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An integrated approach for optimising traffic signals and cooperative vehicle trajectories at urban intersections is proposed. The upper layer determines the optimal signals using enumeration and the lower layer optimises trajectories under each feasible signal plan. In the lower layer, platoon accelerations are optimised considering comfort and delay while satisfying motion constraints and safe requirements. The red phase is enforced as a logic constraint, which restricts vehicles to stay behind the stop-line. Typical platoon manoeuvres such as split and approach can be included in the lower layer. The integrated control approach is adaptive to traffic demands and flexible in incorporating different traffic movements during multiple signal phases. The controller performance is verified by simulation of three designed scenarios. The comparison with trajectory optimization and signal optimization demonstrates the advantages on throughput, fuel economy, delay and vehicle stops, and reveals insights into the optimal patterns on signals and trajectories. [ABSTRACT FROM AUTHOR]

Freeways form an important part of the road network. Yet, driving behavior on freeways, in particular lane changes and the relation with the choice of speed, is not well understood. To overcome this, an online survey has been carried out. Drivers were shown video clips, and after each clip they had to indicate what they would do after the moment the video stopped. A total of 1258 Dutch respondents completed the survey. The results show that most people have a strategy to choose a speed first and stick to that, which is the first strategy. A second, less often chosen, strategy is to choose a desired lane and adapt the speed based on the chosen lane. A third strategy, slightly less frequently chosen, is that drivers have a desired speed, but contrary to the first strategy, they increase this speed when they are in a different lane overtaking another driver. A small fraction have neither a desired speed nor a desired lane. Of the respondents 80% use the right lane if possible, and 80% avoid overtaking at the right. Also 80% give way to merging traffic. The survey was validated by 25 survey respondents also driving an instrumented vehicle. The strategies in this drive were similar to those in the survey. The findings of this work can be implemented in traffic simulation models, e.g., to determine road capacity and constraints in geometric design. [ABSTRACT FROM AUTHOR]

Crowd monitoring systems are more and more used to support crowd management organizations. Currently, counting systems are often used to provide quantitative insights into the pedestrian traffic state, since they are fairly easy to install and the accuracy is reasonably good under normal conditions. However, there are no sensor systems that are 100% accurate. Detection errors might have severe consequences for the density state estimation at large squares. The consequences of these errors for pedestrian state estimation have not yet been determined. This paper studies the impact of one specific type of detection error on the functionality of counting camera systems for density state estimation, namely, a randomly occurring “false negative” detection error. The impact is determined via two tracks, a theoretical track and a simulation track. The latter track studies the distribution of the cumulative number of pedestrians after 24 hours for three stylized cases by means of Monte Carlo simulations. This paper finds that counting camera systems, which have a detection error that is not correlated with the flow rate, provide a reasonably good estimation of the density within an area. At the same time, if the detection error is correlated with the flow rate, counting camera systems should only be used in the situation where symmetric demand patterns are expected. [ABSTRACT FROM AUTHOR]

Technological innovations can be assumed to have made the driving task more complex. It is, however, not yet clear to what extent this complexity leads to changes in longitudinal driving behavior. Furthermore, it remains to be seen how these adaptation effects can best be modeled mathematically. In order to determine the effect of complexity on empirical longitudinal driving behavior we performed a driving simulator experiment with a repeated measures design. Through this experiment we established that complexity of the driving task leads to substantial changes in speed and spacing. In order to provide insight into how complexity is actually related to changes in longitudinal driving behavior we introduce a new theoretical framework based on the Task-Capability-Interface model. Finally in this paper we take some first steps towards modeling of adaptation effects in longitudinal driving behavior in relation to complexity of the driving task through the introduction of a new neurofuzzy car-following model and based on the proposed theoretical framework. In this paper we show that this model yields a relatively good prediction of longitudinal driving behavior in case of driving conditions with differing complexity. The paper finishes with a discussion section and recommendations for future research. [ABSTRACT FROM AUTHOR]

Probe vehicles with Global Positioning Systems (GPS) can provide accurate positions that enable spatial-average speed estimation. However, some probe vehicles cannot provide accurate positions but can provide location-specific information on when and where they are located at the segment or cell level. These topological position (TP) data with segment- or cell-level accuracy cannot provide the distance component that is necessary for traffic speed estimation. However, considering the wide availability of TP data in the existing telecommunications network, there is still hope and benefits to make use of the data for traffic state estimation. In this paper, an algorithm is proposed using low-resolution positioning data. The proposed method is capable of fusing low-resolution positioning data with other data sources, leading to more accurate and reliable speed estimation of relatively low bias. In addition, this method shows strong robustness and error tolerance and can reveal the magnitude of the estimation error, which is helpful for travel time prediction and traffic control. [ABSTRACT FROM PUBLISHER]

Uncertainty of traffic network operations has been a subject of lively debate in the last decade. However, little effort has been put in developing control frameworks that are not only aimed at improving the average performance of the system, but also at improving the system robustness and reliability. In fact, it can be argued that most of the current control approaches are only aimed at improving the efficiency, which can even be counterproductive from a robustness point of view. The main contributions of this article is the proposition of a new control framework based on the notion of controlled Markov processes, which explicitly takes into account the uncertainty in predicted traffic conditions and system performance. Furthermore, in contrast to traditional optimal control approaches, the objective function can include general statistic of the random system performance, such as the mean, standard deviation or 95-percentile. The contribution aims to make clear how different performance function specifications yield different control strategies. This is shown for a relatively simple case study. [ABSTRACT FROM AUTHOR]

Nowadays traffic flow and congestion is one of the main societal and economical problems related to transportation in industrialized countries. In this respect, managing traffic in congested networks requires a clear understanding of traffic flow operations; i.e. insights into what causes congestion, what determines the time and location of traffic breakdown, how does the congestion propagate through the network, etc., are essential. For this purpose, during the past fifty years, a wide range of traffic flow theories and models have been developed to answer these research questions. This paper presents an overview of some fifty years of modelling vehicular traffic flow. A rich variety of modelling approaches developed so far and in use today will be discussed and compared. The considered models are classified based on the level-of-detail with which the vehicular flow is described. For each of the categories, issues like modelling accuracy, applicability, generalizability, and model calibration and validation are discussed. [ABSTRACT FROM AUTHOR]