The use of an inconsistent speed limit determination method can cause low speed limit compliance. Therefore, we developed an objective methodology based on engineering judgment considering the traffic accident rate in road sections, the degree of roadside development, and the geometric characteristics of road sections in urban roads. The scope of this study is one-way roads with two or more lanes in cities, and appropriate sections were selected among all roads in Seoul. These roads have speed limits of the statutory maximum speed of 80 km/h or lower and are characterized by various speeds according to the function of the road, the roadside development, and traffic conditions. The optimal speed limits of urban roads were estimated by applying the characteristics of variables as adjustment factors based on the statutory maximum speed limit. As a result of investigating and testing various influence variables, the function of roads, the existence of median, the level of curbside parking, the number of roadside access points, and the number of traffic breaks were selected as optional variables that influence the operating speed. The speed limit of one-way roads with two or more lanes in Seoul was approximately 10 km/h lower than the current speed limit. The existing speed limits of the roads were applied uniformly considering only the functional road class. However, considering the road environment, the speed limit should be applied differently for each road. In the future, if the collection scope and real-time collection of road environment information can be determined, the GIS visualization of traffic safety information will be possible for all road sections and the safety of road users can be ensured.

Many countries worldwide are introducing public bicycle systems to reduce urban traffic and environmental problems. However, studies on the usage behavior of public bicycles have not considered the trip purpose of riders extensively due to data limitations. Therefore, this study defined the “extra journey time” from usage time and origin–destination (OD) expected time and clustered public bicycle usage behaviors. Subsequently, the effects of the location characteristics of the departure and arrival stations, road environmental factors, and weather conditions for each cluster were analyzed. Three clusters were obtained from the results. Riders in Cluster 1 were inferred to have used the bicycles to commute and for work purposes, and riders in Clusters 2 and 3 used the bicycles for leisure purposes. Moreover, the bike station location characteristics, road environmental factors, and weather conditions influenced the probability of classification into one cluster. In particular, bike lanes near the departure and arrival stations increased the probability of classification under Clusters 2 and 3. The trip patterns according to the extra journey time of public bicycles were classified under these clusters. Furthermore, the differences in the characteristics of the bicycle usage types were identified according to the location and meteorological factors affecting them.

Advanced driver-assistance systems (ADAS) are primarily known for their positive impact in improving the safety of drivers. Previous studies primarily analyzed the positive effects of ADAS with short-term experiments and accident data without considering the long-term changes in drivers’ safety perception. The human factor is the most dominant among factors that cause traffic accidents, and safety effect evaluation should be performed considering changes in human errors. To this end, this study classified the safety effect of ADAS-forward collision warning (FCW) on taxi drivers in Seoul into behavioral control and attitude change to perform analysis on respective factors. With regard to behavioral control, virtual driving simulation was used to analyze the reaction time of drivers and deceleration rate, and for attitude change, autoregressive integrated moving average (ARIMA) time series analysis was employed to predict the long-term perception change of drivers. The analysis results indicated that, in terms of behavioral control, ADAS-FCW reduces the cognitive reaction time of drivers in risk situations on the road, similar to the findings in previous studies. However, in terms of attitude change, ADAS-FCW has the adverse long-term effect of increasing violations in maintaining safety distance in the case of nighttime-drivers under 60 years old. As can be seen from these results, new technologies in the road safety arena can have a short-term effect of improving safety with behavioral control but may have a negative impact in the long term. The results of this study are expected to provide a theoretical basis for reference in the safety evaluation of ADAS and traffic safety facilities.

With increasing global concerns related to global warming, air pollution, and environmental health, South Korea is actively implementing various particulate matter (PM) reduction policies to improve air quality. Accurate data analysis, including the investigation of weather phenomena, monitoring, and integrated prediction, is essential for effective PM reduction. However, the factors influencing the PM generated from domestic road sections have not yet been systematically analyzed, and currently, no predictive models utilize weather and traffic data. This study analyzed the correlations among factors influencing PM to develop models for estimating fine and coarse PM (PM_2.5 and PM₁₀, respectively) concentrations in road sections. Regression analysis models were used to assess the sensitivity of PM_2.5 and PM₁₀ concentrations to the traffic volume, whereas machine learning-based models, including linear regression, convolutional neural networks, and random forest models, were constructed and compared. The random forest models outperformed the other models, with coefficients of determination of 0.74 and 0.71 and mean absolute errors of 5.78 and 9.60 for PM_2.5 and PM₁₀, respectively. These results indicate that the random forest model provides the most accurate PM concentration estimates for road sections. The practical applications of the developed models were considered to inform effective transportation policies aimed at reducing PM. The developed model has practical applications in the formulation of transportation policies aimed at reducing PM. In particular, the model will play an important role in data-driven policymaking for sustainable urban development and environmental protection. By analyzing the correlation between traffic volume and weather conditions, policymakers can formulate more effective and sustainable strategies for reducing air pollution.