On the relationship between PageRank and automorphisms of a graph

PageRank is an algorithm used in Internet search to score the importance of web pages. The aim of this paper is demonstrate some new results concerning the relationship between the concept of PageRank and automorphisms of a graph. In particular, we show that if vertices u and v are similar in a graph G (i.e., there is an automorphism mapping u to v), then u and v have the same PageRank score. More generally, we prove that if the PageRanks of all vertices in G are distinct, then the automorphism group of G consists of the identity alone. Finally, the PageRank entropy measure of several kinds of real-world networks and all trees of orders 10–13 and 22 is investigated.acceptedVersionPeer reviewe

Advances in Public Transport Platform for the Development of Sustainability Cities

Modern societies demand high and varied mobility, which in turn requires a complex transport system adapted to social needs that guarantees the movement of people and goods in an economically efficient and safe way, but all are subject to a new environmental rationality and the new logic of the paradigm of sustainability. From this perspective, an efficient and flexible transport system that provides intelligent and sustainable mobility patterns is essential to our economy and our quality of life. The current transport system poses growing and significant challenges for the environment, human health, and sustainability, while current mobility schemes have focused much more on the private vehicle that has conditioned both the lifestyles of citizens and cities, as well as urban and territorial sustainability. Transport has a very considerable weight in the framework of sustainable development due to environmental pressures, associated social and economic effects, and interrelations with other sectors. The continuous growth that this sector has experienced over the last few years and its foreseeable increase, even considering the change in trends due to the current situation of generalized crisis, make the challenge of sustainable transport a strategic priority at local, national, European, and global levels. This Special Issue will pay attention to all those research approaches focused on the relationship between evolution in the area of transport with a high incidence in the environment from the perspective of efficiency

A Survey on Graph Neural Networks in Intelligent Transportation Systems

Intelligent Transportation System (ITS) is vital in improving traffic congestion, reducing traffic accidents, optimizing urban planning, etc. However, due to the complexity of the traffic network, traditional machine learning and statistical methods are relegated to the background. With the advent of the artificial intelligence era, many deep learning frameworks have made remarkable progress in various fields and are now considered effective methods in many areas. As a deep learning method, Graph Neural Networks (GNNs) have emerged as a highly competitive method in the ITS field since 2019 due to their strong ability to model graph-related problems. As a result, more and more scholars pay attention to the applications of GNNs in transportation domains, which have shown excellent performance. However, most of the research in this area is still concentrated on traffic forecasting, while other ITS domains, such as autonomous vehicles and urban planning, still require more attention. This paper aims to review the applications of GNNs in six representative and emerging ITS domains: traffic forecasting, autonomous vehicles, traffic signal control, transportation safety, demand prediction, and parking management. We have reviewed extensive graph-related studies from 2018 to 2023, summarized their methods, features, and contributions, and presented them in informative tables or lists. Finally, we have identified the challenges of applying GNNs to ITS and suggested potential future directions

Methods and Measures for Analyzing Complex Street Networks and Urban Form

Complex systems have been widely studied by social and natural scientists in terms of their dynamics and their structure. Scholars of cities and urban planning have incorporated complexity theories from qualitative and quantitative perspectives. From a structural standpoint, the urban form may be characterized by the morphological complexity of its circulation networks - particularly their density, resilience, centrality, and connectedness. This dissertation unpacks theories of nonlinearity and complex systems, then develops a framework for assessing the complexity of urban form and street networks. It introduces a new tool, OSMnx, to collect street network and other urban form data for anywhere in the world, then analyze and visualize them. Finally, it presents a large empirical study of 27,000 street networks, examining their metric and topological complexity relevant to urban design, transportation research, and the human experience of the built environment.Comment: PhD thesis (2017), City and Regional Planning, UC Berkele

모바일 소셜네트워크에서 사용자간 기회적인 공유기반트래픽 오프로딩

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2013. 8. 최양희.최근 모바일 트래픽의 빠른 증가는 이동통신 사업자에게 심각한 문제가 되고 있다. 이러한 문제를 해결하기 위해 단거리 통신 기술 및 모바일 소셜 네트워크 등을 이용하여 사용자간 직접 데이터를 주고 받는 오프로딩 기법을 사용하는 것에 대한 연구가 이루어지고 있다. 본 논문에서는 사용자간 직접 통신을 통한 효율적인 콘텐츠 공유 및 오프로딩 기법을 제안하고자 한다. 첫번째 연구로, 모바일 소셜 네트워크에서 사용자간 직접 전송기회를 활용해 데이터를 공유하는 모바일 트래픽 오프로딩의 핵심 기술인 TOSS를 제안 하였다. TOSS에서는 셀룰러 네트워크에서 급속히 증가하고 있는 트래픽으로 인한 네트워크 과부하를 경감시키기 위해 온라인 소셜 네트워크에서 사용자의 연결성 및 오프라인 네트워크에서 사용자의 이동성을 고려하여 콘텐츠를 전달할 사용자를 결정하고 블루투스나 와이파이 다이렉트 등의 기술을 이용해 콘텐츠를 직접 전달 하였다. 또한 소셜네트워크 서비스 사용자의 서로 다른 콘텐츠 접근 패턴, 즉 각 사용자가 콘텐츠 생성으로부터 오프로딩을 위해 콘텐츠에 접근하기까지의 시간을 고려 하였다. 본 연구에서는 이러한 요건을 고려하여 트래픽 오프로딩과 콘텐츠 확산을 모델링하고 분석 하였다. 모바일 소셜 네트워크의 데이타 셋을 기반으로 분석 결과에서 TOSS는 모든 사용자의 딜레이 요구조건을 만족시키면서 최대 86.5의 셀룰러 트래픽을 경감시키는 것을 보였다. 두번째의 연구에서는 모바일 네트워크에서 멀티셀을 고려하여 콘텐츠를 배포하는 프레임워크에 대한 연구를 진행 하였다. 해당 프레임워크에서 콘텐츠는 셀룰러 링크와 모바일 사용자간 로컬 링크를 통해 푸시-공유 기반의 통신으로 전달 되였다. 이러한 기법을 바탕으로 multi-compartment 모델을 이용하여 셀 간 핸드오버 및 콘텐츠 전달을 모델링 및 분석하고, 콘텐츠 전달 딜레이와 에너지 소모 사이의 trade-off를 수학적인 최적화 기법을 사용하여 해결 하였다. 본 논문에서는 이와 같이 기존의 측정 연구에 기반한 trace-driven 분석, 모델링 및 시스템 최적화에 대한 연구를 통해 모바일 소셜 네트워크에서 사용자간 직접 전송을 통한 오프로딩 기법이 고효율적임을 보였다. 또한 본 논문은 제안된 기법의 상용화 전망 및 이를 위한 이슈들에 대한 논의도 포함 하였다.The fast increasing traffic demand becomes a serious concern of mobile network operators. To solve this traffic explosion problem, there have been efforts to offload the traffic from cellular links to local short-range communications among mobile users that are moving around and forming mobile social networks. In my thesis, I mainly focus on the user-to-user opportunistic sharing and try to elaborate its effectiveness and efficiency for to offload mobile traffic. In the first work, I propose the Traffic Offloading assisted by Social network services via opportunistic Sharing in mobile social networks, TOSS. In TOSS, initially a subset of mobile users are selected as initial seeds depending on their content spreading impact in online social network services (SNSs) and their mobility patterns in offline mobile social networks (MSNs). Then users share the content via opportunistic local connectivity (e.g., Bluetooth, Wi-Fi Direct) with each other. Due to the distinct access patterns of individual SNS users, TOSS further exploits the user-dependent access delay between the content generation time and each user's access time for the purpose of traffic offloading. I model and analyze process of the traffic offloading and content spreading by taking into account various options in linking SNS and MSN data sets. The trace-driven evaluation shows that TOSS can reduce up to 86.5% of the cellular traffic while satisfying the access delay requirements of all users. In the second work, I focus on the analytical research on Push-Share framework for content disseminating in mobile networks. One content is firstly pushed the to a subset of subscribers via cellular links, and mobile users spread the content via opportunistic local connectivity. I theoretically model and analyze how the content can be disseminated, where handovers are modeled based on the multi-compartment model. I also formulate the mathematical optimization framework, by which the trade-off between the dissemination delay and the energy cost is explored. Based on the measurement study, trace-driven analysis, theoretical modeling and system optimization in above papers, the traffic offloading by user-to-user opportunistic sharing in mobile social networks is proved to be effective and efficient. Additionally, further discussions on the practical deployment, future vision, and open issues are discussed as well.Abstract i I. Introduction 1 II. RelatedWork 7 2.1 Opportunistic Sharing in DTNs/MSNs 7 2.2 Mobile Traffic Offloading 9 2.3 Information/Content Spreading in SNSs 10 III. TOSS 13 3.1 Framework Details 13 3.1.1 Preliminaries 13 3.1.2 Spreading Impact in the Online SNS 16 3.1.3 Access Delays of Users in the SNS 18 3.1.4 Mobility Impact in the Offline MSN 21 3.2 System Optimization 25 3.3 Trace-Driven Measurement 26 3.3.1 Measurement of the Online SNS 26 3.3.2 Measurement of Offline MSNs, λi j and IM 33 3.3.3 Content Obtaining Delays 36 3.3.4 How C Impacts the Obtaining Delay 38 3.4 Performance Evaluation 39 3.4.1 How C Impacts the Total Access Utility 39 3.4.2 Satisfying 100%, 90%, and 80% of Users 44 3.4.3 On-Demand Delivery 47 3.5 Conclusion 48 IV. Push-Share 50 4.1 Framework Details 50 4.2 System Model 53 4.3 Content Dissemination in Single Cell 56 4.3.1 Content Dissemination by Sharing Only 57 4.3.2 Content Dissemination with Initial Push and Final Push 59 4.3.3 Content Dissemination Energy Cost 62 4.4 Content Dissemination in Multiple Cells 63 4.4.1 Non-steady-state Modeling of MSs in Multiple Cells 66 4.4.2 Steady-State Modeling of MSs in Multiple Cells 66 4.4.3 How Handovers Affect the Content Dissemination 67 4.5 Optimization Framework 69 4.5.1 Minimum Dissemination Completion Delay 69 4.5.2 Minimum Dissemination Completion Cost 70 4.5.3 Conjunctive Minimization of Delay and Cost 71 4.6 Evaluation Results 73 4.6.1 Content Dissemination within One Single Cell 74 4.6.2 Content Dissemination within Multiple Cells 77 4.6.3 Optimization Framework 80 4.7 Conclusion 82 V. Summary and Future Work 84 5.1 A Comparison with Traffic Offloading based on Wi-Fi APs 85 5.2 Practical Deployment and Application 86 5.3 Future Work and Vision 88 Bibliography 90Docto

Estimating Footfall From Passive Wi-Fi Signals: Case Study with Smart Street Sensor Project

Measuring the distribution and dynamics of the population at granular level both spatially and temporally is crucial for understanding the structure and function of the built environment. In this era of big data, there have been numerous attempts to undertake this using the preponderance of unstructured, passive and incidental digital data which are generated from day-to-day human activities. In attempts to collect, analyse and link these widely available datasets at a massive scale, it is easy to put the privacy of the study subjects at risk. This research looks at one such data source - Wi-Fi probe requests generated by mobile devices - in detail, and processes it into granular, long-term information on number of people on the retail high streets of the United Kingdom (UK). Though this is not the first study to use this data source, the thesis specifically targets and tackles the uncertainties introduced in recent years by the implementation of features designed to protect the privacy of the users of Wi-Fi enabled mobile devices. This research starts with the design and implementation of multiple experiments to examine Wi-Fi probe requests in detail, then later describes the development of a data collection methodology to collect multiple sets of probe requests at locations across London. The thesis also details the uses of these datasets, along with the massive dataset generated by the ‘Smart Street Sensor’ project, to devise novel data cleaning and processing methodologies which result in the generation of a high quality dataset which describes the volume of people on UK retail high streets with a granularity of 5 minute intervals since August 2015 across 1000 locations (approx.) in 115 towns. This thesis also describes the compilation of a bespoke ‘Medium data toolkit’ for processing Wi-Fi probe requests (or indeed any other data with a similar size and complexity). Finally, the thesis demonstrates the value and possible applications of such footfall information through a series of case studies. By successfully avoiding the use of any personally identifiable information, the research undertaken for this thesis also demonstrates that it is feasible to prioritise the privacy of users while still deriving detailed and meaningful insights from the data generated by the users