IRSPAS 2016
Permanent URI for this collectionhttp://repository.kln.ac.lk/handle/123456789/15651
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Item Urban traffic simulation using agent-based modelling: A study in the Sri Lankan context(Faculty of Science, University of Kelaniya, Sri Lanka, 2016) Amarasinghe, U.G.L.S.; Rajapakse, C.Traffic congestion is a crucial issue affecting the quality of life of individuals all over the world. In a country like Sri Lanka where the traffic is mostly unorganised and mixed, traffic congestion occurs due to various reasons such as the volume of traffic exceeding the capacity of the road, road accidents, temporary closures of roads due to constructions, as well as the behaviours of pedestrians and drivers. For example, careless lane changing behaviours of drivers and the bad practices of crossing streets of pedestrians account for a larger portion of urban traffic congestion every day. Due to the significant impact of traffic congestion to economic growth, various approaches have been taken by researchers and administrators to reduce the urban traffic congestion. Some popular approaches to solving this issue includes infrastructure development, introducing new traffic rules such as changing peak hour traffic plans in cities, as well as imposing heavy duties on vehicle imports to reduce the growing volume of vehicles on roads. However, despite all these attempts, the traffic congestion remains a serious issue in Sri Lanka. Traffic simulation is one of the most effective tools for the testing of traffic solutions and finds the reasons causing traffic congestion. Traffic conditions are different from region to region due to different factors: traffic laws, vehicle types, drivers’ and pedestrians’ behaviours. Therefore, researches have been done by focusing on modelling traffic simulators considering those factors specific to particular regions. We propose the Agent-Based Modelling and Simulation (ABMS) approach, which is a popular computational research method based on swarm intelligence to study complex social and economic systems, to model a traffic simulator simulating mixed traffic conditions in Sri Lanka which is an unaddressed area of research. In this approach, individual vehicles and pedestrians are modelled as software agents who have a set of individual (i.e. micro level) behavioural rules. When these agents are put together, they behave as the vehicles and pedestrians behave in the real world interacting with each other giving rise to emergent macro-level patterns, which we call traffic congestions. This study aims at modelling vehicle following behavior, seepage behaviour of vehicles and pedestrian’s behaviours at un-signalized crossings. We use the ABMS environment called NetLogo to develop our simulator and Kiribathgoda junction in Western Province, Sri Lanka as the test bed. Data collected from there will be used to calibrate the model with accurate parameter values. Macroscopic statistics such as the rate of traffic flow, average speeds and queue time will be used to validate the model by comparing data from real traffic situations with model outputs. The ultimate objective of this research is to come up with a cost-effective decision support tool for administrators and policy makers to understand various reasons behind congestion in unorganised mixed traffic environments in Sri Lanka, apply and evaluate different traffic control strategies and thereby to make better-informed decisions to control urban traffic congestion in Sri Lanka.Item Instagram sentiment analysis: Discovering tourists’ perception about Sri Lanka as a tourist destination(Faculty of Science, University of Kelaniya, Sri Lanka, 2016) Ranaweera, E.H.; Rajapakse, C.Today the web has changed from static containers of information to dynamic platforms where users can share digital contents such as blog posts, pictures and opinions in a very simple manner. Especially, the social media is largely getting popular due to the fact that most people prefer to share their feelings, thoughts and memories of their daily activities in social networks. One of the most common types of posts in social networks is opinion related posts. Moreover, social network users tend to seek opinions of others before purchasing a product or getting a service. Social media plays a revolutionary role in travel and tourism industry. With the increasing use of social media, tourists not only consume tourism products and services but also prefer to share their experiences with others in the forms of textbased opinions, comments to other’s posts, pictures with descriptions, ratings, etc. Current statistics available with Sri Lanka’s tourism authorities do not reveal whether tourists are happy with the services received during their visit and they have no information regarding common issues that the tourists have to deal with when they are in Sri Lanka. However, reading and analyzing all these online posts is not practically feasible due to the enormous time and human resource that would be required. The objective of this research is to identify how social media contents could be used to extract valuable and meaningful information to develop and promote travel and tourism industry in Sri Lanka. Our approach is to adopt Sentiment analysis techniques to analyze the text-based contents shared by tourists on Instagram, which is a popular social networking site among tourists worldwide, to determine the overall perception of tourists about Sri Lanka as a travel destination. Photo descriptions and user comments are collected, using special keywords related to tourism in Sri Lanka using an online tool and, in the first phase of the research, sentiment classifier with support vector machine algorithm will be develop to identify sentiment polarity of posts. Furthermore in the second phase feature analysis model will be developed through which positive posts with feature words will be used to identify tourists who recommend Sri Lanka to others or potential tourists who plan to visit/revisit Sri Lanka. Moreover, feature categorization method will be used to identify the key areas that require improvements to offer a better service to tourists through negative sentiments.Item Real-time big data video analytics for unorganized traffic environments(Faculty of Science, University of Kelaniya, Sri Lanka, 2016) Ranaweera Arachchi, I.; Jayalal, S.; Rajapakse, C.Traffic on local roads has reached such a level that it is necessary to address the issue of traffic congestion and seek complex transport solutions for the city. Increase of the number of vehicles on the road becomes one of the key reasons for increasing traffic congestion. Traffic congestion is associated with massive financial and manhour loss and therefore attempts to alleviate this has been of keen interest. The basis of almost all those approaches is traffic monitoring and analysis, leading to having an effective traffic management system. Most traffic management systems are applied in well-organized traffic environments such as highways, where driver discipline is high. But in unorganized urban environments as seen in Sri Lanka, road traffic behavior vary from the accepted standards. Driver and pedestrian indiscipline cause huge traffic congestions in urban areas. Hence in such a scenario, a system that monitors road traffic on different traffic environments is very useful. There are several existing techniques such as Magnetic Loops, Microwave RADAR, Infrared Detectors, Ultrasonic Detectors and Camera Based Systems. Traffic monitoring systems require short processing time, low processing cost and high reliability. Therefore, according to the literature, camera-based monitoring is the best-suited technique for traffic monitoring. Real-time video analytics are part of a centralized approach to modern traffic management which is defined as computer vision-based surveillance that provides algorithms for object detection, tracking, classification and trajectory analysis using real-time traffic surveillance video. It usually uses roadside cameras (CCTV) to obtain traffic information and transmit it to central servers, exhibiting real-time operability of big data. In this study, several approaches and algorithms for moving object detection, based on temporal differencing method, optical flow method, background filtering are compared and a novel real-time vehicle detection and classification algorithm based on background filtering will be proposed and re-engineered in order to be applicable to challenging unorganized traffic environments. The solution will classify vehicles individually and their trajectories in real time in unorganized traffic environments in order to analyze the behaviors of the drivers as well as pedestrians on the road. We use OpenCV which is a library of programming functions mainly aimed at real-time computer vision, for implementing and testing algorithms. Data will be collected via pre-recorded video clips from Kiribathgoda junction in the western province, for the testing purpose and real- time CCTV surveillance video is going to be used as the input for implementation. A comprehensive data analysis is required to be conducted to address the higher processing requirement of such videos. The solution will be validated for performance subsequently. The final objective of this research is to come up with an optimum algorithm for vehicle detection and classification in unorganized traffic environments which would help to analyze the behavior of road users. The solution will lead to reduced traffic congestion in the country by enhancing the efficiency and effectiveness of traffic monitoring and analyzing systems.Item A zero configuration protocol stack for device-to-device communication in a private Wi-Fi network(Faculty of Science, University of Kelaniya, Sri Lanka, 2016) Wickramarathne, I.Y.; Jayawardena, B.; Rajapakse, C.Traditional wireless communication involves many Internet-based services, which makes the availability of an Internet connection mandatory to complete the communication. Furthermore, when introducing new devices into the network, preconfiguration, authentication and granted access rights are must to have. This research proposes a stack of protocols for discovering peer nodes, establishing connectivity with them and handling communication between those peers in a Wi-Fi (Wireless Fidelity) network without Internet connectivity as well as configuration and authentication requirements. In this new protocol, all the nodes in the network becomes objects and the user selects appropriate communication channel based on the communication requirement. It could be voice calling, screen sharing, chatting, video calling, file sharing, data transmission, etc. Since the protocol uses local area network, network traffic is not going out from the local router to the Internet. The protocol’s security mechanism is based on different instances. The user is allowed to define his/her own security definition to his/her communication. The protocol supports to network security, application based security, and group based security with encryptions. The research is based on the build and test approach where incrementally developed components of the protocol stack are tested on different Wi- Fi security platforms and device platforms and fine-tuned for minimum bandwidth consumption and data losses. Protocol stack is being developed in accordance with several layers of the TCP/IP (Transmission Control Protocol/Internet Protocol) model such as application, transport, network and physical. Simple chat application is built with all the proposing components and algorithms in order to proof of the concept. Our ultimate objective is to apply this new communication protocol to the IoT (Internet of Things) environments. Since protocol supports to any OS (Operating System) platform and enable Wi-Fi communication with any device without any configurations, this can be used as the core communication protocol used by the devices present in an IoT environment.Item An approach to personalize learning using big data analytics for higher education(Faculty of Science, University of Kelaniya, Sri Lanka, 2016) Jabir, A.; Rajapakse, C.The concept of BYOD (Bring Your Own Device) has gained popularity in studentcentered learning and higher education institutions make significant investments on improving the wireless network to enhance this. Virtual Learning Environment and Learning Management Systems were introduced and personalization of learning becomes the next milestone. The huge streams of data produced by these Wi-Fi networks makes ground for Big Data analytics to identify opportunities in educational environments to adopt personalized learning. The term ‘Personalization’ refers to the tailoring of content and recommending items by inferring what interests a user based on previous or current interactions with that user, and possibly other users. This research proposes an approach to personalize learning on an online learning platform by providing personalized recommendations of educational web resources, comparative feedback and allocate personalized bandwidths based on the concept of deprioritization (lowering priority ranks of heavy users). Concepts of Big Data analytics and data mining techniques will be used to satisfy the objectives. The approach consists of offline phase (modelling phase) and online phase (recommendation /deprioritization) phase. In the offline phase, models will be developed for recommendation and deprioritization separately. For recommendation a hybrid filtering method will be used. k-Nearest Neighbour, a user-based collaborative filtering technique, will be used with correlation based similarity measure with demographic filtering based on demographic classifiers (faculty, year, General/Special/Honors, GPA) to eliminate the cold start problem. To increase the efficiency and accuracy, k-means clustering will be used as an intermediate step to determine usage clusters to group users exhibiting similar browsing patterns and page clusters to discover pages with similar access patterns. For this the access logs of the University of Kelaniya’s Wi-Fi network will be utilized. The parameters for usage clustering would be the timestamp, web resource and category (education, social networking, gaming etc.) whereas the parameters for page clustering would be category and temporal concepts. In the online phase, first the cluster that the current active user belongs to will be identified and k-NN will be applied on that particular cluster to recommend web resources. These techniques also provide the basis for comparative feedback compared to top scorers of the same area of major. For personalized allocation of bandwidth a separate k-means clustering will be performed to identify heavy users during the offline phase. During the online phase deprioritization will be applied accordingly if the current user belongs to the heavy users cluster and there is a heavy traffic in the network. Cross validation will be used to evaluate the models.