Performance analysis of urban drivers encountering pedestrian

Transportation Research Part F 62 (2019) 160–174

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

Performance analysis of urban drivers encountering pedestrian Abbas Sheykhfard, Farshidreza Haghighi ⇑ Department of Civil Engineering, Babol Noshirvani University of Technology, Shariati Ave., PO Box: 4714871167, Babol, Iran

a r t i c l e

i n f o

Article history: Received 8 July 2017 Received in revised form 23 December 2018 Accepted 30 December 2018

Keywords: Driver performance Naturalistic driving study Vehicle-pedestrian interaction Driver behavior Logistic regression model

a b s t r a c t According to the World Health Organization (WHO), Iran is one of the countries with the highest rates of road deaths in the world. About 18,000 people die every year in road accidents in Iran, where about 22 percent of the dead are pedestrians. The purpose of the present paper is to investigate the effective factors on performance of drivers during interaction with pedestrians. Therefore, the Naturalistic Driving Study (NDS) of behaviour of 66 participants (29 males and 27 females, 18–65 years old) has been evaluated in Babol city, Mazandaran province, during 2014–2016. The behavioral studies of the participants were conducted in 216 cases of vehicle-pedestrian interaction in divided road and 485 cases in undivided road through video-recorded process. The results showed that vehicle speed and distance to pedestrians are the most important factors affecting the occurrence of vehicle-pedestrian interaction in both sites. Moreover, the results show that on the divided road, Running when crossing the street by pedestrians, as well as listening to the music by drivers, increases the possibility of interaction. Also, on the undivided road, the attention to the traffic flow of road before crossing by pedestrians, as well as crossing the street in a group increase the probability of driver’s performance. Drivers responded to pedestrian crossing the street by some performances such as decreasing speed, changing the line, stop and acceleration collision. Finally, the probabilistic models of driver performance as well as the type of performances based on the variables affecting the behavior of drivers are determined using the binary logit model and multinomial logit model, respectively. Further model validation and transferability were checked and it has been observed that the driver performance and types of performance models developed in this study represents quite well. The inference of these models will be useful to assess Drivers’ behavioral models and suggest automotive assistance systems for improving pedestrian safety. Ó 2019 Elsevier Ltd. All rights reserved.

1. Introduction Pedestrians are the most vulnerable group of road users with a major share of road accidents and injuries. According to the World Health Organization report, more than 1.2 million people die each year in road accidents, accounting for 22% of the pedestrians’ share (World Health Organization, 2015). The casualties are not the same in different countries, and according to this report, countries with middle-income, although having 50% of the total number of vehicles in the world, account for more than 74% of road deaths. Iran (population: 77,447,168 based on the W.H.O report) is one of the middle-income countries with a road death rate of about 32.1 per 100,000 population, which is 1.84 times the global average (17.1). In 2017, 5478 pedestrians lost their lives in accidents (Iran forensic medicine, 2017). Therefore, studying the interaction between vehicles and ⇑ Corresponding author. E-mail addresses: [email protected] (A. Sheykhfard), [email protected] (F. Haghighi). https://doi.org/10.1016/j.trf.2018.12.019 1369-8478/Ó 2019 Elsevier Ltd. All rights reserved.

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pedestrians can be considered as a safety measure for identifying the accidental causes of pedestrian accidents. Based on the fact that the major share of accidents occurs due to human error, driver and pedestrian behavioral studies can lead to increased pedestrian safety. Studying the behaviour and performance of pedestrians and drivers facing each other is an approach towards identifying behaviour patterns and determining the factors that increases the potential for any possible accidents. Reports of accident databases mainly consist of the statistics of injured or killed people in traffic accidents and represent significant detailed information about the events before the accident or the characteristics of their environment (Larsen, 2004; Shinar, Treat, & McDonald, 1983). Recently, naturalistic driving study (NDS) for identifying causality factors of accidents has gained interest of world traffic researchers. In NDS, vehicles are driven in real-life traffic conditions and are equipped with cameras and sensors that can record video and provide information on the driving, flow of traffic and the characteristics of environment before and at the time of occurrence of an interaction and traffic accidents (Dingus, Klauer, Neale, Petersen, Lee, Sudweeks, & Knipling, 2006). Thus, NDS is a valuable source of information to study interactions causation. In the present study, have been tried the investigation a set of effective factors on the probability of occurrence of interactions between vehicles and pedestrians. Then using these factors, the model of the performance of drivers encountering pedestrians, as well as the types of these performances, will be presented on different conditions. Based on these causal models, it is possible to reduce interactions and rates of pedestrian accidents by developing instructions and conducting traffic control operations. Hence, the research data will be collected through NDS of 66 participants in two different sites. Considering the fact that pedestrian crash statistics are not the same in divided and undivided roads, the present study attempts to evaluate driver behavioral studies facing pedestrians in both places. The outcome of such an approach can lead to a better understanding of the behavioral patterns of drivers and pedestrians in different roads. It seems that users’ behavior patterns are not like on different roads. Therefore, the hypothesis of the present study will be based on the issue that our study approach will lead to the identification of different models of user roads’ behavior in both sites. The questions of the present study are the following: (1) Which factors lead to a possible collision between the vehicle and the pedestrian if the driver or pedestrian (at least one of them) does not make an evasive maneuver to avoid a collision? Are these factors alike on both sites? (2) What variables contribute to causing a safe passage by a pedestrian in both sites? (3) In both sites, how the drivers’ performance model prevents collisions with a pedestrian based on evasive maneuvers? This paper contains 6 sections. Previous studies, as well as their approach to road users’ behavioral studies, are presented in the second section. The third section includes the research method, the study sites, the variables, and the NDS in the present study. The results of modeling and interpretation will be presented in the fourth section and, the validity and transferability of the model will be evaluated in the fifth section. Finally, in the last section, an interpretation of the results and conclusions will be presented. 2. A review of the literature Various studies have been conducted on the behaviour of pedestrians and drivers, some of which have been done in the virtual environments (Dommes, Cavallo, Vienne, & Aillerie, 2012; Liu & Tung, 2014) and some in the real sites (Kadali & Vedagiri, 2013; Koh & Wong, 2014; Onelcin & Alver, 2015). In general, the set of factors that have been evaluated can be grouped into three common categories including. 2.1. Pedestrian and driver characteristics The effect of demographic characteristics of drivers and pedestrians on the possibility of accidents has been assessed on various studies. For instance, the effect of age and sex (Liu & Tung, 2014; Lobjois, Benguigui, & Cavallo, 2013), and size of group (Brosseau, Zangenehpour, Saunier, & Miranda-Moreno, 2013; Dommes, Granié, Cloutier, Coquelet, & HugueninRichard, 2015) has been reported in behavioral analysis of pedestrians crossing. Of course, it should be noted that only pedestrians and drivers do not lead to a possible interaction between them, for example, in Elenora et al., the effect of road type, traffic flow, and traffic control are reported about the behaviours of pedestrians during crossing (Papadakaki, Tzamalouka, Gnardellis, Lajunen, & Chliaoutakis, 2016). Minhas, Batool, Malik, Sanaullah, and Akbar (2017) evaluated the interactions between pedestrians and drivers of vehicles at the intersections of Lahore city in Pakistan. Using the intersection video recording and analyzing 1040 cases recorded, the behavior and characteristics of pedestrians and drivers, as well as the type of access on the probability of occurrence of interactions were identified. According to the results of this study, pedestrians showed more safety behaviors in order to cross the street in developed commercial districts (Minhas et al., 2017). Antic´, Pešic´, Milutinovic´, and Maslac´ (2016) studied the risky behaviours of pedestrians in Serbia through a questionnaire to assess their performance. Data analyses the pedestrians’ questionnaires showed that pedestrians usually show safer behaviours at long distances. Meanwhile, age and gender variables were among the parameters that caused behavioural differences in pedestrians as they crossed the street (Antic´ et al., 2016). Mphele, Selemogwe, Kote, and Balogun (2013) studied distracting behaviors of drivers and pedestrians in interactions with each other. The results of a statistical study based on mathematical probability indicated low - performance of the majority of drivers (80.4%) in interaction with pedestrians. Using a cell phone, text messaging, talking to passengers inside the vehicle were most important distracting behaviors that

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caused the poor performance of the drivers; also talking with other pedestrians, drinking or eating, and use of a mobile phone by pedestrians are the major distractions for pedestrians in interactions (Mphele et al., 2013). In other studies, singing and listening to music (Öztürk & Erzin, 2012) and also pedestrian’s age (Dommes et al., 2012) were defined as factors affecting the performance of drivers in vehicle-pedestrian interactions. 2.2. Their behavior at the time before crossing, during the crossing, and after crossing The behavior of pedestrians and drivers, while encountering each other, can cause a meaningful event on traffic safety. Avoiding the aggressive behaviors by each of them may lead to better cooperation between them, which conclusively form a safe crossing. Alver and Onelcin (2018)studied the behavior of pedestrians near the overpass locations to explore the factors affecting interaction. Pedestrian’ behaviors during the crossing were recorded by a video camera. Finally, the results showed that the impact of gender, age, vehicle position, group size and items carrying in interactions (Alver & Onelcin, 2018). These factors can lead to high-risk behaviors such as Rolling-Gap or crossing without attention to traffic before crossing (Minhas et al., 2017; Pawar & Patil, 2015) and increase the chance of interaction. Nowakowska used logistic regression analysis of 4444 cases of pedestrian-vehicle accidents in Poland based on variables such as the type of vehicle, pedestrians behavior before crossing, alcohol and drugs effects, the pedestrian size of groups and driving skills (Nowakowska, 2011). Habibovic, Tivesten, Uchida, Bärgman, and Ljung Aust (2013) assigned lack of drivers’ attention, visual obstructions at intersections, and unexpected pedestrian behaviors such as inattentiveness to traffic flow and crossing without crosswalks, as risk factors for pedestrian -vehicle interactions; using the driving reliability and error analysis method (DREAM) (Habibovic et al., 2013). Langbroek, Ceunynck, Daniels, Svensson, Laureshyn, Brijs, and Wets (2012), using logistic regression model, studied the information about the performance and behavior of drivers and pedestrians in the crosswalks in 594 cases. The results indicated that factors such as age, gender, number of pedestrians and pedestrians’ crossing styles (walking or running during crossing) were the main causes of the interactions, for example, women and elderly drivers yielded more for the Pedestrians (Langbroek et al., 2012). 2.3. The performance of drivers during crossing of pedestrians Drivers’ performance, in particular, evasive maneuvers at critical moments, can reduce the possibility of a collision with pedestrians. For instance, Studies showed that drivers of vehicles at intersections with traffic signalized also had a greater tendency to allow pedestrians to cross (Minhas et al., 2017). Alferova, Polyatsko, and Gorodokin (2017) studied the accidents of pedestrians in Russia in order to provide a safe pedestrian crossing model. By examining, factors such as the age and gender of pedestrians and drivers as well as the distance of vehicles from possible collision point, the model of the speed of pedestrians and vehicles were presented to reduce the probability of accidents (Alferova et al., 2017). Hunter et al. (2015) studied 975 interactions between pedestrians and vehicles to analyze the driver behavior in USA using logistic regression, which suggests that the probability of drivers yielding to pedestrians increases by the impact of factors such as short distance to crosswalks, low-speed vehicle, pedestrians at the edge of the crosswalks, yielding by opposite direction vehicle driver, group size of pedestrians or students (Hunter et al., 2015). Sucha (2014) analyzed the interaction between vehicle and pedestrian in 1584 cases using the logistic regression model; parameters such as vehicle speed, gap, and traffic density were important factors that affected the decision of pedestrians to cross. Also, use of cell phones by drivers, short distance to pedestrians and low-density traffic flow reduced driver’s performance in interactions with pedestrians (Sucha, 2014). Another research conducted by Salamaty et al, on 1150 interactions between vehicles and pedestrians at crosswalks near two-lane roundabouts predicted the reaction of drivers using logistic regression model. He suggested that the speed of the vehicles, crosswalk locations (near to the input or output roundabouts) and physical condition of pedestrians (use of a cane) influence the decision-making by drivers (Salamati, Schroeder, Geruschat, & Rouphail, 2014). In the present study, Attempts are made to conduct behavioral studies of pedestrians and drivers simultaneously, in divided and undivided roads. This issue may lead to greater recognition of the behavioral differences between drivers and pedestrians as well as the performance of drivers on such roads. Also, the factors consists of three parts which are: (a) factors related to the driver: vehicle speed, listening to music, talking to passengers, attention to road, being hasty, total time of driving license, education, gender, performance of the driver in opposite lane, performance of the front vehicle’s driver, being new in the road, distance between vehicle and pedestrians, vehicle group; (b) factors related to pedestrians: age, gender, crossing places, attention to traffic flow, behavioral status before crossing, location of pedestrians when noticed by approaching drivers, asking to cross, doing other work during crossing, crossing methods and speed, number of pedestrians; (c) factors related to road characteristic: visual obstruction. 3. Methodology and data 3.1. Participants As discussed in the previous section, the pedestrian safety issue has long been studied by researchers. The use of accident database data was initially considered as the most important approach to investigate the causes of accidents. Despite the

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advantage of using these data, the use of new approaches has been taken into account due to limitations of database data approach such as lack of recording of all information, the lack of information on some sites, and so on. The method of data recording by observers in the field and the installation of fixed cameras on the site, were recognized as new generation approaches. Nowadays, although many studies are conducted in different places based on these approaches, however, the lack of precise understanding of the interactions between drivers and pedestrians, as well as the set of behaviors and practices of pedestrians and pedestrians at times of encounter, has motivated us to form a new framework by studying of the behavior of driver and pedestrians, simultaneously. On the other hand, due to the importance of users’ behavioral differences in different segments of the roads, have been tried to assess the pedestrian safety in divided and undivided segments of an urban road. The present Research data was obtained through video data by observations of pedestrian and driver behavior. In other words, the participants were asked to drive in a vehicle equipped with some camera on the study sites. Their behaviors and performance when faced with pedestrians, as well as pedestrian behaviors were considered. Table 1 lists the variables extracted from the examination of the simultaneous behaviors of drivers and pedestrians in study sites. An interaction is an observable situation in which two or more road users approach each other in space and time to such an extent that there is a risk of collision if their movements remain unchanged. So, in the present research, the driver performance is defined as any evasive maneuver (For instance deceleration, changing the lane . . .) that the driver has shown after seeing pedestrians in order to prevent collision with them. 66 participants (39 male drivers, 27 female drivers; 18–65 years; with different jobs and levels of education) were asked to drive on roads. All participants had a valid driving license with average 4.7 years

Table 1 Variable definition. Variable

Code

Description

Type

Value

SPEED

SPEED

Continuous

Kph

DISTANCE

DIST

Pedestrian group size LICENCE Pedestrian Speed

MUP LICENCE P.SPEED

The speed of the vehicle (kph), at the time encountering the pedestrian, recorded from camera (see Fig. 1) The distance between vehicle and pedestrian at the time of encounter The number of pedestrians when crossing Age of driver’s license Pedestrian’s speed during crossing

Place of Crossing Pedestrian Behavior Pedestrian Age

P.C.P P.BEH P.AGE

The place of pedestrian’s crossing Attention to traffic flow before crossing Pedestrians’ age (based on judging by appearance)

Discrete

Crossing Style Driver Gender Pedestrian Gender Place of Encountering

H.CROSS D.GENDER P.GENDER P.SEEN

Pedestrian Position

P.POST

Pedestrian Second work

P.SECWORK

Crossing style by Pedestrian Driver’s gender Pedestrian’s gender Place of pedestrians when seen by driver of approaching vehicle Pedestrian’s behavioral status before crossing Pedestrian’s mode when crossing

Music Conversation Driver Attention Visual obstruction HURRY Platoon

MUSIC D. Talk D. ATT V.O HURRY PLT

ALLOW Vehicle Group

ALLOW MUV

Opposite line Driver

ADY

Previous Vehicle

PREV

Driver education

EDU

Listen to music by driver Talk to passengers by driver Driver’s attention to road Visual obstruction Driver being hasty If the first vehicle was in a platoon or had a close follower Pedestrian asking for crossing If approaching vehicle is a part of vehicles group Does a performance by the driver of vehicle in opposite direction Does a performance by the driver of front vehicle in same direction Driver education

Driver performance

Yield

Driver performance

Meter Number Year Mps Crosswalk = 1, Without crosswalk = 0 Attention = 1, No attention = 0 Under 30 years = 1 30-60 years = 2 + 60 years = 3 Walking = 1, Running = 0 Male = 1, Female = 0 Male = 1, Female = 0 At curb = 1, In middle of street = 0 Walking along the road = 1, Waiting = 0 Speaking with a cell phone = 0, Talk to other pedestrian = 1, Texting = 2, Drinking/Eating = 3, No factor = 4 Yes = 1, No = 0

High school diploma = 0, B.s = 1, M.s & Ph.D. = 2 No yield = 0, Stop = 1 Deceleration = 2, Changing lane = 3, Acceleration = 4

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of driving (ranging from 1 to 11 years) and average of 7600 km per year (ranging from 2600 to 11,500 km). Information about the purpose of the study was not provided to the participants to prevent the possible impact of the study on their driving behavior. On average, NDS was examined about 30 min for each participant and they drove about 18 km in the roads. Three cameras (Fig. 1) installed inside the vehicle during driving recorded behaviour of the participants. The first one, a high-quality dual camera (480
640 pixels and 25 frames per second), was installed under the front mirror of the vehicle that can simultaneously record inside and outside (the front road) of the vehicle. The second camera, high quality (480
720 pixels and 30 frames per second), was installed at the rear cabin of the vehicle in such a way that it can record the pedals of the vehicle. 3.2. Road of study The road (Shariati Street) is a two-way street that is located at the city center of Babol City, Mazandaran Province, Iran. Each line of this street has a width of about 11 m, which includes two lanes of 3.65 m for passing the vehicles and a parking line. In this street, pedestrian traffic is significant as consequence of schools, university and shopping centers. Further, due to the location of the road at along the exit road to the coastal city of Babolsar, there is always a relatively high traffic density. Part of this road has median and other part no, so for modeling the performance of drivers, two parts were studied under separate sites. Fig. 2 shows the aerial and schematic illustrations of the study roads. Behavioural analysis of participants was evaluated in a study course from March 2014 to March 2016. This period was considered in order to investigate the behaviour of drivers in different seasons and the probability of behavioural changes of drivers according to the conditions. Studies the crossing behaviour of pedestrians were conducted in the hours of the day when the volume of pedestrian crossing reached the maximum. For this reason, studies conducted in time intervals in the morning, noon and evening. The recorded videos were examined by frame-to-frame using TRACKER Software. Finally, by observing the films during the driving of the participants, 701 cases of interaction between vehicles and pedestrians were distinguished. To avoid the influence of the camera on the behaviors of participants, as well as to get used to the simulation circumstance of the study, they were asked to drive three rounds on roads. Then, after that, their behaviour and performance were studied in the next round. 3.3. Logistic regression One of the primary goals of regression analysis is prediction of response by a given value of independent variables, which is the main goal of analysis based on regression (Yannis, Papadimitriou, & Theofilatos, 2013). Logistic regression is a special

Fig. 1. Views of the inside and outside of the vehicle in simulation circumstance.

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Fig. 2. An aerial photo and a sketch of the study sites (Site 1: Divided Road; Site 1: Undivided Road).

case of regression with two or more options used for the response variable, In other words the existence of only two or more different modes (Koh & Wong, 2014; Pawar & Patil, 2015). Logistic regression can be binomial, ordinal or multinomial. Binomial or binary logistic regression deals with situations in which the observed outcome for a dependent variable can have only two possible types, ‘‘0” and ‘‘1”. In this study, the dependent variable (target) were considered as two cases; (a) Driver Yielding (Does Performance), and (b) Driver Failure to yielding (Does not performance) when encountering pedestrians crossing the street. The model is in the form of Eqs. (1) and (2):




logitðpÞ ¼ ln

PrðYi ¼ 1jxÞ ¼

p 1p



¼ a þ b1 x1;i þ b2 x2;i þ    þ bk xk;i i ¼ 1; 2;    ; n

elogitðpÞ 1 þ elogitðpÞ

ð1Þ

ð2Þ

Which is the probability of driver’s yield (does-performance) when they see pedestrians at the ith event (Yi ), the independent variables affecting the driver’s performance (xk ) and the independent variable coefficient (bk ). Accordingly, the possibility of driver’s performance with Y = 1 (Does Performance) and Y = 0 (Does not performance) was considered. If the dependent variable is more than two, the multinomial logit can be used. In this study, after defining the effect of independent variables on the driver’s performance using logistic regression model, the impact of variables on the type of driver’s performance were evaluated through the multiple logistic regression. Drivers’ behavior while encountering pedestrian can be categorized into different performance types such as stop, deceleration, acceleration, and changing the lane of road. The general form for multinomial logistic model is in the form of Eq. (3):

PrðY ¼ ijxÞ ¼

e½hi ðxÞ Pn ½h ðx Þ 1 þ i¼1 e i

ð3Þ

where hi ðxÞ is the function of independent variables and Pr (Y = i|x) the probability of performance types. In the simplest case, considering the one of three types of driver’s performance (for example, the stop; Y = 0) as the basis, calculation can be provided based on the Eqs. (4)–(7).

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PðY ¼ 0Þ þ PðY ¼ 1Þ þ PðY ¼ 2Þ þ PðY ¼ 3Þ ¼ 1

ð4Þ

PðY ¼ 0Þ þ PðY ¼ 1Þ– 1

ð5Þ

PðY ¼ 0Þ þ PðY ¼ 2Þ–1

ð6Þ

PðY ¼ 0Þ þ PðY ¼ 3Þ–1

ð7Þ

where P (Y = 0) the possibility of deceleration by the driver, P (Y = 1) the possibility of vehicle stop by driver, P (Y = 2) the possibility of changing the lane of the road by the driver, and P (Y = 3) the possibility of vehicle acceleration by driver. So the probability function for each of the three types of performances in the form of Eqs. (8)–(11) is: (assuming the acceleration as a basis):

PrðY ¼ 0jxÞ ¼

PrðY ¼ 1jxÞ ¼

PrðY ¼ 2jxÞ ¼

PrðY ¼ 3jxÞ ¼

e½h0 ðxÞ ½h1 ðxÞ

1 þ e½h0 ðxÞ þ e

þ e½h2 ðxÞ

e½h1 ðxÞ 1þ

e½h0 ðxÞ

þe

½h1 ðxÞ

þ e½h2 ðxÞ

e½h2 ðxÞ 1 þ e½h0 ðxÞ þ e

½h1 ðxÞ

þ e½h2 ðxÞ

1 1þ

e½h0 ðxÞ

þe

½h1 ðxÞ

þ e½h2 ðxÞ

ð8Þ

ð9Þ

ð10Þ

ð11Þ

4. Analysis and results 4.1. Descriptive statistics 4.1.1. Site (1) the divided road On the first site, there were 216 vehicle-pedestrian interactions (216 ¼ 30:81%). The Table 2 shows the descriptive char701 acteristics of some of the variables examined on site 1. According to the Table 2, about 78% of male pedestrians were involved ) more than female pedestrians. This result was in vehicle-pedestrian interactions, which is more than 3.5 times (78:25% 21:75% because of high-risk behaviours such as lack of attention to the road or sudden running on the street by male pedestrians. Also, the effect of driver gender of driver men and women in the vehicle-pedestrian interaction was relatively similar (30.76% vs. 29.62%), and no significant difference in performance of them. The variable MUP represents the number of pedestrians who intended to cross the street. According to the Table 1, pedestrians who crossed the street in groups (more than two pedestrian) are less likely to be involved in vehicle- pedestrian interactions than one or two pedestrians. The proportion of pedestrians in the event of interactions is 3.35 times as much as pedestrians with more than two people intend to cross the street. Furthermore, out of a total of 216 interactions that took place on the divided road, 139 of those occurred in places where there was no pedestrian crosswalk. In these places, participants drive regardless of the possibility of pedestrian crossing. So the interactions occurred due to factors such as high speed Non-expectation of the possible presence of a pedestrian on the road. According to the Table 1, it can be seen that cross as running increases the risk of interactions by 3 times than walking (Running: 164 cases and walking: 52 cases). Secondary behaviours of pedestrians when occur of interactions were cell phone (101 items), Talk to other pedestrians (22 cases), texting (49 items), and eating/drinking (14 cases). Also, about 20% of pedestrians informed the drivers about their decision to cross, that they were identified in 43 of the interactions. In 80% of the interactions, drivers did not see any gestures from pedestrians regarding their intention to cross the street (173 cases). 4.1.2. Site (2) the undivided road 485 interactions occurred on a part of the road that was no median (69.19%). In this section, in consequence of the behaviour of other drivers of the opposite road, a more complicated situation of the conditions was found on the behaviour of the participant driver. Table 3 describes the statistics of drivers and pedestrians’ behaviour in the event of an interaction between them. According to the above table, 72% of pedestrians on interactions were men, which is more than portion of pedestrian woman. The reason for this difference is in men’s behavioural characteristics, such as the tendency to walk at a faster rate. On site 2, as in the first site, there was no difference among driver gender in occurrence of interactions, although the impact of female drivers was slightly higher than male drivers (40.55% vs. 38.75%) As in the first site, in site 2 alone pedestrian interacted more than the number of other groups. As another point of view, the probability of the involvement of groups of more than 2 pedestrian on site 2 is less than the probability of these groups on site 1. On Site 2, like on site 1,

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Count

Proportion (%)

P.GENDER

Male Female

169 47

78.25 21.75

D.GENDER

Male Female

12 of 39 8 of 27

30.76 29.62

MUP

Alone A group of two More than two

104 81 31

48.15 37.50 14.35

P.C.P

Crosswalk Without Crosswalk

77 139

35.65 64.35

H.CROSS

Walking Running

52 164

24.18 75.92

P.SECWORK

Talk with a cell phone Talk to other pedestrian Texting Drinking/Eating Didn’t Happened

101 22 49 14 30

46.75 10.18 22.68 6.48 13.88

ALLOW (Pedestrian gestures to inform drivers of their intent to cross)

Yes No

43 173

19.90 80.10

Table 3 Descriptive statistics of some behavioral variables of pedestrians and drivers on site 2. Variable

Count

Proportion (%)

P.GENDER

Male Female

351 134

72.35 27.65

D.GENDER

Male Female

15 of 39 11 of 27

38.45 40.75

MUP

Alone A group of two More than two

266 148 61

56.90 30.51 12.59

P.C.P

Crosswalk Without Crosswalk

101 384

20.82 79.18

H.CROSS

Walking Running

143 342

29.48 70.52

P.SECWORK

Talk with a cell phone Talk to other pedestrian Texting Drinking/Eating Didn’t Happened

198 64 96 39 88

40.82 13.19 19.79 8.04 18.16

ALLOW (Pedestrian gestures to inform drivers of their intent to cross)

Yes No

129 356

26.60 73.40

dangerous behaviour, such as running, increased the likelihood of an interaction compared to walking (approximately 70% vs. 30%). Also, on site 2, the secondary behaviours of pedestrians were talking by cell phone (198), talking with other pedestrians (64), texting (96) and eating/drinking (39). Additionally, on this site, the likelihood of interactions with pedestrians who raised a hand to driver approaching them was less than other pedestrian groups. 4.2. Binary logit model of drivers’ performance 4.2.1. Site 1 The variables of Table 1were entered into the SPSS software (version 24) in order to investigate their effect on behaviour and performance of drivers. The binary logit model was used to determine the set of factors that would lead to does or not performance from the driver. The Table 4 shows the final model of the effective variables (95% confidence level); some of the variables are excluded due to the low impact on dependent variable of the model. According to Table 4, among the variables of Table 1, only 7 variables affect the driver’s decision of doing performance or not (95% confidence level). For the speed variable, the negative coefficient of this variable represents the effect of the contrariwise of this factor on the probability of doing a driver’s performance when facing a pedestrian. In other words, with

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Table 4 Estimation binary logit model results of effective factors on Driver performance on site 1. Variable

bi (Coefficient)

p-value

Std. error

t-stat

Speed Distance MUP

1.134 0.928 +0.274 +0.504 0.313 0.626 +0.195 +0.312

0.011 0.015 0.020 0.020 0.025 0.018 0.028 0.033

0.047 0.042 0.018 0.024 0.045 0.043 0.016 0.009

3.25 2.12 2.45 5.12 1.19 1.77 2.88 3.45

H.CROSS MUSIC ALLOW PREV

(In a group of two) (In a group more than two) (Running)

increasing of speed of the vehicle, the chances of performance by the drivers are reduced (Odd ¼ eb ¼ e1:134 ¼ 0:321). So, the 1 ¼ 3:11). Insufficient time to reaction due to the high likelihood of performance decreases by 3.11 times (Odd Ratio ¼ 0:321 speed of the vehicle when facing a pedestrian is one of the reasons that led to failed of performance. The distance between the vehicle and the pedestrian is another important factor that led to behavioural changes in the decision of drivers. At short distance, the likelihood of performance by drivers decreased by 68% (ðe1:134 Þ: In fact, due to short perception-response time to decide, drivers failed to do a proper performance. The effects of vehicle speed and distance between vehicle and pedestrians on drivers are presented in Fig. 3. Fig. 3 shows that at short distances, the drivers do performance at low-speed. On the other hand, drivers at high speeds preferred to continue at the same speed and direction because of observation late or the inability to control the vehicle in order to cross the pedestrian. In long distances, drivers showed another status in terms of performance. As shown in Fig. 3, with increasing vehicle-to-pedestrian distance, drivers were able to operate even at speeds more than the speed limit (30 km/h) and allow pedestrians to cross. These performances were changing the line, decreasing speed and even increasing speed to cross the intersection point of the vehicle and pedestrians (the potential point of the accident). By analysing recorded films, it was observed that driver behaviour is different while encountering different group size of pedestrians. During the 216 interactions detected in the study, 185 vehicle-pedestrian interactions occurred when group sizes of pedestrians were less equal than two. Further, results showed that the number of pedestrians is one of the parameters affecting drivers’ behaviour (sig < 0.05). So, by increasing the number of pedestrian’ group size (more than 2), the likelihood of performance by drivers 65% will increase. Behaviour and performance of the driver, in addition to his cognitive and perceptual characteristics, is connected to the behaviour and performance of other road users. In the present study, among the 216 interaction cases on the divided road, 164 cases occurred when pedestrians crossed the street as running. As a result of this, due to the insufficient time to make decisions by drivers, they could not react to pedestrian and continued on the same speed and direction. This behaviour is very risky if the distance between the vehicle and the pedestrian is less than the amount that the pedestrian can travel through possible collision point with vehicle. The coefficient of the running according to the above table was equal to 0.313, which indicate the 36% reduction (Odd Ratio = 1/0.731 = 1.36) of the driver’s perfor-

Fig. 3. Plot of drivers’ performance encountering pedestrians in site 1.

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mance while pedestrian running across the street in comparison with the pedestrians across as walking. The result show that pedestrians who inform driver of their intent to cross by raise a hand were able to cross 21% more than other pedestrians. Indeed, the likelihood of driver’s performance such as decreasing speed for crossing of this group of pedestrians was 1.21 times than of pedestrians who did not any gesture of their intent to cross. Regarding to playing music, among the 66 participants, 48 of them preferred listen to music, which involved in 73% of the interactions. The results (Table 4) indicate that this factor will reduce the likelihood of performance by the driver by 53%. Part of driving performance can be caused by the impact of the environment factors or other drivers on same direction. As behaviours such as unauthorized speed or sequential changing the lane can lead to disruption of traffic flow, other driving behaviour such as driving on speed limit can influence other drivers’ behaviour and encourage them to do this. In the present study, the PREV factor was evaluated as a factor that could reflect the impact of driver’ performance of front vehicle (a vehicle that is moving ahead of the tested vehicle in this study) on participates in the research. The review of films showed that in 61% of the interactions, drivers do performance (such as decrease speed or stop) by observing driver’ performance of front vehicle performance facing pedestrians. In fact, the behaviour of such drivers (PREV) was identified as an increasing factor in the likelihood of performance on other drivers’ performance (this factor increases possibility of drivers yielding by 1.36). Finally, considering the effective factors in Table 4 and using Eqs. (1) and (2), the binary model of probability the driver’s performance facing pedestrians can be determined on the first site. 4.2.2. Site 2 In this road, the variables affecting the performance/non-performance of the driver were determined after analysing the data extracted from the observations of the films in the SPSS software. According Table 5, in addition to the 7 effective variables known on site 1 (vehicle speed, distance between vehicle and pedestrian, group size of pedestrians, type of pedestrian crossings, music, front driver performance (PREV) and opportunity of crossing for pedestrians (Allow), 4 other variables are also effective on of behavioural decision making. The attention of the driver to the road (D.ATT), the place of the pedestrians in the street when seen by the driver (P.SEEN), the place of pedestrian crossing (P.C.P), and the behaviour of pedestrians before the start of the crossing (P.BEH), affect the performance of drivers. Speed, vehicle-to-pedestrian distance, listening to music, and running the pedestrian during crossing on site 2, like site 1, have reduced the driver’s chances of performing (the variable coefficients listed in the table above). Also, the effects of vehicle speed and distance between vehicle and pedestrians on drivers are presented in Fig. 4. Also, variables such as crossing as a group, the asking for permission by raise a hand and the performance of the frontdriver resulted in an increased probability of the driver being tested on the second site (similar to the first site). In general, the lack of median in the road resulted in behavioural changes on pedestrians and drivers in comparison to the previous road. About behavioural changes of pedestrians, pedestrians in site 2 more than pedestrians in site 1 looked at the traffic flow before begin to crossing (more than 69%). So, pedestrians were better acquainted with the conditions of the traffic flow and could make a proper decision to cross the street. The results show that the drivers were more likely to do the performance of the other pedestrian group 1.17 than pedestrians who crossed the street regardless of the traffic flow and the approaching vehicle. The place of pedestrian crossings was recognized as an effective factor on driver performance (sig = 0.015). Observation the films showed pedestrians crossed the street in various parts of the road (Because of no median). The analysis of the data represented that drivers in this places take different status on pedestrian crossings. Approximately 67% of pedestrians crossed on crosswalks which in result the driver’s performance was increased by 4 times in comparison with crossing of pedestrians from non-crosswalk places. Crossing through non-crosswalk places led to an increase in the likelihood of non-performance by driver due to the lack of mental expectancy from them regarding the possible presence or crossing of pedestrians in such places. This factor can be dangerous situation along with other high-risk factors as unauthorized speed or a short distance from the vehicle to the pedestrian. The result indicates the driver’s inattention to road, because of look to the opposite flow of traffic, the attention to the opposite side billboards or the distraction caused by an incident in the opposite direction (such as an accident, a discussion between two drivers opposition) led to a

Table 5 Estimation results of effective factors on Driver performance on site 2. Variable

bi (Coefficient)

p-value

Std. error

t-stat

Speed Distance MUP

0.842 0.752 +0.117 +0.308 0.483 0.707 +0.263 +0.414 1.386 +0.163 0.493 1.014

0.020 0.035 0.010 0.010 0.014 0.008 0.002 0.009 0.005 0.017 0.025 0.039

0.015 0.012 0.032 0.040 0.006 0.010 0.020 0.018 0.015 0.030 0.022 0.026

2.511 2.236 1.851 3.257 1.632 2.022 3.089 3.747 8.205 1.155 3.419 5.802

H.CROSS MUSIC ALLOW PREV P.C.P P.BEH P.SEEN D.ATT

(In a group of two) (In a group more than two) (Running)

Middle of street

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Fig. 4. Plot of drivers’ performance encountering pedestrians in site 2.

reduction in focus and attention to the front. About 44% (17 participants) of the drivers, at least once, looked at their opposite (Some of them a few times), which was reported during the course of 71 interactions. According to the Table 5, it is obvious that the likelihood of an interaction in the condition that the driver does not pay attention to the traffic flow increased by 2.77 times. On site 1, drivers did similar performances facing pedestrians in the middle of street and who were at curb. But on site 2 this situation was not acted by drivers. According to the analysis of films, the likelihood of hazardous behaviours such as running, crossing without attention to traffic flow (pedestrians who were able to reach the middle of the street in the opposite direction) 80% more than pedestrians were on the curb of the street. In other words, this group of pedestrians showed fewer tendencies to wait and find safe gap for crossing. Further, the risk of interaction was 1.63 times as much as pedestrians on the curb of the street waiting for a safe gap or allowing the driver to cross (performing a state of performance such as a decreasing speed). Therefore, the place of these pedestrians (middle of street) was identified as an effective factor on driver behaviour facing pedestrian. It should be noted that the lack of median as a traffic Refuge Island for pedestrians had an effective role in the incidence of aggressive behaviours by pedestrians for faster crossing and avoiding collision with the vehicle in the middle of the street. Eventually, probability model of drivers’ performance based on the effective factors (Table 5) can be determined on site 2. As shown in Fig. 4, as in the first site, drivers on the second site did not perform at high speeds and low distances encountering pedestrians. Late observation or miss observation the pedestrians by drivers or, the inability of the driver to decide in the short term are the reasons that can be determine of this action. Also based on Fig. 4, drivers tended to perform at lower speeds. 4.3. Multinomial logit model of drivers’ performance 4.3.1. Site 1 Multinomial logistic model were used to determine the type of multiple performance of drivers when facing pedestrians crossing through the street. According to the Section 3, drivers reacted to pedestrians with four types of performances: (1) stop, (2) deceleration, (3) acceleration, and (4) change lane, so that pedestrians could pass across. After determining the probability model of the reaction of binary logit (based on the effective parameters identified), using a model that can determine the type of performance of drivers when performing the reaction was investigated. For this purpose, the variables that were identified in the previous section were analyzed using a multiple model in SPSS software to determine their potential impact on the driver’s performance. Eventually, the probabilistic functions of the various types of driver responses were determined based on the variables defined in the software (significant level of 95%). Viewing and analyzing films showed that the most types of performance of drivers on the first site were the decreasing speed and stop. According to Table 6, the driver’s probability as decreasing speeding was related to changes in some variables such as: speed, distance, and pedestrian asking for crossing, and the front vehicle’ driver performance to pedestrians. In other words, Table 6 shows that drivers at higher speeds tend to respond more as deceleration to pedestrian crossings. Also, the probability of performance of the type of stop increases when the distance between vehicle and pedestrian increase. At lower speeds, drivers more likely to perform stop the vehicle than other types. More, observing asking for the crossing by pedestrians and the reaction of the

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Speed Distance ALLOW PREV

Probability of performance as a decreasing speed

Probability of performance as a stop

Probability of performance as a changing lane

bi

p-value

bi

p-value

bi

p-value

1.614 0.817 +0.604 +0.592

0.014 0.008 0.016 0.020

0.929 1.426 +0.118 +0.224

0.014 0.005 0.018 0.025

0.325 0.293 +0.542 +0.738

0.019 0.018 0.020 0.030

driver of the front vehicle, increase the possibility of performance as deceleration and changing lane. The probability model of various types of performance, defined in the form equations of Section 3.3. 4.3.2. Site 2 Table 7 shows that on the second site, due to the lack of median, drivers in a dangerous situation (short distance to pedestrian and high speed) changed lines, which in some cases violated the opposite line to prevent the collision with pedestrians. Besides, about most cases in pedestrian crosswalk, drivers did performance as decreasing speed so that pedestrians could cross the street. The pedestrian group also increased the likelihood of changing the line by drivers (at short distances) and stopping (at long distance). Eqs. (8)–(11) in Section 3.3 show the model of drivers’ performance based on effective factors (Table 7) using multinomial logit (significant level of 95%). 5. Validation and transferability of driver behavior model 5.1. Validation of model In order to evaluate and develop the model, validation and model transferability were conducted. Firstly, to validate the model, drivers’ performance studies were re-examined in the studied sites. The purpose of this work was to collect the behavioral data of drivers and use them in the models made in the previous section. The NDS studies of the 17 participants (10 male drivers, 7 female drivers; 18–65 years; valid driving license with average 4.2 years and average of 7000 km per year) were recorded during February-August 2017, in which 113 vehicle–pedestrian interactions were detected. It should be noted that the types of equipment, vehicle and data collection measures were carried out through NDS approach like section methodology (See 3.1 and 3.2). Table 8 shows the results of the observed and predicted data through a model constructed based on the parameters of Tables 4 and 5. Out of 113 identified interactions, 72 were on the undivided road

Table 7 Estimation results of effective factors on the types of performance on site 2. Variable

Speed Distance MUP

(In a group more than two) (In a group of two)

P.C.P

Probability of performance as a decreasing speed

Probability of performance as a stop

Probability of performance as a changing lane

bi

p-value

bi

p-value

bi

p-value

0.582 0.155 +0.604 +0.368 +1.643

0.031 0.028 0.016 0.019 0.013

0.335 1.204 +0.118 +0.341 +0.981

0.014 0.011 0.018 0.008 0.003

1.588 0.866 +1.002 +1.363 +0.267

0.030 0.025 0.002 0.005 0.009

Table 8 Result of binary model validation. Observed

Predicted Does performance/Doesn’t performance

Does performance/Doesn’t performance Total

Correct percentage

Does performance

Doesn’t performance

Divided Road

Undivided Road

Divided Road

Undivided Road

Divided Road

Undivided Road

Does performance

23

39

1

3

95.83

92.85

Doesn’t performance

1

2

16

28

94.11

93.33

94.97

93.09

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Table 9 Result of multinomial model validation. Road

Observed

Predicted Acceleration

Stop

Deceleration

Change lane

Correct percentage

Divided

Acceleration Stop Deceleration Change lane Total

12 0 1 0

1 9 0 1

0 0 8 0

0 1 0 8

92.30 90 88.88 88.88 90.01

Undivided

Acceleration Stop Deceleration Change lane Total

20 0 1 1

1 17 0 1

1 1 14 0

0 1 0 14

90.10 89.47 93.33 87.50 90.10

and 41 were on the divided road. Table 8 shows that the binary logistic model constructed in this study with high precision in divided and undivided roads can correctly predict performance or non-performance of the driver. In addition, evaluation of driver performance type models based on the variables in Tables 6 and 7 is shown in Table 9. The results of validating the models in the study roads indicate models of performance type are highly accurate and robust. 5.2. Transferability of model To evaluate the model’s efficiency in other roads, the model’s transferability was conducted. The aim of this operation was to develop and validate the models obtained in other places. Therefore, the NDS studies of 20 participants (10 male drivers, 10 female drivers; 18–65 years; valid driving license with average 4.9 years and average of 8200 km per year) in the months of October, November and December 2017 were recorded on similar roads (similar geometric and traffic characteristics) in Amol city in Mazandaran Province. As the previous section, as well as in the third section, the conditions and equipment of the NDS approach to collecting the data required for Transferability of Model were similar.

Fig. 5. Plot of result of model transferability in Amol City.

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During NDS studies 177 interactions obtained were used to check the model transferability. Fig. 5 shows the plotted graph for the observed values and predicted values by the model in the roads of Amol city. It can be seen from Fig. 5 that the performance/non-performance model of drivers in both divided and undivided roads has the ability to develop in other places. In addition, the probability of correct prediction of the model in both of the selected roads in Amol city was about 91%. 6. Discussion and conclusion The aim of this paper was to investigate factors that affect the behavior and performance of drivers encountering pedestrians in divided and undivided roads. For this purpose, the behavior of the Naturalistic Driving Study (NDS) of 66 participants was carried out through videos recorded on study roads. Analysis of videos showed that a total of 701 interactions between vehicle and pedestrians occurred, of which 216 were on a divided road and 485 occurred on an undivided road. The binary logit model was used to determine the factors affecting which increase the probability driver’s performance of pedestrians crossing. According to the results of the binary model, in divided road, both of the drivers’ behavior and pedestrians’ behaviors affect the probability of interaction. Speed, distance between the vehicle and the pedestrian, and listening to music were the drivers’ behaviors that increased the probability of an interaction. Also, pedestrians’ behaviors such as asking for crossing to drivers, crossing style and crossing as a group were influential on the performance of drivers. In undivided road, in addition to the influential factors on the first site, other factors such as the crosswalk areas, and paying attention to traffic flow before crossing by pedestrians had effects on the performance of drivers. It is to be noted that in the present study, regardless of our approach, aims, and assumptions in the investigation of two sites, some of the findings of the present study are consistent with what has been found by previous studies. For example, in previous studies, some variables such as speed (Chung & Chang, 2015; Mohamed & Bromfield, 2017), distance from vehicle to pedestrian (Alferova et al., 2017; Ni, Wang, Sun, & Li, 2016), listening to music by driver (Brodsky & Slor, 2013; Öztürk & Erzin, 2012; Stelling-Konczak, van Wee, Commandeur, & Hagenzieker, 2017), asking to cross by pedestrian (Gitelman, Carmel, Pesahov, & Hakkert, 2017; Minhas et al., 2017), attention to traffic flow by pedestrian before across the street (Habibovic et al., 2013) are presented as effective causes on possibility of accidents on various scenarios by researchers. Furthermore, a multinomial model was used to determine the types of performance based on effective factors. Multinomial model results showed that drivers at higher speeds are more likely to deceleration to let the pedestrians cross on the divided road. In other words, high speed prevents early detection of pedestrians by drivers in the area, as well as quick decision-making to performance while stopping the car. Moreover, as the distance to the pedestrian increased, the probability of performance of the drivers increased. Also, observing the request for crossing by pedestrians encouraged drivers to decrease speed of the vehicle or change the direction. In the undivided road, vehicle speed, distance to pedestrians and crossing as a group are the most important factors affecting the driver performance. Finally, the probabilistic models of driver performance and its types in both sites were presented based on effective factors. Eventually, it can be concluded that the behavioral patterns of drivers and pedestrians are not the same in divided and undivided roads, which confirms the hypothesis of the present study. Different performance models of drivers on critical condition prior to collision with pedestrians on two sites suggest that the factors and characteristics of the traffic users are not similar to each other, even in the various segments of urban roads. This indicates the complexity of the interaction of road users encountering each other, which should be addressed by the policy makers of traffic safety. 7. Limitations of the study and further research The results of this paper can be considered as a means to improve pedestrian safety crossing the street in urban roads. Determining the statistical models based on the performance of drivers encountering pedestrians, and their development in accordance with the various conditions of the road traffic, can be a valuable step towards improving the pedestrian safety by applying an Advanced Driver-Assistance System (ADAS) in vehicle. In the present study, although the NDS approach has been able to collect various data on the analysis of drivers and pedestrians’ behaviors, however, this approach has some limitations in terms of some opportunities for improving data collection. For example, in some cases, due to wearing glasses by some participants, it was difficult to determine the direction of the eyes of drivers. Also, the exact timing of the pedestrian’s viewing by these participants was somewhat difficult. Therefore, it is suggested that in future research the use of eye-tracker for participants should be considered by researchers on the field of driver behavior studies. 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