New Bayesian combination method for short-term traffic flow forecasting
The Bayesian combination method (BCM) proposed by Petridis et al.(2001) is an integrated
method that can effectively improve the predictions of single predictors. However, research …
method that can effectively improve the predictions of single predictors. However, research …
Using an ARIMA-GARCH modeling approach to improve subway short-term ridership forecasting accounting for dynamic volatility
Subway short-term ridership forecasting plays an important role in intelligent transportation
systems. However, limited efforts have been made to forecast the subway short-term …
systems. However, limited efforts have been made to forecast the subway short-term …
Forecasting the subway passenger flow under event occurrences with multivariate disturbances
Subway passenger flow prediction is of great significance in transportation planning and
operation. Special events, as for vocal concerts and sports games, lead large-scaled …
operation. Special events, as for vocal concerts and sports games, lead large-scaled …
Short‐term traffic flow prediction with linear conditional Gaussian Bayesian network
Traffic flow prediction is an essential part of intelligent transportation systems (ITS). Most of
the previous traffic flow prediction work treated traffic flow as a time series process only …
the previous traffic flow prediction work treated traffic flow as a time series process only …
A real-time passenger flow estimation and prediction method for urban bus transit systems
Bus service is the most important function of public transportation. Besides the major goal of
carrying passengers around, providing a comfortable travel experience for passengers is …
carrying passengers around, providing a comfortable travel experience for passengers is …
Traffic flow forecasting for urban work zones
None of numerous existing traffic flow forecasting models focus on work zones. Work zone
events create conditions that are different from both normal operating conditions and …
events create conditions that are different from both normal operating conditions and …
Multisize patched spatial-temporal transformer network for short-and long-term crowd flow prediction
Y Xie, J Niu, Y Zhang, F Ren - IEEE Transactions on Intelligent …, 2022 - ieeexplore.ieee.org
The prediction of urban crowds is crucial not only to traffic management but also to studies
on the city-level social phenomena, such as energy consumption, urban growth, city …
on the city-level social phenomena, such as energy consumption, urban growth, city …
Road traffic speed prediction: A probabilistic model fusing multi-source data
Road traffic speed prediction is a challenging problem in intelligent transportation system
(ITS) and has gained increasing attentions. Existing works are mainly based on raw speed …
(ITS) and has gained increasing attentions. Existing works are mainly based on raw speed …
[图书][B] Intelligent infrastructure: neural networks, wavelets, and chaos theory for intelligent transportation systems and smart structures
Recent estimates hypothesize that the US will need $1.6 trillion dollars for the rehabilitation,
replacement, and maintenance of existing infrastructure systems within the next 20 years …
replacement, and maintenance of existing infrastructure systems within the next 20 years …
Adaptive seasonal time series models for forecasting short-term traffic flow
S Shekhar, BM Williams - Transportation Research Record, 2007 - journals.sagepub.com
Conventionally, most traffic forecasting models have been applied in a static framework in
which new observations are not used to update model parameters automatically. The need …
which new observations are not used to update model parameters automatically. The need …