6533b823fe1ef96bd127ec0d
RESEARCH PRODUCT
Online shortest paths with confidence intervals for routing in a time varying random network
Stéphane ChrétienChristophe Guyeuxsubject
FOS: Computer and information sciencesMathematical optimizationComputer sciencePopulation02 engineering and technology[INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE][INFO.INFO-IU]Computer Science [cs]/Ubiquitous Computing[SPI]Engineering Sciences [physics][INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR]0502 economics and business11. SustainabilityComputer Science - Data Structures and Algorithms0202 electrical engineering electronic engineering information engineeringFOS: MathematicsData Structures and Algorithms (cs.DS)educationIntelligent transportation systemMathematics - Optimization and ControlRandom graph050210 logistics & transportationeducation.field_of_studyStochastic process[SPI.PLASMA]Engineering Sciences [physics]/Plasmas05 social sciencesApproximation algorithm[INFO.INFO-MO]Computer Science [cs]/Modeling and SimulationStochastic gradient descentOptimization and Control (math.OC)[INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA]Shortest path problem020201 artificial intelligence & image processing[INFO.INFO-ET]Computer Science [cs]/Emerging Technologies [cs.ET]Routing (electronic design automation)[INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC]description
International audience; The increase in the world's population and rising standards of living is leading to an ever-increasing number of vehicles on the roads, and with it ever-increasing difficulties in traffic management. This traffic management in transport networks can be clearly optimized by using information and communication technologies referred as Intelligent Transport Systems (ITS). This management problem is usually reformulated as finding the shortest path in a time varying random graph. In this article, an online shortest path computation using stochastic gradient descent is proposed. This routing algorithm for ITS traffic management is based on the online Frank-Wolfe approach. Our improvement enables to find a confidence interval for the shortest path, by using the stochastic gradient algorithm for approximate Bayesian inference. The theory required to understand our approach is provided, as well as the implementation details.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2018-07-08 |