Low-Carbon Impact of Urban Rail Transit Based on Passenger Demand Forecast in Baoji
| Autor: | Jingni Song, Feng Chen, Na Zhang, Yu Li, Zijia Wang, Jianpo Wang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
four-stage model
residents’ trip survey carbon emission reduction passenger demand urban rail transit Control and Optimization Urban rail transit 020209 energy Energy Engineering and Power Technology chemistry.chemical_element 02 engineering and technology lcsh:Technology Transport engineering Urbanization Range (aeronautics) 0502 economics and business 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Engineering (miscellaneous) 050210 logistics & transportation lcsh:T Renewable Energy Sustainability and the Environment 05 social sciences Mode (statistics) Demand forecasting Travel survey chemistry Greenhouse gas Environmental science Carbon Energy (miscellaneous) |
| Zdroj: | Energies; Volume 13; Issue 4; Pages: 782 Energies, Vol 13, Iss 4, p 782 (2020) |
| ISSN: | 1996-1073 |
| DOI: | 10.3390/en13040782 |
| Popis: | There are increasing traffic pollution issues in the process of urbanization in many countries; urban rail transit is low-carbon and widely regarded as an effective way to solve such problems. The passenger flow proportion of different transportation types is changing along with the adjustment of the urban traffic structure and a growing demand from passengers. The reduction of carbon emissions brought about by rail transit lacks specific quantitative research. Based on a travel survey of urban residents, this paper constructed a method of estimating carbon emissions from two different scenarios where rail transit is and is not available. This study uses the traditional four-stage model to forecast passenger volume demand at the city level and then obtains the basic target parameters for constructing the carbon emission reduction model, including the trip origin-destination (OD), mode, and corresponding distance range of different modes on the urban road network. This model was applied to Baoji, China, where urban rail transit will be available from 2023. It calculates the changes in carbon emission that rail transit can bring about and its impact on carbon emission reductions in Baoji in 2023. |
| Databáze: | OpenAIRE |
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