近年来,快速增长的私人小客车带来的能耗排放问题日益严重,如何构建面向私人小客车的燃油核算模型,分析不同需求管理政策的宏观节能效果,是解决城市交通节能减排问题的关键.本文针对传统私人小客车能耗调查方式所获得数据准确性较差、...近年来,快速增长的私人小客车带来的能耗排放问题日益严重,如何构建面向私人小客车的燃油核算模型,分析不同需求管理政策的宏观节能效果,是解决城市交通节能减排问题的关键.本文针对传统私人小客车能耗调查方式所获得数据准确性较差、无法满足精细化管理的问题,利用现有调查数据和监测数据,基于"OLS(Ordinary Least Square)+稳健标准差"验证法,分析油耗显著性影响因素,提出基于交通大数据的私人小客车能耗核算模型,以实测数据验证其可靠性和有效性,并分析了不同交通需求管理政策(含组合政策)宏观节能效果.结果表明,当政策效果指标变化率相同时,实行"拥堵收费+控制大排量小客车数量"政策对于减少小客车燃油消耗总量的效果最为明显.展开更多
Understanding the characteristics of passenger vehicle use is the prerequisite for effective urban management.However,it has been challenging in the existing literature due to the lack of continuously observed data on...Understanding the characteristics of passenger vehicle use is the prerequisite for effective urban management.However,it has been challenging in the existing literature due to the lack of continuously observed data on passenger vehicle use.Thanks to the advances in data collection and processing techniques,multi-day vehicle trajectory data generated from volunteered passenger cars provide new opportunities for examining in depth how people travel in regular patterns.In this paper,based on a week’s operation data of 6600 passenger cars in Shanghai,we develop a systematic approach for identifying trips and travel purposes,and classify vehicles into four categories using a Gaussian-Mixed-Model.A new method is proposed to identify vehicle travel regularities and we use the Z Test to explore differences in travel time and route choices between four types of vehicles.Wefind that commercially used vehicles present high travel intensity in temporal and spatial aspects and the use intensity in elevated roads is higher for household-used commuting vehicles than semi-commercially used vehicles.The methodologies and conclusions of this paper may provide not only theoretical support for future urban traffic prediction,but also guidance for employing customized active traffic demand management measures to alleviate traffic congestion.展开更多
文摘近年来,快速增长的私人小客车带来的能耗排放问题日益严重,如何构建面向私人小客车的燃油核算模型,分析不同需求管理政策的宏观节能效果,是解决城市交通节能减排问题的关键.本文针对传统私人小客车能耗调查方式所获得数据准确性较差、无法满足精细化管理的问题,利用现有调查数据和监测数据,基于"OLS(Ordinary Least Square)+稳健标准差"验证法,分析油耗显著性影响因素,提出基于交通大数据的私人小客车能耗核算模型,以实测数据验证其可靠性和有效性,并分析了不同交通需求管理政策(含组合政策)宏观节能效果.结果表明,当政策效果指标变化率相同时,实行"拥堵收费+控制大排量小客车数量"政策对于减少小客车燃油消耗总量的效果最为明显.
基金supported by the project of the National Natural Science Foundation of China(No.71734004)。
文摘Understanding the characteristics of passenger vehicle use is the prerequisite for effective urban management.However,it has been challenging in the existing literature due to the lack of continuously observed data on passenger vehicle use.Thanks to the advances in data collection and processing techniques,multi-day vehicle trajectory data generated from volunteered passenger cars provide new opportunities for examining in depth how people travel in regular patterns.In this paper,based on a week’s operation data of 6600 passenger cars in Shanghai,we develop a systematic approach for identifying trips and travel purposes,and classify vehicles into four categories using a Gaussian-Mixed-Model.A new method is proposed to identify vehicle travel regularities and we use the Z Test to explore differences in travel time and route choices between four types of vehicles.Wefind that commercially used vehicles present high travel intensity in temporal and spatial aspects and the use intensity in elevated roads is higher for household-used commuting vehicles than semi-commercially used vehicles.The methodologies and conclusions of this paper may provide not only theoretical support for future urban traffic prediction,but also guidance for employing customized active traffic demand management measures to alleviate traffic congestion.
