Abstract
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.
Highlights
Public transit travel time prediction is one effective measure for improving service reliability, optimizing travel structure, and alleviating traffic problems
This paper studies the travel time prediction of urban public transit based on the Kalman filter in a big data environment
This section is an overview of methods for predicting travel time published in the last five years. These methods can Travel time prediction of urban public transportation based on detection of single routes be divided into five categories: Global Positioning System (GPS) based, Neural Network based, Support Vector Machines (SVM) based, Particle Filtering (PF) based, and Kalman Filter (KF) based
Summary
Public transit travel time prediction is one effective measure for improving service reliability, optimizing travel structure, and alleviating traffic problems. A travel time prediction system of conventional transit under the influence of random factors is constructed, and a short-term ( 35min) travel time prediction model with strong applicability is built. These will work congruously to improve prediction’s real-time accuracy and adaptability and reduce costs associated with data acquisition. This paper provides a scientific theoretical basis and decision support for the practical work of using intelligent technology to improve the prediction accuracy of travel time. It holds practical application value for four stakeholder groups: 1. This improves an urban public transit system’s service levels and provides passengers with real-time travel time information through multiple channels to facilitate their travel choices
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
