Journal of Advanced Transportation
Volume 2021 (2021), Article ID 6667688, 13 pages
https://doi.org/10.1155/2021/6667688

Research Article

Pedestrian Crash Exposure Analysis Using Alternative Geographically Weighted Regression Models

Seyed Ahmad Almasi,¹ Hamid Reza Behnood,¹ and Ramin Arvin²

¹Departmnt of Transportation Planning, Faculty of Engineering, Imam Khomeini International University, Qazvin, Iran
²Department of Civil & Environmental Engineering, University of Tennessee, Knoxville, USA

Correspondence should be addressed to Hamid Reza Behnood; hr.behnood@gmail.com

Received 30 November 2020; Revised 25 January 2021; Accepted 8 February 2021; Published 18 February 2021

Academic Editor: Mohamed Hussein

Copyright © 2021 Seyed Ahmad Almasi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

In order to develop a sustainable, safe, and dynamic transportation system, proper attention must be paid to the safety of pedestrians. The purpose of this study is to analyze the surrogate measures related to pedestrian crash exposure in urban roads, including the use of sociodemographic characteristics, land use, and geometric characteristics of the network. This study develops pedestrian exposure models using geographical spatial models including geographically weighted regression (GWR), geographically weighted Poisson regression (GWPR), and geographically weighted Gaussian regression (GWGR). In general, the results of the GWPR model show that the presence of a bus station, population density, type of residential use, average number of lanes, number of traffic control cameras, and sidewalk width are negatively associated with increasing the number of crashes. In this study, in order to identify traffic analysis zones (TAZ) based on the observed and predicted crash data, spatial distance-based methods using GWPR outputs have been used. This study shows the dispersion and density of pedestrian crashes without possessing the volume of pedestrians. Comparison of the performance of GWPR and Poisson models shows a significant spatial heterogeneity in the analysis.

1. Introduction

Pedestrians are known as vulnerable road users, and the severity of pedestrian injuries in motor vehicle crashes is relatively high. Today, ensuring the safe movement of pedestrians is one of the most challenging concerns for transportation engineers. In general, in urban accidents, drivers and passengers have the largest share of the comprehensive cost of traffic accidents (94%) [1]. In order to develop a sustainable, safe, and dynamic transportation system, proper attention must be paid to the safety of pedestrians. The proportion of pedestrian casualties in the world has increased by an average of 11 percent to 14 percent over the past decade, so addressing pedestrian safety and raising awareness about safe pedestrianization is an important issue. This study was conducted with the aim of addressing the safety of pedestrians and identifying the extent of exposure to pedestrian crashes in urban areas and identifying accident-prone areas. Obviously, the number of people walking on the streets (i.e., pedestrian trips) and the factors that cause the presence of more pedestrians on the streets is one of the best measures for pedestrian exposure [2–5].

However, continuous measurement of pedestrian travels is difficult considering all the effective variables because it requires the use of significant resources and many factors that play a role in creating pedestrian travels. The purpose of this study is to investigate the available criteria and select the most effective measures in order to predict the variable of pedestrian exposure (pedestrian trips) and identify areas prone to pedestrian crashes. In other words, the purpose of this study is to analyze the surrogate measures related to pedestrian exposure in urban roads, including the use of sociodemographic characteristics, land use, and geometric characteristics of the network. The three-step process in the study involves the development of exposure models using geographical spatial models including geographically weighted regression (GWR), geographically weighted Poisson regression (GWPR), and geographically weighted Gaussian regression (GWGR). Exposure models in this study are compared with the study model developed by Lee et al. [5] which were performed using Tobit method and generalized linear models (GLMs) and predicted pedestrian travel. In their study, it was suggested that the effect of the geographical location of exposure variables for pedestrian crashes be investigated in future studies. In the current study, the effect of spatial exposure variables based on their geographical location has also been investigated. Then, in order to identify the best exposure model between GWR, GWPR, and GWGR models, the Akaike Information Criterion (AIC) index and value were used after validating the models to predict pedestrian crashes. This method can be described as a pedestrian safety analysis on urban roads (microlevel) with macrolevel data. Also in this study, in order to identify traffic analysis zones (TAZs) based on the observed and predicted crash data, spatial distance-based methods using GWPR outputs have been used.

The city of Tehran in Iran and its urban areas have been considered in this study. Although the two-step process (i.e., first identifying pedestrian collision variables and second crash prediction and identifying high-risk areas) has a relatively larger modeling error than the one-step model (pedestrian crash prediction only), but still by analyzing the volume of pedestrians and crashes, their output can lead to a better understanding of safety [5]. This study addresses pedestrian safety in the study area by identifying the best model for dealing with pedestrian crashes on major urban roads (first- and second-degree arteries and collector streets) as well as creating a safety analysis process for regions where pedestrian crash data are not available. This process can help transportation authorities create safer paths for pedestrians by implementing appropriate safety interventions.

2. Literature Review

Pedestrian safety is a growing concern, and so extensive studies have been conducted to ensure pedestrian safety. Researchers have tried to identify the factors contributing to pedestrian fatalities as well as identifying the urban areas with the highest risk of crashes for pedestrians by developing spatial relationships. For this purpose, in this section, past studies on the development of exposure models and spatial analysis of crashes have been reviewed.

Lee and Abdel-Aty [6] conducted a comprehensive study of pedestrian crashes at intersections in Florida. This study followed the Keall [7] method to use pedestrian personal travel data to create a rational model of a pedestrian exposure with crashes. In the proposed exposure measure, different walking patterns were reflected by different age groups of pedestrians. Miranda-Moreno et al. [8] analyzed two important relationships between land development and pedestrianization: (a) between land use and pedestrian activities and (b) between risk exposure (pedestrian and vehicle activities) and pedestrian crash frequency. The authors concluded that the land use pattern affects the level of pedestrian activities with a direct impact on pedestrian safety. Ukkusuri et al. [9] developed a pedestrian count crash frequency model for New York City using a negative binomial model and a stochastic parameter. This model found that the ratio of illiterate population, business areas, school areas, functional characteristics of the intersection, type of access control on the roads, and the number of lanes had a positive effect on pedestrian crashes.

In order to select the variables used in the proposed exposure model, several previous studies have been reviewed. Previous researchers have shown that pedestrian volume is a significant measure of exposure that has a positive effect on the occurrence of vehicle-pedestrian collisions [2–5, 10]. Another significant measure of exposure is the effects of land use patterns that have long been studied by researchers [5, 11–13]. Wier et al. [13] found that the number of pedestrian crashes is relatively higher in commercial and residential areas. There are many studies that describe the impact of demographic and socioeconomic characteristics on pedestrian safety (e.g., [12]).

Although previous researchers have attempted to explain pedestrian exposure, there are very few studies that specifically identify the exact causes of this criterion. Another issue is the reliability of pedestrian volume data. There have been many cases where pedestrian volume data were not available or were not sufficiently accurate to perform a safety analysis. A reliable process for identifying surrogate measures is needed to express the pedestrian exposure criteria in such cases. Apart from these issues, the use of the negative binomial (NB) or even zero-inflated negative binomial (ZINB) model in microlevel safety analysis has been questioned by many authors [5, 14–16]. However, some authors have confirmed it in macrolevel analysis [17].

In general, spatial prediction of crashes using localized parameters gives us more accurate predictions compared to methods in Highway Safety Manual [18] that use global parameters. In addition, traffic exposure criteria (such as AADT and length of segment) are considered as predictors of crash frequency and have been widely used by transportation professionals to predict the occurrence of crashes at a particular site. Therefore, in the field of safety performance functions (SPFs), understanding the different spatial relationships between the main factors of exposure and the frequency of crashes has significant potential for the development of localized SPFs that can potentially provide more accurate crash predictions at separate sites [19].

In the current study, in order to identify high-risk TAZs based on observed and predicted crash data, two methods have been adopted: (1) frequency-based methods and (2) distance-based models. The first group measures the severity of point events based on the density of an area. These methods include kernel density estimation (KDE). The second group measures the spatial dependence of point events based on the distance of points from each other. This group includes methods such as nearest neighbor distances, K-functions, and Moran I [20, 21]. Hadayeghi et al. [22] presented traditional crash prediction models for 463 TAZs in Toronto using traditional NB (global) and GWPR general regression models. The results showed that GWPR models were able to partially deal with spatial dependence as well as spatial heterogeneity resulting from these factors and TAZs. Xu and Huang [23] modeled the total crash frequency as a function of road length density, population density, average household income, and percentage of road sections with different speed limits and showed that the GWPR model due to the instability of the crash location has acceptable accuracy compared to the NB model with random parameter. A parametric GWPR model was also developed to estimate some parameters globally and some locally [24]. Similarly, a study by Rhee et al. [25] investigated traffic accidents with spatial correlation and spatial relevance using advanced spatial modeling methods. The results showed that the statistical performance of GWR was superior in the correlation coefficient of localization.

3. Methods

In this study, in the first step, which is the identification of exposure variables, several statistical methods have been used to identify these variables and the crash frequency is examined based on different modeling methods. In the second step, crash prediction models are presented at the TAZ level using surrogate variables. The following is a brief description of the modeling techniques used in these two steps.

3.1. Models Used to Identify Exposure Variables

3.1.1. Generalized Linear Models

Generalized linear models (GLMs) are a general class of statistical models that include many common models with specific features. A typical GLM is as follows:

In this equation, Y is the linear prediction and is the error parameter. In the generalized linear model, the assumptions of independent and normal distribution in Y are given. This distribution includes such cases as normal, Poisson, gamma, and binomial distributions [26]. The GLM is a flexible generalization of ordinary linear regression that allows the use of response variables that have error distribution models other than the normal distribution.

3.1.2. Tobit Model

In this study, in order to eliminate any negative prediction of pedestrian crash, Tobit model was used to identify the measure of exposure. The Tobit model is a statistical model used to describe the relationship between a censored dependent variable and an independent variable (or vector) xi. The Tobit model is as follows:

In this relation, is a hidden variable that can only be seen if it is positive. Also, N is the number of observations, is a dependent variable, is a vector of explanatory variables, is a vector of estimable parameters, and is a normal and independent distribution. The error parameter also has a mean of zero and a variance of [27].

3.1.3. Variable Importance for Exposure Model Using Random Forest

Important explanatory variables can be determined using a random forest exposure model. The first step in this process is to place a random forest of data. During the fitting of this process, an out-of-bag error (a method for measuring random forest prediction error) is recorded for each data point and averaged in the forest. In order to measure the importance of the jth attribute after training, the values of the jth attribute can be changed among the training data and the out-of-bag error is re-estimated in this turbulent dataset [28].

3.2. TAZ Level Crash Predictive Models

3.2.1. Network KDE

As mentioned earlier, many recent studies have used the KDE network method developed by Okabe et al. [29] to examine the spatial correlation of point events in a road network. In this study, the KDE network method has been used to estimate the density of road sections in the Tehran road network. This method is based on the study method of Okabe and Sugihara [30].

In this study, the KDE network was performed on 1000-meter sections, similar to those proposed by Xie and Yan [20] and Nie et al. [31]. Also, according to studies [32, 33] in order to achieve more accurate KDE results, three values of 100, 200, and 500 meters have been considered for bandwidth measure. See Okabe and Sugihara [30] for more details on the computational process.

3.2.2. Geographically Weighted Regression Models

Geographically weight regression (GWR) is an exploratory method that has been adopted in the relevant literature mainly to deal with spatial variables. Past studies on the relationship between urban form and pedestrian behavior have mainly used global regression models. However, since the present study includes urban areas with the main arterial functional class and collectors, the behavioral characteristics of pedestrians may be different in each one. Therefore, it is likely that the relationship between urban form and walking varies across the study area.

In this study, a Gaussian GWR model is used to evaluate the relationship between exposure variables (land use and street characteristics around houses as independent variables). To determine which model is appropriate, a comparison was made between Gaussian GWR and geographically weighted regression and geographically weighted Poisson regression. A model with lower AIC values is a more appropriate model [34–36].

A GWPR model is also used in this study. In a GWPR model, the frequency of crashes is predicted by a set of explanatory variables in which the parameters are allowed to change in space. This model can be written as follows [37]:

In this relation, specifies the coordinates of region i. It should be noted that, in GWPR, is a function of the coordinates of the center for region i.

In this study, GWR4.0 software was used to identify high-risk points in which the chances of determining the walking exposure variable increase because changes in independent variables are given in the first step of modeling. The results of GWR, GWPR, and GWGR in ArcGIS, version 10.2, were mapped to visualize spatial relationships. Also, even if there is a discontinuity in the study area, the optimal bandwidth has been selected based on several experiments to ensure that the blank spaces are outside the crash points of the study area.

3.3. Measures of Goodness of Fit

To evaluate and compare the performance of the models, three statistics were used to measure the accuracy of the estimates. First, we used AIC, which indicates that the lower the AIC, the better the model [38]. The AIC is measured as follows:

where D represents the model deviation and k is the number of parameters. In GWPR, due to the nonparametric framework of the model, the number of parameters is meaningless. Therefore, an effective number of parameters must be considered, which can be written as follows [39]:

where S is the hat matrix. In addition to AIC, we also used mean absolute error (MAE) and root mean square error (RMSE) to compare model performance. Lower MAE and RMSE values indicate better model performance. Finally, Moran’s I statistics model was used to validate the models. Statistically, Moran’s I statistics is a measure of spatial correlation. In this study, the Moran test was used to examine whether the residuals of city-wide crash predictions were spatially related to neighboring TAZs. Negative (positive) value of Moran’s I statistics indicates a negative (positive) spatial correlation at the overall level.

3.4. Data Preparation

The main source of data for this study is Tehran Municipality. The data used in this study are shown in Table 1. In this study, the analyzed zones have been considered for model development based on the variables of exposure in TAZs, which are 560 zones for Tehran. Of course, it should be considered that, in order to match the analyzed zones, the whole city can be divided into equal units, but due to the lack of homogeneous distribution of pedestrian crashes, many of the identical zones will have zero observed crashes.

Table 1: Description of variables used in the study.

Variable
Description

Crash data (CR)
Pedestrian crash data for the years 2017 to 2019 in Tehran

Bus stops (BS)
Location of bus stops in the existing situation and detailed plan of Tehran

Schools (SC)
Location of all schools in Tehran urban zones including both existing and planned schools

Pedestrian bridges (PB)
Location of pedestrian overpasses

Intersections (TS)
Location of all controlled and noncontrolled intersections

Total population (TP0)
Based on the last census in 2016

Children population (TP1)
Based on the last census in 2016

Elder people population (TP2)
Based on the last census in 2016

Motorcycles (TM)
Based on the data recorded in the 2016 census and registered in police databases

Vehicles (TC)
Based on the data recorded in the 2016 census and registered in police databases

Residential land use (RE)
Data gathered by Tehran Municipality’s staff in 2017

Business land use (BU)
Data gathered by Tehran Municipality’s staff in 2017

Recreational land use (RCE)
Data gathered by Tehran Municipality’s staff in 2017

Average road width (AW)
Based on GIS layers of the Tehran Municipality

Average number of lanes (AL)
Based on GIS layers of the Tehran Municipality

Average length of median refuges (TR)
Based on GIS layers of the Tehran municipality

Average sidewalk width (PP)
Average sidewalk width in each traffic zone based on aerial photos

Average speed (SA)
Based on the data adopted from the Traffic Control Center of Tehran Municipality

Average road slope (AS)
Based on GIS layers of the Tehran Municipality

Speed cameras (TCC)
Based on the data adopted from the Traffic Control Center of Tehran Municipality

The TAZ characteristics selected for the crash analysis include all the variables in Table 1. All items selected as exposure variables are items that affect the frequency of crashes. Crash data variable (CR) shows the total number of pedestrian crashes in Tehran. The density of speed cameras (TCC) indicates a risk factor at their installation site, as these devices are typically installed in locations where drivers need more focus and are at greater risk of road crashes [32]. Bus station (BS) can be a risk factor as a large number of pedestrians get on and off in one place and some of them tend to cross the street [5]. The presence of schools (SC) is one of the most important places to attract pedestrians, so the presence of schools in TAZs during the hours of the day is a risk factor for pedestrian crashes [5]. The presence of a pedestrian bridge (PB) based on studies has improved pedestrian safety in conflicting with vehicles [5]. Also, the presence of intersections in any zone increases the risk of pedestrian collisions. It is obvious that population density (TP0) in each zone and the density of vulnerable users (TP1 and TP2) increase the risk of pedestrian collisions [5, 32, 33]. This study was conducted in two steps including [1] identifying the variables of pedestrian exposure and [2] investigating the spatial-geographical relationship between the variables and the spatial crash prediction at the TAZ level. GIS and SPSS software were used to extract and process data for the first step and GWR4 for the second step. The integration of the database with all the information collected in TAZs is done with the help of standard tools in GIS that allow spatial search, layer addition, and spatial operations based on topological relationship.

Table 1 shows the pedestrian crash dataset of Tehran, which includes 1231 observed cases. Descriptive variables were also classified into three categories: “demographic and socioeconomic,” “land use,” and “traffic and geometric”. Out of 25 variables collected, 15 variables are listed in Table 1 based on the results of the first step in the study. Road network in Tehran, including arterial roads and collectors, has been used for analysis. Data were collected from various sources. Figure 1 shows the study area and the status of existing crashes. Figure 2 shows the KDE crash density function based on crash point and crash density per kilometer in three bandwidths of 100, 200, and 500 meters.

Figure 1: The study area and the status of existing crashes.

(a)

(b)

(c)

(d)

(e)

(f)

Figure 2: KDE crash density functions in different bandwidths. (a) KDE crash density function for bandwidth = 100 m. (b) KDE crash density function for bandwidth = 200 m. (c) KDE crash density function for bandwidth = 500 m. (d) KDE crash density function per km for bandwidth = 100 m. (e) KDE crash density function per km for bandwidth = 200 m. (f) KDE crash density function per km for bandwidth = 500 m.

The correlation between the descriptive variables used in the Tobit model (selected model based on the results of the first step of the study) was investigated before the modeling process. Pairs of variables with correlation coefficients higher than 0.6 are not included in the models simultaneously [5]. In the modeling process, first the explanatory variables with the lowest correlation values were included in the model and the variables with relatively lower correlation values were preferred in the model (Tables 2 and 3).

Table 2: Pearson correlation coefficients.

Correlation
SC
BS
PB
TS
TP0

SC
Pearson
1

Sig.

BS
Pearson
0.54
1

Sig.
0.00

PB
Pearson
0.39
0.60
1

Sig.
0.00
0.00

TS
Pearson
0.36
0.41
0.21
1

Sig.
0.00
0.00
0.00

TP0
Pearson
0.63
0.60
0.47
0.37
1

Sig.
0.00
0.00
0.00
0.00

Correlation is significant at the 0.05 level (2-tailed). Correlation is significant at the 0.01 level (2-tailed).

Table 3: Pearson correlation coefficients.

Correlation
SC
BS
PB
TS
TP0
TP1
TP2
TM
TC
RE
BU
REC
AW
AL
TR
AS
TCC

SC
Pearson
1

Sig.

BS
Pearson
0.54
1

Sig.
0.00

PB
Pearson
0.39
0.60
1

Sig.
0.00
0.00

TS
Pearson
0.36
0.41
0.21
1

Sig.
0.00
0.00
0.00

TP0
Pearson
0.63
0.60
0.47
0.37
1

Sig.
0.00
0.00
0.00
0.00

TP1
Pearson
0.56
0.56
0.43
0.29
0.92
1

Sig.
0.00
0.00
0.00
0.00
0.00

TP2
Pearson
0.58
0.43
0.31
0.31
0.85
0.67
1

Sig.
0.00
0.00
0.00
0.00
0.00
0.00

TM
Pearson
0.38
0.22
0.16
0.09
0.63
0.73
0.47
1

Sig.
0.00
0.00
0.00
0.03
0.00
0.00
0.00

TC
Pearson
0.57
0.60
0.48
0.39
0.90
0.78
0.88
0.39
1

Sig.
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

RE
Pearson
0.27
0.07
0.03
0.11
0.38
0.30
0.46
0.45
0.27
1

Sig.
0.00
0.09
0.40
0.00
0.00
0.00
0.00
0.00
0.00

BU
Pearson
0.20
0.32
0.22
0.10
0.28
0.23
0.29
0.06
0.28
0.00
1

Sig.
0.00
0.00
0.00
0.01
0.00
0.00
0.00
0.10
0.00
0.97

REC
Pearson
0.37
0.30
0.22
0.32
0.36
0.22
0.45
0.14
0.35
0.46
0.10
1

Sig.
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01

AW
Pearson
0.12
0.01
−0.01
0.10
0.20
0.22
0.18
0.29
0.10
0.34
0.01
0.12
1

Sig.
0.00
0.81
0.74
0.01
0.00
0.00
0.00
0.00
0.01
0.00
0.68
0.00

AL
Pearson
0.04
0.07
0.05
0.16
0.07
−0.01
0.15
0.20
0.20
0.20
−0.03
0.00
0.41
1

Sig.
0.31
0.09
0.23
0.00
0.06
0.80
0.00
0.00
0.00
0.00
0.37
0.87
0.00

TR
Pearson
0.42
0.65
0.65
0.44
0.58
0.46
0.50
0.11
0.65
0.13
0.23
0.33
0.03
0.20
1

Sig.
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.00
0.44
0.00

AS
Pearson
−0.02
0.05
−0.01
0.02
0.04
0.10
0.21
0.27
0.20
0.07
0.07
0.11
0.04
0.38
0.16
1

Sig.
0.58
0.22
0.81
0.60
0.24
0.01
0.00
0.00
0.00
0.06
0.09
0.00
0.32
0.00
0.00

TCC
Pearson
0.28
0.58
0.67
0.24
0.40
0.33
0.31
0.02
0.45
0.00
0.31
0.19
0.02
0.14
0.76
0.09
1

Sig.
0.00
0.00
0.00
0.00
0.000
0.00
0.00
0.51
0.00
0.98
0.00
0.00
0.52
0.00
0.000
0.02

N
560
560
560
560
560
560
560
560
560
560
560
560
560
560
560
560
560

4. Results and Discussion

A total of six exposure models have been developed in this study (Table 4). Because two different modeling methods are used (GLM vs. Tobit) to compare the best model, it is not appropriate to compare the Akaike Information Criterion (AIC) or the Bayesian Information Criterion (BIC). Therefore, to compare the models, the mean absolute deviation (MAD) and the root mean square error (RMSE) for each model have been used [5]. Table 4 shows that the Tobit model using all variables performs best with the lowest MAD and RMSE values. The Tobit model also shows any predicted negative pedestrian crashes using the exposure variables equal to zero because the lower limit is set at zero.

Table 4: Comparison of the exposure models developed in the study.

Model type (exposure)
MAD
RMSE

Exposure model (GLM) using all variables
26.91
40.12

Exposure model (GLM) using PCA variables
37.21
49.26

Exposure model (Tobit) using all variables
22.69
37.99

Exposure model (GLM) using random forest variables
31.06
42.40

Exposure model (Tobit) using RF variables
33.76
42.43

Exposure model (Tobit) using PCA variables
30.61
43.24

Between 2017 and 2019, 1228 pedestrian crashes were reported in Tehran, in which 44 people died and 1184 were injured. A total of 4979 schools, 4831 bus stops, 927 pedestrian bridges, 801 lighted intersections, and 5386 traffic control cameras located in 560 different TAZs were included in this analysis (Table 1), with an average number of variables ranging from 0 to 80 in all TAZs. This initial benchmark shows that the difference between the scatterings of exposure variables at the level of TAZs is significant. The results of GWR, GWGR, and GWPR models are shown in Tables 5–7, respectively. Table 8 compares the GWR, GWGR, and GWPR models. According to the results shown in these tables, the GWPR model has a higher accuracy in predicting crashes based on exposure variables. In Figure 3, based on crash predictive models and using ArcGis software, a pedestrian crash map of Tehran has been produced. Table 9 shows the ANOVA values for the GWPR model.

Table 5: Summary results of the GWR model for predicting crashes.

Variables
Intercept
C-export
Residual
R² (local)
Std. error
Std. resid

2.053
−0.044
0.39
−0.024

SC

0.248

0.183

BS
0.228
0.196

PB
0.118
0.120

TS
0.178
0.085

TP0
0.725
0.210

TM
−0.423
0.248

TC
0.354
0.155

RE
−0.423
0.122

BU
0.572
0.135

RCE
−0.028
0.123

AW
0.39
0.190

TR
0.121
0.088

TCC
−0.055
0.012

TP1
0.514
0.145

TP2
0.325
0.162

AL
0.254
0.065

PP
−0.416
0.321

SA
0.213
0.174

AS
0.085
0.162

Table 6: Summary results of the GWGR model for predicting crash.

Variables
Estimate
Stand. error
Z (Est/SE)
Mean
Std.
Min
Max
Median
Lower quartile
Upper quartile
Local

Intercept
2.173
0.101
21.462
1.046
1.810
3.093
−2.52
2.009
1.922
2.284
Yes

SC
0.209
0.150
1.394
0.214
0.228
0.531
−0.36
0.285
0.150
0.381
Yes

BB
−0.084
0.170
−0.494
0.553
0.513
1.809
−0.41
0.332
0.128
0.862
Yes

PB
0.012
0.164
0.071
0.227
0.337
0.898
−0.22
0.324
0.181
0.501
Yes

TS
0.202
0.121
1.676
0.173
0.179
0.555
−0.42
0.164
0.107
0.275
Yes

TP0
−0.548
0.519
−1.057
1.081
0.344
3.677
−1.79
-0.007
−0.342
0.817
Yes

TP1
−0.137
0.411
−0.333
1.311
−0.13
4.750
−2.03
-0.518
−0.907
0.385
Yes

TP2
0.154
0.322
0.477
0.742
0.301
1.933
−1.31
0.188
−0.219
0.796
Yes

TM
0.273
0.187
1.465
0.519
0.246
1.123
−2.04
0.330
0.098
0.536
Yes

TC
0.091
0.419
0.218
1.292
−1.32
0.605
−4.83
-1.137
−1.925
−0.244
Yes

RE
−0.429
0.143
−2.999
0.343
−0.56
−0.03
−1.45
-0.545
−0.835
−0.235
Yes

BU
0.229
0.116
1.970
0.520
−0.08
0.923
−1.23
-0.038
−0.509
0.350
Yes

REC
0.556
0.133
4.172
0.253
0.769
1.429
0.139
0.718
0.636
0.886
Yes

AW
0.086
0.121
0.716
0.158
0.005
0.497
−0.39
0.008
−0.125
0.064
Yes

AL
−0.348
0.117
−2.970
2.477
−1.16
−0.00
−11.6
-0.234
−0.576
−0.140
Yes

TR
0.641
0.191
3.347
0.373
0.386
1.473
−0.33
0.324
0.124
0.621
Yes

AS
0.232
0.112
2.068
0.208
0.170
0.715
−0.46
0.201
0.050
0.266
Yes

TCC
0.795
0.175
4.548
0.456
0.813
2.017
0.114
0.665
0.470
1.085
Yes

PP
−0.423
0.125
−3.25
−0.25
0.145
1.025
−0.21
0.361
0.251
0.189
Yes

Table 7: Summary results of the GWPR model for predicting crash.

Variables
Estimate
Stand. error
Z (Est/SE)
Mean
Std.
Min
Max
Median
Lower quartile
Upper quartile
Local

Intercept
−13.165
0.033
−399.863
−14.48
3.208
−31.32
−12.16
−13.15
−14.101
−12.813
Yes

SC
0.104
0.036
2.883
0.111
0.126
−0.160
0.436
0.110
0.008
0.213
Yes

BS
−0.355
0.040
−8.943
−0.028
0.294
−0.824
0.732
−0.073
−0.226
0.139
Yes

PB
0.042
0.038
1.100
0.062
0.168
−0.358
0.433
0.058
−0.068
0.183
Yes

TS
0.218
0.032
6.771
0.077
0.157
−0.401
0.284
0.117
−0.006
0.198
Yes

TP0
−0.312
0.122
−2.559
0.681
1.509
−2.625
5.228
0.402
−0.415
1.370
Yes

TP1
−0.401
0.109
−3.672
−0.467
1.147
−3.593
2.591
−0.618
−1.044
0.201
Yes

TP2
−0.055
0.074
−0.744
0.286
0.416
−0.822
1.068
0.312
−0.012
0.631
Yes

TM
0.254
0.044
5.724
−0.004
0.473
−2.010
1.211
0.026
−0.241
0.290
Yes

TC
0.343
0.105
3.257
−1.014
0.917
−3.034
1.017
−0.992
−1.831
−0.254
Yes

RE
−0.230
0.041
−5.627
−0.224
0.189
−0.709
0.200
−0.261
−0.365
−0.043
Yes

BU
−0.012
0.026
−0.464
−0.220
0.359
−1.242
0.727
-0.101
−0.508
0.049
Yes

REC
0.201
0.026
7.724
0.212
0.189
−0.117
0.764
0.148
0.055
0.328
Yes

AW
0.139
0.034
4.017
−0.019
0.172
−0.631
0.412
0.005
−0.103
0.097
Yes

AL
−0.153
0.039
−3.920
−3.683
7.218
−40.32
0.502
−0.146
−2.609
−0.056
Yes

TR
0.327
0.044
7.352
0.181
0.314
−0.555
1.083
0.157
−0.064
0.367
Yes

AS
0.211
0.028
7.588
0.124
0.323
−0.527
1.141
0.084
−0.078
0.245
Yes

TCC
−0.251
0.037
−6.715
−0.005
0.252
−0.709
0.621
−0.029
−0.149
0.136
Yes

PP
−13.165
0.033
−399.863
−14.48
3.208
−31.32
−12.16
−13.15
−14.101
−12.813
Yes

Table 8: Comparison and fitted goodness model.

Models
Global model result
Regression model

GWGR
GWPR
GWR

Unbiased sigma estimate
2.3961
2.4921
2.1748

−2 log-likelihood
2569.6562
2989.7522
2347.2654

AIC
2587.4516
2767.4426
2507.5604

AICc
2589.0339
2799.0559
2534.7239

Adjusted R square
0.3214
0.44
0.41

MAD
0.851
0.482
0.952

RMSE
1.052
0.591
1.34

Moran’s index
0.042
0.031
0.061

value
<0.001
<0.001
<0.001

Figure 3: Crash predictive maps for GWR, GWGR, and GWPR models.

Table 9: ANOVA table for GWPR model.

Source
SS
DF
MS
F
value

Global residuals
3111.824
542

0.001

GWPR improvement
943.826
83.0214
11.286
0.001

GWPR residuals
2168.097
458.2651
4.730
2.3861
0.001

Tables 5–9 show the results of the GWR, GWGR, and GWPR models with adaptive bisquare kernel for predicting crashes. In the kernel density function model, the lowest AICC value was obtained based on the adaptive bisquare kernel. Notably, we found that the results were largely consistent with the adaptive bisquare kernel. Also, the Lagrange coefficient values for the GWPR model and the global model were 0.12 and 0.15, respectively, which is less than the critical Lagrange value (3.48). The study by Hezaveh et al. [1] also confirms the adaptive bisquare kernel in relation to the Gaussian adaptive for urban TAZs.

The comparison of AIC, AICc, deviation, MAE, and RMSE presented in Table 8 shows that the GWPR model is more appropriate than the global model. The value of Moran I (0.031) indicates that, in the GWPR model, the residues are not related to each other. In addition, VIF values (mean = 1.8; maximum = 3.9) indicate that the local multicollinearity issue is not critical in this study.

In the study by Lee et al. [5], it is proposed to investigate the local effect of pedestrian crash exposure variables. The statistical results of the second step models in the current study show that, in the GWPR model, all variables had a local effect. Figure 3 shows the spatial effect of the estimated parameter on crashes. This figure shows only those coefficients that have a significant effect and the small coefficients are shown in white. It is noteworthy that the estimated coefficients in common fixed models are in the range of similar values in spatial models [1] and this shows that the estimated parameters in global models (i.e., fixed models) are characteristics of the average of the factors affecting the dependent variable.

VIF values do not change between 1.12 and 3.9 (the critical value of VIF between 5 and 10 means a complete spatial correlation between the independent variables). This could be due to excessive scatter in the exposure variables. According to studies [1, 23, 40], the location of VIF in this limit can also justify the geographical distribution of Poisson in crash prediction, but this issue can be explored in the future. Traffic parameters such as intersections and speed cameras have a significant impact on pedestrian crashes and in TAZs where the speed camera density is higher, and fewer crashes will occur due to the variable estimation coefficient (negative).

The model predicts that crashes decrease as population density increases. The sign of population density is negatively associated with crash frequency which is consistent with previous studies [41]. This could be due to the fact that, in residential areas without commercial and recreational land uses, due to the low speed of vehicles and the presence of speed bumps, as well as distracting effects, the crash density has decreased. In general, the results of the GWPR model show that the presence of a bus station, population density, type of residential land use, average number of lanes, number of traffic control cameras, and sidewalk width have a negative effect on increasing the number of crashes. In the GWR model, the number of motorcycles, residential land use, recreational land use, the average number of lanes, and the number of speed cameras in TAZs had a negative relation with increasing pedestrian crashes. Finally, in the GWGR model, the number of bus stops, population density, residential land use density, average number of lanes, and the number of speed cameras were negatively associated with increasing crashes. It should be noted that, in the three mentioned models, a significant relationship between dependent variable and independent variables has been obtained, which has been confirmed by previous studies (e.g., [13, 40, 42]).

One of the explanations for the negative sign of the bus station in urban areas can be the reduction of the volume of motor vehicles around the residential area, which reduces traffic congestion and ultimately exposes the motor traffic of other residents [1, 5]. On the other hand, poor design of a multimode network can negatively affect the safety of nonmotorized users and public transportation. The difference between the signs of the estimated coefficients in several different models requires more details in future studies. We may expect older people to suffer more severe injuries due to vulnerability [1]. Conversely, older people travel less than other groups [43–45]. As a result, in this study, the percentage of elderly people compared to other age groups was negatively associated with the increase in crash density in the GWPR model. The negative sign of motorcycle users on the increase of pedestrian crashes can be due to the increase in motorcycle travel; hence, the number of pedestrian trips has decreased. In this study, the variables of number of schools, number of intersections, and pedestrian bridges have been shown with a positive sign, in which the impact of intersections on pedestrian crashes is not significant but has a positive effect on causing crash, which is confirmed by the study by Lee et al. [5]. Due to the presence of parents and children on the school routes, there is more walking activity in TAZs with more schools. Households with less than two vehicles (0 or 1 vehicle) are another important source of pedestrian activity. Car ownership is directly related to household income levels, which reflects the socioeconomic impact on pedestrian activity [5]. It is obvious that family members without transportation meet their transportation needs through public transportation or walking. On the other hand, the amount of car ownership in TAZs also has a significant impact on increasing pedestrian crash exposure. Of course, the crash may occur with vehicles that are outside the TAZ and collide with a pedestrian while traveling in different TAZs. In this study, the TC variable is the vehicle ownership variable in the study area, considering the two factors, and the model results show a positive effect on pedestrian exposure. As mentioned earlier, in this study, the average width of the sidewalk was negatively associated with pedestrian exposure and did not cause crashes, which is consistent with previous studies (e.g., [5, 45, 46]). These findings are also consistent with studies of human factors that show that some groups (e.g., low income, low education, and young urban road users) are more prone to abnormal behaviors [1, 47, 48].

According to the modeling results, the effect of slope and average width of the route on crashes is positive and the reason could be that pedestrians in wider passages have to travel longer to cross the street so they are more exposed in the passage of vehicles. Also, the medium slope has a positive effect on pedestrian crashes compared to zero-slope roads due to more difficult control of vehicles in adverse weather conditions.

5. Conclusion

In this study, a systematic approach has been developed that uses pedestrian surrogate measures based on exposure information. In this study, in the first step, which is the identification of exposure variables, several statistical methods have been used to identify these variables and the frequency of crashes is investigated based on different modeling methods. In the second step, crash prediction models were presented at the zone level using surrogate variables. In this study, three models GWR, GWGR, and GWPR have been used to spatially predict the crash frequency based on exposure variables, and the results of the study showed that the GWPR model makes more accurate predictions. In addition, identifying effective criteria such as the presence of school, car and motorcycle ownership, bus station, sidewalk width, pedestrian bridges, type of intersection control and the presence of midroad refuge, population density, type of land use, width of roads, average number of routs, average road slopes, and number of speed cameras in dealing with pedestrians is important in this study. In this study, it is emphasized that while providing safety measures for pedestrians, cases such as improving traffic calming should be done in areas with high density of schools as well as schools in the area of intersections and increase the width of sidewalks in areas with more bus stations, because in areas where the bus is the main mode of transportation, there is a tendency to walk and consequently pedestrians are exposed to crashes.

The proposed two-step method in this study involves two consecutive modeling processes. The first model identifies the exposure variables in pedestrian crashes and the second model estimates the number of pedestrian crashes using three spatial models GWR, GWGR, and GWPR. However, this trend is limited because the result can be affected by the errors accumulated in the first stage due to the existence of an uncontrollable confounding variable as well as information biases. It is possible to solve the problem by adopting a simultaneous modeling approach. This study has shown the dispersion and density of pedestrian crashes without possessing the volume of pedestrians and thus by taking safety measures in places prone to pedestrian crashes, social costs, and casualties can be decreased. In this study, Poisson regression was used to evaluate the relationship between sociological variables and crashes at the zone level. Comparison of the performance of GWPR and Poisson models shows a significant spatial heterogeneity in the analysis. The increase in residential density in urban areas has been associated with a decrease in speed and therefore has led to a reduction in crash frequency. On the other hand, increasing travel time and consequently increasing traffic exposure affect the social costs of crashes. Identifying traffic-prone zones can be a useful element in developing policies to support mitigation measures related to pedestrian exposure to traffic. We expect that, in future studies, negative geographic binomial distribution models and the experimental Bayesian geographic model will be used to identify pedestrian exposure variables.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

The authors of this article would like to thank the Deputy of Transportation and Traffic of Tehran Municipality, Legal Medicine Organization, Statistics Center of Iran, and Deputy of Architecture and Urban Planning of Tehran Municipality in order to collect the required data and cooperate in field visits and geometric information of the road network.

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