Survey on traffic prediction in smart cities
The rapid development in machine learning and in the emergence of new data sources
makes it possible to examine and predict traffic conditions in smart cities more accurately …
makes it possible to examine and predict traffic conditions in smart cities more accurately …
Traffic flow prediction with big data: A deep learning approach
Accurate and timely traffic flow information is important for the successful deployment of
intelligent transportation systems. Over the last few years, traffic data have been exploding …
intelligent transportation systems. Over the last few years, traffic data have been exploding …
Short-term traffic forecasting: Where we are and where we're going
EI Vlahogianni, MG Karlaftis, JC Golias - Transportation Research Part C …, 2014 - Elsevier
Since the early 1980s, short-term traffic forecasting has been an integral part of most
Intelligent Transportation Systems (ITS) research and applications; most effort has gone into …
Intelligent Transportation Systems (ITS) research and applications; most effort has gone into …
Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification
J Guo, W Huang, BM Williams - Transportation Research Part C: Emerging …, 2014 - Elsevier
Short term traffic flow forecasting has received sustained attention for its ability to provide the
anticipatory traffic condition required for proactive traffic control and management. Recently …
anticipatory traffic condition required for proactive traffic control and management. Recently …
Review of shared online hailing and autonomous taxi services
As the technology of autonomous vehicle develops, online hailing autonomous taxi system
is regarded as one of the most popular public transportation services in the future. Studies …
is regarded as one of the most popular public transportation services in the future. Studies …
Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions
Most literature on short-term traffic flow forecasting focused mainly on normal, or non-
incident, conditions and, hence, limited their applicability when traffic flow forecasting is most …
incident, conditions and, hence, limited their applicability when traffic flow forecasting is most …
A Hidden Markov Model for short term prediction of traffic conditions on freeways
Y Qi, S Ishak - Transportation Research Part C: Emerging …, 2014 - Elsevier
Accurate short-term prediction of traffic conditions on freeways and major arterials has
recently become increasingly important because of its vital role in the basic traffic …
recently become increasingly important because of its vital role in the basic traffic …
A bayesian dynamic linear model approach for real-time short-term freeway travel time prediction
X Fei, CC Lu, K Liu - Transportation Research Part C: Emerging …, 2011 - Elsevier
This paper presents a Bayesian inference-based dynamic linear model (DLM) to predict
online short-term travel time on a freeway stretch. The proposed method considers the …
online short-term travel time on a freeway stretch. The proposed method considers the …
Hybrid dual Kalman filtering model for short‐term traffic flow forecasting
Short‐term traffic flow forecasting is a fundamental and challenging task since it is required
for the successful deployment of intelligent transportation systems and the traffic flow is …
for the successful deployment of intelligent transportation systems and the traffic flow is …
Multi-task federated learning for traffic prediction and its application to route planning
A novel multi-task federated learning (FL) framework is proposed in this paper to optimize
the traffic prediction models without sharing the collected data among traffic stations. In …
the traffic prediction models without sharing the collected data among traffic stations. In …