6.3. The Co-operative ITS Paradigm
Computing advances have enabled a form of “ubiquitous” communication technology to enhance connectivity among information systems and roads, vehicles and drivers through so-called C-ITS. The International Organization for Standardization (ISO) defines C-ITS as a subset of overall ITS that communicates and shares information between ITS stations to give advice or facilitate actions with the objective of improving safety, sustainability, efficiency and comfort beyond the scope of stand-alone systems.
These next-generation systems use wireless communication to enable constant communications between vehicles and between vehicles and roadside infrastructure. C-ITS can, for example, inform a driver of potential hazards like roadwork or icy surfaces ahead through messages sent either by vehicles (such as incoming vehicles at an intersection) or through the infrastructure. Ultimately, wireless communication technologies are to be installed in vehicles for a networking system designed to maximize efficiency, safety and comfort for passengers. In effect, C-ITS represent an extension of conventional ITS and achieves a greater level of comfort and safety for vehicle users through the provision of real-time information. For example, major vehicle manufacturers including BMW, Chrysler, Fiat, and Volkswagen have organized an industrial association, the Car-to-Car Communication Consortium (C2C-CC), devoted to improving transport safety and efficiency through the application of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) modalities. The consortium aims to establish industrial standards for inter-vehicular communication in Europe from 2015 onwards, with specific technologies covered under this designation after a verification process. Technologies on the agenda for this vehicle-centred ITS includes a warning system for forward collision to protect passengers, route guidance for avoiding congestion and reducing fuel consumption, and inter-vehicular communication links.
6.4. Advanced Transportation Management Systems
Advanced Transportation Management Systems (ATMS) operate with infrastructure fitted with vehicle detection systems, automatic vehicle identification and CCTV to allow for real-time traffic data to be both sent and delivered through various service devices or facilities including Variable Message Sign (VMS), the World Wide Web or mobile devices. The data are transformed into various ITS services including Real-time Traffic Information, BIS and ETCS. The diffusion of such information helps alleviate a range of environmental and congestion problems. In the case of South East Asia, most mega-cities have installed such ATMS technologies as traffic signals, CCTV and VMS in order to optimize their current transportation infrastructures. An interesting illustration is system recently tested in Bangkok (Thailand), which combines sensors fitted on the highways with probabilistic modelling to evaluate the impact of small changes in traffic patterns, and take remedial actions.
7.0. Integrating Cloud Computing for IoT-based Intelligent Transport System
Currently new technology approaches are changing the world in terms of how the people can feel or sense what is happening. As regard of this and focusing on the transportation sector, the inhabitants of whatever country wish to know what is happening on the highways, even when they are travelling, in order to be able to take decisions in their routing and timing. There are several systems allow us knowing about the highways and its conditions, a well-known system is waze. However, waze requires a great amount of memory and phone hardware resources and many people do not have the kind of phone terminals that can support the requirements. In this paper, we present a new way of monitoring taking advantage of an Internet of Things (IoT) approach in order to cooperate with the Intelligent Transportation Systems (ITS).
Iot extends internet of computers to include objects or things in real world & to access them to enable ubiquitous computing. The Internet of Things (IoT) is the network of physical objects, devices, vehicles, buildings and other items which are embedded with electronics, software, sensors, and network connectivity, which enables these objects to collect and exchange data. The Internet of Things allows objects to be sensed and controlled remotely across existing network infrastructure, creating opportunities for more-direct integration between the physical world and computer-based systems, and resulting in improved efficiency, accuracy and economic benefit; when IoT is augmented with sensors and actuators, the technology becomes an instance of the more general class of cyber-physical systems, which also encompasses technologies such as smart grids, smart homes, intelligent transportation and smart cities. Each thing is uniquely identifiable through its embedded computing system but is able to interoperate within the existing Internet infrastructure.
8.0. What Roles Will Connected Transportation Play in Connected Cities?
This section identifies two trends that are emerging as the various sectors within a smart/connected city interact.
Connected Vehicles as Nodes in the Internet of Things: Connected vehicles of all sorts will connect numerous other data-driven systems in connected cities, presenting the possibility of new systemic efficiencies but also new system risks as well.
Mobility as a Service : The option for travelers to use multimodal mobility services in real-time-such as transit, on-demand taxis and vehicle- and ride-sharing services-to achieve the same or better mobility traditionally associated with owning a vehicle is poised to revolutionize transportation operations and planning. Although there are many other trends that will shape the future of connected transportation in smart/connected cities, these trends appear at this point to be among the most powerful in terms of cross-sector linkages.
8.1. Connected Vehicles as Nodes in the Internet of Things
To call connected vehicles nodes in the Internet of Things is to recognize that the infrastructure of a smart and connected city is increasingly a system of systems, or a network of networks, where the networks are composed of nodes in communication with each other. Transportation systems interface with employment, residential, healthcare, utility, and city services systems, and the interface is both physical and in terms of data.
The integration of these data systems offers real promise for congestion management. In a vehicle-to-infrastructure (V2I) or vehicle-to-vehicle (V2V) system, connected vehicles will continuously broadcast location, speed, and other data. This will give traffic management systems real-time data on traffic conditions that are far more detailed and accurate than data available today. Add connected travelers equipped with Smartphone’s to the mix, and the potential for predicting and influencing travel behavior expands dramatically.
What if currently available smart home technology, which allows a house’s energy management system to know which rooms are occupied-the bedroom versus the kitchen, as a commuter heads out to work-could provide new ways to predict travel demand by time, mode and route?
What if transit operators could provide targeted instant electronic coupons to riders, tailored to their buying habits and personal profiles AND their transportation choices? The couponing system could drive increased ridership. The local transit agency in Montreal has begun to do just that. By pairing real-time location of bus riders with a profile of their commercial activities, the agency is able to provide instant e-coupons as rewards for riding the bus. Retailers boost sales while the agency pursues a 40 percent increase in transit ridership (Winterford, 2014; Murphy, 2013).
As vehicles become nodes in the Internet of Things, not only the capacity and efficiency of the transportation network stand to benefit; situational awareness gained from communicating sensor networks has the potential to improve safety. For example, data from vehicles’ external temperature sensors (common on today’s cars), along with the vehicles’ real-time location data, could help an operations center monitor the potential for ice on roadways at a much more granular level than is possible today. Such temperature data, combined with information on vehicle wheel slippage (as indicated by traction control systems or anti-lock brake systems being activated), would alert the operations center about the precise location of ice. The operations center could, in turn, direct deicing equipment to the exact location, and simultaneously issue an ice warning and reduced speed advisory to every vehicle upstream of the ice to reduce the likelihood of a crash.
Emerging intelligent transportation infrastructure can address transportation issues beyond congestion and safety. For example, several cities, including Los Angeles and San Francisco, are currently pilot-testing smart parking systems. These systems use networked sensors in the pavement at each parking space to monitor occupancy, and networked parking meters. Smartphone apps indicate the locations of open parking spaces to reduce the time drivers spend cruising. Such cruising constitutes a significant portion of the traffic in many downtown areas. Other benefits include the ability for cities to implement variable-rate, demand-responsive parking rates. In San Francisco’s system, parking rates are adjusted monthly, and vary from $.25 to $6.00 per hour depending on parking demand.
Adding connected vehicles and connected travelers to the mix will exacerbate those issues. Ensuring interoperability-allowing connected vehicles to send and receive data with the rest of the IoT-will be critical to realize benefits from integration, of course. Finally, systemic risks may arise, with increasingly interconnected data systems creating new possibilities for cascading failures. Thankfully, there are efforts ongoing in many of these areas. The USDOT Connected Vehicle Policy Program is analyzing and developing privacy- and security-preserving options for connected vehicle technology. The USDOT is participating in machine-to-machine working groups to help ensure interoperability. On the private sector side, IBM and Cisco are collaborating on a set of device-to-device standards while competing teams are developing other standards (Clancy, 2013; Murphy, 2012).
Vehicles are already being sold with the capabilities to transmit data to one another and become “nodes” in the IoT. GM’s OnStar was one of the first forays into connected vehicle technologies and telematics, providing users with collision detection, automatic alerts, and remote door management. Many companies provide similar services, such as LoJack, Security Plus, Car Shield, and most if not all automotive manufacturers. Infrastructure technology is being demonstrated through pilot testing and will soon be proven and available for broad nationwide deployment, thus adding additional nodes. The transformation within the smart/connected city will come when these nodes are integrated and communicate with other systems in the city for mutual benefit.
8.2. Mobility as a Service
Advances in vehicle and communication technology, including connected and automated vehicles, are transforming the very concept of mobility where the emphasis is shifting to efficiently reaching destinations rather than committing to a particular transportation mode (UK Autompbil Council, 2012). Increasingly, mobility will be experienced as a just-in-time service rather than owned as an asset, mirroring the embrace of software-as-a-service by many companies over the last decade, where they effectively rent data and software services via the cloud rather than purchase and manage the capability in house (Software as a Service.” (http://www.ibm.com/cloud-computing/us/en/saas.html). In the mobility space, this shift has been toward what is called “mobility on demand” and “mobility as a service” (Weiller, 2012).
Bill Ford, executive chairman of the Ford Motor Company, describes his vision for the future of mobility as “smart roads, even smarter public transport, and going green like never before” (Weiller, 2012). With ongoing increases in the number of cars and urbanization, he predicts “global gridlock,” and he calls for “An integrated system that uses real-time data to optimize personal mobility on a massive scale, without hassle or compromises for travelers.” Ford points to examples from Masdar in the United Arab Emirates, where driverless pod cars shuttle people around; Hong Kong, where the Octopus fare system integrates all transit modes; and of car sharing throughout the world. Ford, BMW, and other automakers are increasingly labeling themselves as mobility service providers rather than as car makers, getting into new mobility business models such as car sharing, and providing mobility on-demand solutions. Fleet companies are now launching corporate mobility-sharing services, even packaging Vespa-type scooters (Hiner, 2012). Whether one’s mobility service is provided by a car company, a rail or transit operator, airline, or bike share, seamless multimodality means on-demand access to the most immediately appropriate combination of transportation options as and when one chooses (Edwards, 2013). According to the World Economic Forum, consumers will increasingly seek such customized and seamless solutions to their transportation and connectivity needs in one single smart phone-type device, along with intermodal terminals and more connected networks.
The asset-to-service mobility shift supports and is supported in turn by three trends: the accelerating growth of:
- Modal integration through transparent back-office data exchange
- New mobility models such as vehicle- and ride-sharing, and
- Smartphone apps that access open data.
- The next sections discuss these trends.
8.2.1. Modal Integration
A key component of the mobility as a service vision is the ability to seamlessly access all transportation services from any origin to any destination in the most efficient way possible. As a result of growing demand for seamless multimodality, established businesses in the transportation sector are rushing to adapt. IT behemoths such as Google are entering the space, and new startups are appearing with disruptive ideas. INRIX’s Transport Protocol Experts Group (TPEG) holds that no one transportation mode can be a solution for rising pollution and congestion in an urbanizing world; rather, the solution lies in integration of multiple modes of transport.
8.2.2. Vehicle Sharing
According to Kent Larson, co-director of the MIT City Science Initiative, mobility as a service implies shared-use systems. Consuming anything as a service necessarily means that users share the assets, whether in space or time. Intelligent mobility (or accessibility) integrates mass transit and ride-sharing, in which multiple users share vehicles at the same time, and car and bike sharing, in which multiple users share the same vehicles at different times. Shared-use systems, like cloud computing or P2P (peer-to-peer) hoteling, mean that fewer assets are needed to meet the needs of an urbanizing population increasingly choosing to “drive light” (Maynerd, 2013). John Markoff, a technology journalist, compares today’s situation in transportation to the historic transition from the mainframe to the personal computer. “If we only need 20 percent of the cars we have,” Markoff notes, “There are some really disruptive things that are going to happen” (Keen, 2013).
For people to be willing to share assets there must be a seamless, low-friction way to do so. The advent of the Internet and wireless networks made the vehicle-sharing business model possible around 2000, according to Roy Russell, the founding CTO of Zipcar. Easy access to vehicles depends on Internet-based reservations that can be made anywhere, on wireless networks that transmit reservation information to the car-share vehicles, and on low-cost RFID key technology. A vehicle-sharing business simply could not have been built in the 1990s (Russel, 2013). Since that time, the availability of smart phones, apps, and GPS has continued enabling ever more sophisticated and easy-to-use business models for vehicle sharing by making it even easier and more frictionless to use.
8.3. Emerging Transportation Modes
In addition to sharing existing transportation modes as discussed above, industry groups and observers foresee new modes entering the mix. Citing forecasts that 60 percent of vehicles by 2017 will be connected vehicles and that there will be 5 billion Smartphone users by 2020, Ryan Chin of MIT asserts that an Autonomous Mobility-on-Demand (A-MoD) network of driverless “swarm cars” will work in conjunction with public transit systems to solve the complete accessibility and mobility puzzle, including the first-mile/last-mile challenge of bridging the gap between transit stops and final destinations. Mirroring similar predictions by the World Economic Forum, this network of automated vehicles would essentially act as driverless taxis (Chin, 2013). Intelligent demand predictions would steer cars to where they are needed, with idle time used for last-mile logistics services or at wireless charging stations.
Other possible new transportation options that may penetrate the mix within 10–25 years include Toyota’s version of the Segway personal mobility device, the Winglet (Strong, 2013). In the air, logistics drones and smaller commercial heli-drones may begin to make deliveries within congested megacities. These unmanned aerial vehicles (UAVs) could become integral parts of our freight and logistics networks in the future (Mitchell et al, 2010). Indeed, the Federal Aviation Administration is likely to open up domestic airspace to large drones by 2015 and expects 10,000 unmanned commercial aircraft to be operating by 2017.
Given the possibility of 2.5 billion cars on the planet by 2050, Ryan Chin emphasizes that sustainably low-energy transportation will require more walking, biking, and smarter mass transit, and not mass motorization. What is also clear is that online retailing will continue to grow and therefore displace personal travel to stores. E-commerce will exceed 20 percent of U.S. retail sales by 2020 and 30 percent by 2025, leading to increasing urban freight volumes and increasing the pressure to develop seamless, last-mile urban-delivery solutions.
8.3.1. Mobility Apps
Following Google’s purchase of Waze, there is significant attention paid to real-time, Smartphone-based transportation management schemes. Investment in intelligent traffic management systems, part of the larger connected cities movement, is growing. Smartphone with GPS location awareness have made new transportation businesses possible and have accelerated the shift to mobility as a just-in-time service rather than a fixed-cost asset. At the same time, growing amounts of data produced and transmitted by the onboard computers in connected and automated vehicles will soon feed larger quantities of real-time data into mobility apps.
The most visible manifestation of mobility apps are the ride-sharing platforms, such as Sidecar and Lyft. But the extent and impact of ubiquitous, real-time, location-aware navigation and ticketing at the tap of a touch screen extends much farther. In addition to P2P services, established taxis can now be easily requested via Smartphone, increasing their utilization and reducing their empty cruise time (Sawers, 2012). Even for people who are not accustomed to using non-automobile transportation modes, the emergence of multimodal navigation apps (and eventually ticketing apps) makes it is easy to figure out how to combine once obscure transit networks and bicycle routes. Hundreds of Smartphone apps now use GPS data to indicate in real-time which bus or subway a traveler should choose when it will arrive, where to get on, where to get off, and other information to make the trip increasingly effortless.
Even companies that have traditionally identified themselves as automakers are entering the multimodal mobility app arena. BMW has launched a series of urban mobility services under the umbrella of “BMW Mobility Services,” including an app called Embark that has real-time information on 12 major public transit systems in the U.S. and the United Kingdom (“Intelligent Solutions for Everyday Life on the Move: BMW Mobility Services.” (http://www.bmw.com/com/en/insights/corporation/bmwi/mobility_services.html.). Daimler has launched an app in Germany called “moovel,” which shows the various options for bus and rail connections, ride-sharing opportunities and a taxicab call function similar to the app Hailo.
8.4. Real-time Parking Management / Multi-level Parking
Real-time parking management systems also provide the information related to available parking lots through a publically displayed electronic sign board. This facility is useful for the staff of parking lots as well as the end-users. Multi level parking benefits with minimal land use, easy entry and exit, multi sensors and safety devices offers low operating and maintenance expenses (shown in these diagrams).
9.0. Development of an Agent–based Intelligent Traffic Information System (ITIS)
There are a large number of heterogonous devices within the traffic monitoring system using IoT. Among challenges of full deployment IoT is making complete interoperability of these heterogeneous interconnected devices which require adaptation and autonomous behavior. The major issue in IoT is the interoperability between different standards, data formats, heterogeneous hardware, protocols, resources types, software and database systems. Another issue is necessity of an intelligent interface and access to various services and applications. It seems that mobile agents are a convenient tool to handle these issues, provide means for communication among such devices and handle the IoT interoperability. Adding to that mobile agent is a perfect choice in cases of disconnection or low bandwidth, passing messages across networks to undefined destination and to handle the interoperability of IoT. All messaging exchanges among agents are established via the TCP/IP Protocol. A software agent is an autonomous executable entity that observes and acts upon an environment and acts to achieve predefined goals. Agents can travel among networked devices carrying their data and execution states, and must be able to communicate with other agents or human users. A multi-agent system is a collection of such entities, collaborating among themselves with some degree of independence or autonomy. Applying agent technology in the process of monitoring and control traffic is new approach. Such technology perfectly fits for distributed and dislocated systems like traffic monitoring and controlling due to its autonomy, flexibility, configurability and scalability thus reducing the network load and overcoming network latency. Agents can also be used to pass messages across networks where the address of destination traffic device is unidentified. Each traffic object is represented as a software agent (an intelligent object agent).
In this infrastructure the extremely large variety of devices will get interconnected, and will be represented by its own intelligent agent that collects information and responds to others requests. Agents will provide their functionality as a service. Autonomous intelligent agents are deployed to provide services necessary for the execution of functional tasks in each layer of the proposed architecture. An agent is embedded within each device and each device supports all agent functions such as migration, execution. Whole system can be controlled by the specific application written for each device’s mobile agent defining how it should behave and act intelligently. Mobile agents within the network migrate from one node to another allowing the devices to pass information to others, retrieve information and discover available resources. Main IoT Traffic agents are: Traffic Mobile Agent: Transmits/receives different types of information to/from other objects the Internet; interprets the data coming from other objects (RFID, sensors, users), and provides a unified view of the context; communicates with other agents in the network to accomplish a specific task. All messages sent from this agent will be transferred to the traffic management system and communicate directly with a static agent of the intended application of the traffic management system mentioned above. User Agent: provides users with real-time information of entities residing in the system. The user agent is a static agent that interacts with the user. It is expected to coordinate with mobile agents.
Monitor Agent: monitors the system to detect contingency situations and triggers some actions to react to some tag reading events on behalf of a smart traffic object, for example in emergency cases. RFID Agent: responsible for reading or writing RFID tags. When reading a tag, according to the data retrieved from it, this agent performs appropriate operations in handling a single task on behalf of a smart object of the associated RFID and to migrate to different platforms at run time. Sensor Agent: receives, processes data that have been read from the associated sensor and saves (or send it somewhere). Traffic Light Agent: detects irregular traffic conditions and changes the traffic control instructions right away. Camera Agent: is responsible for image collecting. All communications between camera agent and video Web server are conducted via the network layer. Camera agent can takes advantage of the existing infrastructure of the camera-based traffic monitoring systems that already available in many cities. The traditional traffic monitoring system based on image processing technology has many limitations. One of them is the impact of the weather. In case of thick dust, heavy rain, etc., the license plate cannot be seen clearly, so its image cannot be captured. The development of e-plate based on RFID provides a good opportunity for intelligent traffic monitoring and vehicle’s identification and tracking (Evizal, 2013).
If no agents are associated with the RFID tags (identification-centric RFID systems), then they may function as an independent set of programs for tag processing and communicate using standardized software agent protocols. The author suggests utilizing the agent technology within the e-plate based on RFID and other traffic objects to fully realize the combined potential of RFID and software agent technology. An RFID-based smart traffic object (code-centric RFID systems) requires a substantial amount of memory space to store traffic object logics and data. The code-centric RFID systems can be used to store a mobile agent into the RFID tags that will enable integration with other parts of the traffic system. Using such technology in the Traffic Information System will eliminate the need for searching of the associated RFID-code information from a database and reduce overall system response time by retrieving service information from the tags, thus achieve faster service responses and perform on-demand actions for different objects in different situations. Each smart vehicle’s RFID object consists of two components, namely, object processing logics and object data (Chen et al, 2010).
Each RFID-tagged traffic object may be assigned an IPv6 Mapped EPC address (Chung et al, 2012). The IoT networks are expected to include billions of devices, and each shall be uniquely identified. A solution to this problem is offered by the IPv6, which provides a larger address space of 128-bit address field to accommodate the increasing number of devices in IoT, thus making it possible to assign a unique IPv6 address to any possible device in the IoT network. RFID can be used as a transponder in vehicle registration plate equipped with a RFID tag and sensors so that each car can get data it needs from the spot and deliver to assigned destination.
The vehicle RFID tag stores information on the vehicle and its owner, such as plate number, vehicle type, speed, time when the car reaches the monitoring point, driver’s name and license number. It can be used to estimate the number of vehicles in the road, average speed of vehicles, vehicle density, etc. The data from each vehicle is captured by fixed or mobile RFID reader at a monitoring station as information of the vehicle and will be sent to central server unit for collecting, processing and storing. Once system connects to the internet, all information of vehicles on each road segment is immediately saved in database and can be used for any purpose and application (vehicle tracking, monitoring or traffic information, etc.). When a vehicle with an RFID tag passes through each monitoring station along the road, the RFID reader at those points will automatically read the tag data related to the vehicle and its owner and transmit to the wireless sensor active nodes. These nodes send accumulated data to the cluster head node. At the same time, a GPS receiver installed at the monitoring station can communicate with GPS satellites to obtain its position information that is taken as a position parameter of the vehicle. Then the data is transmitted using GPRS scheme to the real-time central database where the data is constantly updated to ensure data reliability.
10.0. Traffic Simulation Framework
To justify the proposed system online distributed traffic simulation was conducted. Simulation allows us to observe the properties, characteristics and behaviors of the traffic system. Based on detailed real-time data collected from the distributed online simulations, the IoT traffic system can provide accurate information necessary for near real-time traffic decisions. The whole traffic IoT network is partitioned into dynamic overlapped sections, and a simulation processor is mapped to each section. Each simulation will be supplied with real-time data from nearby RFIDs and sensors and enabled to run continuously. The overall distributed simulation consists of a collection of such segment simulations where each small segment of the overall traffic IoT network is modeled based on local criteria. Each simulation segment is operating in an asynchronous mode, meaning each simulator executes independently of other simulators and the simulation server.
10.1. Distributed online Traffic Simulation Framework
These simulation segments are allowed to exchange information on vehicles moving from one simulation segment to another. Each simulator’s segment locally models current traffic conditions and concentrating only on its area of concern. A simulator’s segment, for example, might model some set of roads and intersections of that segment, and predict the rates of vehicle flow on links carrying vehicles out of that segment. Each segment shares its predictions with other simulation segments to create an aggregated view of both the individual segment’s area of interest and the overall of traffic system. Simulators’ segments publish their current traffic state information (speed, travel time, flow rate, etc.) and their predictions to the simulation server. An aggregation of all simulation segments provides an accurate estimation of a future state of the system. The general model of distributed traffic simulation framework described in Figure 6.
The simulation server disseminates information among the simulator segments, coordinates all simulators’ segments and provides a predictive model of traffic conditions in specified traffic areas by analyzing and integrating the results of distributed simulators of those areas. The simulation server maintains state information of current and future operations of the traffic network such as flow rates, average speed, and the time when that information was generated. Running online simulations are integrated with traffic information system infrastructure to receive real-time traffic data and this overall simulation provides detailed information required for prediction of the system future states of the system. Detailed traffic information (such as speed, location, average acceleration of vehicles on the network segment and the current state of traffic control devices) generated during simulation is saved and managed on the simulation server.
Online distributed traffic simulation is a powerful approach for analyzing the characteristics and behavior of the traffic system and determining traffic conditions and help to reduce vehicle delay time of on the road, traffic congestion without the need of making costly changes in real world; prevent dangerous situations and delays by broadcasting messages informing drivers in the area to avoid congested roads (Shu, 2014). It will be beneficial to transportation management as well as urban planning and architecture working on enhancement of the roads capacity, building new roads or to improve the existing roads and improvement of public transportation systems. The current large-scale distributed simulation methodologies require tremendous network bandwidth and huge amount of computation by each simulator host. Mobile agents are used to reduce the communications loads placed in the network. Agents communicate with a specific simulation segment, providing all of the state information that was sent to the simulator server.
11.0. Intelligent Transportation System with Cloud Computing Big Data
The era of big data is upon us. Hidden inside a flood of heterogeneous raw data is the knowledge. With the advent of cloud computing, resizable infrastructure for data analysis is now available to everyone via an on-demand maybe free model. In order to unlock the potential of big data, there are a significant number of research challenges need to overcome including: managing diverse sources of unstructured data with no common schema, removing the complexity of writing auto-scaling algorithms (e.g. Amazon Elastic Compute Cloud), real-time analytics, suitable visualization techniques for petabyte scale data sets etc. Big data hold great promises for discovering subtle population patterns and heterogeneities that are not possible with small-scale data, which should care (Villars et al., 2011). The global laboratory of IBM defines the future of cities as smarter cities involving following features: (1) using big data and analytics for deeper insights; (2) cloud for collaboration among disparate agencies; (3) mobile to gather data and address problems directly at the source; (4) social technologies for better engagement with citizens. Into and around the city, people and goods are always moving. Intelligent transportation systems(ITS) as fundamental infrastructure services make a city “livable” by improving capacity, enhancing travel experiences and making moving anything safer, more efficient and more secure. For instance, Singapore already set up an excellent stylish example based on Internet of Things (IoT) to connect, collect and comprehend.
The Land Transport Authority plans routes and establishes minimum service standards for bus lines managed by the Singapore Bus Service and Singapore Mass Rapid Transit. To deal with dynamic traffic environments and assist users and city officials better understand traffic problems in large cities, the current trend for powerful intelligent smart city applications is managing data and processing in cloud-enabled large scale sensor networks for actively and autonomously adaptation and smart provision of services and content, which increasingly converge with cloud computing environments.
Figure 7 presents the conceptual framework of urban smart transportation based on cloud and IoT. Contemporary smart urban transport systems:
employ secured IoT to generate big data, which comprise billions of devices that sense, communicate, compute and potentially actuate, massive connected via GIS-T, widely available real time communication network(e.g. 4G, Wi-Fi, Bluetooth);
generate big data with “4Vs” features, as raw material facing great challenges
rely on resilient cloud computing to store, manage, mine and create values for insight as the solution to many of our society’s traffic and transportation problems in an era of data abundance, there is a clear need for visualization tools to provide insight into how coordinated systems should be expected to operate under different parameter settings and to document coordinated system behavior. Cloud computing is utilized to meet the requirements on infrastructure for big data.
Zheng et al. (2011) provided an overview of service-generated big data and big data-as-a-service, employed to provide common big data related services (e.g. accessing service-generated big data and data analytics results) to users to enhance efficiency and reduce cost. Zimmermann et al. (2004) proposed an integration model for service-oriented enterprise architecture based on enterprise services architecture reference cube and the systematic development, diagnostics and optimization of architecture artifacts of service oriented cloud-based enterprise systems for big data applications. Li et al. (2011) proposed urban traffic management systems using intelligent transportation cloud, which generate, store, manage, test, optimize, and use mobile traffic strategy agents to maximize advantages of cloud computing and agent technology to effectively control and manage urban traffic systems.
11.1. Route Map for Deploy Big Traffic Data on the Cloud
With the tendency of asset-light, cloud computing with certain service-level agreement is under consideration. The term “moving to cloud” also refers to an organization moving away from a traditional capital expenditure model (buy the dedicated hardware and depreciate it over a period of time) to the operating expense model (use a shared cloud infrastructure and pay as one uses it). Figure 8 taps big open traffic data on cloud with IoT infrastructure, illustrate with the available options at each level. As a smart city begins with rich IoT involved with big data and cloud computing. When generate, big data transferred real time with streaming process through communication network(such as Bluetooth, Wi-Fi, 4G) to store(e.g. MongoDB), which usually combined with ETL(Extract, Transform and Load) Process. Standardized structure format, combined with open source software to develop planning tools; provide a platform to gather public involved more efficiently create to APPs and maps as well through crowd sourcing (Lantz et al. (2015). As Harry Strasser’s vision, a connected world digital(technological) convergence where everything in people’s life will have computing power, wireless connectivity and a lot of smart sensors
12.0. RFID Applications in Intelligent Transportation Systems (ITS)
Transportation is a crucial industry that affects the national economy and livelihood of the people (Monar et al, 2004). Intelligent Transportation Systems, or ITS, can be defined as the application of computing, information, and communications technologies to the real-time management of vehicles and networks involving the movement of people, goods, and services. When integrated into the transportation system’s infrastructure, and into vehicles themselves, these technologies relieve congestion, improve safety, and enhance productivity.
Intelligent transportation systems (ITS) encompass a broad range of wireless and wire line communications-based information and electronics technologies (Figure 9). The versatile features and benefits of RFID technology have proven that RFID can be widely applied in the field of intelligent transportation to improve driving safety, reduce vehicle collisions, and even help reduce vehicle emissions (Samadi, 2011).The RFID technology has over 16 subcategories in the ITS used in the electronic payment and pricing subcategories among others . The Moscow metro, was the first system in Europe to introduce RFID smartcards in 1998. In Taiwan the transportation system uses RFID operated cards The Easy Card is charged at local convenience stores and metro stations, and can be used in metro, buses, parking lots and taxis. In Singapore, the public transport network of buses and trains employs passive RFID cards (Prakasam, 2009). Traffic into the crowded downtown areas of the country is regulated by variable tolls imposed using an active tagging system combined with the use of stored-value cards .Microwave RFID tags are used in long range access control for vehicles. Since the 1990’s RFID tags have been used in car keys to prevent theft. Without the correct RFID, the car will not start (Mezghani, 2008)..
RFID technology has been known to be one of the noteworthy converging technologies of the 20th century. The technology can be applied in many fields. However, this paper focuses on the application of the technology in the transportation industry. The application of RFID in Intelligent Transport Systems (ITS) is gaining popularity with its widespread use in the field of toll management and the management of the overall transport sector. There are many RFID applications available in the market such as RFID contactless smart card commonly used in buses and LRTs, Automatic Vehicle Identification (AVI), Electronic Toll Collection (ETC), Smart Parking, and congestion zone pricing. In Mashhad, the second largest city of Iran, the “My Card” is used not only in Public transit but also in car parking, and soon in taxis and also other public municipality Services. Driven by such success stories, deployment of RFID technology in Mashhad is thus encouraged.
RFID technology is an important technology that has found its application in many places (Jeong et al, 2003). However, its application in the transportation is one of the best system applications. The technology is applied in the transport sector to perform various tasks such as vehicle or product identification during transportation, security, safety and operations. The system works using a tag that is placed on the vehicle or product to be tracked. The tag carries vital information concerning the vehicle or product identity and location that is transferred to the wireless reader (Figure 10). In spite of their wide potential applications in the sector, the applications of the system are still limited. This paper conducts an extensive survey in the application of RFID technology in the transport sector with the major areas of application being intelligent transportation systems (ITS) and vehicle infrastructure integration.
The advantages of RFID solutions have been recognized by traffic sector & transportation industry in developing countries. It is believed that RFID-based technologies can be extensively exploited to improve transportation safety and security, increase the efficiency of the transportation system, ultimately save costs, and improve people lives. Also, Smartcard-based fare payment provides convenience for passengers and efficiency gains for transport service providers. International experience suggests that successful implementation may take many years, and difficulties are commonplace.
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