Mixed-Use Development Trip Generation Model

2013 ◽  
Vol 2344 (1) ◽  
pp. 98-106
Author(s):  
Jeff Gulden ◽  
J. P. Goates ◽  
Reid Ewing
Keyword(s):  
Generation Model ◽  
Trip Generation ◽  
Mixed Use

Multimodal Trip Generation Model to Assess Travel Impacts of Urban Developments in the District of Columbia

2017 ◽  
Vol 2668 (1) ◽  
pp. 29-37 ◽  
Author(s):  
Ryan Westrom ◽  
Stephanie Dock ◽  
Jamie Henson ◽  
Mackenzie Watten ◽  
Anjuli Bakhru ◽  
...  
Keyword(s):  
Travel Behavior ◽  
Goodness Of Fit ◽  
Research Effort ◽  
Mode Choice ◽  
Local Environment ◽  
Generation Model ◽  
Trip Generation ◽  
Local Data ◽  
Local Site ◽  
Mixed Use

The research effort described in this paper aims to develop a state-of-the-practice methodology for estimating urban trip generation from mixed-use developments. The District Department of Transportation’s initiative focused on ( a) developing and testing a data collection methodology, ( b) collecting local data to complement the ITE’s national data in trip rate estimation, and ( c) developing a model–tool that incorporates contextual factors identified as affecting overall trip rate as well as trip rate by mode. The final model accurately predicts total person trips and mode choice. The full set of models achieves better statistical performance in relation to average model error and goodness of fit than either ITE rates alone or other existing research. The model includes sensitivity to local environment and on-site components. The model advances site-level trip generation research in two major ways: first, it calculates total person trips independent of mode choice; second, it calculates mode choice with sensitivity to the amount of parking provided on site—a major finding in the connection between parking provision and travel behavior at a local-site level. The methodology allows agencies to improve their assessment of expected trips from proposed buildings and therefore the level of impact a planned building may have on the transportation system.

scholarly journals Trip generation model: Example of grammar school students in Belgrade

2006 ◽  
Vol 86 (1) ◽  
pp. 191-202
Author(s):  
Ivan Ratkaj
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Linear Regression Analysis ◽  
Generation Model ◽  
Grammar School ◽  
Trip Generation ◽  
School Students ◽  
Territorial Unit ◽  
Basic Approaches

Trip generation models aim to predict the amount of transportation movements (or the number of potential trip makers) leaving a territorial unit according to the attributes of that unit. There are two basic approaches used for modeling the generation of trips: linear regression and category analysis. This article explains the issue of trip generation modeling based on the methodology of linear regression analysis, on the example of grammar schools in Belgrade.

scholarly journals PEMODELAN BANGKITAN PERGERAKAN BERDASARKAN AKTIVITAS MANDATORY (SEKOLAH DAN BEKERJA) DARI KOMPLEK PERUMAHAN DI KABUPATEN ACEH BARAT

2020 ◽  
Vol 3 (1) ◽  
pp. 410-416
Author(s):  
Murdani Murdani ◽  
Renni Anggraini ◽  
Muhammad Isya
Keyword(s):  
Family Income ◽  
Social Activity ◽  
Generation Model ◽  
Trip Generation ◽  
Work Activity ◽  
Community Activities ◽  
Several Variables ◽  
And Gender ◽  
Main Activity ◽  
School Activity

Johan Pahlawan subdistrict is one of subdistricts in West Aceh. This subdistrict is center of all community activities compared to the sub-districts in West Aceh Regency. This is because there are many government offices, schools and trade centers. So that community activities tend to move to this sub-district. The modeling of trip generation has been performed by individuals in one area that will be needed to know by studying a variety of relationships between the characteristic of movements and the environmental of land use. This research aimed at achieving the modelling movements of generation based on activities in the housing of Caritas, Islamic Relief and IOM  in subdistrict of Johan Pahlawan in West Aceh Regency by identifying the factors which have influenced the occurrence of movements to the workplace by dwellers of housing. The data were collected by surveys, questionnaires and the formation of the model was collected by using SPSS 21 and multiple linear analysis to get the best trip generation model. In this study there are five types of activity, two as main activity and three as an additional activity. they are obtained is school activity (mandatory), work activity (mandatory), shuttle of children activity (maintenance), shuttle household affairs activity (maintenance) and social activity (maintenance). Based on the results of running from several variables there are 5 variables that meet to the criteria of model, the variables are number of family members (X1), family income (X2), age (X8), travel distance (X10) and gender (X11). The best models are: Work Aktivity (Y­­­­1) = 0.988 + 0.169 X1 + 0.582 X2, School Aktivity (Y2) = 1.684 + 0.865 X2 + 0.387 X8, Social Activity (Y3) = 0.885 + 0.564 X2 + 0.334 X10, Shuttle of Children Activity (Y4) = 1.028 + 0.902 X8 + 0.557 X11 and Shuttle Household Affairs Activity (Y5) = 2.367 + 0.931 X1 + 0.858 X2.

Evaluation of Two Modeling Methods for Generating Heavy-Truck Trips at an Intermodal Facility by Using Vessel Freight Data

2005 ◽  
Vol 1906 (1) ◽  
pp. 113-120 ◽  
Author(s):  
Pradeep Sarvareddy ◽  
Haitham Al-Deek ◽  
Jack Klodzinski ◽  
Georgios Anagnostopoulos
Keyword(s):  
Neural Network ◽  
Storage Time ◽  
Learning Algorithm ◽  
Generation Model ◽  
Trip Generation ◽  
Modeling Techniques ◽  
Heavy Truck ◽  
Input Variables ◽  
Lag Variables ◽  
Bpnn Model

A methodology for building a truck trip generation model by use of artificial neural networks from vessel freight data has been developed and successfully applied to five Florida seaports. The backpropagation neural network (BPNN) algorithm was used in the design. Although the methodology was sound, a new model had to be developed for each of these intermodal facilities. Lead and lag variables were necessary input variables for most models to account for commodities stored on port property before export or pickup after import. Other modeling techniques were researched, and a fully recurrent neural network (FRNN) trained by the real-time recurrent learning algorithm was selected to develop a model for Port Canaveral and compare with a BPNN model. FRNN is dynamic in nature and was found to relate to the storage time of the commodities to truck trip generation. A developed Port Canaveral BPNN model was successfully validated at the 95% confidence level with collected field data. It was applied to conduct a short-term forecast of the port's truck traffic for 5 years. The average annual growth of trucks based on the estimated freight activity under the BPNN model was 5.07%. The Port Canaveral FRNN model adequately estimated the current conditions but failed to forecast truck growth. The FRNN model required more data for forecasting than backpropagation. However, when more consecutive data are available for training, FRNN may produce more accurate results.

Trip Generation Modeling Using CHAID, a Criterion-Based Segmentation Modeling Tool

1998 ◽  
Vol 1645 (1) ◽  
pp. 24-31 ◽  
Author(s):  
Orlando Strambi ◽  
Karin-Anne Van De Bilt
Keyword(s):  
Model Development ◽  
Generation Model ◽  
Trip Generation ◽  
Modeling Tool ◽  
Explanatory Variables ◽  
Interaction Detection ◽  
Synthetic Populations ◽  
Exploratory Technique ◽  
Population Forecasting ◽  
Chi Squared

Conventional trip generation models are identified, as are the difficulties of model application typical of segmentation problems: identification and categorization of explanatory variables and of the interactions among them. The use of CHAID (Chi-Squared Automatic Interaction Detection), a criterion-based segmentation modeling tool, is explored to analyze household trip generation rates. CHAID models are presented in the form of a tree, each final node representing a group of homogenous households concerning daily trip making. An application to data from an origin-destination survey for São Paulo produced interesting results, in agreement with theoretical expectations and amenable to interpretation based on the likely activity-travel patterns of each group of households generated by the technique. CHAID can be used as an exploratory technique for aiding model development or as a model by itself. The use of CHAID results as a trip generation model was verified through an evaluation of its predictive capability in a cross comparison of two subsamples and through a comparison of observed versus predicted trips at a zone level; the segmentation of households produced by the technique provided good estimates of trip rates and zone totals. The application of a modeling approach requiring a highly disaggregate projection of the population may become possible considering the advances in methods for the generation of synthetic populations. The use of these methods in conjunction with a segmentation model represents an alternative to conventional trip generation models and an opportunity to introduce new population forecasting techniques into transportation planning practice.

scholarly journals Home Based Trip Generation Model S Analysis in Medan, Binjai, Deli Serdang and Karo (Mebidangro) Urban Area, Indonesia

2020 ◽  
Vol 2 (3) ◽  
pp. 139-143
Author(s):  
Agustinus Panjaitan ◽  
Abdul Rahim Matondang ◽  
Marlon Sihombing ◽  
Agus Purwoko
Keyword(s):  
Mathematical Model ◽  
Urban Area ◽  
Generation Model ◽  
Trip Generation ◽  
Independent Variables ◽  
Home Based ◽  
The Mathematical Model

The purpose of this research is to develop a home-based trip generation model and analyze the variables that influence the trip generation model of people. This study focuses on the trip generation of home-based people in the Medan-Binjai-Deli Serdang (Mebidang) area so that the sample to be used in households that make home-based trips in the region. The mathematical model that generated regression with the dependent variable the number of home-based trips affected by several independent variables that influence it. The resulting model was then validated by the VIF and Anova tests and the Heteroscedasticity test. From the results of this study, it is expected that a trip generation model of home-based trip generation in the Mebidang urban area will be generated so that it can be known what factors influence the trip generation of the area.

scholarly journals A Trip Generation Model for a Petrol Station with a Convenience Store and a Fast-Food Restaurant

2021 ◽  
Vol 13 (22) ◽  
pp. 12815
Author(s):  
Shafida Azwina Mohd Shafie ◽  
Lee Vien Leong ◽  
Ahmad Farhan Mohd Sadullah
Keyword(s):  
Land Use ◽  
Fast Food ◽  
Coefficient Of Determination ◽  
Convenience Store ◽  
Generation Model ◽  
Fast Food Restaurant ◽  
Trip Generation ◽  
Petrol Station ◽  
Independent Variables ◽  
Use Category

A trip generation manual and database are important for transportation planners and engineers to forecast new trip generation for any new development. Nowadays, many petrol stations have fast-food restaurant outlets. However, this land use category has yet to be included in the Malaysian Trip Generation Manual. Therefore, this study attempted to develop a new trip generation model for the new category of “petrol station with convenience store and fast-food restaurant”. Significant factors influencing the trip generation were also determined. Manual vehicle counts at the selected sites were conducted for 3 h during morning, afternoon and evening peak hours. Regression analysis was used in this study to develop the model. A simple trip generation model based on the independent variable number of restaurant seats showed a greater value for the coefficient of determination, R2, compared with the independent variables gross floor area in thousand square feet and number of pumps. The multivariable trip generation model using three independent variables generated the highest R2 among all of the models but was still below a satisfactory level. Further study is needed to improve the model for this new land use category. We must ensure more accuracy in trip generation estimation for future planning and development.

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