Inferring Drivers Behavior through Trajectory Analysis

Carboni, Eduardo M.; Bogorny, Vânia

doi:10.1007/978-3-319-11313-5_73

Cited by 17 publications

(11 citation statements)

References 8 publications

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“…The works of Carboni and Bogorny (2014) and Huang (2015) analyze individual trajectory anomalous behavior, but they evaluate the behavior of drivers according to their trajectory movement, and not in relation to static areas as we propose in this article. Some works in trajectory analysis as Chen et al (2013) discover anomalous patterns from taxi trajectories, in order to automatically detect taxi driving frauds or road network change on real-time situations.…”

Section: Related Workmentioning

confidence: 97%

“…In GPS trajectory data analysis, which is the focus of this article, the main goal has been on extracting patterns of trajectories in relation to other trajectories (Giannotti et al 2007, de Lucca Siqueira and Bogorny 2011, Carboni and Bogorny 2014, Huang 2015. On the other hand, the behavior of moving objects in relation to points of interest (POI), such as the work of Alvares et al (2011), has not received much attention, but such discovery can reveal suspicious movements and unusual behaviors that are interesting for several application domains, mainly for security.…”

Section: Introductionmentioning

confidence: 99%

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Unusual behavior detection and object ranking from movement trajectories in target regions

Barragana

Älvares

Bogorny

2016

International Journal of Geographical Information Science

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Unusual behavior detection has been of interest in video analysis, transportation systems, movement trajectories, and so on. In movement trajectories, only a few works identify unusual behavior of objects around pre-defined points of interest (POI), such as surveillance cameras, commercial buildings, etc., that may be interesting for several application domains, mainly for security. In this article, we define new types of unusual behaviors of moving objects in relation to POI, including surround, escape, and return. Based on these types of unusual behavior, we (i) present an algorithm to compute these behaviors, (ii) define a set of functions to weight the degree of unusual behavior of every moving object in the database, and (iii) rank the moving objects according to the degree of unusual behavior in relation to a set of POIs. We evaluate the proposed method with real trajectory data and show that the closest work does not detect the proposed behaviors and ranks objects considering only one type of unusual movement. ARTICLE HISTORY

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Section: Related Workmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Unusual behavior detection and object ranking from movement trajectories in target regions

Barragana

Älvares

Bogorny

2016

International Journal of Geographical Information Science

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“…Some works which analyze driving behavior using real GPS trajectories will be introduced as follows. For example, Carboni was interested in abnormal behaviors of individual trajectories of drivers, and presented an algorithm for finding anomalous movements and categorizing levels of driving behavior [10]. Li proposed a method to realize the safety analysis of vehicle driving behavior by using GPS vehicle trajectory data [11].…”

Section: Related Workmentioning

confidence: 99%

“…With the popularity of mobile devices such as GPS and cell phones, large amounts of real driving traces are available to analyze the behavior of drivers [9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Inferring and Analysis Drivers Violation Behavior Through Trajectory

Cai¹,

Pei²,

Zhu³

et al. 2017

IFIP Advances in Information and Communication Technology

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In this paper, we present an algorithm for inferring violation movements and categorizing levels of driving behavior. With this algorithm we extract the speeding and retrograde behavior from the real trajectories datasets of Xinjiang, analyze the changing regulation of six streets in the working day and day off on the overall and explore the driving characteristics which is very dangerous(level 4). The results of this study can not only be used for early warning of drivers violations, but also provide the data support and decision basis for the traffic management department to master the situation of traffic violations and formulate the traffic management counter measures.

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“…One of many popular devices in this category is GPS sensor that enables us to collect vehicles position periodically which includes latitude, longitude, and timestamp data. By calculating the trajectory data generated by GPS, the pattern of vehicles moving, such as over speed, abrupt acceleration, and direction [23], can be inferred. Additionally, other sensing tools such as accelerometer, gyroscope, and magnetometer are frequently used as well to recognize vehicles operation pattern.…”

Section: B Unsafe Maneuvers Detector and Driving Situation Analysismentioning

confidence: 99%

An object oriented Bayesian network approach for unsafe driving maneuvers prevention system

Kridalukmana

Naderpour

2017

2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)

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Abstract-As the main contributor to the traffic accidents, unsafe driving maneuvers have taken attentions from automobile industries. Although driving feedback systems have been developed in effort of dangerous driving reduction, it lacks of drivers awareness development. Therefore, those systems are not preventive in nature. To cover this weakness, this paper presents an approach to develop drivers awareness to prevent dangerous driving maneuvers. The approach uses ObjectOriented Bayesian Network to model hazardous situations. The result of the model can truthfully reflect a driving environment based upon situation analysis, data generated from sensors, and maneuvers detectors. In addition, it also alerts drivers when a driving situation that has high probability to cause unsafe maneuver to be detected. This model then is used to design a system, which can raise drivers awareness and prevent unsafe driving maneuvers.

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Inferring Drivers Behavior through Trajectory Analysis

Cited by 17 publications

References 8 publications

Unusual behavior detection and object ranking from movement trajectories in target regions

Unusual behavior detection and object ranking from movement trajectories in target regions

Inferring and Analysis Drivers Violation Behavior Through Trajectory

An object oriented Bayesian network approach for unsafe driving maneuvers prevention system

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