Bus travel time prediction based on deep belief network with back-propagation
Published 2019 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Bus travel time prediction based on deep belief network with back-propagation
Authors
Keywords
-
Journal
NEURAL COMPUTING & APPLICATIONS
Volume -, Issue -, Pages -
Publisher
Springer Science and Business Media LLC
Online
2019-11-02
DOI
10.1007/s00521-019-04579-x
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- An LSTM-Based Method with Attention Mechanism for Travel Time Prediction
- (2019) Xiangdong Ran et al. SENSORS
- Traffic speed prediction for intelligent transportation system based on a deep feature fusion model
- (2019) Linchao Li et al. Journal of Intelligent Transportation Systems
- A data-driven hybrid control framework to improve transit performance
- (2019) Wensi Wang et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- A Tree Based Approach for Data Pre-processing and Pattern Matching for Accident Mapping on Road Networks
- (2018) Arvind Kumar et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES INDIA SECTION A-PHYSICAL SCIENCES
- Allocation method for transit lines considering the user equilibrium for operators
- (2018) Baozhen Yao et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- Multi-output bus travel time prediction with convolutional LSTM neural network
- (2018) Niklas Christoffer Petersen et al. EXPERT SYSTEMS WITH APPLICATIONS
- Traveling time prediction in scheduled transportation with journey segments
- (2017) Avigdor Gal et al. INFORMATION SYSTEMS
- Short-Term Traffic Speed Prediction for an Urban Corridor
- (2016) Baozhen Yao et al. COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
- Bus Travel Time Prediction under High Variability Conditions
- (2016) Kranthi Kumar Reddy et al. CURRENT SCIENCE
- A Discriminated Release Strategy for Parking Variable Message Sign Display Problem Using Agent-Based Simulation
- (2016) Daniel Jian Sun et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- Improving Traffic Flow Prediction With Weather Information in Connected Cars: A Deep Learning Approach
- (2016) Arief Koesdwiady et al. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
- k-Nearest Neighbor Model for Multiple-Time-Step Prediction of Short-Term Traffic Condition
- (2016) Bin Yu et al. JOURNAL OF TRANSPORTATION ENGINEERING
- k-Nearest Neighbor Model for Multiple-Time-Step Prediction of Short-Term Traffic Condition
- (2016) Bin Yu et al. JOURNAL OF TRANSPORTATION ENGINEERING
- Percolation transition in dynamical traffic network with evolving critical bottlenecks
- (2015) Daqing Li et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning
- (2014) Wenhao Huang et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- Bus Travel-Time Prediction with a Forgetting Factor
- (2012) Bin Yu et al. JOURNAL OF COMPUTING IN CIVIL ENGINEERING
- A bus-following model with an on-line bus station
- (2012) Tieqiao Tang et al. NONLINEAR DYNAMICS
- Bus arrival time prediction at bus stop with multiple routes
- (2011) Bin Yu et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- Hybrid model for prediction of bus arrival times at next station
- (2010) Bin Yu et al. JOURNAL OF ADVANCED TRANSPORTATION
- Predictions of Urban Volumes in Single Time Series
- (2009) T. Thomas et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- The Reliability of Travel Time Forecasting
- (2009) Menglong Yang et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- Dynamic multi-interval bus travel time prediction using bus transit data
- (2009) Hyunho Chang et al. Transportmetrica
Create your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create NowAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started