By Daiheng Ni
Creating site visitors versions is a hard activity simply because a few of their interactions and procedure elements are tough to appropriately show in a mathematical shape. Traffic circulate conception: features, Experimental equipment, and Numerical concepts provide site visitors engineers with the required tools and strategies for mathematically representing site visitors movement. The publication starts off with a rigorous yet effortless to appreciate exposition of site visitors move features together with clever Transportation structures (ITS) and site visitors sensing technologies.
- Includes labored out examples and situations to demonstrate ideas, types, and theories
- Provides modeling and analytical tactics for aiding varied elements of site visitors analyses for aiding various movement models
- Carefully explains the dynamics of site visitors circulation over the years and space
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Extra info for Traffic flow theory : characteristics, experimental methods, and numerical techniques
Sample text
2 relates traffic flow characteristics to sensor types. Three categories of traffic flow characteristics are presented: flux, speed, and concentration. The characteristics are considered at two levels of detail: microscopic characteristics are vehicle specific and hence all bear subscript i, and macroscopic characteristics are aggregated measures and the aggregation can be done over vehicles, time, or space. 5 RELATIONSHIPS AMONG CHARACTERISTICS So far, traffic flow characteristics have been introduced with the aid of field observations.
Therefore, such a dilemma inevitably results in the estimation of space-based characteristics from point sensor data. 1 Determining Space-Mean Speed from Point Sensor Data If individual vehicle speeds (˙xi , i = 1, 2, . . , N ) are available from a point sensor, these speeds can be used to determine space-mean speed as follows: vs = 1 1 N N 1 i=1 x˙ i . This mean is called the harmonic mean, in contrast to the arithmetic mean, which is the time-mean speed: 1 vt = N N x˙ i . i=1 Unfortunately, many point sensor systems log only aggregated measures such as time-mean speed.
7] showed how to resolve the problem. 5 Three-dimensional representation examples. the two intersections is the distance traversed by vehicle 2 from t(1) to t(2) . 5), the tangent of the curve denotes the density k at this instant. Note that the tangent slants down (because lower-numbered vehicles are in front), so it has a negative value. Hence, k|t = − dN . 6 The N-x diagram. 7. 7, the intersection of this line and the curve labeled x(2) indicates the time when the vehicle with ID 2 passes location x(2) .