Traffic Variables

The Mathematics of Human Movement

Humans are complicated creatures. Thus, the mathematics of the social sciences can be as complex, or perhaps more complex, as the mathematics of the natural sciences. Scientists studying commuter patterns use traffic variables as part of their traffic-flow models in order to achieve goals as diverse as easing congestion during rush hours, determining where a new stop light may be needed, and predicting the impact of a new convention center on congestion in a downtown metropolitan area.

Traffic variables are quantifiable elements that the developers of traffic models are able to take into account in their efforts to predict the collective human behavior that is the movement of traffic. When used in predictive models, the following traffic variables can be used: the distance between [Page 1412]cars in a given track of roadway, vehicle occupancy, population density in a given area, average weather patterns in the region studied, the density traffic must reach before creating a traffic jam (also known as jam density), the frequency of traffic jams, the average speed of a set of vehicles, the cumulative vehicle arrival function over space and time, the average speed of traffic when there are no constraints on movement (also known as free-flow speed), the speed at which deaths tend to increase, driver behavior, and fatality statistics based on car year, make, and model of vehicle.

Traffic modeling is performed in an effort to predict in a consistent way how individuals will move in a given area in a consistent way. Once transportation analysts compile data that could be used as traffic variables, they may then develop traffic models. Modeling is utilized to both improve existing systems (for example, streets, rail lines, and sidewalks) and plan new systems or elements that may interact with systems (for example, a new building or park).

Case Study of a Traffic Variable: Speed Limit

Vehicle speed is one of the most intuitive and easily studied traffic variables. Maximum and minimum speed limits have the effect of putting most vehicles within a certain speed range. This remains true even if many vehicles travel slightly above the speed limit. For example, if the speed limit were 65 miles per hour (mph) and the minimum speed were 60 mph on Interstate 71 outside Cleveland, Ohio, we could reasonably expect the average vehicle to be traveling somewhere between 60 and 75 mph. Thus, in this example, variation has been minimized and the traffic variable of vehicle speed is able to be integrated into predictive travel models.

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