000203612 001__ 203612
000203612 005__ 20190717172525.0
000203612 0247_ $$2doi$$a10.5075/epfl-thesis-6460
000203612 02470 $$2urn$$aurn:nbn:ch:bel-epfl-thesis6460-7
000203612 02471 $$2nebis$$a10286804
000203612 037__ $$aTHESIS
000203612 041__ $$aeng
000203612 088__ $$a6460
000203612 245__ $$aTraffic modeling, estimation and control for large-scale congested urban networks
000203612 269__ $$a2014
000203612 260__ $$bEPFL$$c2014$$aLausanne
000203612 336__ $$aTheses
000203612 502__ $$aProf. S. Takahama (président) ; Prof. N. Geroliminis (directeur) ; Prof. C.N. Jones,  Prof. J. Laval,  Prof. M. Papageorgiou (rapporteurs)
000203612 520__ $$aPart I of the thesis investigates novel urban traffic state estimation methods utilizing probe vehicle data. Chapter 2 proposes a method to integrate the collective effect of dispersed probe data with traffic kinematic wave theory and data mining techniques to model the spatial and temporal dynamics of queue formation and dissipation in arterials. The queue estimation method captures interdependencies in queue evolutions of successive intersections, and moreover, the method is applicable in oversaturated conditions and includes a queue spillover statistical inference procedure. Chapter 3 develops a travel time reliability model to estimate arterial route travel times distribution (TTD) considering spatial and temporal correlations between traffic states in consecutive links. The model uses link-level travel time data and a heuristic grid clustering method to estimate the state structure and transition probabilities of a Markov chain. By applying the Markov chain procedure, the correlation between states of successive links is integrated and the route-level TTD is estimated. The methods in Part I are tested with various probe vehicle penetration rates on case studies with field measurements and simulated data. The methods are straightforward in implementation and have demonstrated promising performance and accuracy through numerous experiments. Part II studies network-level modeling and control of large-scale urban networks. Chapter 4 is the pioneer that introduces the urban perimeter control for two-region urban cities as an elegant control strategy to decrease delays in urban networks. Perimeter controllers operate on the border between the two regions, and manipulate the percentages of transfer flows between the two regions, such that the number of trips reaching their destinations is maximized. The optimal perimeter control problem is solved by the model predictive control (MPC) scheme, where the prediction model and the plant (reality) are formulated by macroscopic fundamental diagrams (MFD). Chapter 5 extends the perimeter control strategy and MFD modeling to mixed urban-freeway networks to provide a holistic approach for large-scale integrated corridor management (ICM). The network consists of two urban regions, each one with a well-defined MFD, and a freeway, modeled by the asymmetric cell transmission model, that is an alternative commuting route which has one on-ramp and one off-ramp within each urban region. The optimal traffic control problem is solved by the MPC approach to minimize total delay in the entire network considering several control policies with different levels of urban-freeway control coordination. Chapter 6 integrates traffic heterogeneity dynamics in large-scale urban modeling and control to develop a hierarchical control strategy for heterogeneously congested cities. Two aggregated models, region- and subregion-based MFDs, are introduced to study the effect of link density heterogeneity on the scatter and hysteresis of MFD. A hierarchical perimeter flow control problem is proposed to minimize the network delay and to homogenize the distribution of congestion. The first level of the hierarchical control problem is solved by the MPC approach, where the prediction model is the aggregated parsimonious region-based MFD and the plant is the subregion-based MFD, which is a more detailed model. At the lower level, a feedback controller tries to maximize the network outflow, by increasing regional homogeneity.
000203612 6531_ $$aCooperative decentralized control
000203612 6531_ $$aCoordinated urban-freeway control
000203612 6531_ $$aHierarchical control
000203612 6531_ $$aMacroscopic fundamental diagram
000203612 6531_ $$aModel predictive control
000203612 6531_ $$aPerimeter control
000203612 6531_ $$aProbe vehicle data
000203612 6531_ $$aQueue profile estimation
000203612 6531_ $$aSpillover identification
000203612 6531_ $$aTravel time distribution estimation
000203612 700__ $$0245210$$g199994$$aRamezani Ghalenoei, Mohsen
000203612 720_2 $$aGeroliminis, Nikolaos$$edir.$$g196675$$0243522
000203612 8564_ $$zn/a$$yn/a$$uhttps://infoscience.epfl.ch/record/203612/files/EPFL_TH6460.pdf$$s10988875
000203612 909C0 $$xU12124$$pLUTS$$0252222
000203612 909CO $$pthesis-bn2018$$pthesis-public$$pDOI$$pENAC$$ooai:infoscience.tind.io:203612$$qDOI2$$qGLOBAL_SET$$pthesis
000203612 917Z8 $$x108898
000203612 917Z8 $$x108898
000203612 917Z8 $$x108898
000203612 917Z8 $$x108898
000203612 917Z8 $$x108898
000203612 918__ $$dEDCE$$cIIC$$aENAC
000203612 919__ $$aLUTS
000203612 920__ $$b2014$$a2014-12-5
000203612 970__ $$a6460/THESES
000203612 973__ $$sPUBLISHED$$aEPFL
000203612 980__ $$aTHESIS