兰州市作为典型的河谷型城市，其独特的地理环境和复杂的交通布局为交通管理带来了极大的挑战。本数据集是一个针对复杂场景下河谷型城市交通路口的视频流数据，涵盖了兰州市内多个关键交通路口的视频监控数据，包括不同时间段 (如早高峰、平峰时段)、不同天气条件 (如晴天、雾天等)、不同交通状况 (如拥堵、畅通等)、不同光照条件 (如光线昏暗、光线充足等)下的视频流数据。在数据处理方面，对视频数据进行了精细化的标注和分类，包括车辆类型、车辆所在车道等方面。该数据集旨在通过提供丰富多样的交通路口视频数据，为智能交通系统的研发、交通拥堵治理、交通事故预防等方面提供有力的数据支持。
", "ds_source": "数据集的视频数据来源于兰州市内部分路口的道路监控；图片数据来源于视频数据，以 30 秒为间隔，选取视频中的部分帧，将视频转换为图片；图片标注文件使用LabelImg工具标注。
", "ds_process_way": "使用LabelImg标注工具对图片进行标注，标注方法如下：选择YOLO方式对图片中的车辆进行标注，车辆的标注特征分为车道和车型。其中，车型分为S、M和L。S为小型车辆，指明显小于M型的车辆，比如三轮车、摩托车、自行车等；M为中型车辆，比如家庭汽车，出租车等；L为大型车辆，指明显大于M型的车辆，比如公交车，大货车，大客车等。车道划分是指在选取的图片中从左至右依次为1，2，3，4，5车道。
", "ds_quality": "本文对交通监控视频数据进行分析和应用之前，进行了以下数据质量控制和评估：\n
(1)视频数据的完整性和可用性检查。首先检查视频是否完整记录了指定时间段内的交通情况，以及视频文件是否存在损坏或丢失的情况。经检查，本文监控视频数据集中，监控视频完整，无缺失或损坏的部分。\n
(2)清晰度评估。视频的清晰度是数据质量的重要方面。本文通过观察视频中的车辆、目标的细节清晰度来评估。经检查，本文监控视频数据集中，3号陇西路口以及4号硷沟沿工林路存在有雾情况视频较为模糊，其余监控视频数据集分辨率高且没有模糊现象，不会影响后续的识别和分析工作。\n
(3)稳定性检查。视频的稳定性是数据质量的关键指标。经检查，本文监控视频数据集不存在抖动、倾斜等问题。\n
(4)帧图片标注工作校验。对标注结果的准确性和一致性进行检查，以确保标注结果的可靠性。经检查，本文帧图片标注准确无误。
", "ds_acq_start_time": "2021-11-16 00:00:00", "ds_acq_end_time": "2021-12-09 00:00:00", "ds_acq_place": "兰州市内", "ds_acq_lon_east": 103.81166666666667, "ds_acq_lat_south": 36.03805555555555, "ds_acq_lon_west": 103.62944444444443, "ds_acq_lat_north": 36.1, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 2860074717, "ds_files_count": 2, "ds_format": ".mp4 是监控视频数据，.jpg是监控图片，.txt是图片标注信息。", "ds_space_res": "", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "2570a5e8-e7f6-4917-880b-a61948b22299.jpg", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "需申请人提供:(1)姓名或组织名称;(2)联系方式(如电子邮件地址、电话号码);(3)数据用途，包括事务用途(项目、论文、教学等)和确切用途(具体的研究方向及数据在研究中的作用)两方面。如有需要请联系:huojy@mail.lzjtu.cn。", "ds_from_station": null, "organization_id": "52b7b79b-860c-49a5-9083-9a70cf8bed5a", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4520" ], "quality_level": 3, "publish_time": "2024-05-06 17:58:55", "last_updated": "2026-02-24 10:12:26", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.LZJTU.DB6447.2024", "i18n": { "en": { "title": "A video stream dataset of traffic intersections in Lanzhou, a river valley city in complex scenarios", "ds_format": ".mp4,.jpg,.txt", "ds_source": "The video data of the dataset comes from road monitoring at some intersections in Lanzhou city; The image data comes from video data, and at intervals of 30 seconds, select some frames from the video to convert it into images; The image annotation file is annotated using the LabelImg tool", "ds_quality": "
Before analyzing and applying traffic surveillance video data in this article, the following data quality control and evaluation were conducted:\n
(1) Check the integrity and availability of video data. Firstly, check whether the video fully records the traffic situation during the specified time period, and whether the video file is damaged or lost. After inspection, the surveillance video data set in this article is complete with no missing or damaged parts.\n
(2) Clarity assessment. The clarity of a video is an important aspect of data quality. This article evaluates the clarity of details of vehicles and targets in videos. After inspection, it was found that in the surveillance video dataset of this article, there was fog at the intersection of Longxi Road No. 3 and Gonglin Road along Alkali Gully No. 4, and the videos were relatively blurry. The other surveillance video datasets have high resolution and no blurring phenomenon, which will not affect the subsequent recognition and analysis work.\n
(3) Stability check. The stability of the video is a key indicator of data quality. After inspection, the monitoring video dataset in this article does not have any issues such as shaking or tilting.\n
(4) Frame image annotation work verification. Check the accuracy and consistency of the annotation results to ensure their reliability. After inspection, the frame image annotations in this article are accurate and without errors", "ds_ref_way": "", "ds_abstract": "
As a typical river valley city, Lanzhou's unique geographical environment and complex transportation layout pose great challenges to traffic management. This dataset is a video stream data for river valley type urban traffic intersections in complex scenarios, covering video surveillance data of multiple key traffic intersections in Lanzhou city, including video stream data under different time periods (such as morning rush hour, off peak hours), different weather conditions (such as sunny and foggy days), different traffic conditions (such as congestion, smoothness, etc.), and different lighting conditions (such as dim light, sufficient light, etc.). In terms of data processing, video data has been finely annotated and classified, including vehicle types, lanes in which vehicles are located, and other aspects. This dataset aims to provide strong data support for the development of intelligent transportation systems, traffic congestion control, and traffic accident prevention by providing rich and diverse video data of traffic intersections
", "ds_time_res": "", "ds_acq_place": "Within Lanzhou City", "ds_space_res": "", "ds_projection": "", "ds_process_way": "Use the LabelImg annotation tool to annotate the image. The annotation method is as follows: select the YOLO method to annotate the vehicles in the image, and the annotation features of the vehicles are divided into lanes and vehicle types. Among them, the car models are divided into S, M, and L. S is a small vehicle, indicating vehicles that are significantly smaller than the M-type, such as tricycles, motorcycles, bicycles, etc; M is a medium-sized vehicle, such as a family car, taxi, etc; L refers to large vehicles that are significantly larger than the M-type, such as buses, trucks, buses, etc. Lane division refers to the arrangement of lanes 1, 2, 3, 4, and 5 from left to right in the selected image", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "河谷型城市，复杂场景，关键交通路口，视频监控数据" ], "ds_subject_tags": [ "人文地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "甘肃省兰州市" ], "ds_time_tags": [ 2021 ], "ds_contributors": [ { "true_name": "火久元", "email": "huojy@mail.lzjtu.cn", "work_for": "兰州交通大学电子与信息工程学院，国家冰川冻土沙漠科学数据中心，中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "魏金莉", "email": "1910073357@qq.com", "work_for": "兰州交通大学", "country": "中国" }, { "true_name": "孟昱煜", "email": "529267338@qq.com", "work_for": "电子与信息工程学院，兰州交通大学", "country": "中国" }, { "true_name": "王院荣", "email": "wyr20010404@163.com", "work_for": "兰州交通大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "魏金莉", "email": "1910073357@qq.com", "work_for": "兰州交通大学", "country": "中国" } ], "ds_managers": [ { "true_name": "火久元", "email": "huojy@mail.lzjtu.cn", "work_for": "兰州交通大学电子与信息工程学院，国家冰川冻土沙漠科学数据中心，中国科学院西北生态环境资源研究院", "country": "中国" } ], "category": "社会经济文化" }