Bangladeshi Traffic Flow Dataset

In Bangladesh, general public compliance with traffic regulations is notably low. This dataset aims to analyze the traffic flow patterns in Dhaka, focusing on both vehicular movement and pedestrian activities. Data were gathered from four different locations: Shapla Chattar, Arambag, Bashabo, and Abul Hotel. Video recordings were taken from footover bridges, capturing traffic scenarios involving single-lane and double-lane roads, as well as the erratic movement of pedestrians. A total of 23,678 images were extracted from these recordings, which were collected during five distinct time intervals on a weekday, and subsequently annotated using the Roboflow tool. This dataset offers a detailed perspective on Dhaka’s unstructured traffic systems, highlighting various road conditions and heavy traffic environments. Its applications include vehicle fitness monitoring, pedestrian behavior analysis, and traffic flow assessment under diverse environmental conditions, such as daylight, dusk, night, and rain. Additionally, this dataset presents opportunities for researchers to explore and apply machine learning techniques to complex, real-world traffic scenarios.
Readme file contains folder hierarchy of our dataset.

孟加拉国民众对交通法规的普遍遵守程度偏低。本数据集旨在分析达卡市的交通流模式，重点覆盖车辆通行与行人活动两类场景。数据采集自四个不同点位：Shapla Chattar、Arambag、Bashabo以及Abul Hotel。录制设备架设于人行天桥，采集了包含单车道、双车道道路以及行人不规则通行的交通场景影像。研究人员从这些录制影像中共提取23678张图像，这些影像采集于工作日的五个不同时段，随后通过Roboflow工具完成标注。本数据集能够细致展现达卡市非结构化的交通系统，涵盖多样的道路状况与拥堵交通环境。其应用场景包括车辆状态监测、行人行为分析，以及针对日光、黄昏、夜间、雨天等多种环境下的交通流评估。此外，本数据集为研究者探索并将机器学习技术应用于复杂真实交通场景提供了支撑。数据集的文件夹层级结构详见Readme文件。