Applying Design Science Research to Design and Evaluate Real-Time Road Traffic State Estimation Framework

2015 ◽  
pp. 223-233
Author(s):  
Ayalew Belay Habtie ◽  
Ajith Abraham ◽  
Dida Midekso
Keyword(s):  
State Estimation ◽  
Real Time ◽  
Road Traffic ◽  
Design Science ◽  
Science Research ◽  
Design Science Research ◽  
Traffic State ◽  
Traffic State Estimation

scholarly journals Real-Time Urban Traffic State Estimation with A-GPS Mobile Phones as Probes

2012 ◽  
Vol 02 (01) ◽  
pp. 22-31 ◽  
Author(s):  
Sha Tao ◽  
Vasileios Manolopoulos ◽  
Saul Rodriguez ◽  
Ana Rusu
Keyword(s):  
State Estimation ◽  
Real Time ◽  
Mobile Phones ◽  
Urban Traffic ◽  
Traffic State ◽  
Traffic State Estimation

scholarly journals Probability hypothesis density filtering for real-time traffic state estimation and prediction

2013 ◽  
Vol 8 (3) ◽  
pp. 825-842 ◽  
Author(s):  
Matthieu Canaud ◽  
◽  
Lyudmila Mihaylova ◽  
Jacques Sau ◽  
Nour-Eddin El Faouzi ◽  
...  
Keyword(s):  
State Estimation ◽  
Real Time ◽  
Probability Hypothesis Density ◽  
Estimation And Prediction ◽  
Traffic State ◽  
Real Time Traffic ◽  
Traffic State Estimation

Traffic State Estimation for Urban Road Networks Using a Link Queue Model

2017 ◽  
Vol 2623 (1) ◽  
pp. 29-39 ◽  
Author(s):  
Yiming Gu ◽  
Zhen (Sean) Qian ◽  
Guohui Zhang
Keyword(s):  
State Estimation ◽  
Real Time ◽  
Large Scale ◽  
Road Networks ◽  
Entry And Exit ◽  
Urban Road ◽  
Traffic State ◽  
Traffic State Estimation ◽  
Queue Model ◽  
Flow Propagation

Traffic state estimation (TSE) is used for real-time estimation of the traffic characteristics (such as flow rate, flow speed, and flow density) of each link in a transportation network, provided with sparse observations. The complex urban road dynamics and flow entry and exit on urban roads challenge the application of TSE on large-scale urban road networks. Because of increasingly available data from various sources, such as cell phones, GPS, probe vehicles, and inductive loops, a theoretical framework is needed to fuse all data to best estimate traffic states in large-scale urban networks. In this context, a Bayesian probabilistic model to estimate traffic states is proposed, along with an expectation–maximization extended Kalman filter (EM-EKF) algorithm. The model incorporates a mesoscopic traffic flow propagation model (the link queue model) that can be computationally efficient for large-scale networks. The Bayesian framework can seamlessly integrate multiple data sources for best inferring flow propagation and flow entry and exit along roads. A synthetic test bed was created. The experiments show that the EM-EKF algorithm can promptly estimate traffic states. Another advantage is that the EM-EKF can update its model parameters in real time to adapt to unknown traffic incidents, such as lane closures. Finally, the proposed methodology was applied to estimating travel speed for an urban network in the Washington, D.C., area and resulted in satisfactory estimation results with an 8.5% error rate.

A data fusion approach for real-time traffic state estimation in urban signalized links

2018 ◽  
Vol 92 ◽  
pp. 525-548 ◽  
Author(s):  
Majid Rostami Shahrbabaki ◽  
Ali Akbar Safavi ◽  
Markos Papageorgiou ◽  
Ioannis Papamichail
Keyword(s):  
Data Fusion ◽  
State Estimation ◽  
Real Time ◽  
Traffic State ◽  
Real Time Traffic ◽  
Traffic State Estimation ◽  
Fusion Approach
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