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Smartphones as an integrated platform for monitoring driver behaviour: The role of sensor fusion and connectivity

Published on Oct 1, 2018in Transportation Research Part C-emerging Technologies5.78
· DOI :10.1016/j.trc.2018.03.023
Stratis Kanarachos7
Estimated H-index: 7
(Coventry University),
Stavros-Richard G. Christopoulos5
Estimated H-index: 5
(Coventry University),
A. Chroneos28
Estimated H-index: 28
(Coventry University)
Cite
Abstract
Abstract Nowadays, more than half of the world’s web traffic comes from mobile phones, and by 2020 approximately 70 percent of the world’s population will be using smartphones. The unprecedented market penetration of smartphones combined with the connectivity and embedded sensing capability of smartphones is an enabler for the large-scale deployment of Intelligent Transportation Systems (ITS). On the downside, smartphones have inherent limitations such as relatively limited energy capacity, processing power, and accuracy. These shortcomings may potentially limit their role as an integrated platform for monitoring driver behaviour in the context of ITS. This study examines this hypothesis by reviewing recent scientific contributions. The Cybernetics theoretical framework was employed to allow a systematic comparison. First, only a few studies consider the smartphone as an integrated platform. Second, a lack of consistency between the approaches and metrics used in the literature is noted. Last but not least, areas such as fusion of heterogeneous information sources, Deep Learning and sparse crowd-sensing are identified as relatively unexplored, and future research in these directions is suggested.
  • References (83)
  • Citations (8)
Cite
References83
Newest
Published on Jun 1, 2018in Frontiers of Earth Science in China
Tao Xu2
Estimated H-index: 2
(ECNU: East China Normal University),
Xiang Li10
Estimated H-index: 10
(ECNU: East China Normal University)
+ 0 AuthorsChristophe Claramunt23
Estimated H-index: 23
(Naval Academy Research Institute)
Accurate travel time prediction is undoubtedly of importance to both traffic managers and travelers. In highly-urbanized areas, trip-oriented travel time prediction (TOTTP) is valuable to travelers rather than traffic managers as the former usually expect to know the travel time of a trip which may cross over multiple road sections. There are two obstacles to the development of TOTTP, including traffic complexity and traffic data coverage.With large scale historical vehicle trajectory data and m...
Published on Feb 1, 2018in Mobile Networks and Applications2.39
Rita Tse5
Estimated H-index: 5
,
Lu Fan Zhang1
Estimated H-index: 1
+ 1 AuthorsGiovanni Pau (UC: University of California)
In recent years, the growing prevalence of social networks makes it possible to utilize human users as sensors to inspect city environment and human activities. Consequently, valuable insights can be gained by applying data mining techniques to the data generated through social networks. In this work, a practical approach to combine data mining techniques with statistical analysis is proposed to implement crowd sensing in a smart city. A case study to analyze the relationship between weather con...
Published on Jan 1, 2018in Neurocomputing4.07
Weipeng Cao3
Estimated H-index: 3
(SZU: Shenzhen University),
Xi-Zhao Wang36
Estimated H-index: 36
(SZU: Shenzhen University)
+ 1 AuthorsJinzhu Gao2
Estimated H-index: 2
(UOP: University of the Pacific (United States))
Abstract In big data fields, with increasing computing capability, artificial neural networks have shown great strength in solving data classification and regression problems. The traditional training of neural networks depends generally on the error back propagation method to iteratively tune all the parameters. When the number of hidden layers increases, this kind of training has many problems such as slow convergence, time consuming, and local minima. To avoid these problems, neural networks ...
Sina Dabiri2
Estimated H-index: 2
(VT: Virginia Tech),
Kevin Heaslip11
Estimated H-index: 11
(VT: Virginia Tech)
Abstract Identifying the distribution of users’ transportation modes is an essential part of travel demand analysis and transportation planning. With the advent of ubiquitous GPS-enabled devices (e.g., a smartphone), a cost-effective approach for inferring commuters’ mobility mode(s) is to leverage their GPS trajectories. A majority of studies have proposed mode inference models based on hand-crafted features and traditional machine learning algorithms. However, manual features engender some maj...
Published on Jan 1, 2018in Applied Computing and Informatics0.42
Khadija Akherfi1
Estimated H-index: 1
(TUM: Technische Universität München),
Micheal Gerndt1
Estimated H-index: 1
(TUM: Technische Universität München),
Hamid Harroud9
Estimated H-index: 9
(Al Akhawayn University)
Abstract Despite the evolution and enhancements that mobile devices have experienced, they are still considered as limited computing devices. Today, users become more demanding and expect to execute computational intensive applications on their smartphone devices. Therefore, Mobile Cloud Computing (MCC) integrates mobile computing and Cloud Computing (CC) in order to extend capabilities of mobile devices using offloading techniques. Computation offloading tackles limitations of Smart Mobile Devi...
Igor Vasconcelos3
Estimated H-index: 3
(PUC-Rio: Pontifical Catholic University of Rio de Janeiro),
Rafael Oliveira Vasconcelos5
Estimated H-index: 5
(PUC-Rio: Pontifical Catholic University of Rio de Janeiro)
+ 3 AuthorsMethanias Colaço Júnior4
Estimated H-index: 4
(UFS: Universidade Federal de Sergipe)
The majority of fatal car crashes are caused by reckless driving. With the sophistication of vehicle instrumentation, reckless maneuvers, such as abrupt turns, acceleration, and deceleration, can now be accurately detected by analyzing data related to the driver-vehicle interactions. Such analysis usually requires very specific in-vehicle hardware and infrastructure sensors (e.g. loop detectors and radars), which can be costly. Hence, in this paper, we investigated if off-the-shelf smartphones c...
Published on Nov 1, 2017in Expert Systems With Applications4.29
Stratis Kanarachos7
Estimated H-index: 7
(Coventry University),
Stavros-Richard G. Christopoulos5
Estimated H-index: 5
(UoA: National and Kapodistrian University of Athens)
+ 1 AuthorsMichael E. Fitzpatrick24
Estimated H-index: 24
(Coventry University)
Design of a transferable time series anomaly detection method.Novel deep neural network structure facilitates learning short and long-term pattern interdependencies.Detection of anomalies in the Seismic Electrical Signal for predicting earthquake activity.Detection of road anomalies using smartphone data, facilitating crowdsourcing applications. The quest for more efficient real-time detection of anomalies in time series data is critically important in numerous applications and systems ranging f...
Published on Nov 1, 2017in Journal of Network and Computer Applications5.27
Tarun Kulshrestha1
Estimated H-index: 1
(IITs: Indian Institutes of Technology),
Divya Saxena5
Estimated H-index: 5
(IITs: Indian Institutes of Technology)
+ 2 AuthorsManoj Misra17
Estimated H-index: 17
(IITs: Indian Institutes of Technology)
Abstract Localization in both indoor and outdoor environments is a long-studied problem. Using Smartphone for localization has also gained popularity recently. However, none of the existing solutions consider seamless localization and tracking of individuals in both indoor and outdoor stretches with significant accuracy. In this paper, we propose a human identification, monitoring, and location tracking system, called SmartITS , which continuously tracks MAC ids of user equipment (Smartphones, B...
Published on Sep 15, 2017in IEEE Sensors Journal3.08
Shih-Hau Fang17
Estimated H-index: 17
(YZU: Yuan Ze University),
Yu-Xaing Fei1
Estimated H-index: 1
(YZU: Yuan Ze University)
+ 1 AuthorsYu Tsao16
Estimated H-index: 16
(CIT: Center for Information Technology)
In recent years, the importance of user information has increased rapidly for context-aware applications. This paper proposes a deep learning mechanism to identify the transportation modes of smartphone users. The proposed mechanism is evaluated on a database that contains more than 1000 h of accelerometer, magnetometer, and gyroscope measurements from five transportation modes, including still, walk, run, bike, and vehicle. Experimental results confirm the effectiveness of the proposed mechanis...
Cited By8
Newest
Published on Sep 23, 2019in Sensors3.03
Wei Zhao1
Estimated H-index: 1
,
Jiateng Yin + 3 AuthorsTroy Runge
Real-time capturing of vehicle motion is the foundation of connected vehicles (CV) and safe driving. This study develops a novel vehicle motion detection system (VMDS) that detects lane-change, turning, acceleration, and deceleration using mobile sensors, that is, global positioning system (GPS) and inertial ones in real-time. To capture a large amount of real-time vehicle state data from multiple sensors, we develop a dynamic time warping based algorithm combined with principal component analys...
Dewei Yi1
Estimated H-index: 1
(Lboro: Loughborough University),
Jinya Su (University of Essex)+ 2 AuthorsWen-Hua Chen30
Estimated H-index: 30
(Lboro: Loughborough University)
Abstract Reliable driving state recognition (e.g. normal, drowsy, and aggressive) plays a significant role in improving road safety, driving experience and fuel efficiency. It lays the foundation for a number of advanced functions such as driver safety monitoring systems and adaptive driving assistance systems. In these applications, state recognition accuracy is of paramount importance to guarantee user acceptance. This paper is mainly focused on developing a personalized driving state recognit...
Alessio D. Marra1
Estimated H-index: 1
,
Henrik Becker5
Estimated H-index: 5
+ 1 AuthorsFrancesco Corman21
Estimated H-index: 21
Abstract This paper describes development and testing of a passive GPS tracking smartphone application and corresponding data analysis methodology designed to increase the quality of travel behavior information collected in long-term travel surveys. The new approach is intended to replace the pencil-and-paper travel diaries and prompted recall methods that require more user involvement due to requirements for manual data entry and/or high battery usage. Reducing the burden placed on users enable...
Published on May 7, 2019in Sensors3.03
Drivers’ behaviors and decision making on the road directly affect the safety of themselves, other drivers, and pedestrians. However, as distinct entities, people cannot predict the motions of surrounding vehicles and they have difficulty in performing safe reactionary driving maneuvers in a short time period. To overcome the limitations of making an immediate prediction, in this work, we propose a two-stage data-driven approach: classifying driving patterns of on-road surrounding vehicles using...
Xuechi Zhang1
Estimated H-index: 1
(UMD: University of Maryland, College Park),
Ali Haghani23
Estimated H-index: 23
(UMD: University of Maryland, College Park),
Saini Yang9
Estimated H-index: 9
(BNU: Beijing Normal University)
Abstract Optimally deploying sensors into a complex highway network can significantly enhance the socio-economic benefits through accurately providing system users and operators with the latest traffic state information. This paper presents a linear optimization framework for designing a sensor network for travel time surveillance. The presented framework consists of a traditional static network model and a novel dynamic network model as well as an integer cut-based speed-up solution approach. T...
Published on Apr 1, 2019in Expert Systems With Applications4.29
Stratis Kanarachos7
Estimated H-index: 7
(Coventry University),
Jino Mathew3
Estimated H-index: 3
(Coventry University),
Michael E. Fitzpatrick24
Estimated H-index: 24
(Coventry University)
Abstract The high level of air pollution in urban areas, caused in no small extent by road transport, requires the implementation of continuous and accurate monitoring techniques if emissions are to be minimised. The primary motivation for this paper is to enable fine spatiotemporal monitoring based on crowd sensing, whereby the instantaneous fuel consumption of a vehicle is estimated using smartphone measurements. To this end, a surrogate method based on indirect monitoring using Recurrent Neur...
Published on Mar 1, 2019in Measurement2.79
Marco Grossi12
Estimated H-index: 12
(UNIBO: University of Bologna)
Abstract Modern mobile phones, featuring high performance microprocessors, rich set of sensors and internet connectivity are largely diffused all over the world and are ideal devices for the development of low-cost sensing systems, in particular for low-income developing countries and rural areas that lack the access to diagnostic laboratories and expensive instrumentation. In the design of a smartphone based sensing system different elements must be taken in consideration such as sensors perfor...
Oscar Oviedo-Trespalacios11
Estimated H-index: 11
(Universidad del Norte, Colombia),
Mark J. King21
Estimated H-index: 21
(QUT: Queensland University of Technology)
+ 1 AuthorsVerity Truelove2
Estimated H-index: 2
(QUT: Queensland University of Technology)
Abstract Mobile phone use while driving is a pervasive problem that continues to increase, notwithstanding the large crash risk this behaviour constitutes. A number of phone applications have been developed with the intention of utilising the technology to prevent dangerous phone behaviours while driving. Despite the potential these applications have in preventing crashes associated with distracted driving, research is yet to explore these emergent applications. Therefore, this study provided a ...
Hamid Reza Eftekhari2
Estimated H-index: 2
(AUT: Amirkabir University of Technology),
Mehdi Ghatee13
Estimated H-index: 13
(AUT: Amirkabir University of Technology)
Abstract Monitoring and evaluating of driving behavior is the main goal of this paper that encourage us to develop a new system based on Inertial Measurement Unit (IMU) sensors of smartphones. In this system, a hybrid of Discrete Wavelet Transformation (DWT) and Adaptive Neuro Fuzzy Inference System (ANFIS) is used to recognize overall driving behaviors. The behaviors are classified into the safe, the semi-aggressive, and the aggressive classes that are adopted with Driver Anger Scale (DAS) self...
Chengcheng Xu12
Estimated H-index: 12
(SEU: Southeast University),
Junyi Ji1
Estimated H-index: 1
(SEU: Southeast University),
Pan Liu18
Estimated H-index: 18
(SEU: Southeast University)
Abstract The station-free sharing bike is a new sharing traffic mode that has been deployed in a large scale in China in the early 2017. Without docking stations, this system allows the sharing bike to be parked in any proper places. This study aimed to develop a dynamic demand forecasting model for station-free bike sharing using the deep learning approach. The spatial and temporal analyses were first conducted to investigate the mobility pattern of the station-free bike sharing. The result ind...