Numerical Analysis of Freeway Traffic Flow Dynamics for Multiclass Drivers

2003 ◽  
Vol 1852 (1) ◽  
pp. 201-208 ◽  
Author(s):  
Zuojin Zhu ◽  
Gang-len Chang ◽  
Tongqiang Wu
Keyword(s):  
Traffic Flow ◽  
Macroscopic Model ◽  
Traffic Dynamics ◽  
Temporal And Spatial Variation ◽  
Freeway Traffic ◽  
First Order ◽  
Complex Interactions ◽  
Density Relation ◽  
Temporal And Spatial ◽  
Flow Theories

A first-order multiclass model is presented for illustrating freeway traffic dynamics, especially regarding the temporal and spatial variation of both densities and flow rate along freeway segments without the disturbance from ramp flows. The proposed macroscopic model is grounded on the assumption that freeway traffic may consist of multiple classes of drivers who, characterized by their unique speed–density relation, are likely to react differently under the same driving environment. The distribution of various classes of drivers and their differences in responding to perceived driving conditions may contribute significantly to the observed freeway traffic dynamics that remain to be better explained by existing traffic flow theories. The numerical solution and simulation results reported in this study, however, indicate that this proposed first-order multiclass model offers the potential to explore the complex interactions between freeway drivers and their collective impact on traffic flow patterns.

Macroscopic Model and Its Numerical Solution for Two-Flow Mixed Traffic with Different Speeds and Lengths

2003 ◽  
Vol 1852 (1) ◽  
pp. 209-219 ◽  
Author(s):  
Stéphane Chanut ◽  
Christine Buisson
Keyword(s):  
Traffic Flow ◽  
Flow Model ◽  
Macroscopic Model ◽  
Model Output ◽  
Mixed Traffic ◽  
Traffic Flow Model ◽  
Hyperbolic Conservation ◽  
Flux Function ◽  
First Order ◽  
Proposed Model

A new first-order traffic flow model is introduced that takes into account the fact that various types of vehicles use the roads simultaneously, particularly cars and trucks. The main improvement this model has to offer is that vehicles are differentiated not only by their lengths but also by their speeds in a free-flow regime. Indeed, trucks on European roads are characterized by a lower speed than that of cars. A system of hyperbolic conservation equations is defined. In this system the flux function giving the flow of heavy and light vehicles depends on total and partial densities. This problem is partly solved in the Riemann case in order to establish a Godunov discretization. Some model output is shown stressing that speed differences between the two types of vehicles and congestion propagation are sufficiently reproduced. The limits of the proposed model are highlighted, and potential avenues of research in this domain are suggested.

Introducing Buses into First-Order Macroscopic Traffic Flow Models

1998 ◽  
Vol 1644 (1) ◽  
pp. 70-79 ◽  
Author(s):  
J.P. Lebacque ◽  
J.B. Lesort ◽  
F. Giorgi
Keyword(s):  
Simple Model ◽  
Traffic Flow ◽  
Supply And Demand ◽  
Interaction Model ◽  
Macroscopic Model ◽  
Moving Frame ◽  
Large Measure ◽  
Flow Models ◽  
First Order ◽  
Traffic Flow Models

The aim of this paper is to provide a simple model of the interaction between buses and the surrounding traffic flow. Traffic flow is assumed to be described by a first-order macroscopic model of the Lighthill-Whitman-Richards type. As a consequence of their kinematics, which in large measure can be considered to be independent of the flow of other vehicles, buses should be considered as a moving capacity restriction from the point of view of other drivers. This simple interaction model is analyzed, mainly by considering the moving frame associated with the bus in order to derive analytical computation rules for derivation of the effects of the presence of the bus in the traffic flow. After deriving traffic equations in the moving frame associated with a bus, the usual basic concepts of first-order models, including those of relative traffic supply and demand, are generalized to the moving frame. A simple model for the bus-traffic interaction, assuming that the dimension of the bus can be neglected, can be derived from analytical calculations in the moving frame. Finally, some tentative results for the inclusion of buses into first-order traffic flow models, discretized according to Godunov’s scheme, are given.

scholarly journals Phylogenetic paleoecology: macroecology within an evolutionary framework

Paleobiology ◽  
2021 ◽  
Vol 47 (2) ◽  
pp. 171-177
Author(s):  
James C. Lamsdell ◽  
Curtis R. Congreve
Keyword(s):  
Species Diversity ◽  
Spatial Variation ◽  
Geographic Distribution ◽  
Evolutionary Biology ◽  
Phylogenetic Signal ◽  
Evolutionary Rates ◽  
The Other ◽  
Ecological Data ◽  
Temporal And Spatial Variation ◽  
Temporal And Spatial

The burgeoning field of phylogenetic paleoecology (Lamsdell et al. 2017) represents a synthesis of the related but differently focused fields of macroecology (Brown 1995) and macroevolution (Stanley 1975). Through a combination of the data and methods of both disciplines, phylogenetic paleoecology leverages phylogenetic theory and quantitative paleoecology to explain the temporal and spatial variation in species diversity, distribution, and disparity. Phylogenetic paleoecology is ideally situated to elucidate many fundamental issues in evolutionary biology, including the generation of new phenotypes and occupation of previously unexploited environments; the nature of relationships among character change, ecology, and evolutionary rates; determinants of the geographic distribution of species and clades; and the underlying phylogenetic signal of ecological selectivity in extinctions and radiations. This is because phylogenetic paleoecology explicitly recognizes and incorporates the quasi-independent nature of evolutionary and ecological data as expressed in the dual biological hierarchies (Eldredge and Salthe 1984; Congreve et al. 2018; Fig. 1), incorporating both as covarying factors rather than focusing on one and treating the other as error within the dataset.

scholarly journals Analysis of Ozone Pollution Characteristics and Influencing Factors in Northeast Economic Cooperation Region, China

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 843
Author(s):  
Jiaqi Tian ◽  
Chunsheng Fang ◽  
Jiaxin Qiu ◽  
Ju Wang
Keyword(s):  
Wind Speed ◽  
Sunshine Duration ◽  
Meteorological Data ◽  
Economic Cooperation ◽  
Ozone Pollution ◽  
Temporal And Spatial Variation ◽  
Temporal And Spatial ◽  
Pm2.5 And Pm10 ◽  
Peak Pattern ◽  
Pm2.5 Concentration

The increase in tropospheric ozone (O3) concentration has become one of the factors restricting urban development. This paper selected the important economic cooperation areas in Northeast China as the research object and collected the hourly monitoring data of pollutants and meteorological data in 11 cities from 1 January 2015 to 31 December 2019. The temporal and spatial variation trend of O3 concentration and the effects of meteorological factors and other pollutants, including CO (carbon monoxide), SO2 (sulfur dioxide), NO2 (nitrogen dioxide), and PM2.5 and PM10 (PM particles with aerodynamic diameters less than 2.5 μm and 10 μm) on ozone concentration were analyzed. At the same time, the variation period of O3 concentration was further analyzed by Morlet wavelet analysis. The results showed that the O3 pollution in the study area had a significant spatial correlation. The spatial distribution showed that the O3 concentration was relatively high in the south and low in the northeast. Seasonally, the O3 concentration was the highest in spring, followed by summer, and the lowest in winter. The diurnal variation of O3 concentration presented a “single peak” pattern. O3 concentration had a significant positive correlation with temperature, sunshine duration, and wind speed and a significant anticorrelation with CO, NO2, SO2, and PM2.5 concentration. Under the time scale of a = 9, 23, O3 had significant periodic fluctuation, which was similar to those of wind speed and temperature.

Sign in / Sign up

Export Citation Format

Share Document