With a view towards reducing traffic accidents on roadways, various methods have been considered to predict accidents. In this study, we analyze traffic accident frequency models that employ fixed- and random-parameter negative binomial approaches. Random parameters enable the inclusion of unobserved heterogeneity in traffic accident data, which current popular methods with fixed parameters such as Poisson or negative binomial models cannot consider in terms of time variation or segment-specific effects. A continuous, unbalanced panel of accident histories for 208 four-way signalized intersections on national highways in Seoul was used to estimate a traffic accident occurrence model that considered traffic volumes and various geometric characteristics at intersections. The results revealed that the left-turn exclusive lanes and traffic volumes on minor roads had random parameters that affected the likelihood of accident frequencies differently; the other variables were found to significantly affect traffic safety at the intersections on the national highways as fixed parameters. Based on these results, it can be concluded that the same traffic safety facilities have different effects on traffic accidents on major and minor roads. The insights from this study suggest the need for a broader analysis of integrated guidelines for facilities that impact intersection accident propensities.

In the Autonomous Mobility Living Lab, traffic situations with both autonomous vehicles (AVs) and ordinary vehicles driven by humans (HDVs) are explored. Research on countermeasures and efficient transportation management plans has emerged from this context. In this study, we analyzed the effect of AVs with different speeds on signal intersections and road networks to derive efficient traffic operation plans for roads on which various AVs and HDVs with different driving behaviors are mixed in Living Lab cities. To that end, we conducted a simulation-based analysis of the effects of AV mixing rates on continuous signal intersections and the road network in traffic situations where AVs and HDVs were mixed at peak and non-peak driving hours. The simulation scenario was designed by classifying the traffic volume levels at peak and non-peak times and defining various AV mixing rates; we also set the driver behaviors of the AVs as either conservative or aggressive. By performing a small-scale traffic simulation, the average control delay, average stopped delay, average queue length, and average travel time of the signal intersection for each scenario were derived, and the impact of the AV mixing rate on traffic operation was analyzed. The results of the analysis show that higher AV mixing rates were associated with lower measurements of the effectiveness of signal intersections, which had a positive effect on traffic operation. This resulted in a stable and efficient improvement of the traffic flow at intersections as more vehicles passed through at the time of the allocated signal, as the AVs in the simulation could be driven at short intervehicle intervals by receiving real-time traffic information. In the traffic operation on the network, we found that the higher the AV mixing rate, the lower the average travel time, resulting in a greater effect of facilitating the traffic flow of the urban network. These simulated results indicate that higher AV mixing rates were associated with positive outcomes in terms of signal intersections and network traffic operation. We expect that this simulation can be used to establish real traffic operation plans in traffic situations where AVs are mixed at each stage of autonomous driving technology in the future.

Korea has many test beds where various mobility services are provided by automated vehicles. The test beds are operated in their operational design domain (ODD). However, disengagement frequently occurs, even in the ODDs of automated vehicles. In particular, human drivers have to take control of the automated vehicles at SAE Level 3 whenever the vehicles cannot drive by themselves because of an emergency or unknown factors. This study analyzed the driving safety of right turning at signalized intersections where automated vehicles face selfdriving issues because of potential conflicts with other vehicles, crossing pedestrians, and geometric factors. To conduct this analysis, we categorized right-turning intersections into two types with right-turning lanes and channelization islands and divided them into three sections, with a total of six sections. Subsequently, the six sections were compared with each other by disengagements of the automated vehicles as the key index to investigate their self-driving safety. Their significant differences indicate that ODD-related variables must be considered when designing and updating target test beds for automated vehicles.

This study aimed to investigate the factors affecting the severity of taxi traffic accidents at intersections in Busan and propose measures to improve traffic safety. This study collected data on taxi traffic accidents that occurred at intersections in the Metropolitan City of Busan during the past 3 years (2020–2022) from Traffic Accident Analysis System(TAAS) and road views, and analyzed factors affecting their severity by employing an ordered probit model. The severity of taxi traffic accidents worsened with violations of (among others) traffic signals and pedestrian protection during January, April, and September. In addition, when a major street was operated with a permissive left turn, the severity of taxi traffic accidents worsened. Measures to improve traffic safety suggested in this study included safety education that focused on particular violations for taxi drivers, mandatory education for transport employees in an experiential format, support of video storage devices for driving records, policy establishment for the promotion and certification of good and bad driving videos, time adjustment of joint safety management inspection, and left-turn signal operation with an unprotected system and P-turn guidance.

This study points out the traffic safety inhibition problem in which the yellow time length at a signal intersection can be set differently depending on the tram, and proposes a way to overcome it. This study proposes a twofold approach for trams to coordinate with the yellow signal phase designated for general vehicles, utilizing a schedule-speed-based operating speed adjustment and departure time control at stations. To validate the proposed method, simulations were conducted for a planned tram corridor in the Wirye District, Seoul. The simulation environment was developed using the simulation of the urban mobility model. The operational characteristics of tram services in the main and branch-line sections were analyzed. In the upstream section of the mainline, the operating speed was 22.04 km/h, with a total dwell time of 400 s across 10 stations, resulting in zero intersection stops. Regarding the downstream section, the operating speed was 17.71 km/h, and one intersection stop was observed. In contrast, for the upstream section of the branch line, the operating speed was 14.73 km/h and there were two intersection stops, while the downstream section yielded an operating speed of 18.12 km/h, and also comprised two intersection stops. This study examined a novel operational strategy for trams near intersections along tram corridors without directly modifying the signal system. This strategy effectively utilizes tram stop dwell time and traffic signal information. It focuses on adjusting tram speeds to ensure smooth and safe passage through signalized intersections and optimizing operations so that trams approach and cross intersections at a consistent speed. Designed as a universal model, this strategy is adaptable to various regions and environments, rather than being limited to a specific area. It is expected to support tram operation planning by considering diverse factors, such as urban structure, traffic density, and intersection characteristics. Moreover, it provides valuable guidance to ensure the punctuality and stability of tram operations in a range of urban and traffic contexts.

PURPOSES : According to government data, the Black Spot Program has resulted in an average 28.8% reduction in traffic accidents within one year of project implementation in areas where road conditions improved. However, there has been a lack of in-depth analysis of the midto- long-term effects, with a predominant focus on short-term results. This study aimed to analyze the mid-to-long-term effects of the Black Spot Program to assess the sustainability of its reported short-term impact. Additionally, the differences in the mid-to-long-term effects were investigated based on the scale of traffic accidents at intersections and the characteristics of these effects are revealed. METHODS : The mid-to-long-term effects of the Black Spot Program were analyzed at 122 intersections in Seoul, Korea, where the program was implemented between 2013 and 2017, using traffic accident data spanning five years before and after implementation. Additionally, the differences in the program's effects were analyzed at the top-100 intersections with the highest traffic accident concentration in Seoul using the chi-square test to identify these differences. To theoretically validate these differences, the Hurst exponent, commonly used in economics, was applied to analyze the regression to the mean of the intersections and reveal the correlation with improvement. RESULTS : Through the Black Spot Program at 122 intersections, a 33.3% short-term accident reduction was observed. However, the midto- long-term effect analysis showed a 25.8% reduction, indicating a slightly smaller effect than previously reported. Specifically, the top-100 intersections exhibit a 15.4% reduction. A chi-square test with a 95% confidence level indicated significant differences in the program’s effects based on the scale of traffic accidents at intersections. The Hurst index (H ) was measured for the top-100 intersections, yielding H = 0.331. This is stronger than the overall H = 0.382 for all intersections in Seoul, suggesting that the regression to the mean is more pronounced, which may lead to a lower effectiveness of the improvement. CONCLUSIONS : The mid-to-long-term effects of the Black Spot Program were relatively lower than its short-term effects, with larger differences in effectiveness observed based on the scale of traffic accidents at intersections. This indicates the need to redefine the criteria for selecting project targets by focusing on intensive improvements at intersections, where significant effects can be achieved.

PURPOSES : This study aimed to derive the factors that contribute to crash severity in mixed traffic situations and suggest policy implications for enhancing traffic safety related to these contributing factors. METHODS : California autonomous vehicle (AV) accident reports and Google Maps based on accident location were used to identify potential accident severity-contributing factors. A decision tree analysis was adopted to derive the crash severity analyses. The 24 candidate variables that affected crash severity were used as the decision tree input variables, with the output being the crash severity categorized as high, medium, and low. RESULTS : The crash severity contributing factor results showed that the number of lanes, speed limit, bus stop, AV traveling straight, AV turning left, rightmost dedicated lane, and nighttime conditions are variables that affect crash severity. In particular, the speed limit was found to be a factor that caused serious crashes, suggesting that the AV driving speed is closely related to crash severity. Therefore, a speed management strategy for mixed traffic situations is proposed to decrease crash severity and enhance traffic safety. CONCLUSIONS : This paper presents policy implications for reducing accidents caused by autonomous and manual vehicle interactions in terms of engineering, education, enforcement, and governance. The findings of this study are expected to serve as a basis for preparing preventive measures against AV-related accidents.

PURPOSES : Even when autonomous vehicles are commercialized, a situation in which autonomous vehicles and regular drivers are mixed will persist for a considerable period of time until the percentage of autonomous vehicles on the road reaches 100%. To prepare for various situations that may occur in mixed traffic, this study aimed to understand the changes in traffic flow according to the percentage of autonomous vehicles in unsignalized intersections. METHODS : We collected road information and constructed a network using the VISSIM traffic simulation program. We then configured various scenarios according to the percentage of autonomous vehicles and traffic volume to understand the changes in the traffic flow in the mixed traffic by scenario. RESULTS : The results of the analysis showed that in all scenarios, the traffic flow on major roads changed negatively with the mix of autonomous vehicles; however, the increase or decrease was small. By contrast, the traffic flow on minor roads changed positively with a mix of autonomous vehicles. CONCLUSIONS : This study is significant because it proactively examines and designs traffic flow changes in congested traffic that may occur when autonomous vehicles are introduced.

PURPOSES : This study aims to understand the characteristics of accidents involving autonomous vehicles and derive the causes of accidents from road spatial information through autonomous vehicle accident reports. METHODS : For this study, autonomous vehicle accident reports collected and managed by the CA DMV were used as data sources. In addition, spatial characteristics and geometric data for accident locations were extracted by Google maps. Based on the collected data, the study conducted general statistics, text embedding, and cross-analysis to understand the overall characteristics of autonomous vehicle accidents and their relationship with road spatial features. RESULTS : The analysis results for characteristics of autonomous vehicle accidents, applying statistical analysis and text embedding techniques, reveal that the damages caused by autonomous vehicle accidents are often minor, and approximately half of the accidents are triggered by other vehicles. It is noteworthy that accidents where autonomous vehicles are at fault are not uncommon, and when the cause of the accident is within the autonomous vehicle, the accident risk can increase. The accident analysis results using spatial data showed that the severity of accidents increases when on-street parking is present, when dedicated lanes for bicycles and buses exist, and when bus stops are present. CONCLUSIONS : Through this study, geometric and spatial elements that appear to have an impact on autonomous driving systems have been identified. The findings of this study are expected to serve as foundational data for improving the safety of autonomous vehicle operations in the future.

PURPOSES : The main purpose of this study is to identify directions for improvement of triangular islands installation warrants through analysis of the characteristics of crashes and severity with and without triangular islands on intersections. METHODS : The data was collected by referring to the literature and analyzed using statistical analysis tools. First, an independence test analyzed whether statistically significant differences existed between crashes depending on the installation of triangular islands. As a result of the analysis, individual prediction models were developed for cases with significant differences. In addition, each crash factor was derived by comparison with each model. RESULTS : Significant differences appeared in the "crash frequency of serious or fatal" and "crash severity" owing to the installation of triangular islands. As a result of comparing crash factors through the individual models, it was derived that the differences were dependent on the installation of the triangular islands. CONCLUSIONS : As a result of reviewing previous studies, it is found that improving the installation warrants of triangular islands is reasonable. Through this study, the need to consider the volume and composition ratio of right-turn vehicles when installing a triangular island was also derived; these results also need to be referred to when improving the triangular island installation warrants.

PURPOSES : In this study, analyze the characteristics of IOC indicator 'threshold' which is needed when evaluating the traffic signal operation status with ESPRESSO in various grade road traffic environment of Seoul metropolitan city and derive suggested value to use in field practice. METHODS : Using the computerized database program (Postgresql), we extracted data with regional characteristics (Arterial, Collector road) and temporal characteristics (peak hour, non-peak hour). Analysis of variance and Duncan's validation were performed using statistical analysis program (SPSS) to confirm whether the extracted data contains statistical significance. RESULTS: The analysis period of the main and secondary arterial roads was confirmed to be suitable from 14 days to 60 days. For the arterial, it is suggested to use 20 km/h as the critical speed for PM peak hour and weekly non peak hour. It is suggested to use 25 km/h as the critical speed for AM peak hour and night non peak hour. As for the collector road, it is suggested to use 20 km/h as the critical speed for PM peak hour and weekly non peak hour. It is suggested to use 30 km/h as the critical speed for AM peak hour and night non peak hour. CONCLUSIONS : It is meaningful from a methodological point of view that it is possible to make a reasonable comparative analysis on the signal intersection pre-post analysis when the signal operation DB is renewed by breaking the existing traffic signal operation evaluation method.

One major concern of Seoul City is the premature failure occurrence such as fatigue cracking and rutting in the pavement. Due to the acceleration at intersections and low vehicle speed at bus stops that cause higher shear and critical strain on the pavement. Because of this, there is a need to develop a new mixture that can withstand bus stop and intersection traffic while preventing premature failure. In this study, a high modulus asphalt mixture was adapted and developed to address the cracking and rutting concerns at bus stops and intersections of Seoul City. Indirect tensile (IDT) and beam fatigue testing were conducted to determine the fatigue performance of the high modulus asphalt mixture (HMB). In addition, the behaviour of the HMB considering loading speed and temperature were investigated using the IDT dynamic modulus test. It was found that the HMB performs 3 and 1.5 times better compared to conventional asphalt using IDT and beam fatigue test respectively. Moreover, it was observed that modulus value of HMB is two times better at low frequency (high temperature) compared to conventional asphalt. The dynamic modulus value of the HMB was then used as input for bus stop and intersection scenario analyses. It was found that HMB can reduce the total thickness of the pavement around 4 to 6cm compared to the conventional asphalt. It can be concluded that because of the better fatigue and rutting performance and high modulus value of HMB at low frequency, it can perform better in bus stops and intersections. It is recommended to conduct field construction to further evaluate the performance of HMB asphalt mixtures in the field.

PURPOSES: The purpose of this study is to develop a crash prediction model at signalized intersections, which can capture the randomness and uncertainty of traffic accident forecasting in order to provide more precise results. METHODS: The authors propose a random parameter (RP) approach to overcome the limitation of the Count model that cannot consider the heterogeneity of the assigned locations or road sections. For the model’s development, 55 intersections located in the Daejeon metropolitan area were selected as the scope of the study, and panel data such as the number of crashes, traffic volume, and intersection geometry at each intersection were collected for the analysis. RESULTS: Based on the results of the RP negative binomial crash prediction model developed in this study, it was found that the independent variables such as the log form of average annual traffic volume, presence or absence of left-turn lanes on major roads, presence or absence of right-turn lanes on minor roads, and the number of crosswalks were statistically significant random parameters, and this showed that the variables have a heterogeneous influence on individual intersections. CONCLUSIONS : It was found that the RP model had a better fit to the data than the fixed parameters (FP) model since the RP model reflects the heterogeneity of the individual observations and captures the inconsistent and biased effects.

PURPOSES: This study aimed to analyze traffic accidents at circular intersections, and discuss accident reduction strategies based on land use and vehicle type. METHODS : Traffic accident data from 2010 to 2014 were collected from the “traffic accident analysis system”(TAAS) data set of the Road Traffic Authority. To develop the accident rate model, a multiple linear regression model was used. Explanatory variables such as geometry and traffic volume were used to develop the models. RESULTS: The main results of the study are as follows. First, it was found that the null hypotheses that land use and vehicle type do not affect the accident rate should be rejected. Second, 16 accident rate models, which are statistically significant (with high R2 values), were developed. Finally, the area of the central island, number of speed humps, entry lane width, circulatory roadway width, bus stops, and pedestrian crossings were analyzed to determine their effect on accidents according to the type of land use and vehicle. CONCLUSIONS: Through the developed accident rate models, it was revealed that the accident factors at circular intersections changed depending on land use and vehicle type. Thus, selecting the appropriate location of bus stops for trucks, widening entry lanes for cars, and installing splitter islands and optimal lighting for motorcycles were determined to be important for reducing the accident rate. Additionally, the evaluation showed that commercial and mixed land use had a weaker effect on accidents than residential land use.