象山县交通路口流量预测数据

在智能交通管控中，实时融合气象条件、历史通行规律及动态路况数据构成预测引擎的核心框架。当特定区域通行压力逼近阈值时，可灵活调动交通控制系统，暴雨等极端天气下，主动抑制超饱和路段流量输入，配合可变车道灵活切换功能，有效避免区域性交通瘫痪，减少因各路口车流过大造成的不必要交通安全事故，保障城市交通运行的安全性与可持续性。根据前一日统计卡口车流数据获得前日平均每小时车流量Av；根据规定道路最大承载量获得Ma;根据是否节假日，1为普通工作日，1.5为节假日，获得节假日系数W；根据是否高峰时间段，0.8不是高峰时间段，1为高峰时间段，获得高峰时段系数J1；根据实时天气预测数据报告，获得异常天气影响因子J2。将收集的数据通过时间序列分析中的指数平滑法进行计算：Y=Ma x ((Av/Ma)x0.4+ln(J1)x0.3+Wx0.3)xJ2，最终获得预测当前时段车流量，根据预测车辆与前日平均每小时车流量进行对比实现道路交通更合理控制。

In intelligent traffic management, the core framework of the prediction engine is built via real-time fusion of meteorological conditions, historical traffic patterns and dynamic road condition data. When the traffic pressure in a given region approaches its threshold, the traffic control system can be flexibly dispatched. Under extreme weather such as heavy rain, the inflow of traffic at oversaturated road sections can be actively restricted, and in conjunction with the flexible lane changing function of variable lanes, regional traffic gridlock can be effectively prevented, unnecessary traffic safety incidents caused by excessive vehicle queues at intersections can be reduced, and the safety and sustainability of urban traffic operations can be ensured. The average hourly traffic volume of the previous day (denoted as Av) is calculated from the traffic flow data collected by on-site traffic surveillance checkpoints; the maximum road carrying capacity (denoted as Ma) is determined based on the specified road capacity limit. The holiday coefficient W is assigned based on the type of day: 1 for regular weekdays and 1.5 for holidays. The peak hour coefficient J1 is set according to the current time period: 1 for peak hours and 0.8 for non-peak hours. The abnormal weather impact factor J2 is derived from real-time weather forecast reports. The collected data are calculated via the exponential smoothing method in time series analysis, following the formula: Y = Ma * ((Av / Ma) * 0.4 + ln(J1) * 0.3 + W * 0.3) * J2, to obtain the predicted traffic volume of the current time period. Road traffic control can be optimized by comparing the predicted traffic volume with the average hourly traffic volume of the previous day.

搜集汇总

背景与挑战

背景概述

该数据集包含象山县交通路口的流量预测数据，主要用于智能交通管控，数据规模为1001条，每月更新。数据通过时间序列分析中的指数平滑法进行预测，结合节假日、高峰时段和天气等因素，以实现更合理的交通控制。