Analyzing the Effects of Road Type and Rainy Weather on Fuel Consumption and Emissions: A Mesoscopic Model Based on Big Traffic Data
Autor: | Rui Shang, Yi Zhang, Zuo-Jun Max Shen |
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Jazyk: | angličtina |
Rok vydání: | 2021 |
Předmět: |
General Computer Science
Meteorology neural network Taxis 010501 environmental sciences 01 natural sciences Data modeling weather condition Range (aeronautics) 0502 economics and business General Materials Science Fuel consumption and emission factors 0105 earth and related environmental sciences Consumption (economics) 050210 logistics & transportation 05 social sciences General Engineering Energy consumption TK1-9971 Greenhouse gas Trajectory Fuel efficiency Environmental science road type Electrical engineering. Electronics. Nuclear engineering mesoscopic model |
Zdroj: | IEEE Access, Vol 9, Pp 62298-62315 (2021) |
ISSN: | 2169-3536 |
Popis: | Road transportation accounts for significant percentages of urban energy consumption and carbon emissions. Therefore, it is important to predict and analyze the fuel consumption and emissions for on-road vehicles, which are varied under different conditions. Previous studies have shown that some traffic elements such as road type and weather condition have considerable influence on transportation fuel consumption and emissions. However, limited to the data availability, most of the existing studies focus on specific routes or scenarios, and few of them consider the effects of road type and weather condition systematically at large scale. In this research, a data-driven mesoscopic model was developed to investigate the effects of road type and weather condition on the link-level fuel consumption and emission factors based on big traffic data. This built model utilized the neural network for the prediction algorithm with inputs including road type, weather condition, and link-level aggregated operation data obtained through link-based segregation over trajectory snippets. The investigation was carried out with real-world big traffic data collected from 10,944 taxis over a 2-month period of operation in Shenzhen, and produced reliable predictions for four road types with clear and rainy weather conditions. Both statistical analysis and model prediction results showed that fuel consumption and emission factors are lower in low-speed range for freeway and expressway, and are lower in middle-speed range for main road and secondary road. In addition, rainy weather condition tends to have lower fuel consumption and emission factors than clear weather condition. |
Databáze: | OpenAIRE |
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