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Vehicle Kilometer of Traveled is one of most meaningful indicators to represent road transport in particular area. It is an essential data in order to observe vehicle usage and estimate amount of CO2 emissions from road transport. Especially, in developing cities, estimation of CO2 emission based on VKT is the applicable approach. However, it is no...
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Citations
... On the other hand, bottom-up methods encompass both macro-level and micro-level traffic emission estimation approaches (Yu et al., 2020a, c;Zhang et al., 2019a). A macro-level model, relying on Vehicle Kilometers Traveled (VKT) data (Fukuda et al., 2013), offers a comprehensive regional or city-level emission perspective. Furthermore, microsimulation software such as IVE and MOVES can simulate individual vehicle behaviors to facilitate precise emission calculations but necessitate extensive input data. ...
The spatial heterogeneity and temporal variability of traffic in urban environments make traffic emissions inference challenging. To address this challenge, this study introduces a novel geographical context-based approach utilizing high-resolution taxi GPS data, incorporating multidimensional contextual factors such as road data, points of interest (POI), weather data, and population density. The proposed method can enhance the precision of traffic emissions inference compared to conventional macroscopic estimation techniques. To overcome the issue of missing data in traffic emissions inference from taxi data, three ensemble machine learning algorithms-Random Forest, Gradient Boosting Decision Trees (GBDT), and eXtreme Gradient Boosting (XGBoost)-are employed. These algorithms efficiently handle a substantial volume of taxi GPS data, achieving reduced computational time and model complexity. The proposed framework establishes localized models for each road segment, taking into consideration both geographical and external features that characterize the urban environment. This localized modeling contributes significantly to a more profound understanding of traffic dynamics. A thorough comparative analysis is conducted to assess the performance of the proposed method. Results indicate that incorporating multidimensional urban features is advantageous for traffic speed inference. Among the ensemble learning models, Random Forest outperforms others when dealing with a small missing rate or limited sample size, while XGBoost exhibits superior performance for larger missing rates or substantial sample sizes. Additionally, an analysis of the feature importance in traffic speed highlights that road network features are the most significant factors, followed by temporal characteristics, spatial attributes, POI data, and weather information. Finally, leveraging inferred traffic speed and volume information, emissions from large-scale urban road traffic are inferred based on the COPERT model. In contrast to methods relying on complex, multi-source data for emission estimation, our approach utilizes simple and easily accessible data, enabling precise estimation of emissions on a large-scale spatiotemporal basis.
... Sahu et al. [3] estimated that motorcycles emitted approximately 37% of the total emissions from carbon monoxide (CO) in on-road transport in India. Fukuda et al. [4] found that motorcycles consumed about 30% of fuel consumed by passenger cars and emitted about 27% of carbon dioxide (CO 2 ) emitted by passenger cars in Khon Kaen City, Thailand. The Asian Development Bank, ADB [5], stated that the MC fleet contributed approximately 54% of CO and hydrocarbon (HC) pollution at a Hanoi roadside during morning rush hours. ...
This study developed on-road exhaust emission and fuel consumption models for application in traffic microsimulations to estimate motorcycle emissions and fuel consumption in an Asian developing city. The motorcycle onboard measurement system was developed to instantaneously measure and continuously record on-road driving data, including the speed-time profile, exhaust emissions, and fuel consumption per second. The test motorcycle was driven on roads around Khon Kaen City, Thailand, to collect on-road driving data during the morning peak hours for a total of 112 hours. The collected on-road driving data were applied to develop on-road exhaust emission and fuel consumption models using regression analysis. The models were developed with high correlations among the amount of exhaust emissions and fuel consumption and the instantaneous speed and acceleration rate. The developed models were applied with a traffic microsimulation to evaluate the exclusive zone for motorcycles stopping at a signalized intersection. The evaluation results reveal that it could improve the level of intersection service by decreasing travel times, delays, and queue lengths at intersections, as well as by reducing the fuel consumption and emissions of vehicles travelling through intersections compared with these values under the existing conditions.
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This study aims to find the on-road driving parameters influencing fuel consumption and emissions of motorcycle driving on a congested signalized urban corridor. A motorcycle onboard measurement system was developed to measure instantaneously and continuously record on-road driving data, including speed-time profile, emissions, and fuel consumption, by the second. The test motorcycles were driven by 30 sample motorcyclists on a signalized urban corridor in Khon Kaen City, Thailand, to collect their on-road driving behavior during the morning peak period. Cluster analysis was applied to analyze collected driving data and to categorize the drivers by level of fuel consumption and on-road driver behavior. The on-road driving parameter influencing fuel consumption and emissions was then determined. Results revealed that proportion of idle time significantly influenced fuel consumption and emissions of motorcycle driving on a congested signalized urban corridor, though aggressive driving behavior, hard acceleration and deceleration, did not have the same kind of influence.
... Conversely, the number of registered motorcycles (190,153 veh.) is twice the number of registered sedans (88,402 veh.). The CO2 emission from motorcycles was consequently estimated equivalent to 27% of CO2 emission from sedans [13]. To achieve a good driving cycle representing all traffic and road conditions of the Khon Kaen city network, representative routes for on-road data collection would be appropriately selected. ...
This study developed an onboard system to measure the on-road driving pattern for a motorcycle driving cycle in Khon Kaen city, Thailand. The developed system, validated with high accuracy results, could measure and record a driving pattern, i.e. a speed profile of a driving motorcycle. The selected motorcycle was driven along selected routes in Khon Kaen city under the existing traffic conditions to collect the on-road driving pattern. The Khon Kaen motorcycle driving cycle (KMDC) was developed by a repetitive algorithm using the principle of least total variance in the target parameters. The developed KMDC was compared with the existing motorcycle driving cycles for Bangkok and other cities. The result reveals that the KMDC is different from those of other cities.
... So it is important to estimate CO2 emission in chronological order for implementing more concrete policies.4. URBAN STRUCTURE AND TRAVEL ACTIVITYFukuda, et al. (2013) estimated VKT of Khon Kaen City and compared it to the VKT of different cities in Thailand, including Bangkok and Nakhon Ratchasima. These cities are different sized cities. ...
To realize the Sustainable Low Carbon Transport (SLoCaT) in a developing city, the long-run policy including not only introduction of public transport but also effective land-use plan such as Transit Oriented Development (TOD), compact city, etc. as well as technological innovation for vehicles and introduction of renewable energy should be established and evaluated in terms of energy consumption structure and modal share. Thus, in this study, the relationships between urban structure and travel activities based on available transportation mode such as Vehicle Kilometers Travel (VKT), travel distance, etc. were defined and simulated the impacts of such relationships on changing the urban structure and carbon dioxide (CO2) emission dynamically by using the system dynamics model. As the result of this simulation, CO2 emission were obtained in long term and impacts of each scenarios were estimated.
Vehicle emissions are a major source of air pollution in Thulamela Municipality, Limpopo Province, South Africa. The province is currently experiencing an inflow of people from villages, which increases the human population, the number of vehicles, and vehicle kilometres traveled (VKT). However, there is a lack of baseline data on vehicle emission inventory (VEI) and air quality within the district. Therefore, it is important to estimate vehicle emission impact on air quality within Thulamela Municipality. Hence, this paper quantified VEI of CO, NOx and SO2 for ten years between 2012-2021 using a bottom-up approach. This bottom-up approach method combines the vehicle activity and emission factor for different vehicle types. The VKT in this study is the vehicle activity and was based on odometer reading data obtained from individual car owners and registered vehicle population from the National Traffic Information System (eNaTIS). The number of vehicles was obtained from the eNaTIS for 2012 to 2021 (27 699-37 790). The average VKT within this period increased in the following order, Light-duty vehicles (LDV), passenger cars (PC), heavy passenger vehicles (HPV), and heavy-duty vehicles (HDV). This study shows that all the vehicles contributed an average of 3278.11, 2232.60, and 54.72 tons of CO, NOx, and SO2, respectively, into the atmosphere yearly. The total emission of CO, NOx and SO2 pollutants were 32781.12, 22326.02, and 547.16 tons, respectively, with a growth rate of 40.57% for CO, 40.73% for NOx, and SO2 by 39.66% for the study period. High estimated vehicle emissions from the investigated area could be a major contributing factor to local air pollution. Hence, mitigation strategies need to be put in place.
Motorcycle is one of the most widely used transportation in Thailand. Internal Combustion Engine (ICE) is the most common motorcycle type using in Thais daily life. ICE motorcycle is fossil fuel dependence, and one of the most concerned questions about ICE motorcycle is how much CO2 released from it. In this experiment, CO2 emission data is collected from tracking platform. CO2 tracking platform is available on both mobile and web application. CO2 tracking app will calculate emission data from ICE motorcycle by using mathematical models. The model and parameters of CO2 emission will be obtained from the manufacturer's specification. From this experiment, we obtained CO2 emission data from usage in real situations. Not only the CO2 emission, but also graph of carbon footprint, average CO2 per distance, and overall CO2 emission data are collected. Moreover, we also ranked CO2 emission in experiment, and offer basic reward for that best performance. Best ranking data will be analyzed about driving behavior, route, and model of ICE motorcycle. Analysis of these experimental data could be beneficial to CO2 emission of ICE motorcycle.
Vehicle kilometer travelled (VKT) is the total number of vehicles travelled on the road in kilometers. VKT is mostly used in transportation planning for allocating resources, estimating vehicle emission, assessing of traffic impact, analyzing crash statistics, and some necessary information for road policy. This study validates the estimates of VKT from household travel survey method by comparing them with VKT estimates using Average Daily Traffic (ADT) data. The method was validated using the data collected from Uva province of Sri Lanka during July 2020. Additionally travel distances by different modes and Personal Kilometers Travel (PKT) were estimated. Results showed that VKT estimates using ADT data was not significantly different to the VKT estimates using household travel survey (p=0.342). Therefore, household travel survey is a promising method to estimate the VKT for developing countries where ADT data is not regularly collected. Moreover, this method provides mode specific information and PKT data.
Vehicle Kilometers Travelled (VKT) estimation and Personal Kilometer Travelled (PKT) can be considered as key parameters for investigating the travel characteristics. VKT estimates were done through a household travel survey using questionnaire forms and interviews during Covid-19 pandemic in Northern Province of Sri Lanka. The different vehicle usage in Northern Province based on different socio-demographic factors was obtained. The characteristics obtained from the analysis was compared with the similar study conducted before Covod-19 pandemic in 2017. When comparing VKT between years 2017 and 2020, it was found that statistically significant decrease from 2017 to 2020. However, a statistically significant difference on VKT was not observed among the sub-categories of females, unemployed people, and age groups of 15-24 and 45-54 years olds. Study on VKT variations is important for traffic growth estimation and to ensure operation of proper transport system.
