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It is well known that pavement distress negatively affects the drivers and passengers of vehicles. Many studies report that foremost among these negative effects is the vibrations that form within the vehicle. Ride comfort depends on the human response to vibration and vehicle response to the road. The goal of this study was to investigate the effect of pavement condition index on ride comfort and to determine the threshold comfort limits for passenger cars on urban asphalt concrete pavements. The pavement condition index (PCI) was determined for pavement sections subject to different surface distress using the PAVER system. Ride (driving) speeds of 20, 30, 40 and 50 km/h were assessed on the same pavement sections to measure vibrational effects inside the vehicle and on the passenger seat. These measurements were then evaluated using the ISO 2631-1 standard in order to determine the a wz values. Using the logistic regression technique, predictive model that took into account linguistic concepts for estimating ride comfort levels based on PCI values was developed. With the aid of this mathematical model, comfort threshold values were determined for each driving speed within an interval of 0-100 PCI. The study results indicated that increasing driving speed was generally associated with higher PCI comfort thresholds. [ABSTRACT FROM AUTHOR]

Online traffic flow modeling is of increasing importance due to intelligent transport systems and technologies. The flow-density relation plays an important role in traffic flow modeling and provides a basic way to illustrate traffic flow behavior under different traffic flow and traffic density conditions. Until now the research effort has focused mainly on the shape of the relation. The time series of the relation has not been identified clearly, even though the time series of the relation reflects the upstream/downstream traffic conditions and should be considered in the traffic flow modeling. In this paper, the dynamic flow-density relation is identified based on the classification of traffic states and is quantified employing fuzzy logic. The quantified dynamic flow-density relation builds the basis for online application of a macroscopic traffic flow model. The new approach to online modeling of traffic flow applying the dynamic flow-density relation alleviates parameter calibration problems stemming from the static flow-density relation. [ABSTRACT FROM AUTHOR]

Any application of intelligent transportation systems (ITS) to freeway operations and control relies on understanding behavior of freeway bottlenecks. By improving our understanding of the formation and dissipation of queues at active freeway bottlenecks (points characterized by the presence of queued traffic immediately upstream and unqueued traffic immediately downstream) we can improve the techniques used to manage freeway traffic—one of the hallmarks of ITS. It is shown freeway bottlenecks' activations can be diagnosed definitively using archived inductive loop detector data (measured at 20- 30-sec intervals) from two sites in Toronto, Canada. Once the bottlenecks' locations and times of activation were determined, potential signals of their activations were explored. It is shown that certain potential signals were evident immediately before upstream queue formation, and that these were reproducible from day to day (over three or four days examined) at the sites studied. As a result, it is suggested that these signals be used for systematic investigation of bottleneck behavior in either real time or in retrospect, on a more widespread basis as part of a real-time ITS control system. [ABSTRACT FROM AUTHOR]