Huge volumes of all types of data are generated on a daily basis globally. This data is continuously stored in ever expanding storage facilities and it is always available for retrieval by any interested party depending on conditions placed upon it. With great developments in information and communication technology, most of the data produced every day is generated by people all over the world through social networks; however other types of important data are collected using cameras, GPS equipment, satellites and other devices for many uses. Over the last decade, business strategy has become increasingly dependent on information about potential customers and their characteristics. This data is obtained from the huge collection of data referred to as Big Data through processes like data mining and analyzed to help in business strategy. Analytics is the other method of collecting vital consumer information and it involves real time tracking of consumer characteristics. This paper examines how Big data analytics can be used in the transportation industry to improve quality of service, add value to services and develop applications that will enhance service provision in the industry and reduce loss of time and money. The study has defined Big data and some of the theories that enable its application as well as examined the benefits and challenges provided by big data analytics in the transportation industry locally and globally
More data is currently being generated worldwide than at any other point historically. Over the last five years, the volume of data generated globally is estimated to have increased by a factor of six to over 1000 exabytes (Dumbill, 2012). The ‘digital’ universe is expected to reach 8 zettabytes by the year 2015. In general the data explosion is projected to increase with time especially with new data types being developed and increased access to networked devices all over the world including smart phones and geo-positioning devices (Woo et al., 2011).
The data being accumulated comes from a wide range of sources. However, the data growth is driven by two main sources working together with decreasing storage costs. The first source for data is the “internet of things”. A number of sensors collate information on our activities and environment on a daily basis. These connected devices contribute substantially to the amount of information accumulated daily and they are projected to rise from about 4.5 billion devices in 2010 to over 50 billion in 2020 (Dumbill, 2012). The second greatest source of data is the social web of networks where information about human activities is shared on a daily basis. This includes data about human preferences, interests, and locations. On addition to the two major sources of data highlighted above, there are a number of other private sources including hospital records, phone communications, financial transactions, information captured on CCTV and many others.
The McKinsey Global Institute has termed big data as the next frontier for competition, innovation, and global productivity (Manyika, 2011). The analysis of masses of unstructured and semi-structured data which some time ago would have been considered prohibitive in terms of time and money is now considered the next step towards business advantage. One of the reasons why this data has turned out to be very important is that great insight can be gained from the data by monitoring the patterns of human interaction. One of the areas in which big data displays great potential is the transportation industry. This is an industry which increasingly showing great requirement for an industrial big data platform.
With increasing urbanization and expansion of many cities across the world, traffic management and related challenges are getting bigger by the day. In some of the largest and more congested cities in the world, a lot of hours are lost daily on traffic and most people incur economic losses and social stress due to inefficiency in transportation (Intel Corporation, 2013). Cities such as Tokyo, Beijing, New York and London can greatly benefit from good utilization of big data analytics in making their transportation networks and more efficient. However, the greatest beneficiaries of big data analytics are the users of transport networks; people can make their travel and movement as well as that of goods and services more efficient through use of applications taking advantages of big data analytics. According to Intel Corporation (2013) a city in the Zhejiang province of china has been able to connect over one hundred intelligent monitoring checkpoint system as well as similar electronic police and video monitoring systems with the main aim of efficient traffic management. These checkpoint systems basically collect large amounts of traffic data which is stored in central storage centers. The city traffic management division then utilizes this data to carry out analysis of traffic conditions, accidents, violations and other occurrences in real time. This is a great example of how big data analytics can be applied in the management of the transportation industry.
The transportation industry is recognized as a less data-intensive sector but over the past few years it has been facing growing amounts of data. Such data may make it easy to increase transportation efficiency through smart transport management strategies including smart routing. It is also possible that big data can enable provision of new services in transportation and logistics based on smart applications (Dumbill, 2010). Smart routing will mainly be developed on the basis of real-time traffic data collected through navigation systems. With tremendous growth in communication technology, navigation systems are already being operated as software on smart phones or on systems integrated in automobiles. MGI (2011) estimates show that by 2009 the global pool of personal geo-location data exceeded one petabyte and was growing by at least 20% annually. Further projections indicate that by 2020 the pool will save up to USD 500 billion worldwide in terms of time and fuel savings if placed to good use.
Apart from navigation system providers, other parties also contribute significant amounts of data useful to the transportation industry. These include mobile telephone network companies that can use their services to identify patterns related to traffic and accidents on the basis of analytics (Bellier, 2010). Such data can then be sold to third parties including government agencies to help in transportation management. In France, this is already happening with Orange telecommunications Company using its Floating Mobile Data (FMD) technology to collect traffic data through mobile telephones and thus calculate travel time or estimate the formation of traffic jams.
Another area of application of big data with great potential is in security and emergency services. Smart applications based on machine-to-machine (M2M) communication can utilize big data to monitor vehicle movement and location as well as transmit car components (Autonomy, 2012). Car theft protection and navigation services can be offered utilizing such communication systems based on big data analytics. Similarly these systems can make it easy for provision of emergency services by companies developed specifically for this kind of services. It is also getting easier to develop new business models and other forms of taxes, like dynamic road pricing which is based on GPS technology and M2M data. These are generating significant added value and MGI (2011) estimates that by the year 2020 automatic toll collection on the basis mobile phone location can generate between 5 and 10 billion US dollars in value to consumers as well as over 2 billion dollars of revenue to service providers.
Even with the great potential of big data analytics to create value and efficiency in the travel industry, it is clear that good management strategy has to be used to get the best out of big data. Good management is required both in management of the data and in strategic planning of the industry to benefit from big data. A few theoretical models attempt to explain the big data management and its implications in the various sectors including transportation. This paper will examine the models as well as give a broad definition of big data and its implications to management of the transportation industry. Worldwide examples are provided so as to illustrate the scope of big data applications in the industry and make relevant comparisons.
1.3 Aims and Objectives
By exploring the applications of big data implications in the transport industry, this study aims to highlight some of the opportunities made available for improvement of service provision in the sector, enhancement of its public utility, and development of business opportunities related to big data.
The report aims to achieve the following objectives;
Clearly explain big data analytics and its usefulness to the transport industry
Explore some of the theoretical models guiding big data management in business
To identify different values of big data equity in the transportation industry from a business management perspective
To give recommendations on the most appropriate approach to bid data utilization in management of modern transportation
2.0 What is big data?
Big data has been variously defined by different authors but the clearest definition that will be adopted in this study is that big data comprises of datasets of sizes beyond the ability of typical database software tools to handle (Manyika, 2012). Handling of big data includes processes such as capturing, storage, management, and analysis. Although big data is generally assumed to be data in large masses, there is no explicit definition of how big it should be. Considering the large volumes of big data and the fact that it moves fast and does not fit conventional database systems, it requires special technology to handle. Dumbill (2012) describes this technology as Big Data Technologies and indicates that it is a new generation of technologies and architectures for extracting economic value from large volumes of a data of a wide variety.
Big data has got three main attributes that define it apart from size, these are generally referred to as the three Vs of Big Data and they include velocity, volume, and variety. Volume generally refers to the amount of data stored, in the case of big data this may run into the terabytes and exabytes. Companies store a large variety of data from business environment data to financial data; other public utility organizations like government departments also store a wide variety of data for different purposes. On the other hand, the variety of sources from which data can originate is vast; these sources may be internal and external. High growth in data gathering technology has ensured that in any given enterprise or industry relational data, semi-structured data and unstructured data are gathered at ever increasing speeds and with relative ease (IDC, 2012). Figure 1 illustrates the different types of data gathered on a daily basis by organizations.
Figure 1: Types of data making up Big Data (Source: IDC, 2012)
Structured data is that kind of data that is grouped into a relational scheme in a configuration and consistency that allows it to respond to simple computer queries to retrieve usable information. Such data is mostly organized on the basis of organizational parameters and operational needs. Semi-structured data on the other hand comes in a form that does not conform to a particular scheme (Buneman, 2012). Such data is inherently self descriptive and tends to contain different types of markers to indicate hierarchies of records within the data. A good example of such data is social media feeds and weblogs. Finally, unstructured data appears in formats that cannot easily be indexed for querying and analysis. Examples of this type of data include media files (video and audio).
The last and most important characteristic of Big Data is its velocity. This comprises of the speed with which data is generated and delivered, the speed of its storage and how quickly it can be retrieved from storage (Buneman, 2012). While considering Big Data, its unique nature (Volume) means that its velocity is also associated with the speed of flow of the data (IDC, 2012). In organizational terms, extraction of value from Big Data requires a good strategy. This strategy is centered on three factors; informed intuition, intelligence, and insight. Informed intuition is the ability of the manager to predict likely future occurrences and the strategy that is likely to be successful based on such occurrences. Intelligence involves examination of current occurrences in real time and making decisions about the appropriate action. Finally, insight is all about reviewing past occurrences in order to shape future action (Brynjolfsson, 2012).
Big Data is growing in importance because of the fact that business domains converge in a way that there is a new economic relationship between producers, distributors, and consumers of goods and services. Across entire industries and in organizations, it is becoming increasingly difficult for business decisions by managers to depend on experience. In the modern business world, decisions depend on data services to provide access to new insights that can enable effective business competition (IDC, 2011). Recent years have enabled access of organizations and their managers to new technologies at lower costs that enable improvements in data processing. Organizations are now able to capture greater amounts of data from a wide range of sources, analyze data in real time, and visualize it in a variety of new ways. These developments make it easy to take advantage of Big Data for business strategy.
2.1 Current Big Data Technologies
Currently, there are no comprehensive Big Data technology standards in place largely due to the fact that companies dealing with Big Data analytics projects are quite diverse and complex in nature. For that reason as well as other competing interests, proven comprehensive Big Data certification standards have not yet been integrated into the global Big Data management scene (Berners-Lee, 2007). Although big vendors such as IBM and EMC have created their own certification programmes, these are centered mainly on providing training and guidance for their own products, particularly Hadoop.
Hadoop is quite popular for Big Data handling and particularly deals with unstructured data. The Hadoop Distributed File System (HDFS) creates the right data storage and processing environment that can be applied in execution of various large-scale computing projects. For processing structure Big Data, most enterprises apply analytical databases including Greenplum and Aster Data Systems provided by EMC and Teradata. Generally, big data technology can be divided into two main components including hardware and software. The hardware component mainly comprises of the infrastructure layer while the software component comprises of data management software, analytics software, and decision support and automation software (IDC, 2011).
2.2 Theoretical Models in Big Data Management
In terms of business management, a number of theoretical models are applicable to Big Data in general. In the context of this paper, Big Data is viewed as a resource that can be utilized in better management of transportation services and networks. It is also viewed as a resource that creates potential for development of applications that will provide value added services to transport and logistics all over the world. Basically, the bottom line of big data analytics in the transport industry is to improve service, cut down wastage in terms of time and money, and improve service providers’ profits (Sanderson, 2011). In this sense, theoretical models for customer satisfaction as well as those for data management are applicable in explaining the application of Big Data analytics in the transportation industry.
2.2.1 The RFM Model
The RFM analytic model is one of the models applied in data mining and big data analytics. The model was developed by Hughes to differentiate important customers from large data by three main attributes; the interval of customer consumption, the frequency, and amount of money consumed (Hughes, 1994). The RFM model can be described in the following way;
R represents how recent the last purchase of a product was made. It refers to the interval between the last time there was observation on consuming behavior of a customer and the latest consuming behavior. In the model, the shorter the consuming behavior, the bigger the R.
F represents the frequency of purchase; this is basically the number of transactions carried out by a consumer in a particular period of time, for example, two times of a month. F gets bigger with the frequency of purchase.
M represents the monetary value of the purchases made by the consumer. This is the amount of money spent by the consumer in a particular period of time.
According to Wu and Lin (2005), different studies have indicated that the bigger the M is, the more likely that customer will continue buying products or services from the provider. The RFM model has been described as being much more effective for studying customer segmentation than any other attribute (Newell, 1997). This model is basically supported by data collected through big data analytics. It is quite evident that the model has relevant application in the study of customer trends in the transportation service industry. In this industry, customers mainly consume transport and logistics services from both private enterprises and public systems. Their trends and preferences can therefore be studied through big data analytics using the RFM models to help managers develop better strategies for service provision.
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