Percentage Of Trucks Research Articles

Estimating Passenger Car Equivalent using the HCM-6 PCE Methodology on Four-Lane Level Freeway Segments in Western U.S.

The passenger car equivalent (PCE) of a truck is used to account for the presence of trucks in the Highway Capacity Manual (HCM). The HCM-6 employed an equivalency capacity methodology to estimate PCE. It is hypothesized in this paper that the HCM-6 PCE values are not appropriate for the western U.S., which consistently experiences truck percentages higher than 25%. Furthermore, the HCM PCE procedure assumes that truck and passenger cars travel at the same desired free-flow speed on level terrain. However, many heavy trucks in the western U.S. are governed through the use of speed limiters so that their speeds are considerably less than the speed limit. Thirdly, the HCM-6 PCEs are based on the freeways having three lanes per direction, which might not be appropriate for the freeways with two lanes per direction that predominate in the rural sections of the western U.S. Lastly, the trucks used in the HCM-6 simulation might not be representative of the empirical trucks observed on rural freeways in western states. This paper examines these effects on PCEs using data from I-80 in western Nebraska. The PCEs were estimated using the HCM-6 equal-capacity method and VISSIM 9.0 simulation data under (1) the HCM-6 conditions and (2) the Nebraska empirical conditions. It was found that the PCEs recommended in HCM-6 underestimate the effects of trucks on four-lane level freeway segments that experience high truck percentages having large differences in free-flow speed distributions, and which have different truck lengths.

Sensitivity Analysis of Speed Limit, Truck Lane Restrictions, and Data Aggregation Level on the HCM-6 Passenger Car Equivalent Estimation Methodology for Western U.S. Conditions

In the 2016 Highway Capacity Manual (HCM-6), the impact of trucks on freeway operations is measured by passenger car equivalents (PCEs). PCEs are estimated by the equal capacity methodology. The HCM-6 PCE values are based on the assumptions that passenger cars and trucks travel at the same free-flow speed, that they travel on freeways with three lanes per direction, and that they travel in traffic with no more than 25% trucks. On Interstate 80 in western Nebraska, it is observed that the interaction of high truck percentages and large speed differences between passenger cars and trucks may result in moving bottlenecks. It was hypothesized that the current HCM-6 PCEs may be not appropriate for these conditions. A companion paper showed this was true and that the major cause was speed differentials between trucks and passenger cars. In essence, when slow-moving trucks pass each other they create moving bottlenecks, which results in increased PCE values. This paper is an extension to a companion paper and examines a number of issues related to estimation of PCEs. The paper examines the effect of speed limit, truck passing restrictions, and data aggregation interval on PCEs. The results show that: (i) if a higher speed limit is implemented, trucks will affect the passenger cars more severely; (ii) if truck passing is restricted by lane restrictions, the negative impacts of trucks on passenger car operation may be mitigated; and (iii) using a longer data aggregation interval results in lower PCE values, all else being equal.

Streamlined Approach to Identify Projects That Are Not of Air Quality Concern for PM10 Hot-Spot Analysis

Project-level particulate matter (PM) analysis, also known as hot-spot analysis, is required in non-attainment and maintenance areas for transportation projects that are identified as projects of air quality concern (POAQC). The only PM non-attainment area in Texas is El Paso which is currently designated as non-attainment for PM10. This paper presents an analytical methodology that was developed to determine the thresholds for highway activity parameters that would streamline the identification of projects that are not POAQCs, and minimize the risk that the project is misclassified. Researchers used the example provided by EPA, that is, 125,000 annual average daily traffic (AADT) and 8% heavy-duty trucks, as the baseline for the analysis. They then established combinations of AADT and truck percentage that would result in the same amount of PM10 emissions as the baseline scenario. Researchers used a set of conservative assumptions to achieve a very conservative/low-risk determination. The most important assumption among them was to not use a fixed baseline analysis year. Researchers used the proposed methodology to establish traffic activity thresholds for highway projects in El Paso, TX. Researchers established a baseline traffic activity (AADT and truck percentage) threshold curve which is a conservative representative of the lower boundary of POAQCs. Any combination of truck percentage and AADT that falls below this curve can be confidently excluded from POAQC consideration. Researchers developed an easy-to-use spreadsheet tool that would use user-provided AADT and truck percentages to identify whether a project could be confidently classified as not of air quality concern.

Identifying Optimum Taper Lengths for Zipper Merging Applications using Real Data and Microscopic Simulation

Motorists lack of understanding on the proper way to maneuver through lane closures during congested periods cause driver confusion. This confusion directly and indirectly creates inconsistent flow patterns, forced merges, travel time delays, and crashes. Engineers and developers have tried to improve the merge systems used in construction zones to reduce driver frustration, improve travel time, and increase safety. Encouraging drivers to use the zipper merge approach has been assumed by some to target these issues. When implemented, drivers jointly merge together in an alternating fashion at two-to-one lane closures/reductions. There is a difference in opinion between traffic officials concerning the taper length required to efficiently accommodate these types of merging patterns – particularly those that occur near construction sites. Current practice uses the taper design guideline presented in the MUTCD. However, some believe this unique approach to merging at lane reductions should be accompanied by a shorter/longer taper. This study simulated 192 scenarios consisting of eight different percent truck compositions, six different transition lengths, and four different traffic volumes in VISSIM. The simulation models were calibrated with field data taken while a zipper merge configuration was in operation on a freeway. The main objective was to identify the optimum transition length when placing a zipper merge configuration because it visually and physically promoted alternating merging maneuvers. The results indicated none of the six tested taper lengths had a clear advantage over the other under multiple traffic volumes and truck percentages. Although statistically equal, operational differences in response to taper lengths were present and became more pronounced as volumes and truck percentages increased.

Freeway Truck Traffic Safety in Wyoming: Crash Characteristics and Prediction Models

The State of Wyoming experiences a high percentage of truck traffic along all its highways, especially Interstate 80 (I-80). The increased interactions between trucks and other vehicles have raised many operational and safety concerns. This paper presents a safety analysis and a development of safety performance functions (SPFs) along I-80, with a focus on truck crashes. Nine years of historical crash data in Wyoming (2008–2016) were used to observe the involvement of light, medium, and heavy trucks in crashes. Analysis of the major contributory factors showed that 54% of the total truck-related crashes occurred during icy road conditions and about 46% during snowy weather conditions, and approximately 45% involved driving too fast and driving in improper lane. The analysis also included segments with horizontal curves and vertical grades and their impacts on truck crashes. The crash rate analysis showed higher truck crash rate compared with total crash rate considering equal vehicle miles traveled as exposure. A zero-inflated negative binomial model was applied to develop Wyoming-specific SPFs for various truck crash types. The effects of traffic, road geometry characteristics, and weather parameters influencing different truck-related crashes were quantified from these models. Downgrades and steep upgrade sections were found to increase truck-related crashes. The number of rainy days per year was found to be a significant variable affecting truck-related crashes. On the other hand, the presence of climbing lanes has significant safety benefits.

Safety effects of street lighting on roadway segments: Development of a crash modification function

Objective: Nighttime crashes are overrepresented on the U.S. highway system. Roadway lighting, which provides additional visibility by supplementing vehicle headlights, has been identified as an effective countermeasure to improve nighttime safety. However, the existing literature does not provide a thorough understanding of the effects of street lighting photometric characteristics on nighttime crash occurrence on roadway segments. This study aimed to investigate the relationship between lighting photometric measures and nighttime crash risk on roadway segments and develop a crash modification function/factor (CMF).Methods: The research team collected horizontal illuminance data on 440 roadway segments between 2 successive signalized intersections in Florida for 2012–2014 and matched 4 years of nighttime and daylight crash data (2011–2014). Random parameter negative binomial models were estimated for both nighttime and daylight crash frequencies. The expected night-to-day crash odds ratio, as an equivalent of CMF, was derived from the fitted models with the correction of estimation variances. The confidence intervals (CIs) of the developed CMF were estimated using the Cox method.Results: The coefficient of the mean of horizontal illuminance is significantly negative in the nighttime model. The coefficients of the standard deviation of horizontal illuminance are significantly positive and normally distributed in both the nighttime and daylight models. The significance of the standard deviation in the daylight model captures the confounding effects—a high standard deviation correlates with high traffic exposures, poor safety design standards, and low maintenance quality. The CMF based on the expected daylight-to-day odds ratio was developed as an exponential function of the increments and the increment squares of the mean and the standard deviation of horizontal illuminance. Its 95% CIs indicate that the CMF is almost significant over the whole range. Other significant variables contributing to nighttime crash risk include annual average daily traffic, truck percentage, segment length, access density, undivided roads, and urban/city limits.Conclusions: Horizontal illuminance characteristics have a significant impact on nighttime crash risk on roadway segments. An increase in the mean of horizontal illuminance, indicating an improvement in average lighting level, tends to decrease nighttime crash risk; an increase in the standard deviation, representing a poor uniformity of lighting pattern on a roadway segment, is more likely to raise nighttime crash risk. Because the 2 measures are strongly correlated in a low mean range (<0.44 fc), the 2 photometric measures need to be considered together to interpret the safety effects of lighting patterns. The standard deviation shows better performance in measuring lighting uniformity on a roadway segment than the traditional ratios (max-to-min and mean-to-min). However, a new photometric measure is needed to capture the true lighting pattern influencing driver vision at night.

Analysis of truck-related crashes of freeways in China

Truck-related crashes result in tremendous lives and property loss and become a serious safety issue in China. The goal of this article is to identify the influential factors for severity of truck-related crashes using data from Jingjintang freeways in China and to design an ordered probit model to explore their relationship. Records including crashes, traffic flow attributes, and geometric design features ranging from 2009 to 2012 were collected from Jingjintang freeway. Crashes are divided into three severity levels: slight injury, injury, and fatal injury. The injury crashes is ranking the first place occupying 64.37%. Truck-related crashes are likely to occur when truck percentage is around 20% and 80%. The speed of traffic flow decreases with the more appearance of trucks. The ordered probit model is developed to estimate the impacts of influential factors on injury severity of truck-related crashes. Marginal effects for each level of injury severity are calculated. The results reveal that truck-involving crashes are highly sensitive to factors such as time of day, truck percentage, average slope, operating speed, speed difference, and exposure variable. The average slope of road segment and speed gaps has the greatest impact on all severity levels of crashes.

Analysis of crash frequency in motorway tunnels based on a correlated random-parameters approach

The paper provides an analysis of the frequency of total accidents (accidents involving material damage, physical injuries and fatalities), which occurred in 226 unidirectional motorway tunnels over a four-year monitoring period, based on unrelated and correlated random-parameter models. The so-called random-intercept model, in which only the regression intercept is assumed to be random, was also developed a priori for recording the random-effects (temporal correlations among accidents occurring in the same tunnel in different years). The independent variables were: tunnel length (L), annual average daily traffic (AADT) per lane, percentage of trucks (%Tr), presence of a sidewalk (SW), longitudinal slope (LS), and mechanical ventilation (MV). The comparison among the aforementioned three models showed that the correlated random-parameters model, which takes into account the cross correlation among the random-parameters, provided a better goodness-of-fit than the corresponding uncorrelated random-parameter and intercept-random models. This means that more precise estimations of accidents can be obtained when the random-parameters are assumed to be correlated in statistical analysis. The developed model also offers additional insights into showing how different combinations of parameters affect tunnel safety. In particular, through the correlation coefficient matrix of random-parameters, we found that the non-constant longitudinal slope (LS) alleviates the effect of the annual average daily traffic (AADT) per lane on increasing crash frequency. In addition, the presence of the mechanical ventilation (MV) in tunnels makes less significant the influence of AADT per lane on increasing crash frequency, too. The knowledge of these correlations may be useful for future applications, for example, for road engineers in designing tunnel. The model proposed can also be used by Tunnel Management Agencies (TMAs) for estimating possible variations in accident frequency in a specific tunnel due to modifications of traffic control systems.

Estimating Passenger Car Equivalents on Level Freeway Segments Experiencing High Truck Percentages and Differential Average Speeds

In the Highway Capacity Manual (HCM), the passenger car equivalent (PCE) of a truck is used to account for the impacts of trucks on traffic flow. The 2010 HCM PCE values were estimated by the equal-density method using a FRESIM simulation. It was determined that the truck PCE for level freeway segments was 1.5 for all conditions. In the 2016 HCM, the PCE values were based on VISSIM simulation output at 1 min intervals along a three-lane, 13 mile (8 mile level and 5 mile graded) section of a roadway. It was determined that the truck PCE for level freeway segments was 2.0. It is hypothesized in this paper that the HCM PCE values are not appropriate for the western United States, which consistently experiences truck percentages higher than 25%, the maximum truck percentage published in the HCM PCE table. The HCM PCE procedure assumes that truck and passenger cars travel at the same average free-flow speed on level terrain. However, many heavy trucks in the western United States have speed limiters to improve fuel economy, and therefore travel slower than the speed limit. The interaction of high truck percentages and large speed differences may result in moving bottlenecks when trucks pass other trucks at low speed differentials. This may lead to an increased delay for the following passenger car vehicles. The 2016 HCM PCEs are based on three-lane simulations where the PCE is calculated based on near-capacity flows. This approach might not be appropriate for western states where these conditions rarely exist. This paper examines these effects using data from I-80 in western Nebraska. The paper develops new PCE values based on the 2010 HCM equal-density approach using calibrated CORSIM and VISSIM simulation models. It was found that the PCE values in the HCM 2010 and HCM 2016 underestimate the effect of heavy trucks on level terrain freeways that experience high truck percentages, and where different vehicle types have large differences in average free-flow speeds.

Estimating traffic performance on Spanish two-lane highways. Case study validation

Spanish standards rely on the procedures of the U.S. Highway Capacity Manual (HCM) in order to analyze traffic efficiency. Given the differences in road environment, driving behavior and vehicles’ performance, the HCM procedure would not be completely suitable for application to Spanish conditions. Moreover, the effect of passing restrictions is considered through the percentage of no-passing zones and significant variations on the horizontal alignment are not included on the uniform segment identification; which can lead to unrealistic results.The objective of this paper is to twofold: (1) modify the HCM two-lane highway analysis procedure for Spanish conditions considering the effect of passing restrictions and highway alignment variations on two-lane highway operation; (2) compare on one case study the results from the proposed approach and the HCM analysis procedure.The results indicate that solely geometry was not enough to characterize traffic operation on the straighter alignments (almost tangent), where speed dispersion was up to 20 km/h, and passing restrictions should be also included. Moreover, PTSF varied significantly depending on average passing zone lengths. The case study indicated that the proposed methodology provides more accurate estimates than the HCM on actual (in operation) two-lane highways.The proposed approach combines the effect on highway performance of passing restrictions, as considered on the US HCM, and highway alignment, as considered directly on the German HCM. The impact of trucks is modeled through the percentage of trucks rather than PCEs values, as it is common practice in Spain and Germany. This approach is simpler and more flexible to model the impact of trucks, passing restrictions and highway alignment on the different performance measures.

Spatial analysis of macro-level bicycle crashes using the class of conditional autoregressive models

The objective of this study was to investigate the relationship between bicycle crash frequency and their contributing factors at the census block group level in Florida, USA. Crashes aggregated over the census block groups tend to be clustered (i.e., spatially dependent) rather than randomly distributed. To account for the effect of spatial dependence across the census block groups, the class of conditional autoregressive (CAR) models were employed within the hierarchical Bayesian framework. Based on four years (2011-2014) of crash data, total and fatal-and-severe injury bicycle crash frequencies were modeled as a function of a large number of variables representing demographic and socio-economic characteristics, roadway infrastructure and traffic characteristics, and bicycle activity characteristics. This study explored and compared the performance of two CAR models, namely the Besag’s model and the Leroux’s model, in crash prediction. The Besag’s models, which differ from the Leroux’s models by the structure of how spatial autocorrelation are specified in the models, were found to fit the data better. A 95% Bayesian credible interval was selected to identify the variables that had credible impact on bicycle crashes. A total of 21 variables were found to be credible in the total crash model, while 18 variables were found to be credible in the fatal-and-severe injury crash model. Population, daily vehicle miles traveled, age cohorts, household automobile ownership, density of urban roads by functional class, bicycle trip miles, and bicycle trip intensity had positive effects in both the total and fatal-and-severe crash models. Educational attainment variables, truck percentage, and density of rural roads by functional class were found to be negatively associated with both total and fatal-and-severe bicycle crash frequencies.

Impact of real-time traffic characteristics on crash occurrence: Preliminary results of the case of rare events

Considerable efforts have been made from researchers and policy makers in order to explain road crash occurrence and improve road safety performance of highways. However, there are cases when crashes are so few that they could be considered as rare events. In such cases, the binary dependent variable is characterized by dozens to thousands of times fewer events (crashes) than non-events (non-crashes). This paper attempts to add to the current knowledge by investigating crash likelihood by utilizing real-time traffic data and by proposing a framework driven by appropriate statistical models (Bias Correction and Firth method) in order to overcome the problems that arise when the number of crashes is very low. Under this approach instead of using traditional logistic regression methods, crashes are considered as rare events In order to demonstrate this approach, traffic data were collected from three random loop detectors in the Attica Tollway (“Attiki Odos”) located in Greater Athens Area in Greece for the 2008–2011 period. The traffic dataset consists of hourly aggregated traffic data such as flow, occupancy, mean time speed and percentage of trucks in traffic. This study demonstrates the application and findings of our approach and revealed a negative relationship between crash occurrence and speed in crash locations. The method and findings of the study attempt to provide insights on the mechanism of crash occurrence and also to overcome data considerations for the first time in safety evaluation of motorways.

Fragility analysis of bridges due to overweight traffic load

In the U.S. overloading represents the third cause of bridge failures just after hydraulic events and collisions. Large data assembled by Weigh-In-Motion (WIM) systems can be used to obtain improved region-specific or network-specific characterization of vehicle loads on highway bridges for a more accurate evaluation of the safety of critical bridges and the failure consequences to the concerned communities. To achieve this goal it is important to develop tools that allow engineers to estimate the reliability of various types of bridges subjected to realistic ranges of heavy truck load intensities as encountered on highway networks. The objective of this paper is to describe an approach that combines field data and numerical simulations to perform the fragility analysis of bridges due to different percentages of overweight loads and truck traffic. Numerical examples are provided by analyzing typical bridges using field truck data collected at WIM sites in upstate New York. The results of the analysis show that the fragility curves for fatigue are function of the percentage of overweight trucks in New York as a second order polynomial, while the fragility curves of bridges for overstress can be modeled with a copula using both normal distributions for the overweight percentages and Average Daily Truck Traffic.

Pedestrian safety under permissive left-turn signal control

Abstract At intersections with permissive only signal control, pedestrians will move at the phase with the parallel through vehicular movement and the permissive left-turn movement. The left-turn vehicles have to yield to both opposing vehicles and pedestrians at the crosswalk. Under such complicated driving conditions, collision risks rise if left-turn vehicles make misjudgments and fail to yield to the pedestrians. In this research, driving-simulation based experiments were conducted for identifying and assessing the impacts of the factors that contribute to the collision between the left-turn vehicles and the pedestrians during the permissive left-turn phase. The results of this study showed that the percentage of left-turn trucks and the pedestrian volume has significant impacts on the pedestrian safety under permissive left-turn signal control.

Empirical Speed Behavior on Horizontal Ramp Curves in Interchanges in the Netherlands

Several studies in the literature have indicated that interchanges are the most crash-prone areas within the motorway system in number and severity of accidents. The reason is the high level of turbulence as a result of vehicle lane changes and speed variability. To understand the safety consequences of an interchange design (e.g., type of connecting ramps, radii and superelevation of curves, and lane and shoulder widths), an in-depth investigation of driving speed behavior is needed. Such an investigation requires the collection of detailed trajectory data on vehicles on different interchanges. These types of data are rarely available, and as a result, such studies are scarce in the literature. The main objective of this present study was to analyze driver speed behavior on different ramps at interchanges, and to develop an operating speed prediction model as a function of the road design elements. Trajectory data on free-moving vehicles were derived from stabilized video images taken from a camera mounted underneath a helicopter, which hovered over the road areas studied. Data were collected from 29 curves at six freeway–freeway interchanges in the Netherlands. The sample included nine direct connections, 12 semidirect connections, and eight indirect connections. The findings showed that speeds were affected by several road geometric characteristics of the curves, by driver expectancy and design consistency, and by the percentage of trucks in traffic. The operating speed prediction models developed in the study will provide designers with tools to estimate the operating speed during the design process.

Multicontextual Machine-Learning Approach to Modeling Traffic Impact of Urban Highway Work Zones

Impact assessments of highway construction work zones (CWZs) are mandated for all federally funded highway infrastructure improvement projects. However, most existing approaches are ad hoc or project specific, so they are incapable of being benchmarked for any particular spatial region. A novel multicontextual approach to modeling the traffic impact of urban highway CWZs is proposed and tested in this paper. The proposed approach is unique because it models the impact of CWZ operations through a multicontextual quantitative method using big data for improved accuracy. In this study, a machine-learning technique was adopted to predict long-term traffic flow rates and the corresponding truck percentages. With the use of these predicted values, stereotypical patterns of traffic volume-to-capacity ratios were created for typical urban nighttime closures. Third-order curve-fitting models to achieve potential work zone travel time delays in heavily trafficked large urban cores were then developed and validated. This study will greatly help state and local governments and the general traveling public in major cities know the potential traffic flow resulting from construction and thereby facilitate progress on highway improvement projects with the better-informed work zone traffic flow and thus improve safety and mobility in and between CWZs.

Modification of the Highway Capacity Manual two‐lane highway analysis procedure for Spanish conditions

SummaryThe US Highway Capacity Manual (HCM) methodology is used in Spain to evaluate traffic operation and quality of service. The effect of passing manoeuvre on two‐lane highway operational performance is considered through adjustment factors to average travel speeds and percent time spent following. The procedure is largely based on simulations in TWOPAS and passing behaviours observed during US calibrations in the 1970s. It is not clear whether US driving behaviour and vehicles' performance are comparable with Spanish conditions. The objective of this research is to adapt the HCM 2010 methodology to Spanish driver behaviour, for base conditions (i.e. no passing restrictions). To do so, TWOPAS was calibrated and validated based on current Spanish passing field data. The calibration used a genetic algorithm. The case study included an ideal two‐lane highway with varying directional traffic flow rate, directional split and percentage of trucks. The updated methodology for base conditions is simpler than the current HCM 2010 and does not rely on interpolation from tables. Copyright © 2016 John Wiley &amp; Sons, Ltd.

Analysis of vehicle headway distribution on multi-lane freeway considering car–truck interaction

The primary objective of this study is to identify the characteristics of vehicle headway on multi-lane freeway under lane management in China considering car–truck interaction. More specifically, the study focused on answering the following two questions: (1) whether the car–truck interaction has impact on headway, that is, headway varies by different leading and following vehicle types and (2) what is the best-fitted distribution model for particular headway type under lane management. The team collected traffic data, including traffic flow rates, percentage of trucks, speeds, headways, and so on from four segments of Shanghai-Nanjing freeway, Jiangsu Province in China. Then, some statistical methods were used to analyze the vehicle headway. It was found that car–car, car/truck, and truck–truck headways are significantly different from each other. Also, the traffic flow rate, percentage of trucks, and lane position were found to have an influence on the vehicle headway through the tests. Using the maximum-likelihood estimation, Kolmogorov–Smirnov test, and chi-square test techniques, the distribution models and parameter functions for each headway type were built and validated. The results showed that lognormal model is suitable for car–car and truck–truck headway types, and inverse Gaussian model fits the car/truck headway type well.

Methodology for simulating heterogeneous traffic on expressways in developing countries: a case study in India

The prevailing roadway and traffic conditions on expressways in India are vastly different when compared with the other roads in India and also, there is no perfect lane-discipline. The knowledge of roadway capacity is an important basic input required for planning, design, analysis, and operation of roadway systems. Hence, this work aims to model traffic flow on Indian urban expressways with specific reference to Delhi–Gurgaon expressway and estimate its capacity using the micro-simulation model using VISSIM 5·40. For this purpose, the field data collected on traffic-flow characteristics on expressways was used in calibration and validation of the simulation model. The validated simulation model was then used to develop fundamental traffic–flow relationships, namely, speed–flow, speed–area occupancy, and flow–area occupancy for the traffic-flow levels, starting from near-zero until the capacity of the facility. The capacity of an eight-lane divided urban expressway in level terrain with 14·0-m wide road space (one direction of traffic flow) was found to be in the range of 9700–10 000 vehicles/h. Moreover, through sensitivity analysis using simulation model, the following relationships, (i) percentage trucks in the traffic stream and capacity-level flow and (ii) percentage of trucks in the traffic stream and capacity-level traffic-stream speed, were developed. The results and the overall methodology followed in this study for modeling traffic flow on expressways in India to determine the capacity estimates under varying traffic compositions may be very useful for the practitioners and also for the continuing efforts to prepare an Indian Highway Capacity Manual.

교통안전성을 고려한 고속도로 오르막차로 동적운영 알고리즘 개발

본 연구는 다양한 교통상황에서의 오르막차로의 안전성 측면을 고려하여 운영기준 수립 및 운영 알고리즘을 개발하고자 하였다. 교통량과 중차량 비율에 따른 시나리오를 설정하고, 미시적 교통류 시뮬레이션으로 구현하여 분석을 수행하였으며, 오르막차로 개폐여부에 따른 안전성을 평가하였다. 안전성을 평가하기 위하여 교통상충건수와 상충심각도를 평가지표(SSM:Surrogate Safety Measure)로 사용하였다. 분석결과, 서비스 수준이 낮고 중차량 비율이 증가할수록 교통상충건수가 증가하였으며, 서비스수준이 높고 중차량 비율이 증가할수록 상충 심각도가 높아짐을 보였다. 이를 바탕으로 교통 안전성 측면을 고려한 오르막차로 개 폐 기준 및 탄력적 운영을 위한 알고리즘을 제시하였다. 본 연구결과를 활용할 경우, 기존의 운영효율성 측면과 안전성측면 동시에 고려할 수 있는 효율적인 오르막차로 운영이 가능할 것이며, 교통사고 감소 효과도 기대할 수 있을 것이다. 또한 갓길차로, 공사구간관리 등 LCS 운영시 안전성 측면을 고려한 전략적 운영을 지원할 수 있을 것이다. Interest in freeway truck traffic has increased largely due to greater safety concerns regarding truck-related crashes. The negative interactions between slow-moving trucks and other vehicles are a primary cause of hazardous conditions, which lead to crashes with larger speed variations. To improve operational efficiency and safety, providing a climbing lane that separates slow-moving trucks from higher performance vehicles is frequently considered when upgrading geometrics. This study developed an operations strategy for freeway climbing lanes based on traffic conditions in real time. To consider traffic safety when designing a dynamic strategy to determine whether a climbing lane is closed or open, various factors, including the level of service (LOS) and the percentage of trucks, are investigated through microscopic simulations. A microscopic traffic simulator, VISSIM, was used to simulate freeway traffic streams and collect vehicle-maneuvering data. Additionally, an external application program interface, VISSIM's COM-interface, was used to implement the proposed climbing lane operations strategies. Surrogate safety measures (SSM), including the frequency of rear-end conflicts and, were used to quantitatively evaluate the traffic safety using an analysis of individual vehicle trajectories obtained from VISSIM simulations with various operations scenarios. It is expected that the proposed algorithm can be the backbone for operating the climbing lane in real time for safer traffic management.