Анализ предпочтений участников движения на маршрутном общественном транспорте в задаче построения персонализированной рекомендательной системы

В работе рассматриваются теоретические и алгоритмические аспекты построения персонализированной рекомендательной системы (мобильного сервиса), предназначенной для пользователей общественного маршрутного транспорта. Основной упор сделан на выявлении и формализации понятия "пользовательские предпочтения", лежащего в основе современных персонализированных рекомендательных систем. Представлены неформальные (вербальные) и формальные (математические) постановки соответствующих задач определения "пользовательских предпочтений" в определенном пространственно-временном контексте: определение предпочитаемых остановок и определение предпочитаемых "транспортных корреспонденций". Показано, что первая из задач может быть представлена как известная задача классификации, то есть может быть сформулирована и решена с использованием известных методов распознавания образов и машинного обучения. Вторая же сводится к нахождению оценок серии условных распределений. Представлены результаты экспериментального исследования работоспособности предложенных подходов, методов и алгоритмов на примере данных мобильного приложения "Прибывалка-63" сервиса tosamara.ru, используемого в настоящее время для информирования жителей г. Самара о движении общественного транспорта. The paper presents the theoretical and algorithmic aspects for making a personalized recommender system (mobile service) designed for public route transport users. The main focus is on identifying and formalizing the concept of "user preferences", which is the basis of modern personalized recommender systems. Informal (verbal) and formal (mathematical) formulations of the corresponding problems of determining "user preferences" in a specific spatial-temporal context are presented: the preferred stops definition and the preferred "transport correspondence" definition. The first task can be represented as a well-known classification problem. Thus, it can be formulated and solved using well-known pattern recognition and machine learning methods. The second is reduced to the construction of dynamic graphs series. The experiments were conducted on data from the mobile application "Pribyvalka-63". The application is the tosamara.ru service part, currently used to inform Samara residents about the public transport movement.Работа выполнена при финансовой поддержке Министерства науки и высшего образования РФ (уникальный идентификатор проекта RFMEFI57518X0177)

Enhancing travel experience with the combination of information visualization, situation awareness, and distributed cognition

With the new forms of travel introduced by new technologies of transportation and communication, a satisfied travel experience could be affected by various factors before and during a trip. Especially for road trips, traveling by car provides freedom on time control while leading to more possibilities of rescheduling initial plans made under time constraints. When overwhelmed with the need for changed travel context to avoid unexpected events that will require a serious change of initial plans, travelers need to find and access helpful contextual information quickly. This is a context-related decision making process that requires amplifying human situation awareness and supporting distributed cognition, since travel information offers multiple choices. To solve this problem, I applied information visualization as the main design solution. When comparing it with a traditional representation of lists, information visualization displays the advantages of visual representation of abstract data to clarify and depict the information and amplify cognition while improving travel experience intuitively in the domain of user experience design. Therefore in this thesis I will address the approach of implementing recontextualized situation awareness, distributed cognition, and information visualization in a travel-aid system. By using both theoretical and practical design perspectives, I will discuss how to enhance travel experience with represented contextual information that users desire or expect before and during a road trip. I will also explore the new values of this design with strategic business support. Additionally, after conducting research and analysis on existing interaction design parts, I selected a smartphone app to serve as a proper platform with connected multifunctions. Briefly, I begin the thesis with a review of previous theories and aspects of travel planning, information visualization as it relates to travel, situation awareness, and distributed cognition in the design context and related smartphone apps. Then I discuss the process of identifying the specific issues to be solved or improved with a preliminary research of empirical study, followed by an interview, online survey, insights synthesis, and business model design. After a visual-system design was developed, heuristic evaluation was employed to assess the outcome. Lastly, a new round of refined design results is introduced based on outcomes of the evaluation

Design and implementation of a daily activity scheduler in the context of a personal travel information system

How to effectively schedule individual’s daily activities in actual temporal–spatial environments is a challenging task especially when considering various dynamic conditions and constraints. In this chapter, we present a prototype of a personal daily activity scheduler based on our previously developed travel information system, where point of interest (POI) information and travel information have been integrated into an individual’s agenda service. The scheduler provides all operations based on constraints checking, agenda operations (e.g., inserting, updating and deleting activities), recommending locations, detecting deviations from schedule, detecting real-time event consequences and detecting relevant POIs. Initial tests for the basic operations indicate that the approach works well and more comprehensive tests will be conducted in the future

Design and implementation of a daily activity scheduler in the context of a personal travel information system

How to effectively schedule individual’s daily activities in actual temporal–spatial environments is a challenging task especially when considering various dynamic conditions and constraints. In this chapter, we present a prototype of a personal daily activity scheduler based on our previously developed travel information system, where point of interest (POI) information and travel information have been integrated into an individual’s agenda service. The scheduler provides all operations based on constraints checking, agenda operations (e.g., inserting, updating and deleting activities), recommending locations, detecting deviations from schedule, detecting real-time event consequences and detecting relevant POIs. Initial tests for the basic operations indicate that the approach works well and more comprehensive tests will be conducted in the future