QoS Proxy Architecture for Real Time RPC with Traffic Prediction

2007 ◽  
Author(s):  
Pedro N. Nobile ◽  
Roberto R. F. Lopes ◽  
Celio E. Moron ◽  
Luis C. Trevelin
Keyword(s):  
Real Time ◽  
Traffic Prediction

scholarly journals Learning to Recommend Signal Plans under Incidents with Real-Time Traffic Prediction

2020 ◽  
Vol 2674 (6) ◽  
pp. 45-59
Author(s):  
Weiran Yao ◽  
Sean Qian
Keyword(s):  
Real Time ◽  
Domain Knowledge ◽  
Recommendation System ◽  
Time Window ◽  
Traffic Prediction ◽  
Incident Management ◽  
Signal Timing ◽  
Late Response ◽  
Main Question ◽  
Real Time Traffic

The main question to address in this paper is to recommend optimal signal timing plans in real time under incidents by incorporating domain knowledge developed with the traffic signal timing plans tuned for possible incidents, and learning from historical data of both traffic and implemented signals timing. The effectiveness of traffic incident management is often limited by the late response time and excessive workload of traffic operators. This paper proposes a novel decision-making framework that learns from both data and domain knowledge to real-time recommend contingency signal plans that accommodate non-recurrent traffic, with the outputs from real-time traffic prediction at least 30 min in advance. Specifically, considering the rare occurrences of engagement of contingency signal plans for incidents, it is proposed to decompose the end-to-end recommendation task into two hierarchical models—real-time traffic prediction and plan association. The connections between the two models are learnt through metric learning, which reinforces partial-order preferences observed from historical signal engagement records. The effectiveness of this approach is demonstrated by testing this framework on the traffic network in Cranberry Township, Pennsylvania, U.S., in 2019. Results show that the recommendation system has a precision score of 96.75% and recall of 87.5% on the testing plan, and makes recommendations an average of 22.5 min lead time ahead of Waze alerts. The results suggest that this framework is capable of giving traffic operators a significant time window to access the conditions and respond appropriately.

scholarly journals Adarules: Learning rules for real-time road-traffic prediction

2017 ◽  
Vol 27 ◽  
pp. 11-18 ◽  
Author(s):  
Rafael Mena-Yedra ◽  
Ricard Gavaldà ◽  
Jordi Casas
Keyword(s):  
Real Time ◽  
Road Traffic ◽  
Traffic Prediction ◽  
Learning Rules

A Metamodel for Estimating Error Bounds in Real-Time Traffic Prediction Systems

2014 ◽  
Vol 15 (3) ◽  
pp. 1310-1322 ◽  
Author(s):  
Francisco C. Pereira ◽  
Constantinos Antoniou ◽  
Joan Aguilar Fargas ◽  
Moshe Ben-Akiva
Keyword(s):  
Real Time ◽  
Error Bounds ◽  
Traffic Prediction ◽  
Real Time Traffic ◽  
Prediction Systems
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