Trip generation is the first step in the conventional four-step transportation forecasting process used for forecasting travel demands. It predicts the number of trips originating in or destined for a particular traffic analysis zone (TAZ).
Trip generation analysis focuses on residences and residential trip generation is thought of as a function of the social and economic attributes of households. At the level of the traffic analysis zone, residential land uses "produce" or generate trips. Traffic analysis zones are also destinations of trips, trip attractors. The analysis of attractors focuses on non-residential land uses.
This process is followed by trip distribution, mode choice, and route assignment.
A forecasting activity, such as one based on the concept of economic base analysis, provides aggregate measures of population and activity growth. Land use forecasting distributes forecast changes in activities in a disaggregate-spatial manner among zones. The next step in the transportation planning process addresses the question of the frequency of origins and destinations of trips in each zone: for short, trip generation.
The first zonal trip generation (and its inverse, attraction) analysis in the Chicago Area Transportation Study (CATS) followed the “decay of activity intensity with distance from the central business district (CBD)” thinking current at the time. Data from extensive surveys were arrayed and interpreted on a distance-from-CBD scale. For example, commercial land use in ring 0 (the CBD and vicinity) was found to generate 728 vehicle trips per day in 1956. That same land use in ring 5 (about from the CBD) generated about 150 trips per day.
The case of trip destinations will illustrate use of the concept of activity decline with intensity (as measured by distance from CBD) worked. Destination data are arrayed:
The land use analysis provides information on how land uses will change from an initial year (say t = 0) to some forecast year (say t = 20). Suppose we are examining a zone.
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Transportation forecasting is the attempt of estimating the number of vehicles or people that will use a specific transportation facility in the future. For instance, a forecast may estimate the number of vehicles on a planned road or bridge, the ridership on a railway line, the number of passengers visiting an airport, or the number of ships calling on a seaport. Traffic forecasting begins with the collection of data on current traffic. This traffic data is combined with other known data, such as population, employment, trip rates, travel costs, etc.
Trip distribution (or destination choice or zonal interchange analysis) is the second component (after trip generation, but before mode choice and route assignment) in the traditional four-step transportation forecasting model. This step matches tripmakers’ origins and destinations to develop a “trip table”, a matrix that displays the number of trips going from each origin to each destination. Historically, this component has been the least developed component of the transportation planning model.
Mode choice analysis is the third step in the conventional four-step transportation forecasting model of transportation planning, following trip distribution and preceding route assignment. From origin-destination table inputs provided by trip distribution, mode choice analysis allows the modeler to determine probabilities that travelers will use a certain mode of transport. These probabilities are called the modal share, and can be used to produce an estimate of the amount of trips taken using each feasible mode.
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