The objective of this work is to explore the novel use of urban Big Data to analyse social, organisational or behavioural factors affecting low carbon mobility services and their impacts. The major focus of the work will be on demonstrating the use of urban Big Data to improve planning and policy insights in mobility. For example, areas of focus could include: (1) using novel sources of Big Data, Internet-of-Things and associated data science methods to analyse social equity, spatial disparities and accessibility for disadvantaged communities, and to draw implications for planning and operations; (3) using big data for effective strategies for shared, intermodal, active and public transport, for example, regarding bicycle-sharing, car-sharing, parking-sharing, electric vehicle charging infrastructure and autonomlus mobility-on-demand systems.
In the modern society, the infrastructural development is considered as an important sign of economic as well as social growth. With the help of infrastructural development majority of the people will be able to experience modernisation in their lifestyle. In the modern scenario, the development of new urbanisation is considered as one of the most important instants of the continuous growth observed in the economic domain. In the recent scenario, the development of the smart city is a new term of the proper urban development. It has also notified that smart city is a part of urban development that especially integrates multiple information as well as communication technology for securing the overall management of the city design (Simmhan, et. al., 2013).
Designing the smart city with the help of urban computing is considered as one of the most important factors for its future development. With the development of the smart city by taking proper help of the urban computing will help the society to represent a technically developed city. Urban computing is considered to be an interdisciplinary study, which mainly focuses towards managing the development of the smart city through the use of upgraded technological innovation. The technological innovation and its supports are noted to be quite important for brining development within the social as well as economic structure.
The study is mainly focusing towards the importance of big urban data, a new form of technological application that could enable the various authorities to bring huge profitability in their operational process. The principle aim of the study is to portray the way of using big data for bringing development within the society. To satisfy the entire research process and attaining the aim of the study, the study has design few objectives. Those objectives are as follows,
In the modern era, the advent of globalisation has been able to bring huge development in the social as well as economic development process. The urbanisation is also played the role of an instance of the overall development. Also, the rapid of urbanisation has changed the requirements of a developed economy. Therefore, the continuous development of world economy has influences the needs of technological growth that could enable various departments to implement new technology. The increasing rate of migration has relatively influenced the higher authorities to paid utmost attention towards enhancing the urbanisation programme by adopting proper strategies.
In this regard, the advent of big data has been playing an important role in the development of urbanisation. Therefore, to bring urbanisation within the industry, the big data has been converted into urban data. The urban data is one kind of big data or technical approach that mainly developed for managing various data associated with cities. It could state that big data is one kind of large size of data that mainly requires the technological influences for performing in the practical field. It could also notify that the application of big data has gain huge prominences within its operational process for developing the construction of the smart city.
2.1. Introduction
The previous section provided an overview of the importance of Big Data and its impact on affecting low carbon mobility services and their impacts. Hence, this section aims at providing an understanding of the impact of big data in low carbon mobility services through review of relevant literatures.
According to Batty (2013), big data can be considered as the process pertaining to knowledge generation that aims at focusing on the various approaches. Such approaches include data capture involving massive datasets wherein a majority of the population is being studied and data is used from repurposed and purpose-specific forms of data collection. Another approach to big data is data management that incorporates storing data in virtual and decentralized locations and is further associated with management of both unstructured as well as structured data sets. Data analysis is yet another significant approach to big data that is considered automated in nature, since a majority of tasks are performed with the help of computers. Correspondingly, big data is regarded as the large amount of data that requires use of architectures and new technologies towards ensuring that value is extracted from analysis process (Batty, 2013).
On a similar note, Rucks and Kuzma (2012) asserted that big data encompasses a number of properties that can be categorized into variety, volume, velocity, variability, complexity and value. In this regard, it is worth mentioning that big data incorporates both semi-structured information with the help of various resources that includes social media websites, e-mails, web pages, sensor devices and documents, amongst others. In terms of volume, it must be noted that big data incorporates huge volume of data that are measured in terms of petabytes. Velocity pertaining to the data can be considered as yet another significant property underlying Big Data that is associated with determining the speed and flow of data from various sources. Use of big data enables performing the analytics pertaining to the data that is constantly in motion (Rucks and Kuzma, 2012). In addition, The Parliamentary Office of Science and Technology (2014) noted that big data is further associated with variability suggesting that it is able to cope with the inconsistencies in flow of data. Moreover, due consideration further needs to be placed on the fact that big data enables handling complexity since it is able to establish relationship among multiple sets of data, thereby transforming data across various sources. Finally, among several others, big data is capable of creating value based on providing users with the ability to run queries and deduct important results pertaining to the obtained filtered data. It further enables the people to gain an understanding of their business trends that enables them to change their strategies (The Parliamentary Office of Science and Technology, 2014).
According to Hepler (2014), big data can play an imperative role in managing transportation in urban locations with respect to transition from vehicles associated with creating pollution to low carbon mobility services. Specifically mentioning, big data approaches are required to contend with the amount of incoming connected vehicles and traveller data. It can enable the development of a wide range of strategies associated with offering safety, environmental and mobility benefits. Additionally, use of big data can play an important role in the reduction of mechanisms underlying traditional data collection such as traffic detectors that can be replaced by the connected vehicle probes. Certainly, big data has an important role to play in enabling connected vehicle application. There are several anticipated connected vehicle applications that are capable of providing direct as well as societal benefits to the operating agencies. Specifically mentioning, the data created from connected vehicles such as basic safety messages will be requiring huge amount of storage that can be provided through big data. Additionally, there are a number of benefits that can be highlighted with respect to the use of connected vehicle applications through big data. Such benefits are considered as providing social, organizational or behavioural benefits pertaining to big data, such as avoidance of cleanup costs and crash response owing to lower level of accidents and reduction in work-zone accidents (Hepler, 2014).
Similarly, Kitchin (2013) stated that lower costs pertaining to detection of pavement condition and lower level of expenditures with respect to traveller information system and traffic mentoring can be considered as some of the other benefits associated with use of big data. Such benefits can further result in creating many effective and proactive responses towards enhancing mobility and safety in transportation. Traveller – centred transportation strategies focus towards analyzing connected traveller data in order to preparing detailed profiles of behaviours of individual travellers, utilizing the profiles towards analyzing traveller information along with implementation of the strategies through establishing direct communication with individual travellers pertaining to location specific information and making use of handheld devices to gather traveller data. Such techniques underlying use of big data play an important role in offering safety, environmental as well as mobility benefits through spreading of demand in space and time towards enhancing the use of available system capacity (Kitchin, 2013).
As per the facts published by VTT (2014), in the real world scenario, big data has been used in transportation mobility sector to a limited extent. However, some of the examples include Integrated Corridor Management (ICM) in Dallas and fleet connected vehicle projects undertaken by Michigan Department of Transportation agency. Additionally, it must be noted that big data can facilitate transportation systems through focusing on a number of ways that incorporate the importance underlying resolving data, examining the utilization of third-party data broker, developments underlying data standards, lowering the volume of connected vehicles and making use of specific technologies such as federated database systems and crowdsourcing that are capable of ensuring facilitation of transportation operators towards enabling them to extract value from traveller data (VTT, 2014).
Neumann (2015) further suggests that big data can further play an important role in gathering information pertaining to travel demand, traffic events, dynamic traffic conditions, environmental data and data pertaining to transportation infrastructure. Big data has an important role to play with respect to encouraging reliability, enhancing the level of efficiency as well as forecasting. Big data has already been adopted in transportation systems cross several nations. For instance, Israel launched fast lane wherein there is a toll system associated with calculating fee with regards to traffic. In order to manage aviation traffic, Brazil recently introduced system associated with making use of GPS data in order to maximize the utilization pertaining to airspace and enabling lower separation amidst aircraft routes. In Europe, providers of railway infrastructure make use of industrialized approach that aims at distributing track capacity in various slots associated with differing speed profiles with the help of big data (Neumann, 2015).
With respect to transportation systems, UCL (2016) noted that big data offers people specifically belonging to the disadvantaged communities with several accessibility benefits towards fostering social equity. Herein, there is a need to new technologies along with shifts in the open data are playing an important role in the utilization of big data towards evaluating infrastructure and services along with gaining a better understanding of the transportation needs of the disadvantaged communities. At present, big data has been largely applied in informing the decision of transport users, planning pertaining to public transport systems, asset maintenance and management of road traffic (UCL, 2016).
According to the U.S. Department of Transportation (2014), technological developments have played an important role in the use of big data in mobility services that include the use of smart travel cards, location tracking through GPS, sensor equipment along with mobile networks. Social media further provides high amount of readily available information pertaining to people behaviour and their activities that is required for planning and operations. Furthermore, there is a need to note that big data enables maintenance of assets such as rail and road networks through recognizing problems and reducing costs. For instance, use of GPS tracking and mobile sensors are capable of reducing costs as well provide higher benefits. Big data further plays an impeccable role in management of road traffic with the help of using CCTV, traffic sensors and traffic cameras towards offering real-time information of traffic conditions. Use of big data further enables gaining real-time traffic data through media channels. Commendably, big data is used in logistics and freight as part of enhancing performance and delivery towards increasing mileage and lowering carbon emissions (U.S. Department of Transportation, 2014).
From a similar perspective, Katal, Wazid and Goudar (2013) state that big data further enables planning and operations since an assessment of the public transport data can play an important role in assessing the journey patterns of people belonging to disadvantaged communities. This can be analyzed with the help of gathering information pertaining to locations that are travelled by the users, the preferred mode of transportation, reliability of the journey and frequency of travel. Such insights can enable planning low mobility services. An assessment of the public transport data further offers an understanding of the manner in which people belonging to disadvantaged communities make use of the transportation system, thereby offering a better understanding of their preferences. Use of data visualisation as part of big data can enable mapping passengers’ travel routes, apart from informing about operational and planning decisions pertaining to services and explaining transport networks to passengers in a more visual manner (Katal, Wazid and Goudar, 2013).
3.1. Research Design
The study is mainly paid attention towards evaluating the important of big urban data in managing the various operational aspects that help the company in developing the new projects like the smart city. The study will also highlight how the big data or urban computing is helpful in supporting the modern urbanisation progress in a rapid manner. The study with the help of methodology will evaluate the usage process of urban computing and big data for managing the entire process of establishing the smart city. For presenting a proper justification or rationale with satisfying the actual objectives of the study in a systematic way, the study has conducted a literature review session. By conducting the entire research process in a systematic manner with the association of proper methodology, the study mainly represents a significant conclusion. It will also notify that to conduct a literature review session has also proved to provide the study with empirical evidence.
The research design is mainly recognised as a blue print of the study that mainly enables the various research processes to represents in a proper manner. In the process of research methodology, the research design has most of the time played the role of a frame work that always influences the entire study to represent in a professional manner. Basically, in this particular research process, the design needs to be perfect for representing the entire plan in an effective manner. In a simple word, this particular research proposal is mainly focused towards identifying the usefulness of big data in developing the new trend in the urbanisation. Apart from this, actual importance of this particular research is to identify the actual techniques of managing the big data and utilise it in a proper manner for enhancing the development process. Therefore, to identify the actual objectives of the research study, the study has selected the descriptive research design ensuring an effective as well as successful completion of the above-mentioned process. The adoption descriptive research design has been proved to be helpful for this particular research process and encourages for identifying the actual results of the selected topic. Besides, the descriptive research design has also noted to be helpful in identifying the human perception regarding the above-mentioned subject matter (Creswell, 2013).
The research approach is recognised as an important part of the research methodology that primarily responsible for managing the entire research process. With the help of proper research approach, the entire methodology is mainly helpful in managing the actual objective of the study. Therefore, it could be stated that the adoption of appropriate research approach is highly significant for managing the entire research process and helps to attain success significantly. Basically, in the research methodology section, the research approach is considered as a plan (Ng, et. al., 2004).
In this particular research study, the qualitative research approach has been selected for successfully completing the entire project. The qualitative research approach is quite different from quantitative research approach. It is one type of exploratory research that helps to gain understanding regarding human opinion as well as motivation. This is a process that mainly based on the secondary collection data and represents it in the narrative format. Also, the main objective of using this type of research approach is to evaluate the actual reason of the research objective along with human opinion (Creswell, 2013).
In this particular research study, the qualitative research approach has been selected for evaluating its actual objective. The adoption of qualitative research approach it noted to be suitable for evaluating and concluding the actual conclusion of the study. In this research process, the primary topic is associated with the use of big data and its utilisation for developing the urbanisation. With the help of qualitative research approach, the actual importance of big data and urban computing system has been identified in a significant manner.
The qualitative research approach is efficient in representing the data in a story telling process. The story telling process is one type of narrative approach that helps the study to highlights its positive impact on its objective. During the process of literature review session, the qualitative research approach paid main attention towards evaluating the importance and usefulness of the urban big data concept. Furthermore, it also evaluates how the big data can make a positive impact on the construction of smart city (Bryman, 2015).
The data collection method is considered as one of the most important aspects that could enable the research process to be successful in a systematic manner. In this particular research study, the data collection process has been performed in a systematic manner. The study is adopting the qualitative research approach with the help of secondary research data. The adoption of secondary data has been proved to be helpful in drawing an effective conclusion. To conduct the literature review session in a successful manner, the secondary sources have been selected. The secondary data has been gathered by accessing the academic journals as well as books will be used for providing proper justification to the abovementioned question (Bernard, 2011).
Ethical consideration is an integral part of the research process. Recently, in every research process, it is mandatory to maintain the ethical considerations as an inseparable part of the mythology research system. In this particular research process, the ethical consideration has been maintained throughout the study. To develop a successful research, the research proposal should maintain an ethical consideration that mainly depicted about its future authenticity of the research. While conducting the interview session, the confidentiality reliability as well as the validity of the data has been maintained (Schlesinger & Heskett, 1994).
The reliability, as well as validity of the study both, is important for managing the future authentication of this particular research process. The data has been collected from the credible sources for evaluating the empirical evidence systematic manner. It is important for this research process to maintain credibility by collecting data from authentic sources. Therefore, the secondary data that has been collected for this particular research has not been manipulated while conducting the literature review (Creswell, 2013).
Big data is one kind of technological approach that has gain immense prominences within the field of modern business environment. It has also notified that advent of data collection process is noted to be quite important for performing various operational performances within that particular organisation. Basically, big data is also defined as large sets of data that mainly required the assistances of technological supports for performing in a successful manner. Most of the time the big data is also required the support of architectural strategies for evaluating the actual results in the practical life. Basically, it has been used for capturing as well as analysing the large sets of data for enhancing new technical approaches.
In the modern scenario, big data and big data related application has been played an important role for the development of new infrastructure within the society. As per the technical approach the big data has become one of the new strategic assets that enhance the new way of network as well as communication. With helps of advent of big data there are various benefits has been experiences by various users. With the help of this particular application corporate data base, cell phone GPS signals as well as climate sensor has been enjoying by various people. The big data has also influences the social network communication by making a network for various users. In this particular case, data collected from the vast as well as varied sources are been collected and represented in a significant manner.
Now a days the government as well as their governments have planned to paid utmost attention towards developing the smart city. The development of smart city will also enhance the urbanisation concept by adopting the big data and its efficiency. The adoption of big data is not only supported the transportation system but it also created an effective network system for managing the large size of data through the technological supports. The USA is considered to be the first country, which has adopted the concept of smart city with help of big data. In USA the adoption of big data has been largely applied for enhancing their urbanisation system. It mainly contributes in the transport users for planning the public transport systems, asset maintenance such as allocation of house and government land as well as management of road traffic.
The big data application has been adopted by various business institutions for ensuring an exponential growth in business process. It helps the data volumes to get managed easily as well as stored with effective manner. Big data has process the data efficiently as well as provide the accurate information to its various users. It has the proper ability to analyze the entire process in a proper manner for highlighting the positive approach in the practice result. Initially, the big data has been used as an analytical tool for experimenting several new things. Currently, it not only performing as an analytical tool but it also provides various suggestions to its users regarding the future implications of the process. By adopting the big data concept various companies are been able to attaining real results for enhancing their operational process.
Recently, the increasing rate of congestion in the various transportation mode has influences the entire system to adopt new technological approach for reducing various complexities. Apart from this bog data has also showed various beneficial moves for the company while performing their operational process. The adoption of big data is quite beneficial as well as cost effective for the various companies. As it is a cloud based analytical approach, therefore it mainly represents a cost advantages for its users. Apart from this, big data is also useful for managing the entire data management process efficiently for performing a better operational process. There are large size organisations, which recently seeking for adopting big data concept for managing the faster as well as better decision making process.
In the recent scenario, the bog data has been playing an important role for enhancing the logistics department management criteria efficiently. Basically, it could be stated that with the proper application of big data concept majority of the company will be able to manage their operational performances efficiently. In order to sustain within high competitive market structure, the development of the new operational process is considered to be quite efficient. There are various companies which are recently gained huge success in their operational process after adopting the big data concept. The urban big data in Amazon, Face book, Google as well as various social media has influences the better management of the capital as well as resource management.
Big data has also played an important performance in the modern transportation services that could make an effective connection between various places. In the domain of urbanisation, the big data has also played the role of architectural as well as technological supports for development a smart city efficiently. Therefore, to develop the concept of smart city, the adoption of big data has played an important role. In developing a smart city the transportation procedures should be effective. Therefore, the application of big data for measuring the distance of various locations for evaluating the movement of vehicles from one place to another is important. The identification of this particular information associated with transportation is signogfiucnat for the environmental approach as it provides proper information regarding pollution (Neirotti, et. al., 2014).
By indentifying those issues the new strategies regarding its proper management is important. It also enhances the low carbon mobility services among various locations within the smart city urbanisation process. Recently, technological developments have played an important role in developing the proper urbanisation process through the application of big data. In transportation system the low carbon mobility services has been adoption in the logistics process, which includes the use of smart travel cards. The implication of location tracking system through GPS sensor is considered to be another major instance of the big data application.
Big data refers to the huge amount of data that is required to analyse and store the information towards ensuring that the processes are carried out in the most appropriate manner. Specifically mentioning, big data is associated with offering a number of benefits in the transportation and mobile low carbon utility services. Herein, there is a need to note that use of big data enables facilitating resource-efficient transport concerned with respecting the environment, better door-to-door mobility, lower amount of unforeseen delays, lower congestion, reliable as well as safer multi-modal mobility, exchange of personal information in a secured manner, creating global leaderships in transport industry along with social, economic and behavioural developments pertaining to the individuals belonging to disadvantaged communities (W3C, 2015). Use of big data is considered as providing benefits to both the public as well as private sectors (Tufte & Platman, 2015). Big data further enables creation of plot towards determination of speed and volume pertaining to freeway traffic sensors, as can be noted from the diagram below:
In case of public sectors, big data fosters transportation and low carbon mobility services through enabling traffic control, congestion management, logistics and route planning. Correspondingly, big data is used by private sector such as in the case of travel industry, gaining competitive advantages, logistics and route planning. It further enables the individuals to plan their routes and travel (Marr, 2015). Big data has an important role to play in determining the manner in which smart cities are able to accomplish their transportation targets, towards providing an understanding of the manner in which smart cities make use of ICT towards facilitating transportation networks (Oracle, 2015). Herein, there is a need to note that use of big data enables enhancing multi-source traffic, apart from availability and processing of travel data along with the use of techniques that are directed towards increasing travel data fusion and multi-source traffic, such as in the case of enhanced mobility management. It further provides certain socio-economic benefits along with leveraging on innovation (IMSC, 2016). In this regard, the diagram below provides an understanding of the data gathering with the help of big data for moving objects, traffic sensor data, road network data and points-of-interest data.
Big data further plays an important role in traffic management, since it offers new insights pertaining to real time traffic data and traffic patterns. Hence, big data is capable of analysing social equity, spatial disparities and accessibility with respect to disadvantaged communities with the help of analyzing the journey patterns of individuals, providing information to the transport agencies regarding the manner in which such individuals make use of the public transportation systems. In addition to asset maintenance, big data is further considered beneficial for evaluating performance of public transport (Delgado, 2014). For instance, Excess Waiting Time (EWT) is used as the KPIs in big data to assess the performance of transport companies. Additionally, such techniques are capable of providing information regarding causes pertaining to dwell time at bottlenecks, key bottlenecks, worst performing routes along with improvement margins in time table. Specifically mentioning, social media within big data plays a vital role in transportation since it is associated with offering inbound, outbound and continuous information. Big data enables Real-time Traffic Management that offers an understanding of the warnings, predictions underlying traffic flow and lane handling, amongst others (Tufte & Platman, 2015). The use of big data in measurement of travel speed performance can be further understood from the diagram depicted below:
The positive implications of big data and the Internet of Things (IoT) have further played a commendable role in development of Transport for London (TfL). Specifically mentioning, use of big data has enabled focusing on bicycle-sharing, car-sharing, parking-sharing, electric vehicle charging infrastructure and autonomous mobility-on-demand systems. To be specific, data is gathered with the help of ticketing systems, focus groups, traffic signals and social media. The reasons behind gathering huge amount of data can be attributed with offering information to customers and development of planning services (Nemschoff, 2014). Transport for London (TfL) has made use of Oyster prepaid travel cards, wherein, passengers can travel in transportation systems through converting real money to Transport for London money that enable gaining access to trains and buses. This further enables gathering huge data for precise journeys towards producing journey maps that reveals information pertaining to where and when people travel. Similarly, the London City Dashboard makes use of big data towards providing information on tube line status, air pollution, weather and stocks, amongst others (Kitchin, 2014). The diagram depicted below provides an overview of the dashboard.
In addition, big data enables transportation experts to develop an understanding of selection of routes by individuals and preference of mode of transportation. One such example is the tram system of Melbourne in Australia that makes use of big data to reconfigure routes towards overcoming challenges such as natural disaster (Kanniyappan & McQueen, 2014). IoT and big data have together played an important role in fostering public transportation through use of sensors in enhancing efficiency and evaluating problems. Smart Santander RA augmented reality app is one of the vital applications that is based on use of big data towards providing travellers with information regarding the city, travel routes and bus timings (Kitchin, 2014). The diagram below provides an overview of the application.
Big data has further been largely used in the transportation system in Sau Paulo, Brazil towards managing bus fleet. Use of big data has enabled gathering real-time information on number of people utilizing bus services, route timings and driver information (Rijmenam, 2016). This enables the transportation systems to enhance their operations, determining efficient routes and meeting with vehicle demands. Big data has further enabled ensuring the establishment of an efficient transportation system that is considered vital for the development of economy through enhancing the level of operational efficiency, lowering costs of fuel, enhancing customer experiences and providing service offerings (Çolak, Alexander, Alvim, Mehndiratta & González, 2015). Notably, smaller disadvantaged communities that had no access to data in the past are increasingly focusing on the implementation of sensors and other mechanisms of data collection. Additionally, big data provides the benefit of gaining a better understanding of the temporal, daily as well as seasonal variations pertaining to travel behaviour that can enable development of customized strategies for managing transportation. Big data further enables designing traffic speed maps, based on the data collected through sensors (Tufte & Platman, 2015). The diagram below provides an overview of the data pertaining to traffic speed maps.
Transportation Decision-Making (TransDec) is an important real-world system that enables gathering information pertaining to real-time traffic sensor data, along with conducting an assessment of the visualization pertaining to dynamic transportation systems. Developments underlying wireless technologies and sensors have resulted in development of Intelligent Transportation Systems (ITS) that facilitate managing transportation systems, effective monitoring and decision-making (IMSC, 2016). In this regard, the diagram below provides an understanding of the data analysis in transportation systems using big data.
An assessment of the diagram depicted above suggests that the TransDec encompasses three-tier architecture including presentation, query-interface and data tier towards creation of customized spatiotemporal query based on an interactive mapping interface. This enables effective management of complex and dynamic transportation data and reduces queries pertaining to transportation networks (IMSC, 2016).
6. Conclusion & Recommendations
Big data has a considerable level of impact on the social, organisational or behavioural factors affecting low carbon mobility services. In this regard, the study provided an understanding of the manner in which big data can be used along with Internet-of-Things and associated data science methods to analyse social equity, spatial disparities and accessibility for disadvantaged communities. Additionally, the study further elaborated on the use of big data in drawing implications for planning and operations. Correspondingly, the study highlighted several facts suggesting that big data has an important role to play in determining effective strategies for shared, intermodal, active and public transport such as bicycle-sharing, car-sharing, parking-sharing, electric vehicle charging infrastructure and autonomous mobility-on-demand systems. An assessment of the reviews obtained from literatures suggest that big data ion transportation systems enables the development of a wide range of strategies associated with offering safety, environmental and mobility benefits. In addition, big data enables the reduction of mechanisms underlying traditional data collection, apart from enabling connected vehicle application.
The study further provided an understanding of the fact that big data plays an important role in the avoidance of clean-up costs and crash response. On a similar note, lower level of expenditures on traveller information system and traffic mentoring, mobility and safety in transportation were some of the other advantages underlying big data that were highlighted during the study. Witgh respect to examples, it must be noted that Integrated Corridor Management (ICM) in Dallas and fleet connected vehicle projects undertaken by Michigan Department of Transportation agency are some of the areas wherein big data has been effectively used. The study highlighted that big data can foster shared, intermodal and public transportation through resolving data, developments underlying data standards, lowering the volume of connected vehicles, examining the utilization of third-party data broker and making use of specific technologies like federated database systems and crowdsourcing. The study further suggested that big data plays an important role in determining travel demand, traffic events, dynamic traffic conditions, environmental data and data associated with transportation infrastructure.
In addition, the study further provided an understanding of various examples associated with highlighting the use of big data in managing transportation system that have been adopted by several nations. To be specific, some of the examples include launch of fast lane wherein there is a toll system associated with calculating fee with regards to traffic in Israel. The study further noted the example of Brazil that recently introduced system associated with making use of GPS data in order to manage aviation traffic, maximize the utilization pertaining to airspace and enabling lower separation amidst aircraft routes. Furthermore, examples pertaining to providers of railway infrastructure in Europe were further noted that was associated with making use of industrialized approach that aims at distributing track capacity in various slots associated with differing speed profiles with the help of big data. The study further outlined that big data is applied in asset maintenance, planning underlying public transport systems informing decisions pertaining to transport users and management of road traffic. Use of sensors, GPS services, CCTV, traffic sensors and traffic cameras offer real-time information of traffic conditions, which can be considered as another significant benefit underlying big data in transportation systems.
The study noted that big data enables planning and operations based on an assessment of the public transport data that can play an important role in analyzing the journey patterns of people belonging to disadvantaged communities. For instance, due emphasis is laid on gathering information relating to locations travelled by the users, reliability of the journey, preferred mode of transportation and frequency of travel, amongst others. Moreover, findings obtained from the study further suggested that use of big data enables gaining several benefits such as lower congestion, lower amount of unforeseen delays, better door-to-door mobility, exchange of personal information in a secured manner, creating global leaderships in transport industry, reliable as well as safer multi-modal mobility, along with social, economic and behavioural developments pertaining to the individuals belonging to disadvantaged communities.
Based on the facts obtained from the study, a number of recommendations can be made. One such recommendation can be made with respect to use of both qualitative as well as quantitative data analysis techniques in future researches conducted on a similar topic. Moreover, studies can be conducted through inclusion of primary sources of data collection that would encompass gathering information from travellers, disadvantaged communities or employees associated with transportation systems. Moreover, recommendations can be made with regards to development and increase in the use of big data towards managing transportation systems, which can include incorporating stakeholders such as detection algorithm developers, transportation modellers, transportation operations managers, transportation planners, asset managers, transport agency mapping stakeholders along with experts. In addition, identification and association of various programs based on big data use such as Connected Vehicle Program, environmental and road weather programs along with the use of Dynamic Mobility Applications can further enable enhancing the application of big data towards fostering low carbon mobility services in transportation systems.
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