This study aims to develop a network Data Envelopment Analysis (DEA) model for evaluating the efficiency of bus companies of Seoul, Korea. The proposed model is formulated by combining a network DEA model with the Banker, Charnes, and Cooper (BCC) model. The model can reflect the non-storable nature of public transportation services by sequentially considering transportation services provided by operators and consumed by users. The model also reflects simultaneously both desirable and undesirable outputs and thus provides more sufficient information with which to evaluate the efficiency of bus companies than the traditional DEA model does. The proposed model is estimated based on data regarding bus companies in the city of Seoul. Findings suggest several policies to improve the efficiency of bus companies, such as expansion of bus rapid transit systems and establishment of additional stops of median bus lanes for express and arterial bus routes. Furthermore, specific tax-cut polices related to environmental issues should be implemented to encourage inefficient bus companies to operate more pro-environmental vehicles. The developed model and criteria can assess the overall efficiency of bus companies and provide direction for the efforts of inefficient bus companies.

Human errors cause approximately 90 percent of traffic accidents, and drivers with risky driving behaviors are involved in about 52 percent of severe traffic crashes. Driver education using driving simulators has been used extensively to obtain a quantitative evaluation of driving behaviors without causing drivers to be at risk for physical injuries. However, since many driver education programs that use simulators have limits on realistic interactions with surrounding vehicles, they are limited in reducing risky driving behaviors associated with surrounding vehicles. This study introduces surrogate safety measures (SSMs) into simulator-based training in order to evaluate the potential for crashes and to reduce risky driving behaviors in driving situations that include surrounding vehicles. A preliminary experiment was conducted with 31 drivers to analyze whether the SSMs could identify risky driving behaviors. The results showed that 15 SSMs were statistically significant measures to capture risky driving behaviors. This study used simulator-based training with 21 novice drivers, 16 elderly drivers, and 21 commercial drivers to determine whether a simulator-based training program using the SSMs is effective in reducing risky driving behaviors. The risky driving behaviors by novice drivers were reduced significantly with the exception of erratic lane-changing. In the case of elderly drivers, speeding was the only risky driving behavior that was reduced; the others were not reduced because of their difficulty with manipulating the pedals in the driving simulator and their defensive driving. Risky driving behaviors by commercial drivers were reduced overall. The results of this study indicated that the SSMs can be used to enhance drivers’ safety, to evaluate the safety of traffic management strategies as well as to reduce risky driving behaviors in simulator-based training.

Although many countries specify a desirable minimum width for one-way bike lanes, there have been no empirical or methodological studies conducted through extensive field experiments to confirm these specifications. This study devised a measurement method to obtain precise bicycle trajectories using a real-time kinematic global positioning system (RTK GPS). This measurement method achieves very satisfactory accuracy with regard to identifying the desirable minimum bike lane width when considering the bicycle width, Essential Maneuvering Space (EMS), and Comfortable Lateral Clearance (CLC). The width of a bicycle was obtained by investigating common bicycle types in the field. The EMS experiments were performed for speeds of 10, 20, and 30 km/h along test tracks with widths of 1.1, 2.0, and 3.0 m using an RTK GPS. The CLC experiments were repeated 300 times with the same preset speeds as in the EMS tests. Based on the results of this study, the bike lane width should be at least 2.0 m to allow for rider stability on roadways with no curb or gutter. In the future, desirable geometric standards for various types of bike lanes need to be formulated using real data collected with advanced measurement apparatuses.

During road work, lane closures significantly reduce road capacity and negatively impact traffic safety in the upstream segments. This study introduces a merge control strategy for the work zone on freeway that aims to alleviate severe congestion and improve flow efficiency in environments where manual vehicles (MVs) and connected automated vehicles (CAVs) coexist. Using a short‐term prediction model combined with a proportional‐integral‐derivative (PID) controller, this strategy dynamically adjusts merging behavior based on real‐time traffic conditions. The PID controller calculates error values as the difference between current and target states, adjusting responses through proportional, integral, and derivative terms. The predictions of the traffic state based on the density of open lanes in each segment guide the controller’s decision to initiate a “Merge” or “No Merge” guidance. When merging is deemed necessary, the controller estimates the optimal number of vehicles to merge for each segment, using the severe congestion threshold as a reference point. This approach was tested using a microscopic simulation tool on a calibrated real‐world network under mixed traffic conditions. The results indicate that the proposed strategy effectively disperses merging upstream, increases merging speeds, and maintains lane density below critical congestion levels, thus enhancing operation efficiency and safety in work zone areas.