- Doctoral Thesis
Rights / licenseIn Copyright - Non-Commercial Use Permitted
The evolution of information technology brings an entirely new perspective to old issues of transportation and the problem of overloaded road traffic networks. At the forefront of progress in the field of information technology is the opportunity for the driver to acquire knowledge through media.</br>The present study is aimed at investigating effects of spatial orientation in typical situations. To this end, it starts out from the following exemplary scenario: Traffic in the Zurich metropolitan area is congested. Vehicles often move at walking pace. Traffic demand leads to an average volume of 118 vehicles per kilometer. Every driver has planned his itinerary with the help of an off-the-shelf navigation device and sticks to his shortest route. In view of this situation, the question investigated in this study is: How much will the traffic situation improve if part of the drivers use real-time navigation information (such as may be available via smartphone)? The research to answer this question proceeds on the assumption that a driver behaves either in a “conventional” or in a “progressive” manner. The conventional drivers move along on the route they perceived as the shortest one when they planned it before starting on their trip. The progressive drivers are informed about the current traffic situation and head for their destination dynamically by choosing the currently most advantageous link at each traffic node on their trip. The decision processes of the informed drivers will be mapped in a simplified form and microscopically simulated using the MATSim software. A model postulated for the route choice describes the behavior of drivers guided by real-time navigation information, but not obstinately following it; their experience regarding the reliability of the traffic information also influences their route choice. The model analyzes how differing knowledge levels and modes of behavior of the drivers affect the state of the traffic system in the real-world setting of the Zurich metropolitan area. The results of the experiments testify to the existence of great differences in respect of the load on the road network, the mean daily travel times and the consequential properties of a trip up to the driver’s arrival time at his destination. – A key result is that all drivers benefit even when only part of them navigate by using current traffic information. Further results show in detail the time savings that each of the two classes of drivers achieves, and also how the entirety of drivers benefits from certain shares of informed drivers. Especially interesting for the analyst is the finding that the effect of descriptive and normative behavior in respect of route choice varies significantly. The scenario’s estimated mean saving potential of about 25 percent can be fully exploited if the informed drivers behave in a disciplined manner and follow the recommended links. When 30 percent of the drivers in the Zurich metropolitan area are guided by real- time navigation system information and comply exactly with it, the traffic density will be reduced from 118 vehicles to 56 vehicles per kilometer, and traffic speed will increase from four to 22 kilometers per hour. Starting from a share of 50 percent of informed drivers, traffic density will diminish to just above 30 vehicles per kilometer, and a driver will reach his destination at an average speed of little more than 50 kilometers per hour. The better distribution of the traffic may triple the distance of an informed driver, it is true; and yet it amounts to an 84 percent time saving for all drivers. – However, if more than 70 percent of the drivers go by real-time navigation system information, the traffic situation will again deteriorate to as many as 43 vehicles per kilometer moving at a speed of 34 kilometers per hour. This (probably unexpected) deterioration of the traffic situation at a high share of drivers being guided by real-time navigation system information asks for more research. Further analyses are required. Most likely they will show that to prevent this unwanted effect, the quality of the information must be improved. The hypothesis that suggests itself is that navigation system guidance must be based on marginal cost, which in turn requires that the traffic densities and the time-flow- capacity curves of the links Show more
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SubjectMACHINE LEARNING (ARTIFICIAL INTELLIGENCE); GEOMETRISCHES SCHLIESSEN + RÄUMLICHES SCHLIESSEN (KÜNSTLICHE INTELLIGENZ); MASCHINELLES LERNEN (KÜNSTLICHE INTELLIGENZ); GEOMETRIC REASONING + SPATIAL REASONING (ARTIFICIAL INTELLIGENCE)
Organisational unit02115 - Dep. Bau, Umwelt und Geomatik / Dep. of Civil, Env. and Geomatic Eng.
03521 - Axhausen, Kay W. / Axhausen, Kay W.
02226 - NSL - Netzwerk Stadt und Landschaft / NSL - Network City and Landscape
02655 - Netzwerk Stadt und Landschaft D-ARCH
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