INTERNATIONAL JOURNAL OF CIVIL AND STRUCTURAL ENGINEERING Volume 4, No 3, 2014

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1 INTERNATIONAL JOURNAL OF CIVIL AND STRUCTURAL ENGINEERING Volume 4, No 3, 2014 Copyright by the authors - Licensee IPA- Under Creative Commons license 3.0 Research article ISSN Modelling pedestrian road crossing at uncontrolled mid-block locations in developing countries Associate Professor, Department of Civil Engineering, Faculty of Engineering, Port Said University, Port Said, Egypt sadek1234@hotmail.com doi: /ijcser ABSTRACT The aim of this research is to investigate and model pedestrian road crossing behavior at uncontrolled mid-block in Egypt as one of the developing countries. In particular, two aspects of pedestrian crossing behaviors at mid-block locations have been examined, namely the size of traffic gaps accepted by pedestrians and the decision or not to cross the street, as well as the related determinants. A field survey was carried out at nine uncontrolled mid-block locations in different streets in three Egyptian cities. The locations were chosen with various street crossing widths to test the influence of this variable as it has not covered in previous pedestrian models. In this survey, pedestrian crossing decisions were videotaped in real traffic conditions. The survey period was 30 minutes for each location. Ten JPEG files were obtained from each second of video recording with the help of Snapshot Wizard software. The data collected included traffic characteristics (speed, type, etc.), individual characteristics (gender, age etc.), as well as individual behavior (frequency of attempt, rolling gap, etc.). A lognormal regression model was developed in order to examine the effect of various parameters on the size of traffic gaps accepted by pedestrians. It was found that the accepted gaps depend on the speed of incoming vehicle, the pedestrian rolling gap, the frequency of attempts a pedestrian makes before crossing, the crossing width, and the age of the pedestrians. A binary Logit model was also developed in order to examine the effects of various parameters on the decision of pedestrians to cross the street or not. The results suggest that pedestrians decision to cross the street depends on the size of traffic gap, the vehicle speed, the pedestrian rolling gap, and the frequency of attempts before crossing. Keyword: Pedestrian, road crossing behavior, gap acceptance, mid-block, uncontrolled. 1. Introduction The need and ability to model pedestrian movement is a more recent development resulting from an increased interest in the public health, environmental, economic, and social benefits of walking. These are not usually the interests concerned in developing countries like Egypt. In Egypt, pedestrians represented around 75% of the traffic injuries related to accidents involving pedestrians during the year 2010 (Ibrahim et al., 2012). The problem exists in most of the developing countries. The percentage of fatalities was 60% in India, while 85% of them occurred at mid-block locations (Mohan et al., 2009). In Jordan, pedestrians represented 32.5% of all traffic accident fatalities and 23.8% of all traffic accident injuries during the year 2010 (JTI, 2011). These are considered high figures as compared to the developed countries. Based on ( ) statistics, pedestrians represented 20% of all road deaths across the EU countries Received on October, 2013 Published on February

2 ranging from around 9% in the Netherlands to 37% in Romania (NHTSA, 2012). In USA, pedestrians represented 13% of all traffic fatalities and 3% of all traffic injuries during the year 2010 (European Transport Safety Council, 2011). This could be explained by the fact that, in most of the developing countries, there is a low auto ownership, so more journeys are made on foot. Moreover, the behavior of pedestrians is particularly non-compliant and often risk-taking, resulting in more likelihood of people being on the streets facing the risk of being hit by vehicles. This increases the need to study and model pedestrian movement. On most urban roads of Egypt, different types of vehicles move on the same road space occupying any position on the road depending on availability of free space at a given instant of time without complying to any lane discipline. Moreover, there is a complexity of interactions between pedestrians, who have jaywalking behavior, and vehicular traffic which increases mostly at uncontrolled mid-block and unsignalized intersections. Based on the above, it is clear that pedestrian safety is a main issue for transport planners, traffic engineers and policy makers. Hence, it is worth studying the road crossing behavior of pedestrians in mixed traffic condition at uncontrolled mid-block location. This study is an attempt in this direction. 1.1 Background There have been several studies carried out to investigate the pedestrian behavior which is influenced by different factors such as pedestrian perception, roadway, environmental characteristics, etc. Pedestrian crossing behavior is mainly governed by the gap acceptance theory. Each pedestrian has a critical gap to cross the street. Many researches correlate the minimum gap from the vehicle that is accepted by pedestrians who intend to cross streets at mid-block. This parameter may be associated with traffic conditions and with vehicle and pedestrian characteristics. In most of these researches (Oxley et al., 2005; Das et al., 2005) the distance between the vehicles and the pedestrians appears to influence the minimum gap accepted by pedestrians. In addition, an increase in traffic density leads to smaller accepted gaps. These gaps are often described by means of probability distributions or are estimated by means of linear regression modeling. Indicatively, it can be mentioned that the minimum accepted gap has been estimated at two seconds and the mean accepted gap at eight seconds (Das et al., 2005). Behavioral analysis has revealed that pedestrians prefer rolling gap (pedestrian roll over the small vehicular gaps) instead of waiting for larger gaps to cross the road (Brewer et al., 2006; Kadali and Vedagiri, 2013). Individuals judgment about when and where to cross the road are very complex and normally represented by various factors such as comfort, convenience, ease of crossing, and safety. If a pedestrian decides to walk, then the pedestrian cross the road somewhere on midblock and pedestrian behavior changes dynamically. Pedestrians continuously change their actions with respect to environmental characteristics. Several researchers have attempted to identify factors influencing pedestrian behavior including sudden decisions that affect pedestrian walking characteristics such as choice to accelerate or decelerate walking speed, stop or wait and where to cross a street (Ishaque and Noland, 2008). 275

3 Another issue is the decision of pedestrians to cross the road or not. Some studies has found that it depends more on the distance between the vehicle and the pedestrian and not so much on the related time gap (Chu et al., 2002; Sun et al., 2003). As a result, pedestrians may choose inappropriate time gaps, because they are not able to estimate the actual speed of incoming vehicles. However, other studies have found that it depends on the time gap (Chu et al., 2002). Other parameters that affect crossing decisions include the presence of police enforcement and the behavior of other pedestrians (Oxley et al., 2005; Lobjois and Cavallo, 2006). Discrete choice modeling is used by most researchers in order to estimate whether pedestrians are going to cross a street at mid-block or not (Papadimitriou et al., 2009; Lassarre et al., 2007). Road crossing behavior with respect to gender has also been observed in various studies. Males have a tendency to show more hazardous road crossing behavior than females due to less waiting time (Khan et al., 1999; Tiwari et al., 2007). Some studies have focused on detailed experiments to find out the effect of age on road crossing decisions with effect of vehicle distance or speed of vehicle (Oxley et al., 1997; Lobjois and Cavallo, 2007). A recent study has focused on legal versus illegal pedestrian road crossing behavior at midblock location in China (Cherry et al., 2010). Few studies have identified pedestrian behavior in mixed traffic streets and developed a micro-simulation model in order to find out the fundamental characteristics as well as the conflicts of the pedestrian movement (Shahin, 2006). A study in Beijing, investigated pedestrian behavior and traffic characteristics at unsignalized mid-block crosswalk. Authors have explained the pedestrian speed change condition with pedestrian behavior (Jiangang et al., 2007). A few studies have also been carried out at un-controlled intersections to explore the interaction between motorists and pedestrians (Ibrahim et al., 2005). Some studies have focused on the safety impacts of marked and unmarked crosswalks for pedestrian and driver in rural and recreational areas (Mitman et al., 2010). Studies have also explored the pedestrian road crossing behavior and safety issues at unmarked location (Zhuang and Wu, 2011) and in narrow urban streets with mixed traffic condition (Kwon et al., 1998). Recently, a study in India has investigated, in more detail, the pedestrians' behavior at uncontrolled mid-block location under mixed traffic condition (Kadali and Vedagiri, 2013). The study developed models that include detailed behavioral parameters such as pedestrian rolling gap, frequency of attempts, accepted lag or gap, number of observations while crossing, frequency of disturbance while crossing, and driver yield behavior. As a consequence, the results of these researches cannot be transferred and used in a national setting like the one of Egypt, because the Egyptian road and transport network have different characteristics and operational conditions. Not only are the road infrastructure and traffic control often inadequate for pedestrians, but also the behavior of pedestrians is particularly non-compliant and often risk-taking. This is partially reflected in the increased proportion of road accidents involving pedestrians in Egypt. 276

4 In this context, the primary objective of this study is to describe the road crossing behavior of pedestrian at uncontrolled mid-block location in Egypt. In particular, the study focuses on the behavioral characteristics of pedestrian with the help of multiple linear regression (MLR) and binary Logit (BL) models. The study tests the influence of the street crossing width variable which has not been included in previous models. 2. Methodology A field survey was carried out in nine different sites in Egypt. These sites were chosen to have various crossing distances and adequate volume of pedestrians as well as vehicular traffic to allow for collection of pragmatic behavioral data. The sites were chosen in three cities: Port Said, Ismailia, and Cairo. The selected sites were uncontrolled (unmarked and no right of way to the pedestrian) mid-block locations. Street crossing widths at the selected locations ranged from 6 m to m. In this survey, pedestrian crossing decisions were videotaped in real traffic conditions. The survey was conducted in October 2013 during working days in normal weather conditions. The survey period was 30 minutes for each location. The video camera viewed a total of m length along longitudinal direction, out of this only 15 m was used for data collection where the pedestrians are usually crossing the road. Ten JPEG files were obtained from each second of video recording with the help of Snapshot Wizard software. An example of these files is shown in Figure 1. Figure 1: Photograph of one of study locations (in Port Said, Egypt) The data extracted from videotape focused on those pedestrians who intended to cross the street. More specifically, only pedestrians who actually crossed the street, either immediately or after several attempts (i.e. accepting the first traffic gap available or rejecting several gaps before crossing) were captured. Pedestrians who abandoned the crossing task after some attempts, and sought for a crossing opportunity elsewhere, were not included in the sample. Particular care was taken that data were recorded only during the green signal of the nearby traffic lights, so that pedestrians would make an unprotected crossing by interacting with the incoming vehicles. Moreover, congestion conditions were not included in the data. The traffic gap accepted was calculated as the difference between two time points: at the first point, the pedestrian is just ready to set foot on the street, in the second point, the head of the 277

5 vehicle has just passed through vertical virtual line indicating the pedestrian s crossing path. Moreover, the waiting time of the pedestrian started when someone approached the pavement until he set foot on the street. It is noted that these calculations included only the accepted gaps and not the rejected ones. This survey also recorded the frequency of crossing attempt a pedestrian makes due to the increase in waiting time at the curb or median. Pedestrian rolling gaps were also extracted. Rolling gap means that pedestrian cross the road following zigzag path to roll over the small vehicular gaps instead of waiting for larger gaps. This is a usual behavior in developing counties. The speed of the incoming vehicle was measured at the moment when the pedestrian just started to cross, and was calculated by dividing the distance between the two virtual lines by the time the vehicle took considered to be constant during the pedestrians crossing time. From each snapshot, individual characteristics were collected, which include pedestrian gender, age, and whether he/she was accompanied by another pedestrian. The collected variables are shown in Table 1. Variable Street crossing width Traffic gap Vehicle speed Waiting time Frequency of attempt Table 1: List of variables extracted from videotape Pedestrian speed Distance Gender Female = 0, Male = 1 Age Elder (> 60 years) = 0 Middle (30 60 years) = 1 Young (< 30 years) = 2 Accompanied Pedestrian baggage effect Pedestrian rolling gap Description (unit) The crossed width between curb on one side and median or opposite curb from the other side (m) Time gap between two vehicles with reference to crosswalk point (sec) Speed of vehicle at crosswalk area (kph) Time spent at the curb or median for suitable gap (sec) Number of attempts a pedestrian makes to accept a vehicular gap Speed of pedestrian while crossing the road (m/sec) The space between the vehicle and the pedestrian (m) Is the pedestrian accompanied by another pedestrian(s)? No = 0 Yes = 1 Is the pedestrian carrying baggage? No = 0 Yes = 1 Does the pedestrian roll over the available small gap? No = 0 Yes = 1 Type of vehicle Motorcycle = 0 Car, taxi, microbus = 1 Bus, truck = 2 Lane Vehicle in nearside lane = 0 Vehicle in far-side lane = 1 Driver yielding Crossing Parking Does the driver reduce speed or change his path when pedestrian is crossing? No = 0 Yes = 1 Does the pedestrian accept the gap? No = 0 Yes = 1 Presence of illegally parked cars: 278

6 Variable Description (unit) No = 0 Yes = Model framework The pedestrian road crossing behavior at uncontrolled mid-block location can be predicted by two types of models. The first model is used to predict the minimum accepted vehicular time gap. The second model is used to predict mid-block crossing choice. In both models, the functional relationship between input and output variables can be easily represented. The results and discussions for these models are illustrated in the following sections. 3. Modelling accepted traffic gaps The minimum accepted vehicular time gap was modelled with the help of multiple linear regression (MLR) technique. A lognormal regression model was selected given that a normal distribution could be successfully fitted to the logarithm of the gaps. It is noted that lognormal regression assumes a normal distribution for the logarithm of the dependent variable, and was thus preferred over log-linear regression, which assumes a Poisson distribution for the dependent variable (Yannis et al., 2010). The model was developed using (SYSTAT) software package. A logarithm of the accepted gap size was considered as the dependent variable and the remaining variables are independent variables. A partial correlation matrix was used to find the variables that are highly correlated with the dependent variable and to find the independent variables that are correlated. After a number of trials on different groups of independent variables, the final model which has the best statistical results is the following: Log - Gap = * VSPEED * * RGAP * AGE STW * FATM (1) Where: Log-Gap: logarithm of accepted gaps; VSPEED: vehicle speed (kph); STW: street crossing width (m); FATM: frequency of attempt a pedestrian makes; RGAP: the pedestrian rolls over the available gap (= 1) or not (= 0); and AGE: (Elder = 0) (Middle = 1) (Young = 2). The model calibration was considered with 70% data and remaining data were used for validation of the model. The calibrated R 2 value was found as The descriptive statistics of MLR test, t, and p-values are summarized in Table 2. Reported t-values and p values are the statistical test values of each independent variable. The comparison between observed (remain 30% data) and predicted values showed the validity of calibrated model and a valid R 2 value was found Also the critical gap was estimated by Raffs method and it founded as 4.87 sec. 279

7 Table 2: Descriptive statistics results for the MLR gap acceptance model Variable βi (Coefficient) Standard error t-value p-value Constant VSPEED STW FATM RGAP AGE Modelling mid-block crossing choice The pedestrian decision making to cross the road or not was described by the binary Logit Model (BL Model). The probability that a pedestrian crosses the street is as follows: P = e U / [e U + 1] (2) After a number of trials on different groups of independent variables, using (SYSTAT) software package, the final model which has the best statistical results is the following: Where: U = *GAP *RGAP + 3.2*FATM 0.301*VSPEED (3) U: the utility of choosing to cross the road at mid-block; GAP : traffic gap (accepted or rejected) (sec); RGAP: the pedestrian rolls over the available gap (= 1) or not (= 0); FATM: frequency of attempt a pedestrian makes; and VSPEED: vehicle speed (kph). The descriptive statistics of BL Model test are summarized in Table 3. The significance of the independent variable is considered with the effect of t-values and p values. The model validation is carried out with success prediction table and the overall prediction accuracy was found as 93.5%. Hence, the proposed model is strong enough to predict the gap acceptance behavior at uncontrolled mid-block location. Table 3: Descriptive statistics results for the binary Logit crossing choice model Variable βi (Coefficien t) Standard error t-value p-value Constant GAP RGAP FATM VSPEED Analysis and discussion Different pedestrian behavioral characteristics were considered for accepted traffic gap model (MLR), out of which only few (five) variables could explain the pedestrian road crossing behavior, while the remaining variables have insignificant effect. 280

8 In order to show clearly to which extent each of the independent variables affects the dependent variable, an analysis of elasticities (e) is carried out, as shown in Table 4. Moreover, the relative effect (er), as a normalization of the estimated elasticities in relation to the lowest elasticity, is calculated in order to compare the magnitude of effects of all independent variables. The point elasticity (ei) was calculated for each individual in the sample according to the following formula, whereas the overall elasticity (e) is calculated as the average of (ei) in the sample: ei = ( Yi / Xi)(Xi / Yi) = βi (Xi / Yi) (4) Table 4: Elasticity of the accepted gap to the independent variables in the MLR model Independent variable Elasticity (e) Relative elasticity (e r) Vehicle speed (VSPEED) Pedestrian rolling gap (RGAP) Frequency of attempt (FATM) Street crossing width (STW) AGE It is observed that if the variable age has an elasticity of 1, then the variable vehicle speed has an elasticity of 13.6, that is it affects the gap acceptance 13.6 times more than the age. The elasticity analysis shows that, among the different variables, vehicle speed and pedestrian rolling gap are the most influencing variables on gap acceptance as they have the highest elasticities. Frequency of attempts has medium effect while street crossing width and age have the lowest effect. Although type of vehicle is usually an important factor for accepting the gaps (Yannis et al., 2010), in this study, it is observed that pedestrians accepts traffic gaps with respect to vehicle speed, while it has the highest elasticity. It is true, because small vehicles may come with higher speeds. The pedestrian may not accept the available gaps with small vehicle at higher speeds and sometimes gaps with heavy vehicles may be accepted due to less speed. This observation is strongly supported by recent studies in mixed traffic condition in developing countries (Kadali and Vedagiri, 2013; Cherry et al., 2010). Therefore, speed of the vehicle plays important roles in the MLR model. It is clear that at mid-block locations, pedestrians rely mainly on rolling gap and accept traffic gap size without waiting a long time after arriving at the curb. Table 5 shows the pedestrian rolling gap behavior with available vehicular gap size. The mean accepted gap sizes in seconds with and without rolling gap are 2.76 and 5.22, respectively, which is intemperate difference. If pedestrians choose rolling gap they are more likely to accept the minimum gap sizes. This proves that the behavior of pedestrians in Egypt is particularly non-compliant and often risk-taking, which is usual in many developing countries. This observation is strongly supported by recent study in mixed traffic condition in India (Kadali and Vedagiri, 2013). Table 5: Minimum accepted gap size under pedestrian rolling gap behavior Rolling gap condition Gap size (sec) Minimum Maximum Mean With rolling gap Without rolling gap

9 Street width and pedestrian age are also important variables in MLR model for reducing gap size, but they have insignificant effect in crossing choice (BL model). It is observed from Table 2 that pedestrians accept traffic gaps with respect to the crossed width between the curbs (STW). The pedestrians may accept smaller gaps with short crossing distance. This is attributed to the fact that they reach the opposite curb in shorter safe time. Pedestrian age is statistically significant for minimum gap size and there is a significant difference between elders and young pedestrian age groups, which can be observed in Table 6. The mean accepted gap sizes in seconds for elders, middle, and young age groups were 5.85, 3.38, and 3.37 respectively. This indicates that the pedestrian chooses small gap sizes with decrease in age, but there is not much difference between middle and young age groups. This observation is strongly supported by recent study in mixed traffic condition in developing countries (Kadali and Vedagiri, 2013). Table 6: Minimum accepted gap size for different age groups Gap size (sec) Age group Minimum Maximum Mean Elders Middle Young In the BL model for mid-block crossing choice, only four variables; gap size, rolling gap, frequency of attempts, and vehicular speed were significant and included in the model. Moreover, there is a probability of increasing pedestrian gap acceptance with the increase of the gap size, rolling gap, and frequency of attempt, whereas, it reduces with the increase in vehicle speed. It is interesting to note that none of the pedestrians' individual characteristics were found to be significant in the crossing choice model. This is attributed to the fact that most of survey participants may have a strong familiarity with the survey site, as this is located in a very central area, resulting in less uncertainty in the decisions of those groups of pedestrians that are often associated with particular behaviors. The elasticity analysis for the BL model is presented in Table 7. The pedestrian rolling gap has the greatest effect on pedestrians decision to cross the street or not. As expected, it was found that if pedestrians choose rolling gap, they more likely decide to cross the street. The variable with the second greater effect is the traffic gap size. As expected, the higher the available gaps, the easier the crossing. Moreover, frequency of attempt has the lowest effect on crossing decision. Table 7: Parameter elasticities for the crossing choice (BL) model Independent variable Elasticity (e) Relative elasticity (e r) Pedestrian rolling gap (RGAP) Traffic gap (GAP) Vehicle speed (VSPEED) Frequency of attempt (FATM) Conclusions A field survey was carried out at nine uncontrolled mid-block locations in different streets in three Egyptian cities. The purpose was to investigate pedestrian road crossing behavior under 282

10 mixed traffic condition in urban areas. These behavioral characteristics are very useful to control pedestrian jaywalking behavior and for improving pedestrian safety. A lognormal regression model was developed in order to examine the effect of various parameters on the size of traffic gaps accepted by pedestrians. It was found that the accepted gaps depend on the speed of incoming vehicle, the pedestrian rolling gap, the frequency of attempts before crossing, the crossing width, and the age of the pedestrians. A binary Logit model was also developed in order to examine the effects of various parameters on the decision of pedestrians to cross the street or not. The results suggest that pedestrians decision to cross the street depends on the size of traffic gap, the vehicle speed, the pedestrian rolling gap, and the frequency of attempts before crossing. The results of this research show that pedestrians rely mainly on rolling gap and accept small traffic gap size without much waiting at the curb, which proves that the behavior of pedestrians in Egypt is particularly non-compliant and often risk-taking, which is usual in many developing countries. Pedestrians' individual characteristics were found insignificant in crossing choice; only pedestrian's age and frequency of attempts were found to affect gap acceptance. It was found that pedestrians accept vehicular gaps with respect to vehicle speed rather than vehicle type. This can be justified by the fact that small vehicles may come with higher speeds and heavy vehicles may come with less speed. One of the new variables in the gap acceptance model is the crossed width between the curbs. It was observed that pedestrians may accept smaller gaps with short crossing distance. This is attributed to the fact that they reach the opposite curb in shorter safe time. Nevertheless, this variable was not significant in the crossing choice model. 6. References 1. Brewer M. A., Fitzpatrick K., Whitacre J. A., and Lord D., (2006), Exploration of pedestrian gap-acceptance behavior at selected locations, Transportation research record 1982, Journal of the transportation research board, Washington D.C., pp Cherry C., Donlon B., Yan X., Moore S. E., and Xiong J., (2010), Illegal mid-block pedestrian crossings in China: gap acceptance, conflict and crossing path analysis, Journal of injury control and safety promotion, 19(4), pp Chu X., Gittenplan M., and Baltes M., (2002), Why people cross where they do - The role of the street environment, In the proceedings of the TRB 81st annual meeting, Transportation research board, Washington. 4. Das S., Manski C.F., and Manuszak M., (2005), Walk or wait? An empirical analysis of street crossing decisions, Journal of applied econometrics, 20(4), pp European Transport Safety Council (2011), Unprotected Road Users: a Key Concern of Road Safety, Road safety performance index (Flash 19) report, ARRB Group Limited, Vermont South, Victoria. 283

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