Travel time prediction of urban public transportation based on detection of single routes

• Published in: PloS one Vol. 17; no. 1; p. e0262535
• Main Authors: Zhang, Xinhuan, Lauber, Les, Liu, Hongjie, Shi, Junqing, Xie, Meili, Pan, Yuran
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 14.01.2022; Public Library of Science (PLoS)
• Subjects: Accuracy; Algorithms; Big Data; Biology and Life Sciences; Computer and Information Sciences; Data analysis; Data mining; Dwell time; Engineering; Engineering and Technology; Error analysis; Forecasting - methods; Global positioning systems; GPS; Kalman filters; Methods; Model accuracy; Models, Theoretical; Motor Vehicles; Neural networks; Physical Sciences; Prediction (Logic); Prediction models; Public Sector - trends; Public transportation; Reliability engineering; Reproducibility of Results; Research and Analysis Methods; Smartphones; Social Sciences; Structural reliability; Time Factors; Traffic control; Transportation - methods; Travel; Travel - economics; Travel - statistics & numerical data; Travel time; Travel time (Traffic engineering); Urban transportation; Volatility; China; Zhejiang China; Kansas; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0262535

Improving travel time prediction for public transit effectively enhances service reliability, optimizes travel structure, and alleviates traffic problems. Its greater time-variance and uncertainty make predictions for short travel times (≤35min) more subject to be influenced by random factors. It requires higher precision and is more complicated than long-term predictions. Effectively extracting and mining real-time, accurate, reliable, and low-cost multi-source data such as GPS, AFC, and IC can provide data support for travel time prediction. Kalman filter model has high accuracy in one-step prediction and can be used to calculate a large amount of data. This paper adopts the Kalman filter as a travel time prediction model for a single bus based on single-line detection: including the travel time prediction model of route (RTM) and the stop dwell time prediction model (DTM); the evaluation criteria and indexes of the models are given. The error analysis of the prediction results is carried out based on AVL data by case study. Results show that under the precondition of multi-source data, the public transportation prediction model can meet the accuracy requirement for travel time prediction and the prediction effect of the whole route is superior to that of the route segment between stops.