Changes In Traffic Volume Research Articles

Simulation-Based Analysis of the Effect of Significant Traffic Parameters on Lane Changing for Driving Logic “Cautious” on a Freeway

Lane changing of traffic flow is a complicated and significant behavior for traffic safety on the road. Frequent lane changing can cause serious traffic safety issues, particularly on a two-lane road section of a freeway. This study aimed to analyze the effect of significant traffic parameters for traffic safety on lane change frequency using the studied calibrated values for driving logic “conscious” in VISSIM. Video-recorded traffic data were utilized to calibrate the model under specified traffic conditions, and the relationship between observed variables were estimated using simulation plots. The results revealed that changes in average desired speed and traffic volume had a positive relationship with lane change frequency. In addition, lane change frequency was observed to be higher when the speed distribution was set large. 3D surface plots were also developed to show the integrated effect of specified traffic parameters on lane change frequency. Results showed that high average desired speed and large desired speed distribution coupled with high traffic volume increased the lane change frequency tremendously. The study also attempted to develop a regression model to quantify the effect of the observed parameters on lane change frequency. The regression model results showed that desired speed distribution had the highest effect on lane change frequency compared to other traffic parameters. The findings of the current study highlight the most significant traffic parameters that influence the lane change frequency.

Developing Cold Region Winter Weather Traffic Models and Testing Their Temporal Transferability and Model Specification

It is well known from past studies that inclement winter weather conditions such as heavy snowfall and harsh temperature affect the variation of traffic volume. In order to quantify the change in traffic volume caused by winter weather conditions, a winter weather model explaining the relationship between weather conditions and traffic volume has been generally developed. However, none of the studies in the past have evaluated the performance of the developed model in terms of temporal transferability. In addition, there is no research showing that model specification for predicting correct traffic under winter weather conditions can be optimized for different types of vehicles. This paper presents an application of dummy-variable regression to develop winter weather traffic models. In an effort to fine-tune winter weather model specification by the vehicle class, two model validation tests were conducted. First, a temporal transferability test is conducted to determine whether the model developed is suitable to be used regardless of time. Second, a model specification test is conducted to determine whether the winter weather model specification initially structured is the most appropriate format or not. To pursue this, 6 years of traffic and weather data sets from weigh-in-motion (WIM) and weather stations were collected at Highway 2A in Alberta, Canada. The study results indicate that a dummy-variable winter weather traffic model that includes a temperature variable shows good performance in terms of accuracy and temporal transferability for passenger cars. In contrast, the naïve winter weather model specification that excludes a temperature variable is preferable in predicting truck traffic volume. It is very interesting to note that even if vehicles are traveling in the same traffic stream under the same environmental conditions, different model specifications are preferable for different vehicle types to predict more accurate traffic in winter season.

Potential Benefits of Vessel Slowdowns on Endangered Southern Resident Killer Whales

A voluntary commercial vessel Slowdown trial was conducted through 16 nm of shipping lanes overlapping critical habitat of at-risk southern resident killer whales (SRKW) in the Salish Sea. From August 7 to October 6, 2017, the trial requested piloted vessels to slow to 11 knots speed-through-water. Analysis of AIS vessel tracking data showed that 421 of 951 (44%) piloted transits achieved speeds within one knot of the target (i.e., ≤12 knots) and 55% achieved speeds ≤13 knots. Slowdown results were compared to ‘Baseline’ noise of the same region, matched across lunar months. A local hydrophone listening station in Lime Kiln State Park, 2.3 km from the shipping lane, recorded 1.2 dB reductions in median broadband noise (10-100,000 Hz, rms) compared to the Baseline period, despite longer transit durations during the trial. Filtering for commercial vessels within 6 km radius of Lime Kiln listening station (aiming to remove confounding effects of variability in vessel numbers), and excluding small boat noise, high current and wind speed periods, median noise was reduced by 2.5 dB. The reductions were highest in the 1st decade band (-3.1 dB, 10-100 Hz) and lowest in the 4th decade band (-0.3 dB reduction, 10-100 kHz). A regional vessel noise model predicted noise for a range of traffic volume and vessel speed scenarios for a 1133 km2 ‘Slowdown region’ containing the 16 nm of the trial’s shipping lanes. A temporally and spatially-explicit simulation model was used to evaluate the changes in traffic volume and speed on SRKW in their foraging habitat within this Slowdown region. The simulation model tracked the number and magnitude of noise-exposure events that impacted each of 78 SRKW across different traffic volume-speed scenarios. These metrics were simplified to a cumulative effect termed ‘potential lost foraging time’. The model predicted that the voluntary Slowdown trial achieved 22% reduction in ‘potential lost foraging time’ for SRKW, with 40% reductions under 100% 11 knot participation. Slower vessel speeds reduced underwater noise in the Slowdown area despite longer passage times and therefore suggest this is an effective way to benefit SRKW habitat function in the vicinity of shipping lanes.

Two-tier anomaly detection based on traffic profiling of the home automation system

Smart building equipment and automation systems often become a target of attacks and are used for attacking other targets located out of the Home Area Network. Attacks are often related to changes in traffic volume, disturbed packet flow or excessive energy consumption. Their symptoms can be recognized and interpreted locally, using software agent at Home Gateway. Although anomalies are detected locally at the Home Gateway, they can be exploited globally. Thus, it is significantly important to detect global attack attempts through anomalies correlation.Our proposal in this paper is the involvement of the Network Operator in Home Area Network security. Our paper describes a novel strategy for anomaly detection that consists of shared responsibilities between user and network provider. The proposed two-tier Intrusion Detection System uses a machine learning method for classifying the monitoring records and searching suspicious anomalies across the network at the service provider's data center. Result show that local anomaly detection combined with anomaly correlation at the service providers level can provide reliable information on the most frequent IoT devices misbehavior which may be caused by infection.

Concrete Modular Pavements – Types, Issues And Challenges

According to the European Asphalt Pavement Association, more than 90 per cent of the European road network is paved with asphalt. Constantly increasing traffic volume and climate change accelerate deterioration of current pavements. As a result, there arises a need to rehabilitate them prematurely. Repair and rehabilitation work lead to traffic congestion, which is one of the most significant concerns in highly trafficked roads and urban streets. Concrete modular pavements consisting of precast concrete slabs are a reasonable solution to deal with the road works since their construction, as well as repair, is time-saving. Repair works typically are implemented during a low traffic period (usually at night). A primary purpose of concrete modular pavements is heavily trafficked roads and other transport areas. This paper focuses on concrete modular pavements, their types, issues and challenges related to their design, slab fabrication and pavement construction. The conducted analysis revealed 15 different types of concrete modular pavements that differ from the techniques of slab joints and load transfer between the adjacent slabs. More than 20 issues and challenges related to the design of modular elements, slab fabrication and pavement construction were identified. Finally, the existing practice of concrete modular pavements was summarised and the gaps of scientific knowledge, as well as a need for comprehensive research, were defined.

Safety evaluation of four safety countermeasures on freeways in South Korea

We used data from seven freeways in South Korea to evaluate the safety impact of four safety countermeasures: (1) automated speed enforcement cameras, (2) chevron signs, (3) delineator posts, and (4) shoulder rumble strips. The evaluation was based on crash data from 2001 to 2008 and provided three years of before data and three years of after data for each countermeasure. We developed crash modification factors (CMFs) to show the safety impact of the countermeasures. We used the empirical Bayes before and after method to avoid regression-to-the-mean bias and take into account the impact of traffic volume changes in the before and after periods. We developed a safety performance function using a set of reference sites and a negative binomial regression technique. The CMFs for all crash data appeared that automated speed enforcement cameras and chevron signs decreased the frequency of crashes while delineator posts and shoulder rumble strips did not. The results were expanded by developing CMFs separately according to crash severity and crash type. The crash severity was divided into fatal and injury (FI) and property damage only (PDO) while the crash type was only focused on speeding-related crashes due to data limitations.

Safety sensitivity to roadway characteristics: A comparison across highway classes

This paper examined the accident risk factors associated with highway traffic and roadway design, for each of three highway classes in the United States using a bivariate modeling framework involving two levels of accident severity. With regard to the highest class (Interstates), the results suggest that, compared to no-casualty accidents, casualty accidents are more sensitive to traffic volume and average vertical grade, but less sensitive to the inside shoulder width and the median width. For US Roads, it was determined that, compared to no-casualty accidents, casualty accidents are more sensitive to traffic volume, outside shoulder width, pavement condition, and median width but less sensitive to the average vertical grade. For the relatively lowest-class roads (State Roads), it was determined that, compared to no-casualty accidents, casualty accidents are more sensitive to the traffic volume, lane width, outside shoulder width, and pavement condition. Compared to the relatively lower-class highways, accidents at higher-class highways are more sensitive to: changes in traffic volume, average vertical grade, median width, inside shoulder width, and the pavement condition (no-casualty accidents only); but less sensitive to changes in lane width, pavement condition (casualty accidents only), and the outside shoulder width. This variation in sensitivity across the different road classes could be attributed to the differences in road geometry standards across the road classes, as the results seem to support the hypothesis that these standards strongly influence accident occurrence. It is hoped that the developed bivariate negative binomial models can help highway engineers to evaluate their current design standards and policy, and to assess the safety consequences of changes in these standards in each road class.

Evaluating Time Trend Correction Approaches Associated with Empirical Bayes Before-after Study for Road Safety

The Empirical Bayes (EB) before-after study is used extensively for reliable safety evaluation. The EB method has the advantage of addressing confounding factors, including the regression-to-the-mean phenomenon and change of traffic volume. The EB method can also address the time trend effect by two approaches, i.e., the Yearly Multiplier (YM) and the Simple Crash Ratio of a Reference group (SCRR). However, the use of the YM without adjusting the over-dispersion parameter may induce a biased estimation of the Bayesian inference. This study aims to provide the systematic evaluation of the two approaches for correcting the time trend. To achieve this, a numerical example and its sensitivity analysis are presented. The bias due to the use of the YM is also measured based on the empirical data from Korean freeways. The findings of this study show the EB method with SCRR would be a useful approach as a time trend correction approach, especially when the reference group has experienced a considerable time trend.

Effects of vehicle restriction policies: Analysis using license plate recognition data in Langfang, China

To mitigate externalities of the urban transportation system, various travel demand management policies have been tested across the world in the past decades. Among them, vehicle restriction is considered the most straightforward way to reduce travel demands. The effects of this policy, however, are still inconclusive. Using the massive license plate recognition (LPR) data in Langfang, China, this study investigates the differences of two typical license-plate-number-based vehicle restriction strategies, i.e., One-Day-Per-Week (ODPW) and Odd-And-Even (OAE). This study compares ODPW and OAE from two perspectives: travel demand and traffic condition over the road network. In addition to the change of traffic volume, the analysis of travel demand also includes the investigations of “illegal” travel, which denotes the road use of restricted vehicles, and travel intensity, which represents the travel frequency of certain vehicles in a time period. The changes of traffic condition are assessed by travel speed. Findings confirm that traffic volume declines as ODPW shifts to OAE, but not as significantly as expected. This less-than-full adjustment can be explained by the increase of illegal travel and travel intensity. The policy shift from ODPW to OAE significantly improves the travel speed in most of the road segments, whether in peak hours or off-peak hours. The conclusions in this study provide in-depth insights into the influence of vehicle restriction policies and contribute to the effective design of travel demand management strategies.

Evaluation of Traffic Density Parameters as an Indicator of Vehicle Emission-Related Near-Road Air Pollution: A Case Study with NEXUS Measurement Data on Black Carbon.

An important factor in evaluating health risk of near-road air pollution is to accurately estimate the traffic-related vehicle emission of air pollutants. Inclusion of traffic parameters such as road length/area, distance to roads, and traffic volume/intensity into models such as land use regression (LUR) models has improved exposure estimation. To better understand the relationship between vehicle emissions and near-road air pollution, we evaluated three traffic density-based indices: Major-Road Density (MRD), All-Traffic Density (ATD) and Heavy-Traffic Density (HTD) which represent the proportions of major roads, major road with annual average daily traffic (AADT), and major road with commercial annual average daily traffic (CAADT) in a buffered area, respectively. We evaluated the potential of these indices as vehicle emission-specific near-road air pollutant indicators by analyzing their correlation with black carbon (BC), a marker for mobile source air pollutants, using measurement data obtained from the Near-road Exposures and Effects of Urban Air Pollutants Study (NEXUS). The average BC concentrations during a day showed variations consistent with changes in traffic volume which were classified into high, medium, and low for the morning rush hours, the evening rush hours, and the rest of the day, respectively. The average correlation coefficients between BC concentrations and MRD, ATD, and HTD, were 0.26, 0.18, and 0.48, respectively, as compared with −0.31 and 0.25 for two commonly used traffic indicators: nearest distance to a major road and total length of the major road. HTD, which includes only heavy-duty diesel vehicles in its traffic count, gives statistically significant correlation coefficients for all near-road distances (50, 100, 150, 200, 250, and 300 m) that were analyzed. Generalized linear model (GLM) analyses show that season, traffic volume, HTD, and distance from major roads are highly related to BC measurements. Our analyses indicate that traffic density parameters may be more specific indicators of near-road BC concentrations for health risk studies. HTD is the best index for reflecting near-road BC concentrations which are influenced mainly by the emissions of heavy-duty diesel engines.

Commuters’ traffic pattern and prediction analysis in a metropolitan area

A metropolitan area is an area with dynamic demands and is one of the main indicators of economic growth of nation. It involves the complexity of efficient analysis and prediction of patterns of growth or decline of traffic volume and patterns of resource utilization with respect to time and place. To solve these complexities, we propose agent-based commuters’ traffic pattern and prediction analysis model in a metropolitan area. The proposed system model is capable of analyzing and predicting the patterns of commuters’ traffic flow volume and resource utilization in each zone and region, using the population density, availability of resources, type of place, time period, and commuters’ and vehicles’ arrival rates. The proposed model provides qualitative form of traffic; increases the probability of measure of unpredictable information; and aids in emergency traffic planning and route-finding services. The result shows the effectiveness of the model at different time periods in a day for forecasting of the resource utilization and changes in traffic volumes in zones and regions in the metropolitan area.

Understanding the emission impacts of high-occupancy vehicle (HOV) to high-occupancy toll (HOT) lane conversions: Experience from Atlanta, Georgia

ABSTRACTConverting a congested high-occupancy vehicle (HOV) lane into a high-occupancy toll (HOT) lane is a viable option for improving travel time reliability for carpools and buses that use the managed lane. However, the emission impacts of HOV-to-HOT conversions are not well understood. The lack of emission impact quantification for HOT conversions creates a policy challenge for agencies making transportation funding choices. The goal of this paper is to evaluate the case study of before-and-after changes in vehicle emissions for the Atlanta, Georgia, I-85 HOV/HOT lane conversion project, implemented in October 2011. The analyses employed the Motor Vehicle Emission Simulator (MOVES) for project-level analysis with monitored changes in vehicle activity data collected by Georgia Tech researchers for the Georgia Department of Transportation (GDOT). During the quarterly field data collection from 2010 to 2012, more than 1.5 million license plates were observed and matched to vehicle class and age information using the vehicle registration database. The study also utilized the 20-sec, lane-specific traffic operations data from the Georgia NaviGAtor intelligent transportation system, as well as a direct feed of HOT lane usage data from the State Road and Tollway Authority (SRTA) managed lane system. As such, the analyses in this paper simultaneously assessed the impacts associated with changes in traffic volumes, on-road operating conditions, and fleet composition before and after the conversion. Both greenhouse gases and criteria pollutants were examined.Implications: A straight before-after analysis showed about 5% decrease in air pollutants and carbon dioxide (CO2). However, when the before-after calendar year of analysis was held constant (to account for the effect of 1 yr of fleet turnover), mass emissions at the analysis site during peak hours increased by as much as 17%, with little change in CO2. Further investigation revealed that a large percentage decrease in criteria pollutants in the straight before-after analysis was associated with a single calendar year change in MOVES. Hence, the Atlanta, Georgia, results suggest that an HOV-to-HOT conversion project may have increased mass emissions on the corridor. The results also showcase the importance of obtaining on-road data for emission impact assessment of HOV-to-HOT conversion projects.

Time series trends of the safety effects of pavement resurfacing

This study evaluated the safety performance of pavement resurfacing projects on urban arterials in Florida using the observational before and after approaches. The safety effects of pavement resurfacing were quantified in the crash modification factors (CMFs) and estimated based on different ranges of heavy vehicle traffic volume and time changes for different severity levels. In order to evaluate the variation of CMFs over time, crash modification functions (CMFunctions) were developed using nonlinear regression and time series models. The results showed that pavement resurfacing projects decrease crash frequency and are found to be more safety effective to reduce severe crashes in general. Moreover, the results of the general relationship between the safety effects and time changes indicated that the CMFs increase over time after the resurfacing treatment. It was also found that pavement resurfacing projects for the urban roadways with higher heavy vehicle volume rate are more safety effective than the roadways with lower heavy vehicle volume rate. Based on the exploration and comparison of the developed CMFucntions, the seasonal autoregressive integrated moving average (SARIMA) and exponential functional form of the nonlinear regression models can be utilized to identify the trend of CMFs over time.

Tourist traffic simulation as a protected area management tool. The case of Serengeti National Park in Tanzania

The principal economic source of protected areas is tourism. However, tourism may negatively affect habitats. The growth of wildlife tourism has raised international concern regarding whether there is an equilibrium between tourism and conservation. Management actions should help provide this equilibrium. In relation to transportation management, most studies have focused on visitors' experiences and disregarded the consequences of traffic volume changes. Because tourist traffic is acknowledged as being harmful to habitats, this study develops a tool that simulates changes in traffic volume to facilitate the assessment of management actions. The tool is a simple transport model based on transport network characteristics and 2680 trip registrations in Serengeti National Park. The analyses show that a northern road through the Park may distribute tourist traffic, whereas the potential new international airport may reduce the overall tourist traffic on roads.

Reducing emissions at land border crossings through queue reduction and expedited security processing

Vehicle border crossings between Mexico and the United States generate significant amounts of air pollution, which can pose health threats to personnel at the ports of entry (POEs) as well as drivers, pedestrians, and local inhabitants. Although these health risks could be substantial, there is little previous work quantifying detailed emission profiles at POEs. Using the Mariposa POE in Nogales, Arizona as a case study, light-duty and heavy-duty vehicle emissions were analyzed with the objective of identifying effective emission reduction strategies such as inspection streamlining, physical infrastructure improvements, and fuel switching. Historical traffic information as well as field data were used to establish a simulation model of vehicle movement in VISSIM. Four simulation scenarios with varied congestion levels were considered to represent real-world seasonal changes in traffic volume. Four additional simulations captured varying levels of expedited processing procedures. The VISSIM output was analyzed using the EPA’s MOVES emission simulation software for conventional air pollutants. For the highest congestion scenario, which includes a 200% increase in vehicle volume, total emissions increase by around 460% for PM2.5 and NOx, and 540% for CO, SO2, GHGs, and NMHC over uncongested conditions for a two-hour period. Expedited processing and queue reduction can reduce emissions in this highest congestion scenario by as much as 16% for PM2.5, 18% for NOx, 20% for NMHC, 7% for SO2 and 15% for GHGs and CO. Other potential mitigation strategies examined include fleet upgrades, fuel switching, and fuel upgrades. Adoption of some or all of these changes would not only reduce emissions at the Mariposa POE, but would have air-quality benefits for nearby populations in both the US and Mexico. Fleet-level changes could have far-reaching improvements in air quality on both sides of the border.

Evaluating safety effectiveness of surface treatment at signalised intersections: a before and after study

In a metropolitan region of Melbourne, Australia, 136 signalised intersections were identified to have been resurfaced with asphalt over the period 2005–2010. In this study, the safety effectiveness of surface treatment was evaluated using Empirical Bayes (EB) approach to account for regression to the mean bias and traffic volume change through using safety performance function (SPF). Safety effects were estimated for total casualty, high severity (fatality and serious injury) and other injury crashes. For conducting EB method a reference group was selected with similar traffic volumes and site characteristics to the treated sites. Negative Binomial regression was applied to develop SPFs that were used to predict the expected number of crashes at the treated sites. The results of EB approach revealed that the treatment effect was found to be significant at 95% confidence level for all crash severity levels. The evaluation results also showed that total casualty crashes were reduced by 21.3% with a standard error of 3.13% and high severity (fatality and serious injury) crashes were reduced by 15.3% with a standard error of 5.56%. Pavement surface treatment was found to reduce other injury crashes by 21.4% with a standard error of 3.75%.

WAYS OF IMPROVING EFFICIENCY OF PASSENGER COMPLEX OF UKRAINE

The importance of passenger transport in the economically developed society is the key topic of the article. Passenger transportation included the variety of modes of transport, place, role and geographical position and each of them depends on many factors. Maximum quality and meeting the demand of people in traffic mostly accompanied by rational and economic use of technology. Passengers considering the quality of transport services, choosing a mode of transport what finally forms transportation cost.Competition leads to better quality transport service for passengers and efficiency of all systems components. Conditions of transportation performance of cause to many complex problems, characterized by constant changes in traffic volumes, increased requirements for their implementation and in conducting energy conservation policy, compliance efficiency of fixed assets of transport and lack of depreciation assets. When objective and subjective difficulties of socio-economic importance are present, the demand of passenger transport to society is constantly increasing.The article disclose the ways of improving the efficiency of passenger transport by counting the Ukraine possible further improvement of complex government regulation and legal framework of the transportation system of passengers restructuring passenger transport management based on market economy principles and orientation of the system to the needs of the passenger. In addition, it is important to ensure maximum coordination of those integration of all elements of the transportation process each mode of transport, cooperation of all, to ensure the introduction of integrated transport systems authorized for passenger traffic. Providing high quality services to the population, combined with effective use of rolling stock can achieve rationalization organization of passenger transport schedules movement of vehicles on the basis of a compromise on service passengers, who recognized the comparison of costs, revenues and profit definition.

Modifying PIARC’s Linear Model of Accident Severity Index to Identify Roads' Accident Prone Spots to Rehabilitate Pavements Considering Nonlinear Effects of the Traffic Volume

Pavement rehabilitation could affect the accident severity index (ASI) since restoration measures means more safety for road users. No research or project has been carried out to identify hazard points to build a linear model based on crash severity index. One of the very popular accident severity index models used in all countries is based on linear models to rehabilitate pavements and this paper is aiming at correcting the deficiency of PIARC’s related model i.e. lack of sensitivity to changes in the traffic volume flow, to modify crash severity index (which is based on linear models) making an allowance for the nonlinear effects of traffic on eventful locations on dual carriageways. To do so, traffic volume has been chosen as the hazard criteria and, using multiple regression and statistical models, the coefficients and variables of the new model have been calculated by means of the SPSS software. This study presents the structural defects for the correction of linear models based on the accident severity (sensitive to changes in traffic volume). This research provides a linear model based on the crash severity index considering the nonlinear effects of the traffic volume to identify roads main eventful locations. Recommended that the model for a comprehensive database of accident data be built for all other roads in order to enhance the research accuracy.

Incidents between Straight-ahead Cyclists and Right-turning Motor Vehicles at Signalised Junctions

Accidents between right-turning motor vehicles and straight-ahead cyclists are one of the most common accident types leading to cyclist injuries at signalised junctions in Denmark. A before-after safety evaluation of applying staggered stop lines in 189 arms at 123 signalised junctions is presented. The evaluation accounts for long-term accident trends and changes in motor vehicle traffic volumes. Applying staggered stop lines gives no decline in accidents between right-turning motor vehicles and straight-ahead cyclists. However, there is a statistical tendency to a decline of these right-turn accidents involving heavy vehicles. There are several questions about factors leading to right-turn accidents that cannot be answered by recorded accident data. A study of conflicting behaviour focuses on factors leading to conflicts. Video observations have been carried out in 10 arms at signalised junctions. A total of 45 situations with conflicting behaviour between right-turning motor vehicles and straight-ahead cyclists have been investigated and compared to a reference group of simultaneous arrivals. The relative risk is lowest when both parties stop on red before entering the junction. Upon simultaneous arrival of both parties at a green light, the relative risk is highest. Cyclists tend to have a higher relative risk of being involved in conflicts if they; a) ride through on yellow, b) have a time distance of at least 2seconds to other cyclists, c) wear a black jacket, and/or d) arrive at the junction at a speed of at least 25km/h. Much less can be said about the motor vehicles or their drivers on the basis of these video observations, but motor vehicles stopping in the cycle crossing in order to yield to pedestrians or cyclists have a higher relative risk of being involved in conflicts.

An Effectiveness Analysis of Pilot Enforcement for Overweight Vehicles(Trucks) using High-Speed Weigh-In-Motion System

On January 16 to May 31, 2012, Korea Expressway Corporation was carried out an pilot overweight enforcement using high-speed weigh-in-motion at Gyeongbu expressway 195.0k (Gimcheon) and Jungbunaeryuk expressway 119.5k (Seonsan). In this study, it is attempted to analyze the practical effect of high-speed weigh-in-motion by comparing the average total weight and traffic volume of eight weeks before and after the these overweight enforcement, respectively. The main results are as follows: First, the result of analysis of the change in average total weight and traffic volume, it was found that it did not differ after as in previous traffic volume, and the total weight is reduced. This means that the total weight is not reduced by decreasing freight traffic, but by decreasing the total weight. Therefore, it can be seen that there is an effect of pilot overweight enforcement using high-speed weigh-in-motion. Second, the average total weight and total weekly traffic volume decreased rapidly starting from the start of the overweight enforcement, but there was showing a tendency to increase gradually again.