Beyond First/Last Mile Active Transportation - BikeShare@UH

Bike sharing is a new green transportation solution that has been developed and adopted at various cities around the world. In this paper, we present the process and results of the design and prototypes that a group of undergraduate students developed for a BikeShare@UH program during Summer 2017. After presenting the detailed results of four project teams focusing on customer discovery, bike share station (BSS) location identification, cloud-based mobile computing platform for user engagement and bike share program operation and management, smart lock, and alternative energy source based on PV panel. With the phase one implementation at the University planned in Spring 2018, we anticipate gathering real time data and feedback to improve the system.Cockrell School of Engineerin

Location-aware recommendation systems: Where we are and where we recommend to go

Recommendation systems have been successfully used to provide items of interest to the users (e.g., movies, music, books, news, images). However, traditional recommenda- tion systems do not take into account the location as a relevant factor when providing suggestions. On the other hand, nowadays, there exist an increasing amount of geo- referenced data and users are usually interested only in nearby items (e.g., restaurants, museums, cinemas). Hence, the emergence of location-aware recommendation systems have acquired a great attention by the research community in the last decade. In this paper, we provide a survey of location-aware rec- ommendation systems in mobile computing scenarios. Firstly, we describe briefly the fundamentals of recommendation sys- tems. Then, we introduce some of the most relevant existing approaches for location-aware recommendation. Moreover, we present the main applications of this type of systems in several recommendation scenarios, such as music, news, restaurants, etc. Finally, we discuss new avenues and open issues in the area

A review of the role of sensors in mobile context-aware recommendation systems

Recommendation systems are specialized in offering suggestions about specific items of different types (e.g., books, movies, restaurants, and hotels) that could be interesting for the user. They have attracted considerable research attention due to their benefits and also their commercial interest. Particularly, in recent years, the concept of context-aware recommendation system has appeared to emphasize the importance of considering the context of the situations in which the user is involved in order to provide more accurate recommendations. The detection of the context requires the use of sensors of different types, which measure different context variables. Despite the relevant role played by sensors in the development of context-aware recommendation systems, sensors and recommendation approaches are two fields usually studied independently. In this paper, we provide a survey on the use of sensors for recommendation systems. Our contribution can be seen from a double perspective. On the one hand, we overview existing techniques used to detect context factors that could be relevant for recommendation. On the other hand, we illustrate the interest of sensors by considering different recommendation use cases and scenarios

IoTRec: The IoT Recommender for Smart Parking System

This paper proposes a General Data Protection Regulation (GDPR)-compliant Internet of Things (IoT) Recommender (IoTRec) system, developed in the framework of H2020 EU-KR WISE-IoT (Worldwide Interoperability for Semantic IoT) project, which provides the recommendations of parking spots and routes while protecting users’ privacy. It provides recommendations by exploiting the IoT technology (parking and traffic sensors). The IoTRec provides four-fold functions. Firstly, it helps the user to find a free parking spot based on different metrics (such as the nearest or nearest trusted parking spot). Secondly, it recommends a route (the least crowded or the shortest route) leading to the recommended parking spot from the user’s current location. Thirdly, it provides the real-time provision of expected availability of parking areas (comprised of parking spots organized into groups) in a user-friendly manner. Finally, it provides a GDPR-compliant implementation for operating in a privacy-aware environment. The IoTRec is integrated into the smart parking use case of the WISE-IoT project and is evaluated by the citizens of Santander, Spain through a prototype, but it can be applied to any IoT-enabled locality. The evaluation results show the citizen’s satisfaction with the quality, functionalities, ease of use and reliability of the recommendations/services offered by the IoTRec

What attracts vehicle consumers’ buying:A Saaty scale-based VIKOR (SSC-VIKOR) approach from after-sales textual perspective?

Purpose: The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint. Design/methodology/approach: A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint. Findings: The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior. Research limitations/implications: The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation. Originality/value: Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective

A Design Concept for a Tourism Recommender System for Regional Development

Despite of tourism infrastructure and software, the development of tourism is hampered due to the lack of information support, which encapsulates various aspects of travel implementation. This paper highlights a demand for integrating various approaches and methods to develop a universal tourism information recommender system when building individual tourist routes. The study objective is proposing a concept of a universal information recommender system for building a personalized tourist route. The developed design concept for such a system involves a procedure for data collection and preparation for tourism product synthesis; a methodology for tourism product formation according to user preferences; the main stages of this methodology implementation. To collect and store information from real travelers, this paper proposes to use elements of blockchain technology in order to ensure information security. A model that specifies the key elements of a tourist route planning process is presented. This article can serve as a reference and knowledge base for digital business system analysts, system designers, and digital tourism business implementers for better digital business system design and implementation in the tourism sector

Electric vehicle route recommender system

This paper presents a recommender system responsible for processing information that will help the driver in the daily use of his Electric Vehicle (EV), minimizing the problem of range anxiety through a personalized range prediction and by presenting in real time relevant information about the charging stations that can be reached within the range autonomy. Given the success of recommendation systems on automatic delivery of relevant information in numerous areas of usage, this type of systems can also be applied in the electric mobility scenario, with the objective of maximizing the relevance of the information presented to the driver, which should be the strictly needed data for the driver to make important decisions, filtering out the unnecessary information.This work is financed by FEDER Funds, through the Operational Programme for Competitiveness Factors – COMPETE, and by National Funds through FCT – Foundation for Science and Technology of Portugal, under the project PTDC/EEA-EEL/104569/2008 and the project MIT-PT/EDAM-SMS/0030/2008

Mobile Data Science: Towards Understanding Data-Driven Intelligent Mobile Applications

Due to the popularity of smart mobile phones and context-aware technology, various contextual data relevant to users' diverse activities with mobile phones is available around us. This enables the study on mobile phone data and context-awareness in computing, for the purpose of building data-driven intelligent mobile applications, not only on a single device but also in a distributed environment for the benefit of end users. Based on the availability of mobile phone data, and the usefulness of data-driven applications, in this paper, we discuss about mobile data science that involves in collecting the mobile phone data from various sources and building data-driven models using machine learning techniques, in order to make dynamic decisions intelligently in various day-to-day situations of the users. For this, we first discuss the fundamental concepts and the potentiality of mobile data science to build intelligent applications. We also highlight the key elements and explain various key modules involving in the process of mobile data science. This article is the first in the field to draw a big picture, and thinking about mobile data science, and it's potentiality in developing various data-driven intelligent mobile applications. We believe this study will help both the researchers and application developers for building smart data-driven mobile applications, to assist the end mobile phone users in their daily activities.Comment: Journal, 11 pages, Double Colum