基于居民属性数据的出行碳排放预测模型

Travel Carbon Emission Prediction Model Based on Resident Attribute Data

准确分析居民出行方式的碳排放及方式选择影响因素的重要性和敏感性,是精准制定交通减排措施的基础。根据居民出行调查的家庭属性、个人属性、出行属性和环境属性等影响因素综合分析,基于LightGBM(Light Gradient Boosting Machine)构建了居民出行方式预测模型并进行验证,结合出行活动水平、各种能源类型的碳排放系数、标准煤系数等参数,构建了基于居民属性数据的出行碳排放预测模型;最后,以广州市为例进行实证分析,对居民出行方式和碳排放总量进行预测,并分析了出行方式选择影响因素的重要程度和重要因素敏感性。结果表明:基于居民属性数据构建的碳排放预测模型,能较为精确地预测各种出行方式的碳排放,较好地分析碳排放的影响因素重要性和敏感性,以及全面揭示出行行为、出行方式和出行碳排放之间的关系。其中,起终点距最近公交站的距离或距最近地铁站的距离、自驾车费用、出行距离等是影响居民出行方式选择的重要因素。当起终点距最近地铁站距离下降55%时,地铁出行竞争力随着距离缩短而明显提升;在公交站点密度较大的区域,起终点距最近公交站距离对居民出行方式选择不敏感;当碳排放费用增加400%时为居民出行方式和碳排放的转折点,超过转折点后小汽车出行方式难以转移;当出行距离下降幅度在90%以内时,碳排放下降速度最快,最大降幅为90.4%。

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%.