石黄高速节假日闯口逃费筛查10月份数据

石黄高速节假日闯口逃费筛查 10 月份数据，旨在聚焦该时段内可能存在的闯口逃费情况。其数据涵盖多方面来源，有收费站收费系统记录的车辆入口、出口（含无出口情况）信息、车型、时间等基础数据，收费站监控视频呈现的车辆实际通行画面，以及 ETC 系统留存的车辆 ETC 相关通行记录等。这些数据基于节假日闯口逃费筛查系统，利用各数据间逻辑关系，结合节假日逃费的特征与业务规则，借助如分类、决策树、逻辑回归等机器学习算法进行深度挖掘。通过分析处理，能够精准筛查出那些利用节假日免费期间 “紧急” 车道等途径逃费的车辆，助力减少通行费损失，保障收费秩序，实现通行费足额收缴。The October dataset for toll evasion screening of unauthorized lane breaches during holidays on the Shihuang Expressway is intended to focus on potential toll evasion scenarios occurring within this period. The dataset encompasses data from multiple sources: basic information including vehicle entry, exit (including cases with no recorded exit), vehicle type, and timestamp, logged by toll station charging systems; actual vehicle passing footage captured by toll station surveillance cameras; and ETC-related passage records retained by the ETC system. Based on the holiday toll evasion screening system, this dataset conducts in-depth data mining by leveraging logical correlations across different data sources, combining the characteristics of holiday-related toll evasion and operational business rules, and employing machine learning algorithms such as classification, decision trees, and logistic regression. Through analysis and processing, it can accurately identify vehicles that evade tolls via channels like "emergency" lanes during holiday toll-free periods, thereby helping to reduce toll losses, maintain orderly toll collection operations, and ensure full collection of toll fees.

搜集汇总

背景与挑战

背景概述

该数据集聚焦石黄高速2024年10月份节假日期间的闯口逃费行为筛查，整合了收费站收费系统、监控视频和ETC系统等多源数据，利用机器学习算法如决策树和逻辑回归进行深度分析，旨在精准识别逃费车辆以减少通行费损失。数据具有显著的经济和管理价值，可优化资源配置、提升行业形象，并为交通运输、安防及人工智能领域提供技术借鉴和合作机会。