Community-Based Behavioral Understanding of Mobility Trends and Public Attitude through Transportation User and Agency Interactions on Social Media in the Emergence of Covid-19

The increased availability of technology-enabled transportation options and modern communication devices (smartphones, in particular) is transforming travel-related decision-making in the population differently at different places, points in time, modes of transportation, and socio-economic groups. The emergence of COVID-19 made the dynamics of passenger travel behavior more complex, forcing a worldwide, unparalleled change in human travel behavior and introducing a new normal into their existence. This dissertation explores the potential of social media platforms (SMPs) as a viable alternative to traditional approaches (e.g., travel surveys) to understand the complex dynamics of people’s mobility patterns in the emergence of COVID-19. In this dissertation, we focus on three objectives. First, a novel approach to developing comparative infographics of emerging transportation trends is introduced by natural language processing and data-driven techniques using large-scale social media data. Second, a methodology has been developed to model community-based travel behavior under different socioeconomic and demographic factors at the community level in the emergence of COVID-19 on Twitter, inferring users’ demographics to overcome sampling bias. Third, the communication patterns of different transportation agencies on Twitter regarding message kinds, communication sufficiency, consistency, and coordination were examined by applying text mining techniques and dynamic network analysis. The methodologies and findings of the dissertation will allow real-time monitoring of transportation trends by agencies, researchers, and professionals. Potential applications of the work may include: (1) identifying spatial diversity of public mobility needs and concerns through social media platforms; (2) developing new policies that would satisfy the diverse needs at different locations; (3) introducing new plans to support and celebrate equity, diversity, and inclusion in the transportation sector that would improve the efficient flow of goods and services; (4) designing new methods to model community-based travel behavior at different scales (e.g., census block, zip code, etc.) using social media data inferring users’ socio-economic and demographic properties; and (5) implementing efficient policies to improve existing communication plans, critical information dissemination efficacy, and coordination of different transportation actors to raise awareness among passengers in general and during unprecedented health crises in the fragmented communication world

IEEE Access Special Section Editorial: Big Data Technology and Applications in Intelligent Transportation

During the last few years, information technology and transportation industries, along with automotive manufacturers and academia, are focusing on leveraging intelligent transportation systems (ITS) to improve services related to driver experience, connected cars, Internet data plans for vehicles, traffic infrastructure, urban transportation systems, traffic collaborative management, road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plans, and the development of an effective ecosystem for vehicles, drivers, traffic controllers, city planners, and transportation applications. Moreover, the emerging technologies of the Internet of Things (IoT) and cloud computing have provided unprecedented opportunities for the development and realization of innovative intelligent transportation systems where sensors and mobile devices can gather information and cloud computing, allowing knowledge discovery, information sharing, and supported decision making. However, the development of such data-driven ITS requires the integration, processing, and analysis of plentiful information obtained from millions of vehicles, traffic infrastructures, smartphones, and other collaborative systems like weather stations and road safety and early warning systems. The huge amount of data generated by ITS devices is only of value if utilized in data analytics for decision-making such as accident prevention and detection, controlling road risks, reducing traffic carbon emissions, and other applications which bring big data analytics into the picture

An improved text mining approach to extract safety risk factors from construction accident reports

Workplace accidents in construction commonly cause fatal injury and fatality, resulting in economic loss and negative social impact. Analysing accident description reports helps identify typical construction safety risk factors, which then becomes part of the domain knowledge to guide safety management in the future. Currently, such practice relies on domain experts' judgment, which is subjective and time-consuming. This paper developed an improved approach to identify safety risk factors from a volume of construction accident reports using text mining (TM) technology. A TM framework was devised, and a workflow for building a tailored domain lexicon was established. An information entropy weighted term frequency (TF-H) was proposed for term-importance evaluation, and an accumulative TF-H was proposed for threshold division. A case study of metro construction projects in China was conducted. A list of 37 safety risk factors was extracted from 221 metro construction accident reports. The result shows that the proposed TF-H approach performs well to extract important factors from accident reports, solving the impact of different report lengths. Additionally, the obtained risk factors depict critical causes contributing most to metro construction accidents in China. Decision-makers and safety experts can use these factors and their importance degree while identifying safety factors for the project to be constructed

The Role of Safety Culture in Empowering Safety Behavior at Rail Transport Industry in Malaysia: A Review of Literature

The awareness on safety culture and behavioral safety is in higher demand to reduce occupational incidents. For instance, safety culture is an essential element that could influence employees’ occupational behavior. The increasing number of passengers over the past few decades is evidence of the rapid regional development facilitated by the expansion of the railway industry. It has been implemented to alleviate congestion on highways and airways, as well as to promote global efforts to cut the emissions and employ sustainable energy.  However, the inadequacies in the railway industry's safety culture implementation led to a number of events, including train derailments and collisions, which has contributed to an increase in the rate of occupational accidents. Therefore, the objective of this study is to review the literature on the safety culture as a role in empowering the behavior of railway employees towards safety performance. Thus, the quantitative research design will be conducted through close-ended questionnaire. The sample of this study consist on employees that working in Malaysian rail transport industry. Therefore, SmartPLS will be used to test the model. Moreover, the findings expected to acknowledge an extensive literature review to propose a conceptual framework. It will be accomplished through integrating safety culture into the perceptions of workers, which is expected to influence their safety behavior at workplace. Thus, the Malaysia rail transport industry and related party can use this study as recommendations to improve their safety culture practices. &nbsp

Reliability Centered Maintenance: A Case Study of Railway Transit Maintenance to Achieve Optimal Performance, MTI Report 10-06

The purpose of this qualitative case study was to identify the types of obstacles and patterns experienced by a single heavy rail transit agency located in North America that embedded a Reliability Centered Maintenance (RCM) Process. The outcome of the RCM process also examined the impact of RCM on availability, reliability, and safety of rolling stock. This qualitative study interviewed managers (10 cases), and non-managers (10 cases) at the transit agency obtain data. The data may serve to help rail transit leaders determine future strategic directions that would improve this industry. Despite the RCM record in other fields, it has infrequently been used in heavy rail transit agencies. The research method for the first portion of this qualitative case study was to collect data from subjects by administering an open-ended, in-depth personal interview, of manager and non-managers. The second portion of the study explored how the RCM process affected rolling stock for availability, reliability, and safety. The second portion of the study used data derived from project documents and reports (such as progress reports, email, and other forms of documentation) to answer questions about the phenomena. The exploration and identification of the patterns and obstacles is important because organizational leaders in other heavy rail transit systems may use this knowledge to assist in embedding the process more smoothly, efficiently, and effectively to obtain the desired end results

Estimating Workforce Development Needs for High-Speed Rail in California, Research Report 11-16

This study provides an assessment of the job creation and attendant education and training needs associated with the creation of the California High-Speed Rail (CHSR) network, scheduled to begin construction in September 2012. Given the high profile of national and state commitment to the project, a comprehensive analysis that discusses the education, training, and related needs created during the build out of the CHSR network is necessary. This needs assessment is achieved by means of: 1) analyzing current high-speed rail specific challenges pertaining to 220mph trains; 2) using a more accurate and robust “bottom-up” approach to estimate the labor, education, skills, and knowledge needed to complete the CHSR network; and 3) assessing the current capacity of railroad-specific training and education in the state of California and the nation. Through these analyses, the study identifies the magnitude and attributes of the workforce development needs and challenges that lie ahead for California. The results of this research offer new insight into the training and education levels likely to be needed for the emergent high-speed rail workforce, including which types of workers and professionals are needed over the life of the project (by project phase), and their anticipated educational level. Results indicates that although the education attained by the design engineers of the system signifies the most advanced levels of education in the workforce, this group is comparatively small over the life of the project. Secondly, this report identifies vast training needs for the construction workforce and higher education needs for a managerial construction workforce. Finally, the report identifies an extremely limited existing capacity for training and educating the high-speed rail workforce in both California and in the U.S. generally

Railway Research

This book focuses on selected research problems of contemporary railways. The first chapter is devoted to the prediction of railways development in the nearest future. The second chapter discusses safety and security problems in general, precisely from the system point of view. In the third chapter, both the general approach and a particular case study of a critical incident with regard to railway safety are presented. In the fourth chapter, the question of railway infrastructure studies is presented, which is devoted to track superstructure. In the fifth chapter, the modern system for the technical condition monitoring of railway tracks is discussed. The compact on-board sensing device is presented. The last chapter focuses on modeling railway vehicle dynamics using numerical simulation, where the dynamical models are exploited