公路交通运维需求驱动的能源负荷预测数据集

主要面向适配公路交通智能化绿色化发展的能源负荷预测方法，数据为关键年份，高速公路、普通国道交通量预测结果，包含未来关键年车流总量和分车型流量，公路里程及公路行驶量。数据空间范围覆盖了高速公路和普通国道我国两类主要道路类型。预测结果基于2015年至2020年客货运周转量、GDP以及路网里程数，采用弹性系数法，对未来关键年高速公路、普通国道车流量进行了预测。
京藏高速（G6）在北京、甘肃、青海等地流量监测数据主要面向交通需求驱动的能源负荷动态演化分析方法。数据为2019年1月1日至2019年12月31日京藏高速（G6）在北京、甘肃、青海等地路段逐小时和分车型流量。数据时间分辨率为逐小时，空间分辨率为所在行政区道路尺度，车流分辨率为分车型。数据采集根据《国家高速公路网交通量调查观测站点布局规划》的相关要求，交通运输部采用自动化监测站为主、利用监控数据和收费数据为辅的综合调查方法，获取路网宏观交通流量特征。
车辆碳排放因子数据主要面向公路交通运维需求驱动的能源负荷预测软件工具模块。不同车辆类型、不同燃料类型在公路不同行驶速度下的车辆碳排放因子，可以用来计算在不同流量和道路长度下路段对应碳排放量。数据测算基准年为2020年，时间分辨率为逐小时、空间分辨率为路段尺度。
公路交通系统智能化、绿色化用能场景集主要涵盖不同用能场景。数据包含面向建管养运的用能场景集，包含公路干线、隧道、服务区和收费站；智能化绿色化用能场景集包含孤立微网、微网群互济、自用为主上网为辅、自用为辅上网为主、全额上网等场景。数据来自于课题组实地调研及收集整理的京沪高速公路、京哈高速公路等近40条公路相关资料。

This dataset is primarily intended for energy load forecasting methods adapted to the intelligent and green development of road transportation. The data comprises traffic volume prediction results for expressways and national ordinary highways in key years, including total traffic volume, vehicle-type specific traffic flow, road mileage and road traffic volume for future critical years. Its spatial scope covers the two primary road types in China: expressways and national ordinary highways. The prediction results were generated using the elastic coefficient method, based on passenger and freight turnover volume, GDP and road network mileage data from 2015 to 2020, to forecast the traffic flow of expressways and national ordinary highways in future key years.

This traffic flow monitoring dataset of the Beijing-Tibet Expressway (G6) in Beijing, Gansu, Qinghai and other regions is mainly targeted at the dynamic evolution analysis method of energy load driven by traffic demand. The data contains hourly and vehicle-type specific traffic flow data of sections of the Beijing-Tibet Expressway (G6) in Beijing, Gansu, Qinghai and other regions from January 1, 2019 to December 31, 2019. The temporal resolution is hourly, the spatial resolution is at the administrative district road scale, and the traffic resolution is vehicle-type specific. According to the relevant requirements of "Layout Plan for Traffic Volume Survey and Observation Stations of the National Expressway Network", the Ministry of Transport adopted a comprehensive survey method that primarily uses automated monitoring stations, supplemented by monitoring data and toll data, to obtain the macro traffic flow characteristics of the road network.

This vehicle carbon emission factor dataset is mainly intended for the energy load forecasting software tool module driven by road transportation operation and maintenance demands. The vehicle carbon emission factors under different vehicle types, fuel types and road driving speeds can be used to calculate the corresponding carbon emissions of road sections under different traffic volumes and road lengths. The benchmark year for data calculation is 2020, with hourly temporal resolution and section-scale spatial resolution.

This intelligent and green energy consumption scenario set for road transportation systems mainly covers various energy consumption scenarios. The data includes energy consumption scenario sets for construction, management, maintenance and operation, covering highway trunk lines, tunnels, service areas and toll stations; the intelligent and green energy consumption scenario set includes scenarios such as isolated microgrid, microgrid cluster mutual support, self-use priority with grid feed-in as supplement, grid feed-in priority with self-use as supplement, and full grid feed-in. The data is derived from on-site investigations and collected materials of nearly 40 highways including the Beijing-Shanghai Expressway, Beijing-Harbin Expressway and other highways organized by the research team.

搜集汇总

背景与挑战

背景概述

该数据集主要面向公路交通智能化绿色化发展的能源负荷预测，包含高速公路和普通国道的未来关键年交通量预测、京藏高速2019年逐小时分车型流量监测、车辆碳排放因子以及用能场景集。数据基于弹性系数法预测和自动化监测站采集，覆盖全国范围，用于支持交通自洽能源系统基础设施规划与设计技术研究。