Travel Carbon Emission Prediction Model Based on Resident Attribute Data

doi:10.12141/j.issn.1000-565X.230355

Abstract

Abstract:

It is an important basis for precise formulation of transportation emission reduction measures to accurately analyze the importance of factors influencing residents’ travel mode and the sensitivity of carbon emissions. According to the comprehensive analysis of the influencing factors such as family attributes, personal attributes, travel attributes and environmental attributes of the residents’ travel survey, the prediction model of residents’ travel mode was constructed based on LightGBM (Light Gradient Boosting Machine) and verified. Combined with the travel activity level, the carbon emission coefficient of various energy types, the standard coal coefficient and other parameters, the travel carbon emission prediction model based on the resident attribute data was constructed. Finally, taking Guangzhou as an example, the carbon emission intensity and total amount of residents’ travel mode were predicted, and importance of factors influencing travel mode and sensitivity was analyzed.The results indicate that the carbon emission prediction model constructed based on the attribute data of residents can more accurately predict the carbon emission of various modes of travel, better analyze the importance and sensitivity of the influencing factors of carbon emission, and comprehensively reveal the relationship between travel behavior, travel mode and travel carbon emission. Among them, the distance between the start and the end and the nearest bus station or the distance from the nearest subway station, the cost of self-driving and travel distance are important factors affecting the choice of residents’ travel mode. The competitiveness of subway travel increases significantly with the decrease of distance when the distance between the starting and the end point and the nearest subway station drops by 55%. In the area with high density of bus stops, the distance between the start and the nearest bus station is not sensitive to residents’ travel mode choice. It is the turning point of the residents’ travel mode and carbon emission when the carbon emission cost increases by 400%. After passing the turning point, the car travel mode is difficult to transfer. The carbon emissions fell the fastest, with a maximum reduction of 90.4% when the reduction in travel distance was within 90%.

Key words: urban traffic, resident attribute data, travel mode prediction, carbon emission prediction, sensitivity analysis

CLC Number:

U491

SU Yuejiang, WEN Huiying, YUAN Minxian, WU Dexin, ZHOU Lulu, QI Weiwei. Travel Carbon Emission Prediction Model Based on Resident Attribute Data[J]. Journal of South China University of Technology(Natural Science Edition), 2024, 52(8): 23-33.

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