Proposal of geographic information systems methodology for quality control procedures of data obtained in naturalistic driving studies

Balsa-Barreiro, José; Valero‐Mora, Pedro M.; Pareja-Montoro, Ignacio; Sánchez-García, Mar

doi:10.1049/iet-its.2014.0243

Cited by 12 publications

(9 citation statements)

References 20 publications

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“…Its length was 15.9 km and it was traveled in both directions being the departure time between 8:00 am and 9:00 am. This same route was already introduced in previous articles by the same authors [19][20][21][22][23][24]. The traffic conditions were optimal, with hardly any traffic jams.…”

Section: Data and Study Areamentioning

confidence: 73%

“…In [19,20], we implemented a methodology for estimating the geoposition of an experimental car in areas where the GPS instrument ceased to function. In [21,22], we developed a reliable method for checking the quality of this type of data, which allowed us to reduce the number of false positives and detect events and incidents more efficiently than with other methods. In [23,24], we implemented and discussed strategies for mapping kinematic data related to driving performance by using Geographic Information Systems (henceforth GIS).…”

Section: Introductionmentioning

confidence: 99%

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Extraction of Naturalistic Driving Patterns with Geographic Information Systems

et al. 2020

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A better understanding of Driving Patterns and their relationship with geographical driving areas could bring great benefits for smart cities, including the identification of good driving practices for saving fuel and reducing carbon emissions and accidents. The process of extracting driving patterns can be challenging due to issues such as the collection of valid data, clustering of population groups, and definition of similar behaviors. Naturalistic Driving methods provide a solution by allowing the collection of exhaustive datasets in quantitative and qualitative terms. However, exploiting and analyzing these datasets is complex and resource-intensive. Moreover, most of the previous studies, have constrained the great potential of naturalistic driving datasets to very specific situations, events, and/or road sections. In this paper, we propose a novel methodology for extracting driving patterns from naturalistic driving data, even from small population samples. We use Geographic Information Systems (GIS), so we can evaluate drivers’ behavior and reactions to certain events or road sections, and compare across situations using different spatial scales. To that end, we analyze some kinematic parameters such as speeds, acceleration, braking, and other forces that define a driving attitude. Our method favors an adequate mapping of complete datasets enabling us to achieve a comprehensive perspective of driving performance.

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Section: Data and Study Areamentioning

confidence: 73%

Section: Introductionmentioning

confidence: 99%

Extraction of Naturalistic Driving Patterns with Geographic Information Systems

et al. 2020

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“…Until the GNSS antenna receives a reliable GNSS signal, the MLS system uses GNSS and IMU observation data for comprehensive processing. As a general rule, when the positional accuracy is below the threshold, the MLS system stops recording data [43] or records the data but does not process it. Figure 10a-b shows the point cloud results of the MLS system output when the train just entered the station, colored by elevation and intensity, respectively.…”

Section: Position Accuracy Descriptionmentioning

confidence: 99%

A Fast Algorithm for Rail Extraction Using Mobile Laser Scanning Data

et al. 2018

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Railroads companies conduct regular inspections of their tracks to maintain and update the geographic data for railway management. Traditional railroad inspection methods, such as onsite inspections and semi-automated analysis of imagery and video data, are time consuming and ineffective. This study presents an automated effective method to detect tracks on the basis of their physical shape, geometrical properties, and reflection intensity feature. This study aims to investigate the feasibility of fast extraction of railroad using onboard Velodyne puck data collected by mobile laser scanning (MLS) system. Results show that the proposed method can be executed rapidly on an i5 computer with at least 10 Hz. The MLS system used in this study comprises a Velodyne puck/onboard GNSS receiver/inertial measurement unit. The range accuracy of Velodyne puck equipment is 2 cm, which fulfills the need of precise mapping. Notably, positioning STD is lower than 4 cm in most areas. Experiments are also undertaken to evaluate the timing of the proposed method. Experimental results indicate that the proposed method can extract 3D tracks in real-time and correctly recognize pairs of tracks. Accuracy, precision, and sensitivity of total test area are 99.68%, 97.55%, and 66.55%, respectively. Results suggest that in a multi-track area, close collaboration between MLS platforms mounted on several trains is required. terrestrial laser scanning (TLS), and MLS is common in recent years because laser scanners provide massive high-quality range measurements with intensity features in a fast manner. Many remarkable practices are made in MLS, ALS, and TLS. The ALS technology has been commercialized in the generation of high-precision digital elevation and surface models [3]. The first MMS [4] used onboard satellite receivers and stereo cameras to capture road images. At that time, precise point positioning [5] technology and differential positioning [6] technology were not introduced yet. IMUs are expensive and the application of mobile mapping systems (MMSs) is limited. Currently, Crucial high-end MMSs such as the Optech Lynx mobile mapping system, Trimble MX-9 MLS system, and RIGEL VMX-2HA MLS system could capture over millions of points every second. Commercial MMSs are applied to point cloud data acquisition of urban road networks and railroad corridors. However, commercial MMSs do not provide a user programming interface, which makes object detection and extraction applications based on them impossible. Consequently, previous studies were focused on the combined use of those devices [7].In general terms, an MLS platform is a vehicle-mounted MMS that is integrated with multiple sensors (e.g., GNSS antenna, IMU, digital cameras) and equipped with a centralized computing system for data synchronization and management. Existing urban reconstruction algorithms using data from different source (e.g., ALS, TLS, MLS) obtain convincing results in urban object modeling and management. Data collected by vehicle-mounted MMSs can be used to detect o...

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“…In [26,27], a methodology for georeferencing data through kinematics and video was developed. In [28,29], some procedures for the quality control of naturalistic driving data were proposed. Both methodologies were also implemented in GIS.…”

mentioning

confidence: 99%

“…The implementation of strategies for quality control of data, taking into account that misleading data may appear due to eventual malfunctions in some of the devices [28,29].…”

mentioning

confidence: 99%

GIS Mapping of Driving Behavior Based on Naturalistic Driving Data

Balsa-Barreiro

Valero‐Mora

Berné-Valero

et al. 2019

IJGI

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Naturalistic driving can generate huge datasets with great potential for research. However, to analyze the collected data in naturalistic driving trials is quite complex and difficult, especially if we consider that these studies are commonly conducted by research groups with somewhat limited resources. It is quite common that these studies implement strategies for thinning and/or reducing the data volumes that have been initially collected. Thus, and unfortunately, the great potential of these datasets is significantly constrained to specific situations, events, and contexts. For this, to implement appropriate strategies for the visualization of these data is becoming increasingly necessary, at any scale. Mapping naturalistic driving data with Geographic Information Systems (GIS) allows for a deeper understanding of our driving behavior, achieving a smarter and broader perspective of the whole datasets. GIS mapping allows for many of the existing drawbacks of the traditional methodologies for the analysis of naturalistic driving data to be overcome. In this article, we analyze which are the main assets related to GIS mapping of such data. These assets are dominated by the powerful interface graphics and the great operational capacity of GIS software.Undoubtedly, one of the most interesting topics related to traffic and transportation issues is road safety [16]. This is highly complex and goes beyond the number of deaths and injuries in crashes within a certain period. Road fatalities have a huge impact, not only in terms of the number of casualties, but also in terms of the costs related to caring for the injured people, the disruption of traffic flows just after accidents, and so forth [17]. For this reason, it becomes increasingly important to deploy prevention policies to manage infrastructures and road flows as well as to provide a quick and efficient response in emergencies [18].Most studies analyze road safety by considering the different elements that are involved, such as the vehicle, driver, road, and infrastructure. However, they generally do it by isolating one of these elements separately or assessing the interaction of two elements at most. Some recent studies suggest the growing need for integrating the maximum number of elements that can affect driving performance to ensure a bigger picture of the whole driving scenario. Thus, more light is shed on why a certain incident happens and under what circumstances [19].In the last few years, the naturalistic driving method has gained more relevance. This research method aims to characterize the driving behavior of people in real-world situations. In order to do that, the different elements and factors involved in the driving performance are recorded using a large array of sensors and video cameras inconspicuously installed in the vehicle. This equipment allows data related to the driving performance to be collected at high temporal rates. These kinds of data are collected using massive and blind strategies. They are massive because their aim is to collec...

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Proposal of geographic information systems methodology for quality control procedures of data obtained in naturalistic driving studies

Cited by 12 publications

References 20 publications

Extraction of Naturalistic Driving Patterns with Geographic Information Systems

Extraction of Naturalistic Driving Patterns with Geographic Information Systems

A Fast Algorithm for Rail Extraction Using Mobile Laser Scanning Data

GIS Mapping of Driving Behavior Based on Naturalistic Driving Data

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