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Title
Traffic efficiency optimization through V2X communication / by Christian Backfrieder
AuthorBackfrieder, Christian
CensorMecklenbräuker, Christoph
PublishedWien, 2018
Descriptionxiv Seiten, Seite 15-131 : Illustrationen, Diagramme
Institutional NoteTechnische Universität Wien, Dissertation, 2018
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Zusammenfassung in deutscher Sprache
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Link noch nicht aktiv
LanguageEnglish
Document typeDissertation (PhD)
Keywords (DE)Intelligente Verkehrssysteme / Fahrzeugkommunikation
Keywords (EN)Intelligent Transport Systems / Vehicular Communication
URNurn:nbn:at:at-ubtuw:1-108653 Persistent Identifier (URN)
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 The work is publicly available
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Traffic efficiency optimization through V2X communication [10.31 mb]
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Abstract (English)

Transport delays due to traffic jams are manifest in many urban areas worldwide. For the purpose of making road traffic networks more efficient, Intelligent Transport Systems (ITSs) are currently being developed and deployed. In order to mitigate (or even avoid) congestion, Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication provide a means for cooperation and intelligent route management in transportation networks. In this thesis, the novel Predictive Congestion Minimization in combination with an A*-based Router (PCMA*) algorithm is introduced, which provides a comprehensive framework for detection, prediction and avoidance of traffic congestion. It assumes utilization of Vehicle-to-Everything (V2X) communication for transmission of contemporary vehicle data such as route source and destination or current position, as well as for provision of routing advice for vehicles. By processing the vehicle data, an early congestion detection and subsequently the calculation of alternative routes becomes possible. As a consequence of the early detection, detours over a wide area can be a solution in appro- priate situations to bypass traffic jams and avoid critical areas completely, whereas the degree of improvement of course also depends on the structure of the road network. The routing component takes the current road conditions and predicted future congestion into consideration. PCMA* further contains a component for intelligent and target-oriented selection of vehicles to be rerouted in case of a congestion. In the first part of this thesis, the performance is proven by dynamic, microscopic traffic simulations in an artificial and real-world road network scenario. Due to the well performing prediction, the results reveal substantial advantages in terms of time and fuel consumption and hence also CO2 emissions, compared to situations with no active rerout- ing system. PCMA* is further contrasted with simple rerouting algorithms without any functionality for congestion prediction but with different approaches of how the current situation on the road is assessed and quantified. Additionally, a more sophisticated predictive approach from literature is evaluated and the results are compared to those which can be achieved by applying PCMA*. Within the very same configuration for environment and traffic emergence, all the reference algorithms are outperformed by PCMA*. With the objective of optimizing traffic flow and in the best case avoiding congestion, it was assumed that 100% of all vehicles participate actively in the system in the initial simulation configurations so far. Notwithstanding, the transition from very low penetration rates of vehicles that are equipped with communication functionality to a situation where basically all vehicles have the capability to send and receive information will not be completed overnight. To a greater degree, the penetration of connected vehicles will increase more and more, which further will result in a very long period of mixed composition. The second part of this thesis focuses on the analysis of a variable ratio of vehicles having routing and communication functionality to those who do not have these capabilities. It analyzes the performance of the rerouting algorithm when a varying percentage of vehicles is unable to communicate for distinct traffic densities, and proves by simulations that even penetration rates far below hundred percent lead to improvements of the average time and fuel consumption as well as CO2 emissions per vehicle. Finally, the router requires a functional communication infrastructure to contribute route guidance to vehicles which are affected by traffic jams. However, variable message delays or a complete loss of messages can influence the rerouting performance significantly, even if the penetration rate is 100%, since either route advice could fail to reach their recipient, or the supposed knowledge of the road conditions could be outdated at the side of the router. The delay requirements of various routers may be divergent, and therefore two delay models which are independent of the underlying communication standard and the applied routing algorithm are proposed in the third part of the thesis. PCMA* is evaluated concerning its performance with varying delays and message loss probabilities by applying the introduced delay models in the traffic simulations. Furthermore, constraints are defined for both the delay and message loss probability, which define boundary conditions that are required to achieve certain improvements ensuing from intelligent rerouting. The results reveal a high robustness of PCMA* with regard to delays and message loss probabilities, which expresses itself by similarly low achieved average vehicle travel times for a large amount of the investigated communication setups, compared to a parametrization without message delays.

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