• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.33 no.6
• /
• pp.2465-2471
• /
• 2013

This paper develops a travel time prediction algorithm that can be used for real-time application. The algorithm searches for the most similar pattern in historical travel time database as soon as a series of real-time data become available. Artificial neural network approach is then taken to forecast travel time in the near future. To examine the performance of this algorithm, travel time data from Gyungbu Highway were obtained and the algorithm is applied. The evaluation shows that the algorithm could predict travel time within 4% error range if comparable patterns are available in the historical travel time database. This paper documents the detailed algorithm and validation procedure, thereby furnishing a key to generating future travel time information.

• Journal of Korean Society of Transportation
• /
• v.17 no.5
• /
• pp.19-32
• /
• 1999

In this study, the trip characteristics of workers in the city are systematically analyzed. The trip behaviors and socioeconomic characteristics of workers are analyzed using Person Trip Survey Data of 1988 and 1992 in Taegu Metropolitan area. With the results of behavioral analyses, the daily travel pattern of workers is shown as one tour contained two trips and it is relatively simple and stable. Also the rate using the same mode in a day is Presented as high ratio. So, it can be explained that the choice of worker\`s first trip is fixed his/her travel mode for his/her daily travel mode. Based on these analyses, the mode choice model for workers is developed by applying the Multi-nominal Logit Model with the choice set of bus, taxi, and car. The explanatory variables of this model include sex, age, auto, travel time, and cost. Empirical tests of the model show encouraging results. After that, the temporal transferability of the model is examined by the Pairwise t-test and five indexes far the model of 1988 and 1992. The results of examination are satisfied with each significance level of the explanatory variables and five indexes. Therefore. it can be concluded that the temporal transferability of this model developed in this study is resonable.

• Journal of Korean Society of Transportation
• /
• v.15 no.3
• /
• pp.39-49
• /
• 1997

본 연구는 서울대도시권을 대상으로 토지이용패턴과 통행수단선택간의 관계를 경헙적으로 분석하여 수도권 토지이용 및 교통정책에 시사점을 제시하는 것을 목적으로 하고 있다. 다중 로짓모형을 이용한 분석에서 직장 및 주거빌도가 높을수록 전철과 버스 등 대중교퉁수단에 대한 의존도를 보이는 것으로 나타났다. 또한 직장중심지의 경우 버스보다는 자가용이용율이 높은 것으로 나타나 고밀도 정책이 반드시 대중교통수단으로의 이동을 의미하는 것은 아니라는 사실을 경험적으로 입증하고 있다.

• Journal of Korean Society of Transportation
• /
• v.30 no.1
• /
• pp.103-112
• /
• 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
• /
• v.13 no.2
• /
• pp.68-79
• /
• 2014

This paper analyzes the characteristics of trip chaining activities of elderly and explores temporal and spatial distribution. The research also estimates ordered probit model and binary logistic model to investigate various factors affecting trip chaining and mode choice patterns. We utilized household survey data for elderly conducted in 2006 and 2010 in Seoul metropolitan area. Research results indicate that trip chaining showed an increasing trend and simple trip chaining counts for more than 85%. GIS mapping expressed spatial distribution of trip departure and arrival areas, particularly showing regional changes in job-related trips. We also found that more factors influence trip chaining in 2010, compared with 2006, and travel cost is more sensitive than travel time in determining travel mode. The research contributes to establish transportation policies based on travel behavior of elderly in a upcoming super-aged society.

• Journal of the Economic Geographical Society of Korea
• /
• v.9 no.3
• /
• pp.379-395
• /
• 2006

The purpose of this study is to analyze the spatial characteristics of travel patterns and travel behaviors of transit users in the Metropolitan Seoul area. We apply the data mining techniques to explore the travel patterns of transit users from the T-money card database which has been produced over 10,000,000 transaction records per day. The database contains the information of locations and times of origin, transfer, and destination points for each transaction as well as the informations of transit modes taken via the transaction. We develop an data mining algorithm to explore traversal patterns from the enormous information. The algorithm determines the travel sequences of each passenger, and produce the volumes of support on each points (stops) of transportation networks in the Metropolitan Seoul area. In order to visualize the spatial patterns of travel demands for transit systems we apply GIS techniques, and attempt to investigate the spatial characteristics of travel patterns and travel demand. Subway stops located in the Gangnam area appear the highest peak for the travel origin and destination, while the CBD in the Gangbuk stands at the second position. Two or three sub-peaks appear at the densely populated residential areas developed as the high-rise apartment complex. Subway stations located along the Subway Line 2, especially from Guro to Samsung receive heavy travel demand (total support), while bus stops located at the CBD in the Gangbuk stands the highest travel demand by bus.

• The Korean Journal of Applied Statistics
• /
• v.29 no.5
• /
• pp.949-960
• /
• 2016

This paper uses all sections of highway data (VDS) for two years (Jan. 2014-Dec. 2015), with 15 minute units. The first purpose of this study is to find clusters with similar patterns that appear repeatedly with time variables of month, week and hour. The cluster analysis results indicate a variety of patterns of average traffic speeds by time variables depending on the clusters; subsequently, these can be utilized to model for the forecast of the speed at a specific time. The second purpose is to do cluster analysis for grouping sections by effect nets that are closely related to each other. For the similarity measure we use cross-correlation functions calculated after pre-whitening the speed of each section. The cluster analysis gets 19 clusters, and sections within a cluster are geographically close. These results are expected to help to forecast a real-time speed.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.39 no.6
• /
• pp.833-839
• /
• 2019

Bus Information System (BIS) collects information related to the operation of buses and provides information to users through predictive algorithms. Method of predicting through recent information in same section reflects the traffic situation of the section, but cannot reflect the characteristics of the target line. The method of predicting the historical data at the same time zone is limited in forecasting peak time with high volatility of traffic flow. Therefore, we developed a pattern recognition bus arrival time prediction algorithm which could be overcome previous limitation. This method recognize the traffic pattern of target flow and select the most similar past traffic pattern. The results of this study were compared with the BIS arrival forecast information history of Seoul. RMSE of travel time between estimated and observed was approximately 35 seconds (40 seconds in BIS) at the off-peak time and 40 seconds (60 seconds in BIS) at the peak time. This means that there is data that can represent the current traffic situation in other time zones except for the same past time zone.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.34 no.1
• /
• pp.213-221
• /
• 2014

Since 2005, when "The Mobility Enhancement for the Mobility Impaired Act" was enacted, special transport systems(SPS) has been introduced by each responsible local entity. For its efficient operations and service enhancements, a clear understanding of travel patterns of SPS users is required. Yet we currently have a very limited understanding about them due to a lack of necessary data. This study represents an attempt to provide a better understanding of SPS user's travel patterns with the data generated by Gyeongsangnam-do SPS Call Center. The data include the number, time and day of calls, origins and destinations of callers, types of callers' impairement etc. The data thus allow one to analyze users' travel patterns, including area-wide O-D patterns. There were a number of interesting findings. For example, wheelchair users are only about 42% and the trips are made mostly on non-peak daytime periods. The results are expected to provide a helpful information not just for Center's SPS operations, but for other local entities that are interested in developing similar call centers as well. By refining the SPS system, periodic patterns of callers could be identified in the future.

• Journal of the Economic Geographical Society of Korea
• /
• v.17 no.4
• /
• pp.759-770
• /
• 2014

The aim of this study is to examine how time-use and activities are affected by work hours. To achieve this, we focused on the weekday time-use of full-time workers in Seoul Metropolitan Area(SMA). The long 'work hours' are under active discussions since it is related to the quality of life. However, many Social researcher thought that problem of Korean working hours is linked to quality of life in the abstract. Because activity connects time-use and quality of life, the key point is activity under time constraints. Therefore, travel patterns should be understood by time-use and activity patterns. This study composes trip-chains from travel data of 2010 Household Travel Survey(HTS). Grouping trip-chains by activity patterns, we could make sure that a few of activities after work is affected by a short free time. This study has potential implications for the policy of work hours and traffic problems in the evening, and will provide new geographical perspective related to measuring quality of life.