• Proceedings of the Korean Society of Disaster Information Conference
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• 2016.11a
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• pp.263-264
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• 2016

본 연구는 첨두 비첨두시간에 발생되는 교통사고의 주요 요인들을 발견하고 비교하여 각 시간대의 사고특성을 제시하고자 한다. 이에 로지스틱 회귀분석을 통해 분석한 결과 흐린 날씨의 경우 첨두시간에 발생되는 사망사고 위험도가 비첨두시간의 경우보다 더 높게 나타났고, 비첨두시간 사망사고 위험도의 경우 안개가 낀 날 증가하고 맑은 날 감소하는 모습이 나타났다. 과속의 경우 비첨두시간이 첨두시간의 경우 보다 크게 나타났고, 횡단 중인 보행자와 발생되는 사고에 대해서는 첨두시간의 사망위험도가 상대적으로 높은 것으로 나타났다. 차량단독 사고의 경우 공작물과 충돌로 인한 사망위험도는 첨두시간이 높은 것으로 나타났고, 도로 이탈로 인한 사망위험도는 비첨두시간이 높은 것으로 나타났다.

• Journal of Korean Port Research
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• v.10 no.2
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• pp.91-101
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• 1996

Today transportation problems are severer with the increase of the vehicles and travel demand in urban areas, but could not be completely solved with only the expansion of the new transportation facilities. Because the expansion of the new transportation facilities are limited in urban areas. As one of the Transportation System Management(TSM) techniques in this study, the simulation results of the existing signal systems which were operated based upon the peak time periods for increasing the efficiency on the pretimed signalized intersections(PSI) during the different time periods : the AM on-Peak, the AM off-Peak, the PM off-Peak, and the PM on-Peak, were as follows : i) There was no distinct difference in the total traffic volumes concentrated on the signalized intersections during the different time periods, but a considerably big difference in the directional traffic volumes for those time periods. ii) There were about 53% reduction of the average delay and 51% reduction of the fuel consumption when applying the different signal systems to the different time periods regardless of the CBD and Non-CBD. iii) There were about 36% increase of the average delay and 33% increase of the fuel consumption when applying the same signal systems during the peak time periods to the different time periods regardless of the CBD and Non-CBD. Based on the above results, it was concluded that constructing the different signal systems for the different time periods would be better than construction the same ones for those periods on the pretimed signalized intersections in urban areas.

• Proceedings of the Korean Society of Disaster Information Conference
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• 2017.11a
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• pp.207-208
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• 2017

본 연구는 전국 고속도로별 첨두 비첨두 시간에 발생되는 교통사고 중 사망사고의 주요 요인들을 발견하고 분석하여 각 노선별 사고 특성을 제시하고자 한다. 이에 로지스틱 회귀분석을 통해 분석한 결과 남해선의 경우 첨두 시간에 발생되는 사망사고의 요인 중 주시태만이 첨두가 비첨두의 경우보다 높게 나타났고, 논산천안선, 호남선과 중부내륙선의 경우 모두 졸음의 사망사고 위험도가 첨두일 경우 비첨두의 경우보다 높게 나왔으며 논산천안선, 호남선의 경우 비첨두일 때 과속에도 영향을 받는 경향을 나타냈다. 특이하게 경부선의 경우 졸음의 사망사고 위험도가 오히려 비첨두일 경우가 첨두의 경우보다 높게 나타났다. 비첨두일 경우 경인선, 서해안선, 영동선 등의 노선에서도 졸음, 주시태만과 과속의 위험도가 나타났다.

• Journal of Digital Convergence
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• v.19 no.5
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• pp.93-103
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• 2021

The special means of transportation introduced to improve the mobility of the transportation vulnerable met the number of legal standards in 2016, but lack of development in terms of quality, such as the existence of long waiting times. In order to streamline the operation of special means of transportation, long-term standby traffic, which is the top 25% of the wait time, was extracted from the Daegu Metropolitan Government's special transportation history data, and spatial autocorrelation analysis and social network analysis were conducted. As a result of the analysis, the correlation between the average waiting time of special transportation users and the space was high. As a result of the analysis of internal degree centrality, the peak time zone is mainly visited by general hospitals, while the off-peak time zone shows high long-term waiting demand for visits by lawmakers. The analysis of external degree centrality showed that residential-based traffic demand was high in both peak and off-peak hours. The results of this study are considered to contribute to the improvement of the quality of the operation of special transportation means, and the academic implications and limitations of the study are also presented.

• Journal of Korean Society of Transportation
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• v.25 no.3
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• pp.137-144
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• 2007

This study involves an analytical approach to determine transit dispatching schedules (headways) Determining a time schedule is an important process in transit system planning. In general, the transit headway should be shorter during the peak hour than at non-peak hours for demand-responsive service. It allows passengers to minimize their waiting time under inelastic, fixed demand conditions. The transit headway should be longer as operating costs increase, and shorter as demand and waiting time increase. Optimal headway depends on the amount of ridership. and each individual vehicle dispatching time depends on the distribution of the ridership. This study provides a theoretical foundation for the dispatching scheme consistent with common sense. Previous research suggested a dispatching scheme with even headway. However, according to this research, that is valid for a specific case when the demand pattern is uniform. This study is a general analysis expanding that previous research. This study suggests an easy method to set a time table without a complex and difficult calculation. Further. if the time axis is changed to the space axis instead, this study could be expanded to address the spacing problems of some facilities such as roads. stations, routes and others.

• International Journal of Highway Engineering
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• v.14 no.2
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• pp.93-99
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• 2012

This study analyzes traffic data which are collected by VDS(Vehicle Detection System) to research the relationship between spacing distribution and vehicles' relative speed. The collected data are relative speed between preceding and following vehicles, passing time and speed. They are also classified by lane and direction. For the result of the analysis, in the same platoon, we figure out that mean of spacing is 40m, which can be a value to determine section A to D. To compare spacing according to time interval, this study splits time intervals to peak hour and non-peak hour by peak hour traffic volume. In conclusion, vehicles in peak hour are in car following because most drive similar speed as preceding vehicle and they have relatively small spacing. On the other hand, non-peak hour's spacing between vehicles is bigger than that of peak hour. This implies driver's behaviors that the less spacing, the more aggressive and want to reduce their travel time in peak hour, whereas most drive easily in non-peak hour and recreational trip purpose because of less time pressure.

• Journal of Korean Society of Transportation
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• v.16 no.2
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• pp.169-176
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• 1998

버스운행시격은 주어진 고정된 승객수요와 버스운행시간에 대해 최적의 버스운행시격을 결정하고, 이러한 운행시격을 요일과 시간대에 관계없이 그대로 유지하는 것이 일반적이다. 여기에 첨두시와 비첨두시의 운행시격으로 구분하여 결정하기도 한다. 그러나 실제 버스운행시간과 승객수요는 요일별 시간대별로 변화하고 이에 맞는 운행시격으로 조정하지 못함에 따라 버스운영의 비효율성을 초래하기 쉽다. 본 연구의 목적은 요일별, 시간대별로 변화하는 시내버스의 승객수요 및 버스운행 소요시간을 변수로 하여 운영자비용과 이용자비용의 합으로 본 총교통비용 모형을 수립하고, 요일별, 시간대별 최적의 버스운행시격을 구하는데 있다. 또한 최적의 버스보유대수를 구하고, 이에 따른 운행시격 조정 방법을 제안하였다. 또한 서울시에서 운행되고 있는 시내버스 노선 1개에 대한 사례연구를 통해 요일별, 시간대 별로 버스운행시격을 탄력적으로 조정함에 따라, 총교통비용을 상당히 절감할 수 있음을 입증하였고, 버스보유대수의 최적화도 유사한 결과를 나타내었다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.3
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• pp.1087-1094
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• 2013

This study aims at enhancing the objective estimation of social cost of air quality due to mobile emission. More specifically, it examines the difference between the daily oriented and hourly oriented estimation results of social air quality cost and draws implications from the comparative analysis. The result indicates that the social cost of air quality differs up to approximately 24 times depending on the analysis time period. Moneywise, the difference between daily and hourly assignments amounts to the average of 653.5 billion won whereas only 1% of error occurred in the estimation result based on peak and nonpeak based hourly assignment. This study reaffirms the need for time-based travel demand management for emission reduction, and confirms the feasibility of emission estimation by travel demand forecasting method over the conventional method employed by the CAPSS.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.3
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• pp.28-38
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• 2017

The study is to analyze the aggressive driving behavior in the protected/permissive left turn(PPLT) intersections in Gunsan City. As a result of the logistic regression model, increasing of driver's age and driving experience, non-peak time, no company, sedan and male have a tendency to behave aggressive driving to the opposite vehicles. When the vehicles try to turn the unprotected left in the PPLT intersection, the opposite vehicle drivers recognize them at the aggressive driving behavior if the distance to opposite vehicles is not enough. The relationship between driver characteristics and the distance to the opposite vehicles is analyzed under aggressive driving behavior. increasing of age and company, peak time tend to influence the short distance opposite vehicles while male and higher driving experience the middle and long distance. Sedan has the aggressive possibility to shorter distance opposite vehicles rather than others.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 1999.10a
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• pp.131-140
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• 1999

Today freeway is experiencing a severe congestion with incoming or outgoing traffic through freeway ramps during the peak periods. Thus, the objectives of this study is to identify the traffic characteristics, analyze the relationships between the traffic characteristics and finally construct the delay predictive models on the ramp junctions of freeway with 70mph speed limit. From the traffic analyses, and model constructions and verifications for delay prediction on the ramp junctions of freeway, the following results were obtained: ⅰ) Traffic flow showed a big difference depending on the time periods. Especially, more traffic flows were concentrated on the freeway junctions in the morning peak period when compared with the afternoon peak period. ⅱ) The occupancy also showed a big difference depending on the time periods, and the downstream occupancy(Od) was especially shown to have a higher explanatory power for the delay predictive model construction on the ramp junction of freeway. ⅲ) The speed-occupancy curve showed a remarkable shift based on the occupancies observed ; Od < 9% and Od$\geq$9%. Especially, volume and occupancy were shown to be highly explanatory for delay prediction on the ramp junctions of freeway under Od$\geq$9%, but lowly for delay predicion on the ramp junctions of freeway under Od<9%. Rather, the driver characteristics or transportation conditions around the freeway were through to be a little higher explanatory for the delay perdiction under Od<9%. ⅳ) Integrated delay predictive models showed a higher explanatory power in the morning peak period, but a lower explanatory power in the non-peak periods.