Journal articles: 'Trip generation model'

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Alho, André Romano, and João de Abreu e. Silva. "Freight-Trip Generation Model." Transportation Research Record: Journal of the Transportation Research Board 2411, no. 1 (2014): 45–54. http://dx.doi.org/10.3141/2411-06.

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Ali Safwat, K. Nabil, and Thomas L. Magnanti. "A Combined Trip Generation, Trip Distribution, Modal Split, and Trip Assignment Model." Transportation Science 22, no. 1 (1988): 14–30. http://dx.doi.org/10.1287/trsc.22.1.14.

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Fadhlansyah, Andika, and Nahry. "Home Delivery Trip Generation Model." IOP Conference Series: Earth and Environmental Science 1000, no. 1 (2022): 012003. http://dx.doi.org/10.1088/1755-1315/1000/1/012003.

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Abstract As a result of online shopping’s rapid development, more goods and services are being delivered directly into residences. The number of people who shop online using e-commerce platforms also increases with the Covid-19 pandemic and causes an increase in freight trip generation. This research aims to analyze the factors that affect freight trip generation and develop a freight trip generation model for home deliveries. Data collection is done using questionnaires with 273 people who reside in Jabodetabek as samples to understand the individual characteristics, household characteristics, and the number of freight trips generated by each residential unit. Collected data is analyzed using the multiple linear regression method into a multiple linear regression model. The result shows on multiple linear regression modeling only the home type, household income, and the number of vehicles have significance towards the model. This research can be used by city transportation authorities as a reference to predict the impact of urban area’s development on freight trip generation.

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Sreeparvathy, C. M., R. T. Arjun Siva Rathan, and K. Jayakesh. "Development of Trip Generation Model Using Linear Regression for Areas in Hyderabad City." IOP Conference Series: Earth and Environmental Science 1326, no. 1 (2024): 012096. http://dx.doi.org/10.1088/1755-1315/1326/1/012096.

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Abstract Trip generation represent the measure of motive of travel and is an important parameter for predicting the future number of trips. In Indian scenario; trip generation tables that show relation between the trips originating from a specific land-use based on the number of units or on the floor area as in ITE manual is unavailable. The trips initiated from Veterinary colony, Shaikpet in Hyderabad and their contributing attributes are observed for the purpose of the study. Finding a trip generation model based on the observed parameters is the goal of this project. Characteristics that could contribute to trip generation, along with frequency of trip and other features of trips were taken as the data for generating a trip based model. Online survey through Google forms was adopted to gather the required data for trip generation model. The Google form was developed keeping in view of the parameters that would be required for the development of the model that includes distance, time of travel, mode and socio-economic background of the trip maker. For the trip generation model, linear regression analysis was chosen as the analytical method. In order to predict the trip generation pattern of individuals, the degree of correlation of the dependent variables on other independent variables, such as age, gender, income, travel time, travel cost, etc., was examined. Using R software’s statistical analysis tools, the obtained data were examined, and the best fitting answer was discovered based on the input variables. It was observed that the efficiency of the model can be escalated using further and intense data collection.

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Dinda, Raina Permitalia, Renni Anggraini, and Sugiarto Sugiarto. "MODEL BANGKITAN PERGERAKAN RUMAH TANGGA BAGI PENGGUNA SEPEDA MOTOR BERDASARKAN LOKASI TUJUAN PERJALANAN DI KOTA BANDA ACEH." Jurnal Arsip Rekayasa Sipil dan Perencanaan 1, no. 3 (2018): 19–30. http://dx.doi.org/10.24815/jarsp.v1i3.11759.

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Population growth in banda Aceh city is increasing every year resulting in increased community movement. Community movement in Banda Aceh city dominated by motorcycle users, there are several factors that influence the Trip generation of Household in Banda Aceh city. One of which is the characteristic of trip behaviour which is the location of the choosen destination of the community. This research aims to know the variables of socio-economic characteristics, demography, and trip behavior in 4 sub-districts that are jaya Baru sub-dsitrict, banda raya, Lueng bata, and Kuta raja sub-dsitrict in Banda Aceh city, obtained trip generation model of motorcycle users on the household base don selection of destination location travel, what factors that affect the trip generation and analyzing the probability and error rate from the selection of each generation. This method is done by using home interview survey method. Sampling of household population to be studied by using stratified random sampling method. Method use to obtained model and probability also the relationship of variables that influenced in this research is by binomial logistic regression analysis model then processed with the help of SPSS v20 software program. From this research obtained 2 models which are the trip generation model based on destination location of trip to other places with the most influential variables are job status and last education. The generation probability is ≤ 3 movements of 77% and generation probability 3 with movement of 23%. The second model is the trip generation model is based on the destination location of trip to office/school with the most influential variables are number of family members who attened school and number of family members, the probability of trip generation is. ≤ 5 movements of 81% and probability of trip generation5 movements of 19%. Both of two models also meets the specifications error value which si below 10%..

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Wang, Ying, and Kuan Min Chen. "Trip-Generation Forecasting Model Based on Entropy of Land-Use Mixing." Applied Mechanics and Materials 738-739 (March 2015): 479–82. http://dx.doi.org/10.4028/www.scientific.net/amm.738-739.479.

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This study aimed to modify the traditional method of trip-generation by investigating the relation between trip generation and land use. Based on the interaction between urban land-use sorts and trip generation, the trip generation weights among different urban land-use sorts are determined by multiple regression analysis. Given full consideration of the land-use mixing degree, the entropy of traffic-zone-land-use mixing was calculated. An improved trip-generation model based on the entropy of land-use mixing was proposed by analyzing the relationship between trip-generation weight and land-use mixing degree. This method was tested through applying it to Xi’an urban trip generation forecasting. The result of the test shows that this method effectively illustrates the correlation between trip-generation demand and land-use mix sort, and has a better application prospect due to simple calculation, high reliability and feasibility.

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Haryanta, Adinda Rifqi Aprilliana, Verdy Ananda Upa, and Eka Apriliasi. "Trip Generation Model of Sawah Baru Residential, Ciputat District, South Tangerang Municipality." Jurnal Poli-Teknologi 23, no. 1 (2024): 1–9. http://dx.doi.org/10.32722/pt.v23i1.6503.

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Traffic congestion in Ciputat area due to increasing of trip generation unsupported by transportation facilities and adequate road network. Generally, solution that implemented is road network capacity upgrades with widening based on trip generation analysis. The purpose of this research to determine trip generation model, total of trip generation, factors that influence of trip generation, and characteristics of trip users on Sawah Baru residence Ciputat Tangerang Selatan. The research method used by household interview survey about number of family members, amount of income per month, number of people working, number of students, number of vehicle owners, amount of transportation costs per month, also trip data from family members daily. Data that has been collected then analyzed with multiple linear regression method used SPSS 25.0 application with significant level 95%. Trip generation model of Sawah Baru residence can be attained Y = 0,450 +0,833 X1 with R2(R square) = 0,833. Total of trip generation that is generated by Sawah Baru residence = 333 trip/day. Based on t Test with significant level <0,05, therefore an influential factors are number of family members, amount of family income, number of people working, number of students, vehicle owners, and transportation cost.

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Zhou, Zhong, Anthony Chen, and S. C. Wong. "Alternative formulations of a combined trip generation, trip distribution, modal split, and trip assignment model." European Journal of Operational Research 198, no. 1 (2009): 129–38. http://dx.doi.org/10.1016/j.ejor.2008.07.041.

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Mchi, Akaawase Alexander, and Victor Umoren. "Trip Generation Model for Gboko Town, Benue State, Nigeria." Journal of Sustainability and Environmental Management 2, no. 3 (2023): 190–202. http://dx.doi.org/10.3126/josem.v2i3.59311.

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Nowadays many urban planning authorities especially in less developed countries have been struggling with traffic related challenges like traffic congestion, air pollution and traffic accidents in their cities. However, solutions to these problems cannot be achieved without rational planning decisions and cannot be attained without the basic understanding of the urban transport system. This study was carried out to assess commuter trip generation in Gboko town, Benue State with a view to estimate long-range future travel demand that would accommodate future transportation needs. Trip generation data was collected from 440 households using questionnaire and travel diary. The information from questionnaire and travel diary was used to prepare the origin-destination matrix, and gravity model was used to translate trip distribution into trip length frequency. Work trip had the greatest trip percentage (%) share among other trip purposes and was proxy for trip distribution. The 25-minute work trips dominated the urban trip pattern in Gboko town with 17.01 % annual increase. It implied that people would take much longer time and distance to reach their destination in Gboko town due to the large expanse of the town, or due to deficiency in public transit facilities and poor road infrastructure. Trip generation and distribution models for Gboko town provided accurate scenarios of the current travel pattern in the town and aided in forecasting future travel situations in the study area. The study recommended that urban transportation policy should encourage provision of public transit buses, incentives for public transit users and improve on the road network system to shorten the trip length frequency of the town.

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Gulden, Jeff, J. P. Goates, and Reid Ewing. "Mixed-Use Development Trip Generation Model." Transportation Research Record: Journal of the Transportation Research Board 2344, no. 1 (2013): 98–106. http://dx.doi.org/10.3141/2344-11.

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binti Mohd Shafie, Shafida Azwina, Ahmad Farhan Mohd Sadullah, Meor Othman Hamzah, and Lee Vien Leong. "The Alternative Trip Generation Model for Flat/Apartment/Condominium and Low Cost Housing Subcategories." Applied Mechanics and Materials 802 (October 2015): 369–74. http://dx.doi.org/10.4028/www.scientific.net/amm.802.369.

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Malaysian Trip Generation Manual (MTGM) is an important document to assist transport planners in forecasting the estimated trip attraction and trip production from a land use. The forecast is crucial in estimating trip generation from a proposed development on the existing road network. Therefore, this study is to verify the accuracy of the existing trip generation model published in MTGM for flat/apartment/condominium and low cost housing subcategories. By applying variable transformation, four alternative models were developed. They were the logarithmic model, the inverse model, the linear-logarithmic model and the logarithmic-linear model. Using residual analysis, influential data was identified and taken out for second analysis. Model selection was based on R2 value, t-test and Kolmogorov-Smirnov test results. Besides linear model, logarithmic model is also truly representing trip generation model for both subcategories. There is some difference in the trip generation estimation between the study model and the existing model in MTGM. Sensitivity analysis shows the level of sensitivity between study model and existing linear model. One of the implications in using the studied trip generation model is in evaluating level of service of the junction.

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Yang, Fan, Linchao Li, Fan Ding, Huachun Tan, and Bin Ran. "A Data-Driven Approach to Trip Generation Modeling for Urban Residents and Non-local Travelers." Sustainability 12, no. 18 (2020): 7688. http://dx.doi.org/10.3390/su12187688.

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Trip generation modeling is essential in transportation planning activities. Previous modeling methods that depend on traditional data collection methods are inefficient and expensive. This paper proposed a novel data-driven trip generation modeling method for urban residents and non-local travelers utilizing location-based social network (LBSN) data and cellular phone data and conducted a case study in Nanjing, China. First, the point of interest (POI) data of the LBSN were classified into various categories by the service type, then, four features of each category including the number of users, number of POIs, number of check-ins, and number of photos were aggregated by traffic analysis zones to be used as explanatory variables for the trip generation models. We used a random tree regression method to select the most important features as the model inputs, and the trip models were established based on the ordinary least square model. Then, an exploratory approach was used to test the performance of each combination of the variables with various test methods to identify the best model for residents’ and travelers’ trip generation functions. The results suggest land use compositions have significant impact on trip generations, and the trip generation patterns are different between urban residents and non-local travelers.

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Denno, Habte Debisa, Raju Ramesh Reddy, and M. Durga. "Development of Trip Generation Model by Using Artificial Neural Network Algorithm: Wolaita Sodo City as a Case Study." ECS Transactions 107, no. 1 (2022): 2611–26. http://dx.doi.org/10.1149/10701.2611ecst.

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Trip generation is the first step model in the transportation forecasting process. In this study, there are six developed models. Such as the general trip generation model, work trip generation model, educational trip model, shopping trip model, recreational trip generation model, and social trip generation model by using an artificial neural network. The mail-back survey method was adapted to collect pilot data, and household interview surveys were adapted to collect main trips and socio-economic household data. A sample size selected by stratified random distribution method from each TAK of Wolayta-Soddo city is 718 households. And the essential data is collected from all the households that enable to development of different models. The results of the developed models are to identify the possible modifications required in the study area to improve the transportation facilities and policy decisions furthermore for the economic development of the country in general and state in particular.

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Panjaitan, Agustinus, Abdul Rahim Matondang, Marlon Sihombing, and Agus Purwoko. "Home Based Trip Generation Model S Analysis in Medan, Binjai, Deli Serdang and Karo (Mebidangro) Urban Area, Indonesia." Journal of World Conference (JWC) 2, no. 3 (2020): 139–43. http://dx.doi.org/10.29138/prd.v2i3.233.

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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.

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Mohd Shafie, Shafida Azwina, Lee Vien Leong, and Ahmad Farhan Mohd Sadullah. "A Trip Generation Model for a Petrol Station with a Convenience Store and a Fast-Food Restaurant." Sustainability 13, no. 22 (2021): 12815. http://dx.doi.org/10.3390/su132212815.

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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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Ratkaj, Ivan. "Trip generation model: Example of grammar school students in Belgrade." Glasnik Srpskog geografskog drustva 86, no. 1 (2006): 191–202. http://dx.doi.org/10.2298/gsgd0601191r.

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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.

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Anderson, Michael D., and Dilip N. Malave. "Dynamic Trip Generation for a Medium-Sized Urban Community." Transportation Research Record: Journal of the Transportation Research Board 1858, no. 1 (2003): 118–23. http://dx.doi.org/10.3141/1858-17.

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Traditional transportation modeling activities in medium-sized urban communities follow the four-step planning process: trip generation, trip distribution, mode split, and traffic assignment. These models forecast daily traffic volumes on major roadways of the communities to support infrastructure investment decisions. Recently, researchers have focused on dynamic traffic-assignment models, which provide time as a measure in the trip-modeling process, to support incident management and intelligent transportation system–based decisions. As a methodology to support the dynamic traffic-assignment models, development of a dynamic trip-generation model for a medium-sized urban community is considered. An overview of the need for dynamic trip-generation data is presented, and a methodology and a data-collection effort to develop a dynamic trip-generation model are discussed. Results are included of a validation study performed on the model. It is concluded that the models developed in the effort can provide dynamic trip-generation data and represent a first step toward making the dynamic transportation model a viable resource.

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Moeckel, Rolf, Leta Huntsinger, and Rick Donnelly. "From Macro to Microscopic Trip Generation: Representing Heterogeneous Travel Behavior." Open Transportation Journal 11, no. 1 (2017): 31–43. http://dx.doi.org/10.2174/1874447801711010031.

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Background: In four-step travel demand models, average trip generation rates are traditionally applied to static household type definitions. In reality, however, trip generation is more heterogeneous with some households making no trips and other households making more than a dozen trips, even if they are of the same household type. Objective: This paper aims at improving trip-generation methods without jumping all the way to an activity-based model, which is a very costly form of modeling travel demand both in terms of development and computer processing time. Method: Two fundamental improvements in trip generation are presented in this paper. First, the definition of household types, which traditionally is based on professional judgment rather than science, is revised to optimally reflect trip generation differences between the household types. For this purpose, over 67 million definitions of household types were analyzed econometrically in a Big-Data exercise. Secondly, a microscopic trip generation module was developed that specifies trip generation individually for every household. Results: This new module allows representing the heterogeneity in trip generation found in reality, with the ability to maintain all household attributes for subsequent models. Even though the following steps in a trip-based model used in this research remained unchanged, the model was improved by using microscopic trip generation. Mode-specific constants were reduced by 9%, and the Root Mean Square Error of the assignment validation improved by 7%.

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Yao, Liya, Hongzhi Guan, and Hai Yan. "Trip generation model based on destination attractiveness." Tsinghua Science and Technology 13, no. 5 (2008): 632–35. http://dx.doi.org/10.1016/s1007-0214(08)70104-4.

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Wilson, F. R., and Ahmed E. T. Abl El Megeed. "Stability of province/state wide transport planning model." Canadian Journal of Civil Engineering 17, no. 2 (1990): 192–97. http://dx.doi.org/10.1139/l90-024.

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This paper assesses the temporal stability of trip generation, distribution, and assignment process at a state or provincial level. The assignment used data from a test study area that included the southwest part of the Province of New Brunswick with a total area of 30 000 km2. For the test year of 1979, trip generation data were developed using three models. The models were based on stepwise regression, principal components, and ridge regression. Following the trip generation phase, a gravity model was used to distribute the output of each model. Given the limited number of alternative routes, an all-or-nothing assignment was used. The stability of the models was assessed using data for 1971. Of the three models evaluated only the one based on ridge regression proved to be reasonably stable over the term period. A stepwise regression model without intercept was also developed and reasonable results were obtained for the stability of this model. Key words: planning, model, stability, trip generation, distribution, assignment, provincial.

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Zhang, Qin, Kelly J. Clifton, Rolf Moeckel, and Jaime Orrego-Oñate. "Household Trip Generation and the Built Environment: Does More Density Mean More Trips?" Transportation Research Record: Journal of the Transportation Research Board 2673, no. 5 (2019): 596–606. http://dx.doi.org/10.1177/0361198119841854.

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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.

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Badoe, Daniel A., and Chin-Cheng Chen. "Unit of analysis in conventional trip generation modelling: an investigation." Canadian Journal of Civil Engineering 31, no. 2 (2004): 272–80. http://dx.doi.org/10.1139/l03-098.

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This paper examines the importance of the unit of analysis selected for trip generation modelling when the model estimation data are collected in a household travel survey. The paper reviews the literature on the arguments made for the use of the "individual" or the "household" as the unit of analysis in trip production modelling, and then through a statistical exposition it determines what should be the appropriate unit of analysis. An empirical test of the forecast performance of household- and person-trip generation models is conducted using data collected in a household-travel-behaviour survey in the Greater Toronto Area of Canada. The paper concludes that the household is theoretically the preferable analysis unit to use in trip production modelling when the model estimation data are collected in a household travel survey in which the household is the sampling unit. The empirical test indicates that household-trip generation models yield predictions of trips at the household and traffic zone level, respectively, that are marginally more accurate than those yielded by person-trip generation models.Key words: trip generation, travel demand forecasting, household trip generation, person trip generation, sampling unit, travel demand modeling, activity-based travel forecasting.

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Mousavi, Amir, Jonathan Bunker, and Jinwoo (Brian) Lee. "Exploring Socio-Demographic and Urban Form Indices in Demand Forecasting Models to Reflect Spatial Variations: Case Study of Childcare Centres in Hobart, Australia." Buildings 11, no. 10 (2021): 493. http://dx.doi.org/10.3390/buildings11100493.

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This study investigated whether indices for socioeconomic, demographic and urban form characteristics can reflect the overall effect of each category in a demand forecasting model. Regression equations were developed for trip generation of the land use of long day care centres (LDCC) in the metropolitan region of Hobart, Australia, to estimate the morning peak hourly private car trip generation of the centres. The independent variables for the model were functions of socioeconomic, demographic and urban form related indices, while the dependent variable was private car trip generation per number of staff or children. Findings show that using indices for socioeconomic, demographic and urban form characteristics enhances overall model performance, while the models based on the commonly used method for estimating trip generation present acceptable results in just some specific sites. The use of socioeconomic, demographic and urban form indices can reflect differences in these characteristics across suburbs when estimating trip generation.

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Yin, Chaoying, Xiaoquan Wang, and Chunfu Shao. "Do the Effects of ICT Use on Trip Generation Vary across Travel Modes? Evidence from Beijing." Journal of Advanced Transportation 2021 (August 4, 2021): 1–11. http://dx.doi.org/10.1155/2021/6699674.

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With the development of information and communication technologies (ICTs), considerable attention is being paid to the relationship between ICT use and travel behavior. However, it is unclear whether the effects of ICT use on trip generation vary across travel modes. Based on the data of 1022 respondents collected by a web-based questionnaire survey in Beijing, this study used a zero-inflated Poisson model to investigate the effects of ICT use on trip generation in different travel modes, in which ICT use was measured by both the time spent online and the Internet use frequency. The results indicated that the effects of ICT use on trip generation vary across auto, transit, and active trips. Moreover, two measurements of ICT use play essential roles in influencing a trip generation. Specifically, only the frequency of ordering food online showed a positive association with the likelihood of generating any transit trips. These findings demonstrate the importance of considering the differences across travel modes when analyzing the relationship between ICT use and trip generation.

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Tian, Guang, and Reid Ewing. "A walk trip generation model for Portland, OR." Transportation Research Part D: Transport and Environment 52 (May 2017): 340–53. http://dx.doi.org/10.1016/j.trd.2017.03.017.

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Muttaqien, Abied Rizky Putra, and Yudi Basuki. "Trip Rate Model of Attraction in Higher Education Zone." Journal of Advanced Civil and Environmental Engineering 3, no. 1 (2020): 1. http://dx.doi.org/10.30659/jacee.3.1.1-8.

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Land use and transportation have a very close relationship. As the first stage in the four-step trip demand model that trip generation can explain the relationship between the two variables. In the analysis of trip generation and attraction it can be predicted how many movements result from a certain land use. One of the land uses that have a fairly high number of perch is in the higher education zone. Sultan Agung Islamic University (Unissula) Semarang is a campus located on Jalan Kaligawe km. 4. The rise arising from the existence of the tertiary education area is a high enough tourist attraction, causing problems such as traffic jams and traffic accidents during busy times morning and evening. This study aims to analyze the trip generation of Unissula Semarang higher education which has a total building area of 102,754.40 m2 with activities in and out of vehicles both two-wheeled and four-wheeled from morning to evening. The approach used in this research is quantitative descriptive. The analytical method used is trip-rate analysis. The results of this study indicate that vehicles entering the type of car experience peak hours at 08.00 - 08.30 as many as 210 pcu / hour while motorbikes at 07.30 - 08.00 as many as 94 pcu / hour. However, cumulatively, the highest trip rate occurred at 07.30-08.00. in the amount of 0.3 pcu / hour. While the provisions of the Institute of Transportation Engineers (ITE) states that the trip rate for tertiary institutions is 0.11 pcu / hour. Thus the need for efforts to distribute vehicles so that the traffic volume density can be decomposed.

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SASAKI, Tsuna, and Kazuo NISHII. "An estimation model of the car-used trip generation for business trip chainings." Doboku Gakkai Ronbunshu, no. 371 (1986): 115–24. http://dx.doi.org/10.2208/jscej.1986.371_115.

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Schmöcker, Jan-Dirk, Mohammed A. Quddus, Robert B. Noland, and Michael G. H. Bell. "Estimating Trip Generation of Elderly and Disabled People." Transportation Research Record: Journal of the Transportation Research Board 1924, no. 1 (2005): 9–18. http://dx.doi.org/10.1177/0361198105192400102.

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The aging of populations has implications for trip-making behavior and the demand for special transport services. The London Area Travel Survey 2001 is analyzed to establish the trip-making characteristics of elderly and disabled people. Ordinal probit models are fitted for all trips and for trips by four purposes (work, shopping, personal business, and recreational), with daily trip frequency as the latent variable. A log-linear model is used to analyze trip length. A distinction must be made between young disabled, younger elderly, and older elderly people. Retired people initially tend to make more trips, but as they become older and disabilities intervene, trip making tails off. Household structure, income, car ownership, possession of a driver's license, difficulty walking, and other disabilities are found to affect trip frequency and length to a greater or lesser extent.

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Riyana, Raihan Icthiar, Renni Anggraini, and Sugiarto Sugiarto. "MODEL BANGKITAN PERGERAKAN RUMAH TANGGA BERDASARKAN GENDER UNTUK AKTIFITAS MANDOTORY DI KOTA BANDA ACEH." Jurnal Arsip Rekayasa Sipil dan Perencanaan 1, no. 3 (2018): 64–75. http://dx.doi.org/10.24815/jarsp.v1i3.11767.

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Trip generation in the household is very influential on the activity it has. Routine activity that should be done by an individual itself also called as mandatory activity. Also with the trip generation performed can be affected by social status owned, one of which is gender. Gender is the division of roles between men and women which can have a difference. The occurrence of traffic density due to the characteristics of community within a household of trip generation making the purpose of this research is to know the socio-economic characteristics, demography and tip behavior, knowing factors which influence the trip generation of a household based on gender for mandatory activity and to get the model, and the difference of utility and probability value. Area of this research are 4 sub-districts in Banda Aceh city which are Jaya Baru sub-districts, Banda raya, Lueng Bata, and Kuta raja sub-district with the sampe numbers of 400 samples. Method used is Model of Binomial Logit Difference then processed by using SPSS v20. Characteristics of Socio-economic, demography, and trip behavior which produced by 19 variables in this research one of its variable is gender that is 54% for men and 46% for women. From the utility model obtained, then the factors that affecting the movement of the household based on mandatory activity on men are number of family members and activity duration, while for women are number of family members who attend school and activity duration. Probability for the household Trip generation is probabilitas ≤ 3 movement and 3 movements based on the mandatory activity for men are 75% and 25%, while for women are 72% and 28%.

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Strambi, Orlando, and Karin-Anne Van De Bilt. "Trip Generation Modeling Using CHAID, a Criterion-Based Segmentation Modeling Tool." Transportation Research Record: Journal of the Transportation Research Board 1645, no. 1 (1998): 24–31. http://dx.doi.org/10.3141/1645-04.

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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.

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Biala, Oreoluwa Temidayo. "A COMPARATIVE STUDY OF CATBOOST AND ARTIFICIAL NEURAL NETWORKS IN ENHANCING TRIP GENERATION MODELLING FOR ILORIN CITY." Journal of Civil Engineering, Science and Technology 15, no. 1 (2024): 18–29. http://dx.doi.org/10.33736/jcest.6196.2024.

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Trip generation plays a crucial role in transportation planning, and the choice of an appropriate model is essential for predicting future travel patterns. This study focuses on comparing the suitability and performance of CatBoost and ANN for trip generation (production and attraction) modelling of Ilorin City. By incorporating Ilorin household and trip characteristics, population data, and maps, this study evaluates the performance of the models. The two models demonstrated high accuracy and performance. In terms of trip production, the CatBoost model displayed exceptional accuracy, attaining an R-squared value of 0.99999992016446, accompanied by an impressively low mean squared error (MSE) of 3.93870930136429e-05. In contrast, the neural network exhibited a slightly lower accuracy of 0.999873850524181, with an error value of 0.0581313408911228. Similarly, for trip attraction, the CatBoost model showcased remarkable accuracy and precision, achieving an accuracy score of 0.9999999999999994 and an extremely low error value of 2.26762031965784e-13. The neural network model demonstrated an accuracy of 0.99999999990335 and a negligible error value of 0.000000041994. These findings underscore the strong predictive capabilities of both models for trip production and attraction, with the CatBoost model notably excelling in achieving nearly flawless accuracy and minimal error values across both aspects in Ilorin. Further research can explore the application of other advanced machine-learning techniques and combine their strengths to enhance the accuracy and robustness of trip-generation models.

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Ahmed, Tanjeeb, Suman Kumar Mitra, Rezwana Rafiq, and Sanjana Islam. "Trip Generation Rates of Land Uses in a Developing Country City." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 9 (2020): 412–25. http://dx.doi.org/10.1177/0361198120929327.

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In recent decades, a major shift in the land use pattern has been observed in Dhaka, the capital city of Bangladesh. To understand and model the impact of these land use changes on transportation demand, this study aimed to determine the trip generation rates for six different land use categories adjacent to Mirpur Road in Dhaka. A total of 20 establishments consisting of six land use categories were selected for the collection of data on person trip rates and respective modal share by manual counts and intercept surveys. These data were used to develop vehicular trip generation rates for each land use category in passenger car equivalents as a uniform unit of comparison. Results showed that commercial and healthcare land uses had the highest average and peak-hour trip rates. There was also a significant variation in the share of eight transport mode categories among the trips generated by the land uses. The peak-hour trip generation rates of the study area were found to be different from the values established by the Institute of Transportation Engineers which corresponds to the fact that trip generation depends on a host of factors, such as surrounding land uses, modal share, the economic condition of a region, and so forth, rather than on a single factor inherent to the land use. The findings of this research can help to determine the trip generation impact of new establishments and consequently identify suitable locations to minimize the impact.

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Purvis, Charles L., Miguel Iglesias, and Victoria A. Eisen. "Incorporating Work Trip Accessibility in Nonwork Trip Generation Models in San Francisco Bay Area." Transportation Research Record: Journal of the Transportation Research Board 1556, no. 1 (1996): 37–45. http://dx.doi.org/10.1177/0361198196155600106.

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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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Zakiyah, Qonita, Tedy Murtejo, and Nurul Chayati. "Analysis of generation and attraction in Bogor Regency (Case study: Tenjolaya sub-District, Tamansari sub-District and Tenjolaya sub-District)." astonjadro 12, no. 1 (2023): 1. http://dx.doi.org/10.32832/astonjadro.v12i1.4263.

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Tenjolaya Subdistrict, Tamansari Subdistrict, and Ciomas Subdistrict of Bogor District has an area of 61.74 km2, divided into 25 villages, and 1 urban-village with those total populations density are 4,819.7 people/km2. The increasing population and significant development in the region has increased the movement of traffic flows to and from the region, which is expected to cause some particular problems in traffic congestion on Road segments. This research aims to make the model of the trip generation and the trip attraction caused by land use such as education area, medical, office, lodging, physical fitness center and tourism in the three sub-districts. MKJI 2017 is used as a data processing guideline for the method of calculation of transport analysis. Then use Trip Generation Manual and modelled into SATURN software. Total trip generations from 3 sub-districts of the study area are 4,403.78 trips/hour, and with total trip attractions are 6,165.33 SMP/hour. The design of the transportation modelling equation for Ciomas subdistrict, Tamansari District, and Tenjolaya subdistrict is 21,230 – 0,950 (X). The Model of trip generation and trip attraction has a value of R2 = 0,9687. The level of service is obtained index in the range of A until D, with an average is B, indicating a relatively stable condition. although in some areas, it is still necessary to repair and improve road network infrastructure, and strive for a comfortable and efficient alternative public transport system (time, cost, energy) to transfer people from private vehicles to Public transport.

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Anand, Akash, and Varghese George. "Modeling Trip-generation and Distribution using Census, Partially Correct Household Data, and GIS." Civil Engineering Journal 8, no. 9 (2022): 1936–57. http://dx.doi.org/10.28991/cej-2022-08-09-013.

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The efficiencies of urban transport systems in several cities are drastically affected due to difficulties imposed by rapid urbanization and the proliferation of private modes of transport. The conventional four-stage travel demand modeling approach provides an ideal platform to formulate strategies to rectify problems in urban transport. Trip generation is the first stage in this exercise (where trip production and trip attractions are modelled), followed by trip distribution in the second stage. The present work related to the development of models for trip generation and trip distribution necessitated the use of census data related to the number of households in each zone since the available revealed preference (RP) data compiled based on household interview surveys was partially incorrect. A review of the literature indicated that studies on the use of sparsely available and partially inaccurate data such as revealed preference and zone-specific secondary data on trip generation and trip distribution were limited. In the present study, the use of the initial trip generation regression models developed based on existing household survey data resulted in prediction errors ranging between 26% and 32%. Modeling efforts after applying corrections to zone-specific characteristics based on secondary data and the use of trip rate per household later resulted in prediction errors of less than ±5%. In the latter phase of work related to trip distribution modeling, a log-linear regression model was developed based on a smaller refined set of the revealed preference data obtained by eliminating erroneous data in a stage-wise manner. The use of the calibrated and validated model ensured that the errors in predicted trip frequencies were less than 0.6%. Here, the information on the inter-zonal aerial distances that formed part of the trip distribution model was obtained using GIS approaches that employed the moment area method, which considered the intensity of land use at the sub-zone level. The combined strategy incorporates the use of GIS-based approaches to determine inter-zonal aerial distances, and the use of the refined relationship between trip interchanges and the inter-zonal aerial distances in the development of a reliable log-linear regression model for trip distribution contributed towards attaining higher accuracies in travel demand estimation. The modeling approaches described herein do not rely on the use of sophisticated technology, and time-consuming data processing. The study will provide the basic framework for transport planners to formulate better strategies for travel demand modeling where available data is noisy and less reliable. Doi: 10.28991/CEJ-2022-08-09-013 Full Text: PDF

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Moeckel, Rolf, Nico Kuehnel, Carlos Llorca, Ana Tsui Moreno, and Hema Rayaprolu. "Agent-Based Simulation to Improve Policy Sensitivity of Trip-Based Models." Journal of Advanced Transportation 2020 (February 25, 2020): 1–13. http://dx.doi.org/10.1155/2020/1902162.

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The most common travel demand model type is the trip-based model, despite major shortcomings due to its aggregate nature. Activity-based models overcome many of the limitations of the trip-based model, but implementing and calibrating an activity-based model is labor-intensive and running an activity-based model often takes long runtimes. This paper proposes a hybrid called MITO (Microsimulation Transport Orchestrator) that overcomes some of the limitations of trip-based models, yet is easier to implement than an activity-based model. MITO uses microsimulation to simulate each household and person individually. After trip generation, the travel time budget in minutes is calculated for every household. This budget influences destination choice; i.e., people who spent a lot of time commuting are less likely to do much other travel, while people who telecommute might compensate by additional discretionary travel. Mode choice uses a nested logit model, and time-of-day choice schedules trips in 1-minute intervals. Three case studies demonstrate how individuals may be traced through the entire model system from trip generation to the assignment.

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Syed, Aaqib Javed, Debnath Mithun, Hossain Nadim Md., Ajwad Anwar Md., and Chowdhury Sabuj. "Estimation of Trip Attraction Rates and Models for Shopping Centers in Dhaka City." Journal of Transportation Systems 5, no. 1 (2020): 28–34. https://doi.org/10.5281/zenodo.3733088.

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Planinig of transportation facilities for any city depends on trip generation stepy. In this paper, trip attraction rate of six shopping centers located at different places (Uttara, Gulshan and Dhanmondi) in Dhaka city is determined. Physical features like floor area (ft2), parking space, number of shops, number of entry, number of employees (per shop) are considered to determine the trip attraction rate. The number of vehicles (car) and persons entering the shopping center on weekend and weekday during peak hour for every 15 minutes time interval are counted. A similar trend showed in all the graphs between the trip rate on weekend and weekdays. In all cases, trip rate on weekend is higher than the trip rate on weekday. This data used to determine the trip attraction rate with respect to different physical features. This trip attraction rate can be used for estimating the traffic volume and evaluating the traffic impacts adjacent a new shopping center. Two regression models were also developed as a function of different physical features to estimate the trip attractions of the shopping centers. The result showed that all the physical features have a strong correlation except number of entry. This models will be helpful in estimating the trip attraction rate for any shopping centers.

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Patel, Vinodkumar R., H. R. Varia, and Gargi Rajpara. "Development of Regional Industrial Trip Generation Model Using SPSS." Indian Journal of Science and Technology 11, no. 7 (2018): 1–9. http://dx.doi.org/10.17485/ijst/2018/v11i7/97742.

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Poltavskaya, Yuliya. "MODELING TRANSPORT PROCESS BASED ON TRIP GENERATION." Modern Technologies and Scientific and Technological Progress 2020, no. 1 (2020): 177. http://dx.doi.org/10.36629/2686-9896-2020-1-177-177.

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Matsui, Hiroshi, Motohiro Fujita, and Eiji Kamiya. "A Time-of day Trip Generation/Attraction Model Using Transition Matrices of Trip Purposes." Journal of the City Planning Institute of Japan 27 (October 25, 1992): 367–72. http://dx.doi.org/10.11361/journalcpij.27.367.

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Guo, Bao, Hu Yang, Fan Zhang, and Pu Wang. "A Hierarchical Passenger Mobility Prediction Model Applicable to Large Crowding Events." Journal of Advanced Transportation 2022 (June 1, 2022): 1–12. http://dx.doi.org/10.1155/2022/7096153.

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Predicting individual mobility of subway passengers in large crowding events is crucial for subway safety management and crowd control. However, most previous models focused on individual mobility prediction under ordinary conditions. Here, we develop a passenger mobility prediction model, which is also applicable to large crowding events. The developed model includes the trip-making prediction part and the trip attribute prediction part. For trip-making prediction, we develop a regularized logistic regression model that employs the proposed individual and cumulative mobility features, the number of potential trips, and the trip generation index. For trip attribute prediction, we develop an n -gram model incorporating a new feature, the trip attraction index, for each cluster of subway passengers. The incorporation of the three new features and the clustering of passengers considerably improves the accuracy of passenger mobility prediction, especially in large crowding events.

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Yang, Min, Dan Li, and Wei Wang. "A Structural Equation Model to Analyze Interrelationship between Activity Participation and Trip-Chaining of Nuclear Household." Applied Mechanics and Materials 178-181 (May 2012): 1923–29. http://dx.doi.org/10.4028/www.scientific.net/amm.178-181.1923.

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Travel is derived from the necessity to engage in spatially-separated activities. Previous researches have demonstrated that socio-demographics have effects on activity-travel behavior, but few of them focus on the interactions between activity and trip-chaining, especially the influence of trip-chaining on activity. Based on the activity-travel survey data of Suzhou, China, in 2009, complex interactions between activity participation and trip-chaining behavior of nuclear household are explored using structural equation model. Model estimation results show that trade-off and complementarity exist among different types of activities and trip chains. Besides, trip-chaining generation is deeply affected by activity participation. Subsistence activity negatively affects trip-chaining characteristics, while maintenance and leisure activities positively affect it. Furthermore, feedbacks from trip-chaining characteristics to activity participation do exist. Travel time and the number of trip-chaining have significant effects on activity duration.

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Westrom, Ryan, Stephanie Dock, Jamie Henson, et al. "Multimodal Trip Generation Model to Assess Travel Impacts of Urban Developments in the District of Columbia." Transportation Research Record: Journal of the Transportation Research Board 2668, no. 1 (2017): 29–37. http://dx.doi.org/10.3141/2668-04.

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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.

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Tian, Guang, Keunhyun Park, and Reid Ewing. "Trip and parking generation rates for different housing types: Effects of compact development." Urban Studies 56, no. 8 (2018): 1554–75. http://dx.doi.org/10.1177/0042098018770075.

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Guidelines for trip and parking generation in the United States come mainly from the Institute of Transportation Engineers (ITE). However, their trip and parking manuals focus on suburban locations with limited transit and pedestrian access. This study aims to determine how many fewer vehicle trips are generated and how much less parking demand is generated, by different housing types (single-family attached, single-family detached, and apartment and condo) and in different settings (from low density suburban environments to compact, mixed-use urban environments). Using household travel survey data from 30 diverse regions of the United States, we estimate a multilevel negative binomial model of vehicle trip generation and a multilevel Poisson model of vehicle ownership, vehicle trip generation and vehicle ownership being logically modelled as count variables. The models have the expected signs on their coefficients and have respectable explanatory power. Vehicle trip generation and vehicle ownership (and hence parking demand) decrease with the compactness of neighbourhood development, measured with a principal component that depends on activity density, land use diversity, percentage of four-way intersections, transit stop density and employment accessibility (after controlling for sociodemographic variables). The models capture the phenomena of ‘trip degeneration’ and ‘car shedding’ as development patterns become more compact. Reducing the number of required parking spaces, and vehicle trips for which mitigation is required, creates the potential for significant savings when developing urban projects. Guidelines are provided for using study results in transportation planning.

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Khatri, Kajal. "Planning an Optimal Road Trip Analysis and Drowsiness Detection using Genetic Algorithm." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 675–80. http://dx.doi.org/10.22214/ijraset.2021.35054.

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One of the Machine Learning Projects which can promptly affect our lives is the Road Trip Analyzer. With our reliance on information and applications these days, going to new places has become the space of the excursion analyser. A solid Trip-generation Forecasting Model is the most essential piece of the traffic determining model. The undertaking has been based on the genetic algorithm which has extraordinary Worldwide Global search ability. It will permit the trip-generation forecasting model to improve the exactness of the expectation. Perhaps the greatest trouble in arranging an excursion is choosing where to stop en route. The proposed framework endeavours to coordinate with the drivers' requirement with the quickest course accessible so the clients have the smartest possible solution.

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Winters, Philip L., Rafael A. Perez, Ajay D. Joshi, and Jennifer Perone. "Work Site Trip Reduction Model and Manual." Transportation Research Record: Journal of the Transportation Research Board 1924, no. 1 (2005): 197–206. http://dx.doi.org/10.1177/0361198105192400125.

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Today's transportation professionals often use the ITE Trip Generation Manual and the Parking Generation Manual for estimating future traffic volumes to base off-site transportation improvements and identify parking requirements. But these manuals are inadequate for assessing the claims made by specific transportation demand management (TDM) programs in reducing vehicle trips by a certain amount at particular work sites. This paper presents a work site trip reduction model (WTRM) that can help transportation professionals in assessing those claims. WTRM was built on data from three urban areas in the United States: Los Angeles, California; Tucson, Arizona; and nine counties in Washington State. The data consist of work sites’ employee modal characteristics aggregated at the employer level and a listing of incentives and amenities offered by employers. The dependent variable chosen was the change in vehicle trip rate that correlated with the goals of TDM programs. Two different approaches were used in the model-building process: linear statistical regression and nonlinear neural networks. For performance evaluation the data sets were divided into two disjoint sets: a training set, which was used to build the models, and a validation set, which was used as unseen data to evaluate the models. Because the number of data samples varied from the three areas, two training data sets were formed: one consisted of all training data samples from three areas and the other contained equally sampled training data from the three areas. The best model was the neural net model built on equally sampled training data.

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Lad, Shivangkumar, L. B. Zala, and Pinakin Patel. "Development of Trip Generation Model: Case Study of Makarpura GIDC." Journal of Transportation Systems 05, no. 03 (2020): 18–27. http://dx.doi.org/10.46610/jots.2020.v05i03.003.

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El-Desouky, A., K. Kandil, A. Mostafa, and S. Easa. "DEVELOPMENT OF A LONG DISTANCE TRIP GENERATION MODEL: CASE STUDY." International Conference on Civil and Architecture Engineering 7, no. 7 (2008): 186–96. http://dx.doi.org/10.21608/iccae.2008.45522.

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Yousif Ahmed, Thamir. "Trip generation production model for North zone of Fallujah city." Iraqi Journal of Civil Engineering 7, no. 2 (2012): 59–70. http://dx.doi.org/10.37650/ijce.2012.68987.

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Nazem, Mohsen, Martin Trépanier, and Catherine Morency. "Integrated Intervening Opportunities Model for Public Transit Trip Generation–Distribution." Transportation Research Record: Journal of the Transportation Research Board 2350, no. 1 (2013): 47–57. http://dx.doi.org/10.3141/2350-06.

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Journal articles on the topic 'Trip generation model'

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