雄安窄路密网环境下面向数字道路的多粒度交通仿真数据集

窄路密网是雄安新区实现80/90绿色出行目标、落实交通强国战略的核心路网形态，但其高密度交叉口引发的交通方式冲突、信号协同难题，亟需针对性的多粒度交通数据支撑仿真与协同控制技术研发。当前国内外交通数据集多适配常规密度路网，在场景匹配度、数据维度完整性上难以满足窄路密网研究需求。本数据集依托“面向雄安新区窄路密网的多粒度交通系统仿真与协同控制技术研究”项目，通过路侧毫米波雷达、路口信号机及公交车载GPS等多源感知设备，采集容东片区不少于100个示范路口及5条公交线路的核心数据，经系统化清洗、筛选与加工，形成历史流量、信号配时、公交运行3类.xlsx格式子数据集。数据集已通过项目多次交通信号仿真优化实验验证，数据真实性与科学可信度符合研究与工程应用标准。该数据集可为窄路密网下交通流特性建模、信号配时优化及公交优先控制等研究提供关键数据支撑，助力新区破解高密度路网交通运行效率与安全难题。

Narrow Roads and Dense Grid (NRDG) is the core road network form for Xiong'an New Area to achieve its 80/90 green travel targets and implement the National Strategy for Transport Power. However, traffic mode conflicts and signal coordination problems caused by high-density intersections urgently require targeted multi-granularity traffic data to support the research and development of simulation and cooperative control technologies. Most current domestic and international traffic datasets are adapted to conventional-density road networks, and they fail to meet the research needs of narrow roads and dense grids in terms of scene matching degree and integrity of data dimensions. This dataset is developed based on the research project titled "Research on Multi-granularity Traffic System Simulation and Cooperative Control Technologies for Narrow Roads and Dense Grids in Xiong'an New Area". Core data of no less than 100 demonstration intersections and 5 bus lines in the Rongdong Area are collected via multi-source sensing equipment including roadside millimeter-wave radars, intersection signal controllers and on-board GPS of buses. After systematic cleaning, screening and processing, three subsets of data in .xlsx format are formed, namely historical traffic flow, signal timing and bus operation. This dataset has been validated through multiple traffic signal simulation and optimization experiments conducted in the project, and its data authenticity and scientific credibility conform to the standards for research and engineering applications. It can provide key data support for studies such as traffic flow characteristic modeling, signal timing optimization and bus priority control under narrow roads and dense grids, and help the new area address the issues of traffic operation efficiency and safety in high-density road networks.

搜集汇总

背景与挑战

背景概述

该数据集是针对雄安新区窄路密网环境设计的交通仿真数据资源，通过多源感知设备采集了容东片区超过100个路口和5条公交线路的数据，形成历史流量、信号配时和公交运行三类子数据集。数据集以.xlsx格式为主，已通过实验验证，主要用于支持交通流建模、信号优化和公交优先控制研究，以解决高密度路网的交通效率和安全问题。