Developing a Representative Driving Cycle for Paratransit that Reflects Measured Data Transients: Case Study in Stellenbosch, South Africa

Hull, Christopher; McCulloch, Malcolm; Collett, Katherine A.

doi:10.2139/ssrn.4355437

Cited by 2 publications

(1 citation statement)

References 46 publications

(92 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the driving cycle constructed by these traditional clustering algorithms may not adequately represent the real-world driving cycle due to the sensitivity to the cycle data including diversity in the time series of micro-trips. In this regard, the use of the Dynamic Time Warping (DTW) technique can be more useful in the driving cycle constructions since DTW allows measuring of the similarity between two sequences varying in time, speed or frequency of accelerations and decelerations [28,29]. Sundarkumar et al [30] carried out the construction of a representative driving cycle using k-means along with DTW, Euclidean, and Pearson correlation distances to cluster the micro-trips.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Framework for Driving Cycle Generation and Analysis

Keskin,

Yıldız,

Arslannur

2023

Preprint

View full text Add to dashboard Cite

This paper presents a methodology for generating realistic driving cycles using Markov chain modelling, Monte Carlo simulation and dynamic time warping. The study focuses on the construction of a representative driving cycle for the city of Igdir, taking into account its unique traffic characteristics. The methodology involves segmenting the original driving datasets based on traffic conditions and road types, creating a detailed representation of driving behaviour. Dynamic time warping is used to identify reference segments for each group of segments, ensuring accurate matching with the generated driving cycles. The integration of Markov chain-Monte Carlo simulation introduces variability and randomness. The proposed methodology provides an advanced algorithm for generating highly accurate representative driving cycles, which can contribute to energy consumption analysis, vehicle performance and emission evaluation. The comprehensive approach provided by the methodology enables an accurate understanding of driving patterns, promoting the development of sustainable mobility solutions and advancing transport research.

show abstract

Section: Introductionmentioning

confidence: 99%