Pedestrian Safety: Drivers Stopping Behaviors at Crosswalk

1. Introduction

1.2. Pedestrian mobility in the City of Kigali

According to the 2018 Transport Master Plan, the population of Kigali stood at approximately 1.5 million. It was forecasted to grow to approximately 3.8 million in 2050. This is something that may raise the pressure on existing transport infrastructure, and thence on traffic safety measures that have been put in place [8].

According to this same survey, on average, the majority of employed citizens live within 2 km from their place of work. When comparing the mode of transport used to travel to work to the average monthly household income, it has been clear that the areas where people earn a higher income are also the areas where the usage of private car travel is more common. Since most urban citizen do not fall in this category, it was then concluded that the majority of employed citizens use walking as a mode of transport to their place of work [9].

More so, on schooling and other motives of people movements, it has been noticed that walking is the most popular mode of transport for students travelling to school [9]. In addition to this, the time of day that people will likely use to travel to work in Kigali is between 06:00 and 09:00 in the morning [10].

Figure 1. Population concentration and potential mobility density in the City of Kigali [8]

Figure 1. Population concentration and potential mobility density in the City of Kigali [8]

According to police data, road accidents that happen in the City of Kigali, do involve a lot of pedestrians and motorcycles. For instance, in the year of 2017 alone, 71% of total registered road accidents involved motorcycles (moto-taxis), pedestrians and bicycles. The KN 7 road, the KN 5 Road and city circle junctions, remain hotspots for fatal accidents in Kigali as determined from a study done from the police database [11].

2. Materials and Methods

2.6. Statistical Tests

To begin with, all data was stored in a central dataset to perform cleaning and critical joins. Using spatial dimensions attached to each entry, a spatial analysis was made. Using Tableau (Version 2021.4), spatial visualizations have been built to compare population densities, traffic densities and pedestrian crossing compliance on each surveyed site. Descriptive statistics have been used to portray safety challenges caused by vehicles. Cross tabulations and Pearson Chi-square tests have been used to evaluate probable dependencies of subject variables. To wind up the analysis part, Binary logistic regression models were built and have been used to evaluate driver’s stopping behaviors against traffic flow characteristics.

2.6.1. Chi-Square

Chi-square tests of independence test whether two qualitative variables are independent, that is, whether there exists a relationship between two categorical variables [27]. If the test shows no association between the two variables (i.e., the variables are independent), it means that knowing the value of one variable gives no information about the value of the other variable [26].

The Chi-square test of independence is a hypothesis test so it has a null (H₀) and an alternative hypothesis (H₁):

H₀: the variables are independent, there is no relationship between the two categorical variables. Knowing the value of one variable does not help to predict the value of the other variable

H₁: the variables are dependent, there is a relationship between the two categorical variables. Knowing the value of one variable helps to predict the value of the other variable

The calculation aspects of this test stems from the logic of Mathematical Probability. For two independents events A and B, the following probability equation should always hold true:

$$P\left(A\cap B\right)=P\left(A\right)\ast P\left(B\right)$$

Therefore, the Chi-square test of independence works by comparing the observed frequencies (so the frequencies observed in your sample) to the expected frequencies if there was no relationship between the two categorical variables (so the expected frequencies if the null hypothesis was true). This difference, referred as the test statistic (or t-stat) and denoted by χ2, and is computed as follows:

$$\mathsf{\chi }2=\sum _{\mathrm{i},\mathrm{j}}^{}\frac{{({\mathrm{O}}_{\mathrm{i}\mathrm{j}}-{\mathrm{E}}_{\mathrm{i}\mathrm{j}})}^2}{{\mathrm{E}}_{\mathrm{i}\mathrm{j}}}$$

O represents the observed frequencies and E the expected frequencies.

The test statistic alone is not enough to conclude for independence or dependence between the two variables. As previously mentioned, this test statistic (which in some sense is the difference between the observed and expected frequencies) must be compared to a critical value to determine whether the difference is large or small.

The critical value can be found in the statistical table of the Chi-square distribution and depends on the significance level, denoted α, and the degrees of freedom, denoted $df=(rows-1)(columns-1)$. The significance level is usually set equal to 5%.

Figure 2. Chi-square Distribution.

Figure 2. Chi-square Distribution.

Like for many other statistical tests, when the test statistic is larger than the critical value, the null hypothesis is rejected at the specified significance level.

2.6.2. Binary Logistic Regression

Collected data was also analyzed using binary logistic regression. This one can predict the probability (P) of a specific event when the dependent variable is dichotomous [30] . In this study, the occurrence of a specific stopping behavior can be considered as a binary response variable. The driver will either stop or continue driving during a pedestrian crossing event. The log odds (logit) of P equals the natural logarithm of P/(1-P). Therefore, Binary logistic regression estimates the log odds of the independent variables as a linear combination as shown in the equation below:

$$\mathrm{L}\mathrm{o}\mathrm{g}\mathrm{i}\mathrm{t}\left(\mathrm{P}\right)=\mathrm{L}\mathrm{n}\left(\frac{\mathrm{P}}{1-\mathrm{P}}\right)={\mathsf{\beta }}_0+{\mathsf{\beta }}_1{\mathrm{X}}_1+{\mathsf{\beta }}_2{\mathrm{X}}_2+\dots {\mathsf{\beta }}_{\mathrm{n}}{\mathrm{X}}_{\mathrm{n}}$$

In this equation, P is the probability of the behavior occurrence; X_n is the independent variable; and β_n is the logistic regression coefficient. For each research question, the significance value of 0.05 was accepted as an influential predictor variable. For each model, the odds ratio was calculated to determine if there is an increase (>1) or decrease (0–1) in the probability of the behavior when the independent variables increase with one unit [31]. The Cox & Snell R Squared and Nagelkerke R Squared tests have been used to assess each model’s goodness of fit. The test determines the percentage of variance caused by independent variables in the equation [28]. SPSS 25.0 has been used for all statistical computations.

3. Results

3.1. Zebra Characteristics

According to the Figure 3, using satellite imagery, it is easy to track subject zebra crossings. Using remote sensing, on the 6m scale, on precise coordinates, it’s possible to estimate zebra crossing characteristics and track asymmetries. Generally, in road design, it’s normal to get non-uniform dimensions depending on terrain nature. The focus of this research is on investigating the relationship between zebra characteristics and vehicle’s (un)stopping behaviors. Stopping behaviors of vehicles are subject of pedestrian safety.

Figure 3. From left to right, satellite visualization of data collection sites: 15, 14 and 12. The last mockup shows various dimensions that were measured using a tape measure. It’s already obvious that stopping line are not uniform on each zebra crossing.

Figure 3. From left to right, satellite visualization of data collection sites: 15, 14 and 12. The last mockup shows various dimensions that were measured using a tape measure. It’s already obvious that stopping line are not uniform on each zebra crossing.

A pedestrian crossing can be characterized by various aspects. Some are inherently due to the way the road infrastructure has been set up. Other factors may depend of how markings have been drawn. In both cases, data collectors have collected important information using tape measures.

While sites have been trimmed down to granular uniformity details, recorded measurements still show that there are clear inconsistencies in Zebra characteristics in the City of Kigali. For instance, one site (Site 9) did not have a stop line one side of the lane. Zebra lines lengths also greatly vary in measure with μ=3.7m (mean) and σ=0.96m (standard deviation). Zebra lines lengths are arguably designed depending on pedestrian traffic on both ends of the road facility.

Table 1. Zebra characteristics for selected sites. Quick analyses involving spark lines and bars have been done. Additional columns to show average measurements and standard deviations is shown as well. All are measured in meters.

Table 1. Zebra characteristics for selected sites. Quick analyses involving spark lines and bars have been done. Additional columns to show average measurements and standard deviations is shown as well. All are measured in meters.

One other major characteristic with variability is the distance between stopping lines and zebra lines themselves. They were measured, on both lanes with maximum μ=2.9m and σ=1.25m. Zebra lines markings vary from country to country. Like all other road markings, there is no fixed or correct length. Most markings are adapted to the traffic situation, to address factors like overtaking possibility, design speed, and more. Therefore, design standards suggest acceptable ranges. Nonetheless, it is of great interests, to investigate whether zebra characteristics have any, or, are in anyway associated to drivers stopping behaviors in cases of pedestrian crossings. According to our observations, most measurements fell into acceptable ranges with some exceptions. This was partly due to the fact that data collection sites were hand-picked upon various factors.

Pedestrian walkways may affect pedestrian safety at pedestrian crossings. According to collected data, at least 3 out of 10 sites, did not have a pedestrian walkway on either of its lanes. It’s unknown whether this characteristic has a positive or negative stopping behavior toward drivers, but it’s arguably a non-ignorable safety risk for pedestrians. In most cases, when pedestrians are crossing, they tend to feel safer when the crossing destination is safer. The lack of pedestrian walkways in some roads therefore poses a significant hazard toward pedestrian safety. At worst cases, the lack of walkways was associated with existence of non-covered water canals and drenches. It’s therefore undeniably risky to cross over such a facility.

3.2. Spatial Analysis

This study was carried out in the capital city of Rwanda, Kigali. This analysis aims to visualize traffic flow on zebra crossings across the capital. Data collection sites were purposively distanced.

Figure 4. From the left: Data collection sites on Kigali Map, Vehicle frequencies by type on stopping occasions, Vehicle activity when pedestrian are crossing on each site.

Figure 4. From the left: Data collection sites on Kigali Map, Vehicle frequencies by type on stopping occasions, Vehicle activity when pedestrian are crossing on each site.

Spatial analysis data almost corresponds to population density map portrayed earlier in Figure 1. Transport by foot being the most used mode of transport to reach workplaces and schools, it shows on Figure 3. It is as, for relatively dense areas, there will be a lot of pedestrian traffic, thus prompting multiple vehicles to stop on crossing occasions. This is particularly true for site 2 and 4.

On a second glance, it’s almost noticeable that buses and bicycles are rarely spotted, which motorcycles and cars are in abundance. For dense areas, motorcycles do greatly outnumber cars. One of the reasons is arguably due to the economic profile of densely populated households. In places where there are a few residents, for instance, site 9 and 6, there will be relatively more cars since residents are more affluent.

3.3. Descriptive Analysis

The collection of data was long and interesting. Over 22,000 vehicles were identified in 10,259 stopping occasions. Across 10 sites; drivers, vehicles, pedestrians and road characteristics were different. This variety of situations came to interesting findings summarized in the following table:

Table 2. Cross tabulation of major frequencies.

Table 2. Cross tabulation of major frequencies.

Variable Name	Evening		Morning		Totals
	N	%	N	%	N	%
	11,354	(51.5)	10,704	(48.5)	22,058	(100)
Did the vehicle stop?
No	9,124	(41.4)	9,049	(41.0)	18,173	(82.4)
Yes	2,230	(10.1)	1,655	(7.5)	3,885	(17.6)
Vehicle Type
Bicycle	95	(0.4)	245	(1.1)	340	(1.5)
Motorbike	7,499	(34.0)	7,366	(33.4)	14,865	(67.4)
Bus	89	(0.4)	116	(0.5)	205	(0.9)
Car	3,428	(15.5)	2,401	(10.9)	5,829	(26.4)
Stopping Distance
Breached	9,124	(41.4)	9,049	(41.0)	18,173	(82.4)
< 0.5 Meters	142	(0.6)	109	(0.5)	251	(1.1)
< 1.0 Meters	120	(0.5)	48	(0.2)	168	(0.8)
< 1.5 Meters	147	(0.7)	110	(0.5)	257	(1.2)
< 2.0 Meters	344	(1.6)	148	(0.7)	492	(2.2)
< 2.5 Meters	296	(1.3)	162	(0.7)	458	(2.1)
< 3.0 Meters	429	(1.9)	299	(1.4)	728	(3.3)
< 3.5 Meters	265	(1.2)	370	(1.7)	635	(2.9)
Greater than 4m	487	(2.2)	409	(1.9)	896	(4.1)
Week Day
Monday	1,230	(5.6)	1,175	(5.3)	2,405	(10.9)
Tuesday	1,400	(6.3)	1,690	(7.7)	3,090	(14.0)
Wednesday	1,536	(7.0)	2,064	(9.4)	3,600	(16.3)
Thursday	2,020	(9.2)	1,937	(8.8)	3,957	(17.9)
Friday	1,327	(6.0)	1,863	(8.4)	3,190	(14.5)
Saturday	2,241	(10.2)	1,224	(5.5)	3,465	(15.7)
Sunday	1,600	(7.3)	751	(3.4)	2,351	(10.7)

At a glance, zebra compliance is arguably in a bad shape and safety at pedestrian crossings is not the best. In all observed occasions, from Monday to Sunday, in morning and evening peak hours, only 17.6% of drivers stopped to allow pedestrian to cross. Everyone else just continued to drive while pedestrians are crossing, not to mention that those who stopped didn’t necessary stop far away from pedestrians. According to recorded observations, only 896 vehicles (4.1%) stopped at least 4m away from the Zebra center line.

The survey has identified four types of vehicles. Big ones on more than four wheels (Cars and Buses) and two-wheeled ones (Motorcycles and Bicycles). In terms of frequency, it’s clear that motorcycles are the most present vehicles on surveyed roads, with a prominence of 14,865 counts (67.4% of all surveyed vehicles). Cars come second with about a quarter prominence while bicycles and buses are rare with a collective negligible percentage of about 1.5% and 0.9 % respectively.

Figure 5. Integral visualization of vehicle stopping behavior on all sites throughout the week.

Figure 5. Integral visualization of vehicle stopping behavior on all sites throughout the week.

In this study, researchers tried to visualize four variables of traffic flow and pedestrian crossing events in one graph. These include: vehicle types, days of the week, peak hour periods (hours, minutes and seconds) and stopping distances. Advanced statistical and data computational tools have been used to come up with such a graphs

Figure 4 shown above, is a clear and integral visualization of pedestrian safety on zebra crossings in Kigali. From the left, on the upper axis, there are consecutive days of the week when the survey was carried. On the bottom x-axis, there are stopping distances per zebra crossings. Distance 0 is recorded for every vehicle which crossed the center line of the zebra, thence breaching safety regulations and putting pedestrians lives at risk.

On the Y-axis, the graph portrays traffic peak hours in a continuous fashion. By trimming out outliers and other edge observations, the graph shows data from the morning at 6:00 AM till 7:00 PM in the evening. A 4 – semi-transparent color legend variable has been used to portray vehicle type, while circle radii were used to showcase the density of the same.

On the granular level, there are obvious observations that can be noted. For instance, on the traffic flow aspect, there are a few vehicles in weekend mornings. Same is for a Wednesday evening, nevertheless this could have been an anomaly since it rained heavily in that evening halting pedestrian mobility and data collection altogether.

In addition, on the first day of recording data, observers and data analysts were at first agnostic of vehicle type, until when they realized that vehicles behave differently upon various crossing events. As shows the videos, two wheeled vehicles are likely to continue moving while pedestrian are crossing, even when four-wheeled (or more) ones have stopped to allow safe passage of pedestrians. From this point forward, vehicle types were recorded as part of the survey.

3.4. Stopping Behaviors

To compute various equation elements of the Chi-Square equation, the following table has been made:

Table 3. Cross Tabulation and Chi-Square Values (Expected values in Bold).

Table 3. Cross Tabulation and Chi-Square Values (Expected values in Bold).

Did the vehicle stop?	No			Yes			Totals
	N	%		N	%		N	%
	18173	(82.4)		3885	(17.6)		22058	(100.0)
1.Peak Hour Period – X2 (1) =66.3, p<.001
Evening	9124	(41.4)	(42.4)	2230	(10.1)	(9.1)	11354	(51.5)
Morning	9049	(41.0)	(40.0)	1655	(7.5)	(8.5)	10704	(48.5)
2.Vehicle Type – X2 (4) =2774, p<.001
Bicycles	323	(1.5)	(1.3)	17	(0.1)	(0.3)	340	(1.5)
Motorcycles	13519	(61.3)	(55.5)	1346	(6.1)	(11.9)	14865	(67.4)
Buses	131	(0.6)	(0.8)	74	(0.3)	(0.2)	205	(0.9)
Cars	3522	(16.0)	(21.8)	2307	(10.5)	(4.7)	5829	(26.4)
Null	678	(3.1)	(3.1)	141	(0.6)	(0.7)	819	(3.7)
3.Day of the Week – X2 (6) =259.2, p<.001
Monday	1897	(8.6)	(9.0)	508	(2.3)	(1.9)	2405	(10.9)
Tuesday	2391	(10.8)	(11.5)	699	(3.2)	(2.5)	3090	(14.0)
Wednesday	2898	(13.1)	(13.4)	702	(3.2)	(2.9)	3600	(16.3)
Thursday	3197	(14.5)	(14.8)	760	(3.4)	(3.2)	3957	(17.9)
Friday	2625	(11.9)	(11.9)	565	(2.6)	(2.5)	3190	(14.5)
Saturday	3107	(14.1)	(12.9)	358	(1.6)	(2.8)	3465	(15.7)
Sunday	2058	(9.3)	(8.8)	293	(1.3)	(1.9)	2351	(10.7)
4.Vehicle Density – X2 (2) =181.3, p<.001
Low	3039	(13.8)	(14.3)	788	(3.6)	(3.1)	3827	(17.3)
Medium	4426	(20.1)	(18.6)	564	(2.6)	(4.0)	4990	(22.6)
High	10708	(48.5)	(49.5)	2533	(11.5)	(10.6)	13241	(60.0)
5.Pedestrian Density – X2 (3) =138.4, p<.001
Less Dense	6327	(28.7)	(27.6)	1061	(4.8)	(5.9)	7388	(33.5)
Dense	1138	(5.2)	(5.3)	291	(1.3)	(1.1)	1429	(6.5)
Very Dense	4863	(22.0)	(23.2)	1353	(6.1)	(5.0)	6216	(28.2)
Extremely Dense	5845	(26.5)	(26.2)	1180	(5.3)	(5.6)	7025	(31.8)

Model Fit

A logistic regression was conducted to determine if five traffic flow variables (Vehicle type, peak hour period, day of the week, vehicle density and pedestrian density) are able to predict stopping behaviors of drivers to allow pedestrian use zebra crossings in safety. The results of the logistic regression were highly statistically significant, with X (13) =2954.6, p<.001, using the Omnibus Test.

Effect size

The variance in driver’s stopping behaviors predicted by traffic flow variables in the model ranges from 12.5% to 20.7% based on the Cox & Snell R Square or Nagelkerke R Square, respectively.

Classification Table

Without any predictor, the model would have predicted 82.4% of the stopping behavior correctly. On the flipside, with independent variables included, this number increases slightly with 0.50 basis points to 82.9%. Some statisticians refer this to percentage accuracy(PAC) in classification too.

Sensitivity

Percentage of cases in which Stopping at a Zebra crossing was observed ("yes") and were correctly predicted by the model. 9.5% of drivers who stopped were predicted by the model to have stopped. The positive predictive value is the percentage of correctly predicted cases with the observed characteristic (those who stopped) compared to the total number of cases that were predicted to stop. That is, of all cases predicted to stop, 93.7% were correctly predicted.

Specificity

Percentage of cases in which Stopping at a Zebra crossing was observed ("no") and were correctly predicted by the model. 98.6% of drivers who breached the zebra crossing were predicted by the model to have violated. The negative predictive value is the percentage of correctly predicted cases with the observed characteristic (those who breached) compared to the total number of cases that were predicted to breach. That is, of all cases predicted to violate, 99.9% were correctly predicted.

Table 4. Logistic regression equation variables and their statistical significance.

Table 4. Logistic regression equation variables and their statistical significance.

							Lower	Upper
Variable	B	S.E.	Wald	df	Sig.	Exp(B)	95% C.I. - Exp(B)
Intercept	-3.390	0.266	162.468	1	.000	0.034
1. Peak Hour Period	-0.311	0.040	59.095	1	.000	0.733	0.677	0.793
2.Vehicle Type
Bicycle (1)			2289.644	4	.000
Moto	0.415	0.252	2.704	1	.100	1.514	0.924	2.481
Bus	2.208	0.290	57.838	1	.000	9.094	5.148	16.064
Car	2.341	0.252	86.486	1	.000	10.389	6.343	17.014
Null	0.992	0.273	13.157	1	.000	2.696	1.578	4.609
3.Day of the Week
Monday (1)			168.105	6	.000
Tuesday	-0.078	0.081	0.923	1	.337	0.925	0.789	1.085
Wednesday	-0.162	0.080	4.061	1	.044	0.851	0.727	0.996
Thursday	-0.269	0.079	11.601	1	.001	0.764	0.655	0.892
Friday	-0.276	0.083	10.936	1	.001	0.759	0.645	0.894
Saturday	-0.884	0.088	100.973	1	.000	0.413	0.348	0.491
Sunday	-0.599	0.093	41.426	1	.000	0.549	0.458	0.659
4.Vehicle Density*	0.671	0.044	233.313	1	.000	1.956	1.795	2.132
5.Pedestrian Density*	-0.494	0.056	77.360	1	.000	0.610	0.547	0.681

Model Summary

The model (traffic variables name them) explained between 12.5% (Cox & Snell R²) and 20.7% (Nagelkerke R²) of the variance in Driver’s stopping behaviors and correctly classified 82.9% of cases. Sensitivity was 9.5% and Specificity was 98.6%. The positive predictive value was 93.7%. and the negative predictive value was 99.9%.

4. Discussion

5. Conclusions

References

1. D. Antov, T. Rõivas, M. Pashkevich and &. E. Ernits, "Safety Assessment of Pedestrian Crossings," WIT Transactions on State of the Art in Science and Engineering, vol. 74, pp. 41-53, 2013.
2. H. Raiful and H. Ragib, "Pedestrian Safety Using the Internet of Things and Sensors:Issues, Challenges, and Open Problems," 2022.
3. UNCTAD, "Road Safety-Considerations in Support of the 2030 Agenda for Sustainable Development," UNITED NATIONS, Geneva, 2017.
4. P. Metaxatos and P. S. Sriraj, "Pedestrian Safety at Rail Grade Crossings: Focus Areas for Research and Intervention," Urban Rail Transit, vol. I, no. 4, p. 238–248, 2015. [CrossRef]
5. M. Haghighi, F. B. Aghdam, H. Sadeghi Bazargani and H. Nadrian, "What are the challenges of pedestrian safety from the viewpoints of traffic and transport stakeholders?A qualitative Study," Research Square, 2020.
6. R. Mukamana, "DRIVERS’ COMPLIANCE WITH ZEBRA CROSSING LAWS IN RWANDA: Kigali as a case study," UNIVERSITY OF RWANDA, KIGALI, 2015.
7. B. Olga, P. Luca and S. Davide, "A methodology to assess pedestrian crossing safety," Eur. Transp. Res. Rev, vol. II, no. SPECIAL ISSUE, p. 129–137, 23 September 2010.
8. CoK, "Transport Plan,Kigali Master Plan 2050," Kigali Master Plan Review, Kigali, 2020.
9. D. Nkurunziza. and R. Tafahomi, "Assessment of Pedestrian Mobility on Road Networks in The City of Kigali," Global Journal of Pure and Applied Sciences, vol. 26, pp. 179-190, 2020. [CrossRef]
10. J. Nyirajana, A. O. Coker and F. O. Akintayo, "Traffic flow rate on Kigali roads: a case of national roads (RN1 and RN3)," East African Journal of Science, Technology and Innovation, vol. II, no. 3, 25 June 2021. [CrossRef]
11. NISR, "Stattistical Year Book," National Institute of Statistics of Rwanda, Kigali, 2017.
12. NZTA, "Traffic Control Devices Manual," 2011.
13. D. Nkurunziza, R. Tafahomi and I. A. Faraja, "Assessment of Road Safety Parameters in the City of Kigali, Rwanda,," Global Journal of Pure and Applied Sciences, vol. 27, pp. 209-219, 2021. [CrossRef]
14. W. Wang, H. Guo, Z. Gao and H. Bubb, "Individual differences of pedestrian behavior in midblock crosswalk and intersection," International Journal of Crashworthiness, vol. 16, pp. 1-9., 2011.
15. D. Nkurunziza, "Investigation into Road Construction Safety Management Techniques," Open Journal of Safety Science and Technology, vol. X, no. 3, pp. 81-90, 2020. [CrossRef]
16. MININFRA, "RWANDA LAUNCHES ROAD SAFETY WEEK," 5 May 2019. [Online]. Available: https://www.mininfra.gov.rw/updates/news-details/rwanda-launches-road-safety-week. [Accessed 15 May 2023].
17. MININFRA, "NATIONAL TRANSPORT POLICY AND STRATEGY FOR RWANDA," Ministry of Infrastructures, Kigali, 2021.
18. P. Anjni, K. Elizabeth, A. Luciano, R. Stephen, R. ,. V. João and A. S. Catherine, "The epidemiology of road traffic injury hotspots in Kigali, Rwanda from police data," BMC Public Health, 2016.
19. J. J. Fruin, "Planning and design for pedestrians," in Time sever standards for urban design, New York, McGraw-Hill Companies, Inc, 2003, pp. 621-637.
20. R. Tafahomi, "Qualities of the green landscape in primary schools, deficiencies and opportunities for health of the pupils," J. Fundam. Appl . Sci., vol. 13, no. 2, pp. 1093 -1116, 2021.
21. R. Tafahomi and R. Nadi, "Insight into the missing aspects of therapeutic landscape in psychological centers in Kigali, Rwanda," Cities & Health, vol. 6, no. 1, pp. 136-148, 2022.
22. R. Tafahomi, "Application of physical and nonphysical elements in the conservation of historic core of city," South African Journal of Geomatics, vol. 10, no. 1, pp. 75-86, 2021b. [CrossRef]
23. R. Tafahomi and R. Nadi, "The interpretation of graphical features applied to mapping SWOT by the architecture students in the design studio," Journal of Design Studio, vol. 3, no. 2, pp. 205-221, 2021. [CrossRef]
24. M. Bonnes and M. Bonaiuto, "Environmental psychology: From spatial-physical environment to sustainable development," in Handbook of environment psychology, R. B. Bechtel and A. Churchman, Eds., New York, John Wiley & Sons, Inc., 2002, pp. 28-54.
25. D. Nkurunziza, R. Tafahomi and I. A. Faraja, "Identification of challenges and opportunities of the transport master plan implementation in the city of Kigali, Rwanda," in International Conference in Urban and Maritime Transport 2021, Wessex, 2021.
26. D. Mindrila and P. Balentyne, The Chi Square Test, 2023.
27. D. S. Moore, W. I. Notz and M. A. Flinger, "The Chi Square Test," in The basic practice of statistics (6th edition), New York, W. H. Freeman and Company, 2013.
28. S. Sandro, "Understanding logistic regression analysis," Biochemia Medica, February 2014.
29. P. Sathya and K. Krishnamurthy, "Parametric Study on the Influence of Pedestrians’ Road Crossing Pattern on Safety," The Open Transportation Journal, vol. XVII, 8 March 2023.
30. H. Nasser, Logistic Regression Using SPSS, University of Miami, 2020.
31. J. Mathias and V.-J. Hannah, Statistics Made Easy, Graz: ©DATAtab e.U., 2023.