A distributed evolutionary approach to cooperative vehicular traffic optimization
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Mathematisch-Naturwissenschaftliche Fakultät
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Abstract
Durch ein zunehmendes Verkehrsaufkommen wächst die Notwendigkeit den Verkehr in irgendeiner Form "intelligent" zu organisieren. In diesem Kontext sind die sogenannten Intelligent Transportation Systems (ITS) in den Fokus der Forschung gerückt. Diese Systeme zielen in der Regel auf die dynamische Optimierung der Routenwahlen von Verkehrsteilnehmern ab und sollen dadurch die Effizienz des Verkehrs verbessern. Grundsätzlich kann die Vorstellung einer optimalen Routenwahl in eine von zwei Kategorien eingeteilt werden: das Nash Gleichgewicht und die systemoptimale Routenwahl. Während Nash Gleichgewichte vergleichsweise einfach erzielt werden können-beispielsweise dadurch, dass jeder Fahrer seine Route egoistisch optimiert-ist das Erreichen des Systemoptimums ungleich schwerer. Dieses setzt nämlich voraus, dass alle Fahrer miteinander kooperieren und gemeinsam eine Lösung finden, von der die Gesamtheit als solche profitiert. In dieser Dissertation diskutieren wir das Design eines dezentralisierten ITS, welches in der Lage ist, eine systemoptimale Routenzuweisung im Straßennetzwerk zu approximieren, so dass die Fahrzeuge den price of anarchy nicht mehr in voller Höhe bezahlen müssen. Der besondere Fokus liegt hierbei auf der Anwendbarkeit des Ansatzes in realistischen Umgebungen, in denen eine Vielzahl von Schwierigkeiten zu erwarten ist. Dies beinhaltet beispielsweise eine unvollständige oder inkorrekte Sicht auf die aktuelle Verkehrssituation, das Fehlen von Wissen über Fahrzeuge, die erst in der Zukunft das Straßennetz betreten sowie ein nicht perfekter oder ressourcenlimitierter Kommunikationskanal.
The increasing amount of road traffic necessitates approaches that somehow "intelligently" organize traffic. In this context, the study of intelligent transportation systems (ITS) has been performed for some time. The goals of such systems include, e.g., is the dynamic optimization of route choices in a road network and hence the improvement of traffic conditions. There are two main methodologies how an optimization can be performed: the optimization towards a Nash equilibrium or towards a system optimum. While Nash equilibria can be easily reached, e.g., when every driver selfishly optimizes his own route, reaching the system optimum is a challenging task and requires all drivers to cooperate in an altruistic manner in favor of the system from a global perspective. In this work, we discuss the design of a decentralized ITS that is capable of approximating system optimal route choices in the network avoiding that the drivers have to pay the full price of anarchy. The focus, in this context, lies on the applicability to real life situations where a number of difficulties has to be expected, e.g., an incomplete or incorrect view on the current traffic situation, the lack of future knowledge and an imperfect or limited communication channel. Facing these challenging questions, we develop solutions to a number of research questions, that arise from the aforementioned difficulties. Before we can do so, we focus on the fundamental concepts of traffic optimization with an emphasis both on the theoretical concepts as well as their applicability in real world environments.
The increasing amount of road traffic necessitates approaches that somehow "intelligently" organize traffic. In this context, the study of intelligent transportation systems (ITS) has been performed for some time. The goals of such systems include, e.g., is the dynamic optimization of route choices in a road network and hence the improvement of traffic conditions. There are two main methodologies how an optimization can be performed: the optimization towards a Nash equilibrium or towards a system optimum. While Nash equilibria can be easily reached, e.g., when every driver selfishly optimizes his own route, reaching the system optimum is a challenging task and requires all drivers to cooperate in an altruistic manner in favor of the system from a global perspective. In this work, we discuss the design of a decentralized ITS that is capable of approximating system optimal route choices in the network avoiding that the drivers have to pay the full price of anarchy. The focus, in this context, lies on the applicability to real life situations where a number of difficulties has to be expected, e.g., an incomplete or incorrect view on the current traffic situation, the lack of future knowledge and an imperfect or limited communication channel. Facing these challenging questions, we develop solutions to a number of research questions, that arise from the aforementioned difficulties. Before we can do so, we focus on the fundamental concepts of traffic optimization with an emphasis both on the theoretical concepts as well as their applicability in real world environments.
Description
Keywords
Navigation, Verkehr, Simulation, Optimierung, VANETs, SUMO, Optimization, Navigation, Simulation, Traffic, VANETs, SUMO
Dewey Decimal Classification
004 Informatik
Citation
Cagara, Daniel.(2017). A distributed evolutionary approach to cooperative vehicular traffic optimization. 10.18452/17675