A hybrid strategy for real-time traffic signal control of urban road networks

The recently developed traffic signal control strategy known as traffic-responsive urban control (TUC) requires availability of a fixed signal plan that is sufficiently efficient under undersaturated traffic conditions. To drop this requirement, the well-known Webster procedure for fixed-signal control derivation at isolated junctions is appropriately employed for real-time operation based on measured flows. It is demonstrated via simulation experiments and field application that the following hold: 1) The developed real-time demand-based approach is a viable real-time signal control strategy for undersaturated traffic conditions. 2) It can indeed be used within TUC to drop the requirement for a prespecified fixed signal plan. 3) It may, under certain conditions, contribute to more efficient results, compared with the original TUC method

Store-and-forward based methods for the signal control problem in large-scale congested urban road networks

The problem of designing network-wide traffic signal control strategies for large-scale congested urban road networks is considered. One known and two novel methodologies, all based on the store-and-forward modeling paradigm, are presented and compared. The known methodology is a linear multivariable feedback regulator derived through the formulation of a linear-quadratic optimal control problem. An alternative, novel methodology consists of an open-loop constrained quadratic optimal control problem, whose numerical solution is achieved via quadratic programming. Yet a different formulation leads to an open-loop constrained nonlinear optimal control problem, whose numerical solution is achieved by use of a feasible-direction algorithm. A preliminary simulation-based investigation of the signal control problem for a large-scale urban road network using these methodologies demonstrates the comparative efficiency and real-time feasibility of the developed signal control methods

Control and optimization methods for traffic signal control in large-scale congested urban road networks

The problem of designing real-time traffic signal control strategies for large-scale congested urban road networks via suitable application of control and optimization methods is considered. Three alternative methodologies are proposed, all based on the store-and-forward modeling (SFM) paradigm. The first methodology results in a linear multivariable feedback regulator derived through the formulation of the problem as a linear-quadratic (LQ) optimal control problem. The second methodology leads to an open-loop constrained quadratic optimal control problem whose numerical solution is achieved via quadratic-programming (QP). Finally, the third methodology leads to an open-loop constrained nonlinear optimal control problem whose numerical solution is effectuated by use of a feasible-direction algorithm. A simulation-based investigation of the signal control problem for a large-scale urban network using these methodologies is presented. Results demonstrate the efficiency and real-time feasibility of the developed generic control methods

Coevolutionary Genetic Algorithms for Establishing Nash Equilibrium in Symmetric Cournot Games

We use co-evolutionary genetic algorithms to model the players' learning process in several Cournot models, and evaluate them in terms of their convergence to the Nash Equilibrium. The "social-learning" versions of the two co-evolutionary algorithms we introduce, establish Nash Equilibrium in those models, in contrast to the "individual learning" versions which, as we see here, do not imply the convergence of the players' strategies to the Nash outcome. When players use "canonical co-evolutionary genetic algorithms" as learning algorithms, the process of the game is an ergodic Markov Chain, and therefore we analyze simulation results using both the relevant methodology and more general statistical tests, to find that in the "social" case, states leading to NE play are highly frequent at the stationary distribution of the chain, in contrast to the "individual learning" case, when NE is not reached at all in our simulations; to find that the expected Hamming distance of the states at the limiting distribution from the "NE state" is significantly smaller in the "social" than in the "individual learning case"; to estimate the expected time that the "social" algorithms need to get to the "NE state" and verify their robustness and finally to show that a large fraction of the games played are indeed at the Nash Equilibrium.Comment: 18 pages, 4 figure

A rolling-horizon quadratic-programming approach to the signal control problem in large-scale congested urban road networks

The paper investigates the efficiency of a recently developed signal control methodology, which offers a computationally feasible technique for real-time network-wide signal control in large-scale urban traffic networks and is applicable also under congested traffic conditions. In this methodology, the traffic flow process is modeled by use of the store-and-forward modeling paradigm, and the problem of network-wide signal control (including all constraints) is formulated as a quadratic-programming problem that aims at minimizing and balancing the link queues so as to minimize the risk of queue spillback. For the application of the proposed methodology in real time, the corresponding optimization algorithm is embedded in a rolling-horizon (model-predictive) control scheme. The control strategy’s efficiency and real-time feasibility is demonstrated and compared with the Linear-Quadratic approach taken by the signal control strategy TUC (Traffic-responsive Urban Control) as well as with optimized fixed-control settings via their simulation-based application to the road network of the city centre of Chania, Greece, under a number of different demand scenarios. The comparative evaluation is based on various criteria and tools including the recently proposed fundamental diagram for urban network traffic

Adaptive performance optimization for large-scale traffic control systems

In this paper, we study the problem of optimizing (fine-tuning) the design parameters of large-scale traffic control systems that are composed of distinct and mutually interacting modules. This problem usually requires a considerable amount of human effort and time to devote to the successful deployment and operation of traffic control systems due to the lack of an automated well-established systematic approach. We investigate the adaptive fine-tuning algorithm for determining the set of design parameters of two distinct mutually interacting modules of the traffic-responsive urban control (TUC) strategy, i.e., split and cycle, for the large-scale urban road network of the city of Chania, Greece. Simulation results are presented, demonstrating that the network performance in terms of the daily mean speed, which is attained by the proposed adaptive optimization methodology, is significantly better than the original TUC system in the case in which the aforementioned design parameters are manually fine-tuned to virtual perfection by the system operators

Εξελιγμένες Στρατηγικές Ελέγχου για την Επίτευξη Μηδενικής Ενεργειακής Κατανάλωσης σε Κτήρια [ = Developing control strategies toward nearly-zero energy buildings]

Στην παρούσα εργασία παρουσιάζονται η κεντρική ιδέα και τα αποτελέσματα του προγράμματος PEBBLE: το PEBBLE είναι ένα πρόγραμμα το οποίο στοχεύει στην ανάπτυξη εξελιγμένων Τεχνολογιών Πληροφορικής και Επικοινωνιών, οι οποίες θα υποστηρίξουν τη λειτουργία Κτηρίων Θετικού ή Μηδενικού Ισοζυγίου. Στο σχεδιασμό και τη λειτουργία τέτοιων κτηρίων ρεαλιστικό στόχο αποτελεί η μεγιστοποίηση της Καθαρής Παραγόμενης Ενέργειας, μέσω ευφυούς διαμόρφωσης της ζήτησης, ώστε να επιτευχθεί σύγκλιση παραγωγής-κατανάλωσης. Με την πεποίθηση ότι η μεγιστοποίηση της Καθαρής Παραγόμενης Ενέργειας για Κτήρια Θετικού Ισοζυγίου επιτυγχάνεται με τη λήψη καλύτερων αποφάσεων ελέγχου, παρουσιάζεται μια μεθοδολογία ελέγχου και βελτιστοποίησης, η οποία συνδυάζει τεχνικές Προβλεπτικού Ελέγχου βασισμένου σε Μοντέλα και Προσαρμοστικής Βελτιστοποίησης. Στο σύστημα PEBBLE συνυπάρχουν τρία βασικά συστατικά: α) μοντέλα θερμικής προσομοίωσης, β) αισθητήρες, επενεργητές, και διεπαφές χρήστη, και γ) γενικά εργαλεία ελέγχου και βελτιστοποίησης. Το πιθανό περιθώριο εξοικονόμησης ενέργειας χρησιμοποιώντας εξελιγμένες στρατηγικές ελέγχου παρουσιάζεται με τη βοήθεια πειραμάτων προσομοίωσης: υπάρχουν σημαντικά ενεργειακά οφέλη από τη χρήση εξελιγμένων στρατηγικών ελέγχου, συγκριτικά με παραδοσιακές μεθόδους ελέγχου βασισμένες σε κανόνες

Tumor-associated antigen human chorionic gonadotropin beta contains numerous antigenic determinants recognized by in vitro-induced CD8+ and CD4+ T lymphocytes.

The beta subunit of human chorionic gonadotropin (hCG beta) is markedly overexpressed by neoplastic cells of differing histological origin including those present in colon, breast, prostate and bladder tumors. We have previously shown that some patients with hCG beta-producing urothelial tumors have circulating T cells that proliferate in response to hCG beta. To make a comprehensive study of hCG beta as a potential target for cancer immunotherapy, we investigated whether hCG beta peptides could induce CD4+ or CD8+ T-cell responses in vitro. By stimulating peripheral blood mononuclear cells (PBMCs) from three donors with mixtures of overlapping 16-mer synthetic peptides analogous to portions of either the hCG beta 20-71 or the hCG beta 102-129 region, we established six CD4+ T-cell lines that proliferated specifically in response to five distinct determinants located within these two hCG beta regions. Three antigenic determinants (hCG beta 52-67, 106-121 and 114-125) were presented by HLA-DR molecules, while the two other antigenic determinants (hCG beta 48-63 and 56-67) were presented by HLA-DQ molecules. Interestingly, one T-cell line specific for peptide hCG beta 106-121 recognized hCG beta peptides comprising, at position 117, either an alanine or an aspartic acid residue, with the latter residue being present within the protein expressed by some tumor cells. In addition, three other hCG beta-derived peptides that exhibited HLA-A*0201 binding ability were able to stimulate CD8+ cytotoxic T cells from two HLA-A*0201 donors. These three immunogenic peptides corresponded to regions hCG beta 40-48, hCG beta 44-52 and hCG beta 75-84. Our results indicate that the tumor-associated antigen hCG beta possesses numerous antigenic determinants liable to stimulate CD4+ and CD8+ T lymphocytes, and might thus be an effective target antigen for the immunotherapy of hCG beta-producing tumors

In vivo study of the GC90/IRIV vaccine for immune response and autoimmunity into a novel humanised transgenic mouse

Parathyroid hormone-related protein (PTH-rP), a secreted protein produced by prostate carcinoma and other epithelial cancers, is considered a key agent for the development of bone metastases. We investigated the construct GC90/IRIV, composed of immunopotentiating reconstituted influenza virosomes (IRIV) containing PTH-rP gene plasmids (GC90), as a potential tool for human anticancer immunotherapy into humanised mice transgenic for HLA-A(*)02.01, the human-β2 microglobulin, and the human CD8α molecule. Intranasal administration of GC90/IRIV resulted in the induction of a PTH-rP-specific multiepitope cytotoxic T-cell (CTL) response. Cytotoxic T cells derived from vaccinated mice were capable of lysing in vitro syngenic murine PTH-rP transfectants and human HLA-A(*)02.01+/PTH-rP+ prostate carcinoma LNCaP cells as well. The immune response capacity and the absence of any sign of toxicity and/or autoimmunity in vivo suggest the GC90/IRIV vaccine as a valid tool for active specific immunotherapy of human cancers and metastases overexpressing PTH-rP