DTG Big Data Analysis for Fuel Consumption Estimation

doi:10.3745/jips.04.0031

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…A large volume of GPS data generated from the devices along with various operating history of the vehicle could be utilised for multiple purposes, such as vehicle routing, driving education and traffic safety regulation. The data has been analysed to evaluate the traffic patterns or promote eco-driving or safe driving [23][24][25], but only a few recent studies have examined an association between driving behaviours and actual traffic accidents using the limited data [26,27].…”

Section: Methodsmentioning

confidence: 99%

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

Cho,

Kim,

Khan

et al. 2023

PROMTT

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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.

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Section: Methodsmentioning

confidence: 99%

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

Cho,

Kim,

Khan

et al. 2023

PROMTT

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“…Cho and Choi [6] studied the estimation of fuel consumption mileage using the driving patterns extracted from the digital tachographs (DTGs) big data of commercial vehicles. Instead of using the conventional means of measuring fuel consumption by using the On-Board Diagnostics II (OBD-II) device, they used actual DTG data and OBD-II fuel consumption data.…”

Section: Related Workmentioning

confidence: 99%

Advances in Algorithm, Multimedia, and Network for Future IT

2017

J Inf Process Syst

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A Review of Big Data in Road Freight Transport Modeling: Gaps and Potentials

Shoman

Yeh²,

Sprei³

et al. 2023

Data Sci. Transp.

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Road transport accounted for 20% of global total greenhouse gas emissions in 2020, of which 30% come from road freight transport (RFT). Modeling the modern challenges in RFT requires the integration of different freight modeling improvements in, e.g., traffic, demand, and energy modeling. Recent developments in 'Big Data' (i.e., vast quantities of structured and unstructured data) can provide useful information such as individual behaviors and activities in addition to aggregated patterns using conventional datasets. This paper summarizes the state of the art in analyzing Big Data sources concerning RFT by identifying key challenges and the current knowledge gaps. Various challenges, including organizational, privacy, technical expertise, and legal challenges, hinder the access and utilization of Big Data for RFT applications. We note that the environment for sharing data is still in its infancy. Improving access and use of Big Data will require political support to ensure all involved parties that their data will be safe and contribute positively toward a common goal, such as a more sustainable economy. We identify promising areas for future opportunities and research, including data collection and preparation, data analytics and utilization, and applications to support decision-making.

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DTG Big Data Analysis for Fuel Consumption Estimation

Cited by 3 publications

References 8 publications

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

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

Advances in Algorithm, Multimedia, and Network for Future IT

A Review of Big Data in Road Freight Transport Modeling: Gaps and Potentials

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