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Traffic Flow Theory
Published in Dušan Teodorović, The Routledge Handbook of Transportation, 2015
With the exception to research conducted by Kerner (2004), the fundamental hypothesis of all traffic flow theories is the existence of a site-specific unique relationship between traffic stream flow and traffic stream density, commonly known as the fundamental diagram, or the traffic stream motion model. The assumption is that all steady-state model solutions lie on the fundamental diagram and thus are referred to as fundamental diagram approaches (Kerner 2004). Given that traffic stream space-mean speed can be related to the traffic stream flow and density, a unique speed-flow-density relationship is derived from the fundamental diagram for each roadway segment.
Intelligent Mobility for Minimizing the Impact of Traffic Incidents on Transportation Networks
Published in Nishu Gupta, Joel J. P. C. Rodrigues, Justin Dauwels, Augmented Intelligence Toward Smart Vehicular Applications, 2020
Among these features, real-time traffic information is one of the most important features since the forecast should be updated every time the traffic condition changes. Therefore, most of the recent studies carried out have analyzed the impact of incidents by incorporating real-time traffic data [13][14]. In particular, it is essential to consider both important traffic variables, i.e., speed and flow, in the analysis which can be explained by the fundamental diagram of speed-flow, as shown in Figure 10.3.
An efficient variational Bayesian algorithm for calibrating fundamental diagrams and its probabilistic sensitivity analysis
Published in Transportmetrica B: Transport Dynamics, 2023
X. Jin, W. F. Ma, R. X. Zhong, G. G. Jiang
The fundamental diagram that depicts the relationship between traffic flow, velocity and density is the foundation of traffic flow theory. The empirical and analytic properties of the fundamental diagram are substantially discussed in the literature. Previously, researchers mainly focused on single-regime fundamental diagrams (FDs) (Greenberg 1959; Greenshields et al. 1935; Newell 1961; Underwood 1961), which assume a single pre-defined functional form for the entire domain, thus is simple to estimate and easy to analyze. However, these FDs are not accurate enough for complex traffic scenarios. Some recent studies try to reduce the number of parameters and improve the accuracy of the single-regime models (Cheng et al. 2021). Multi-regime FDs are then established to fit different regimes of FDs with different pre-defined functional forms (Drake, Schofer, and May 1967; Edie 1961; Kerner 2009; May 1990; Sun and Zhou 2005). They are more accurate than single-regime FDs and have been widely used in traffic management and control fields. Besides, in reality, inherent uncertainty is revealed in observed traffic flow data. Therefore, Qu, Zhang, and Wang (2017), Siqueira et al. (2016), Bai et al. (2021), Z. Liu et al. (2022), Makridis et al. (2023), Yu, Hua, and Wang (2023), and Rao et al. (2023) proposed different stochastic traffic models to further describe the heterogenous driving behaviors, speed heterogeneity, multiple vehicles and other stochastic features of the traffic flow. And some studies also focus on the stochastic of the network (Huang, Sun, and Zhang 2022; Moshahedi, Kattan, and Tay 2021).
A novel framework for automated monitoring and analysis of high density pedestrian flow
Published in Journal of Intelligent Transportation Systems, 2020
Muhammad Baqui, Manar D. Samad, Rainald Löhner
The fundamental diagram is a plot of speed versus density and is considered an important design tool for any traffic analysis. It provides a snapshot of the flow at a particular time and enables one to decide if the flow needs to be regulated. Figure 6 illustrates a fundamental diagram for the representative case with ten images from the second image set (Table 2). Here, velocities are obtained for both the ground truth and derived cases using PIV method and then transformed to the 3D world coordinate. Similarly, the pedestrian density is obtained for ground truth pedestrian counts and from the Ferns regressor model. The fundamental diagram obtained from Predtechenskii and Milinskii (1978) is also shown in the figure as a solid line. It is evident that our framework derived data from PIV and Ferns regressors closely match the ground truth and the diagram obtained by Predtechenskii and Milinskii (1978).
Expressway bottleneck pattern identification using traffic big data—The case of ring roads in Beijing, China
Published in Journal of Intelligent Transportation Systems, 2020
Yanni Yang, Meng Li, Jiaying Yu, Fang He
To identify bottlenecks on the expressway by mining huge amount of traffic data, it is important to distinguish the traffic states. In traffic flow theory, the fundamental diagram provides the relationship among traffic flow, density and speed. In this paper, the speed-flow diagram (Figure 1(a)) is used to determine the speed at which the optimum flow occurs, namely the critical speed, which is related to traffic demand and road segment feature. The speed-flow curve can be generally divided by the critical speed into two parts, the oversaturated part and the unsaturated part.