Factors influencing traffic signal violations by car drivers, cyclists, and pedestrians: A case study from Guangdong, China

Transportation Research Part F xxx (2016) xxx–xxx

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Transportation Research Part F journal homepage: www.elsevier.com/locate/trf

Factors influencing traffic signal violations by car drivers, cyclists, and pedestrians: A case study from Guangdong, China Guangnan Zhang a, Ying Tan a, Rong-Chang Jou b,⇑ a Center for Studies of Hong Kong, Macao and Pearl River Delta, Collaborative Innovation Center for the Cooperation and Development of Hong Kong, Macao and Mainland China, Sun Yat-Sen University, Xingang Xi Road, Guangzhou, China b Department of Civil Engineering, National Chi Nan University, No. 1, University Rd, Puli, Nantou County 54561, Taiwan

a r t i c l e

i n f o

Article history: Received 22 September 2015 Received in revised form 4 August 2016 Accepted 4 August 2016 Available online xxxx Keywords: Traffic violations Traffic signal violations Risk factors Road safety

a b s t r a c t Traffic signal violation is an important factor in causing accidents involving vehicles and pedestrians. Thus, studying factors influencing traffic signal violations in China seem extremely urgent and important. Using data collected from Guangdong Province in China, this study applies the Logistic model to analyze risk factors influencing traffic signal violations for three different groups, car drivers, cyclists, and pedestrians. Results indicate that road types and lighting conditions have different effects on traffic signal violations for the three groups. In addition, different ages have different effects on traffic signal violations for car drivers and pedestrians. Finally, occupations have different effects on cyclists and pedestrians. Therefore, China might establish policies and promulgate regulations based on the different risk factors for the three groups. Ó 2016 Elsevier Ltd. All rights reserved.

1. Introduction Noncompliance with traffic signals and stop signs at intersections is an important factor in the cause of accidents, and the risk of causing injuries after violating traffic signal is even higher than other types of accidents. For example, Retting, Williams, Preusser, and Weinstein (1995) found that running red lights is one of the most common traffic signal violations which explains 22% of urban accidents and 27% of all accidents with injuries in four cities of the United States (Akron, Ohio; New Orleans, Louisiana; Yonkers, New York and Arlington, Virginia). The injury rate for accidents caused by running red lights is 45%, and the injury rate for other accidents is 30%. In developing countries, bicycle is one of the primary forms of transportation. In China bicycles are the form of transportation used by more than 38% of daily commuters (Zhang & Lu, 2010), and the bicycle usage rate in many big cities exceeds 50% (Zhou, Lu, & Xu, 2007). In recent years, e-bikes have gradually become a popular transportation method (Weinert, Ma, Yang, & Cherry, 2007). Accidents caused by bicycles cannot be ignored (Zhang & Wu, 2013), and red light violation is the primary factor in accidents related to bicycles and other non-automobile vehicles (Spence, Dykes, Bohn, & Wesson, 1993). For example, in 2010, e-biking resulted in 178 deaths in Hangzhou, China, accounting for 23.4% of traffic casualties. CRTASR statistics (2004), CRTASR statistics (2007) indicate that more than 60% of fatal accidents involving two-wheeled vehicles were caused by traffic violations, and the most common violation was running red lights. E-bike riders running red lights caused 15% of the accidents in Hangzhou, China (Xinhua News, 2010). Approximately 80% of accidents involving e-bike riders in Shaoxin,

⇑ Corresponding author. E-mail addresses: [email protected] (G. Zhang), [email protected] (R.-C. Jou). http://dx.doi.org/10.1016/j.trf.2016.08.001 1369-8478/Ó 2016 Elsevier Ltd. All rights reserved.

Please cite this article in press as: Zhang, G., et al. Factors influencing traffic signal violations by car drivers, cyclists, and pedestrians: A case study from Guangdong, China. Transportation Research Part F (2016), http://dx.doi.org/10.1016/j.trf.2016.08.001

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G. Zhang et al. / Transportation Research Part F xxx (2016) xxx–xxx

China were caused by commuters running red lights (Pei, 2011). Approximately 76.9% of e-bike related casualty accidents in Haikou, China were due to commuters running red lights (Nanguo City Newspaper, 2008). Cyclists running red lights were regarded as the common behavior that annoys drivers most (Basford, Reid, Lester, Thomson, & Tolmie, 2002; O’Brien, Tay, & Watson, 2002). In addition, given the high population density, rapid modernization and urbanization, inadequate legislation, and noncompliance of drivers and pedestrians, accidents involving pedestrians violating traffic signal were a more serious problem in developing countries (Hamed, 2001; Khan, Jawaid, Chotani, & Luby, 1999; Yang, Deng, Wang, Li, & Wang, 2006). Approximately 40% of human in China choose to walk (Yang et al., 2006). Traffic violations by pedestrians, including poor judgment, dangerous behavior, ‘‘illegal crossing” which means that pedestrians cross roads at unmarked crosswalks and typical ‘‘Chinastyle crossing” were common occurrences at intersections (Zhou, Horrey, & Yu, 2009). ‘‘China-style crossing” is a phenomenon of Chinese pedestrians’ crossing behavior while waiting for red lights. That is, pedestrians accumulating to a certain number of group can cross the street regardless of the traffic lights. For example, pedestrians do not comply with traffic signals caused 682 accidents resulting in 276 injuries and 245 deaths in China in 2014 (Xinhua Net, 2015). Violating traffic signal is becoming an increasingly prominent problem in China. The number of accidents for traffic signal violations in China are growing at a considerable rate (increased from 4768 cases in 2012 to 7415 cases in 2014) (Xinhua Net, 2015). Some researchers reported various rates of traffic signal violations all around the world. Compared with other countries, China has a higher rate of traffic signal violation. For example, approximately 20% of drivers reported having one or more red light running in the United States (Porter & Berry, 2001). Johnson, Newstead, Charlton, and Oxley (2011) discovered that 6.9% of urban commuter cyclists infringed at red lights by observational study in Melbourne, Australia. While almost 56% two-wheelers riders ran red light in Beijing, China (Wu, Liu, & Zhang, 2012). However, empirical studies on traffic signal violation in China remain rare. Because traffic safety in China is becoming more important, studying the risk factors for traffic signal violations by cars, bicycles, and pedestrians seems urgent and important. Based on accident reports related to cars, bicycles and pedestrians between 2006 and 2010 in the Chinese Ministry of Public Security accident database, this study used a Logit model to empirically investigate risk factors for traffic signal violations in China and analyzed the individual, vehicular, road, and environmental characteristics of traffic violations caused by cars, bicycles, and pedestrians. 2. Literature review Previous empirical studies on vehicle drivers, cyclists, and pedestrians violating traffic signal indicate that risk factors affecting traffic signal violations primarily include four aspects: human, vehicles, road, and environment. 2.1. Human Being young and male, committing other traffic violations, and past traffic violation records are all factors affecting vehicle drivers’ traffic signal violation behavior. Studies have demonstrated that young male drivers are the most ‘‘typical red light runners” (Porter, 1999; Retting, Ulmer, & Williams, 1999). Other driving behaviors, such as speeding, driving under the influence of alcohol, distraction, and the use of safety belts, are also important factors affecting traffic signal violations. First, speeding significantly increases traffic signal violations and accident risk because driving fast or at a speed higher than the speed limit increases the distance necessary for stopping at intersections and accordingly reduces the braking distance available for responding to traffic signal changes (Federal Highway Administration – FHWA, 2005). Second, the likelihood of drivers under the influence of alcohol violating traffic signal is relatively high (Retting et al., 1999; Romano, Tippetts, & Voas, 2005). Third, distractions such as drowsiness, talking to passengers, eating, and using cellphones and other electronic devices reduce drivers’ concentration and sensitivity to signals, making them unable to predict signal changes in a timely manner, resulting in traffic signal violations (FHWA, 2005). Fourth, drivers not using a safety belt have a greater tendency to violate traffic signal (Deutsch, Sameth, & Akinyemi, 1980; Porter & England, 2000). Harper, Marine, Garret, Lezotte, and Lowenstein (2000) also found that drivers not using a safety belt, driving while intoxicated by alcohol, speeding, or using an invalid license have a greater tendency to run red lights. In addition, drivers with a record of violations predictably have a higher risk of running red lights. For example, Retting and Williams (1996) found that drivers under the age of 30 with accident records have a high risk of running red lights. The characteristics of cyclists who violate traffic signal are similar to those of car drivers: young males with histories of traffic violations and cyclists who are influenced by the behavior of other cyclists and the number of human crossing the street. Young male cyclists are more likely to run a red light (Basford et al., 2002; Johnson, Charlton, Oxley, & Newstead, 2013; Johnson et al., 2011; Richardson & Caulfield, 2015). The behavior of other cyclists and the number of human crossing the street also affect the behavior of cyclists. When only a few cyclists are waiting for a signal to change, other cyclists have already run the red light; when traffic is light, cyclists are more likely to run red lights (Johnson et al., 2011; Wu et al., 2012); formal or informal cyclist groups in which members ride together have a higher rate of running red lights (O’Connor & Brown, 2007). In addition, cyclists’ history of other traffic violations can indicate a risk of additional violations. For example, Johnson et al. (2013) have demonstrated that cyclists who have been punished for violations on motorcycles also have a high tendency to run red lights.

Please cite this article in press as: Zhang, G., et al. Factors influencing traffic signal violations by car drivers, cyclists, and pedestrians: A case study from Guangdong, China. Transportation Research Part F (2016), http://dx.doi.org/10.1016/j.trf.2016.08.001

G. Zhang et al. / Transportation Research Part F xxx (2016) xxx–xxx

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Gender and age affect pedestrians’ traffic violation behavior most significantly, and personality traits, marital status, education, income, and employment also affect pedestrians’ tendency to violate traffic signal. Young male pedestrians or adults with sensation seeking trait which always would seek fancy and complicated experience have higher risks of violating traffic regulations than women and elderly pedestrians (i.e., Díaz, 2002; Rosenbloom, 2003; Rosenbloom, 2006; Rosenbloom, Barkan, & Nemrodov, 2004). By contrast, the elderly are more likely to comply with intersection crossing and traffic signal rules (Bernhoft & Carstensen, 2008; Dommes, Granié, Cloutier, Coquelet, & Huguenin-Richard, 2015). Marital status, education, income, and employment conditions also affect pedestrians’ behavior (Gueguen & Pichot, 2001; LaScala, Gerber, & Gruenwald, 2000). Notably, personality traits also affect street crossing behavior. First, for individuals with a greater tendency for social compliance, complying with other human’s behavior might be an important reason for risking illegally crossing streets at intersections with signals (Hamed, 2001; Wu & Huang, 2006; Zhou et al., 2009), and the compliance rate of teenagers at the same age has a strong predicative power for risky behavior (Santor, Messervey, & Kusumaka, 2000). Second, the number of other pedestrians also affects pedestrians’ crossing behavior. For example, Hamed (2001) found that when the number of pedestrians increased, the waiting time of crossing pedestrians decreased and pedestrians were more inclined to cross streets along with others. Zhou et al. (2009) found that a pedestrian was likely to violate a traffic signal if other pedestrians violated the signal. In fact, a group of pedestrians violating traffic signal is common behavior (Public Security of China First Blog, 2006). Third, the behavior of drivers also affects pedestrians’ behavioral decisions. Studies indicate that drivers’ willingness to yield to pedestrians at crosswalks is low and that only one-quarter of drivers would slow down at intersections (Varhelyi, 1998). However, drivers are more inclined to slow down or yield to a group of pedestrians rather than a single pedestrian (Katz, Zaidel, & Elgrishi, 1975). Khan et al. (1999) also found that a group of pedestrians is 1.8 times more likely to cause a vehicle to swerve than a single pedestrian. 2.2. Vehicle Size, weight, and speed significantly affect the behavior of drivers and pedestrians at intersections. For example, Himanen and Kulmala (1988) demonstrated that the number of pedestrians, the size of the city, and the speed and size of the vehicle affected the likelihood that drivers and pedestrians would run red lights. In addition, when the traffic signal changes to yellow, vehicles with heavy loads need more time to decelerate and stop, but drivers of this type of vehicle might forget or ignore the effects of weight on braking distance, which causes them to violate traffic signal (FHWA, 2005). 2.3. Road The configuration of traffic lights at intersections might cause drivers to violate traffic signal because some designs and configurations might not provide sufficient breaking distance and could cause drivers to become confused or the designs and configuration might limit the visibility of traffic control devices (FHWA, 2005). For cyclists, the type of intersection, traffic flow, and time span of red lights affects the risk of traffic signal violations. Studies have indicated that cyclists have a greater tendency to run a red light at T- or Y-type intersections and intersections with light traffic flow and a short time span for red lights (Johnson et al., 2011; Pai & Jou, 2014). Road engineering factors such as signal lights, road width, and the presence of safety islands also affect the crossing behavior of pedestrians. Comparative studies indicate that signal lights or other road signs have significant effects on pedestrian behavior (Hatfield & Murphy, 2007; Jahangiri & et al., 2016; Ragland & Mitman, 2007; Rosenbloom, 2009; Tiwari, Bangdiwala, Saraswat, & Gaurav, 2007). Pedestrians prefer intersections with good signal lights over underpasses and pedestrian footbridges (Tanaboriboon & Jing, 1994). However, at intersections with signals where accidents involving pedestrians occur often (Tiwari, Mohan, & Fazio, 1998), pedestrians are more likely to violate traffic signals because the time span for signal changes increases (Tiwari et al., 2007). In addition, Mitman, Ragland, and Zegeer (2008) found that pedestrians were more careful when crossing streets at unmarked crosswalks. 2.4. Environment Factors such as weather and time of day also affect drivers’ behavior to a certain extent. Severe weather, bright sun, dust, and debris reduce visibility, distract drivers and affect drivers’ ability to observe signs, signals, and other traffic control devices in a timely manner. Rain and snow can also make roads slippery and increase breaking distance, further affecting drivers’ behavior at intersections. In addition, the specific time of day also has effects. For example, the morning and afternoon sunlight reduces the visibility of the color of signal lights (FHWA, 2005). For cyclists, bright sun and high temperature affect illegal behavior. The likelihood that cyclists will run red lights can be reduced significantly by installing sunshades (Zhang & Wu, 2013). Temporal factors such as season, day of the week and specific times of day also cause differences in pedestrians’ crossing decisions. For example, the Federal Highway Administration (2004) found that among all injury accidents involving pedestrians, 62% occurred at night. In addition, on weekend and holiday the red light violation rates for pedestrians were higher (Yan, Li, Zhang, & Hu, 2015). The degree of a country’s development, its social beliefs, and its regulations all affect pedestrians’ illegal activities (Hamed, 2001; Kouabenan, 1998). The percentage of pedestrians and drivers who comply with traffic rules is lower in developing countries, and low income countries generally have higher injury rates than developed countries (WHO, 2002). The Please cite this article in press as: Zhang, G., et al. Factors influencing traffic signal violations by car drivers, cyclists, and pedestrians: A case study from Guangdong, China. Transportation Research Part F (2016), http://dx.doi.org/10.1016/j.trf.2016.08.001

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G. Zhang et al. / Transportation Research Part F xxx (2016) xxx–xxx

number of extremely traditional pedestrians violating traffic rules is more than three times that of ordinary pedestrians (Rosenbloom et al., 2004). Social punishment also has more threatening influence on behavior than formal punishment (Hirschi & Gottfredson, 1994). In sum, although more studies have analyzed the problem of signal violation, previous research has commonly ignored the effects of the special economic and social development of developing countries on traffic safety and has failed to systematically compare the internal connections and distinctions between different transportation methods, such as driving, bicycling, and walking, and the risk factors for traffic signal violations. Although traffic signal violations in China are becoming more common and severe, empirical studies based on traffic signal violations in China are still rare due to the limited availability of accident data. Previous studies on traffic signal violations in China are primarily based on field observations (Wu et al., 2012; Zhang & Wu, 2013; Zhuang & Wu, 2011) or questionnaire surveys (Zhou et al., 2009) to collect data, but this type of data is not universal and might have sampling errors that cause researchers to miss many important variables, leading to estimate errors in analyzing risk factors for traffic signals violations. This study attempts to respond to the shortcomings in previous research by improving upon the following aspects: First, previous studies primarily focus on the effects of drivers’ individual characteristics, such as age, gender, and driving experience, on signal violations and ignore the effects of driving habits of different occupations and accident risks. Because occupation and identity characteristics, such as education, income, social status, and reputation, can reflect the effects of social ranking and class on risk attitude and driving behavior (Ganzeboom, De Graaf, & Treiman, 1992), this article analyzes the differences in risk for clerks, migrant workers, self-employed workers, and human in other occupations. Second, previous studies focus on separately analyzing signal violation risks of individual transportation methods, ignoring the differences in signal violation risk factors for different transportation modes. Thus, this study distinguishes the risk factors for signal violations based on transportation modes, including driving a car, riding a bicycle, and walking, and systematically compares the internal connection and differences of risk factors for traffic signal violations in connection with different transportation modes. Third, previous studies pay more attention to traffic signal violations in developed countries, and studies about developing countries are rare, although the situation in those countries is more serious. This study selects China, the largest developing country, as its research subject, and systematically examines the risk factors for violating traffic signals by cars, bicycles, and pedestrians. Research results have significant reference implications for policies and measures related to traffic signal violations for China and other developing countries. 3. Data and method 3.1. Data explanation and variable definition We analyze the traffic crash data for 2006–2010 in Guangdong Province, China. Data obtained from the Guangdong Provincial Security Department are extracted from the Traffic Management Sector-Specific Incident Case Data Report, which is the only officially available, most detailed, abundant, and reliable source of traffic crash data in China. Data are recorded and reported by the traffic police who conduct on-scene assessments and provide feedback within 24 h to the headquarters of the Traffic Management Department. The information is recorded according to the Code of Traffic Crash Information issued by the Computer and Information Processing Standardization Commission under the Security Department of the country. Each sample includes detailed indexes about demographic information, injury severity, vehicle characteristics, road conditions, crash time, as well as environmental conditions, such as the level, form, and cause of the accident, damage severity of the vehicles, type of the responsibilities of the parties, injury severity of the parties, trip purpose, vehicle status, type of the drivers, and insurance condition (Zhang & Wu, 2013; Zhang, Yau, & Gong, 2014). The study includes information regarding approximately 6220 car-related accidents, 2507 cyclist-related accidents, and 4817 pedestrian-related accidents, respectively. Accidents include those with no injuries, slight injuries and serious injuries. In light of the differences in risk factors for traffic signal violations by users of different transportation tools, this study categorized car drivers, cyclists, and pedestrians into three groups. To confirm the risk factors for traffic violations, this study set traffic signal violation as a dependent variable. The entry ‘‘1” indicates that traffic signal violations exist and cause accidents, and ‘‘0” indicates that traffic violations do not exist. To confirm the risk factors affecting traffic signal violations by individuals, this study established three types of variables: individual factors, road factors, and environmental factors. Individual Factors: In 2013, the UN World Health Organization established new categories for age. According to the policy, human ages 44 and younger are considered young, those 45–59 are considered middle aged, and those who are 60 years old and older are elderly. Based on the World Health Organization’s age categorization, this study divided drivers into four age groups: <24, 25–44, 45–59, and >60. Considering the relatively limited reaction capacities of new drivers, their lack of driving experience, and the fact that driving skills improve with experience, this study divided drivers into two groups based on experience: <1 and >1 year driving experience. Although an individual’s education, income, and social status are all prospective risk factors and relevant to traffic violations, the traffic violation accident database lacks such information. Occupation and identity characteristics include education, income, social status, and reputation, and they can reflect the effects of social status and class on risk attitude and driving behavior (Ganzeboom et al., 1992). Accordingly, this study categorized drivers’ occupations into farmer, clerk, self-employed, and other. This study established driving experience for the car driver

Please cite this article in press as: Zhang, G., et al. Factors influencing traffic signal violations by car drivers, cyclists, and pedestrians: A case study from Guangdong, China. Transportation Research Part F (2016), http://dx.doi.org/10.1016/j.trf.2016.08.001

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model, but it did not include the driving experience variable in the cyclists’ empirical model or in the pedestrian empirical model. Because China does not require regular bicycles to be registered or display license plates, and cyclists do not need to obtain any license; therefore, determining the experience of cyclists is difficult. Road Factors: The type and quality of roads affect traffic violations. Roads in China are divided into highway, regular road, and city road based on their type and rank. Regular roads include first class roads, second class roads, third class roads, fourth class roads, and roads without a class. City roads include city express roads, regular city roads, and other city roads. The road ranking in this study’s dataset does not include highways or city express roads, which do not have traffic lights. Different intersection types also significantly affect individuals’ traffic signal violations (Pai & Jou, 2014). Accordingly, this study considered the effects of different types of crossings, including regular roads, intersections, forked road, roads with multiple forks, and other crossings. Other crossings primarily included bridges, tunnels, and elevated roads. Many roads in China have physical isolation devices to restrict pedestrians and non-motor vehicles from randomly crossing. Physical isolation is the type of separation constructed on roads to divide traffic flows in opposite directions to ensure the safety of vehicles and pedestrians and smooth traffic flow. These separations primarily include barriers, green barriers, the sides of the road, and cement barriers. This study set a dummy variable for road physical isolation (1 represents an existing isolation, 0 represents no isolation) to test its effects on traffic signal violations. Environmental Factors: Environmental factors include lighting, weather, weekends, time period, season, and year. Lighting includes daytime, evening when roads are lit, and evening when road lights are off. This study set a dummy variable for weather because it is an important influential factor for traffic signal violations (FHWA, 2005). Existing studies also found that temporal factors such as season, different days of the week and specific time periods during a day lead to differences in crossing decisions (Federal Highway Administration, 2004). To test the effects of time on accident risks, this study sets Saturday, Sunday and any time after 5:00 pm Friday afternoons as dummy variables for the weekend. The time period is divided into early hours (midnight to 6:59am), morning peak hours (7:00–8:59am), after work peak hours (5:00– 7:59 pm) and other time periods. Years range from 2006 to 2010. 3.2. Method Because the dependent variable ‘‘traffic signal violation” is binary, this study adopted the Logistic model, which is appropriate to analyze binary variables:



ln

Pi 1
Pi



¼ a þ bi  X i þ li

ð1Þ

In the formula, Pi is the likelihood a traffic signal violation will occur, 1
Pi is the likelihood a traffic signal violation will not occur, bi is the estimated coefficient, li is a stochastic error term, and Xi denotes the independent variables affecting traffic accidents or injuries, including Human, Vehicle, Road, and Environment. Accordingly, the empirical model can be written as:



ln

Pi 1
Pi



¼ a þ b1  Humani þ b2  Vehiclei þ b3  Roadi þ b4  Env iormenti þ li

ð2Þ

In this model, the logit is the natural logarithm of the odds or the likelihood ratio that the dependent variable is 1 (a traffic signal violation) as opposed to 0 (not a traffic signal violation). The probability P of a traffic signal violation is expressed in Eq. (3):

Pr½yi ¼ 1jxi  ¼

expðx0i bj Þ 1 þ expðx0i bj Þ

ð3Þ

Among other variables, xi includes risk factors affecting traffic signal violations, 0 < Pr[yi = 1|xi] < 1. The model coefficient indicates the border effects of the relative probability an incident will occur. The ratio of the probability of incident occurrence to nonoccurrence is called the Odds Ratio (OR): Odds ¼ Pr½y ¼ 1=Pr½y ¼ 0 ¼ expðx0i bj Þ. The value range for the OR is [0, 1], which can be used to analyze the ratio change resulting from changes to one independent variable when other independent variables are constant. It is also the change ratio for the probability of occurrence of one variable and that of its reference level. OR > 1 indicates the increase in the red light violation accident occurrence ratio, OR < 1 indicates the decrease in the red light violation accident occurrence ratio, and OR = 1 indicates that the ratio is constant. According to the different of the transportation modes, we chose the three groups: car drivers, cyclists, and pedestrians. This study constructed three Logit models to separately examine risk factors for traffic signal violations by car drivers, cyclists, and pedestrians, considering the effects of different risk factors for traffic signal violations involving different modes of transportation. The 35 variables in 12 categories that this study tested are listed in Table 1, including gender, age, driving experience, occupation, the existence of physical road separations, road class, crossing type, lighting conditions, weather, whether it was a weekend, the time of day, and the year. Please refer to Appendix A for further details.

Please cite this article in press as: Zhang, G., et al. Factors influencing traffic signal violations by car drivers, cyclists, and pedestrians: A case study from Guangdong, China. Transportation Research Part F (2016), http://dx.doi.org/10.1016/j.trf.2016.08.001

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G. Zhang et al. / Transportation Research Part F xxx (2016) xxx–xxx

Table 1 The frequency of variables across different groups. Items

Variables N

Car drivers 6220

Cyclists 2507

Pedestrians 4817

x_signal

597

108

207

(1) Gender

Male

5834

1660

2852

(2) Age:

624 25–44 45–59 P60

585 4839 765 31

486 933 627 464

1431 1604 848 939

(3) Driving experience

61 year >1 year

560 5660

(4) Job

Farmers General staffs Migrant workers Boss Other occupations

255 740 1804 1107 2314

657 40 898 88 825

963 87 1609 178 1975

(5) Physical isolation

()Physical isolation

3701

1161

1932

(6) Type of road

The first class highways Substandard highway and the second third and fourth class highways Urban ordinary highways Urban other highways

765 1337 2476 373

451 993 885 175

713 1782 1936 361

(7) Type of junction

Ordinary road Triple intersection Quadruple intersection Multiple intersection Other intersection

4734 386 603 174 323

1930 201 226 50 98

3878 299 366 82 193

(8) Light condition:

Daylight Dark but lighted Dark

3099 2333 790

14576 725 326

2221 1782 819

(9) Weather condition

:Bad

1580

562

1079

(10) Day of week

Weekends

1729

632

1358

(11) Time of day

00:00–06:59 07:00–08:59 (a.m. peak hours) 17:00–19:59 (p.m. peak hours) Others

1207 435 964 3620

248 328 524 1407

650 376 1012 2779

(12) Years

2006 2007 2008 2009 2010

1057 1101 1275 1344 1418

471 489 539 514 484

944 939 1002 997 910

4. Results 4.1. Descriptive statistics The descriptive statistics of variables for car drivers, cyclists, and pedestrians are displayed in Table 1. As shown in the table, a total of 6220 car-related accidents, 2507 cyclist-related accidents, and 4817 pedestrian-related accidents, respectively, occurred between 2006 and 2010 in Guangdong Province, China. The frequencies (percentages) of traffic signal violations for car drivers, cyclists, and pedestrians were 597 (9.6%), 108 (4.3%) and 207 (4.3%), respectively. The results indicated that car drivers clearly had more accidents and a higher rate of traffic signal violations than cyclists and pedestrians. 4.2. Logistic regression analysis This study estimates three Logistic models to separately examine important risk factors related to traffic signal violations for car drivers, cyclists, and pedestrians. The models are estimated by using Stata version 14 (STATACorp., 2014) statistical software and the estimation results are listed in Table 2. It shall be noted that the estimation results obtained from this study are based on the recorded data of traffic signal violations which all caused accidents (violations without causing accident are excluded in this study).

Please cite this article in press as: Zhang, G., et al. Factors influencing traffic signal violations by car drivers, cyclists, and pedestrians: A case study from Guangdong, China. Transportation Research Part F (2016), http://dx.doi.org/10.1016/j.trf.2016.08.001

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G. Zhang et al. / Transportation Research Part F xxx (2016) xxx–xxx Table 2 Factors influencing traffic signal violations by car drivers, cyclists, and pedestrians in China. Factors

Car drivers (Odds ratio)

Cyclists (Odds ratio)

Pedestrians (Odds ratio)

Male

0.922

1.111

1.006

Age (base: 25–44) 624 45–59 P60

1.148 1.307** 0.715

0.996 1.317 0.736

0.713* 0.932 0.777

Driving experience (base: >1) 61

0.889

NA

NA

Job (base: farmers) General staffs Migrant workers Boss Other occupations Physical isolation

1.035 0.742 0.817 0.729 1.183*

0.763 1.968** 0.748 1.650 2.501***

2.248* 1.814** 1.359 1.591* 1.856***

Type of road (base: the first class highways) Substandard highway and the second third and fourth class highway Urban ordinary highways Urban other highways

1.612*** 1.846*** 1.096

0.898 1.431 0.461

0.534** 1.236 0.550

Type of junction (base: Ordinary road) Triple intersection Quadruple intersection Multiple intersection Other intersection

0.963 1.848*** 1.439 1.213

1.221 1.552 2.148 2.655**

0.588 0.711 1.140 1.490

Light condition (base: daylight) Dark but lighted Dark Weather condition: Bad Weekends

1.270** 0.456*** 0.955 1.147

0.656* 0.467* 0.709 1.272

0.992 0.439*** 1.198 0.880

Time of day (base: Others) 00:00–06:59 07:00–08:59 17:00–19:59

1.046 1.371* 0.978

0.874 0.897 1.159

0.890 1.106 0.974

Years (base: 2006) 2007 2008 2009 2010 Correct percent ROC N

0.725** 0.568*** 0.549*** 0.542*** 90.129 0.655 6220

0.887 1.060 1.023 0.583 95.732 0.728 2507

0.679* 0.477*** 0.493*** 0.468*** 95.723 0.714 4817

‘NA’: not applicable. * p < 0.1. ** p < 0.05. *** p < 0.01.

4.2.1. Car drivers Age is an important factor among individual factors for car drivers. The likelihood of middle aged human between 45 and 59 violating traffic lights (OR = 1.307) is higher than other age groups. Drivers’ gender, driving experience, and occupation does not have significant effects. With regards to road factors, road class has a significant effect on drivers’ traffic signal violation behavior. The risk of a traffic signal violation on a regular city road (OR = 1.846), second, third, or fourth class road or a road without a class designation (OR = 1.612) is significantly higher than that for first class city roads. However, the risk of a traffic signal violation on other city roads is not significantly different from that of commuting on a first class city road. The type of crossing also affected the occurrence of traffic signal accidents. The likelihood of traffic signal accidents at intersections is 1.848 times (OR = 1.848) that of regular roads. However, the risk of traffic signal violations at a forked road, roads with multiple forks, and other crossings is not significantly different from that of regular roads. In addition, physical road separations (OR = 1.183) also significantly increase the likelihood that a driver will violate traffic signals. Among environmental factors, lighting conditions affect traffic signal violations for car drivers. The likelihood that a violation will occur during the evening, when roads are lit, (OR = 1.270) is higher than for daytime, and the likelihood of violating traffic signal, during the evening, when road lights are off, (OR = 0.456) is lower than during daytime. These results indicate that when lighting conditions are relatively good, drivers are more likely to violate traffic signals. In addition, drivers are most likely to violate traffic signals during the morning peak time from 7:00 to 8:59 am. The year is negatively correlated Please cite this article in press as: Zhang, G., et al. Factors influencing traffic signal violations by car drivers, cyclists, and pedestrians: A case study from Guangdong, China. Transportation Research Part F (2016), http://dx.doi.org/10.1016/j.trf.2016.08.001

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to traffic signal violations. The risk of traffic signal violations decreases as the year increases: 2007 (OR = 0.725), 2008 (OR = 0.568), 2009 (OR = 0.549), and 2010 (OR = 0.542), which is possibly the result of increasing regulations for traffic signal violations and the improvement of drivers’ safety awareness. The effects of factors such as weekends and weather are not significant. In summary, for car drivers, compared to the drivers between 25 and 44, traffic signal violations are more likely to occur when drivers are between ages 45 and 59; when the road is physically separated or on a regular city road, second class road, third class road, fourth class road, or road without class; when at intersections; during the evening, when roads are unlit, or during morning peak morning hours between 7:00 and 8:59 am. However, the risk of car drivers violating traffic signals decreases during the evening, when there is no lighting. 4.2.2. Cyclists Among individual characteristics for cyclists, the effects of age and gender are not significant, but occupation has significant effects on traffic signal violations. The likelihood of migrant workers violating traffic signals is 1.968 (OR = 1.968) times that of farmers, but no significant difference in the number of traffic signal violations exists between farmers, clerks, selfemployed workers, and those in other occupations. In terms of road factors, road class does not have significant effects on the manner of traffic signal violations by cyclists. However, the risk of traffic signal violations differs at different types of crossings. The risk of traffic signal violations at other crossings, such as bridges, tunnels, and elevated roads, is 2.655 times (OR = 2.655) that of regular roads, but no significant difference exists between forked roads, intersections, roads with multiple forks, and regular roads in terms of traffic signal violations. In addition, the risk of traffic signal violations on roads with physical separations is 2.501 times (OR = 2.501) that of roads without physical isolation. The effects of environmental factors on traffic signal violations for bicycles are not significant. In other words, different light conditions, time periods, weather, and year do not cause significant differences in traffic signal violations by cyclists. In general, for cyclists, the likelihood of violating traffic signals is higher when they are migrant workers, when the road has physical isolation, when the crossing type is classified as other, such as bridges, tunnels, and elevated roads, or when it is daytime. The risk of cyclists violating traffic signals is lower during the evening, with relatively poor lighting conditions. 4.2.3. Pedestrians Among individual characteristics of pedestrians, age and occupation lead to significant differences in traffic signal violations. The likelihood of traffic signal violations by human younger than the age of 24 (OR = 0.713) is lower than for those ages 25–44, but no significant difference exists among the age groups of 25–44, 45–59 and 60 and older. In terms of occupation, clerks (OR = 2.248), migrant workers (OR = 1.814), and workers in other occupations (OR = 1.591) are all at significantly higher risk of traffic signal violations than farmers, but no significant difference in risk exists between self-employed workers and farmers. Pedestrians face risks similar to car drivers and cyclists in terms of road factors. The risk of traffic signal violations on roads with physical isolation is higher than on roads without such barriers (OR = 1.856). Road class also has significant effects on traffic signal violations by pedestrians. The likelihood of traffic signal violations is the lowest on second, third, and fourth class roads as well as on roads without class (OR = 0.534), and no significant difference in traffic signal violation risk exists among first class roads, regular city roads, and other roads. The type of crossing has significant effects on traffic signal violations by drivers. Among environmental factors, lighting conditions affect pedestrians’ traffic signal violations. The likelihood of traffic signal violations during the evening hours, when road lights are off, (OR = 0.439) is lower than during daytime hours, but no significant difference exists between evening times, when road lights are on, and daytime. These results indicate that the risk of pedestrians violating traffic signals is higher under relatively good lighting conditions. In addition, the risk of pedestrians violating traffic signals gradual decreases over the years. Overall, for pedestrians, those ages 25–44 who are clerks, workers, and in other occupations have a high risk of violating traffic signals when roads are physically separated. The risks of pedestrians violating traffic lights are lower on second class, third class and fourth class roads as well as on roads without a designated class and when it is evening time and roads are unlit. 4.3. Discussion By comparing empirical results of the models for car drivers, cyclists, and pedestrians, this study has found that the effects of gender are insignificant for all three groups, indicating that men and women in China have no significant difference in terms of traffic signal violation risk. This finding is apparently different from the view of many foreign studies that males are the ‘‘classic red light runners” (Porter, 1999; Retting et al., 1999). In terms of age, middle age drivers between 45 and 59 and young pedestrians between 25 and 44 are more likely to have traffic signal violations. These results are consistent with results from existing studies that young drivers are more likely than the elderly to violate traffic rules (Bernhoft & Carstensen, 2008). Nevertheless, age is not a significant risk factor for cyclists. In addition, individual occupation has significant effects on traffic signal violations for cyclists and pedestrians, while this is not the case for car drivers. More specifically, for cyclists and pedestrians, workers including migrants and clerks have the Please cite this article in press as: Zhang, G., et al. Factors influencing traffic signal violations by car drivers, cyclists, and pedestrians: A case study from Guangdong, China. Transportation Research Part F (2016), http://dx.doi.org/10.1016/j.trf.2016.08.001

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highest risk of violating traffic signals. Individual occupation can be viewed as a combination effect of other factors, such as individuals’ income, education level, and social status. The physical isolation of roads significantly increases the risk of traffic signal violations by car drivers, cyclists, and pedestrians. Generally speaking, road physical isolation can increase the degree of safety. The likelihood of traffic light violations is even lower when the physical isolation exists. Human might think that the risk of accidents caused by traffic signal violations becomes lower when physical isolation exists between motor vehicle lanes and non-motor vehicle lanes. Meanwhile regulation of traffic signal violations is not enough in china. These reasons might lead to the higher tendency toward traffic signal violations. Road class also affects the risk of traffic signal violations for car drivers and pedestrians but not cyclists. Among the types of crossing, accident risk involving car drivers is higher at intersections, and accident risk involving cyclists is higher at other crossings, such as bridges, tunnels, and elevated roads. Previous studies found that the risk of pedestrian traffic signal violations was higher at forked roads (Pai & Jou, 2014), and this study reveals that the risk of traffic signal violations by pedestrians in China is similar. These results are most likely due to the common ‘‘China-style crossing” used by Chinese pedestrians, and the difference among violations at different types of crossing is small. Among environmental factors, lighting conditions and year both have significant effects on car drivers and pedestrians, and the likelihood of traffic light violations is even lower during evening times when road lights are off. Under the dark condition, traffic signal violations have higher risks and would be more prone to cause accidents. Roadway users in dark have not confidence to detect approaching vehicles and cross the street cautiously and safely. Thus, they tend to wait for a green signal rather than going on red. The risk of traffic signal violations by car drivers and pedestrians decreased with the years, and no significant difference in cyclists’ traffic signal violations exists in different years. Different time periods also significantly affect traffic signal violations by car drivers. The risk of traffic signal violations by car drivers is higher during the morning peak between 7:00 and 8:59 am, and no significant difference in pedestrians’ traffic signal violations exists in different time periods. In addition, weekends and weather do not have significant effects in any of the three models. Thus, road physical isolation and lighting conditions are the common risk factors for traffic signal violations for car drivers, cyclists, and pedestrians. The likelihood of traffic signal violations for car drivers, cyclists, and pedestrians increases when the road is physically isolated and decreases in the evening and when roads are unlit. Gender, weekends, and weather have no significant effects on traffic signal violations for car drivers, cyclists, and pedestrians.

5. Conclusion Using data collected from Guangdong Province in China, this study analyzes risk factors for traffic signal violations by car drivers, cyclists, and pedestrians. Research indicates that traffic signal violations are more likely to occur when drivers are between the ages of 45 and 59; when the location is a second, third, or fourth class or uncategorized regular city road, a physically separated road, or an intersection; or when the commuting time occurs in the evening, when roads are lit, or at the morning peak time, between 7:00 am and 8:59 am. Cyclists are more likely to violate traffic signals when they are workers, when the road is physically separated, and during the daytime. Young pedestrians between the ages of 25 and 44 who work as clerks, laborers, or in other occupations are at high risk of violating traffic signals when the road is physically isolated. Some of the findings could be applied to reduce, to some extent, traffic signal violations and to improve road safety. First of all, there is an urgent need for more policies and regulations to deter traffic signal violations and punish offenders. Secondly, in order to enhance compliance with traffic signals, traffic safety campaign and mandatory requirement should be developed for road users, especially for middle-age drivers (45–59 years old), young pedestrians (25–44 years old), and some specific workers, such as migrant, clerks and workers in other occupations. Moreover, during the morning peak (from 7:00 to 8:59 am) car drivers at intersections, and at other crossings, such as bridges, tunnels, and elevated roads are considered to have a higher risk of being involved in traffic signal violations. Thus, traffic enforcements and control devices shall be setup effectively (spatially and temporally) to reduce traffic light violations at key spots that involve traffic accidents frequently. Finally, it is necessary to improve the infrastructure to modify this prohibited behavior of road users. For example, an automated traffic enforcement camera system should be installed and widespread applied. In addition, road physical isolation should be replaced by other road isolation facilities and strategies. Due to data availability, the current study only focused on the factors influencing traffic signal violations in Guangdong Province, and did not take driving behaviors, such as speeding, driving under the influence of alcohol, and the use of safety belts, into consideration, though these factors are deemed important. Also, future work can expand geographically to include other provinces of China to obtain more comprehensive and comparable conclusions.

Acknowledgements This research was supported in part by the National Natural Science Foundation of China Grant 71573286. Please cite this article in press as: Zhang, G., et al. Factors influencing traffic signal violations by car drivers, cyclists, and pedestrians: A case study from Guangdong, China. Transportation Research Part F (2016), http://dx.doi.org/10.1016/j.trf.2016.08.001

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Appendix A

Categories

Variables

Description of variables

x_signal

Red light running behavior = 1; otherwise = 0

(1) Gender

Male

Male = 1; otherwise = 0

(2) Age:

624 25–44 45–59 P60

Younger than 24 or 24 = 1; otherwise = 0 25–44 = 1; otherwise = 0 45–69 = 1; otherwise = 0 60 or older than 60 = 1; otherwise = 0

(3) Driving

experience Driving less than 1 year or 1 year = 1; otherwise = 0 Driving above 1 year = 1; otherwise = 0

61 year

(4) Job

Farmers General staffs Migrant workers Boss Other occupations

Farmers = 1; otherwise = 0 Staffs = 1; otherwise = 0 Workers from other cities or counties = 1; otherwise = 0 Owning a private business = 1; otherwise = 0 Other occupations = 1; otherwise = 0

(5) Physical isolation

()Physical isolation

Road has physical isolation = 1; otherwise = 0

(6) Type of road

The first class highways Substandard highway and the second third and fourth class highways Urban ordinary highways Urban other highways

The first class highways = 1; otherwise = 0 Substandard highway, the second class highways, the third class highways or the fourth class highways = 1; otherwise = 0 Urban ordinary highways = 1; otherwise = 0 Urban other highways = 1; otherwise = 0

(7) Type of junction

Ordinary road Triple intersection Quadruple intersection Multiple intersection Other intersection

Ordinary road = 1; otherwise = 0 Triple intersection = 1; otherwise = 0 Quadruple intersection = 1; otherwise = 0 Multiple intersection = 1; otherwise = 0 Bridge, tunnel or elevated road and so on = 1; otherwise = 0

(8) Light

condition: Daylight = 1; otherwise = 0 Dark but lighted In the night without street light = 1; otherwise = 0

Daylight

(9) Weather condition

:Bad

Bad weather = 1; otherwise = 0

(10) Day of week

Weekends

Weekends = 1; otherwise = 0

(11) Time of day

00:00–06:59 07:00–08:59 (a.m. peak hours) 17:00–19:59 (p.m. peak hours) Others

00:00–06:59 = 1; otherwise = 0 07:00–08:59 = 1; otherwise = 0 17:00–19:59 = 1; otherwise = 0 Others = 1; otherwise = 0

(12) Years

2006 2007 2008 2009 2010

Years Years Years Years Years

>1 year

Dark

In the night with street light = 1; otherwise = 0

of of of of of

2006 = 1; 2007 = 1; 2008 = 1; 2009 = 1; 2010 = 1;

otherwise = 0 otherwise = 0 otherwise = 0 otherwise = 0 otherwise = 0

Please cite this article in press as: Zhang, G., et al. Factors influencing traffic signal violations by car drivers, cyclists, and pedestrians: A case study from Guangdong, China. Transportation Research Part F (2016), http://dx.doi.org/10.1016/j.trf.2016.08.001

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