• Proceedings of the KOR-KST Conference
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• 1995.02a
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• pp.101-113
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• 1995

The objective of this research is to test the feasibility of developing a statewide truck traffic forecasting methodology for Wisconsin by using Origin-Destination surveys, traffic counts, classification counts, and other data that are routinely collected by the Wisconsin Department of Transportation (WisDOT). Development of a feasible model will permit estimation of future truck traffic for every major link in the network. This will provide the basis for improved estimation of future pavement deterioration. Pavement damage rises exponentially as axle weight increases, and trucks are responsible for most of the traffic-induced damage to pavement. Consequently, forecasts of truck traffic are critical to pavement management systems. The pavement Management Decision Supporting System (PMDSS) prepared by WisDOT in May 1990 combines pavement inventory and performance data with a knowledge base consisting of rules for evaluation, problem identification and rehabilitation recommendation. Without a r.easonable truck traffic forecasting methodology, PMDSS is not able to project pavement performance trends in order to make assessment and recommendations in the future years. However, none of WisDOT's existing forecasting methodologies has been designed specifically for predicting truck movements on a statewide highway network. For this research, the Origin-Destination survey data avaiiable from WisDOT, including two stateline areas, one county, and five cities, are analyzed and the zone-to'||'&'||'not;zone truck trip tables are developed. The resulting Origin-Destination Trip Length Frequency (00 TLF) distributions by trip type are applied to the Gravity Model (GM) for comparison with comparable TLFs from the GM. The gravity model is calibrated to obtain friction factor curves for the three trip types, Internal-Internal (I-I), Internal-External (I-E), and External-External (E-E). ~oth "macro-scale" calibration and "micro-scale" calibration are performed. The comparison of the statewide GM TLF with the 00 TLF for the macro-scale calibration does not provide suitable results because the available 00 survey data do not represent an unbiased sample of statewide truck trips. For the "micro-scale" calibration, "partial" GM trip tables that correspond to the 00 survey trip tables are extracted from the full statewide GM trip table. These "partial" GM trip tables are then merged and a partial GM TLF is created. The GM friction factor curves are adjusted until the partial GM TLF matches the 00 TLF. Three friction factor curves, one for each trip type, resulting from the micro-scale calibration produce a reasonable GM truck trip model. A key methodological issue for GM. calibration involves the use of multiple friction factor curves versus a single friction factor curve for each trip type in order to estimate truck trips with reasonable accuracy. A single friction factor curve for each of the three trip types was found to reproduce the 00 TLFs from the calibration data base. Given the very limited trip generation data available for this research, additional refinement of the gravity model using multiple mction factor curves for each trip type was not warranted. In the traditional urban transportation planning studies, the zonal trip productions and attractions and region-wide OD TLFs are available. However, for this research, the information available for the development .of the GM model is limited to Ground Counts (GC) and a limited set ofOD TLFs. The GM is calibrated using the limited OD data, but the OD data are not adequate to obtain good estimates of truck trip productions and attractions .. Consequently, zonal productions and attractions are estimated using zonal population as a first approximation. Then, Selected Link based (SELINK) analyses are used to adjust the productions and attractions and possibly recalibrate the GM. The SELINK adjustment process involves identifying the origins and destinations of all truck trips that are assigned to a specified "selected link" as the result of a standard traffic assignment. A link adjustment factor is computed as the ratio of the actual volume for the link (ground count) to the total assigned volume. This link adjustment factor is then applied to all of the origin and destination zones of the trips using that "selected link". Selected link based analyses are conducted by using both 16 selected links and 32 selected links. The result of SELINK analysis by u~ing 32 selected links provides the least %RMSE in the screenline volume analysis. In addition, the stability of the GM truck estimating model is preserved by using 32 selected links with three SELINK adjustments, that is, the GM remains calibrated despite substantial changes in the input productions and attractions. The coverage of zones provided by 32 selected links is satisfactory. Increasing the number of repetitions beyond four is not reasonable because the stability of GM model in reproducing the OD TLF reaches its limits. The total volume of truck traffic captured by 32 selected links is 107% of total trip productions. But more importantly, ~ELINK adjustment factors for all of the zones can be computed. Evaluation of the travel demand model resulting from the SELINK adjustments is conducted by using screenline volume analysis, functional class and route specific volume analysis, area specific volume analysis, production and attraction analysis, and Vehicle Miles of Travel (VMT) analysis. Screenline volume analysis by using four screenlines with 28 check points are used for evaluation of the adequacy of the overall model. The total trucks crossing the screenlines are compared to the ground count totals. L V/GC ratios of 0.958 by using 32 selected links and 1.001 by using 16 selected links are obtained. The %RM:SE for the four screenlines is inversely proportional to the average ground count totals by screenline .. The magnitude of %RM:SE for the four screenlines resulting from the fourth and last GM run by using 32 and 16 selected links is 22% and 31 % respectively. These results are similar to the overall %RMSE achieved for the 32 and 16 selected links themselves of 19% and 33% respectively. This implies that the SELINICanalysis results are reasonable for all sections of the state.Functional class and route specific volume analysis is possible by using the available 154 classification count check points. The truck traffic crossing the Interstate highways (ISH) with 37 check points, the US highways (USH) with 50 check points, and the State highways (STH) with 67 check points is compared to the actual ground count totals. The magnitude of the overall link volume to ground count ratio by route does not provide any specific pattern of over or underestimate. However, the %R11SE for the ISH shows the least value while that for the STH shows the largest value. This pattern is consistent with the screenline analysis and the overall relationship between %RMSE and ground count volume groups. Area specific volume analysis provides another broad statewide measure of the performance of the overall model. The truck traffic in the North area with 26 check points, the West area with 36 check points, the East area with 29 check points, and the South area with 64 check points are compared to the actual ground count totals. The four areas show similar results. No specific patterns in the L V/GC ratio by area are found. In addition, the %RMSE is computed for each of the four areas. The %RMSEs for the North, West, East, and South areas are 92%, 49%, 27%, and 35% respectively, whereas, the average ground counts are 481, 1383, 1532, and 3154 respectively. As for the screenline and volume range analyses, the %RMSE is inversely related to average link volume. 'The SELINK adjustments of productions and attractions resulted in a very substantial reduction in the total in-state zonal productions and attractions. The initial in-state zonal trip generation model can now be revised with a new trip production's trip rate (total adjusted productions/total population) and a new trip attraction's trip rate. Revised zonal production and attraction adjustment factors can then be developed that only reflect the impact of the SELINK adjustments that cause mcreases or , decreases from the revised zonal estimate of productions and attractions. Analysis of the revised production adjustment factors is conducted by plotting the factors on the state map. The east area of the state including the counties of Brown, Outagamie, Shawano, Wmnebago, Fond du Lac, Marathon shows comparatively large values of the revised adjustment factors. Overall, both small and large values of the revised adjustment factors are scattered around Wisconsin. This suggests that more independent variables beyond just 226; population are needed for the development of the heavy truck trip generation model. More independent variables including zonal employment data (office employees and manufacturing employees) by industry type, zonal private trucks 226; owned and zonal income data which are not available currently should be considered. A plot of frequency distribution of the in-state zones as a function of the revised production and attraction adjustment factors shows the overall " adjustment resulting from the SELINK analysis process. Overall, the revised SELINK adjustments show that the productions for many zones are reduced by, a factor of 0.5 to 0.8 while the productions for ~ relatively few zones are increased by factors from 1.1 to 4 with most of the factors in the 3.0 range. No obvious explanation for the frequency distribution could be found. The revised SELINK adjustments overall appear to be reasonable. The heavy truck VMT analysis is conducted by comparing the 1990 heavy truck VMT that is forecasted by the GM truck forecasting model, 2.975 billions, with the WisDOT computed data. This gives an estimate that is 18.3% less than the WisDOT computation of 3.642 billions of VMT. The WisDOT estimates are based on the sampling the link volumes for USH, 8TH, and CTH. This implies potential error in sampling the average link volume. The WisDOT estimate of heavy truck VMT cannot be tabulated by the three trip types, I-I, I-E ('||'&'||'pound;-I), and E-E. In contrast, the GM forecasting model shows that the proportion ofE-E VMT out of total VMT is 21.24%. In addition, tabulation of heavy truck VMT by route functional class shows that the proportion of truck traffic traversing the freeways and expressways is 76.5%. Only 14.1% of total freeway truck traffic is I-I trips, while 80% of total collector truck traffic is I-I trips. This implies that freeways are traversed mainly by I-E and E-E truck traffic while collectors are used mainly by I-I truck traffic. Other tabulations such as average heavy truck speed by trip type, average travel distance by trip type and the VMT distribution by trip type, route functional class and travel speed are useful information for highway planners to understand the characteristics of statewide heavy truck trip patternS. Heavy truck volumes for the target year 2010 are forecasted by using the GM truck forecasting model. Four scenarios are used. Fo~ better forecasting, ground count- based segment adjustment factors are developed and applied. ISH 90 '||'&'||' 94 and USH 41 are used as example routes. The forecasting results by using the ground count-based segment adjustment factors are satisfactory for long range planning purposes, but additional ground counts would be useful for USH 41. Sensitivity analysis provides estimates of the impacts of the alternative growth rates including information about changes in the trip types using key routes. The network'||'&'||'not;based GMcan easily model scenarios with different rates of growth in rural versus . . urban areas, small versus large cities, and in-state zones versus external stations. cities, and in-state zones versus external stations.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.4
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• pp.897-905
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• 2015

This research suggested the traffic signal calculation model of active transit signal priority using a shockwave model. Using this signal priority timing optimization model, the shockwave area is computed under the condition of Early Green and Green Extension among active transit signal priority techniques. This study suggested the speed estimation method of backward shockwave using average travel time and intersection passing time. A shockwave area change is calculated according to signal timing change of transit signal priority. Moreover, this signal timing calculation model could determine the optimal signal priority timings to minimize intersection delay of general vehicles. A micro simulation analysis using VISSIM and its user application model ComInterface was applied. This study checked that this model could calculate the signal timings to minimize intersection delay considering saturation condition of traffic flow. In case studies using an isolated intersection, this study checked that this model could improve general vehicle delay of more over ten percentage as compared with equality reduction strategy of non-priority phases. Recently, transit priority facilities are spreading such as tram, BRT and median bus lane in Korea. This research has an important significance in that the proposed priority model is a new methodology that improve operation efficiency of signal intersection.

• Journal of Korean Society of Transportation
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• v.33 no.5
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• pp.449-460
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• 2015

The reliability of air quality evaluation results for green transportation could be improved by applying correct emission factors. Unlike previous studies, which estimated emission factors that focused on vehicles in laboratory experiments, this study investigates emission factors according to road types and time using real driving data. The real driving data was collected using a Portable Activity Monitoring System (PAMS) according to road types and time, which it compared and analyzed fuel consumption from collected data. The result of the study shows that fuel consumption on national highway is 17.33% higher than the fuel consumption on expressway. In addition, the average fuel consumption of peak time is 4.7% higher than that of non-peak time for 22.5km/h. The difference in fuel consumption for road types and time is verified using ANOCOVA and MANOVA. As a result, the hypothesis of this study - that fuel consumption differs according to road types and time, even if the travel speed is the same - has proved valid. It also suggests correction factor of emission factors by using the difference in fuel consumption. It is highly expected that this study can improve the reliability of emissions from mobile pollution sources.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.5
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• pp.124-134
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• 2011

To make popular the NEV(Neighborhood Electric Vehicles) uses, it must be considered the supply of infrastructure and the political decision for NEV. However, the guidelines of infrastructure for NEV are not prepared. The guidelines of infrastructure for NEV should be performed in many research and case. The purpose of this study is to reveal the influence of NEV on the two-lane highway traffic flows by TWOPAS simulation model. The main items to check the influence are Average Travel speed, Percent Time Spent Following and Total Delay. The scenario were setup by traffic volume. And the NEV percentages are changed from 1% ~ 30%. The scenario 1 which traffic volume are 650veh/h and the scenario 4 which traffic volume are 2,600veh/h are less influenced by NEV, compare to scenario 2, scenario 3. Because the scenario 1 is more free to make passing other cars and Scenario 4 is fully saturated with existing traffic volumes. The urban two-lane highway which has much traffic volume and the rural two-lane highway which has little traffic volume has affinity for NEV than the other two-lane highway.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.12
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• pp.778-784
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• 2017

In this paper, we propose wired and wireless distributed control systems designed to improve the freight logistics efficiency and verify wired distributed control systems. The verification condition required that 50 cargo vehicles be connected and operated to travel 21 km from Busan Sinhang station to Jinlye Station at an average speed of about 100km/h. The verification results show that the traction output and braking output of the control and controlled cars are dispersed by the wired distributed control system. The application is expected to more than double the efficiency of the logistics compared to the existing freight transportation system. However, in the case of the wired distributed control system, cable installation and maintenance are difficult, and it is impossible to change the combination of freight vehicles. Through the verification of the wired distributed control system, the applicability of distributed control systems to freight vehicles in Korea was confirmed and the system was further developed to produce a wireless distributed control system. In order to apply the wireless distributed control system, a propagation environment analysis for the ISM band was performed in the testbed and, as a result, it was confirmed that Wifi technology using the ISM band could be utilized. In order to use the WDP (Wireless Distributed Power) devices newly installed in the target vehicles, the transmission / reception control signals associated with the propulsion / braking / total control devices are defined. In the case of wireless distributed control systems, the convenience of their application and operation is guaranteed, but reliability and emergency safety measures should because of the dependence of the control of the vehicle on radio signals.

• Journal of Korean Society of Transportation
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• v.36 no.2
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• pp.67-85
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• 2018

Vehicle platooning through wireless communication and automated driving technology has become realized. Platooning is a technique in which several vehicles travel at regular intervals while maintaining a minimum safety distance. Truck platooning is of keen interest because it contributes to preventing truck crashes and reducing vehicle emissions, in addition to the increase in truck flow capacity. However, it should be noted that interactions between vehicle platoons and adjacent manually-driven vehicles (MV) significantly give an impact on the performance of traffic flow. In particular, when vehicles entering from on-ramp attempt to merge into the mainstream of freeway, proper interactions by adjusting platoon size and inter-platoon spacing are required to maximize traffic performance. This study developed a methodology for establishing operational strategies for truck platoonings on freeway on-ramp areas. Average speed and conflict rate were used as measure of effectiveness (MOE) to evaluate operational efficiency and safety. Microscopic traffic simulation experiments using VISSIM were conducted to evaluate the effectiveness of various platooning scenarios. A decision making process for selecting better platoon operations to satisfy operations and safety requirements was proposed. It was revealed that a platoon operating scenario with 50m inter-platoon spacing and the platoon consisting of 6 vehicles outperformed other scenarios. The proposed methodology would effectively support the realization of novel traffic management concepts in the era of automated driving environments.

• Journal of Korean Society of societal Security
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• v.1 no.3
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• pp.59-67
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• 2008

The most influential facility causing traffic disaster on the urban road is intersection. Accordingly, this study elected a region for case study from seabang three-way junction, partial section of Dongmoon Ro in Kwang-Ju city, to the intersection of Mudeung Library Entrance. It is believed that the signal progression is very effective on the basis of short interval of intersection and massive traffic volume. The signal progression was simulated by using TRANSYT-7F model. The following is summary of the simulation: According to the change of cycle length, P.I. delay and fuel consumption showed the tendency of being increased in case that cycle length becomes long or short, centering around the best cycle length. In the event of progressing the cycle length, the average speed per vehicle is increased by 11.39Km per hour and P.I value is improved by 40.65% so that it resulted in 42.86% improvement in the total travel time. Moreover, the fuel consumption in line with the progression practice produced fuel saving of 31.04%.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.6
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• pp.15-22
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• 2012

A performance index of singalized intersections is a standard to optimize signal control variables and to manage traffic flow. Traffic delays is generally used to minimize the average delay time on intersections or networks, progression efficiency is used to improve travel speed of main cooridors or to provide transit signal priority. We manage traffic flows with only selecting one index between delays and progression according to the objective of traffic management and field characteristics. In real field, the driver's satisfaction is high in any performance criteria when the waiting time is shorter and the unnecessary stop in front of traffic is smaller. This paper aims to develop simulation model to represent real progression with concurrently considering delays and progression. In order to reflect an effect of level of traffic volumes and residual queues which don't be considered in prior progression model, we apply shockwave model with flow-density diagram. We derive Cell Transmission Model of Daganzo in order to develop the delay index and the progression index for the macroscopic simulation model. In order to validate the effect, we analysis traffic delays and progression efficiency with comparing this model to Transyt-7F and PASSER V.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.36 no.1
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• pp.97-104
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• 2016

In Korea, the number of crash accident victims per 100,000 population is three times higher than the average of OECD. In particular, 60% of it occurs on the community road. Thus, this study intends to analyze the causes of such accidents through a pedestrian and vehicle traffic survey. The purpose is to establish practical safety enhancement measures for community roads. In recent years, lots of changes have occurred in the pedestrian environment. A traffic survey shows that 65% of pedestrians walk on the right and 17% of people use smart-phones while walking. An eye camera experiment shows that the operation load of drivers on the community roads is more than 4 times higher than those in urban roads. According to a speed survey, 62% of vehicles drive at 30km/h or above. The characteristics of accidents on community roads are as follows. First, the ratio of accidents on the edge of the road is 2.3 times as high as those on other roads. Second, when people walk on the right, the ratio of accidents is 2.5 times as high as that of walking on the left. Third, it becomes more dangerous when people cross the road from the right to the left. The majority of accidents is caused by unsafe driving (84.4%). When a vehicle makes a left turn, the likelihood of accidents is 2.3 times as high as those caused by a right turn. The ratio of accidents caused by vehicles going backwards is 14% among all accidents. In community roads, the focus of drivers should be at least 4 times higher than those on urban roads. Thus, walking in the opposite direction of vehicles and careless behaviors are highly likely lead to accidents.