• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.5D
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• pp.657-662
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• 2011

The relationship between household attributes and trip generation can only be found in Seoul Metropolitan Household Travel Survey, which has been implemented every 5 years. However, various household attributes' impact on trip generation has not been analyzed closely. This paper compared and analyzed those impact. The results could be useful when trip generation models are studied in the future. They are as follows. The household size should be an important classification criteria when household trip generation is estimated. The traditional assumption that the relationship between household auto ownership and trip generation is positive and linear correlation should be reconsidered. Weekday travel data only did not showed that housing type has an influence on trip generation. Household income is unrelated with trip generation among single-person household, while multi-person household is related strongly. However, when trips are classified by purpose, impact of household income on trip generation are varied by trip purpose. Especially, the increase in single-person household can not be overlooked when trip generation is forecasted.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.6
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• pp.2503-2508
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• 2013

Single-person households in the Seoul Metropolitan Area have increased sharply during recent 20-30 years. Despite of decreasing in the total population, the number of single-person household is predicted to increase continuously. However, the effect of single-person household growth on the domestic transport sector has not been studied concretely. In this study, the differences on trip generation characteristics by household size and attributes were figured out by analyzing Seoul Metropolitan Area Household Travel Behavior Survey (SMA-HTBS). Firstly, trip generation rates (trips/day/person) were produced by household attribute, household member attribute and trip attribute based on SMA-HTBS. Secondly, trip generation rate of single-person household and that of multi-person (2 or more) household were compared by significance test. It was found that trips generation characteristics of single-person household is quite different from those of multi-person household by housing type, residential type, living area, and transport mode. The result of this paper is expected to contribute developing more sophisticated trip generation model and transport policy reflecting trip generation characteristics of single-person household.

• Journal of the Korean Society for Railway
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• v.18 no.5
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• pp.481-488
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• 2015

The recent trend in transportation planning information is to reduce traffic survey costs and enhance accuracy by using and converging various sources of external data. In Korea, mobile phone data can help generate useful transportation planning information, thanks to the universal use of mobile phones, which are present in a number greater than that of the population. This paper addresses measures to derive trip generation information from mobile phone data and verifies the value of the system for practical use by correlation analysis with KTDB trip generation data. The results show that trip generation information produced by mobile phone data correlates with existing (KTDB) trip generation data.

• International Journal of Railway
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• v.5 no.4
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• pp.139-143
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• 2012

In Korea, almost 700 industrial parks are under operation. Generally, industrial parks consist of national industrial parks and local industrial parks which are managed by a central government and by local governments respectively. The developing countries such as Korea, China and Vietnam etc. have constructed many industrial parks, which result in the change of land use pattern and also affect future trip demands. Therefore, in estimating traffic demands, it is very important to consider the industrial park development. This study aims to improve the methodology in estimating a freight trip generation rate with the data based on a nationwide commodity freight survey. The result showed that it is desirable to apply freight trip generation rate by the industry sector in estimating freight trip generations and using the production area of firm as an indicator. Specially, the reliability of the rates through a survey could be made sure because a sample rate based on firms in industrial parks was over 25% and the response rate was over 67%. The sample rate and response rate are very superior as compared to surveys conducted in many other countries. Because industrial parks have significant effects on forecasting transportation demand in pre-feasibility studies of transport and logistics projects, it is expected that the accuracy of freight trip demands would be improved through the results of this study.

• Journal of Korean Society of Transportation
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• v.28 no.6
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• pp.159-166
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• 2010

Trip distribution is to connect travel demand for each OD pair based on travel cost, trip production and attraction derived from trip generation step. In real world the travel cost is a function of travel demand, but existing models could not fully consider such functional relation between travel cost and demand, which leads to an equilibrium in trip distribution model. This paper proves the equilibrium trip distribution by using gravity model. In order to obtain such equilibrium this paper also presents a solution algorithm based on fixed point theorem. The algorithm will be tested with an example and confirmed the equilibrium solution of trip distribution.

• International Journal of Highway Engineering
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• v.12 no.4
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• pp.93-100
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• 2010

Trip generation is the first step in the conventional four-step model and has great effects on overall demand forecasting, so accuracy really matters at this stage. A linear regression model is widely used as a current trip generation model for such plans as urban transportation and SOC facilities, assuming that the relationship between each socio-economic index and trip generation stays linear. But when rapid urban development or an urban planning structure has changed, socio-economic index data for trip estimation may be lacking to bring many errors in estimated trip. Hence, instead of assuming that a socio-economic index widely used for a general purpose, this study aims to develop a new trip generation model by type based on the market separation for the variables to reflect the characteristics of various zones. The study considered the various characteristics (land use, socio-economic) of zones to enhance the forecasting accuracy of a trip generation model, the first-step in forecasting transportation demands. For a market separation methodology to improve forecasting accuracy, data mining (CART) on the basis of trip generation was used along with a regression analysis. Findings of the study indicated as follows : First, the analysis of zone characteristics using the CART analysis showed that trip production was under the influence of socio-economic factors (men-women relative proportion, age group (22 to 29)), while trip attraction was affected by land use factors (the relative proportion of business facilities) and the socio-economic factor (the relative proportion of third industry workers). Second, model development by type showed as a result that trip generation coefficients revealed 0.977 to 0.987 (trip/person) for "production" 0.692 to 3.256 (trip/person) for "attraction", which brought the necessity for type classifications. Third, a measured verification was conducted, where "production" and "attraction" showed a higher suitability than the existing model. The trip generation model by type developed in this study, therefore, turned out to be superior to the existing one.

• Journal of Korean Society of Transportation
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• v.30 no.1
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• pp.103-112
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• 2012

This research introduces a trip generation model reflecting time-series effects derived from a panel analysis with the data collected from the national household trip surveys conducted in 1996, 2002 and 2006. The existing methods are unable to reflect time-series effects from the change of socioeconomic conditions because the parameters applied to the model were basically from the base year of study - the parameter values were unchanged. This study proposes a new trip generation model developed through a panel analysis performed with the data collected from the last three national household trip surveys. From the results, it was found that the number of school trips increases and that the number of shopping trips decreases as time passes. The results showed that there are time-series effects affecting in trip generation.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.6
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• pp.126-139
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• 2011

In the traditional four-step travel demand models, each step is sequentially conducted following the model estimation at the previous step. The accuracy of the following model is partly dependent on whether the model at the former stage was properly established or not. Therefore, trip generation, which is the first step in this conventional model, has great effects on the modeling process and forecasting results. Linear regression models for trip generation of Seoul Metropolitan Area might increase the forcasting errors, since a variety of land-use characteristics are not considered. Hence, in this study, zonal factors such as socioeconomic and land use variables are included to improve the elaboration of trip generation. Comparing the %RMSE with the existing models, which contain bigger errors in the zones highly based on the secondary and tertiary industries than residence-based, the trip generation models including those variables seem more appropriate overall.

• Journal of Korean Society of Transportation
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• v.22 no.7 s.78
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• pp.79-90
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• 2004

In this study, we applied the Bayesian Networks for the case of the trip generation models using the Seoul metropolitan area's house trip survey Data. The household income was used for the independent variable for the explanation of household size and the number of cars in a household, and the relationships between the trip generation and the households' social characteristics were identified by the Bayesian Networks. Furthermore, trip generation's characteristics such as the household income, household size and the number of cars in a household were also used for explanatory variables and the trip generation model was developed. It was found that the Bayesian Networks were useful tool to overcome the problems which were in the traditional trip generation models. In particular the various transport policies could be evaluated in the very short time by the established relationships. It is expected that the Bayesian Networks will be utilized as the important tools for the analysis of trip patterns.

• Journal of Korean Society of Transportation
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• v.18 no.4
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• pp.95-105
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• 2000

The Purpose of this study is to present a new Trip Generation Model using Backpropagation Neural Networks. For this purpose, it is compared the performance between existing linear/nonlinear Regression models and a new TriP Generation model using Neural Networks. The study was performed according to the below. First, it is analyzed the limits of conventional Regression models, next Proved the superiority of Neural Networks model in theoretical and empirical aspects, and lastly Presented a new approach of Trip Generation methodology. The results show that Backpropagation Neural Networks model is predominant in estimation and Prediction comparable to Regression analysis. Such results mean the possibility of Neural Networks\` application in Trip Generation modeling. Specially under the circumstances of the chancing transportation situations and unstable transportation on vironments, its application in transportation fields will be extended.