Real-time holding control strategies are implemented, among other reasons, in order to protect transfers. In the context of highfrequency services, there is a need to reconcile between striving for single-line regularity and synchronizing inter-line arrivals. Their operationalization depends on the predictions regarding passenger flows across the network. We examine the influence of real-time passenger data on the performance of transfer synchronization control. To this end, we develop two real-time transfer synchronization controllers which make use of different passenger data sources. The controllers differ in their assumptions concerning capacity constraints as well as on-board crowding conditions. The results show that each transferring passenger saves on average 2-10 min thanks to the proposed strategy, while on-board passengers experience a delay of 1-2 min each in most cases. The highest time saving per transferring passenger is obtained when the demand level is low and the controller opts for synchronizing more frequently.
Highlights• Rule-based holding controller selects transfer synchronization or line regularity • The impact of different passenger data on controller performance is investigated • On-board crowding conditions are considered by the real-time controller • On-board occupancy is the most valuable real-time passenger data source
Cycling research at the operational behavioral level is limited, mainly because of the lack of empirical data. To overcome this data shortage, we performed a controlled, large-scale cycling experiment in the Netherlands. In this paper we describe the methodology for setting up and implementing such an experiment, from the motivation of its design using a conceptual model describing cyclist behavior to adjustments that were required during the experiment. The main contribution of this paper is, therefore, to be used as a guide in future experimental data collections. Moreover, we present the characteristics of the participants and their bicycles, and provide a qualitative description of phenomena observed during the experiment. Finally, we elaborate on the potential that the collected dataset holds for future research into understanding and modeling operational cycling behavior.
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