Home-Based Urban Commute and Non-Commute Trip Generation in Less-Studied Contexts: Evidence from Cairo, Istanbul, and Tehran

2021 ◽  
Author(s):  
Houshmand Masoumi
Keyword(s):  
Trip Generation ◽  
Home Based

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.

Household Trip Generation and the Built Environment: Does More Density Mean More Trips?

2019 ◽  
Vol 2673 (5) ◽  
pp. 596-606
Author(s):  
Qin Zhang ◽  
Kelly J. Clifton ◽  
Rolf Moeckel ◽  
Jaime Orrego-Oñate
Keyword(s):  
Built Environment ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Trip Generation ◽  
Transportation Demand ◽  
Home Based ◽  
Environment Variables ◽  
Binomial Regression ◽  
Demand Models ◽  
New Development

Trip generation is the first step in the traditional four-step trip-based transportation model and an important transport outcome used in evaluating the impacts of new development. There has been a long debate on the association between trip generation and the built environment, with mixed results. This paper contributes to this debate and approaches the problem with two hypotheses: 1) built environment variables have significant impacts on household total trip generation; and 2) built environment variables have different impacts on trip generation by purpose. This study relied on data from the Portland, Oregon, metropolitan area to estimate negative binomial regression models of household trip generation rates across all modes. Results show that the built environment does have significant and positive influences on trip generation, especially for total number of trips, total number of tours, and home-based shopping-related trips. Moreover, log likelihood ratio tests implied that adding built environment to the base model contributed significantly to improving model explanatory and predictability. These findings suggest that transportation demand models should be more sensitive to the effects of the built environment to better reflect the variations in trip making across regions.

scholarly journals Forecasting Trip Generation For High Density Residential Zones of Akure, Nigeria: Comparability of Artificial Neural Network And Regression Models

2018 ◽  
Vol 9 (2) ◽  
pp. 2
Author(s):  
J.E. Etu ◽  
O. J. Oyedepo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multiple Linear Regression Model ◽  
High Density ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Trip Generation ◽  
Home Based ◽  
Artificial Neural ◽  
Household Travel

Evidence from literature has shown the absence of the use of Artificial Neural Network techniques in formulating trip generation forecasts in Nigeria, rather the practice has consisted more on use of regression techniques. Therefore, in this study, the accuracy of Radial Basis Function Neural Network (RBFNN) and Multiple Linear Regression model (MLR) in formulating home-based trips generation forecasts was assessed. Datasets for the study were acquired from a household travel survey in the high density zones of Akure, Nigeria and were analysed using SPSS 22 statistical software. Results of data analysis showed that the RBFNN model with higher Coefficient of Determination (R2) value of 0.913 and lower Mean Absolute Percentage Error (MAPE) of 0.421 performed better than the MLR with lower R2 value of 0.552 and higher MAPE of 0.810 in predicting the number of home-based trips generated in the study area. The study demonstrated the higher accuracy of the RBFNN in producing trip generation forecasts in the study area and is consequently recommended for researchers in executing such forecasts.

scholarly journals The effects of critical peak pricing for electricity demand management on home-based trip generation

2014 ◽  
Vol 37 (2) ◽  
pp. 89-97 ◽  
Author(s):  
Masanobu Kii ◽  
Keiji Sakamoto ◽  
Yoichi Hangai ◽  
Kenji Doi
Keyword(s):  
Demand Management ◽  
Electricity Demand ◽  
Trip Generation ◽  
Home Based ◽  
Peak Pricing

scholarly journals EFFECT OF ELECTRICITY CRITICAL PEAK PRICING ON HOME BASED TRIP GENERATION

2013 ◽  
Vol 69 (5) ◽  
pp. I_247-I_252
Author(s):  
Masanobu KII ◽  
Shintaro SONE ◽  
Hitoi ONO ◽  
Yoichi HANGAI ◽  
Kenji DOI
Keyword(s):  
Trip Generation ◽  
Home Based ◽  
Peak Pricing

A note on the stabilities of work trip travel demands in Toronto

1986 ◽  
Vol 13 (3) ◽  
pp. 389-395 ◽  
Author(s):  
B. G. Hutchinson
Keyword(s):  
Labour Force ◽  
Spatial Differentiation ◽  
Labour Force Participation ◽  
Careful Consideration ◽  
Trip Generation ◽  
Home Based ◽  
Temporal And Spatial ◽  
Work Trip ◽  
Census Metropolitan Area ◽  
Over Time

The 1971 and 1981 census journey-to-work data are used to examine the temporal and spatial stabilities of home-based work trip travel demands in the Toronto census metropolitan area (CMA). Regression analysis is used to establish consistent trip generation equations at the census tract level using population, household, and dwelling unit data; the stabilities of alternative equations over time are examined. All of the partial regression coefficients shifted over time, reflecting the substantial changes that have occurred in household structure, female labour force participation, and the characteristics of the housing market. The spatial distributions of the residuals are examined in terms of the spatial differentiation that exists in the household sector in the Toronto CMA in terms of variables such as household size, population age, and occupation status. The use of traditional trip generation techniques is difficult to sustain given the temporal and spatial variations in the trip generation rate. It is concluded that travel demands can only be estimated from a careful consideration of the residential dynamics of the major subareas in a region.

Incorporating Work Trip Accessibility in Nonwork Trip Generation Models in San Francisco Bay Area

1996 ◽  
Vol 1556 (1) ◽  
pp. 37-45 ◽  
Author(s):  
Charles L. Purvis ◽  
Miguel Iglesias ◽  
Victoria A. Eisen
Keyword(s):  
San Francisco ◽  
San Francisco Bay Area ◽  
Trip Duration ◽  
Trip Generation ◽  
Household Level ◽  
Bay Area ◽  
Home Based ◽  
Trip Frequency ◽  
Work Trip ◽  
Social Recreation

Efforts to include disaggregate work trip accessibility in models of non-work trip generation are described. Reported household-level, one-way, average home-based work trip duration is used in home-based shop/other and home-based social/recreation models for the San Francisco Bay Area. The survey data and models show an inverse relationship between work trip duration and home-based nonwork trip frequency: as work trip duration increases, nonwork trip frequency decreases. Hybrid trip generation models using multiple regression techniques, cross-classified by workers in household level and vehicles in household level, are estimated using data from the 1981 and 1990 household travel surveys. Work trip duration is excluded in models estimated for nonworking households and is included in models estimated for single-worker and multiworker households. Elasticity analyses show that a 10 percent decrease in the regional work trip duration yields a 1.2 percent increase in regional home-based shop/other trips and a 0.9 percent increase in regional home-based social/recreation trips. The research helps to identify practical means to incorporate workplace accessibility in regional travel demand model forecasting systems, to better analyze the issue of induced trip making, and to provide a better understanding of the linkage between congestion and trip frequency choice behavior.

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