Browsing by Author "Mandir, Eileen"

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    Potential of traffic information to optimize route and departure time choice
    (2012) Mandir, Eileen; Friedrich, Markus (Prof. Dr.-Ing.)
    Intelligent traffic management has been the means to utilize existing capacities on overloaded road networks for quite some time. In recent years traffic information has become more and more important. The increasing popularity of navigation systems as well as the growing availability of current travel times makes it possible to provide a vast number of car drivers with traffic state information via onboard navigation devices or dynamic roadside traffic signs. The first part of this work analyses drivers’ acceptance of traffic information and compliance with route guidance today on the basis of an empirical study of almost 300 car commuters in the greater Munich area over a period of eight weeks. An analysis of the collected data reveals that traffic information is not only used for route guidance in case of needing directions but it is also used rather frequently on everyday trips, such as from home to work. Today, the level of drivers’ information on potential alternative routes and current travel times is based on incomplete as well as temporally delayed information and thus is greatly dependent on the driver’s own knowledge of historical daytime-dependent travel times in the road network. In the second part of this work econometric choice models are presented based on the collected GPS trajectories as well as questionnaire data using Maximum-Likelihood estimation. These choice models identify the variables influencing route and departure time choice. Special focus is given to the influence of traffic reports received via radio, route guidance via navigation devices or via dynamic roadside traffic signs, traffic state information displayed as Level-of-Service map on navigation devices, and travel time information on dynamic roadside traffic signs. The analysis of over 16,000 trips shows that survey participants have a strong preference towards their usual main route. Besides the current travel time, traffic information via radio or via Level-of-Service map has the strongest influence on the choice to divert from the main route. The probability of diverting to an alternative route is equal for a 5-minute increase in travel time as for 6 kilometres of congestion or 10 kilometres of minor delays when the information is broadcast over the radio or displayed through the Level-of-Service map. The evaluation of the questionnaire shows the influence of current travel time information on departure time choice. The results greatly depend on the flexibility of the working hours of the decision maker. For a usual commuting distance of 30 kilometres, persons with flexible working hours show a willingness to change their usual departure time by 15 minutes for a 10-minute travel time reduction, whereas persons with fixed working hours are willing to change their departure time for a 25-minute travel time reduction. The third part of this work includes modelling concepts to determine the potential of traffic information to reduce the transport time expenditure as well as fuel consumption in the entire survey area. Therefore, a comparison of today’s traffic situation with a state with perfect information and a system-optimal state from the transport planning perspective is performed. The separate models of route and departure time choice are integrated within a macroscopic traffic assignment model. Optimization of route choice by providing all drivers with perfect information on current travel times reduces the transport time expenditure by 4% in the morning peak hour. A substantially larger reduction of almost 8%, and thus close to a system-optimal state, is achieved by perfect information on the optimal departure time which causes a temporal distribution of the travel demand. The empirical analysis within this work and the route and departure time choice models based thereon determine impacts of traffic information in the study area of Munich today as well as possibly in the future. The results show the potential of traffic information for the optimization of traffic flows in road networks. Furthermore, this work provides an important contribution to model-based decision guidance for practitioners in optimizing traffic management strategies as well as for politicians deciding on future investments in information infrastructure and technologies.