Mobility Trace Analysis for Intelligent Vehicular Networks

2021 ◽  
Vol 54 (3) ◽  
pp. 1-38
Author(s):  
Clayson Celes ◽  
Azzedine Boukerche ◽  
Antonio A. F. Loureiro
Keyword(s):  
Large Volume ◽  
Vehicular Networks ◽  
Smart Cities ◽  
Data Communication ◽  
Transportation Systems ◽  
Data Driven ◽  
Mobility Data ◽  
Spatiotemporal Information ◽  
Research Problems ◽  
Hidden Knowledge

Intelligent vehicular networks emerge as a promising technology to provide efficient data communication in transportation systems and smart cities. At the same time, the popularization of devices with attached sensors has allowed the obtaining of a large volume of data with spatiotemporal information from different entities. In this sense, we are faced with a large volume of vehicular mobility traces being recorded. Those traces provide unprecedented opportunities to understand the dynamics of vehicular mobility and provide data-driven solutions. In this article, we give an overview of the main publicly available vehicular mobility traces; then, we present the main issues for preprocessing these traces. Also, we present the methods used to characterize and model mobility data. Finally, we review existing proposals that apply the hidden knowledge extracted from the mobility trace for vehicular networks. This article provides a survey on studies that use vehicular mobility traces and provides a guideline for the proposition of data-driven solutions in the domain of vehicular networks. Moreover, we discuss open research problems and give some directions to undertake them.

scholarly journals Data Dissemination Based on Fuzzy Logic and Network Coding in Vehicular Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Xiaolan Tang ◽  
Zhi Geng ◽  
Wenlong Chen ◽  
Mojtaba Moharrer
Keyword(s):  
Fuzzy Logic ◽  
Network Coding ◽  
Vehicular Networks ◽  
Data Dissemination ◽  
Smart Cities ◽  
High Reliability ◽  
Transportation Systems ◽  
Delivery Ratio ◽  
Velocity Change ◽  
Backbone Network

Vehicular networks, as a significant technology in intelligent transportation systems, improve the convenience, efficiency, and safety of driving in smart cities. However, because of the high velocity, the frequent topology change, and the limited bandwidth, it is difficult to efficiently propagate data in vehicular networks. This paper proposes a data dissemination scheme based on fuzzy logic and network coding for vehicular networks, named SFN. It uses fuzzy logic to compute a transmission ability for each vehicle by comprehensively considering the effects of three factors: the velocity change rate, the velocity optimization degree, and the channel quality. Then, two nodes with high abilities are selected as primary backbone and slave backbone in every road segment, which propagate data to other vehicles in this segment and forward them to the backbones in the next segment. The backbone network helps to increase the delivery ratio and avoid invalid transmissions. Additionally, network coding is utilized to reduce transmission overhead and accelerate data retransmission in interbackbone forwarding and intrasegment broadcasting. Experiments show that, compared with existing schemes, SFN has a high delivery ratio and a short dissemination delay, while the backbone network keeps high reliability.

scholarly journals Visualization of Urban Mobility Data from Intelligent Transportation Systems

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 332 ◽  
Author(s):  
Thiago Sobral ◽  
Teresa Galvão ◽  
José Borges
Keyword(s):  
Intelligent Transportation Systems ◽  
Smart Cities ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Urban Mobility ◽  
Multiple Perspectives ◽  
Data Types ◽  
Mobility Data ◽  
Extensive Body ◽  
Visualization Techniques

Intelligent Transportation Systems are an important enabler for the smart cities paradigm. Currently, such systems generate massive amounts of granular data that can be analyzed to better understand people’s dynamics. To address the multivariate nature of spatiotemporal urban mobility data, researchers and practitioners have developed an extensive body of research and interactive visualization tools. Data visualization provides multiple perspectives on data and supports the analytical tasks of domain experts. This article surveys related studies to analyze which topics of urban mobility were addressed and their related phenomena, and to identify the adopted visualization techniques and sensors data types. We highlight research opportunities based on our findings.

scholarly journals Smart Cities: Data-Driven Solutions to Understand Disruptive Problems in Transportation—The Lisbon Case Study

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3044
Author(s):  
Vitória Albuquerque ◽  
Ana Oliveira ◽  
Jorge Lourenço Barbosa ◽  
Rui Simão Rodrigues ◽  
Francisco Andrade ◽  
...  
Keyword(s):  
Smart Cities ◽  
Research Work ◽  
Data Availability ◽  
Data Driven ◽  
City Management ◽  
Cascading Effect ◽  
Mobility Data ◽  
Local Municipality ◽  
Big Picture ◽  
The City

Transportation data in a smart city environment is increasingly becoming available. This data availability allows building smart solutions that are viewed as meaningful by both city residents and city management authorities. Our research work was based on Lisbon mobility data available through the local municipality, where we integrated and cleaned different data sources and applied a CRISP-DM approach using Python. We focused on mobility problems and interdependence and cascading-effect solutions for the city of Lisbon. We developed data-driven approaches using artificial intelligence and visualization methods to understand traffic and accident problems, providing a big picture to competent authorities and supporting the city in being more prepared, adaptable, and responsive, and better able to recover from such events.

scholarly journals On-demand multi-hop forwarding for sustainable vehicular data transfer network

2021 ◽  
Vol 17 (5) ◽  
pp. 155014772110151
Author(s):  
Ayoub el Bendali ◽  
Anis Ur Rahman ◽  
Asad Waqar Malik ◽  
Muazzam Ali Khan ◽  
Sri Devi Ravana
Keyword(s):  
Internet Of Things ◽  
Intelligent Transportation Systems ◽  
Vehicular Networks ◽  
Data Transfer ◽  
Smart Cities ◽  
Transportation Systems ◽  
Vital Role ◽  
Vehicle Speed ◽  
Coverage Area ◽  
Smart Hospitals

Smart cities play a vital role to develop a sustainable infrastructure with efficient management of the Internet of things devices. The infrastructure is used to support various applications for smart hospitals, smart factories, and intelligent transportation systems. With the extensive deployment of Internet of things devices, unprecedented growth in data has lead to capacity and transfer issues. In this article, we proposed an efficient data transfer mechanism based on self-sustainable networks over the vehicular environment. Depending on whether the network is connected with vehicles available to support direct connection from the source to destination, we propose end-to-end and hop-by-hop forwarding for vehicular networks that are inherently disconnected. The evaluation results demonstrate that the lifetime of the discovered paths depends on the coverage area, vehicle mobility, and vehicle speed. Therefore, at times redundant disjoint paths are selected for communication. In the proposed work, selected vehicles are used to reach the destination.

scholarly journals Exploiting Mobile Edge Computing for Enhancing Vehicular Applications in Smart Cities

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1073 ◽  
Author(s):  
Hesham El-Sayed ◽  
Moumena Chaqfeh
Keyword(s):  
Traffic Safety ◽  
Intelligent Transportation Systems ◽  
Vehicular Networks ◽  
Data Dissemination ◽  
Smart Cities ◽  
Transportation Systems ◽  
Edge Computing ◽  
Communication Overhead ◽  
Mobile Edge Computing ◽  
Communication Frameworks

Mobile edge computing (MEC) has been recently proposed to bring computing capabilities closer to mobile endpoints, with the aim of providing low latency and real-time access to network information via applications and services. Several attempts have been made to integrate MEC in intelligent transportation systems (ITS), including new architectures, communication frameworks, deployment strategies and applications. In this paper, we explore existing architecture proposals for integrating MEC in vehicular environments, which would allow the evolution of the next generation ITS in smart cities. Moreover, we classify the desired applications into four major categories. We rely on a MEC architecture with three layers to propose a data dissemination protocol, which can be utilized by traffic safety and travel convenience applications in vehicular networks. Furthermore, we provide a simulation-based prototype to evaluate the performance of our protocol. Simulation results show that our proposed protocol can significantly improve the performance of data dissemination in terms of data delivery, communication overhead and delay. In addition, we highlight challenges and open issues to integrate MEC in vehicular networking environments for further research.

scholarly journals Highway Speed Prediction Using Gated Recurrent Unit Neural Networks

2021 ◽  
Vol 11 (7) ◽  
pp. 3059
Author(s):  
Myeong-Hun Jeong ◽  
Tae-Young Lee ◽  
Seung-Bae Jeon ◽  
Minkyo Youm
Keyword(s):  
Neural Network ◽  
Intelligent Transportation Systems ◽  
Moving Average ◽  
Computational Cost ◽  
Nonlinear Problems ◽  
Transportation Systems ◽  
Mobility Data ◽  
Challenges And Opportunities ◽  
Moving Average Model ◽  
Gated Recurrent Unit

Movement analytics and mobility insights play a crucial role in urban planning and transportation management. The plethora of mobility data sources, such as GPS trajectories, poses new challenges and opportunities for understanding and predicting movement patterns. In this study, we predict highway speed using a gated recurrent unit (GRU) neural network. Based on statistical models, previous approaches suffer from the inherited features of traffic data, such as nonlinear problems. The proposed method predicts highway speed based on the GRU method after training on digital tachograph data (DTG). The DTG data were recorded in one month, giving approximately 300 million records. These data included the velocity and locations of vehicles on the highway. Experimental results demonstrate that the GRU-based deep learning approach outperformed the state-of-the-art alternatives, the autoregressive integrated moving average model, and the long short-term neural network (LSTM) model, in terms of prediction accuracy. Further, the computational cost of the GRU model was lower than that of the LSTM. The proposed method can be applied to traffic prediction and intelligent transportation systems.

scholarly journals The core academic and scientific disciplines underlying data-driven smart sustainable urbanism: an interdisciplinary and transdisciplinary framework

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Simon Elias Bibri
Keyword(s):  
The Body ◽  
Data Driven ◽  
New Era ◽  
The Core ◽  
Sustainable Urbanism ◽  
Scientific Disciplines ◽  
Specific Direction ◽  
Research Problems ◽  
Urban Planning And Development ◽  
Application Potential

AbstractA new era is presently unfolding wherein both smart urbanism and sustainable urbanism processes and practices are becoming highly responsive to a form of data-driven urbanism under what has to be identified as data-driven smart sustainable urbanism. This flourishing field of research is profoundly interdisciplinary and transdisciplinary in nature. It operates out of the understanding that advances in knowledge necessitate pursuing multifaceted questions that can only be resolved from the vantage point of interdisciplinarity and transdisciplinarity. This implies that the research problems within the field of data-driven smart sustainable urbanism are inherently too complex and dynamic to be addressed by single disciplines. As this field is not a specific direction of research, it does not have a unitary disciplinary framework in terms of a uniform set of the academic and scientific disciplines from which the underlying theories can be drawn. These theories constitute a unified foundation for the practice of data-driven smart sustainable urbanism. Therefore, it is of significant importance to develop an interdisciplinary and transdisciplinary framework. With that in regard, this paper identifies, describes, discusses, evaluates, and thematically organizes the core academic and scientific disciplines underlying the field of data-driven smart sustainable urbanism. This work provides an important lens through which to understand the set of established and emerging disciplines that have high integration, fusion, and application potential for informing the processes and practices of data-driven smart sustainable urbanism. As such, it provides fertile insights into the core foundational principles of data-driven smart sustainable urbanism as an applied domain in terms of its scientific, technological, and computational strands. The novelty of the proposed framework lies in its original contribution to the body of foundational knowledge of an emerging field of urban planning and development.

Hybrid-Data Approach for Estimating Trip Purposes

2021 ◽  
pp. 036119812110184
Author(s):  
Xiaoling Luo ◽  
Adrian Cottam ◽  
Yao-Jan Wu ◽  
Yangsheng Jiang
Keyword(s):  
Transportation Systems ◽  
Data Driven ◽  
Trip Purpose ◽  
Additional Information ◽  
Point Of Interest ◽  
Average Accuracy ◽  
Hybrid Data ◽  
Added Benefit ◽  
Data Source ◽  
High Level

Trip purpose information plays a significant role in transportation systems. Existing trip purpose information is traditionally collected through human observation. This manual process requires many personnel and a large amount of resources. Because of this high cost, automated trip purpose estimation is more attractive from a data-driven perspective, as it could improve the efficiency of processes and save time. Therefore, a hybrid-data approach using taxi operations data and point-of-interest (POI) data to estimate trip purposes was developed in this research. POI data, an emerging data source, was incorporated because it provides a wealth of additional information for trip purpose estimation. POI data, an open dataset, has the added benefit of being readily accessible from online platforms. Several techniques were developed and compared to incorporate this POI data into the hybrid-data approach to achieve a high level of accuracy. To evaluate the performance of the approach, data from Chengdu, China, were used. The results show that the incorporation of POI information increases the average accuracy of trip purpose estimation by 28% compared with trip purpose estimation not using the POI data. These results indicate that the additional trip attributes provided by POI data can increase the accuracy of trip purpose estimation.

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