Trip Generation Forecast Model PDF

GROUP 1 TRIP GENERATION FORECAST MODEL TRANSPORTATION ENGINEERING  CONTENT 01 02 03 04 Travel Trip Objective Approach Demand and Traffic Generation Forecasting 05 06 07 08...

GROUP 1 TRIP GENERATION FORECAST MODEL TRANSPORTATION ENGINEERING  CONTENT 01 02 03 04 Travel Trip Objective Approach Demand and Traffic Generation Forecasting 05 06 07 08 Method Typical trip Trip Summary generation generation models with count data models  TRAVEL DEMAND AND TRAFFIC FORECASTING Introduction Traffic volumes will be affected by any significant modification of a highway network, which would include items such as new road construction or operational changes on existing roads. Analysts therefore must develop methodological approaches for forecasting changes in traffic volumes Travelers Decision Travelers can be viewed as making four distinct but interrelated decisions regarding trips: temporal decisions, destination decisions, modal decisions, and spatial or route decisions. Scope of Travel Demand and Traffic Forecasting Problems Because travel demand and traffic forecasting are predicated on the accurate forecasting of traveler decisions, two factors must be addressed in the development of an effective travel demand and traffic forecasting methodology: the complexity of the traveler decision-making process and system equilibration.  TRIP GENERATION Definition Definition Trip generation is the process of determining the number of trips that The first traveler decision to will begin orend in each traffic analysis be modeled in the zone within a study area. Since the trips are determinedwithout regard to sequential approach to destination, they are referred to as trip travel demand and traffic ends. Each trip has two ends,and these are described in terms of trip purpose, forecasting is trip or whether the trips are either pro? generation. duced by a traffic zone or attracted to a traffic zone.  OBJECTIVE OF TRIP GENERATION 01 The objective of trip generation modeling is to develop an expression that predicts exactly when a trip is to be made. This is an inherently difficult task due to the wide variety of trip types (working, social/recreational, shopping, etc.) and activities (eating lunch, exercising, visiting friends, etc.) undertaken by a traveler in a sample day, as is schematically shown in Fig. 8.4  APPROACH Temporal aggregation. To address the complexity of the trip generation decision, the following approach is typically taken: Although research has been undertaken to develop mathematical expressions that predict Segmentation of trips by type. when a traveler is likely to make a trip (Hamed and Different types of trips have Mannering 1993), trip Aggregation of decision-making different characteristics that generation more often units. make them more or less likely focuses on the number of to be taken at various times trips made over some Predicting trip generation behavior of the day. For example, work period of time. Thus trips is simplified by considering the trip trips are more likely to be are aggregated generation behavior of a taken in the morning hours temporally, and trip household (a group of travelers than are shopping trips, generation models seek to sharing the same domicile) as which are more likely to be predict the number of trips opposed to the behavior of taken during the evening per hour or per day. individual travelers. hours.  Trip generation analysis has two functions: (1) to develop a relation?ship between trip end production or attraction and land use and (2) to use the rela?tionship to estimate the number of trips generated at some future date under a newset of land use conditions Method 1 Cross-Classification Cross-classification is a technique developed by the Federal Highway Administration(FHWA) to determine the number of trips that begin or end at the home. Home?based trip generation is a useful value because it can represent a significant propor?tion of all trips.  Method 2 Rates Based on Activity Units The preceding section illustrated how trip generation is determined for residentialzones where the basic unit is the household. Trips generated at the household end arereferred to as productions, and they are attracted to zones for purposes such as work,shopping, visiting friends, and medical trips.  Method 3 Balancing Trip Productions and Attractions A likely result of the trip generation process is that the number of trip productionsmay not be equal to the number of trip attractions. Trip productions, which are basedon census data, are considered to be more accurate than trip attractions TYPICAL TRIP GENERATION MODELS Trip generation models generally assume a linear form in which the number of vehicle-based (automobile, bus, or subway) trips is a function of various socioeconomic and/or distributional (residential and commercial) characteristics. An example of such a model, for a given trip type, is where Ti = number of vehicle-based trips of a given type (shopping or social/recreational) in some specified time period made by household i, bk = coefficient estimated from traveler survey data and corresponding to characteristic k, and zki = characteristic k (income, employment in neighborhood, number of household members) of household i. SOLUTION TRIP GENERATION WITH COUNT DATA MODELS Because fractions of trips are not realistic, a modeling approach that gives the probability of making a nonnegative-integer number of trips (0, 1, 2, 3,...) may be more appropriate [Washington et al. 2011]. One such model is the Poisson regression, which can be formulated for trip generation (for a given trip type) as where Ti = number of vehicle-based trips of a given type (shopping or social/recreational) made in some specified time period by household i, P(Ti) = probability of household i making exactly Ti trips (where Ti is a nonnegative integer), e = base of the natural logarithm (e = 2.718), and λi = Poisson parameter for household i, which is equal to household i’s expected number of vehicle-based trips in some specified time period, E[Ti]. TRIP GENERATION WITH COUNT DATA MODELS Poisson regressions are estimated by specifying the Poisson parameter λi (the expected number of trips of a specific type made by household i over some time period). The most common relationship between explanatory variables (variables that determine the Poisson parameter) and the Poisson parameter is the log-linear relationship where B = vector of estimable coefficients, Zi = vector of household characteristics determining trip generation, and Other terms are as defined previously. TRIP GENERATION WITH COUNT DATA MODELS  SUMMARY Trip Generation Approach Methods The first traveler - Aggregation of - Cross-Classification decision to be decision-making units - Rates Based on Activity modeled in the - Segmentation of trips Units sequential approach by type - Balancing trip to travel demand and - Temporal Aggregation production and traffic forecasting. attraction SUMMARY Dani Martinez Typical Trip Generation Trip Generation with Model Count Data Models Because fractions of trips are not Trip generation models generally assume realistic, a modeling approach that a linear form in which the number of gives the probability of making a vehicle-based (automobile, bus, or nonnegative-integer number of trips subway) trips is a function of various (0, 1, 2, 3,...) may be more socioeconomic and/or distributional appropriate [Washington et al. 2011]. One such model is the Poisson (residential and commercial) regression, which can be formulated characteristics. for trip generation (for a given trip type) as REFERENCE: Principles of Highway Engineering and Traffic Analysis Fred F. Mannering & Scott Washburn Traffic and Highway Engineering Nicholas J. Garber & Lester A. Hoel  GROUP 1 THANK YOU (BEYONCÉ)