Adverse weather conditions at night are very fatal to drivers, causing serious traffic accidents. Road lighting is a facility that can alleviate these dangerous situations. Nevertheless, road lighting has only rarely been studied during adverse weather. The reason is that the current road lighting performance evaluation method is presented based on normal weather. The current road lighting performance evaluation method uses a luminance meter to measure the road surface, which is not suitable due to scattering during adverse weather such as rain and fog. Therefore, this study proposes obstacle recognition distance as a measure of effectiveness to evaluate the performance of road lighting during adverse weather. There is a lack of actual research on whether obstacle recognition distance can be used as a measure of effectiveness for road lighting during adverse weather. Therefore, in this study, 30 subjects were used to measure the subjects’ obstacle recognition distance according to changes in weather conditions, road lighting grade, and road lighting color temperature. As a result, it was analyzed that there was a clear trend of change in obstacle recognition distance depending on the change in each condition. It was found that, under the same road lighting performance conditions, there was a difference of up to 72.86% by weather condition; under the same weather conditions, there was a difference of up to 22.75% by road lighting grade; and by color temperature, there was a difference of up to 21.87%. In addition, a statistical significance test was performed to support the existence of a difference, and the results were synthesized to suggest that obstacle recognition distance can be used as a performance measure of effectiveness of road lighting in adverse weather.

A dynamic offset with coordinated signal control system (DYCCS) was developed in this study. When configuring the DYCCS, the Critical Intersection (CI) of a coordinated corridor was operated based on saturation equalization signal control, and the adjacent intersections were controlled by coordinated semi-actuated signal control, with the background cycle corresponding to the suggested cycle length of the CI. An algorithm for the dynamic offset was suggested to integrate in the coordinated semi-actuated signal control algorithm and the saturation equalization signal control algorithm. Moreover, simulations were conducted for pre-timed signal control using one or three optimized signal timings and the DYCCS. The simulations demonstrated that the DYCCS can decrease the average vehicle delay relative to an optimized time of day and a Coordinated Signal Control System (CCS) utilizing a fixed offset. It is anticipated that the implementation of this DYCCS in newly constructed intersections or existing intersections controlled by a pretimed signal control may significantly decrease vehicle delay and may enhance progression effects in a coordinated corridor. A local controller equipped with this algorithm combined with an existing traffic responsive control, such as COSMOS, would be efficient under various conditions.

This study proposes an intelligent surveillance framework that integrates image preprocessing, illuminance-adaptive object detection, multi-object tracking, and pedestrian abnormal behavior recognition to address the rapid degradation of image recognition performance under low-illuminance street lighting conditions. In the preprocessing stage, image quality was enhanced by correcting color distortion and contour loss, while in the detection stage, illuminance-based loss weighting was applied to maintain high detection sensitivity even in dark environments. During the tracking process, a Kalman filter was employed to ensure inter-frame consistency of detected objects. In the abnormal behavior recognition stage, temporal motion patterns were analyzed to detect events such as falls and prolonged inactivity in real time. The experimental results indicate that the proposed method maintained an average detection accuracy of approximately 0.9 and adequate tracking performance in the 80% range under low-illuminance conditions, while also exhibiting stable recognition rates across various weather environments. Although slight performance degradation was observed under dense fog or highly crowded scenes, such limitations are expected to be mitigated through sensor fusion and enhanced processing efficiency. These findings experimentally demonstrate the technical feasibility of a real-time intelligent recognition system for nighttime street lighting environments.

Variable-message signs (VMS) are essential visual systems that convey roadway information to drivers; however, their legibility declines under foggy conditions. In this study, the effect of fog on VMS legibility was examined and legibility distances between elderly and non-elderly drivers were compared. Experiments were conducted in a facility capable of simulating foggy nighttime environments by measuring legibility distances for various display formats (symbols, numbers, and text) at varying fog densities. The results showed that heightened fog density significantly reduced legibility distance. Elderly drivers performed better with symbolic displays, whereas non-elderly drivers showed greater legibility for numeric and textual displays. Under dense-fog conditions, the minimum legibility distance requirements were not met, highlighting the need for luminance standards suitable for foggy conditions. These findings provide empirical evidence for improving VMS operation to foster safer driving environments, particularly for elderly drivers.