查看更多>>摘要：Difficulty in obtaining accurate car-following data has traditionally been regarded as a considerable drawback in understanding real phenomena and has affected the development and validation of traffic microsimulation models. Recent advancements in digital technology have opened up new horizons in the conduct of research in this field. Despite the high degrees of precision of these techniques, estimation of time series data of speeds and accelerations from positions with the required accuracy is still a demanding task. The core of the problem is filtering the noisy trajectory data for each vehicle without altering platoon data consistency; i.e., the speeds and accelerations of following vehicles must be estimated so that the resulting intervehicle spacings are equal to the real one. Otherwise, negative spacings can also easily occur. The task was achieved in this study by considering vehicles of a platoon as a sole dynamic system and reducing several estimation problems to a single consistent one. This process was accomplished by means of a nonstationary Kalman filter that used measurements and time-varying error information from differential Global Positioning System devices. The Kalman filter was fruitfully applied here to estimation of the speed of the whole platoon by including intervehicle spacings as additional measurements (assumed to be reference measurements). The closed solution of an optimization problem that ensures strict observation of the true intervehicle spacings concludes the estimation process. The stationary counterpart of the devised filter is suitable for application to position data, regardless of the data collection technique used, e.g., video cameras.
查看更多>>摘要：The development of accurate and robust models in the field of car following has suffered greatly from the lack of appropriate microscopic data. Because of this lack, little is known about differences in car-following behavior between individual driver-vehicle combinations. This paper studies the car-following behaviors of individual drivers by making use of vehicle trajectory data extracted from high-resolution digital images collected at a high frequency from a helicopter. The analysis was performed by estimating the parameters of different specifications of the well-known Gazis-Herman-Rothery car-following rule for individual drivers. This analysis showed that a relation between the stimuli and the response could be established in 80% of the cases. The main contribution of this paper is that considerable differences between the car-following behaviors of individual drivers could be identified. These differences are expressed as different optimal parameter values for the reaction time and the sensitivity, as well as different car-following models that appear to be optimal on the basis of the data for individual drivers.
查看更多>>摘要：The study of car-following dynamics is useful for capacity analysis, safety research, and traffic simulation. There is also growing interest in its applications in intelligent transportation systems, such as advanced vehicle control and safety systems and autonomous cruise control systems. A large number of car-following models have been developed in the past five decades. Some of them were investigated and validated against experimental data; nevertheless, the results were not that consistent for some models, e.g., those for the General Motors (GM) model. As a part of the problem, the data acquisition and calibration techniques were not advanced then. The past few decades have seen remarkable advancements in these techniques, e.g., the use of the differential Global Positioning System (GPS) for position measurement, the use of Doppler's principle for speed measurements, and the use of genetic algorithms for optimization. It might be useful to reassess some outstanding issues in car-following dynamics in light of the latest technological advancements. This paper attempts to investigate car-following dynamics on the basis of the real-time kinematic GPS data collected from test track experiments. The GM model was evaluated along with some well-known simulation models, including the Gipps model and the Leutzbach and Wiedemann model. A genetic algorithm-based optimization technique was adapted for calibration. The sensitivities of drivers to their speeds and spacings from the vehicle ahead were found to vary among drivers. The interpersonal variations in model performance were significant. The GM model parameters were identified with improved reliability. The stability of traffic flow was analyzed experimentally.
查看更多>>摘要：This paper presents a new car-following model that aims to capture some of the key motorway flow characteristics, namely, traffic breakdown, hysteresis, and shock wave propagation, as well as close-following behavior. The model proposes three different driving states: nonalert, alert, and close following. Under the different driving states, drivers apply different reaction times and accelerations. This paper presents the formulation and algorithmic implementation of the model. The theoretical analysis of the macroscopic flow-density relationships of the model is discussed. Simulation experiments were conducted, and the results are examined at both the macroscopic level (speed breakdown and traffic hysteresis) and the microscopic level (gap distribution and shock wave propagation). The results show that the model is able to capture realistically the speed drop, traffic hysteresis, and shock wave propagation as well as close-following behavior. Further studies of the sensitivities of key model parameters suggest that the drivers' reaction times have a significant effect on the modeled capacity and occupancy, while the effect of the speed threshold that distinguishes congested from noncongested traffic flow is less significant.
查看更多>>摘要：This paper discusses the validity of first-order traffic flow theory for the description of two-dimensional pedestrian flow operations in the case of an oversaturated bottleneck in front of which a large high-density region has formed. The paper shows how observations of density, speed, and flow that have been collected from laboratory walking experiments can be interpreted from the viewpoint of first-order theory. It is observed that pedestrians present at the same cross section inside of the congested region may encounter different flow conditions. This mainly depends on the lateral position of the pedestrian with respect to the center of the congested region. In the lateral center, high densities and low speeds are observed. However, on the boundary of the congested region, pedestrians may walk in nearly free-flow conditions and literally walk around this congested region. Visualization of these data in the flow-density plane results in a large scatter of points that have similar flows (bottleneck capacity) but different densities. This can be explained by noticing that observations of congestion of pedestrian traffic over the total width of the cross section do not belong to a single fundamental diagram but belong to a set of different fundamental diagrams. This observation has consequences for estimation of the fundamental diagram describing pedestrian traffic.
查看更多>>摘要：The evermore widespread use of microscopic traffic simulation in the analysis of road systems has refocused attention on submodels, including car-following models. The difficulties of microscopic-level simulation models in the accurate reproduction of real traffic phenomena stem not only from the complexity of calibration and validation operations but also from the structural inadequacies of the submodels themselves. Both of these drawbacks originate from the scant information available on real phenomena because of the difficulty with the gathering of accurate field data. In this study, the use of kinematic differential Global Positioning System instruments allowed the trajectories of four vehicles in a platoon to be accurately monitored under real traffic conditions on both urban and extraurban roads. Some of these data were used to analyze the behaviors of four microscopic traffic flow models that differed greatly in both approach and complexity. The effect of the choice of performance measures on the model calibration results was first investigated, and intervehicle spacing was shown to be the most reliable measure. Model calibrations showed results similar to those obtained in other studies that used test track data. Instead, validations resulted in higher deviations compared with those from previous studies (with peaks in cross validations between urban and extraurban experiments). This confirms the need for real traffic data. On comparison of the models, all models showed similar performances (i.e., similar deviations in validation). Surprisingly, however, the simplest model performed on average better than the others, but the most complex one was the most robust, never reaching particularly high deviations.
查看更多>>摘要：Empirical speed-density relationships are important not only because of the central role that they play in macroscopic traffic flow theory but also because of their connection to car-following models, which are essential components of microscopic traffic simulation. Multiregime traffic speed-density relationships are more plausible than single-regime models for representing traffic flow over the entire range of density. However, a major difficulty associated with multiregime models is that the breakpoints of regimes are determined in an ad hoc and subjective manner. This paper proposes the use of cluster analysis as a natural tool for the segmentation of speed-density data. After data segmentation, regression analysis can be used to fit each data subset individually. Numerical examples with three real traffic data sets are presented to illustrate such an approach. Using cluster analysis, modelers have the flexibility to specify the number of regimes. It is shown that the K-means algorithm (where K represents the number of clusters) with original (nonstandardized) data works well for this purpose and can be conveniently used in practice.
查看更多>>摘要：Unlike the freeway traffic stream, the urban traffic stream is generally interrupted by signals, and the traffic data observed may differ from the true (uninterrupted) flow characteristics. This paper deals with the transformation between interrupted and uninterrupted speeds on an urban road with signalized intersections. The conventional shock wave model and two modified versions of the shock wave boundary that address unrealistic characteristics of the conventional shock wave model are developed. A method of integrating probe and detector data for speed transformation is also discussed. The proposed methods are tested with a virtual isolated signalized intersection by varying the approach demand. The numerical results suggest that all models can significantly reduce the difference between the actual and the estimated uninterrupted (or interrupted) speeds compared with the case of doing nothing. The applications of the proposed methods, including the development of a flow-speed model and link travel time estimation, are also given.
Ruihua TaoHeng WeiYinhai WangVirginia P. Sisiopiku...
p.83-93页
查看更多>>摘要：This paper explores driver behavior in a paired car-following mode in response to a speed disturbance from a front vehicle. A current state-control action-expected state (SAS) chain is developed to provide a framework for modeling of the hierarchy of expected actions incurred during the need for speed disturbance absorption. Three car-following scenarios and one lane-changing scenario are identified with defined perceptual informative variables to describe the process of speed disturbance absorption. Those variables include dynamic spacing versus the follower's speed, disturbance-effecting and -ending spacing, headway, acceleration-deceleration, speed recovery period, speed advantage, and lane-changing duration. A significant improvement in car-following modeling introduced in the paper is the integration of car-following and lane-changing behaviors in the SAS chain. Moreover, critical values of perceptual informative variables are statistically developed as a function of the follower's speed by using observed vehicle trajectory data. Furthermore, models that determine the probability of a lane change in response to a speed disturbance and models for acceptable lane-changing decision-making conditions at the adjacent lanes are developed on the basis of the analysis of observed vehicle trajectory data. The work presented in this paper provides an analysis of speed disturbance and speed absorption phenomena and car-following and lane-changing behaviors at the microscopic level. This work establishes the foundation for further research on multiple speed disturbance absorption and its impact on traffic stabilities at the macroscopic analysis level.
查看更多>>摘要：The evolution of traffic from freely flowing to queued conditions and from queued to freely flowing conditions was studied along a 14-km section of German Autobahn 9 near Munich. Several bottlenecks were identified by a systematic examination of the excess vehicle accumulation and excess travel time (delay) that arose between measurement locations. It is shown that a bottleneck arose repeatedly downstream of an on-ramp on a busy freeway. The analysis tools used were curves of cumulative vehicle arrival number versus time and curves of cumulative time-averaged velocity versus time. The data required to construct these curves were available in archived form from inductive loop detectors embedded in the freeway. These cumulative plots were carefully and systematically transformed to produce the resolution necessary to reveal important details of the evolution of traffic flow features. These high-resolution transformed curves have made it possible to identify key time-dependent features related to the activation and deactivation of the bottleneck.
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