers, to Reduce the risk of customers (Dawar 2016).

Smart parking:

Smart parking is a relatively new concept. The parking application provides users with available parking spaces. If possible, choose the preferred parking space according to their own preferences, depending on the user’s different purposes or different requirements, for example, business, travel, and parking spaces (Di Napoli et al., 2014).

There are many definitions of smart parking. In my text, smart parking is defined as a comprehensive parking system built with big data. Everyone can share his private parking space on my system. Everyone can use it to be looking for parking spaces. According to time, positioning, distance to the destination and personal preferences to recommend the appropriate parking space for the user, and a complete GPS navigation guide the user to the destination. Intelligently calculate the parking time and parking fees to perfectly match the vehicles to leave and the vehicles that are about to arrive at the parking space.

The literature reviews:

Due to the seriousness of the parking problem, there are many projects implemented to solve this problem. Parking Guidance and Information System (PGIS) is the most commonly used solution in the past period. However, PGIS provides limited information and therefore cannot provide personalized information, such as which parking space is closest to the driver, which parking space is cheaper than other parking space, and how the driver avoids traffic congestion to the parking space. Due to these shortcomings, the impact of PGIS systems may be relatively limited from the point of view of not being able to reduce the time to the search for parking spaces (Waterson et al., 2001).  In order to solve these restrictions, a better solution is proposed for the parking lot location and the time of arrival at the parking lot. Shin and Jun (2014) considers the driving time and the distance to the parking space and proposes a parking guidance algorithm to look for the parking space. The vehicle is assigned to the most suitable parking space, walking distance from the parking space to the destination, expected parking cost, and traffic congestion due to parking guidance itself. It is based on their research that will make my proposal feasible.

The closest study to my research was proposed by Di Napoli. Di Napoli (2014) believes that the problem of parking in big cities is the dispersion of public and private parking providers, each using their own technology to collect occupancy data, so it cannot be easily shared between different owners or through applications. His view is that in order to provide smart parking applications to drivers, public and private parking providers are encouraged to share their data and build smart parking software applications, coordinate individual parking solutions for end users without involving them in the fragmentation of parking owners.

His research is mainly about sharing information about parking lots, whether private or public, to centralize this data into an application, to facilitate customer access to information, and to centralize the use of scattered parking spaces through coordinated parking systems. The price is set and depends on the preferences of the user and the owner of the car park. His research direction is to integrate information from private and public parking lots, but my research, in addition to parking lots, also tends to open access to street parking spaces and apartment parking spaces. Integration of transportation networks through integration with other GPS programs such as Google Maps. Not only know the parking space information but also know the time when I arrived at the destination. According to the departure time set by the previous parking space, it will help me make an appointment with my satisfied parking space. The most important thing is that my research is to change the fixed shape of the parking space in a flowing form. My goal is not limited to parking lots and it must be extended to every apartment, every community, even every house. Providing these parking spaces to people who needed when these parking spaces are available. Although information security of the apartment and the access control system is a problem, there will be a complete information security system to solve this problem later, so let’s not mention it here.

Reference List:

• Chen, M., Mao, S. and Liu, Y. (2014), “Big data: a survey”, Mobile Networks and Applications, Vol. 19 No. 2, pp. 171-209.
• Dawar, N (2016), Use Big Data to Create Value for Customers, Not Just Target Them<https://hbr.org/2016/08/use-big-data-to-create-value-for-customers-not-just-target-them>
• Di Napoli, C., Di Nocera, D., & Rossi, S. (2014). Agent negotiation for different needs in smart parking allocation. Lecture Notes in Computer Science (including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8473, 98-109.
• Gantz, J. and Reinsel, D. (2011), “Extracting value from chaos”, IDC iView, Vol. 1142, pp. 1-12.
• James, M., Michael, C., Brad, B., Jacques, B., Richard, D., Charles, R. and Angela, H. (2011), Big Data: The Next Frontier for Innovation, Competition, and Productivity, The McKinsey Global Institute, New York.
• Pawar, A.M. (2016), “Big Data mining: challenges, technologies, tools and applications”, Database Systems Journal, Vol. 7 No. 2, pp. 28-33.
• Shin, J & Jun, H. (2014). A study on smart parking guidance algorithm. Transportation Research Part C, 44, 299-317.
• Soup, D (2007). Cruising for parking Access, 30 (2007), pp. 16-22
• Waterson et al., (2001). Quantifying the potential savings in travel time resulting from parking guidance systems – a simulation case study J. Oper. Res. Soc., 52 (10) (2001), pp. 1067-1077