07-04-2011, 03:55 PM
PRESENTED BY:
ANUP RANJAN MOHANTA
KHAIDEM PATHOU
UTKARSH SAHU
RAJA SURYA PRATAP
VIVEK PAYENG
[attachment=11871]
TRIP GENERATION
Travel Demand Modeling
Travel Demand Modeling is a 4-step process:
Trip Generation (how many trips?)
Trip Distribution (where do you want to go?)
Mode Choice (how do you want to get there?)
Trip Assignment (which route?)
TERMINOLOGY
Journey is an out way movement from a point of origin to a point of destination, where as the word trip denotes an journey and return journey.
If either origin or destination of a trip is the home of the trip maker then such trips are called home based trips and the rest of the trips are called non home based trips.
Trip production– trip end connected with a residential land use in a zone.
Trip attraction – trip end connected to a nonresidential land use in a zone.
Trip generation is the first step in the conventional four-step transportation forecasting process widely used for forecasting travel demands. It predicts the number of trips originating in or destined for a particular traffic analysis zone. In other words this stage answers the questions to “how many trips" originate at each zone, from the data on household and socioeconomic attributes.
The trip-generation models used to predict the number of trips generated by a zone. These models try to mathematically describe the decision-to-travel .
Trip-generation models attempt to quantify a populations urge or propensity to travel.
The term trip-generation is used to mean trip production -- generally the trips made from households --and trip attraction -- the trips made to a particular urban location or activity.
The factors (for any given trip purpose) which affect the trip generation of a zone are:
The number of potential trip-makers in the zone; this data could be captured by variables like residential density, average household occupancy, age distribution of occupants, and so forth.
The propensity of a potential trip-maker to make a trip; this is related to automobile ownership, accessibility to public transportation. For example, persons who own automobiles make more non-work trips than persons who do not own automobiles.
Accessibility of the zone to potential destinations for a given trip-purpose satisfaction; variables like distance to potential destinations can affect this factor. For example, persons who live close to various recreational facilities may make more number of recreational trips than persons who live in areas which do not have nearby recreational facilities.
. In trip generation modeling in addition to personal trips, freight trips are also of interest. Although the latter comprises about 20 percent of trips, their contribution to the congestion is significant. Freight trips are influenced by number of employees, number of sales and area of commercial forms.
Based o the existing survey data of warangal city the following parameters were taken in to consideration for trip generation :-
Household level information:
No of family members
No of households
Income range
Vehicle ownership
Expenditure on private and public transport
No of earners
Population by age and sex
Trip information
Trip origin
Trip destination
Household Data Distribution Zone 1
Distribution of Households by family members
Distribution of households by Income (Rs/month)
Distribution of Household by Expenditure on Transport
Distribution of Household by Earners (Nos. per HH)
Distribution of Population by Age
Distribution of Households by family members
Distribution of households by Income (Rs/month)
Household Data Distribution Zone 1-53
Distribution of Household by Expenditure on Transport
Distribution of Household by Earners (Nos. per HH)
Distribution of Population by Age
General approaches to trip generation
Cross-classification analysis
Regression models
Experience based
Growth factor modeling.
Regression models
The general form of a trip generation model is
Ti = f(x1, x2, x3, ….xi,….xk)
Where xi's are prediction factor or explanatory variable. The most common form of trip generation model is a linear function of the form
Ti = a0+ a1 x1 + a2 x2 + …..ai xi ….. + ak xk