Digital Tachograph Research Articles

Daily estimation of NO2 concentrations using digital tachograph data.

Traffic information is crucial for estimating NO2 concentrations, but it is static and limited in predicting constantly changing NO2 levels. To overcome these challenges, this study utilized real-time spatial big data to capture both the spatial and temporal fluctuations in traffic. Digital tachograph (DTG) data, sourced from digital devices in all commercial vehicles, are employed to construct a DTG land use regression (LUR) model, and its performance is compared with that of a non-DTG-LUR model. The DTG-LUR model exhibits superior performance, with an explanatory power of 0.46, in contrast to the 0.36 of the non-DTG model. This significant improvement stems from the spatially and temporally dynamic DTG variables such as cargo traffic. This study introduces a novel approach for incorporating DTG data in correlating with NO2 concentrations. It underscores the advantage of DTG data in predicting daily NO2 fluctuations at a precise 200-m grid, which is not feasible with conventional data. The findings of the study highlight the immense potential of spatial big data for fine-grained analyses, which could enable hourly predictions of air pollution.

Predicting Bus Travel Time in Cheonan City Through Deep Learning Utilizing Digital Tachograph Data

Urban transportation systems are increasingly burdened by traffic congestion, a consequence of population growth and heightened reliance on private vehicles. This congestion not only disrupts travel efficiency but also undermines productivity and urban resident’s overall well-being. A critical step in addressing this challenge is the accurate prediction of bus travel times, which is essential for mitigating congestion and improving the experience of public transport users. To tackle this issue, this study introduces the Hybrid Temporal Forecasting Network (HTF-NET) model, a framework that integrates machine learning techniques. The model combines an attention mechanism with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) layers, enhancing its predictive capabilities. Further refinement is achieved through a Support Vector Regressor (SVR), enabling the generation of precise bus travel time predictions. To evaluate the performance of the HTF-NET model, comparative analyses are conducted with six deep learning models using real-world digital tachograph (DTG) data obtained from intracity buses in Cheonan City, South Korea. These models includes various architectures, including different configurations of LSTM and GRU, such as bidirectional and stacked architectures. The primary focus of the study is on predicting travel times from the Namchang Village bus stop to the Dongnam-gu Public Health Center, a crucial route in the urban transport network. Various experimental scenarios are explored, incorporating overall test data, and weekday and weekend data, with and without weather information, and considering different route lengths. Comparative evaluations against a baseline ARIMA model underscore the performance of the HTF-NET model. Particularly noteworthy is the significant improvement in prediction accuracy achieved through the incorporation of weather data. Evaluation metrics, including root mean squared error (RMSE), mean absolute error (MAE), and mean squared error (MSE), consistently highlight the superiority of the HTF-NET model, outperforming the baseline ARIMA model by a margin of 63.27% in terms of the RMSE. These findings provide valuable insights for transit agencies and policymakers, facilitating informed decisions regarding the management and optimization of public transportation systems.

Heinrich’s Law for Traffic Incidents – Using the Digital Tachograph Data to Identify Traffic Accident Hotspots

Heinrich’s Law indicates an empirical ratio between serious accidents, minor accidents and near misses in industrial sites, but has not been discussed in the context of road traffic accidents. Digital tachographs (DTG), a type of IoT device collecting spatiotemporal big data of vehicle trajectories, allow0 for examining a linkage between abnormal driving behaviours and the prevalence of road traffic incidents. According to the Traffic Safety Act implemented in 2011, DTG has been mandatorily pre-installed on most commercial vehicles in South Korea. The data have been analysed to evaluate the data processing method or promote eco-driving or safe driving, but only a few studies have examined an association between driving behaviours and actual traffic accidents using the limited data. We obtained 7,785,124 DTG sensing records from 1,523 commercial taxis driving within the city limits of Seoul at least once in 2013 and integrated them with 57,139 traffic accident cases during the same period. Using the integrated GIS database, we performed a grid-based spatiotemporal mapping and analysis to calculate a ratio among abnormal driving events, minor and major traffic accidents by road type. The findings suggest a potential for enhancing road safety by monitoring and controlling abnormal driving patterns as a precursor for accidents.

Classification of Speed Change and Unstable Flow Segments Using Geohash-Encoded Vehicle Big Data

Precise and detailed speed information is indispensable for ensuring safe and efficient transportation. This is particularly true within unstable flow (UF) segments, which are especially prone to accidents due to the significant speed variations between vehicles and across lanes, and in the context of evolving transportation systems, where autonomous and non-autonomous vehicles are increasingly mixing. To address the limitations of existing methods in providing such data, this study aims to improve the detail, accuracy, and granularity of road information for micro-segments by leveraging individual vehicle big data. The proposed approach utilizes the geohash algorithm for spatial segmentation and introduces a novel criterion for identifying UF segments based on the relationship between space mean speed (SMS) and time mean speed (TMS). The presented strategy was validated through a comprehensive analysis of DTG (Digital Tachograph) data from freight vehicles on Expressway No. 50 in the Gyeonggi region in the Republic of Korea. As a result, a total of 301 segments were identified, including 178 eastbound and 123 westbound segments. UF segments corresponded to partitions falling beyond the reference standard deviation range. Compared with VDS (Vehicle Detection System) and conzone speeds, the proposed method provided more precise and continuous speed information, surpassing those obtained from conventional link-based approaches.

A methodology for prioritizing safety indicators using individual vehicle trajectory data

A methodology for assessing crash risk using vehicle driving trajectories based on data mining techniques was developed in this study. A variety of safety indicators reflecting the characteristics of traffic and road geometric conditions were evaluated in terms of their capability of capturing hazardous traffic flow. Comprehensive data preparation was conducted by matching driving trajectory data obtained from in-vehicle digital tachograph devices and crash data to classify and analyze hazardous and normal traffic flows. The random forest approach was adopted to quantify the importance of safety indicators. The crash risks were evaluated using the logistic regression model and multivariate adaptive regression splines model based on the set of safety indicators with high importance. The results show that the dangerous driving events rate and driving volatility indicators were found to be particularly significant in identifying hazardous conditions. The multivariate adaptive regression splines model showed better performance and a classification accuracy of 86% was achieved. The proposed methodology will be useful for deriving effective countermeasures to prevent crashes, which is the backbone of proactive traffic safety management.

Influence of Public Bus Driver’s Driving Behaviors on Passenger Fall Incidents: An Analysis Using Digital Tachograph Data

Owing to the low occurrence of public transport accidents, most existing studies have focused on improving the traffic safety of passenger cars. However, traffic accidents related to public transport should also be investigated for the safety of public transport users, particularly the vulnerable ones. This study analyzed the behavioral factors affecting passenger fall incidents on buses to enhance the safety of public bus passengers. This study considered potential influential factors, such as acceleration, deceleration, braking, and steering maneuvers, calculated using data from digital tachographs installed on the buses. Negative binomial and Poisson lognormal regression models were built using the Bayesian method. The two models yielded similar results. In all cases, deceleration-related behavioral factors (abrupt deceleration and abrupt stop) significantly influenced passenger fall incidents. The findings from this study are significant for establishing effective strategies to reduce fall incidents by providing safety education to bus drivers.

Acceleration and Deceleration Rates in Interrupted Flow Based on Empirical Digital Tachograph Data

The acceleration and deceleration rates are crucial road design reference values in terms of traffic safety. The purpose of acceleration and deceleration lanes is to reduce the speed difference between the mainline and ramp to minimize rear-end collision. Thus, in traffic safety and traffic flow operational aspects, the acceleration and deceleration lane lengths need to be long. Moreover, excessively long minimum acceleration or deceleration lane length regulations may cause hesitancy to connect new facilities with the road. Despite their relevance, acceleration and deceleration rates have not been updated for many decades. In this study, we analyze the digital tachograph data of vehicles stopped at signalized intersections at red lights and empirically deduce the acceleration and deceleration rates that reflect recent vehicle performance and driver behavior. Finally, we suggest a new corner clearance distance to safely connect the new road near a signalized intersection in urban and rural areas derived from our empirical acceleration and deceleration rates.

자동차 CCOS 기록장치를 활용한 화재조사 연구

Big data and AI-based automobile service technology should be understood not only in the coming future but also in the advanced technology such as artificial intelligence, and CCOS automobile recorder should be understood and applied to automobile fire investigation. There are many recording devices such as OBD (On Board Diagnosis), EDR (Event Data Recorder), and DTG (Digital Tachograph) in the vehicle. However, when the vehicle is burned down, the recording devices installed in the vehicle are lost and the recorded information disappears. Recently, the problem that the recorded information is recorded and stored in the Internet server by using the wireless communication network is also solved. The information collected in the CCOS-based recording device in the vehicle should be combined with the fire investigation to help identify the cause and reinforce it as evidence to enhance external credibility

Study on Bus Clearance Time Calculation at Bus Station Using Digital Tacho Graph

Study on Bus Clearance Time Calculation at Bus Station Using Digital Tacho Graph

Pavement slipperiness detection using wheel speed and acceleration sensor data

Faced with high rates of traffic accidents on slippery roads, it is recommended that road managers promptly identify the slippery spots, remove the slipperiness and inform drivers of the location of the dangerous spots on the route ahead, to allow them to prepare for the slipperiness. In this paper, the wheel slip-based and wheel acceleration-based approaches are suggested for detecting road slipperiness using sensor data from a digital tachograph (DTG), which is mandatory in commercial vehicles. Support vector machine algorithms were employed to categorize slippery and non-slippery states. The performances were outstanding (with accuracy more than 98%) when evaluated using experimental data, collected on a road weather-proving ground. Considering a large number of commercial vehicles equipped with DTG devices which are connected via cellular communications, the suggested methods could be practically applied to real-world DTG-based fleet management systems.

A Crash Prediction Method Based on Artificial Intelligence Techniques and Driving Behavior Event Data

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.

Highway Speed Prediction Using Gated Recurrent Unit Neural Networks

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.

Relationship between sleep problems and dangerous driving behaviors in Japanese short-haul commercial truck drivers: a cross-sectional survey using digital tachograph data

The aim of the study was to examine the relationship between sleep problems and objective driving behaviors of short-haul commercial truck drivers in Japan. One hundred and ten male short-haul commercial truck drivers’ sleep duration, sleep quality (Pittsburgh Sleep Quality Index), and frequency of sleepiness at the wheel were measured by survey in June 2019. In addition, their dangerous driving behaviors (i.e., overspeeding, sudden start and acceleration, sudden deceleration, and sudden braking) during June were calculated using digital tachograph data. Logistic regression analysis (LRA) was performed. The dependent variables were the presence/absence of dangerous driving behaviors, and sleep duration, sleep quality, and sleepiness were assigned as independent variables. Body mass index was adjusted as a confounding variable. LRA for sleep duration revealed that the “<5 h group” was associated with tendency of a higher adjusted odds ratio (2.87, 95% confidence interval = 0.87–9.45, p = 0.08) for sudden braking compared with the “6 h group.” LRA for sleep quality revealed that the “poor quality group” was associated with a higher adjusted OR (2.56, 95% CI = 1.09–6.01) for sudden braking compared with the “normal quality group.” LRA for the frequency of sleepiness revealed that the “frequently group” was associated with a higher adjusted OR (5.15, 95% CI = 1.10–24.13) for sudden braking compared with the “never group.” Nevertheless, sleep problems were nonsignificantly associated with other dangerous driving behaviors. Truck drivers with poor sleep quality and/or frequent sleepiness at the wheel displayed a 2.5–5.1 times greater risk of sudden braking than did drivers without these sleep problems.

A Tamper-Resistant Algorithm Using Blockchain for the Digital Tachograph

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.

A Framework of an Integrated Livestock Vehicle Trajectory Database Using Digital Tachograph Data

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.

Examination of the development of the number and use of trucks up to 3.5 tons total weight

In EU Member States, it is still the case that, except where provided for by national law, the rules on access to the occupation of road transport operator do not apply to undertakings engaged in the occupation of road transport operator exclusively by motor vehicles with a maximum permissible laden weight not exceeding 3,5 tonnes or combination vehicle with the specified maximum weight. The European Commission has pointed out for long time that the number of such companies or vehicles operating both nationally and internationally are also growing. In order to avoid possible gaps and to ensure, through common rules, a minimum level of professionalisation in the sector in which vehicles used exclusively for the carriage of goods with a maximum permissible laden weight not exceeding 3,5 tonnes are used, thereby approximating the conditions of competition between all operators. Requirements for access to the profession should become mandatory for operators using motor vehicles or combinations of vehicles intended exclusively for the carriage of goods in international transport, the maximum permissible laden weight of which exceeds 2.5 tonnes but does not exceed 3.5 tonnes. Therefore, the aim of the paper is to examine the development of the number of trucks up to 3.5 tons in the past time in the Slovak Republic and Poland, but also their use in traffic surveys on a selected transport network in the Slovak Republic. The results of the survey show a relatively large share of these vehicles in the statistics of newly registered vehicles as well as a large share in the traffic flow and on the highway between the two countries. Tightening the requirements for access to the profession and introducing the obligation for digital tachographs to control the driver’s driving mode will have implications for logistical planning, especially for long-distance express services.

Analysis and research plan of commercial truck drivers’ potentially dangerous driving behaviors caused by the changes of Regulation (EC) No. 561/2006

Regulation (EC) No 561/2006 - known as the Driving Time Regulation - lays down rules on driving times, breaks and rest periods for drivers of Lorries and buses to improve working conditions and road safety. A number of new amendments to this regulation were adopted on 15 July 2020. An important change has been made in connection with the extension of driving time, which may endanger road safety. The aim of the study is on the two independent types of research to verify that current regulation and the possible extension of driving time by two hours will not increase driver fatigue and risky driver behavior. The first analysis will be based on the collection of GPS data, digital tachograph data and video data of drivers faces monitored while driving. The second study will be realized on the Driving simulator SNA-211 at the University of Zilina. Data will be collected from each driver and each device and evaluated.

디지털운행기록장치 데이터를 활용한 세종시 버스 유형별 위험운전 행동 분석

본 연구는 세종시를 운행하는 버스(시내버스, 마을버스, 시외버스, 전세버스 등)의 위험운전행동 유형을 분석하여 어느 구간(도로)에서 위험운전행동 유형이 반복적으로 발생하는지 확인하고, 세종시 민원 자료와 매칭하여 실제 민원이 발생하는 구간이 어디인지, 위험운전행동을 감소시킬 수 있는 정책 등을 제언하는 것을 목적으로 수행되었다. 2019년 10월 7-13일까지의 버스 DTG 자료를 분석한 결과 시내버스는 위험운전행동 중 급가속 유형이 한누리 대로와 새롬중앙로를 중심으로 시간대 상관없이 많이 발생하였고, 마을버스는 위험운전행동 중 급감속 유형이 전동면과 연서면을 중심으로 오전첨두에 빈번하게 발생하는 것으로 분석되었다. 시외버스는 위험운전행동이 거의 발생하지 않는 것으로 나타났다. 전세버스는 위험운전행동 중 급가속 유형이 조치원읍의 국도 1호선(세종로)을 중심으로 많이 발생하는 것으로 분석되었다. 이는 버스 유형별 운행하는 지역이 상이하기 때문으로 판단되며, 실제 시내 버스 위험운전행동 민원과 비교 분석한 결과 일치함을 확인하였다. 향후 DTG 자료와 버스 노선번호와 매칭하여 실제 어느 버스 노선에서 위험운전행동이 빈번하게 발생하는지, 어떤 도로에서 위험운전행동 유형이 발생하는지에 대한 분석 수행이 가능할 것으로 판단된다. 해당 연구가 진행된다면 향후 세종시 도로별, 차종별 위험운전행동구간 통계 및 집계 정보 활용이 가능하며, 세종시에 대한 위험운전 유형별 발생 분포에 대해 확인이 가능할 것으로 보인다.

Wheel Slip-based Road Surface Slipperiness Detection

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.

디지털운행기록을 활용한 소형 택배트럭의 대표 운행 사이클 개발

국내에서는 차량의 공인 성능평가를 위해서 미국(FTP-75)과 유럽(NECD)의 표준 운행 사이클을 사용한다. 그러나 택배 차량과 같이 특이 운행 환경(저속주행, 가다-서다 운행의 반복 등)을 가지는 차량의 경우에는 해외 조건에서 개발된 운행 사이클이 최대 속도, 평균 속도, 유휴 시간 비율 등에서 큰 차이가 있어 성능을 평가하기에 적합하지 않다. 따라서 본 연구에서는 차량의 실 운행 특성을 반영하는 대표운행사이클을 도출하기 위하여 사업용 차량에 부착된 운행기록계(Digital TachoGraph, DTG)에서 수집한 빅 데이터를 활용한다. 해당 데이터의 일일 운행을 정차-정차 구간 단위로 구분하고, 단위별 특성을 추출한다. 추출된 단우를 K-means clustering 방법을 사용하여 클러스터링 후 클러스터별 출현 비율에 따라 가중 추출된 대표운행사이클을 생성하는 방법을 제안한다. 그 결과 길이가 2,254초, 평균 속도가 6.86km/h, 유휴비율 48%의 사이클이 도출되었으며, 이는 2017년 2/4분기에 조사된 택배 차량 평균 운행속도 5.9km/h(KOTI)와 유사하고 택배 배송에 사용되는 차량의 실 운행기록과 비교하였을 때 유사한 부분을 확인하였다.