Development of a Real-World Driving Cycle: Comparison
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Please note this is a comparison between Version 2 by Dean Liu and Version 1 by Thaned Satiennam.

Global greenhouse gas (GHG) emissions reached a new high in 2019. Although 2020 GHG emissions were lower than those in 2019 due to the COVID-19 crisis and associated actions, GHG concentrations in the atmosphere are continuing to rise. An improvement in on-road driving behavior that would reduce fuel consumption would benefit a huge number of motorcycle riders, resulting in significant reductions in fuel consumption and CO2 emissions. Therefore, it is essential to aggregate sufficient realistic data to generate representative real-world driving cycles that can be employed reliably for fuel consumption and exhaust emission assessment in the future. 

  • eco-driving cycle
  • motorcycle
  • CO2 emissions
  • real-world data

1. Introduction

Global greenhouse gas (GHG) emissions reached a new high in 2019. Although 2020 GHG emissions were lower than those in 2019 due to the COVID-19 crisis and associated actions, GHG concentrations in the atmosphere are continuing to rise. CO2 emissions account for 65% of the total greenhouse gas emissions, resulting in increased GHG emissions. To reduce global warming, net-zero CO2 emission reductions must be sustained. The transport sector has contributed to roughly 14% of global GHG emissions during the last decade [1]. The road transport sector is principally responsible because its growth is increasing, particularly in Asia due to rapid economic growth. More than half of global CO2 emissions are emitted in Asia [2]. Therefore, the development of transportation must be based on environmental sustainability.
Hayashi et al. (2012) [3] suggested the CUTE matrix for establishing a low carbon society. The CUTE matrix introduced three strategies, including AVOID, SHIFT, and IMPROVE, to minimize fuel consumption and emissions in the transportation sector. Four measurements, including technology, regulatory, information, and economics, were also introduced. Fukuda et al. (2014) [4] proposed three strategies for establishing a low-carbon society in Asia according to the CUTE matrix. They were AVOID (e.g., transit-oriented development, TOD), SHIFT (e.g., shift to public transit) and IMPROVE (e.g., improving driving behavior using eco-driving cycles).
Among ASEAN countries, Indonesia, Vietnam, and Thailand have the highest accumulated number of motorcycles, as displayed in Figure 1 [5]. The number of newly registered motorcycles in Thailand has risen to 21.4 million [6], leading to an increase in fuel consumption and CO2 emissions.
Figure 1. The number of motorcycles registered in ASEAN countries.
An improvement in on-road driving behavior that would reduce fuel consumption would benefit a huge number of motorcycle riders, resulting in significant reductions in fuel consumption and CO2 emissions. The term “eco-driving” refers to actions that the driver can perform while driving to improve fuel efficiency or reduce CO2 emissions. These actions include reducing deceleration, avoiding frequent use of the brakes, maintaining a suitable distance, using proper acceleration, maintaining a constant speed, and reducing the idling time [7]. Many studies have revealed that eco-driving training in passenger car drivers could improve fuel efficiency and reduce CO2 emissions [8[8][9],9], but eco-driving training for motorcycle riders does not yet exist. Previously, eco-driving cycles for motorcycles have been developed by an optimal controller in laboratory tests [10,11][10][11]. However, these engine-controlled eco-driving cycles may not ensure that motorcycle riders can be effectively trained for eco-driving behavior because they were not developed using representative data from the real-world driving behavior of motorcycle riders. In addition, fuel consumption and air pollutant emissions of real-world driving were reported differently from those of laboratory-tested driving cycles because drivers’ behavior and traffic and road conditions were not considered. Therefore, it is essential to aggregate sufficient realistic data to generate representative real-world driving cycles that can be employed reliably for fuel consumption and exhaust emission assessment in the future [12]. Nevertheless, the previous study did not reveal the development of a real-world eco-driving cycle for motorcycles, which is necessary for reducing fuel consumption and CO2 emissions.

2. Development of a Driving Cycle

There have been many previous studies on real-world driving patterns, as indicated in Table 1. Many driving cycles have been developed according to vehicle type, road hierarchy, and the urban environment. In general, the development of a driving cycle can be divided into three steps: (1) route selection, (2) real-world data collection, and (3) driving cycle construction.
Table 1. Previous studies on real-world driving patterns.
Previous Studies Data Collection Analysis Method Measurement and Calculation of Fuel Consumption and CO2 Emissions Results
Tzeng and Chen (1998) [14][13] Chasing vehicle technique Statistical method for determining the driving cycle Chassis dynamometer test The driving cycles for motorcycles in Taipei, Taiwan
Chen et al. (2003) [21][14] Onboard measurement Repetitive algorithm for selecting micro-trips at random Chassis dynamometer test The driving cycles for motorcycles for cities in Taiwan
Tsai et al. (2005) [22][15] Onboard measurement Repetitive algorithm for selecting micro-trips at random Chassis dynamometer test The driving cycles for motorcycles in Kaohsiung, Taiwan
Tong et al. (2011) [23][16] Onboard measurement Repetitive algorithm for selecting micro-trips at random Not considered The driving cycles for motorcycles and light-duty vehicles in Vietnam
Seedam et al. (2015) [24][17] Onboard measurement Repetitive algorithm using principle of least total variance in target parameters from micro-trips Not considered The driving cycles for motorcycles in Khon Kaen city, Thailand
Tutuianu et al. (2015) [25][18] Onboard measurement Repetitive algorithm using principle of least total variance in target parameters from short trips Not considered The driving cycles for light-duty vehicles
Satiennam et al. (2017) [29][19] Onboard measurement Linear regression analysis Onboard fuel consumption sensor and gas analyzer Real-world exhaust emission and fuel consumption models for motorcycles
Mayakuntla and Verma (2018) [15][20] GPS Repetitive algorithm using target parameters from trip segment Not considered The driving cycles for passenger cars in Indian cities
Wang et al. (2018) [9] Onboard measurement Descriptive statistics Calculation of fuel consumption and emissions Eco-driving training efficiency according to road type
Koossalapeerom et al. (2019) [26][21] Onboard measurement Repetitive algorithm using principle of least total variance in target parameters from micro-trips Onboard fuel consumption sensors and gas analyzer

Calculation of CO2 equivalent emissions of electric motorcycle		 The driving cycles for electric and gasoline motorcycles
Mahesh et al. (2019) [16][22] GPS Emission rate equations Onboard gas analyzer Real-world emission factors and emission models for motorcycles in India
Zhang et al. (2019) [17][23] GPS Micro-trip method and Markov Monte Carlo method Onboard energy consumption sensor The driving cycles for electric vehicles considering road environment
Lois et al. (2019) [30][24] Onboard measurement Multivariate analysis Onboard energy consumption sensor Eco-driving affected by driving behavior and fuel consumption influenced by congestion and road slope
Ma et al. (2019) [12] GPS Markov chain method Calculation of fuel consumption The driving cycles for large-sized vehicles
Desineedi et al. (2020) [18][25] GPS K-means clusters and Markov modeling method Not considered The driving cycles for buses in Chennai, India
Zhao et al. (2020) [13][26] Chasing vehicle technique and onboard measurement Markov Monte Carlo simulation method Onboard energy consumption sensor and emissions not considered The driving cycles for electric vehicles in Xi’an, China
Coloma et al. (2020) [8] GPS Multivariate data analysis Calculation of fuel consumption and emissions Eco-driving efficiency depending on city and road section
Liu et al. (2021) [19][27] GPS Combination of clustering and Markov chain algorithm Onboard energy consumption sensor and emissions not considered The driving cycles for plug-in hybrid electric vehicles
Ghaffarpasand et al. (2021) [20][28] GPS Repetitive algorithm using principle of least total variance in target parameters Onboard measuring gas analyzer The driving cycles for motorcycles in Isfahan
The real-world driving cycle data were collected using three methods: (1) the chasing vehicle technique—measuring the speed of the targeted vehicle, e.g., [13,14][13][26], (2) data collection using a global positioning system (GPS), e.g., [8[8][20][22][23][25][27][28],15,16,17,18,19,20], and (3) onboard measurements by installing a measuring device on a test vehicle, e.g., [21,22,23,24,25,26][14][15][16][17][18][21]. However, it was difficult for the driver to carry out the chasing in the circumstance of mixed and congested traffic. Furthermore, the GPS was unable to accurately measure the speed profile. On the other hand, the onboard measurement with a measuring device installed on a test vehicle was more effective.
Previously, the micro-trip method was commonly utilized to construct driving cycles, for example, [12,17,27,28][12][23][29][30]. This method divided the raw on-road driving data into many micro-trips by successive stops. The algorithm randomly and repeatedly selected a micro-trip and connected it to a previous micro-trip (if one existed) to construct a combination of micro-trips until the length of the combination of micro-trips was close to the predefined duration of the driving cycle. Recently, Mayakuntla and Verma [15][20] proposed an alternative trip segment method for constructing a driving cycle. This method divided raw on-road driving data into trip segments, which were more homogenous than micro-trips. This method provided a more specific indication of the driving cycle in mixed and congested traffic. However, the construction of the driving cycle requires a more complex algorithm.
Many previous studies, such as [14[13][14][15],21,22], used the chassis dynamometer test to measure the fuel consumption and emissions of driving cycles. In other studies, such as [8[8][21],26], equations have been used to calculate the fuel consumption and emissions. Many studies have recently developed and installed onboard sensors to measure the real-world fuel consumption and emissions, such as [13,16,17,19,20,26,29][19][21][22][23][26][27][28].
The eco-driving cycle was defined as a driving characteristic that consumes less gasoline fuel and emits fewer emissions. To the best of the authors’ knowledge, there is a lack of research on the development of a real-world eco-driving cycle for motorcycles. A few studies [11[11][31][32],31,32], have attempted to develop eco-driving cycles for two-wheel and four-wheel vehicles using mathematical models and algorithms. Coloma et al. [8] developed a real-world eco-driving cycle for passenger cars by comparing the behavior of drivers before and after an eco-driving training course. Nevertheless, no study has been done on a real-world eco-driving cycle for motorcycles.

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