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Vehicle occupancy measurement is an important part of transportation congestion
management and it is used for evaluating the efficiency of road system, High
Occupancy Vehicle (HOV) lanes or particular congestion reduction programs.
The measure occupancy is a function of speed and length of individual vehicle
and thus, it could consider the effects of varying vehicle length and speed.
Hence, it can be considered as a logical substitute of density.
In other words, occupancy, based on practical consideration, is defined as the
percentage of time the detection zone is occupied by the vehicles.
Therefore, occupancy measured using detectors depends on the length of detection
zone, each detector type has a differing zone of influence (detector length) and
the zone of influence is effectively added to vehicle length.
Hence, the measured occupancy may be different for different detection zones
even for the same site having identical traffic, depending on the size and
nature of the detectors.
Development of intelligent systems that extract traffic density and vehicle
classification information from traffic surveillance systems is crucial in
traffic management.
It is important to know the traffic density of the roads real time especially in
HOV lanes for effective traffic management.
Time estimation of reaching from one location to another and recommendation of
different route alternatives using real time traffic density information are
very valuable for metropolitan city residents.
Travel time can be defined as the period of time to transverse a route between
any two points of interest.
It is a fundamental measure in transportation.
Travel time is also one of the most readily understood and communicated measure
indices used by a wide variety of users, including transportation engineers,
planners, and consumers.
Travel time data is useful for a wide range of transportation analyses including
congestion management, transportation planning, and traveler information.
Congestion management systems commonly use travel time-based performance
measures to evaluate and monitor traffic congestion.
In addition, some metropolitan areas provide real-time travel time prediction as
part of their advanced traveler information systems (ATIS).
Travel time data can be obtained through a number of methods.
Some of the methods involve direct measures of travel times along with test
vehicles, license plate matching technique, and ITS probe vehicles.
Additionally, various sensors (e.g. inductance loop detectors, acoustic sensors)
in ITS deployment collect a large amount of traffic data every day, especially
in metropolitan areas.
Such data can be used for travel time estimation for extensive applications when
direct measurements of travel times are not available.
The delay defines as ``The additional travel time experienced by a driver,
passenger, or pedestrian''.
Delay is thus the difference between an ``ideal'' travel time and ``actual''
travel time.
Since the definition of delay depends on a hypothetical ``ideal travel time'',
delay is not always directly measurable in the field.
If the ideal travel time is defined as off-peak travel time, then the measured
delay is difference between the actual measured travel time during peak period,
and the actual measured travel time during off-peak period.
If the ideal travel time is defined as travel at the posted speed limit, then
the delay cannot be directly measured in the field.
It is estimated by subtracting the hypothetical travel time at the posted speed
limit from the measured mean travel time in the field.
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