The invention relates to an
urban rail transit scheduling method and
system based on
big data. The
system comprises a real-
time data collection module, a
station density change prediction module, a
line rate section division module, a rate section dynamic adjustment module and a
train stop time optimization module. According to the
urban rail transit scheduling method based on the
big data, by dynamically optimizing the
train speed and the stopping time, the operation efficiency and the passenger flow safety are remarkably improved. The
system adjusts the
train speed according to the real-time
density change, the speed is reduced in a high-density area, the
arrival time is delayed, buffering is achieved for evacuation, and congestion and potential safety hazards are avoided; the speed is increased in a low-density area,
time loss is made up, and the whole-line operation efficiency is ensured. And through regional speed synchronization, train aggregation is avoided, and balanced
resource allocation under the condition of multiple stations and
high density is realized. Meanwhile, the system dynamically adjusts the
stop time, prolongs the stop of the previous
station to reduce pressure for the high-density
station, shortens the stop of the high-density station, and accelerates the circulation.