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Journal articles on the topic 'The digital tachograph'

Author: Grafiati

Published: 4 June 2021

Last updated: 17 February 2022

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1

Kim, Yongbae, Juyong Back, and Jongweon Kim. "A Tamper-Resistant Algorithm Using Blockchain for the Digital Tachograph." Electronics 10, no. 5 (March 2, 2021): 581. http://dx.doi.org/10.3390/electronics10050581.

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A tachograph in a vehicle records the vehicle operating conditions, such as speed, distance, brake operation conditions, acceleration, GPS information, etc., in intervals of one second. For accidents, the tachograph records information, such as the acceleration and direction of a vehicle traveling in intervals of 1/100 s for 10 s before and after the accident occurs as collision data. A vehicle equipped with a tachograph is obliged to upload operation data to administrative organizations periodically via other auxiliary storage devices like a USB attached external memory or online wireless communication. If there is a problem with the recorded contents, data may be at risk of being tampered with during the uploading process. This research proposed tamper-resistant technology based on blockchain for data in online and offline environments. The suggested algorithm proposed a new data recording mechanism that operates in low-level hardware of digital tachographs for tamper-resistance in light blockchains and on/offline situations. The average encoding time of the proposed light blockchain was 1.85 ms/Mb, while the average decoding time was 1.65 ms/Mb. With the outliers in statistical tests removed, the estimated average encoding and decoding time was 1.32 ms/Mb and 1.29 ms/Mb, respectively, and the tamper verification test detected all the tampered data.

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2

Szokało, Agnieszka Aleksandra, and Marcin Rychter. "Use of Cryptography Principles in Digital Registration Devices." Journal of KONES 26, no. 2 (June 1, 2019): 159–66. http://dx.doi.org/10.2478/kones-2019-0045.

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Abstract Cryptography is a field of science necessary to solve problems with encryption of classified messages. The security of electronic information is an integral part of the digital tachography system. From year to year, there is a noticeable increase in electronic data logging systems in many aspects of life. The control measures created for the purpose of performing roadside checks are not sufficient to detect all violations in the recording equipment. Many violations that have occurred during the use of the vehicle are stored in the device’s memory. The article describes the basic mathematical rules illuminating cryptography. The hazards to which the motion sensor installed in the vehicle may be exposed are described. Breaking safety in transport systems may lead to incorrect results of inspections carried out while the vehicle is stopped by authorized services. The article also provides information on the authorities responsible for issuing cards for digital tachographs. Nowadays, documents are increasingly issued with the use of a digital signature. The digital signature of its origin is based on the principles of cryptography. The security key seems to be complicated for the average user. The article discusses the topic of tachograph construction, in particular the schematic responsible for information encryption.

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3

Kang, Joon-Gyu, Yoo-Won Kim, Ung-Taeg Lim, and Moon-Seog Jun. "Digital Tachograph Vehicle Data Digital Authentication System." Journal of the Korea Society of Computer and Information 18, no. 6 (June 28, 2013): 47–54. http://dx.doi.org/10.9708/jksci.2013.18.6.047.

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4

Nowacki, Gabriel, Izabella Mitraszewska, Andrzej Wojcechowski, and Tomasz Kamiński. "SOME PROBLEMS OF DATA SECURITY OF DIGITAL TACHOGRAPH SYSTEM." TRANSPORT 23, no. 1 (March 31, 2008): 82–87. http://dx.doi.org/10.3846/1648-4142.2008.23.82-87.

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The paper refers to some introduction problems of Digital Tachograph System (DTS) in EEA. It affects 25 states of the European Union and EFTA (Island, Liechtenstein, Norway) and Switzerland. The legislation principles and structure of DTS in the EU are characterized, especially Polish elements. The current state of DTS introduction in the EU Member States was also noted. Some problems of data security concern TACHONET system that ensures reliable and secure exchange of data between Member States issuing tachograph cards. The digital tachograph security principles (ITSEC) of the motion sensor, the vehicle unit and the smart cards were taken into consideration.

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5

Rychter, Marcin, and Piotr Sułek. "Digital Recording Devices is Element of Safety of the Road Transport." Journal of KONES 26, no. 2 (June 1, 2019): 115–20. http://dx.doi.org/10.2478/kones-2019-0039.

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Abstract The duty of applying recorders in the road transport was implemented in states of the European Union on regulations (EWG) no. 3820 / 85 on harmonizations of some social welfare legislation referring to the transport, which was changing with regulation (EWG) no. 3821/85 on recorders applied in the road transport. The duty of implementation of the digital tachograph is also considered in the context of improving road safety. Through the analysis of the records of the registering devices can be defined in each specific case of speeding by more than the allowable value in the area. Technical requirements for your device recorded in the resolution, which defines the main parameters, are measured, among things other, the traversed path length of the car, speed, time driving, other periods of work, politeness of the driver and the correct operation of the check authorized for those authorities. At present led Regulation (EU) No 165/2014 of European Parliament establishing requirements concerning structure, checking, installation, use and repairs of tachographs and their building blocks is implementing the second generation of the digital tachographs connected with the device GNSS (Global Navigation Satellite System). Organizing the early detection from a distance is showing data officers from the digital tachograph and information concerning mass and mass falling on the wasp of the entire team of vehicles. Experience in work, economic pressures and competition in transport has led the drivers through a transport company in the event noncompliance, and in particular, in relation to the driving time and breaks. This article contains responsibilities after part rest on the drivers of vehicles takes the issues of system security and optimum performance of recording devices.

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6

Kang, Joon-Gyu, Yoo-Won Kim, Ung-Taeg Lim, and Moon-Seog Jun. "An Improved Vehicle Data Format of Digital Tachograph." Journal of the Korea Society of Computer and Information 18, no. 8 (August 30, 2013): 77–85. http://dx.doi.org/10.9708/jksci.2013.18.8.077.

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7

Baek, Sung Hoon. "A Reliable and Fast Data Recovery in a Digital Tachograph." Advanced Science Letters 22, no. 11 (November 1, 2016): 3511–15. http://dx.doi.org/10.1166/asl.2016.7861.

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8

LEE, Jaeyun, and Jihye BYUN. "Development of Representative Driving Cycle for Delivery Truck Using Digital Tachograph." Journal of Korean Society of Transportation 38, no. 4 (August 31, 2020): 324–33. http://dx.doi.org/10.7470/jkst.2020.38.4.324.

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9

CHO, Jongseok, Hyunsuk LEE, Jaeyoung LEE, and Ducknyung KIM. "The Hazardous Expressway Sections for Drowsy Driving Using Digital Tachograph in Truck." Journal of Korean Society of Transportation 35, no. 2 (April 30, 2017): 160–68. http://dx.doi.org/10.7470/jkst.2017.35.2.160.

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10

Sim, HyeonJeong, Chandle Chae, Minju Kang, and Jonghoon Lee. "Analysis on Accuracy of GPS installed in Digital Tachograph of Commercial vehicles." Journal of The Korea Institute of Intelligent Transport Systems 18, no. 6 (December 31, 2019): 164–75. http://dx.doi.org/10.12815/kits.2019.18.6.164.

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11

Kang, Joon-Gyu, Yoo-Won Kim, and Moon-Seog Jun. "Real-time Dangerous Driving Behavior Analysis Utilizing the Digital Tachograph and Smartphone." Journal of the Korea Society of Computer and Information 20, no. 12 (December 30, 2015): 37–44. http://dx.doi.org/10.9708/jksci.2015.20.12.037.

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12

Kim, Yoo-Won, and Joon-Gyu Kang. "Implementation of Real-time Dangerous Driving Behavior Analysis Utilizing the Digital Tachograph." Journal of the Korea Society of Computer and Information 20, no. 2 (February 28, 2015): 55–62. http://dx.doi.org/10.9708/jksci.2015.20.2.055.

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13

Baek, Sung Hoon, and Myunghee Son. "A Design and Implementation for a Reliable Data Storage in a Digital Tachograph." KIPS Transactions on Computer and Communication Systems 1, no. 2 (November 30, 2012): 71–78. http://dx.doi.org/10.3745/ktccs.2012.1.2.071.

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14

Park, Jongjin, Geonwoo Joh, and Jongchan Park. "Study on Reliability of New Digital Tachograph for Traffic Accident Investigation and Reconstruction." Transactions of the Korean Society of Automotive Engineers 23, no. 6 (November 1, 2015): 615–22. http://dx.doi.org/10.7467/ksae.2015.23.6.615.

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15

jang jae min, 임재경, and Young-Ihn Lee. "Analysis of Risk Driver Discrimination Considering Driver's Behavior Based on Digital TachoGraph(DTG)." Journal of Transport Research 24, no. 4 (December 2017): 63–77. http://dx.doi.org/10.34143/jtr.2017.24.4.63.

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16

Jeong, Heehyeon, Jungyeol Hong, and Dongjoo Park. "A Framework of an Integrated Livestock Vehicle Trajectory Database Using Digital Tachograph Data." Sustainability 13, no. 5 (March 2, 2021): 2694. http://dx.doi.org/10.3390/su13052694.

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The outbreak of African swine fever virus has raised global concerns regarding epidemic livestock diseases. Therefore, various studies have attempted to prevent and monitor epidemic livestock diseases. Most of them have emphasized that integrated studies between the public health and transportation engineering are essential to prevent the livestock disease spread. However, it has been difficult to obtain big data related to the mobility of livestock-related vehicles. Thus, it is challenging to conduct research that comprehensively considers cargo vehicles’ movement carrying livestock and the spread of livestock infectious diseases. This study developed the framework for integrating the digital tachograph data (DTG) and trucks’ visit history of livestock facility data. The DTG data include commercial trucks’ coordinate information, but it excludes actual livestock-related vehicle trajectories such as freight types and facility visit history. Therefore, the integrated database we developed can be used as a significant resource for preventing the spread of livestock epidemics by pre-monitoring livestock transport vehicles’ movements. In future studies, epidemiological research on infectious diseases and livestock species will be able to conduct through the derived integrating database. Furthermore, the indicators of the spread of infectious diseases could be suggested based on both microscopic and macroscopic roadway networks to manage livestock epidemics.

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17

SEYAMA, Shunki, Naoya AKITA, and Michiyasu ODANI. "STUDY ON TRUCK MOVEMENT FOR COLLECTING AND DELIVERING RAILWAY CONTAINER CARGO USING DIGITAL TACHOGRAPH." Journal of Japan Society of Civil Engineers, Ser. D3 (Infrastructure Planning and Management) 75, no. 5 (2019): I_659—I_668. http://dx.doi.org/10.2208/jscejipm.75.i_659.

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18

Shikanai, Takeshi, and Senlin Guan. "Recording Operation of Sugar Cane Harvester and Analysis of Working Efficiency by Digital Tachograph System." Agricultural Information Research 24, no. 4 (2015): 101–11. http://dx.doi.org/10.3173/air.24.101.

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19

Seya, Hajime, Junyi Zhang, Makoto Chikaraishi, and Ying Jiang. "Decisions on truck parking place and time on expressways: an analysis using digital tachograph data." Transportation 47, no. 2 (June 21, 2018): 555–83. http://dx.doi.org/10.1007/s11116-018-9899-y.

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20

Kwon, Yeongmin, Jiho Yeo, and Jihye Byun. "Analysis of Bus Drivers’ Working Environment and Accidents by Route-Bus Categories : Using Digital TachoGraph Data." Journal of The Korea Institute of Intelligent Transport Systems 18, no. 2 (April 30, 2019): 1–11. http://dx.doi.org/10.12815/kits.2019.18.2.1.

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21

Kim, Do-Gyeong, Chungwon Lee, and Byung-Jung Park. "Use of Digital Tachograph Data to Provide Traffic Safety Education and Evaluate Effects on Bus Driver Behavior." Transportation Research Record: Journal of the Transportation Research Board 2585, no. 1 (January 2016): 77–84. http://dx.doi.org/10.3141/2585-09.

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22

PARK, Seungjun, Kiman HONG, Taegyun KIM, Hyeon SEO, and Young Suk HONG. "A Study on Spatial Pattern of Impact Area of Intersection Using Digital Tachograph Data and Traffic Assignment Model." Journal of Korean Society of Transportation 36, no. 2 (April 30, 2018): 155–68. http://dx.doi.org/10.7470/jkst.2018.36.2.155.

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23

Rychter, M., and R. Rychter. "The impact of European Registers of Road Transport Undertakings on security and enforcement of the system of digital tachograph." IOP Conference Series: Materials Science and Engineering 148 (September 2016): 012050. http://dx.doi.org/10.1088/1757-899x/148/1/012050.

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24

Zhou, Tuqiang, and Junyi Zhang. "Analysis of commercial truck drivers’ potentially dangerous driving behaviors based on 11-month digital tachograph data and multilevel modeling approach." Accident Analysis & Prevention 132 (November 2019): 105256. http://dx.doi.org/10.1016/j.aap.2019.105256.

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25

Jeong, Myeong-Hun, Tae-Young Lee, Seung-Bae Jeon, and Minkyo Youm. "Highway Speed Prediction Using Gated Recurrent Unit Neural Networks." Applied Sciences 11, no. 7 (March 29, 2021): 3059. http://dx.doi.org/10.3390/app11073059.

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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.

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26

LEE, Jooyoung, and Jiho YEO. "Analysis of Driving Behavior and Correlation with Crashes in Rainy Weather Using Digital Tachograph - Focusing on Driving Record of Taxi in Seoul -." Journal of Korean Society of Transportation 36, no. 6 (December 31, 2018): 493–502. http://dx.doi.org/10.7470/jkst.2018.36.6.493.

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27

Ikeda, Hiroki, Shun Matsumoto, Tomohide Kubo, Shuhei Izawa, and Masaya Takahashi. "Relationship between sleep problems and dangerous driving behaviors in Japanese short-haul commercial truck drivers: a cross-sectional survey using digital tachograph data." Sleep and Biological Rhythms 19, no. 3 (March 29, 2021): 297–303. http://dx.doi.org/10.1007/s41105-021-00318-9.

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28

Kim, Yunjong, Juneyoung Park, and Cheol Oh. "A Crash Prediction Method Based on Artificial Intelligence Techniques and Driving Behavior Event Data." Sustainability 13, no. 11 (May 28, 2021): 6102. http://dx.doi.org/10.3390/su13116102.

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Various studies on how to prevent and deal with traffic accidents are ongoing. In the past, the key research emphasis was on passive accident response measures that analyzed roadway-based historical data to identify road sections with high crash risk. Through assessing crash risks by analyzing simulation data and actual vehicle driving trajectory data, this study suggests a method of effectively preventing accidents before they happen. In this analysis, using digital tachograph (DTG) data, which is the vehicle trajectory data for commercial vehicles running on Korean highways, hazardous and normal traffic flows were identified and extracted. Driving behavior event data for both types of traffic flow was processed by measuring safety indicators through the extracted data. Safety indicators with a high impact on traffic flow classification were then extracted using gradient boosting, a representative ensemble technique. A neural network analysis was performed using the extracted safety indicators as independent variables to create a traffic flow classifier, which had a high accuracy of 94.59%. The DTG data set was also classified based on the severity of each accident that occurred in the studied roadway, the time of the accident, and the weather; the results were compiled to enable comprehensive accident prediction. It is expected that proactive crash prevention will be possible in the future by evaluating real-time accident risks using the findings and ensemble-based methodologies of this paper.

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29

HORIMO, Sadao. "2S1-5 Ergonomic analysis of road spots inducing frequent abrupt brake on large trucks through “cloud service oriented operation management system” based on big data of vehicle mounted device: video driving recorder and digital tachograph." Japanese journal of ergonomics 52, Supplement (2016): S60—S61. http://dx.doi.org/10.5100/jje.52.s60.

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30

ŚMIEJA, Michał, Sławomir WIERZBICKI, and Jarosław MAMALA. "CAN network in real time on board systems." Combustion Engines 171, no. 4 (November 1, 2017): 211–14. http://dx.doi.org/10.19206/ce-2017-436.

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The network system of data exchange between the various components is an inherent element of every car. Because of the specific and different requirements for data transfer between specific devices, currently used communication protocols have different properties of performance, security, and degree of determinism. The paper presents the increasing complexity of the data exchange system based on the example of the latest requirements for digital tachographs. The article describes also the data transmission initialization methods in the context of network data exchange organization. The hybrid use of time triggering and event triggering mechanisms has been presented in relation to the operation of the CAN network under increased real-time conditions requirements implemented as TTCAN (time triggered CAN).

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31

Rychter, Marcin. "POSSIBILITIES OF MANIPULATING RECOMMENDATIONS OF ELEMENTS OF THE DIGITAL TACHOGRAPHS SYSTEM AND WAYS OF PREVENTING THIS ACTION." Journal of KONES. Powertrain and Transport 19, no. 2 (January 1, 2015): 481–91. http://dx.doi.org/10.5604/12314005.1138263.

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32

Jang, Jinhwan. "Wheel Slip-based Road Surface Slipperiness Detection." Open Transportation Journal 14, no. 1 (September 8, 2020): 186–93. http://dx.doi.org/10.2174/1874447802014010186.

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Background: Faced with the high rate of traffic accidents under slippery road conditions, agencies attempt to quickly identify slippery spots on the road and drivers want to receive information on the impending dangerous slippery spot, also known as “black ice.” Methods: In this study, wheel slip, defined as the difference between both speeds of vehicular transition and wheel rotation, was used to detect road slipperiness. Three types of experiment cars were repeatedly driven on snowy and dry surfaces to obtain wheel slip data. Three approaches, including regression analysis, support vector machine (SVM), and deep learning, were explored to categorize into two states-slippery or non-slippery. Results: Results indicated that a deep learning model resulted in the best performance with accuracy of 0.972, only where sufficient data were obtained. SVM models universally showed good performance, with average accuracy of 0.965, regardless of sample size. Conclusion: The proposed models can be applied to any connected devices including digital tachographs and on-board units for cooperative ITS projects that gather wheel and transition speeds of a moving vehicle to enhance road safety in winter season though collecting followed by providing dangerous slippery spots on the road.

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33

Szokało, Agnieszka Aleksandra, and Marcin Rychter. "Reducing Emission of Harmful Toxic Substances by Trucks in the Aspect of Analysis of Selected Vehicle Operating Parameters and Human Factor." Journal of KONES 26, no. 3 (September 1, 2019): 227–34. http://dx.doi.org/10.2478/kones-2019-0077.

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Abstract Digital tachographs as devices recording the work of a lorry and driver’s work are a constant factor in the work of a professional driver. It is therefore important to preserve the security of recording and storing electronic information. In this matter, cryptography also becomes an inseparable field of science. The control services set up to conduct roadside inspections do not detect all violations in the recording devices by encrypting information in information systems. One of the frequent vehicle operating parameters that can be falsified are the speed of the vehicle and its rotational speed, as well as the time and efficiency of the driver’s work. These parameters affect the amount of fuel consumption. The reduction of fuel consumption translates directly to the reduction of harmful substances emitted by the car to the environment. Variable compression engines are becoming the future of motoring in the case of internal combustion engines. It is noticeable that there are more frequent deliberate of regulations; and more violations rules concerning the proper functioning of recording devices by recording driver activity. Current methods of preventing violations are insufficient. The article points out the violations related to the actual rotational speed of the crankshaft of the internal combustion engine in relation to the vehicle speed profile, which is directly related to the fuel consumption and the emission of harmful substances. The article describes the ways to reduce fuel consumption, of which the correct driving technique is important in addition to the previously marked parameters. The factors that increase the awareness of professional drivers in the aspect of proper driving techniques are described.

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34

"Digital tachograph market drives forward." Card Technology Today 16, no. 10 (October 2004): 4. http://dx.doi.org/10.1016/s0965-2590(04)00177-x.

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35

"G&D wins digital tachograph contract." Card Technology Today 17, no. 4 (April 2005): 6. http://dx.doi.org/10.1016/s0965-2590(05)00277-x.

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