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Road transportation is one of the main sources of air pollution in Macao. This study mainly explores four major roadside locations with high traffic flow in Macao from March to May 2022 and measures their pollutant concentrations (PM10 and PM2.5), traffic flow and their fuel type, as well as considering the meteorological parameters and pollutant concentration of SMG Macao to analyze the relationship between traffic flow and pollutants on roadside locations. Under the measuring distance between 3 and 6 meters, showing that the four locations had a good correlation with the roadside station data provided by SMG on both weekdays and weekends/holidays (PM2.5: R2 is 0.59 to 0.81 on weekdays and 0.79 to 0.88 on weekends/holidays, p<0.01; PM10: R2 is 0.33 to 0.82 on weekdays and 0.30 to 0.58 on weekends/holidays, p<0.05), the overall PM2.5 is 41 to 86% higher than that of the same period of Macao roadside station (SMG), and 68 to 186% higher than that of Taipa Ambient (SMG), indicating that it is more harmful to daily pedestrians. The overall relationship between PM concentration and traffic flow is small on the long-term scale (PM2.5: R2 is 0.01 to 0.13; PM10: R2 is 0.00 to 0.02). This study also analyzed air quality on EBL, the overall PM2.5 and PM10 decreased by 12.3% to 24.8% compared with non-EBL during the period, so that is indeed beneficial to the reduction of pollutant concentrations. In addition, narrower roads were overall higher when road widths added for comparison. Lastly, meteorological data added for comparison, except for relative humidity, it can be found that there is a significant correlation with long-term pollutants (p<0.05). While previous studies have found that single-day traffic flow is related to the increase in PM concentration, this paper is more inclined to their two-way effect when exploring their long-term relationship
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This paper introduces a concept proposal for accessing driving behavior in public transportation through Mobile Crowd Sensing (MCS), as part of a long-term research project on Advanced Public Transportation System (APTS). The proposed concept makes use of mobile device's accelerometer and passengers' qualitative evaluation to identify aggressive driving behavior, which is believed to be a major factor for unnecessary accidents and fuel consumption. A survey and comparison of IT services (mobile applications and websites) provided by Macau Government and private bus companies in Macau, regarding bus-related information, such as fares, routes and route diversions is also provided.
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This thesis introduces, implements and evaluates an innovative concept for assessing driving behavior in public transportation through Mobile Crowd Sensing (MCS), under the field of Advanced Public Transportation System (APTS) - a sub-group of Intelligent Transportation Systems (ITS). Aggressive driving behavior is known to be a cause of avoidable accidents and to increase fuel consumption. In public transportations, it is also a case for costumers’ dissatisfaction. Monitoring the quality of driving behavior is a key element to overcome this issue and to improve road safety and customer satisfaction. In this research project, a software application (app) for mobile devices was developed as an experimental tool / proof-of-concept, to monitor aggressive driving behavior in bus drivers, collecting data coming from mobile device’s accelerometer and passengers’ qualitative evaluation. The experimental procedure took place in public transportation in Macau (bus only) and consisted of data collection of drivers’ aggressive driving behavior using the developed application. The analysis of collected data suggests that MCS is a viable way to assess drivers’ behavior in public transportation, thus contributing to the improvement of the service and increase of road safety. Although the methodology has been tailor-made for Macau public transportation, it is believed that the same concept can be applied to other cities, leading them towards the goal of becoming smarter cities. Keywords: driving behavior; mobile crowd sensing; crowdsourcing; smart city; advanced public transportation system; intelligent transportation system; road safety; mobile device accelerometer
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Investor sentiment and emotions have a strong impact on financial markets. In recent years there has been increasing interest in analyzing the sentiment of investors for stock price prediction using machine learning. Existing prediction models mostly depend on the analysis of trading data and company profit. few prediction theories have been built based on individual investors' sentiments. The fundamental reason is the difficulty to measure individual investors' sentiment.
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Sentiment analysis technologies have a strong impact on financial markets. In recent years there has been increasing interest in analyzing the sentiment of investors. The objective of this paper is to evaluate the current state of the art and synthesize the published literature related to the financial sentiment analysis, especially in investor sentiment for prediction of stock price. Starting from this overview the paper provides answers to the questions about how and to what extent research on investor sentiment analysis and stock price trend forecasting in the financial markets has developed and which tools are used for these purposes remains largely unexplored. This paper represents the comprehensive literature-based study on the fields of the investors sentiment analytics and machine learning applied to analyzing the sentiment of investors and its influencing stock market and predicting stock price.
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Nowadays marketers and practitioners recognize the importance of social marketing as a strategy to acquire customers. Accordingly, the enterprises and brands are embracing the use of social network in communicating with potential consumers ad actual consumers, in order its performance so as to increase its sales. Among all those social media platforms, Instagram is one of the platforms which is image orientated and can easily connect with users and provide information with photos and images. As Macau is small‐sized city where there are abundant small to medium‐sized enterprises, the local small firms are switching their marketing strategies by utilizing the Instagram to promote their product online. In order to get a better grasp of how Instagram affects the promotion of the food and beverage product in Macau, the research question has been formulated: “To what extend can the local food sectors in Macau use Instagram as a marketing tool to engage purchasing intention and strengthen their brand equity? From the perspective of customers.” Semi‐structured interviews were conducted with 13 participants who are the Instagram users, with diverse situations of engagement on Instagram. According to the interviews, participants expressed that how Instagram can associate effects with arousal and brand equity (brand awareness, brand image, perceived quality and brand loyalty) and formation of purchase intention eventually through the Instagram marketing activities. The results showed that Instagram marketing activities strongly correlated the brand equity (brand awareness, brand image, and perceived quality while which in turn led to purchasing intention towards the brand. This research comprehensively illustrates the influences of Instagram marketing activities on customer‐based brand equity. The findings of this study will enable local food brands to more accurately forecast the future purchasing behaviors of their customers through Instagram marketing activities and provide a guide in managing brand equity as well
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This paper shows how mathematical concepts can be displayed on World Wide Web pages. A few of the most interesting solutions are outlined although a few others are missing and a few more will be invented very soon. It is for authors to decide which system is the most suitable for their purposes.
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