3,030 research outputs found
Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools
Big data has been used widely in many areas including the transportation
industry. Using various data sources, traffic states can be well estimated and
further predicted for improving the overall operation efficiency. Combined with
this trend, this study presents an up-to-date survey of open data and big data
tools used for traffic estimation and prediction. Different data types are
categorized and the off-the-shelf tools are introduced. To further promote the
use of big data for traffic estimation and prediction tasks, challenges and
future directions are given for future studies
A Map-matching Algorithm with Extraction of Multi-group Information for Low-frequency Data
The growing use of probe vehicles generates a huge number of GNSS data.
Limited by the satellite positioning technology, further improving the accuracy
of map-matching is challenging work, especially for low-frequency trajectories.
When matching a trajectory, the ego vehicle's spatial-temporal information of
the present trip is the most useful with the least amount of data. In addition,
there are a large amount of other data, e.g., other vehicles' state and past
prediction results, but it is hard to extract useful information for matching
maps and inferring paths. Most map-matching studies only used the ego vehicle's
data and ignored other vehicles' data. Based on it, this paper designs a new
map-matching method to make full use of "Big data". We first sort all data into
four groups according to their spatial and temporal distance from the present
matching probe which allows us to sort for their usefulness. Then we design
three different methods to extract valuable information (scores) from them: a
score for speed and bearing, a score for historical usage, and a score for
traffic state using the spectral graph Markov neutral network. Finally, we use
a modified top-K shortest-path method to search the candidate paths within an
ellipse region and then use the fused score to infer the path (projected
location). We test the proposed method against baseline algorithms using a
real-world dataset in China. The results show that all scoring methods can
enhance map-matching accuracy. Furthermore, our method outperforms the others,
especially when GNSS probing frequency is less than 0.01 Hz.Comment: 10 pages, 11 figures, 4 table
Hybrid group anomaly detection for sequence data: application to trajectory data analytics
Many research areas depend on group anomaly detection. The use of group anomaly detection can maintain and provide security and privacy to the data involved. This research attempts to solve the deficiency of the existing literature in outlier detection thus a novel hybrid framework to identify group anomaly detection from sequence data is proposed in this paper. It proposes two approaches for efficiently solving this problem: i) Hybrid Data Mining-based algorithm, consists of three main phases: first, the clustering algorithm is applied to derive the micro-clusters. Second, the kNN algorithm is applied to each micro-cluster to calculate the candidates of the group's outliers. Third, a pattern mining framework gets applied to the candidates of the group's outliers as a pruning strategy, to generate the groups of outliers, and ii) a GPU-based approach is presented, which benefits from the massively GPU computing to boost the runtime of the hybrid data mining-based algorithm. Extensive experiments were conducted to show the advantages of different sequence databases of our proposed model. Results clearly show the efficiency of a GPU direction when directly compared to a sequential approach by reaching a speedup of 451. In addition, both approaches outperform the baseline methods for group detection.acceptedVersio
State of the Art, Trends and Future of Bluetooth Low Energy, Near Field Communication and Visible Light Communication in the Development of Smart Cities
The current social impact of new technologies has produced major changes in all
areas of society, creating the concept of a smart city supported by an electronic infrastructure,
telecommunications and information technology. This paper presents a review of Bluetooth Low
Energy (BLE), Near Field Communication (NFC) and Visible Light Communication (VLC) and their
use and influence within different areas of the development of the smart city. The document also
presents a review of Big Data Solutions for the management of information and the extraction of
knowledge in an environment where things are connected by an âInternet of Thingsâ (IoT) network.
Lastly, we present how these technologies can be combined together to benefit the development of
the smart city
Patterns of mobility in a smart city
Transportation data in smart cities is becoming increasingly available. This data allows building meaningful, intelligent solutions for city residents and city management authorities, the so-called Intelligent Transportation Systems. Our research focused on Lisbon mobility data, provided by Lisbon municipality. The main research objective was to address mobility problems, interdependence, and cascading effects solutions for the city of Lisbon. We developed a data-driven approach based on historical data with a strong focus on visualization methods and dashboard creation. Also, we applied a method based on time series to do prediction based on the traffic congestion data provided. A CRISP-DM approach was applied, integrating different data sources, using Python. Hence, understand traffic patterns, and help the city authorities in the decision-making process, namely more preparedness, adaptability, responsiveness to events.Os dados de transporte, no Ăąmbito das cidades inteligentes, estĂŁo cada vez mais disponĂveis. Estes dados permitem a construção de soluçÔes inteligentes com impacto significativo na vida dos residentes e nos mecanismos das autoridades de gestĂŁo da cidade, os chamados Sistemas de Transporte Inteligentes. A nossa investigação incidiu sobre os dados de mobilidade urbana da cidade de Lisboa, disponibilizados pelo municĂpio. O principal objetivo da pesquisa foi abordar os problemas de mobilidade, interdependĂȘncia e soluçÔes de efeitos em cascata para a cidade de Lisboa. Para alcançar este objetivo foi desenvolvida uma metodologia baseada nos dados histĂłricos do transito no centro urbano da cidade e principais acessos, com uma forte componente de visualização. Foi tambĂ©m aplicado um mĂ©todo baseado em series temporais para fazer a previsĂŁo das ocorrĂȘncias de transito na cidade de Lisboa. Foi aplicada uma abordagem CRISP-DM, integrando diferentes fontes de dados, utilizando Python.
Esta tese tem como objetivo identificar padrÔes de mobilidade urbana com anålise e visualização de dados, de forma a auxiliar as autoridades municipais no processo de tomada de decisão, nomeadamente estar mais preparada, adaptada e responsiva
(So) Big Data and the transformation of the city
The exponential increase in the availability of large-scale mobility data has fueled the vision of smart cities that will transform our lives. The truth is that we have just scratched the surface of the research challenges that should be tackled in order to make this vision a reality. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders in building knowledge discovery pipelines over such data sources. At the same time, this widespread data availability also raises privacy issues that must be considered by both industrial and academic stakeholders. In this paper, we provide a wide perspective on the role that big data have in reshaping cities. The paper covers the main aspects of urban data analytics, focusing on privacy issues, algorithms, applications and services, and georeferenced data from social media. In discussing these aspects, we leverage, as concrete examples and case studies of urban data science tools, the results obtained in the âCity of Citizensâ thematic area of the Horizon 2020 SoBigData initiative, which includes a virtual research environment with mobility datasets and urban analytics methods developed by several institutions around Europe. We conclude the paper outlining the main research challenges that urban data science has yet to address in order to help make the smart city vision a reality
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