位基服務商業模式研究---海量資料價值創造 - 政大學術集成

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(1)國立政治大學商學院經營管理碩士學程碩士學位論文. 位基服務商業模式研究---海量資料價值創造治. 立. 政. 大. ‧ 國. services. 學. A study of creating value from big data on location-based. sit. y. ‧. Nat. n. er. io. 指導教授：尚孝純博士 a v i 研 l究生：崔紹成 n Ch engchi U 中華民國 103 年 7 月.

(2) Acknowledgement First of all, I would like to express my sincere gratitude to my advisor Prof. Shari S.C. Shang for the continuous support of my academy study and research, for her patience, motivation, enthusiasm, and immense knowledge. Without her guidance helped me in all the time of research and writing of this thesis, this paper would have never been accomplished.. Besides my advisor, I would like to special thank two of my thesis oral exam committee: Prof. Victor Tsan (Ph.D. Department of Information Management, National Central University), and Prof. Minder Chen (Ph.D. Management Information Systems, University of Arizona), for their encouragement, valuable comments, and hard questions during the oral defense.. 政治大 My sincere thanks also goes to David W.H. Chen, Daisy Wu, and Mei-Shing Chen for 立 offering me help and encourage in the period of academy study, and providing me literature. ‧ 國. 學. materials and cases data advices.. ‧. I would like to thank my classmates of class 101 ICICT of NCCU EMBA and especially. er. io. sit. Nat. through the most pleasant and enjoyable time.. y. the academic advisor Professor Ruey Lin Hsiao, for their endless friendship. Leading me to go. al. v i n dear wife and two little daughters, C who offered their encouragement through phone calls and hengchi U message of Line. As a family, we have experienced some ups and downs in the past two years. n. Most importantly, none of this could have happened without my family. My parents, my. Shao-Cheng Tsui July, 2014 Taipei, Taiwan. ii.

(3) 中文摘要近年來, 智慧型手機使用率的快速成長, 以及 App 程式的開發, 使得手機的功能越來越多元. 其中 GPS (Global Position System)功能最廣為被應用, 並且在手機的平台上開發位置基礎服務(Location-Based Service)的技術. LBS(Location-Based Service)會產生非常大量的數據, 通常 LBS 所產生的資料是即時性的、大量的、非結構性的, 例如記錄一些移動的軌跡, 包含時間序位的路徑, 地理位置的資料. 企業管理者開始研究如何利用這些 LBS 產生的大量資料, 本研究試圖去了解下列 3 項問題. 1)被收集到的資料有甚麼樣的特徵?. 2)這些大量的資料如何被分析. 3) 企業. 如何去應用這些資料, 以及如何使用這些研究的結論.. 政治大. 為了去研究經營管理 LBS 產生的海量資料(Big data), 本研究收集 8 個不同的個案. 立. 作分析, 研究這些 LBS 海量資料的特徵、資料收集和分析方法. 並檢視其研究結果在經. ‧ 國. 學. 營管理上的執行和績效.. 從最後的研究結論可以發現, 對應到商業模式的四個大區塊來分析, 以 ”客戶端”. ‧. (Customers)的效益提升最為顯著. “基礎設施”(Infrastructure)的部分次之.. sit. y. Nat. n. al. er. io. 關鍵字: Location-Based Service、Big Data、Business Model. Ch. engchi. iii. i n U. v.

(4) Abstract In recent years, there is a growing trend in the use of smartphone, and the applications developed on smartphone have become increasingly diversified. GPS (Global Position System) is one of the widely adopted techniques in developing Location-Based Service (LBS) on the mobile platform. A very large number of data has been generated off those LBS. The LBS data sets are usually generated in real time, in large volumes and in unstructured forms. Such as the moving track record, timeline routing path, and geographic data. It has become critical for businesses to learn how to leverage these massive data from LBS. This study strives to understand:. 1) what kind of LBS data features can be collected?. 2). do firms interpret the data and 政治 3) How 大. How can the big volume of LBS data be analyzed?. 立. make use of findings?. ‧ 國. 學. In order to understand the management of LBS big data, this study collected 8 different type of LBS cases to understand the features, collection and analysis methods of the big data on LBS and examine the management efforts and benefits of the use of big data results from. ‧. LBS.. sit. y. Nat. Conclusions of this study can be mapped in the four blocks of business model, and it seems that “Customer” can experience significant value from the LBS big data and whole business. io. n. al. er. “Infrastructure” can also receive big improvement in the back of office process.. Ch. engchi. i n U. v. Keywords: Location-Based Service、Big Data、Business Model .. iv.

(5) Content ACKNOWLEDGEMENT ........................................................................................................ II 中文摘要 .................................................................................................................................. III ABSTRACT ............................................................................................................................. IV CONTENT ................................................................................................................................ V LIST OF TABLE ....................................................................................................................VII LIST OF FIGURES ............................................................................................................... VIII CHAPTER 1 INTRODUCTION ............................................................................................... 1 1.1 GENERAL BACKGROUND INFORMATION ............................................................................ 1 1.2 MOTIVATION ...................................................................................................................... 3 1.3 RESEARCH OBJECTIVE ....................................................................................................... 4 1.4 RESEARCH PROCESS .......................................................................................................... 5 CHAPTER 2 LITERATURE REVIEW .................................................................................... 6 2.1 GENERAL ASPECTS OF LOCATION-BASED SERVICES .......................................................... 6 2.2 BIG DATA GENERATED FROM LOCATION BASED SERVICES ................................................... 7 2.2.1 Special features of data collected from LBS ............................................................. 7 2.2.2 Technology involved in managing big data from LBS ............................................. 9 2.2.3 Management effort involved in exploiting big data from LBS ............................... 11 2.3 BUSINESS MODEL............................................................................................................. 13 CHAPTER 3 RESEARCH METHOD ..................................................................................... 15 3.1 CONTENT ANALYSIS ........................................................................................................ 15 3.2 DATA COLLECTION .......................................................................................................... 16 3.3 DATA ANALYSIS ............................................................................................................... 17 CHAPTER 4 LOCATION BASE SERVICE APPLICATION IN MARKETPLACE ........... 18 4.1 TAXI FLEET LOCATION AND DISPATCH SYSTEM ................................................................. 18 4.2 REAL-TIME TRAFFIC INFORMATION SERVICE .................................................................. 19 4.3 CHINESE NEW YEAR MASS MIGRATION WITH BAIDU HEAT MAP .................................... 21 4.4 SMART LOGISTIC NETWORK ............................................................................................ 24 4.5 CAR POOL NETWORK SYSTEM .......................................................................................... 27 4.6 VEHICLE MONITORING AND TRACKING SYSTEM ............................................................... 28 4.7 PUBLIC BICYCLE SHARING SYSTEM .................................................................................. 30 4.8 NAVIGATION SERVICES .................................................................................................... 31 CHAPTER 5 RESULT AND DISCUSSION .......................................................................... 34 5.1 CROSS ANALYSIS OF LBS IN DIFFERENT DATA FEATURES ................................................. 34 5.2 RELATED TECHNOLOGIES WITH DATA MINING ................................................................. 35 5.3 EFFORTS IN BIG DATA MANAGEMENT ............................................................................... 37 5.4 DEVELOP VALUE FROM BIG DATA ..................................................................................... 38 CHAPTER 6 CONCLUSION .................................................................................................. 40 6.1 SUMMARY ....................................................................................................................... 40 6.2 CONTRIBUTION AND MANAGEMENT IMPLICATION ............................................................ 40. 立. 政治大. ‧. ‧ 國. 學. n. er. io. sit. y. Nat. al. Ch. engchi. v. i n U. v.

(6) 6.3 LIMITATION AND FUTURE RESEARCH ................................................................................ 41 REFERENCE ........................................................................................................................... 43. 立. 政治大. ‧. ‧ 國. 學. n. er. io. sit. y. Nat. al. Ch. engchi. vi. i n U. v.

(7) List of Table Query counts, in 2012 July~Nov ..................................... 3 Research flow chart ............................................... 5 List of studied cases .............................................. 15 Data source of studied cases ....................................... 16 The detail of collected data ........................................ 17 Data list of Baidu Migration ....................................... 22 Travel statistics from 2009 to 2011 .................................. 23 Data of Bachelor’s Day in 2013 ..................................... 25 Data features of studied cases ...................................... 35 Technology involved of studied cases ................................ 36 Management effort of studied cases .................................. 38 Develop value of studied cases ..................................... 39. 立. 政治大. 學 ‧. ‧ 國 io. sit. y. Nat. n. al. er. Table 1-1 Table 1-2 Table 3-1 Table 3-2 Table 3-3 Table 4-1 Table 4-2 Table 4-3 Table 5-1 Table 5-2 Table 5-3 Table 5-4. Ch. engchi. vii. i n U. v.

(8) List of Figures 24 satellite GPS constellation with the Earth .......................... 6 Business Model Framework ...................................... 13 Baidu Migration heat map ....................................... 21 Diagram of data analysis of Baidu ................................. 23 Alibaba Group data exchange platform ............................. 26 Transit path of GPS data ......................................... 32. 立. 政治大. 學 ‧. ‧ 國 io. sit. y. Nat. n. al. er. Figures 2-1 Figures 2-2 Figures 4-1 Figures 4-2 Figures 4-3 Figures 4-4. Ch. engchi. viii. i n U. v.

(9) Chapter 1 Introduction 1.1 General Background Information Location services are starting from the 1970s, the U.S. Department of Defense has been operating the global positioning system (GPS), a satellite infrastructure serving the positioning of people and objects. Initially, GPS was conceived for military purposes, but the U.S. government decided in the 1980s to make the system's positioning data freely available to other industries worldwide. A location-based service can be defined as an information service provided by a device that knows where it is, and modifies the information it provides accordingly (Spiekermann, 2004).. 政治大. Location-based services (LBS) are a general class of computer application services. Which is a kind of information service. They are used in the field of commercial and consumer. 立. marketing. Mobile phones and the Internet have revolutionized the communication and with it. ‧ 國. 學. the lifestyle of people. An increasing number of mobile phones and tablet pc allow people to access the Internet where ever they are and whenever they want. The advances in portable. ‧. devices and wireless communication technologies enables a new form of services named location based services (Location-based service, 2014).. sit. y. Nat. Here, I would like to introduce “Taipei e-bus system” (Department of Transportation,. io. er. 2014), which is a good demonstration of location-based service with Big Data. That is why I am going to take a further study about the Location-based service with Big Data. Following. n. al. paragraph are the description about the detail.. Ch. engchi. i n U. v. The Taipei e-bus system is defined by a specific service schedule, and it makes designated stops. Each bus driver is required to transmit his/her current bus location to the GIS server on schedule in order to notify passengers and management in search of bus location services. The current bus location features a real-time signifier, when the bus approaches the next stop, the built-in direction-orientation mechanism will notify the network control center, allowing passengers to acquire the current bus information either online, or via their PDA’s and mobile phones. Fleet managers can also easily keep tracks of all buses operating in Taipei. Taipei’s public transit system is extremely well developed, it covers practically every possible destination with relatively few transfers. The "Taipei e-bus System" is established by the Department of Transportation, Taipei City Government. The system is named “Tele Point2100”, It is a wonderful e-bus system in the ITS industry. The basic idea of the Tele point 2100 can solve the problem on the issue of oil resource. The system is the most accurate, most 1.

(10) advance system that can provide real time information useful in the daily life of the ever changing cities. Blow figure is the system diagram of Taipei e-bus system (Department of Transportation, 2014).. 立. 政治大. ‧. ‧ 國. 學. n. al. Ch. er. io. sit. y. Nat Figures 1-1. Taipei e-bus system diagram. i n U. v. Source: Department of Transportation of Taipei Government, 2014. engchi. Over the past ten years location-based services (LBS) have been variously adopted as popular technology; as the killer application that will bring the GPS industry to its next level of prosperity. This research attempts to provide a professional introduction to LBS, and a discussion of some major issues surrounding them. Since then mobility has become an increasingly important factor in digital devices, and we expect to develop a devices which are high performance available. Mobility has in turn opened up the possibility of providing information about the user’s location in space and time, about the user’s surroundings, and about features in the environment that are nearby but beyond the user’s own sensory perception (Goodchild, 2009).. 2.

(11) 1.2 Motivation The concepts described in this research range from general application-related concepts to technical aspects. We use an outside-in approach, reaching from a general use of the application—inside to the data management technical levels. Including data feature, source, type, and analysis. Moreover, all of the concepts described in this research are illustrated using a reference application given at the following section. In order to make location applications work, the industry had already overcome several challenges of both a technological and economic nature over the past years (Schiller & Voisard, 2004). From previous section, this research has presented a good demonstration of locationbased service. But, how do I have motivation to do this further study. First of all, I would like to show some data from Taipei e-bus system. And, understanding how the data management is. 政治大. so important for e-bus system.. 立. Query counts average per month 5284 data base query counts. smartphone app. 8,884,106. ‧. 5,311,604. y. July ~ Nov 2012. Nat. Query method. 學. ‧ 國. Table 1-1. sit. e-bus website e-bus voice. 26,757. er. io. e-bus mobile. 17,136,009. n. a l subtotal: v i 31,358,476 n Ch U e n g c h i34,453,140 API Query Total:. 65,811,616. Source: Department of Transportation of Taipei Government, 2014. From the data of table, we already knew the location-based service generated huge number of data. And the data are so jealousy to manager team. The most commonly used e-bus information system approach to the "Watch the stop sign LED marquee" 95.5% of the maximum, 82.1 percent of bus passengers expressed satisfaction with the e-bus information system (Department of Transportation, 2014). It is a very successful system of location-based service with big data. The governors are proud of this system. The civilizations are eager to use this system. This system has been expanded to other cities and counties, so that the results can be spread throughout the country 3.

(12) of world wide. GPS provides the simplest and most obvious form of LBS, allowing a user to determine position on the Earth’s surface, in the precise coordinates of a formally specified location, to within meters and in some cases centimeters (Kennedy, 2002; Leick, 2004). The U.S. Global Positioning System was originally developed for military purposes, with only a degraded signal available to civilians. But a change of policy in the 1990s opened full accuracy to all, and today GPS is used worldwide as a free, ubiquitous means of determining position. It is based on comparing the timing of signals arriving from that subset of a constellation of 24 satellites that is above the user’s horizon (Spiekermann, 2004). A next-generation wireless technology will be able to track the behavior of individual customers from internet, update their position, and model their behavior in real time. If the data. 治政大 of this research. Big Data will mining, as well as system interoperability, are the prime focuses 立 and entirely new categories of companies. The use of help to create new growth opportunities. can be analyzed and managed. Which are the core value of this research. Data collection and. ‧ 國. 學. Big Data is becoming a crucial way for leading companies to outperform their peers. In most industries, established competitors will leverage data-driven strategies (McGuire et al., 2012).. ‧ y. Nat. sit. 1.3 Research Objective. er. io. Location-based services have been used for many years. There is a growing need for. al. v i n C h data that areUavailable today. Based on the nature manage the large quantities of geo-referenced i been adopted to address different e n g c h have of the research objective, different research methods n. mobile data management teams, which are able to store, manipulate, retrieve, most importantly,. issues. In view of the problem statement and research questions, the study will interpret the questions asked and motivate the benefits of using mobile devices (Kock, 2003).. We now discuss in more detail the research issues. Which are going to be investigated in the following chapter. The research questions are: 1) What kind of LBS data features can be collected? 2) How can the big volume of LBS data be analyzed? 3) How do firm interpret the data and make use of findings?. 4.

(13) 1.4 Research Process To meet the goals and answer the research questions (which were described in previous section 1.3). The research process followed five steps of below Table 1-2.. Flow Chart. Description Identify factors that could improve location-based services.. Research Objectives. Proposing asked question.. Literature data collect and data analysis.. Literature Review. 立. 政治大. ‧ 國. Content Analysis. generate valuable data.. ‧. Nat. y. Study cases of location-based services in current market.. n. al. er. io. sit. Cases Study. Results and Conclusions. 學. Examine how LBS data collected and can be analyzed to. Ch. engchi. i n U. v. Develop lesson learned from LBS cases.. Table 1-2. Research flow chart. Most of location-based services are built for outdoor applications. Since positioning technology covers everything from short-range wireless technology to wide area mobile device. This study focuses on a particular application, based on mobile and handheld devices, from wireless Internet and Global Positioning Systems (Wang, 2008).. 5.

(14) Chapter 2 Literature Review. 2.1 General Aspects of Location-Based Services Location services have been a long tradition. Since from 1970s, the U.S. Department of Defense has been operating the global positioning system (GPS), a satellite infrastructure serving the positioning of people and objects. GPS provides the simplest and most obvious form of LBS, allowing a user to determine position on the Earth’s surface, (Spiekermann, 2004) A GPS signal alone is not of much value. Position can be determined relative to other features. If the GPS signal can be integrated with a digital map, or hardware facility with wireless network. But the real power of GPS comes from its integration into other functions,. 治政 established a marketing strategy as a new opportunity to 大 access position data through GPS. 立products and service for customers. Below figure shows 24 They are trying to enhance their such as the GIS with a digital database or social network. Many industries have already. ‧. ‧ 國. 學. satellite GPS surrounding Earth (Goodchild, 2009).. n. er. io. sit. y. Nat. al. Ch. engchi. i n U. v. Figures 2-1 24 satellite GPS constellation with the Earth Source: Wikipedia Global Positioning System, 2014. Simple GPS devices are available that contain digital maps, databases of features. And GPS has been integrated into the in-vehicle navigation systems. The present research have also recognized the potential of location based service in field work. Wikitude Drive (wikitude, 2014) is the world's first mobile navigation app using fun and intuitive to guide the driver along a line drawn onto reality. Wikitude Drive has three great advantages: 1. No need to read maps - just follow a line. 2. See the live video of what's ahead of you and therefore never take your eyes off the road again. 6.

(15) 3. Use it in car or pedestrian mode. Information on the user’s location is of substantial commercial value to the operator of a search service. It’s by providing the user with information that is beyond his or her sensory perception, such as Google or foursquare, since it can be used to precisely hit in geographic proximity to the consumer. Such precision is of value to enterprise, who are willing to adopt the search service and phase in commercial application. GPS system which was created by US department of defense for the navigation of military in any part of world under circumstances. But, this system is now being used for many other purposes and GPS system has proved to be a revolutionary technology in today's world. GPS is extremely easy to navigate as it tells you to the direction for each turns you take or you have. 政治大. to take to reach to your destination. There are several advantages of GPS at present (Virrantaus, et al., 2002).. 立. GPS system works in any time (24hrs whole day).. . GPS system works in all condition of the weather, doesn’t need to care about the climate.. . The operation software is very popular in most of mobile device.. . The cost is very low, even is free.. . The protocol of signal is standardized, no competition issue.. . The coverage area is all over the planet.. . The signal is very stable and advance.. n. er. io. sit. y. Nat. al. ‧. ‧ 國. 學. . Ch. engchi. i n U. v. 2.2 Big data generated from location based services Learned from previous section 2-1, there are growing data from location based service. This section will separator into 3 portions to describe how to manage these data. Starting from mention of data feature to what technology be used and how the management effort involved.. 2.2.1 Special features of data collected from LBS The content of location data come from typically mobile users with a wireless device like a PDA, mobile phone, or a tablet pc. The wireless networks will enable new forms of mobile 7.

(16) services. Location Based Services (LBS) are such services for mobile users that take the current position of the user into account when performing their task. Map information and GIS services and infrastructures are crucial helper services. There are many wireless localization techniques that can be used to obtain the location of a mobile device. However, the most popular techniques allow one to determine one’s own position. The GPS (Satellite-based positioning) offers meter accuracy positioning almost everywhere on the planet. This type of positioning is passive, meaning that the mobile devices determine their own position and the satellites cannot determine the location of mobile devices on earth (Zipf, 2008). So far, we already understood that where the location data come from and how the data be generated. Then we are going to study what kind of the feature included? Here I would like to list down 6 items which collected by research team.. 學. ‧ 國. . 政治大 Routing path: For social scientists interested in understanding human behavior in 立 space-time and its complex relationship with the urban environment, the possibility of collecting and using data derived from LBS offer new opportunities and pose many challenges at the same time (Kwan, 2001).. ‧. . Non-structure: The data of LBS a kind of non-structured is irregular, the request of. io. al. v i n C h according to U data. User locations are sampled e n g c h i some specific protocol. The sample Imprecision and Varying Precision: Imprecision is a fundamental aspect of location. n. . er. analysis and administration (Huang et al., 2012).. sit. y. Nat. the data is dynamic, and these characteristics raise new challenges for the data's. imprecision is dependent on the positioning technology used and the circumstances under which a specific technology is used (Morten, 2005). . Digital data: Global Positioning System can also be collected and then imported into a GIS. A current trend in data collection gives users the ability to utilize field computers with the ability to edit live data using wireless connections or disconnected editing sessions (Geographic information system, 2014).. . Real Time: After data has been collected, it should be transmitted, received and stored into a database that is able to store large amounts of data and quickly executes queries. To be able to provide real-time „push‟ and „pull‟-services, the information needs to be sent to the end-user as soon as the data arrives and matches his requested service. 8.

(17) To do so, the database should be able to store large amounts of data and execute the queries almost at the same time as new data is being inserted (Ekkebus et al., 2004). . Social network: Facebook and Twitter were pioneers of social networking, with mobile they have started extending their reach to include geo-social marketing. Geosocial networking allows users to interact relative to their current locations. Thus you can search for users in your network who are nearby, or by venue. For business this means potential group messaging and ad targeting. Users can share likes, maybe meet at a specified location. At every step of their interaction there is the potential for mobile marketing and advertising (WebMapSolution, 2012).. 立. 政治大. 2.2.2 Technology involved in managing big data from LBS. ‧ 國. 學. New technologies make it possible to realize value from Big Data. There is a new wave of. ‧. economic opportunity that businesses should get ready to exploit this trend. To establish a strategy of Big Data is a main stream activity for company survival in current environment. It. Nat. sit. y. is all about location-based services.. er. io. In traditional, a company operated data in a way of MRP, ERP, and CRM… They feed. al. n. v i n Ctoh an age of BI (Business a modern enterprise. They are going e n g c h i U Intelligent). Business should. these data into a data warehouse for analysis and reporting. That will no longer good enough to. need to know what their customer needs where they are, why they buy, when they buy.. Fortunately, these answer already behind the data warehouse. What they need to do is to dig and mine the database by an algorithms (Stepney, 2014). Big Data relates to data collection, storage, querying and analysis that is category in terms of volume, variety, and velocity. It is also a term used to refer to massive and complex datasets made up of a variety of data structures, including structured, semi-structured, and unstructured data. Businesses are aware that this huge volume of data can be used to generate new opportunities and process improvements through their processing and analysis Here are the 6 items of technology which involved in managing Big Data from LocationBased Service (Rodrigues, 2012). 9.

(18) . Schema-less databases: The data collected from capturing from user in any type of data. They are a kind of non-structure data. It means data is not perform in a consolidation form. Some of data may loss or no-data. So, if we stored to a normal database, could be initialled a syntax error (Cassandra, 2014).. . Cloud computing: Cloud computing is a term used to refer to a model of network computing where a program or application runs on a connected server or servers rather than on a local computing device such as a PC, tablet or smartphone (Huang et al., 2012).. . .. Storage Technologies: One of the key characteristics of big data applications is that they demand real-time or near real-time responses. If a police officer stops a car they. 政治大 growing very quickly, especially unstructured data. As we move forward, this will 立 only likely increase, with data augmented by that from growing numbers and types need data on that car and its occupants as quickly as possible. Data volumes are. ‧ 國. 學. of machine sensors as well as by mobile data, social media and so on. (Adshead & Dubash, 2014). ‧. . MapReduce: A MapReduce program is composed of a Map procedure that performs. sit. y. Nat. filtering and sorting and a Reduce procedure that performs a summary operation. The "MapReduce System" orchestrates by marshalling the distributed servers, running the. io. n. al. er. various tasks in parallel, managing all communications and data transfers between. i n U. v. the various parts of the system, and providing for redundancy and fault tolerance (Rodrigues, 2012). . Ch. engchi. Hadoop: The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures (Rodrigues, 2012).. . Hive and PIG: The Apache Hive data warehouse software facilitates querying and managing large datasets residing in distributed storage. Hive provides a mechanism to project structure onto this data and query the data using a SQL-like language called 10.

(19) HiveQL. At the same time this language also allows traditional map/reduce programmers to plug in their custom mappers and reducers when it is inconvenient or inefficient to express this logic in HiveQL. Apache Pig is a platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs. The salient property of Pig programs is that their structure is amenable to substantial parallelization, which in turns enables them to handle very large data sets (Rodrigues, 2012).. 政治大 In a traditional industry, many of the routing companies predict routing path by computing 立. 2.2.3 Management effort involved in exploiting big data from LBS. company start to adopt the management of Big Data.. 學. ‧ 國. of mathematic. But something is changing, this is not enough to satisfy user’s needs. Some. Users are looking for stability and. distribution system expertise more than the latest algorithms. However, one of the challenges. ‧. that all of these companies will face is the ability to process data to re-optimize routes.. Nat. sit. y. Here we would like to discuss about the management effort in Big Data. It included 3. er. io. portions as below: 1) Database management. 2) Clustering Data. 3) Recommendation system, which represented 3Vs (volume, variety, velocity) (Beyer & Laney, 2012) of Big Data.. al. n. v i n C U adopted big data, it’s a large Database management: it’shconcerning e n g c the h idatabase. . data repository that integrates data from several sources into structures expressly designed for analytical purposes. Database typically employ a multidimensional model for organizing data. This type of model typically categorizes data as either business facts with associated measures, which are numerical in nature, or. dimensions, which characterize the facts and are mostly textual. Each dimension is organized into a hierarchical structure of levels, which enables the aggregation of facts to the desired levels of granularity. Services supported by non-conventional databases, characterized by the spatial and temporal dimension, i.e., spatiotemporal databases. Due to this, data involved in LBS have not been really examined in depth. Consequently, LBS data semantics are not captured properly, LBS data models do not fully accommodate application requirements, and the final system does not 11.

(20) always meet user needs (Jensen, et al., 2003). . Clustering Data: The scientist organized a collection of objects into a classification or a hierarchy. Not feasible to “label” large collection of objects, there are no prior knowledge of the number and nature of groups in data. Clusters may evolve over different domain, which provides efficient querying, search, storage and organization of data. More and more data are collected from multiple sources or represented by multiple views, where different views describe distinct perspectives of the data. Clustering is an exploratory technique and essential methodology. The collected data are used in every scientific field. The scientist depends on data to choice of clustering algorithm and factors. Clustering is essential for solving issues of Big Data. The methodology of K-means provides good trade-off between data. 政治大. size and accuracy. The number of challenges are extensibility, huge quantity of. 立. clusters, various data, solidity data, and validity. Although each view could be. ‧ 國. 學. individually used for finding patterns by clustering, the clustering performance could be more accurate by exploring the information among multiple views. Several multi-view clustering methods have been proposed to unsupervised integrate. ‧. different views of data. However, they are graph based approaches. It based on. y. Nat. spectral clustering, such that they cannot handle the large-scale data. How to. io. n. al. er. has become a challenging problem (Cai et al., 2013). . sit. combine these heterogeneous features for unsupervised large-scale data. Clustering. Ch. i n U. v. Recommendation system: The system of real time recommendation methodology. engchi. which generating location based involves building a dataset on usage data of the user based. The usage data collected from capturing device location data. The data determine the travelling patterns of the individual, and send to server in real time. The system is a valuable but unique application in location-based social networking services, in terms of what a recommendation is and where a recommendation is to be made. Recommender system can record people’s routes by taking advantage of the category information of a user’s location history. Always taking into account usage context that match user personal interests within a geospatial. Machine learning algorithms can be used effectively to identify the regular routes, weekend vacation, most frequented routes and other travel pattern that can be used to build the user profile (Shamah, 2014).. 12.

(21) 2.3 Business model A business model is a description of how a business makes (or intends to make) money. It is the centerpiece of the business plan. Constructing a business model is the first step in planning to start a business.. The purpose of a business model is to insure that all the factors. needed to operate a successful business are considered and analyzed to make sure they are reasonable and achievable. Following is a template describing the contents of a business model developed by Alex Osterwalder (Osterwalder et al., 2009).. 立. 政治大. ‧. ‧ 國. 學 Business Model Framework. er. io. sit. y. Nat Figures 2-2. n. a lSource: Osterwalder et al., 2009 v i n Ch engchi U. There are 4 parts to this business model Framework.. Part 1- The offering – this is what the business produces and sells. Part 2- Infrastructure– this is the part of the business that creates expenses. Part 3- Customers– this is the part of the business that generates revenue. Part 4- Finances – this is the part of the business that determines its financial performance and profit. In theory and practice, the term business model is used for a broad range of informal and formal descriptions to represent core aspects of a business. A systematic review and analysis of. 13.

(22) manager responses to a survey defines business models as the design of organizational structures to enact a commercial opportunity. Further extensions to this design logic emphasize the use of narrative or coherence in business model descriptions as mechanisms by which entrepreneurs create extraordinarily successful growth firms. Building upon the service-oriented studied cases, several authors propose Data-as-aservice and Analytics-as-a-service as new service types. However, most of these papers focus on technical or organizational aspects (Delen and Demirkan, 2013; Stipic and Bronzin, 2012). An exception is provided by Chen et al. (2011), who focused on the analytics ecosystem. Although the term ‘data-driven business model’ has not yet been defined in the scholarly literature the term is commonly used by practitioners (Hartmann et al., 2014).. 立. 政治大. ‧. ‧ 國. 學. n. er. io. sit. y. Nat. al. Ch. engchi. 14. i n U. v.

(23) Chapter 3 Research Method. 3.1 Content Analysis This study is designed to explore identification classification and application of LocationBased Service. A survey was conducted to identify the factor that support or impede of acceptance LBS application. This study employs a qualitative approach for reviewing LBS scholarship, in which existing knowledge is presented in narrative form and is critiqued thematically. In this study we propose the following different contents. The majority of study cases have collected 8 industries of variety application. Which will. 治政大at the industry background, data application in current marketplace. Each of cases are shown 立 feature, major technology of big data, advantage of application, management effort, and. present detail on the chapter 4. These different cases are represented the most popular. ‧ 國. ‧. Table 3-1 List of studied cases Case Description Taxi Fleet location and dispatch system Real-Time Traffic Information Service Chinese New Year Mass Migration with Baidu Heat Map Smart Logistic Network Car pool network system Vehicle monitoring and tracking system Public bicycle sharing system Navigation Services. n. al. er. io. sit. y. Nat. Items 1 2 3 4 5 6 7 8. 學. business model.. Ch. engchi. i n U. v. The content analysis begins with identifying research questions and choosing sample to explain how it works. While we were reading materials, we noticed that the all items of cases were related to location based service and big data. They were giving evidence of how advantage is improved to a general application, when we were trying to understand that case issues are different in its own application. The present research is trying to conduct this study in more systematic manner.. 15.

(24) 3.2 Data Collection Learned from previous section 3.1, the present research has adopted 8 industries application as a demonstration to increase our understanding of the location-based service with big data. The data collected from different sources to each independent case. The sources are shown as below. Items 1. 2. Table 3-2 Data source of studied cases Case Description Taxi Fleet location and dispatch system --1) Taiwan Taxi 2) Singapore Comfort http://www.taiwantaxi.com.tw/taiwantaxi/english_about.asp http://www.taxisingapore.com/taxi-companies/comfort-taxi/ Real-Time Traffic Information Service --1) EU-wide Real-time traffic information services 2) The Service Interface for Real Time Information http://ec.europa.eu/transport/themes/its/consultations/2014-03-14-rtti_en.htm http://user47094.vs.easily.co.uk/siri/. 立. ‧ 國. Chinese New Year Mass Migration with Baidu Heat Map --1) Baidu Migration http://qianxi.baidu.com/ Smart Logistic Network --1) Alibaba Group 2) Cainiao Internet Technology http://www.alibabagroup.com/en/global/home http://en.wikipedia.org/wiki/China_Smart_Logistic_Network. ‧. 4. 學. 3. 政治大. 8. sit. er. Car pool network system --1) TwoGo powered by HERE, Nokia’s location cloud, https://www.twogo.com/ Vehicle monitoring and tracking system --1) OnStar ( General Motor ) 2) Tobe (Yulon Motor ) http://www.gm.com/vision/design_technology/onstar_safe_connected.html http://www.tobe-motor.com.tw/home/index.asp Public bicycle sharing system --1) YouBike ( Taipei City ) 2) Citi Bike ( New York City ) http://www.youbike.com.tw/ https://www.citibikenyc.com/. al. n. 7. io. 6. y. Nat. 5. Ch. engchi. Navigation Services 1) TomTom International BV. http://www.tomtom.com/en_gb/. 16. i n U. v.

(25) 3.3 Data Analysis. Learned from previous section, the present research has adopted as a demonstration to increase our understanding of the location-based service with big data. The data collected from different sources to each independent case. This research collected different cases, which represented different application of famous objects in the current marketplace. In order to understand the data of LBS with big data. The content of data was consisted of data features, data analysis technology, and manage efforts. Those of 3 portions are separated into more detail of small parts, showed as below table.. Table 3-3 Items of data analysis. 立. 政治大. ‧. ‧ 國. 學. 1. Data Features:. The detail of collected data Detail Description Routing path Non-structure Imprecision and Varying Precision Digital data Real Time Social network Schema-less databases Cloud computing Storage Technologies Hadoop Hive and PIG MapReduce Database management Clustering Data Recommendation system. n 3. Management effort. er. io. al. sit. y. Nat. 2. Technology involved. Ch. engchi. 17. i n U. v.

(26) Chapter 4 Location base service application in marketplace 4.1 Taxi Fleet location and dispatch system This section will refer to 2 famous companies which are Comfort (Singapore) and Taiwan Taxi (Taipei). Both of 2 companies are using a location-based service with high technology of GPS and wireless network system. Performing in taxi fleet management and dispatch operation. A taxicab is a type of vehicle for hire with a driver, used by a single passenger or small group of passengers, Global Positioning System (GPS) fleet management system is a network that allows the vehicles in a fleet to be tracked via satellite and with the results available to company supervisors in real time. Each vehicle equipped with a box device, it’s named as a "black box," that receives signals from GPS satellites to positioning the location. Then the data. 治政 manage these data on the back end side (Zhang et al., 2013). 大立 Previous paragraph are well-known information. This study would discuss about how will transmit to control center through the wireless network. There is a team to monitor and. ‧ 國. 學. they can be improved by new technology from Big Data. Past research has been constrained by using aggregated data to assume all vehicles with the same travel pattern as the aggregated. ‧. average. Taxi fleet used a large-scale data set containing real-time trajectories of all taxis in downtown retrieved by GPS systems for one week to explore the impacts of individual travel. Nat. sit. y. patterns. Each data point includes a unique taxi ID, the time of the recording, and the position. io. er. (longitude and latitude) of the taxi at the specific time. Depending on the GPS device setup in each vehicle, the frequency of recording ranges from few seconds to minutes. After collecting. n. al. Ch. i n U. v. all data in the data server, the engineers would take these data to analyze by computing program.. engchi. Fleet management capabilities such as computerized dispatch, real-time monitoring, and vehicle tracking work together. Moreover, enhance accuracy and enhance efficiency of dispatch. Monitor vehicle location, enable passenger self-service reservation, and provide safety credit card authorizations. A well-construct integrated and scalable fleet management platform enables you to manage as much flexibility and functionality as you need (VeriFone, 2014).. Learned from previous information, we concluded the data features of taxi fleet as below items. Which are 1) non-regular route path, 2) real time, and 3) digital data. Taiwan Taxi established a cloud computing system. Which embedded power processors and huge data serve to develop a variety of IT services that provide passengers and drivers with a comfortable and safe riding experience. The system serve over 350,000 members, 100,000 calling per day, 15,000 taxicab on duty. Taiwan Taxi is looking to set up a business intelligence 18.

(27) system (BIS). Using passenger information collected over the years, the BIS will analyze and compile taxi service schedules and passenger pick-up routines, and identify service shortage areas. There are currently as many as 20 major IT systems in operation at Taiwan Taxi. Many of these systems, such as the taxi calling service systems, are further developed into smaller service applications. As a result, each system supports an average of 10 different functions, expanding the company’s software use that proved challenging for their operations. Taiwan Taxi is a success story for merging traditional enterprise with IT. Having implemented its cloud system, Taiwan Taxi continues to strive for service improvements that will enable its taxis to provide passengers with better leisure and business trips (Qiang, 2012). The service uses collected data in an aggregated way to generate predictions that are generated from traceable of individual taxis or passengers. It would be a management effort on. 治政大solution for management team. variety changed, using a schemaless database is a necessary 立traffic pattern are for recognizing the user’s behavior, the Another effort in clustering the database management and recommendation system. In order to analyze the traffic pattern. ‧ 國. 學. purpose is for the customer relationship system.. Using Big Data mining techniques, which examines real-time vehicle trajectory data for. ‧. all taxis in fleet to characterize the travel patterns of individual taxis. We then evaluate the behavior of vehicles in the taxi fleet based on the characterized individual travel patterns. The. Nat. sit. y. results indicate that, 1) the largest gasoline displacement can be achieved by adopting data. er. io. analysis. 2) Reducing gasoline cost has the largest impact on increasing vehicle mileage traveled. 3) Government subsidies can be more effective to reduce air pollution, and 4) taxi fleet. n. al. Ch. i n U. v. can increase greenhouse gas emissions by less CO2 emission (Cai & Xu, 2013).. engchi. 4.2 Real-Time Traffic Information Service. The provision of EU-wide Real-time traffic information services, it is a kind of intelligent transport system, with a target to provide road users with helpful, precise and up-to-date information on the road network, traffic regulations. It would recommend driving routes and real-time traffic data, including estimated travel time, traffic jam information, accidents alert, road works, weather conditions, and other relevant safety-related information. (Intelligent transport systems, 2014) Target group of users are citizens, transportation companies, local or regional public 19.

(28) authorities, national public authorities, who have an interest in the issue of provision and usage of real-time traffic information services.. The real world in-vehicle route planning problems are dynamic, and real-time traffic information is one of the most important and essential criteria for drivers during route selection, most current systems have been based on static algorithms. Recent developments in information and communication technologies and their impact on transportation researches lead to intelligent transportation systems. In many countries there is a traffic control center which is a part of their intelligent transportation systems. The traffic information is available to the control center in real-time (Nadi & Delavar, 2010). There are 2 major algorithms. 1) Determining the shortest path between two specified. 治政 with travel times depending on the departure time; and 大 finding the shortest path between specified endpoints that passes立 through specified intermediate nodes (Dreyfus 1967), and 2) nodes or all pairs of nodes of a network…, determining of the fastest path through a network. ‧ 國. 學. For a given source vertex (node) in the graph, finding the least expected travel time path through networks with lowest cost and the shortest path between that vertex and every other vertex. ‧. (Dijkstra 1959). There are different methodologies from other researches to find minimum shortest path (Nadi & Delavar, 2010).. Nat. sit. y. The Service Interface for Real Time Information (SIRI) is an XML protocol to allow. er. io. distributed computers to exchange real time information about public transport services and vehicles. The protocol is a CEN technical specification, developed with initial participation by. n. al. Ch. i n U. v. France, Germany, Scandinavia, and the UK (RTIG). SIRI is based on the Trans model abstract. engchi. model for public transport information, and comprises a general purpose model, and an XML schema for public transport information (SIRI, 2014). The service interface for Real Time Information is a protocol to allow distributed computers to exchange real time information about public transport services and vehicles. It is based on the position model for public transport information, and comprises a general purpose model, and a data schema for public transport information. It also allows pairs of server computers to exchange structured real-time information about schedules, vehicles, and connections, together with general informational messages related to the operation of the services. The information can be used for many different purposes (SIRI, 2014).. 20.

(29) 4.3 Chinese New Year Mass Migration with Baidu Heat Map Chunyun is referred to as the Spring Festival travel season or the Chunyun period, is a period of travel in China with extremely high traffic load around the time of the Chinese New Year. The period usually begins 15 days before the Lunar New Year's Day and lasts for around 40 days. This is a famous network enterprise name “Baidu” in China. Who has equipped with a high performance search engine, and launched "Baidu Migration", use big data technology, its own location-based services to calculate and analyze big data, and using innovative visualization presentation. Baidu is the first enterprise to achieve the full, dynamic, real-time, and intuitive to show China Spring Festival around the track with a large population migration characteristics. It’s an online map tracking the movement of Chinese people migrating to other. 治政 its 200 million registered users. The data is gathered from大 smart phone usage, and is updated 立 4-1 shown as migrate heat map. hourly (Baidu, 2014). Below Figures. parts of the country for the spring Festival. This map uses location-based technology to track. ‧. ‧ 國. 學. n. er. io. sit. y. Nat. al. Ch. Figures 4-1. engchi. i n U. v. Baidu Migration heat map Source: Baidu,2014. The most affected modes of transportation are intercity passenger transportation systems. The traffic system majorly including railway and road networks. Most of middle city can’t sustain the huge transportation flow during the short period. People usually take a trip to way home at this moment every year. It is a high challenge to the government and transportation companies. Actually, they have done a data collection of transportation at route between each city. The data showed a very complication map of connection network. The Chunyun of 2014 21.

(30) covers 40 days, and a total of 3.6 billion trips are expected to be made during the period. Baidu has launched a heat map of where Chinese travelers are heading to, coming from, and which routes are most popular during Chinese New Year, the country’s largest national holiday. (Baidu, 2014) Below table is the major data of items. We realized the numbers are huge and big.. Table 4-1. Data list of Baidu Migration. Description. Data. Duration. 40 days. Registered users. 200 M. Passenger Trips (total). 3.6 B. 3.5 B / day 政治大 The flow of train ticket purchase website 12306 8.4 B 立 Position requests. 240 K / second. greatest concentration of passengers flows. 學. ‧ 國. clicks request from users. ( 40,000/sec). 200 ~ 800 Km. ‧. Source: qianxi.baidu.com. sit. y. Nat. Baidu Migration includes a search function so you can see stats from specific cities and time frames. Here’s a few stats as of press time:. io. The most popular destination is Beijing, followed by Chongqing. The hub cities in. n. al. er. . Hunan and Guangxi provinces tie for third.. Ch. engchi. i n U. v. . Beijing is also the most popular city to leave, followed by Shanghai and Guangzhou.. . The trip both to and from Chengdu and Beijing take up the top two most popular routes.. However, big data also suggests that many migrant workers are no longer traveling to work in big cities far from their home towns. The greatest concentration of passenger’s flows was to areas 200-800 km from home. Along with the development of mobile internet, big data will play a more and more important role in providing analysis in such areas as population migrations, the urbanization process, city administration, Chunyun transportation strategy, and cultural communications. Below table is a statistic data. It shows different traffic mode with increasing number of trips (Baidu, 2014). 22.

(31) Table 4-2. Travel statistics from 2009 to 2011. Source: Baidu, 2011. 政治大 type any word. The firm, which says its search engine attracts more than a number of million 立. Chinese Internet giant Baidu will be watching closely when users of its search engine. users daily through mobile devices alone, is working with the Chinese Center for Disease. ‧ 國. 學. Control and Prevention to use big data. From Big Data Technology Conference, BDTC, Baidu presented how they are using several type of technology, for example: Hadoop, MapReduce,. ‧. and Hive. Below figure was presented on the BDTC 2012. (包研, 2012). n. er. io. sit. y. Nat. al. Figures 4-2. Ch. engchi. i n U. v. Diagram of data analysis of Baidu Source: Baidu, 2012. 23.

(32) 4.4 Smart Logistic Network Alibaba Group is a privately owned Hangzhou-based group of Internet-based ecommerce businesses including business-to-business online web portals, online retail and payment services, a shopping search engine and data-centric cloud computing services. Alibaba operate leading online and mobile marketplaces in retail and wholesale trade, as well as cloud computing and other services. They provide technology and services to enable consumers, merchants, and other participants to conduct commerce in their ecosystem. In 2012, two of Alibaba’s portals together handled 1.1 trillion Yuan ($170 billion) in sales, more than competitor eBay and Amazon.com combined. The company primarily operates in the People’s Republic of China. (Alibaba, 2014) After creating a huge business of E-commerce, who face a big challenge, the question is. 治政大 in China is far more of a logistical challenge than a technological challenge. China's Ecommerce companies are facing立 a big challenge stemming from the lack of efficient and reliable how they can deliver goods fluent to customer in a short time. The growing E-commerce market. ‧ 國. 學. last-mile delivery.. Alibaba's New Courier Service, Cainiao Internet Technology, is poised to monopolize. ‧. Chinese market, threatening competing players such as state-owned China Post and smaller logistics businesses. Former Alibaba CEO Jack Ma will be the president of Cainiao Internet. Nat. sit. y. Technology Ltd., a new joint venture formed by Alibaba in partnership with eight e-retailers. er. io. and leading courier services, which will operate a national courier service website named China Smart Logistic Network (CSN). The network, with an investment of 5 billion Yuan (RMB). n. al. Ch. (China Smart Logistic Network, 2013).. engchi. i n U. v. Cainiao won't build a logistics network but establish an innovative system based on internet technologies and ideas to provide more efficient services to logistics companies. Jack Ma said: “the logistics network should enable a courier to reach a fourth-tier city buyer in 24 hours. But for users in really remote areas like Xinjiang it is a very challenging target." Ma is aiming to establish a non-traditional and new type of logistics provider that can adapt to the big data. Cainiao aims for its smart logistics system, which will include its own national network of warehouses, to deliver online orders to customers in 2,000 Chinese cities within 24 hours. Currently, 25 million parcels are delivered each day in China. The figure will grow to 200 million in 10 years. The existing logistics network won’t be able to cope with the extra capacity. Cainiao will use Alibaba’s location data to select prime locations for warehouses and optimal delivery routes. The completed network will provide a logistical infrastructure, ultimately 24.

(33) lowering their costs of doing business. The logistics network will also strengthen Alibaba’s hand against rival Jingdong Mall, which is the second-largest e-commerce firm in China and has a dedicated logistics network of its own. China’s Bachelor’s Day in 2013 created 152 million packages; and, in 2012, the number was 78 million. About 100,000 packages were created in one minute. It is a huge challenge for logistic companies. Cainiao helped Chinese logistic companies to sort out packages more efficiently. In 2013, the packages sorted on November 11 hit 60 million, which was nearly twice of last year. Cainiao sent out alerting signals about weather and transportation to related logistic companies and enterprises (Sabrina, 2013). Data of Bachelor’s Day in 2013. Table 4-3. Data 政治大 Date: 2013 , 11, 11. Description :. 立. Bachelor’s Day in 2013. 152 M ( 100,000 / minute). ‧ 國. 學. Package were created Sorted of package. 60 M 16.7 B. y. Nat. 35 B Yuan (USD 5.71 billion). sit. Total Revenue. ‧. The number of order. n. al. 100 M Yuan (USD 16.3 million). er. io. First 55 seconds transaction. i n U. v. After 6 min 7 sec transaction. 1 B Yuan (USD 163 million). After 5 hr. 49 min transaction. e n g c10hBi Yuan (USD 1.63 billion). Ch. Source: Sabrina, 2013 Big Data will allow Alibaba to understand consumers’ behavior in the past and to some degree predict how they will act in the future, making it a great asset in developing product roadmaps as well as understanding credit risks in its lending business. “It’s a very serious advantage”. Big Data will also be instrumental in the development of its national logistics network in which Alibaba and its partners will invest up to RMB 100 billion over the next five to eight years (Fulco, 2013). With 1.3 billion people, a quickly expanding urban economy, and rising rates of Internet and smartphone penetration, China generates an immense amount of data annually. If streams 25.

(34) of that data can be appropriately sifted, analyzed, and stored, companies seeking to understand China’s often-fickle consumers could have access to valuable real-time insights—and perhaps early warning to the next big consumer trends. A number of companies in China that do own large quantities of user-generated data— such as Alibaba and Baidu, hold the cards and may profitably sell that valuable information to other vendors (Larson, 2013). Aligroup, the holding company of online wholesale and retail channels Alibaba and Taobao; and Tencent, creator of the highly popular QQ and WeChat chatting software and other mobile applications. Each of these companies monopolizes its respective market and has access to transaction and interaction data generated by millions of customers. (cbkcuhk, 2013) Alibaba intending to dig deep for e-commerce gold. The group executives frequently tell. 治政大 are gathering. The country's the large amount of data that the company's e-commerce operations largest e-commerce company,立 which operates the consumer-to consumer Taobao.com and. their employees that the company is sitting on a gold mine, a reference to the huge potential of. ‧ 國. 學. business-to-consumer Tmall.com, has for years emphasized the potential of data mining. Indeed, every year Taobao.com hosts more than 100 million consumers and handles nearly 10 trillion. ‧. yuan worth of transactions (Shanshan, 2013).. Hadoop&BigData Technology Conference 2012，(HBTC 2012). Alibaba presented a. y. Nat. n. er. io. al. sit. diagram to show the platform of data exchange (包研, 2012).. Ch. Figures 4-3. engchi. i n U. v. Alibaba Group data exchange platform. 26.

(35) 4.5 Car pool network system For thousands of men and women, the daily experience is 4 hrs a day stuck in traffic commuting. These people suffer the daily grind of getting up early, getting their kids ready for the child-minder. Then they have a two hour drive to downtown, a hard day’s work, the long road home to spend some time with their children before they collapse into bed and wait to do it all again. Day in Day Out. Monday to Friday (Collins, 2006). The ride-sharing app “TwoGo” application, first unveiled in 2013, is aimed at corporations with large workforces or smaller firms that share a location (such as an office or industrial park). TwoGo is powered by HERE, Nokia’s location cloud, and launched by SAP. Nokia’s HERE mapping technology and has been in use internally at SAP since July 2011. SAP and its employees have derived about $5 million in benefits from the application thanks to lower. 治政大way, which delivers one of the To help locate desirable matches in an easy and clear 立 across multiple screens and operating systems. A user leading map and location experiences fuel usage, employee travel reimbursements and other savings (Goldberg, 2013).. ‧ 國. 學. registers for the cloud-based TwoGo application using workplace email account. User enters location information about the start of his commute and the destination, preferences about travel. ‧. times to and from work, whether User wants to be a driver or passenger, and her preferred routes (such as how far user is willing to detour, in minutes, to pick up a rider).. Nat. sit. y. TwoGo is a particularly interesting service as it combines location, mobility, and. er. io. sustainability in an innovative way. And, which is available on a desktop or smartphone, delivers potential ride matches for her schedule, both to and from work. Rides selected appear. n. al. Ch. i n U. v. as appointments in a work calendaring application. The calendaring function is a key design. engchi. element, because “it’s where corporate life happens” (Kanaracus, 2013). The technology of Here is based on a cloud-computing model, in which location data and services are stored on remote servers so that users have access to it regardless of which device they use. Here captures location content such as road networks, buildings, parks and traffic patterns. It then sells or licenses that mapping content, along with navigation services and location solutions to other businesses such as Garmin, BMW, Oracle and Amazon.com. Here has maps in nearly 200 countries, offers voice guided navigation in 94 countries, provides live traffic information in 33 countries and has indoor maps available for about 49,000 unique buildings in 45 countries. Currently, Here Maps is available in 196 countries and its features include turn-by-turn walking navigation, offline availability, 3D landmarks and indoor venue maps for 29,000 unique buildings in 45 countries. A favorites list shows the top 25 most popular places in the 27.

(36) vicinity looking at positive reviews, search queries and other user data (Here (Nokia), 2014). Here draws on more than 80,000 data sources including a vehicle fleet, which collects data through panoramic cameras, position sensors and laser technology for 3D footprints. The cars have an array of cameras, which capture 360-degree street views and LIDAR sensors, which capture 1.3 billion data points every minute. Another bank of high-resolution cameras capture signs such as speed limits and street names (China Smart Logistic Network, 2013).. Carpooling is thought to be part of the solution to resolve traffic congestion in regions where large companies dominate the traffic situation because coordination and matching between commuters is more likely to be feasible in cases where most people work for a single employer. Moreover, carpooling is not very popular for commuting. In order for carpooling to. 治政 large community involved. Such service is necessary but大 not sufficient because carpooling 立 requires rerouting and activity rescheduling along with candidate matching (Bellemansa, et al., be successful, an online service for matching commuter profiles is indispensable due to the. 4.6 Vehicle monitoring and tracking system. Nat. y. ‧. ‧ 國. 學. 2012).. sit. GM VEHICLES & ONSTAR (a powerful combination), OnStar in GM vehicles is driven. er. io. by a powerful and simply-stated promise. It was the industry’s first embedded telematics system. al. v i n C h system. Since itsUdebut, OnStar and GM have never automatic crash notification and security e n g csafe, h isecure and connected (General-Motor, ceased to drive advancements in keeping drivers n. when it debuted in 1996. OnStar in GM vehicles was the first available comprehensive. 2014). Capabilities such as remote vehicle diagnostics, turn-by-turn navigation, the ability to slow down stolen vehicles and the possibility to talk to a live advisor with a simple push of a button have all changed what customers worldwide experienced driving. Big data is not a new phenomenon in the automotive industry. The key reason for this industry to zoom into big data is the increasing maintenance service. Recently, new vehicle is equipped some sensor inside the vehicle. It should be to have the ability to collect gigabytes of data from a variety of sensors. These data was sent to data center to analyze for an objective of highly cautious in advance. There are 3 reasons to collect and analyze data from vehicle sensors. 1) Cut down the 28.

(37) warranty cost. 2) three-way data sharing network between the sales, customer, and the motor maker. 3) Crucial ecosystem partners who can use this data for value-added services (Manohar, 2013).. Yulon Nissan Motors Chooses QNX for New-Generation Telematics System in Taiwan. QNX Software Systems, the global leader in operating systems and middleware for the in-car telematics and infotainment market, it is working closely with Yulon Nissan Motors and its partners Sin Etke Technology Co. Ltd, and Freescale Semiconductor to develop the nextgeneration TOBE telematics system for Yulon Nissan Motors vehicles in Taiwan (OTTAWA, 2006). The system will provide a variety of services, including real-time information, news and. 治政 information, roadway guide, roadside assistance, speed大 limit alarms, tow-away alarms, 立 stolen car tracking, collision reports, car device control (air integrated GSM hands-free module,. weather reports, discount offers, flight and hotel reservations, POI (Point of interest). ‧ 國. 學. conditioning, car audio, gas mileage), concierge service, and other services yet to be announced. The new system is expected to be introduced to the market in the near future (OTTAWA, 2006).. ‧. The TOBE service platforms include four parts, 1) an information counter, 2) a life counter, 3) a traffic counter, and an e-interface set. The information counter provides real-time. Nat. sit. y. information includes news, weather report, etc. The life counter gives discounts messages and. er. io. valet order service such as on-line shopping for souvenirs, flight and hotel reservation… etc. for daily life. The traffic counter provides scenic spot messages, roadway guide, exceeding the. n. al. Ch. i n U. v. speed limit alarm, and tow-away alarms with the help of GPS. The e-interface set installed in. engchi. the car includes an integrated GSM mobile module. The e-interface set automatically provides anti-theft notice and collision report (Chiu, Fang, & Wang, 2005). The situation was usually happened on the car driver. Car driver spend lots of time do nothing to wait the traffic jam, to find a parking lot, lost way to find the direction to destination. This system equipped a computer base system. The Tobe system provide services from their control center. Control center collected the car location data and sent to the clouding based server through the wireless internet. To provide the recommendation and decision service like new path, available parking lot, store information, tow service and traffic jam updated (OTTAWA, 2006).. 29.

(38) 4.7 Public bicycle sharing system Bicycle Sharing systems allow citizens to hire a bicycle from an automated docking station for a short journey. The number of systems has grown rapidly in the last ten years and it is estimated that there are currently just over 700 systems in active use (Meddin and DeMaio 2014), and been set up from 100+ cities around the world (O'Brien, 2014). This study provide 2 famous systems of the world. First, is the YouBike which is founded in Taipei city, another is the Citi Bike which is founded in New York City. The YouBike pilot program was launched in March 2009 on a small scale, with a mere 500 bicycles at 11 stations, all within a short radius of Taipei City Hall. But Taipei residents greeted YouBike with a yawn. By the end of the year (2014), 4,455 of these bikes had been stationed at 133 electronically monitored racks along the periphery of the city’s Mass Rapid. 治政 November 2013, and ridership sometimes exceeded 60,000大 on peak days (SMITH, 2014). 立 it collected the positioning data and sent to cloud based There is an app, name RunKeeper, Transit (MRT) system grid. Meanwhile, the total number of YouBike rentals hit 10 million in. ‧ 國. 學. server for analysis in real time. The server stored huge data and clustering the data. This system is a kind of smart internet of thing. The system send valuable data to the marketing manager for. ‧. some promotion activities. The RFID technology was adopted to access the user’s riding path automatically. User can check the available bike stations and riding time schedule. For manager. Nat. sit. y. team, the system provided the results of user behavior. It helps enterprise to plan a marketing. al. er. io. strategy (杜胤廣, 2014). The smart system manage all bike usage. Recording the mileage and. n. maintenance schedule (吳韻萱, 2014).. Ch. engchi. i n U. v. New York City’s new bike-share program, Citi Bike, has been underway for a couple of years now. Its level of success is still up for debate, but the stats are impressive: as of June 10 ‘2013, there had been 173,516 trips traveled over 510,782 miles since the launch. Oliver O’Brien, a researcher and software developer at the Centre for Advanced Spatial Analysis (CASA), and a contributor to OpenStreetMap, has developed a visualization of bike share use in real time. The top of the map also links to real-time bike share information in other cities around the world. O’Brien explains that the data is updated automatically every two to 10 minutes, noting that data generally comes from the bike share provider’s website or their official API, but he also gathers data from third-party data collectors, such as citybik.es (O'Brien, 2013). We also found out how important those data was for everyone, and that there's no reason 30.

(39) for companies to keep it private. Most of these bike sharing systems are built using public money, we believe this data should be available to their own citizens. The main reason for this project to be is for people to realize the benefits of providing free data. CityBikes is a cost free service, and provide an interface of API to access their database. What if there was a product that could anticipate bike and dock availability, much as Google Maps is able to forecast traffic for any day and time of the week? One year in, we’ve found that Citi Bike has fairly predictable ridership patterns. For example, some stations in the East Village empty out in the morning, while many in Midtown fill up around the same time – but, in many cases, an available bike or dock is just a short walk away (CitiBikeFiner, 2014). In an ideal world, this product would also: • Provide functionality for riders to report any issues they might encounter with Citi. 治政大and provide detail on those rides • Use the phone's GPS to map routes taken by riders 立 (or integrate with a service like Strava that does this already). Bike within the app.. ‧ 國. services.. 學. • Integrate with one or more of Twitter, Foursquare, or other mobile, location-based. ‧. • Do anything else that we haven't mentioned – but that you think would markedly improve our riders' experiences.. y. Nat. al. er. io. sit. (CitiBikeFiner, 2014). n. It is important to predict and guide users to the right station. Users of this system may. Ch. i n U. v. have complex and diverse paths so it is hard to satisfy everybody, especially in the rush hour.. engchi. Supstat built up a program to scrap data and save them to our database automatically. Using these data we utilized models from time series theory and machine learning to predict bike numbers in all the stations precisely. Based on the models, Supstat build a website for this citibike system. This application helps users of citibike arrange their trips better (Supstat, 2013).. 4.8 Navigation Services An automotive navigation system is a satellite based GPS navigation device to acquire position data to locate the user on a road in the unit's map database. Using the road database, the unit can give directions to other locations along roads also in its database. Some newer systems can not only give precise driving directions, they can also receive and display information on traffic congestion and suggest alternate routes. These may use either TMC 31.