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In order to improve the driver's physiological and psychological state, the driver's mental load which is caused by sight distance, lighting, and other factors in the tunnel environment should be quantified via modeling the spatiotemporal data. e experimental schemes have been scientifically designed based on methods of traffic engineering and human factor engineering, which aims to test the driver's spatiotemporal data of eye movement and ECG (electrocardiogram) index in the tunnel environment. Firstly, the changes in the driver's spatiotemporal data are analyzed to judge the changing trend of the driver's workload in the tunnel environment. e results show that the cubic spline interpolation function model can fit the dynamic changes of average pupil diameter and heart rate (HR) growth rate well, and the goodness of fit for the model group is above 0.95. So, tunnel environment makes the driver's typical physiological indicators fluctuate in the coordinates of time and space, which can be modeled and quantified. Secondly, in order to analyze the classification of tunnel risk level, a fusion model has been built based on the functions of average pupil diameter and HR growth rate. e tunnel environmental risk level has been divided into four levels via the fusion model, which can provide a guidance for the classification of tunnel risk level. Furthermore, the fusion model allows tunnel design and construction personnel to adopt different safety design measures for different risk levels, and this method can effectively improve the economy of tunnel operating safety design.

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As an effective method, Traffic Conflict Technology (TCT) is widely applied to estimate the safety level of some risky areas, especially for the merging areas in the urban roads. Most of researchers prefer to just exploit the promising safety assessment models using realistic traffic data to predict the numbers of conflicts. There are a few types of research focusing on how to predict traffic conflict assessment indexes precisely in merging areas. Despite some related studies have realized this critical dilemma, significant lane-change characteristics are usually ignored and it is worthwhile devoting much effort to this. Hence, a modified Post Encroachment Time (PET) model is proposed in this study, to figure out lane-change characteristics as well as accurately forecast traffic safety of the merging area. Unlike other conventional methods, such as Time to Collision (TTC) or PET models, the proposed model not only fully takes the lanechange characteristics of merging vehicles into consideration, but also it explores the safety requirements in the process of lane changing in details. Moreover, the calculation formula of the modified PET model is gradually deduced by the trajectory of merging behavior and velocity formula. Besides, for the sake of pursuing a high validity, this paper exceptionally adopts two crucial compositions of PET, and eventually gives a unified calculation formula. In order to determine an appropriate threshold, traffic conflict data collected from Guangyuan Road in Guangzhou are analyzed. The results clearly prove that PET < 0.7 means a serious conflict, 0.7 ≤ PET <1.31 means a general conflict, 1.31 ≤ PET < 2.25 means a slight conflict, and PET ≥ 2.25 means a potential conflict. Finally, 50 groups of PET data and a comparative experiment are collected to demonstrate the effectiveness and reliability of the modified PET model. INDEX TERMS Urban road, merging area, safety evaluation, traffic conflicts, post encroachment time.

Disporum xilingense is described from Sichuan, China. The new species is related to D. leucanthum and D. bodinieri, with which it shares similar terminal inflorescences, widely open and nodding flowers with tepals slightly saccate at the base. However, its narrowly lanceolate tepals 23–26 mm long with tapered lower part 5–6 mm long, stamens distinctly shorter than the tepals, and style equaling or slight longer than the tepals support recognition at the species rank. The new species is also similar to D. acuminatissimum in flower size and color, whereas D. xilingense differs by its acute apices of leaves and tepals, widely opening flowers, glabrous tepals only minutely papillate on the lower margin and inside, sharply narrowed and navicular tepal bases, stamens 14–16 mm long and style 24–26 mm long. Furthermore, data are reported to exclude D. leucanthum from the flora of China. The main morphological features of the new species are discussed and illustrations and an identification key are provided.

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Effective identification of the risk area of the bus bay stop is a prerequisite for the enhancement of traffic safety. This study proposes a method of identifying the risk area based on the distribution of traffic conflicts. Firstly, the traffic flow data of the bus stop is collected by drones and video recognition software, and the traffic flow characteristics of the bus stop are analyzed by the mathematical and statistical methods. Secondly, using the gray clustering evaluation theory, on the basis of the rasterization of the functional area of the bus bay stop, a risk level model based on the index system of conflict rate, conflict severity, and potential conflict risk is proposed. Finally, take a bus stop in Guangzhou as an example to verify the solution. The results show that the constructed model can effectively identify the risk areas of bus bay stops. The risk areas of the bus bay stops are concentrated in the middle and lower reaches of the bus stop, which proves that the impact of bus exiting the stop on the surrounding traffic is greater than the process of bus entering the stop; the traffic risk areas of lanes near the bus stop are concentrated, and the severity of conflicts is low. The traffic risk zone of the lane far away from the bus stop is widely distributed, and the severity of conflict is higher. The research results can provide a basis for the micro safety performance evaluation and safety optimization of bus bay stops, which has strong theoretical and practical significance.