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The parameter value chosen to measure driving performance affects the accuracy of the estimated fatigue level. Methods to analyze the sensitivity of these parameter values were proposed. Standard deviation of lane position (SDLP) and steering reversal rate (SRR) were considered to assess fatigue, and the sensitivity of these parameters was analyzed from the time domain and value domain. Thirty-six male drivers participated in a field test. Lane position, steering wheel angle data, and self-reported fatigue level (scored on the Karolinska sleepiness scale) were recorded. SDLP results indicate that the maximum average coefficient with fatigue level reached .11, with a unified statistical interval of 202 s when the consecutive analysis method was used; the maximum average coefficient was .12 with a unified interval of 120 s when the maximum analysis method was used. SRR results indicate that a steering angle difference of 6° was the most sensitive threshold for driver fatigue level and has an average correlation coefficient of .42, which demonstrated that SRR was more reliable than SDLP for monitoring fatigue level. With the use of the optimal parameter value, the variation results of SDLP and SRR at each fatigue level were examined, and results indicate that driving ability was impaired as fatigue level increased. The methods and results can be applied to analyses of fatigued or drowsy driving.

The parameter value chosen to measure driving performance affects the accuracy of the estimated fatigue level. Methods to analyze the sensitivity of these parameter values were proposed. Standard deviation of lane position (SDLP) and steering reversal rate (SRR) were considered to assess fatigue, and the sensitivity of these parameters was analyzed from the time domain and value domain. Thirty-six male drivers participated in a field test. Lane position, steering wheel angle data, and self-reported fatigue level (scored on the Karolinska sleepiness scale) were recorded. SDLP results indicate that the maximum average coefficient with fatigue level reached .11, with a unified statistical interval of 202 s when the consecutive analysis method was used; the maximum average coefficient was .12 with a unified interval of 120 s when the maximum analysis method was used. SRR results indicate that a steering angle difference of 6° was the most sensitive threshold for driver fatigue level and has an average correlation coefficient of .42, which demonstrated that SRR was more reliable than SDLP for monitoring fatigue level. With the use of the optimal parameter value, the variation results of SDLP and SRR at each fatigue level were examined, and results indicate that driving ability was impaired as fatigue level increased. The methods and results can be applied to analyses of fatigued or drowsy driving.

Circadian rhythms, inherent in all humans, consist of 24-h biological patterns that affect a person's fatigue level. The effect of circadian rhythms on driving performance was explored in an on-road driving study. Fifteen middle-aged professional daytime drivers were recruited to participate in the experiment. Participants were classified into three groups: (a) a morning group that started driving at 09:00, (b) a noon group that started driving at 12:00, and (c) an evening group that started driving at 21:00. Each group completed a 6-h driving task. The self-reported Karolinska sleepiness scale score was recorded every 5 min, and data on driving performance parameters, such as steering and lane positioning, were also acquired. The results indicated that both circadian rhythms and driving duration had significant effects on self-reported fatigue levels and that the fatigue level increased faster in the evening group than the morning and noon groups. The results of the circadian rhythm analysis showed that a driver was most likely to feel tired between 14:00 and 16:00 and between 02:00 and 04:00, when the ability to stay within designated lane lines (lane maintenance) was significantly impaired for drivers in all three groups. The evening group drivers were the most at risk. The steering performance did not show a significant relationship with the self-reported fatigue level. The self-reported fatigue level is the result of the interactive effect of circadian rhythms and driving duration. The standard deviation of lane position was more correlated with circadian rhythms than with the steering reversal rate.

Circadian rhythms, inherent in all humans, consist of 24-h biological patterns that affect a person's fatigue level. The effect of circadian rhythms on driving performance was explored in an on-road driving study. Fifteen middle-aged professional daytime drivers were recruited to participate in the experiment. Participants were classified into three groups: (a) a morning group that started driving at 09:00, (b) a noon group that started driving at 12:00, and (c) an evening group that started driving at 21:00. Each group completed a 6-h driving task. The self-reported Karolinska sleepiness scale score was recorded every 5 min, and data on driving performance parameters, such as steering and lane positioning, were also acquired. The results indicated that both circadian rhythms and driving duration had significant effects on self-reported fatigue levels and that the fatigue level increased faster in the evening group than the morning and noon groups. The results of the circadian rhythm analysis showed that a driver was most likely to feel tired between 14:00 and 16:00 and between 02:00 and 04:00, when the ability to stay within designated lane lines (lane maintenance) was significantly impaired for drivers in all three groups. The evening group drivers were the most at risk. The steering performance did not show a significant relationship with the self-reported fatigue level. The self-reported fatigue level is the result of the interactive effect of circadian rhythms and driving duration. The standard deviation of lane position was more correlated with circadian rhythms than with the steering reversal rate.

Planned special events attract thousands of attendees from nearby cities or suburbia by car and transit. In most cases, the majority of attendees use personal automobiles, and a high parking demand results in a short time, with a consequent parking shortage. Parking guidance information systems can solve the problem by displaying information on parking lot availability to dynamically divert vehicles. This study focused on optimizing dynamic parking guidance information for automobile drivers at special events. An original multimode traffic network was converted to a novel network by considering parking lots as dummy links; therefore the shortest path and traffic assignment could be implemented in this extended network. A bilevel programming model based on quasi-dynamic route choice and linear programming was proposed to optimize the dynamic parking guidance information. On the basis of travelers' reaction to the guidance, stochastic dynamic user optimal route choice was employed within the lower-level model. The upper-level model was a linear program aimed at minimizing network total travel time. The solutions of the bilevel programming model were based on discrete particle swarm optimization and the method of successive average algorithms. Results of a case study implemented with a hypothetical network indicated that the optimization model could reduce the system total travel time by 4%.

Planned special events attract thousands of attendees from nearby cities or suburbia by car and transit. In most cases, the majority of attendees use personal automobiles, and a high parking demand results in a short time, with a consequent parking shortage. Parking guidance information systems can solve the problem by displaying information on parking lot availability to dynamically divert vehicles. This study focused on optimizing dynamic parking guidance information for automobile drivers at special events. An original multimode traffic network was converted to a novel network by considering parking lots as dummy links; therefore the shortest path and traffic assignment could be implemented in this extended network. A bilevel programming model based on quasi-dynamic route choice and linear programming was proposed to optimize the dynamic parking guidance information. On the basis of travelers' reaction to the guidance, stochastic dynamic user optimal route choice was employed within the lower-level model. The upper-level model was a linear program aimed at minimizing network total travel time. The solutions of the bilevel programming model were based on discrete particle swarm optimization and the method of successive average algorithms. Results of a case study implemented with a hypothetical network indicated that the optimization model could reduce the system total travel time by 4%.

Many studies have dealt with modeling crash occurrences on urban arterials. There is a dearth of research on urban arterials with mixed-traffic patterns in China, however, because of the large traffic flow volume of bicyclists and pedestrians in most Chinese cities. This study investigates the risk factors associated with severe crash occurrences on arterial roads in Beijing. Through use of the generalized estimating equations modeling technique with a negative binomial link function, statistical relationships were established to relate severe crashes to a variety of factors related to geometric design, traffic control, and other traffic-related characteristics. Crash records from 2004 to 2007 for 108 signalized intersections and 123.5 km of road segments were used to estimate the models. Results showed that arterial roads with heavier traffic volumes, more road lanes, and higher speed limits tended to have more severe crashes. Medians were helpful in reducing severe crash risk. Higher risks of severe crashes were generally associated with intersections having small angles and count-down signals and road segments having higher side-access densities and the presence of bus stops. With regard to nonmotorist protection facilities, results revealed that a combined use of crosswalks and overpasses was the most desired pedestrian-crossing facility for safety, especially at sites with heavy traffic or sites located in primarily residential areas. Barriers that separated bikeways from roadways on minor roads were found effective in significantly reducing severe crash risk at intersections.

Public transportation plays a key role in providing transport services for the public in most cities of China. Safety is a top priority for improving the level of services of public transportation. This study aims to identify crash risk factors associated with demographic characteristics, driving-related experiences, and aberrant driving behaviors of the drivers of public transportation vehicles as well as to establish the influence of risk perception, risk-taking attitudes, and risky driving behaviors. The data used for analyses were obtained from a self-reported questionnaire survey carried out among 248 taxi and bus drivers in Wuhan, China. The results showed that drivers who both reported more tendencies toward aggressive violations and ordinary violations and had previously been involved in crashes were at high risk of crash involvement. Moreover, through the use of a structural equation model, it was found that drivers' attitudes toward rule violations and speeding significantly affect risky driving behaviors. Two risk perception scales, likelihood of crash and concern, have indirect effects on risky driving behaviors through their influence on drivers' attitudes toward rule violations and speeding. The significant risk factors and influential paths identified in this study are expected to result in better planning of road safety campaigns aimed at the occupational drivers in public transportation.

Many studies have dealt with modeling crash occurrences on urban arterials. There is a dearth of research on urban arterials with mixed-traffic patterns in China, however, because of the large traffic flow volume of bicyclists and pedestrians in most Chinese cities. This study investigates the risk factors associated with severe crash occurrences on arterial roads in Beijing. Through use of the generalized estimating equations modeling technique with a negative binomial link function, statistical relationships were established to relate severe crashes to a variety of factors related to geometric design, traffic control, and other traffic-related characteristics. Crash records from 2004 to 2007 for 108 signalized intersections and 123.5 km of road segments were used to estimate the models. Results showed that arterial roads with heavier traffic volumes, more road lanes, and higher speed limits tended to have more severe crashes. Medians were helpful in reducing severe crash risk. Higher risks of severe crashes were generally associated with intersections having small angles and countdown signals and road segments having higher side-access densities and the presence of bus stops. With regard to nonmotorist protection facilities, results revealed that a combined use of crosswalks and overpasses was the most desired pedestrian-crossing facility for safety, especially at sites with heavy traffic or sites located in primarily residential areas. Barriers that separated bikeways from roadways on minor roads were found effective in significantly reducing severe crash risk at intersections.

Public transportation plays a key role in providing transport services for the public in most cities of China. Safety is a top priority for improving the level of services of public transportation. This study aims to identify crash risk factors associated with demographic characteristics, driving-related experiences, and aberrant driving behaviors of the drivers of public transportation vehicles as well as to establish the influence of risk perception, risk-taking attitudes, and risky driving behaviors. The data used for analyses were obtained from a self-reported questionnaire survey carried out among 248 taxi and bus drivers in Wuhan, China. The results showed that drivers who both reported more tendencies toward aggressive violations and ordinary violations and had previously been involved in crashes were at high risk of crash involvement. Moreover, through the use of a structural equation model, it was found that drivers’ attitudes toward rule violations and speeding significantly affect risky driving behaviors. Two risk perception scales, likelihood of crash and concern, have indirect effects on risky driving behaviors through their influence on drivers’ attitudes toward rule violations and speeding. The significant risk factors and influential paths identified in this study are expected to result in better planning of road safety campaigns aimed at the occupational drivers in public transportation.