Low-Carbon Impact of Urban Rail Transit Based on Passenger Demand Forecast in Baoji

• Main Authors: Na Zhang, Zijia Wang, Feng Chen, Jingni Song, Jianpo Wang, Yu Li
• Format: Article
• Language: English
• Published: MDPI AG 2020-02-01
• Series: Energies
• Subjects: four-stage model; residents’ trip survey; carbon emission reduction; passenger demand; urban rail transit
• Online Access: https://www.mdpi.com/1996-1073/13/4/782

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.