In China, around 90% of traffic crashes at signalized intersections take place within the signal change intervals, especially during signal change from green to red. Hence, yellow time, which is a part of inter-green time, is of great significance to the safety of signalized intersections. The conventional calculation method for duration of yellow light (DYL) ignores the stochastic characteristics of drivers, which we believe is an important factor in this calculation. Therefore, the purpose of this research is to investigate a new approach to calculate DYL based on safety reliability theory in which the randomness of human factors is taken into consideration. Firstly, a comprehensive literature review concerning the conventional calculation methods of DYL is conducted. Secondly, a theoretical calculation method of DYL based on safety reliability theory is put forward which, different from the conventional methods, accounts for the stochastic characteristics of drivers. Additionally, a driving simulation experiment is designed to obtain two driving behavior parameters of Chinese drivers: perception–reaction time (PRT) and safe acceptable acceleration (SAA). Thirdly, a Monte Carlo simulation is employed to simulate the interactive process of PRT and SAA, and solve the proposed model. Finally, according to the Monte Carlo simulation results, a look-up table describing the relationship between DYL, safety reliability (50–90%) and approaching speed (15–40 km/h) is made. Results show that this method successfully incorporates the probabilistic nature of driving behavior. Taking the safety reliability into consideration can provide a more reasonable method to calculate the DYL of signalized intersections.
With rapidly developing communication and autonomous-driving technology, traffic flow on road networks will change from homogeneous human-driven vehicle (HDV) traffic flow to heterogeneous mixed traffic flow (MTF) comprising HDVs, autonomous vehicles (AVs), and connective-and-autonomous vehicles (CAVs). To understand the changes in the MTF of transportation engineering, we investigated the reserved capacity (RC) and right-of-way (ROW) reallocation policy that should be utilized under MTF scenarios. We established an MTF-based theoretical model to calculate the expressway segment capacity, theoretically analyzed the influence of the market penetration rate (MPR) on capacity and validated the model through numerical analysis. The results showed that the MPR of AVs and CAVs can enhance the MTF RC that is within 0–200% and that the platooning rate of CAVs positively influences the MTF RC. CAV popularization does not necessarily lead to a rapid increase in the transportation system efficiency when the MPR is <40% but significantly improves the efficiency of existing urban transportation facilities. When the MPR is >40%, the greatest enhancement is 4800 pcu/h/lane in terms of RC. A ROW reallocation policy that equips CAV-dedicated lanes according to the MPR of AVs and CAVs can enhance the capacity of expressway systems by 500 pcu/h/lane in terms of RC.
ADAS and autonomous driving are booming. As technologies continue to innovate and mature, whether travelers understand, accept, and buy them will directly impact the technological development, popularization, and profitability of these products. This study analyzes the influence of urban residents’ personal, family, and commuting characteristics on their willingness to choose and pay for ADAS and autonomous driving functions. Using the questionnaire survey data for Jiading and Meishan in China, Logit models are established for willingness to choose, and linear regression models are established for willingness to pay. Although Jiading and Meishan are similar in terms of city size and population, there are some differences in the influencing factors for willingness to choose and pay because of the differences in industrial structure, city culture, and residents’ commuting habits. The results show that significant influencing factors vary for different levels of ADAS and autonomous driving functions. The findings of this research can provide a reference for city authorities, designers, and sellers of ADAS products or autonomous vehicles to identify potential buyers and promote related products.
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