Analysis of Characteristics of the Dynamic Flow-Density Relation and its Application to Traffic Flow Models

Abstract

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.     

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通行能力预测是公路设施规划、设计与运营中的基础环节.本文基于埃及曼努菲亚省12条农村双车道路段数据,研究了公路线形等几何特征对通行能力的影响以及线形由切线变为平曲线时通行能力的损失.所选取的路段线形包括直线和随后的平曲线.同时,采集了各路段上车流量及速度数据,利用基于基本图的外推法研究流量和密度关系.路段上不同车型的车辆均转换为小客车当量.此外,针对不同情形（切线、平曲线及对应流量损失）,分别建立了最佳回归模型.结果表明,切线型道路的关键自变量是路宽、肩宽以及切线长;平曲线道路的关键变量是曲线半径和路宽.通行能力和几何特征关系最佳模型的自变量为曲线半径.本文的模型可用于分析评价农村双车道道路的通行能力,尤其是评价文中所研究路段的通行能力.     

Modeling traffic's flow-density relation: Accommodation of multiple flow regimes and traveler types

This research investigates freeway-flow impacts of different traveler types by specifying and applying a latent-segmentation model of congested and uncongested driving behaviors. Drivers in uncongested conditions are assumed to drive at self-chosen speeds, while drivers in congested conditions are assumed to take speed as given and choose a spacing (between their vehicle and the previous vehicle). Several classes of driver-vehicle combinations are distinguished in a data set based on double-loop-detector pulses and a household travel survey. These classifications are made on the basis of vehicle type and gender, leading to class estimates of speeds and spacings. The segmentation model is specified as a logit function of density, weather, and vehicle type, leading to estimates of congested-condition probabilities. Unobserved heterogeneity is incorporated in all models via common error assumptions.Results indicate that segmentation models are promising tools for traffic data analysis and that information on travelers, their vehicles, and weather conditions explains significant variation in flow data. By clarifying a greater understanding of traffic conditions and traveler behavior explains much scatter in the fundamental relation between flow, speed, and density, can assist regions in their traffic-management efforts and engineers in their design of roadway facilities. Ultimately, such improvements to travel networks should enhance quality of life.