Currently, several traffic conflict indicators are used as surrogate safety measures. Each indicator has its own advantages, limitations, and suitability. There are only a few studies focusing on fixed object conflicts of highway safety estimation using traffic conflict technique. This study investigated which conflict indicator was more suitable for traffic safety estimation based on conflict-accident Pearson correlation analysis. First, a high-altitude unmanned aerial vehicle was used to collect multiple continuous high-precision videos of the Jinan-Qingdao highway. The vehicle trajectory data outputted from recognition of the videos were used to acquire conflict data following the procedure for each conflict indicator. Then, an improved indicator T_i was proposed based on the advantages and limitations of the conventional indicators. This indicator contained definitions and calculation for three types of traffic conflicts (rear-end, lane change and with fixed object). Then the conflict-accident correlation analysis of TTC (Time to Collision)/PET (Post Encroachment Time)/DRAC (Deceleration Rate to Avoid Crash)/T_i indicators were carried out. The results show that the average value of the correlation coefficient for each indicator with different thresholds are 0.670 for TTC, 0.669 for PET, and 0.710 for DRAC, and 0.771 for T_i, which T_i indicator is obviously higher than the other three conventional indicators. The findings of this study suggest TTC often fails to identify lane change conflicts, PET indicator easily misjudges some rear-end conflict when the speed of the following vehicle is slower than the leading vehicle, and PET is less informative than other indicators. At the same time, these conventional indicators do not consider the vehicle-fixed objects conflicts. The improved T_i can overcome these shortcomings; thus, T_i has the highest correlation. More data are needed to verify and support the study.

Existing dynamic bus lanes (DBLs) typically use a fixed and long blocking space to guarantee bus priority, leading to a conflict between bus priority and traffic capacity. This study proposes an innovative approach in a connected-vehicle (CV) environment, called dynamic bus lane with moving block (DBLMB). The length of the moving block can be adjusted with the bus speed in real time, aiming to ensure bus priority with fewer road resources, thus improving traffic capacity. A three-lane cellular automata model is established to evaluate DBLMB in comparison with two other strategies, bus lane without priority and bus lane with intermittent priority (BLIP). First, the benefits of DBLMB over the established DBLs are analyzed qualitatively by the macroscopic fundamental diagram. Next, simulation experiments are conducted to compare the coordination between bus priority and capacity under different strategies. Then, the impact of DBLMB on microscopic traffic flow is investigated through evaluation indicators including lane density, lane speed, lane-changing frequency, and travel speed. Finally, the sensitivity of bus delay and traffic capacity to CV penetration is discussed. The results show that: (1) the capacity of a three-lane road adopting DBLMB strategy can be stabilized above 5200 passenger car units per hour (pcu/h) when the bus departure interval is higher than 60 s and the expected level of service is below B. (2) With 100%, 80%, 60%, 40%, and 20% of CV penetration, DBLMB improves the average capacity of the road by 523 pcu/h compared with BLIP, with only an incremental bus delay of 6.43 s per vehicle.

The lack of vehicle feature information and the limited number of pixels in high-definition remote-sensing images causes difficulties in vehicle detection. This paper proposes U-YOLO, a vehicle detection method that integrates multi-scale features, attention mechanisms, and sub-pixel convolution. The adaptive fusion module (AF) is added to the backbone of the YOLO detection model to increase the underlying structural information of the feature map. Cross-scale channel attention (CSCA) is introduced to the feature fusion part to obtain the vehicle’s explicit semantic information and further refine the feature map. The sub-pixel convolution module (SC) is used to replace the linear interpolation up-sampling of the original model, and the vehicle target feature map is enlarged to further improve the vehicle detection accuracy. The detection accuracies on the open-source datasets NWPU VHR-10 and DOTA were 91.35% and 71.38%. Compared with the original network model, the detection accuracy on these two datasets was increased by 6.89% and 4.94%, respectively. Compared with the classic target detection networks commonly used in RFBnet, M2det, and SSD300, the average accuracy rate values increased by 6.84%, 6.38%, and 12.41%, respectively. The proposed method effectively solves the problem of low vehicle detection accuracy. It provides an effective basis for promoting the application of high-definition remote-sensing images in traffic target detection and traffic flow parameter detection.