Shortest Path Trajectory System Based on Dijkstra Algorithm

In the master project, the researcher discussed the shortest path solution to a single source problem based on Dijkstra algorithm as resolving the basic concepts. Everybody can travel by different routes to reach a different destination point. This can be time consuming if they do not travel trough the best route. This project aims to determine locations of the node that reflect all the items in the list, build the route by connecting nodes and evaluate the proposed algorithm for the single source shortest path problem. This project includes the modification of main algorithm which has been implemented in the prototype development. This study discussed the emphasis on the single source shortest path at the location of specific studies. The study will produce a decision-makers prototype

SHORT TERM TRAVEL BEHAVIOR PREDICTION THROUGH GPS AND LAND USE DATA

The short-term destination prediction problem consists of capturing vehicle Global Positioning System (GPS) traces and learning from historic locations and trajectories to predict a vehicle’s destination. Drivers have predictable trip destinations that can be estimated through probabilistic modeling of past trips. This dissertation has three main hypotheses; 1) Employing a tiered Markov model structure will permit a shorter learning period while achieving similar accuracy results, 2) The addition of derived trip purpose information will increase accuracy of the start of trip and in-route models as a whole, and 3) Similar methodologies of travel pattern inference can be used to accurately predict trip purpose and socio-economic factors. To study these concepts, a database of GPS driving traces (120 participants for 70 days) is collected. To model the user’s trip purpose, a new data source was explored: Point of Interest (POI)/land use data. An open source land use/POI dataset is merged with the GPS dataset. The resulting database includes over 20,000 trips with travel characteristics and land use/POI data. From land use/POI data, and travel patterns, trip purpose is calculated with machine learning methods. A new model structure is developed that uses trip purpose when it is available, yet falls back on traditional spatial temporal Markov models when it is not. The start of trip model has an overall increase of accuracy over other start of trip models of 2%. This comes quickly, needing only 30 days to reach this level of accuracy compared to nearly a year in many other models. When adding trip purpose and the start of trip model to in-route prediction methods, the accuracy of the destination prediction increases significantly: 15-30% improvement of accuracy over similar models between 0-50% of trip progression. Certain trips are predicted more accurately than others: work and home based trips average of 90% correct prediction, whereas shopping and social based trips hover around the 50% mark. In all, the greatest contribution of this dissertation is the trip purpose methodology addition and the tiered Markov model structure in gaining fast results in both the start of trip and in-route models

Collective Behaviour: From Cells to Humans

Living in organised groups is a strategy that can be observed in a multitude of diverse species. Among such species, the behaviour of an individual on their own is not the same as within a group: the environment is modified by the presence of more subjects, individuals interact with each other, and from those interactions complex patterns of behaviour can emerge. Some species of animals almost exclusively exist as groups, and as a consequence, studying them in a social context is the only way to understand their behaviour in nature. This is the idea that drives all the research presented in this thesis: the particular behaviour exhibited by the group is so robust that it will emerge even in a very simplified environment. By observing the individual and the group in those simplified experimental conditions, it is possible to deduce rules that might govern the interaction. The importance of interactions in the group’s behaviour can then be demonstrated by implementing a computer model of agents following those rules and comparing it with natural and experimental behaviour. This thesis presents different examples of such analyses, and gives illustrations of the range of questions that can be answered through this method. Groups of stem cells, juvenile sea bass and human beings were successively observed and tracked in suitable environments, with or without perturbation. The data extracted from those experiments were then processed so as to correct recording errors, and individual and collective behaviours were derived from those data, returning new insights on the nature of the interaction at the individual level, their consequences at the global level, as well as the effects of the interaction on both. Finally, I present the computer models derived from those analyses. Many systems in nature share this property of global behaviours emerging from deterministic local interaction, and as a consequence studies of this kind could shed light on important questions, of which cancer treatment, ocean acidification and human organisations are but a few examples

Contextualized and personalized location-based services

Advances in the technologies of smart mobile devices and tiny sensors together with the increase in the number of web resources open up a plethora of new mobile information services where people can acquire and disseminate information at any place and any time. Location-based services (LBS) are characterized by providing users with useful and local information, i.e. information that belongs to a particular domain of interest to the user and can be of use while the user remains in a particular area. In addition, LBS need to take into account the interactions and dependencies between services, user and context for the information filtering and delivery in order to fulfill the needs and constraints of mobile users. We argue that consequently it brings up a series of technical challenges in terms of data semantics and infrastructure, context-awareness and personalization, as well as query formulation and answering etc. They can not be simply extended from existing traditional data management strategies. Instead, they need a new solution. Firstly, we propose a semantic LBS infrastructure on the basis of the modularized ontologies approach. We elaborate a core ontology which is mainly composed of three modules describing the services, users and contexts. The core ontology aims at presenting an abstract view (a model) of all information in LBS. In contrast, data describing the instances (of services user and actual contextual data) are stored in three independent data stores, called the service profiles, user profiles and context profiles. These data are semantically aligned with the concepts in the core ontology through a set of mappings. This approach enables the distributed data sources to be maintained in a autonomous manner, which is well adapted to the high dynamics and mobility of the data sources. Secondly, we separately address the function, features, and our modelling approach of the three major players, i.e. service, context and user in LBS. Then, we define a set of constructs to represent their interactions and inter-dependencies and illustrate how these semantic constructs can contribute to personalized and contextualized query processing. Service classes are organized in a taxonomy, which distinguishes the services by their business functions. This concept hierarchy helps to analyze and reformulate the users' queries. We introduce three new kinds of relationships in the service module to enhance the semantics of interactions and dependencies between services. We identify five key components of contexts in LBS and regard them as a semantic contextual basis for LBS. Component contexts are related together by specific composition relationships that can describe spatio-temporal constraints. A user profile contains personal information about a given user and possibly a set of self-defined rules, which offer hints on what the user likes or dislikes, and what could attract him or her. In the core ontology clustering users with common features can help the cooperative query answering. Each of the three modules of the core ontology is an ontology in itself. They are inter-related by relationships that link concepts belonging to two different modules. The LBS fully benefits from the modularized structure of the core ontology. It allows restricting the search space, as well as facilitating the maintenance of each module. Finally, we studied the query reformulation and processing issues in LBS. How to make the query interface tangible and provide rapid and relevant answers are typical concerns in all information services. Our query format not only fully obeys the "simple, tangible and effective" golden-rules of user-interface design, but also satisfies the needs of domain-independent interface and emphasizes the importance of spatio-temporal constraints in LBS. With pre-defined spatio-temporal operators, users can easily specify in their queries the spatio-temporal availability they need for the services they are looking for. This allows eliminating most of irrelevant answers that are usually generated by keyword-based approaches. Constraints in the various dimensions (what, when, where and what-else) can be expressed by a conjunctive query, and then be smoothly translated to RDF-patterns. We illustrate our query answering strategy by using the SPARQL syntax, and explain how the relaxation can be done with rules specified in the query relaxation profile