Внедрение технологий Big Data в CRM-системы для повышения качества эксплуатации

В статье рассматривается использование технологий BigData, возможность внедрения ее в CRM системы для эффективного управления бизнесом компании. Анализируются инновационные технологии построения BigData

Prediction and analysis the causes of increasing an illegal e-taxi in Bangladesh municipalities: A case study of Pabna municipality

Purpose - This study aims to find out the causes for an increase in the number illegal E-taxis and the extent of these vehicles in the municipalities of Bangladesh. Design/methodology/approach - Based on extensive literature review and field investigation, a set of questionnaires was developed to explore the actual causes for an increase in the number of illegal E-taxis, where ten predicted hypotheses were tested. Findings - The result proved that the illegal E-taxi is very active in the study area. Besides the socio-economic condition of the commuter, education level of taxi drivers and commuter satisfaction level (safety and comfort) and provision of continuous and door-to-door service system are the main causes for increasing number of E-taxis in the municipality of Bangladesh. Originality/value - Moreover, this study provides an effective thinking on socio-economic condition of drivers and the legalization of illegal E-taxis in the study area

Developing an Interactive Baseline Data Platform for Visualizing and Analyzing Rural Crash Characteristics in RITI Communities

This project focused on developing an interactive baseline crash data platform, termed as Rural Crash Visualization Tool System (RCVTS), to visualize and analyze rural crash characteristics in RITI communities. More than 975 thousand crash records were collected in the state of Alaska, Idaho, and Washington, from 2010 to 2016. Data fusion is applied to unify the collected data. In the proposed RCVTS platform, three main functions are defined: crash data visualization, data analysis, and data retrieval. Crash data visualization includes an on-street map based crash location tool and a graphic query tool. Data analysis involves a number of visualization approaches, including static charts— i.e., the scatter chart—the line chart, the area chart, the bar chart, and interactive graph— i.e., the sunburst chart. Users are allowed to generate customized analytical graphs by specifying the parameters and scale. The three types of authorized users are defined to download crash information in the data retrieval section following corresponding limitations. The proposed RCVTS was illustrated using a sample case with crash records of the State of Alaska. It showed that the proposed RCVTS functions well. Recommendations on future research are provided as well

An Unlicensed Taxi Identification Model Based on Big Data Analysis

Social networks and mobile networks are exposing human beings to a big data era. With the support of big data analytics, conventional intelligent transportation systems (ITS) are gradually changing into data-driven ITS ((DITS)-I-2). Along with traffic growth, (DITS)-I-2 need to solve more real-life problems, including the issue of unlicensed taxis and their identification, which potentially disrupts the taxi business sector and endangers society safety. As a remedy to this issue, a smart model is proposed in this paper to identify unlicensed taxis. The proposed model consists of two submodel components, namely, candidate selection model and candidate refined model. The former is used to screen out a coarse-grained suspected unlicensed taxi candidate list. The list is taken as an input for the candidate refined model, which is based on machine learning to get a fine-grained list of suspected unlicensed taxis. The proposed model is evaluated using real-life data, and the obtained results are encouraging, demonstrating its efficiency and accuracy in identifying unlicensed taxis, helping governments to better regulate the traffic operation and reduce associated costs.Social networks and mobile networks are exposing human beings to a big data era. With the support of big data analytics, conventional intelligent transportation systems (ITS) are gradually changing into data-driven ITS ((DITS)-I-2). Along with traffic growth, (DITS)-I-2 need to solve more real-life problems, including the issue of unlicensed taxis and their identification, which potentially disrupts the taxi business sector and endangers society safety. As a remedy to this issue, a smart model is proposed in this paper to identify unlicensed taxis. The proposed model consists of two submodel components, namely, candidate selection model and candidate refined model. The former is used to screen out a coarse-grained suspected unlicensed taxi candidate list. The list is taken as an input for the candidate refined model, which is based on machine learning to get a fine-grained list of suspected unlicensed taxis. The proposed model is evaluated using real-life data, and the obtained results are encouraging, demonstrating its efficiency and accuracy in identifying unlicensed taxis, helping governments to better regulate the traffic operation and reduce associated costs

Информационные технологии в образовании, науке и производстве

Цель конференции – распространение опыта использования современных информационных технологий в образовательном процессе в ходе проведения научно-исследовательских работ, а также в производственной сфере. Научные направления работы мероприятия (секции): 1. Современные информационные технологии в преподавании технических и гуманитарных дисциплин. 2. Информационные технологии в производстве и научных исследованиях. 3. Дистанционное образование: особенности обучения в техническом ВУЗе