Expressway travel time prediction model based on multi-source data fusion

2021 ◽  
Author(s):  
Jianzhen Liu
Keyword(s):  
Data Fusion ◽  
Prediction Model ◽  
Travel Time ◽  
Travel Time Prediction ◽  
Time Prediction ◽  
Model Based ◽  
Source Data

Travel Time Prediction of Road Network Based on Multi-Source Data Fusion

2012 ◽  
Vol 490-495 ◽  
pp. 850-854
Author(s):  
Wen Ting Liu
Keyword(s):  
Data Fusion ◽  
Travel Time ◽  
Road Network ◽  
Traffic Data ◽  
Traffic Patterns ◽  
Travel Time Prediction ◽  
Time Prediction ◽  
Real Time Traffic ◽  
Source Data ◽  
Two Phases

This paper is concerned with the task of travel time pre-diction of urban roadway. For improving the travel time predication ac-curacy, a travel time predication model based multi-source data fusion is proposed. The prediction procedure is divided into two phases, the estimation phase and the prediction phase The method is combined the historical traffic patterns with real-time traffic data as a linear. The resulting model is tested with realistic traffic data, and is found to perform well.

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

PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0262535
Author(s):  
Xinhuan Zhang ◽  
Les Lauber ◽  
Hongjie Liu ◽  
Junqing Shi ◽  
Meili Xie ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Prediction Model ◽  
Travel Time ◽  
Public Transportation ◽  
Service Reliability ◽  
Travel Time Prediction ◽  
Filter Model ◽  
Time Prediction ◽  
Study Results ◽  
Source Data

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.

Sign in / Sign up

Export Citation Format

Share Document