Tweets and Facebook posts, the novelty techniques in the creation of origin-destination models

Abstract: Social media and big data have emerged to be a useful source of information that can be used for planning purposes, particularly transportation planning and trip-distribution studies. Cities in developing countries such as South Africa often struggle with out-dated, unreliable and cumbersome techniques such as traffic counts and household surveys to conduct origin and destination studies. The emergence of ubiquitous crowd sourced data, big data, social media and geolocation based services has shown huge potential in providing useful information for origin and destination studies. Perhaps such information can be utilised to determine the origin and destination of commuters using the Gautrain, a high-speed railway in Gauteng province South Africa. To date little is known about the origins and destinations of Gautrain commuters. Accordingly, this study assesses the viability of using geolocation-based services namely Facebook and Twitter in mapping out the network movements of Gautrain commuters. Explorative Spatial Data Analysis (ESDA), Echo-social and ArcGis software were used to extract social media data, i.e. tweets and Facebook posts as well as to visualize the concentration of Gautrain commuters. The results demonstrate that big data and geolocation based services have the significant potential to predict movement network patterns of commuters and this information can thus, be used to inform and improve transportation planning. Nevertheless use of crowd sourced data and big data has privacy concerns that still need to be addressed

Big Data Security Issues in Three Perspectives: A Review

Big data is a term that is used to describe data that is high volume, high velocity, and/or high variety; requires new technologies and techniques to capture, store, and analyze it; and is used to enhance decision making, provide insight and discovery, and support and optimize processes. With regard to the definition of big data, IBM Company uses volume, velocity, variety, value and veracity as 5Vs to summarize the concept of big data.  There are different types of big data, for example, structured, semi-structured and un-structured data. The contents of big data can be text data, audio data, video data and still image and it indicates that the big data may have diverse data types as well as data qualities. Big data has variety of sources such as healthcare center, commercial system, industries, social media, telecommunication, transportation, sensor machines and others. In this paper, I reviewed three the most security challenging perspectives and I studied lack of concentrations in these areas by most research works. To confirm security in the big data platforms, it is critical to ascertain the data rendering points and their security techniques to safeguard the data in this pacing digital world. Then I envisage directions for the future research. In this paper, I have reviewed the big data sources and its security issues in the three directions such as data at rest, data at communication and data in process/use. Keywords: Big Data, Big Data Security, Big Data source, Attribute based encryption, storage path, Transport layer security, secure shell, Cloud service Provider DOI: 10.7176/CEIS/12-3-01 Publication date: November 30th 202

Human dynamics in the age of big data: a theory-data-driven approach

The revolution of information and communication technology (ICT) in the past two decades have transformed the world and people’s lives with the ways that knowledge is produced. With the advancements in location-aware technologies, a large volume of data so-called “big data” is now available through various sources to explore the world. This dissertation examines the potential use of such data in understanding human dynamics by focusing on both theory- and data-driven approaches. Specifically, human dynamics represented by communication and activities is linked to geographic concepts of space and place through social media data to set a research platform for effective use of social media as an information system. Three case studies covering these conceptual linkages are presented to (1) identify communication patterns on social media; (2) identify spatial patterns of activities in urban areas and detect events; and (3) explore urban mobility patterns. The first case study examines the use of and communication dynamics on Twitter during Hurricane Sandy utilizing survey and data analytics techniques. Twitter was identified as a valuable source of disaster-related information. Additionally, the results shed lights on the most significant information that can be derived from Twitter during disasters and the need for establishing bi-directional communications during such events to achieve an effective communication. The second case study examines the potential of Twitter in identifying activities and events and exploring movements during Hurricane Sandy utilizing both time-geographic information and qualitative social media text data. The study provides insights for enhancing situational awareness during natural disasters. The third case study examines the potential of Twitter in modeling commuting trip distribution in New York City. By integrating both traditional and social media data and utilizing machine learning techniques, the study identified Twitter as a valuable source for transportation modeling. Despite the limitations of social media such as the accuracy issue, there is tremendous opportunity for geographers to enrich their understanding of human dynamics in the world. However, we will need new research frameworks, which integrate geographic concepts with information systems theories to theorize the process. Furthermore, integrating various data sources is the key to future research and will need new computational approaches. Addressing these computational challenges, therefore, will be a crucial step to extend the frontier of big data knowledge from a geographic perspective. KEYWORDS: Big data, social media, Twitter, human dynamics, VGI, natural disasters, Hurricane Sandy, transportation modeling, machine learning, situational awareness, NYC, GI

Edge-based mining of frequent subgraphs from graph streams

In the current era of Big data, high volumes of valuable data can be generated at a high velocity from high-varieties of data sources in various real-life applications ranging from sensor networks to social networks, from bio-informatics to chemical informatics. In addition, Big data are also available in business, education, engineering, finance, healthcare, scientific, telecommunication, and transportation domains. A collection of these data can be viewed as a big dynamic graph structure. Embedded in them are implicit, previously unknown, and potentially useful knowledge. Consequently, efficient knowledge discovery algorithms for mining frequent subgraphs from these dynamic streaming graph structured data are in demand. On the one hand, some existing algorithms discover collections of frequently co-occurring edges, which may be disjoint. On the other hand, some other existing algorithms discover frequent subgraphs by requiring very large memory space. With high volumes of Big data, available memory space may be limited. To discover collections of frequently co-occurring connected edges, we present in this paper two efficient algorithms that require small memory space. Evaluation results show the efficiency of our edge-based algorithms in mining frequent subgraphs from graph streams

Using crowdsourced data (Twitter & Facebook) to delineate the origin and destination of commuters of the Gautrain public transit system in South Africa

Abstract: The study of commuters’ origins and destinations (O_D) promises to assist transportation planners with prediction models to inform decision making. Conventionally O_D surveys are undertaken through travel surveys and traffic counts, however data collection for these surveys has historically proven to be time consuming and having a strain on human resources, thus a need for an alternative data source arises. This study combines the use social media data and geographic information systems in the creation of a model for origin and destination surveys. The model tests the potential of using big data from Echo echo software which contains Twitter and Facebook data obtained from social media users in Gauteng. This data contains geolocation and it is used to determine origin and destination as well as concentration levels of Gautrain commuters. A krigging analysis was performed on the data to determine the O-D and concentration levels of Gautrain users. The results reveal the concentration of Gautrain commuters at various points of interest that is where they work, live or socialise. The results from the study highlight which nodes attract the most commuters and also possible locations for the expansion for Gautrain. Lastly, the study also highlights some weakness of crowdsourced data for informing transportation planning. (208

Ridesourcing and Travel Demand: Potential Effects of Transportation Network Companies in Bogotá

This paper proposes a modal-shift analysis methodology based on a mix of small-scale primary data and big data sources to estimate the total amount of trips that are reallocated to transportation network companies (TNCs) services in Bogotá, Colombia. The analysis is focused on the following four modes: public transportation, private vehicles, conventional taxis, and TNC services. Based on a stated preferences survey and secondary databases of travel times and costs, the paper proposes a methodology to estimate the reallocation of travel demand once TNCs start operating. Results suggests that approximately one third of public transportation trips are potentially transferred to TNCs. Moreover, potential taxi and private vehicle–transferred trips account for almost 30% of the new TNC demand. Additionally, approximately half of the trips that are reallocated from public transport demand can be considered as complementary, while the remaining share can be considered as potential replacing trips of public transportation. The paper also estimates the potential increase in Vehicle-km travelled in each of the modes before and after substitution as a proxy to the effects of demand reallocation on sustainability, finding increases between 1.3 and 14.5 times the number of Vehicle-km depending on the mode. The paper highlights the role of open data and critical perspectives on available information to analyze potential scenarios of the introduction of disruptive technologies and their spatial, social, and economic implications

Transportation Research Challenges Based on the Analysis of EU Projects

In recent years several projects have been realised in the field of transportation, but there is a lack of systematic analysis of research challenges connected to these projects. Thus, the main aim of this paper is to provide an overview of these challenges through EU funded projects in the field of smart, green and integrated transport. Based on EU strategic documents, reports and roadmaps, 10 topics are identified playing a crucial role in transportation-related research. A systematic analysis of the projects is realised, where the projects collected from an online database in the Horizon 2020 framework programme from 2015 to 2020 are categorised into these topics. The results show that travel behaviour, big data and open data, sustainable mobility planning and smart solutions are covered by several projects which reflect the main research trends. While active and shared modes, multimodal transportation, trip optimisation and Mobility as a Service are also popular topics. Based on the results, the most underrepresented research areas are artificial intelligence and social networks. The analysis of the connections between the research topics could enable the achievement of a long-term paradigm shift in urban mobility, which is beneficial for researchers, professionals and policy makers

Investigation of route choice behaviour in passenger transport

The continually increasing population, the rising economic activity and the permanent race for the rise of economic prosperity, led us to the increase of motorisation. Transportation sector has an important role, because it is not only using the lacking fossil energy sources and polluting the environment, but because of our social and economic structure it is the sector that can hardly change because of the big social resistance. One of the substantial preconditions for the change into an environmental friendly, sustainable system is correct data provision to the regulator (quality and quantity as well). One way for reduction of transport related environmental pollution is to give right price signal to the users with internalisation of transport related external costs. In our article we tried to examine the route choice behaviour in Hungary focusing on toll roads. Our aim in this paper is to analyze the correlation of control possibilities of such econometric phenomena with mathematic tools

A Framework for Integrating Transportation Into Smart Cities

In recent years, economic, environmental, and political forces have quickly given rise to “Smart Cities” -- an array of strategies that can transform transportation in cities. Using a multi-method approach to research and develop a framework for smart cities, this study provides a framework that can be employed to: Understand what a smart city is and how to replicate smart city successes; The role of pilot projects, metrics, and evaluations to test, implement, and replicate strategies; and Understand the role of shared micromobility, big data, and other key issues impacting communities. This research provides recommendations for policy and professional practice as it relates to integrating transportation into smart cities

Classification of Community Complaints Against Public Services on Twitter

Abstract— Information on public services is an important part of increasing community satisfaction with government policies. Complaints and Complaints of the community become mediators to improve public services according to community needs.Twitter is one of the most widely used social media in the community to post activities, experiences, and complaints about public services through the internet easily and realtime.The amount of information on Twitter is mixed between satisfaction and extensibility of public services, making it difficult for the government to make decisions in public policy. The role of Big Data can be a solution to classifying data to predict satisfaction or extensibility of public services with parameters: markets, transportation and hospitals.Data sources taken from Twitter are 700 data texts. The twitter classification of public service complaints is built using the Naïve Bayes Algorithm Method, because the algorithm can classify based on probability values. Text processing is done by filtering text and selecting text to be ordered.The results of this study indicate that the Naïve Bayes Method is able to properly classify public service complaints based on 3 parameters, transportation, markets and hospitals. System testing using 700 data obtained the best results accuracy value: 86%, and precision: 72%, recall 81% and f-measure: 83%