Observation method based on data driving for macroscopic driving fluctuation of urban public transportation

Abstract

The invention provides an observation method based on data driving for a macroscopic driving fluctuation of urban public transportation. The method comprises the steps of (S1) establishing a full travel time data set {A<l>(f)} of each line for the collected bus arrival data, (S2) calculating a total travel time T(l)<f,p>(k) at a time period k and percentile position p according to the data set {A<l>(f)}, (S3) calculating an observed full travel time fluctuation amplitude alpha(l)(k) of a line l under the percentile position p at the time period k, (S4) calculating a frequency number F alpha, p, v (k) of a public transportation line whose fluctuation amplitude alpha(l)(k) at the time period k is larger than v, and (S5) calculating a fluctuation ratio gamma p, v(k), gamma p of the timeperiod k and the condition v, wherein the v(k) is used for observing a ratio of a macroscopic fluctuation value of the urban public transportation in the k-time period beyond a v threshold, and the overall macroscopic fluctuation of the urban public transportation can be visually observed through gamma p and v(k). The method comprises advantages that the macroscopic fluctuation observation of urban travel based on public transportation data is achieved, the method is used for comparing the macroscopic fluctuation characteristics of an urban road network at different time points on different dates, and a decision-making basis is provided for urban public transportation planning.

Description

technical field [0001] The invention relates to an urban computing information processing technology in the field of intelligent transportation, in particular to a data-driven observation method for the macroscopic driving fluctuation of urban public transport. Background technique [0002] With the development of information technology, many domestic cities have installed positioning systems on buses and established corresponding cloud platforms, realizing real-time positioning of bus vehicles and real-time query of bus arrival information, which facilitates passenger travel information query, At the same time, it also improves the management optimization level of the operating company. [0003] Urban travel will show a big difference between weekdays and weekends. During the morning and evening peak hours of weekdays, there will be high average travel time and high fluctuations. On weekends, there will be no obvious average peaks and valleys but high fluctuations. The dyn...

AI Technical Summary

Problems solved by technology

The dynamic model of bus travel time volatility is often difficult to accurately model. Therefore, when the travel time shows high volatility, the difficulty of forecasting will increase, which will increase the difficulty of scheduling and optimization for operating companies, which will easily lead to bus schedules in time and space. Intersect disorder, exacerbating the unevenness of bus frequency distribution in space

In the city, the fluctuation of bus driving can easily lead to the bus on the same line after the next bus catches up with the previous bus, resulting in the phenomenon that there is no bus service for a long period of time after the bus is dense during this period, which reduces the quality of bus service

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