12,958 research outputs found
Twitter mobility dynamics during the COVID-19 pandemic: A case study of London
The current COVID-19 pandemic has profoundly impacted people's lifestyles and travel behaviours, which may persist post-pandemic. An effective monitoring tool that allows us to track the level of change is vital for controlling viral transmission, predicting travel and activity demand and, in the long term, for economic recovery. In this paper, we propose a set of Twitter mobility indices to explore and visualise changes in people's travel and activity patterns, demonstrated through a case study of London. We collected over 2.3 million geotagged tweets in the Great London Area (GLA) from Jan 2019 -Feb 2021. From these, we extracted daily trips, origin-destination matrices, and spatial networks. Mobility indices were computed based on these, with the year 2019 as a pre-Covid baseline. We found that in London, (1) People are making fewer but longer trips since March 2020. (2) In 2020, travellers showed comparatively reduced interest in central and sub-central activity locations compared to those in outer areas, whereas, in 2021, there is a sign of a return to the old norm. (3) Contrary to some relevant literature on mobility and virus transmission, we found a poor spatial relationship at the Middle Layer Super Output Area (MSOA) level between reported COVID-19 cases and Twitter mobility. It indicated that daily trips detected from geotweets and their most likely associated social, exercise and commercial activities are not critical causes for disease transmission in London. Aware of the data limitations, we also discuss the representativeness of Twitter mobility by comparing our proposed measures to more established mobility indices. Overall, we conclude that mobility patterns obtained from geo-tweets are valuable for continuously monitoring urban changes at a fine spatiotemporal scale
A stigmergy-based analysis of city hotspots to discover trends and anomalies in urban transportation usage
A key aspect of a sustainable urban transportation system is the
effectiveness of transportation policies. To be effective, a policy has to
consider a broad range of elements, such as pollution emission, traffic flow,
and human mobility. Due to the complexity and variability of these elements in
the urban area, to produce effective policies remains a very challenging task.
With the introduction of the smart city paradigm, a widely available amount of
data can be generated in the urban spaces. Such data can be a fundamental
source of knowledge to improve policies because they can reflect the
sustainability issues underlying the city. In this context, we propose an
approach to exploit urban positioning data based on stigmergy, a bio-inspired
mechanism providing scalar and temporal aggregation of samples. By employing
stigmergy, samples in proximity with each other are aggregated into a
functional structure called trail. The trail summarizes relevant dynamics in
data and allows matching them, providing a measure of their similarity.
Moreover, this mechanism can be specialized to unfold specific dynamics.
Specifically, we identify high-density urban areas (i.e hotspots), analyze
their activity over time, and unfold anomalies. Moreover, by matching activity
patterns, a continuous measure of the dissimilarity with respect to the typical
activity pattern is provided. This measure can be used by policy makers to
evaluate the effect of policies and change them dynamically. As a case study,
we analyze taxi trip data gathered in Manhattan from 2013 to 2015.Comment: Preprin
Optimisation of Mobile Communication Networks - OMCO NET
The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University.
The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing
Identifying and understanding road-constrained areas of interest (AOIs) through spatiotemporal taxi GPS data: A case study in New York City
Urban areas of interest (AOIs) represent areas within the urban environment featuring high levels of public interaction, with their understanding holding utility for a wide range of urban planning applications.
Within this context, our study proposes a novel space-time analytical framework and implements it to the taxi GPS data for the extent of Manhattan, NYC to identify and describe 31 road-constrained AOIs in terms of their spatiotemporal distribution and contextual characteristics. Our analysis captures many important locations, including but not limited to primary transit hubs, famous cultural venues, open spaces, and some other tourist attractions, prominent landmarks, and commercial centres. Moreover, we respectively analyse these AOIs in terms of their dynamics and contexts by performing further clustering analysis, formulating five temporal clusters delineating the dynamic evolution of the AOIs and four contextual clusters representing their salient contextual characteristics
The Possibility of Big Data Spatio-Temporal Analytics for Understanding Human Behavior and Their Spatial Patterns in Urban Area
13301甲第4630号博士(学術)金沢大学博士論文本文Ful
Sensing Human Activity for Smart Cities’ Mobility Management
Knowledge about human mobility patterns is the key element towards efficient mobility management. Traditionally, these data are collected by paper/phone household surveys or travel diaries and serve as input for transportation planning models. In this chapter, we report on current state-of-the-art techniques for sensing human activity and report on their applicability for smart city mobility management purposes. We particularly focus on the use of location-enabled devices and their potential towards replacing traditional data collection approaches. Furthermore, to illustrate applicability of smartphones as ubiquitous sensing devices we report on the use of Routecoach application that was used for mobility data collection in the city of Leuven, Belgium. We provide insights into lessons learned, ways in which collected data were used by different stakeholders, and identify existing gaps and future research needs in this field
都市の持続可能性に向けた旅行行動と知的移動データ統合に関する包括的研究
過去数十年にわたり世界中で都市の持続可能性がトレンドとなり研究対象となっている.人々は,非効率な天然資源の消費や社会経済活動による環境破壊など,地球環境に有害な活動を行い,これには都市計画や交通計画を始め,多くの分野が密接に関係している.現在では,これらを解決する新技術の開発や応用が広範囲な研究分野で日々取り組まれている.本研究では観光に関する問題を,交通と都市の研究の観点からさまざまなビックデータを使用し,持続可能な都市開発を目標とした具体的な解決策を示した.本研究では都市や地域の持続可能性に資するデータの活用方法として,Wi-Fiパケットセンサーを使用した旅行者にとって魅力的な観光目的地マネジメントに関する研究,およびETCプローブデータを使用した旅行時間の信頼性の観測における天候の影響に関する分析を組み合わせて示した.本論文では,都市の移動性の認知に対して以下に示す3つの研究から,特徴的な結果と有効な分析手法を確立した.1)Wi-Fiパッケージセンシング調査を使用した,広域観光エリアでの周遊パターンのマイニングベースの関連法則の調査,2)Wi-Fi追跡データでの大規模な観光地の持続可能な開発に向けた魅力的な目的地の抽出,3)ETC2.0プローブデータを使用して,様々な道路タイプを考慮した旅行の信頼性に対する降雪の影響の評価.以上の研究から,複数視点の考察を積み重ね,包括的な評価と提案を行い,いくつかの重要な結果が得られた.この論文の貢献は,より良い社会への問題解決への糸口となり,今後の政策立案者にとって有意義な内容となるだろう.According to sustainability, the trend is spreading out around the world for past decades. There are many area subjects involved, such as city planning, transportation planning, and so on, because people realized human activities harmful to the environment by consuming natural resources with less efficiency process or damage environment by social and economic movements. Currently, emerging technologies considered for the proactive procedure in extensive study areas regarding new technology application and knowledge based. In term of transport and urban study, including tourism concerns, we used intelligent data from deferent sources to be demonstrating the possible solutions which involve sustainable urban development concept. In this study, as a method of utilizing data that contributes to the sustainability of cities and regions, consideration of attractive destination management for tourists by using wireless probe data, and the weather impact on travel time reliability observation by using electronic toll collection probe data, it represented as combination experiments throughout comprehensive study. This dissertation addressed three contribution studies to the composed acknowledgment of urban mobility, and it obtained the intelligent data and specific method of research-based. It consists of; 1) an association rule mining-based exploration of travel patterns in wide tourism areas using a Wi-Fi package sensing survey, 2) Attractive destinations mining towards massive tourism area sustainable development on Wi-Fi tracking data, and 3) Assessment of the impact of snowfall on travel reliability considering different road types using ETC2.0 probe data. Hence, a stack of varying viewpoints researches provided a comprehensive review and suggestion throughout significant results. The contribution of this dissertation could be an advantage substance for strategy and policies planner to recognize alternative solutions leading to a better society.室蘭工業大学 (Muroran Institute of Technology)博士(工学
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