采用低频浮动车数据的行程时间估计

为解决利用低频浮动车数据进行路径行程时间估计时精度不高的问题,从分析浮动车数据特征的角度出发进行行程时间分布的估计,提出并讨论利用浮动车数据估计行程时间的潜在误差,针对每种潜在误差提出修正模型,并选取上海市长寿路部分路段进行实证分析,利用1 500辆出租车数据,对各种修正方法下的行程时间进行估计,与改进内插值估计方法进行对比,并与车牌识别装置提供的直接行程时间估计结果进行相似性分析.结果表明:所有误差均修正的行程时间估计与改进内插值方法相比,平均估计精度提高9.5%,且估计的中位数、25%分位数和75%分位数与车牌识别方法有较高的匹配度.考虑低频浮动车数据误差修正的行程时间估计可以改善估计的精度,可提供有效的行程时间信息.

Travel time estimation using low-frequency floating car data

In order to solve the low accuracy problem of route travel time estimation when using low-frequency floating car data, the characteristics of low-frequency floating car data are analyzed from the perspective of travel time distribution estimation. Potential errors are presented and discussed, each of them has an error correction model. Selecting Changshou Road of Shanghai for empirical analysis using floating car data from 1 500 taxies. Travel time under various correction methods would be directly estimated, which compares with estimation results provided by the vehicle license plate recognition device. The results reveal that travel time with all errors corrected improves the average estimation accuracy by 9.5%, and median, 25th and 75th percentile have higher matching with the license plate recognition method. The low-frequency floating car data error correction model can improve the accuracy of the travel time estimation, and provide an effective travel time information.