Aiming at prevalent violations of non-motorists at urban intersections in China, this paper intends to clarify the characteristics and risks of non-motorist violations at signalized intersections through questionnaire...Aiming at prevalent violations of non-motorists at urban intersections in China, this paper intends to clarify the characteristics and risks of non-motorist violations at signalized intersections through questionnaires and video recordings, which may serve as a basis for non-motorized vehicle management. It can help improve the traffic order and enhance the degree of safety at signalized intersections. To obtain the perception information, a questionaire survey on the Internet was conducted and 972 valid questionnaires were returned. It is found that academic degree contributes little to non-motorist violations, while electrical bicyclists have a relatively higher frequency of violations compared with bicyclists. The video data of 18 228 non-motorist behaviors indicate that the violation rate of all non-motorists is 26.5%; the number of conflicts reaches 1 938, among which violation conflicts account for 66.8%. The study shows that the violation rates and the violation behavior at three types of surveyed intersections are markedly different. It is also concluded that the conflict rates and the violation rates are positively correlated. Furthermore, signal violation, traveling in the wrong direction, and overspeeding to cross the intersection are the most dangerous among traffic violation behaviors.展开更多
Comparative analyses were conducted to compare the effects of the behavioral characteristics of the drivers of taxis and private cars on the capacity and safety of signalized intersections. Data were collected at sixt...Comparative analyses were conducted to compare the effects of the behavioral characteristics of the drivers of taxis and private cars on the capacity and safety of signalized intersections. Data were collected at sixteen signalized intersections in the Nanjing area in China. The risk-taking behaviors of the drivers of taxis and private cars were compared. The results suggest that 19.9% of taxi drivers have committed at least one of the identified risky behaviors, which is 2.37 times as high as that of the drivers of private cars(8.4%). The traffic conflicts technique was used to estimate the safety effects of taxis and private cars. The overall conflict rate for taxis is 21.4% higher than that for private cars, implying that taxis are more likely to be involved in conflicts. Almost all of the identified traffic conflicts can be attributed to certain levels of risk-taking behaviors committed by either taxi drivers or the drivers of private cars, and taxi drivers are more likely to be at fault in a conflict. Failure to yield to right-of-way and improper lane change is the leading causes of the conflicts in which taxis are at-fault. The research team further studied the effects of taxis on the queue discharge characteristics at signalized intersections. The results show that the presence of taxis significantly reduces both start-up lost time and saturation headways. The effects of taxis on saturation flow rates are dependent on the proportion of taxis in the discharge flow, and the saturation flow rates increase with the increase in the proportion of taxis. The adjustment factors for various proportions of taxis for different turning movements are then calculated to quantitatively evaluate the effects of taxis on the capacity of signalized intersections.展开更多
The authors propose a two-stage method for recognizing driving situations on the basis of driving signals for application to a safe human interface of an in-vehicle information system. In first stage, an unknown drivi...The authors propose a two-stage method for recognizing driving situations on the basis of driving signals for application to a safe human interface of an in-vehicle information system. In first stage, an unknown driving situation is determined as stopping behavior or non-stopping behavior. In second stage, a Hidden Markov Model （HMM）-based pattern recognition method is used to model and recognize six non-stopping driving situations. The authors attempt to find the optimal HMM configuration to improve the performance of driving situation recognition. Center for Integrated Acoustic Information Research （CLAIR） in-vehicle corpus is used to evaluate the HMM-based recognition method. Driving situation categories are recognized using five driving signals. The proposed method achieves a relative error reduction rate of 30.9% compared to a conventional one-stage based HMMs.展开更多
基金The National Key Technology R&D Program during the 11th Five-Year Plan Period(No.2009BAG13A05)the National Natural Science Foundation of China(No.51078086)
文摘Aiming at prevalent violations of non-motorists at urban intersections in China, this paper intends to clarify the characteristics and risks of non-motorist violations at signalized intersections through questionnaires and video recordings, which may serve as a basis for non-motorized vehicle management. It can help improve the traffic order and enhance the degree of safety at signalized intersections. To obtain the perception information, a questionaire survey on the Internet was conducted and 972 valid questionnaires were returned. It is found that academic degree contributes little to non-motorist violations, while electrical bicyclists have a relatively higher frequency of violations compared with bicyclists. The video data of 18 228 non-motorist behaviors indicate that the violation rate of all non-motorists is 26.5%; the number of conflicts reaches 1 938, among which violation conflicts account for 66.8%. The study shows that the violation rates and the violation behavior at three types of surveyed intersections are markedly different. It is also concluded that the conflict rates and the violation rates are positively correlated. Furthermore, signal violation, traveling in the wrong direction, and overspeeding to cross the intersection are the most dangerous among traffic violation behaviors.
基金Projects(51322810,50908050)supported by the National Natural Science Foundation of China
文摘Comparative analyses were conducted to compare the effects of the behavioral characteristics of the drivers of taxis and private cars on the capacity and safety of signalized intersections. Data were collected at sixteen signalized intersections in the Nanjing area in China. The risk-taking behaviors of the drivers of taxis and private cars were compared. The results suggest that 19.9% of taxi drivers have committed at least one of the identified risky behaviors, which is 2.37 times as high as that of the drivers of private cars(8.4%). The traffic conflicts technique was used to estimate the safety effects of taxis and private cars. The overall conflict rate for taxis is 21.4% higher than that for private cars, implying that taxis are more likely to be involved in conflicts. Almost all of the identified traffic conflicts can be attributed to certain levels of risk-taking behaviors committed by either taxi drivers or the drivers of private cars, and taxi drivers are more likely to be at fault in a conflict. Failure to yield to right-of-way and improper lane change is the leading causes of the conflicts in which taxis are at-fault. The research team further studied the effects of taxis on the queue discharge characteristics at signalized intersections. The results show that the presence of taxis significantly reduces both start-up lost time and saturation headways. The effects of taxis on saturation flow rates are dependent on the proportion of taxis in the discharge flow, and the saturation flow rates increase with the increase in the proportion of taxis. The adjustment factors for various proportions of taxis for different turning movements are then calculated to quantitatively evaluate the effects of taxis on the capacity of signalized intersections.
文摘The authors propose a two-stage method for recognizing driving situations on the basis of driving signals for application to a safe human interface of an in-vehicle information system. In first stage, an unknown driving situation is determined as stopping behavior or non-stopping behavior. In second stage, a Hidden Markov Model （HMM）-based pattern recognition method is used to model and recognize six non-stopping driving situations. The authors attempt to find the optimal HMM configuration to improve the performance of driving situation recognition. Center for Integrated Acoustic Information Research （CLAIR） in-vehicle corpus is used to evaluate the HMM-based recognition method. Driving situation categories are recognized using five driving signals. The proposed method achieves a relative error reduction rate of 30.9% compared to a conventional one-stage based HMMs.
