Development of Urban Driving Cycle with GPS Data Post Processing

Lipar, Peter; Strnad, Irena; Česnik, Martin; Maher, Tomaž

doi:10.7307/ptt.v28i4.1916

Cited by 16 publications

(11 citation statements)

References 16 publications

(20 reference statements)

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“…For installed measuring systems, we can connect to the vehicle's On-Board Diagnostic (OBD) computer with Controller Area Network (CAN bus) communication, and read data, usually velocity and distance travelled, but also collect other information such as engine performance and fuel consumption [31] [34] [35] [39] [43]. If the vehicle cannot be fitted with a device, for example on a motorcycle, because of the size of the instrument is too large, then a mobile device can be used to measure [33], for example, a handheld GPS receiver [31] [35] or if we use multiple vehicles, and it is not necessary to make more measurement in the same time, we can also move the device [41].…”

Section: Hardware and Software Requirements 21 Hardware For Data Collectionmentioning

confidence: 99%

“…That is why GPS is usually used as a supplement (taking into account altitude values, identifying stopping points, etc.) [35] [47] [48] or checking and correcting the values measured with on board instrument [32] [39] [43]. They can also be used to determine the nature of travel (downtown, suburban, express) or to connect the result of measuring to Geographic Information System (GIS) software [42] [43] [49].…”

Section: Hardware and Software Requirements 21 Hardware For Data Collectionmentioning

confidence: 99%

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Review on Construction Procedures of Driving Cycles

Czégé,

Vámosi,

Kocsis

2020

IJEMS

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The goal of this paper is to give an overview of the literature of construction techniques of driving cycles. Our motivation for the overview is the future goal of constructing our own driving cycles for various types of vehicles and routes. This activity is part of a larger project focusing on determination of fuel and energy consumption by dynamic simulation of vehicles. Accordingly, the papers dealing with sample route determination, data collection and processing, driving cycle construction procedures, statistical evaluation of data are in our focus.

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Section: Hardware and Software Requirements 21 Hardware For Data Collectionmentioning

confidence: 99%

Section: Hardware and Software Requirements 21 Hardware For Data Collectionmentioning

confidence: 99%

Review on Construction Procedures of Driving Cycles

Czégé,

Vámosi,

Kocsis

2020

IJEMS

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“…Besides, more research for other cases than Europe and China would be useful to create multi-regional models or models across affiliated cities such as the C40 Cities [35]. Since international standardized driving cycles can considerably differ from the uniqueness of local driving patterns [36][37][38], there is a need to estimate energy consumption and to analyze driving cycles for various cases.…”

Section: Utilization Of Large Datasets In Energy Researchmentioning

confidence: 99%

OSeMOSYS-PuLP: A Stochastic Modeling Framework for Long-Term Energy Systems Modeling

Dreier¹,

Howells²

2019

Energies

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Recent open-data movements give access to large datasets derived from real-world observations. This data can be utilized to enhance energy systems modeling in terms of heterogeneity, confidence, and transparency. Furthermore, it allows to shift away from the common practice of considering average values towards probability distributions. In turn, heterogeneity and randomness of the real-world can be captured that are usually found in large samples of real-world data. This paper presents a methodological framework for an empirical deterministic–stochastic modeling approach to utilize large real-world datasets in long-term energy systems modeling. A new software system—OSeMOSYS-PuLP—was developed and is available now.It adds the feature of Monte Carlo simulations to the existing open-source energy modeling system (the OSeMOSYS modeling framework). An application example is given, in which the initial application example of OSeMOSYS is used and modified to include real-world operation data from a public bus transport system.

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“…However, in almost all previous studies, the selected parameters mainly reflect the driving characteristics, without parameters reflects well vehicle emission characteristics as vehicle specific power (VSP) parameter [8,12]. In addition, most of previous studies often use driving cycle parameters following the experience of previous studies without presenting an explanation of their choice, as in [7], [13], [15], [9] and so on. Meanwhile, the study of Torp et al (2013) showed that on the different data sets, selected parameters could be very different although the data mining method are the same.…”

Section: Introductionmentioning

confidence: 99%

Determining the set of representative variables of real-world driving cycle of bus: a case study of Hanoi

Yen-Lien

2020

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This paper analysed the real-world driving data to determine the representative parameters of driving cycle for the purpose of the typical driving cycle development of bus in Hanoi. The realworld driving data of bus in Hanoi were collected by using the Global positioning system technique with 1Hz data update rate. The real-world driving data of fifteen bus routes in the inner city were collected continuously, on weekdays as well as at weekends. The data, then, were used to calculate 33 kinematics parameters reflecting the realistic driving characteristics, including vehicle-specific power. The hierarchical agglomerative clustering method was used to determine a minimal set of representative variables from the 33 kinematics parameters. The 14 representative parameters of the real-world driving cycle of bus in Hanoi were determined.

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Development of Urban Driving Cycle with GPS Data Post Processing

Abstract: ABSTRACT

Cited by 16 publications

References 16 publications

Review on Construction Procedures of Driving Cycles

Review on Construction Procedures of Driving Cycles

OSeMOSYS-PuLP: A Stochastic Modeling Framework for Long-Term Energy Systems Modeling

Determining the set of representative variables of real-world driving cycle of bus: a case study of Hanoi

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