How does built environment influence pedestrian …tram.mcgill.ca/Research/Publications/How does built...Miranda-Moreno, Morency, El-Geneidy 2 1 2 Abstract 3 This paper studies the

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How does built environment influence pedestrian activity and pedestrian collisions 2

at intersections? 3

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Luis F. Miranda-Moreno, PhD 6 Assistant Professor 7

Department of Civil Engineering and 8

Applied Mechanics 9

McGill University 10

Phone: 514-398-6589 11

E-mail: [email protected] 12

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Morency P., M.D. 15 Montreal Department of Public Health 16

Montreal Health and Social Service Agency 17

Phone: (514) 528-2400 ext: 3327 18


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Ahmed M. El-Geneidy 22 Assistant Professor 23

School of Urban Planning 24

McGill University 25

Montreal, Quebec H3A 2K6 26

Phone: 514-398-8741 27


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Submitted to the 89th Annual Meeting of the Transportation Research Board 33

July 31, 2009 34

Word count 5600 + *250(8) 7,600 35

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The first author acknowledges the financial support of Natural Sciences and Engineering Research Council of 37

Canada. All remaining errors and the views expressed in this research are however solely ours. We would also 38

like to add our thanks to Lee Haber, Kevin Manaugh, Sabrina Chan and Tyler Kreider for their help in the data 39

preparation. 40

Miranda-Moreno, Morency, El-Geneidy

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Abstract 2

This paper studies the influence of built environment in the vicinity of an intersection on pedestrian 3

activity and collision frequency. In doing so, a two-equation model to predict pedestrian activity and 4

collision occurrence is formulated and validated using data from 509 signalized intersections in the City 5

of Montreal. The applicability of this modeling framework is illustrated through an elasticity analysis 6

and a model validation study. Among other results, it was found that the built environment (BE) in the 7

proximity of an intersection has a powerful association with pedestrian activity but a small direct effect 8

on collision frequency. That is, the impact of BE is mainly mediated through pedestrian activity and 9

traffic volume. In accordance with previous studies, pedestrian activity and traffic volume are the main 10

determinants of pedestrian collision frequency at signalized intersections. Our analysis illustrates how 11

urban policies aiming to increase population density, transit offer and road network connectivity may 12

have important health and safety benefits by encouraging pedestrian activity. In addition, our results 13

show that a reduction of 30% in the traffic volume in each of the studied intersections would greatly 14

reduce the average risk of pedestrian collision (-50%) and the total number of injured pedestrians (-35%) 15

in the area under analysis. Arterials and urban highways seem to have a double negative effect on 16

pedestrian safety: major roads are negatively related with pedestrian activity and positively associated 17

with traffic volume. Those results support the idea of retrofitting major urban roads into more complete 18

streets. 19

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1. INTRODUCTION 21

Non-motorized transportation (NMT) such as walking is essential for the development of sustainable 22

transportation systems, whether for short trips, access and egress to/from motorized modes (especially 23

transit) or recreation. As recognized in the literature, NMT can offer substantial benefits in countries like 24

Canada by reducing automobile traffic, increasing transit travel, reducing energy consumption and 25

emissions, and improving health and fitness - e.g., see Dora (1) and Krizek (2). 26

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In spite of the importance and benefits of walking, road facilities in urban areas are still an important 28

source of harm to pedestrians. Every year, a large number of pedestrians are killed or seriously injured 29

in crashes involving motor vehicles. In Canada between the year 2002 and 2006, 1,829 pedestrians were 30

killed comprising approximately 13% of total road user fatalities. In addition, around 23,920 were 31

seriously injured. To address this problem, local government and urban transportation agencies, not only 32


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in Canada but also in other countries around the world, have identified the safety and mobility of 1

pedestrians as high priorities. To this end, investments are constantly allocated through different safety 2

improvement programs. The development of cost-effective safety improvement programs requires 3

however modeling tools to guide decision makers. 4

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In the past decade, considerable research effort has been directed towards addressing safety and mobility 6

issues of motorized transportation modes with relatively few dealing with NMT. Significant knowledge 7

and methodology gaps still remain. Specifically, there is a lack of empirical studies and modelling tools 8

that help to understand the complex relationships between built environment (BE), pedestrian activity 9

and accident occurrence in urban intersections1. To our knowledge, these relationships have been 10

separately investigated in previous studies, yet none has looked all of them together. 11

12

Accordingly the aim of this paper is two-fold: 13

1) To propose a framework to investigate the impact of built environment on pedestrian activity and 14

collision frequency at signalized intersections. 15

2) To develop and evaluate a two-equation model for predicting pedestrian activity and collision 16

frequency at signalized intersections in Montreal. 17

The paper is structured in several sections. Section two offers a literature review on pedestrian safety 18

with particular focus on safety analysis at intersections. Section three presents a conceptual framework 19

to analyse the interaction between BE, pedestrian exposure and accident occurrence. Section four 20

describes the empirical data used in this research – which involves a sample of 509 signalized 21

intersections located in the City of Montreal. The results of pedestrian activity and collision frequency 22

models are presented in section five – public policy implications and model validation are also part of 23

this section; and section six includes the final conclusions and directions for future work. 24

2. LITERATURE REVIEW 25

In recent years, a large number of studies have been published addressing different issues related to the 26

development of collision prediction models -see (3), (4) and (5). Relative to the amount of research 27

being devoted to motor vehicle-only collisions, few studies have dealt with pedestrian activity and safety 28

at signalized intersections (6-10). Furthermore, past studies have looked at the effect of traffic and 29

1 Where built environment is represented by land use, demographics characteristics, transit facilities and road network facilities in the proximity of an intersection.


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pedestrian volumes on pedestrian safety, with a small amount studies incorporating geometry design 1

characteristics [(7) and (10)]. Also, very little work has been carried out to investigate the link between 2

BE, pedestrian activity and collisions at the intersection level. The effect of BE on accident occurrence 3

has been mostly studied at the aggregated level – see (11) and (12). 4

5

For pedestrian safety analysis, pedestrian volume referred here as pedestrian activity is a key element. In 6

the literature, several possible definitions of pedestrian activity can be found depending on the data 7

available and unit of the analysis. In general, measures of pedestrian activity can be classified in site-8

specific or area-wide2. For urban intersections, pedestrian exposure can be determined using 1) raw 9

pedestrian volumes observed during a period of time (e.g. morning peak, noon, and afternoon) or 2) 10

pedestrian volumes estimated according to prediction methods such as Space Syntax or built 11

environment models. Some estimation methods and alternative measures of pedestrian exposure have 12

been suggested in the literature (for instance, see (13), (14), (15) and (16). 13

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Despite the fact that pedestrian volumes are an essential element in road safety analysis; few 15

transportation agencies collect pedestrian data from a large number of sites on a regular basis. Among 16

other reasons, this is due to the fact that site-specific pedestrian count studies are expensive and time-17

consuming3. To address this lack of data, a simple and efficient way is to develop prediction built-18

environment models based on a sample of intersections in an urban area (15, 16). The goal is to estimate 19

pedestrian activity based on built-environment attributes in the proximity of an intersection or a 20

crosswalk. In spite of this, very little empirical evidence exists in the literature following this approach. 21

One of the few studies is the recent work done by Pulugurtha and Repaka (15) which develops a model 22

to measure pedestrian activity using data collected for a small sample of signalized intersections (176 23

intersections) in the city of Charlotte, North Carolina. Based on a standard regression analysis, they 24

found that population, total employment, urban residential area, and the number of transit stops are 25

statistically significant variables related with pedestrian activity. This study however has some 26

2 At the area-wide level (e.g. neighbourhood, municipality, city, etc), several measures have been proposed such as observed

pedestrian volumes at a sample of sites and estimated pedestrian volumes based on travel surveys or network analysis

models, for instance. An extensive literature review of previous studies is provided by Greene-Roesel (13). 3 Alternative methods have been also proposed to collect pedestrian activity at the site-specific level (e.g. signalized

intersections or pedestrian crossings. A common method is to obtain pedestrian volumes from manual counts taken directly

in the field by using video recording with manual review or automated methods using video cameras. A review of these

alternative counting strategies and pedestrian detection technology has been carried out by (13).


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limitations. It was done in a medium-size North American city with a low population density and land 1

use mix using a relatively small sample of intersections. Another similar and recent study is the one of 2

Schneider et al. (16). Using a small sample of 50 intersections in Alameda, CA, these authors linked 3

pedestrian activity (volume) at the intersection level to a variety of surrounding land uses, transportation 4

system attributes, and neighbourhood socioeconomic characteristics. In a similar way, some significant 5

variables include total population, employment, number of commercial retail properties, and the 6

presence of a regional transit station in the proximity of an intersection. Although in this work 7

pedestrian count data were extrapolated to weekly volume estimates, it has also the limitation of a small 8

sample size. Despite these recent efforts, studies considering a number of intersections in large Canadian 9

cities (Toronto, Montreal and Vancouver) are lacking in the literature. In these cities, central 10

neighbourhoods are characterized by a high population density and rich land use mix. In addition, 11

previous studies have not validated the prediction capability of developed models. 12

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3. CONCEPTUAL FRAMEWORK 14

A conceptual framework showing the potential relationships between BE, pedestrian activity and safety 15

at the site-specific (intersection) level is presented in Figure 1. This conceptual framework is inspired 16

and supported by previous research – including (10), (17), (18), (19) and (20). The elements of this 17

framework and their relationships are discussed as follows: 18

19

Built environment (BE): BE is represented by land use characteristics (commercial, parks, 20

employment density, number of schools, etc.), demographics (total population, children, seniors, 21

etc.), transit network density (kilometres of bus lanes, number of transit stops, the presence of metro 22

stations, etc) and road network characteristics (kilometres of streets and major roads, number of 23

intersections, speed limits, etc.) in the vicinity of an intersection. In Figure 1, pedestrian activity is 24

assumed to be directly affected by the BE. However, we challenge the assumption that the direct 25

effect of BE on collision frequency is negligible, having its major impact through pedestrian activity 26

and traffic volume. 27

Risk exposure: Exposure is a function of pedestrian activity, traffic volume and motor vehicle 28

speeds. In the literature, pedestrian and traffic volume have been found to be the main determinants 29

of pedestrians collision frequency, while motor vehicle types (e.g., car versus SUV) and speeds are 30

the main contributing factor associated to severity. 31


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-Traffic volume: Past studies generally show that daily traffic volume (measured usually as 1

average annual daily ADDT) is the main determinant of pedestrian collision frequency. In 2

addition, published studies agree that the risk faced by pedestrians with respect to traffic volume 3

is non-linear, and that individual pedestrian risk increases as the number of motor vehicles goes 4

up [(21), (22)]. The elasticities reported in the literature range between 0.4 and 1.2 (see e.g., 5

Harwood et al. (10), Elvik, (18) and Leden (21)]. 6

-Pedestrian volume: referred here as pedestrian activity during a period of time (peak-hourly or 7

daily volume, for instance). It has also been found to be one of the most influential factors in 8

predicting pedestrian-vehicle collisions (10). Among others, (8), (21) and (23), have found a 9

statistically significant and positive relationship between pedestrian activity and collision 10

frequency at different types of intersections. More importantly, past studies also suggest that the 11

risks faced by pedestrians are highly non-linear (as traffic) and that as the number of pedestrians 12

increases, the risk faced by each pedestrian decreases. The literature reports elasticity estimates 13

between 0.2 and 0.7 for the pedestrian volume and collision frequency relationship (see Elvik, 14

(18). 15

Geometry design: Few studies have investigated the effects of geometry design attributes at 16

intersections on pedestrian safety including, road width or number of lanes, presence of marked 17

pedestrian crosswalks, presence of median, types of turn restrictions, etc. For instance, Zegeer et al. 18

have completed comprehensive studies on the pedestrian accident occurrence and the effect of some 19

geometry attributes. In their study, they found that after controlling for pedestrian and traffic 20

volumes, the number of lanes and raised medians have significant effects on collision frequency. 21

The effect of curb parking has been also documented by Box (24). Moreover, geometry design may 22

also have an effect on severity. For instance, road width or traffic calming measures should be 23

associated with operating speeds (17). 24

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Figure 1. Conceptual framework - BE, risk exposure and pedestrian safety at intersections 2

As illustrated in Figure 1, there are several dimensions to the problem of how to study the linkages 3

between BE, risk exposure and pedestrian safety. In this research, we narrow the analysis to focus on the 4

relationship between BE, pedestrian activity, and accident frequency at signalized intersections. The gap 5

we wish to address is the absence of studies simultaneously exploring the effect of BE on both 6

pedestrian activity and collision occurrence at intersections. To study the mechanisms by which BE 7

characteristics link directly to pedestrian activity and pedestrian collision frequency, a two-equation 8

modeling framework is proposed. To define our modeling framework, the notation for each vector of 9

variables is first defined as follows: 10

L – Land use types in the proximity of i, 11

D – Demographic characteristics in the proximity of i, 12

T – Transit offer characteristics in the proximity of i, 13

R - Road network infrastructures in the proximity of i, 14

G – Geometry design characteristics of i, 15

P – Pedestrian activity (in a day or in an hourly-base period) at i 16

V – Vehicle volumes intersection (daily average) at i 17

A – Pedestrian accident frequency (in one or more year period) at i 18

where i stands for intersection (i = 1,2,... n). 19

P and A – Other unobserved heterogeneities 20

The two-equation model is then defined as: 21

Land use and demographics

patterns

Geometry design

Transit offer and road network connectivity

Pedestrian activity

Operating speed (driver behaviour)

Pedestrian-vehicle collision frequency

and severityTraffic volume

Built environment Risk exposure Traffic Safety


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(i) P = f(L, D, T, R, P) 1

(ii) A = f(V, P, G, A) (1) 2

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In the first equation, the hypothesis is that pedestrian activity (P) is directly associated to BE attributes 4

(L, D, T and R) and unobserved variations. In the second equation, the assumption to be tested is that the 5

number of accidents in an intersection during a given period of time depends mainly of vehicle traffic 6

volume (V), pedestrian activity (P), intersection geometry and unobserved variations (such as driver 7

behaviour and weather conditions). That is, BE in the proximity of an intersection is assumed to have a 8

direct marginal effect on the collision frequency. 9

10

A limitation of much of the current research is the direct association of BE variables with pedestrian 11

collision frequency without specifying whether BE patterns affect collision risk by affecting directly 12

pedestrian activity, the number of pedestrian accidents or both. For instance, at a given intersection with 13

high commercial land use, does pedestrian collision risk increase because there are likely to be more 14

pedestrians, or because there is greater traffic volume or other behavioural reasons associated to this 15

particular land use that increase the likelihood of a collision? As mentioned above, this paper challenges 16

the hypothesis that the effects of BE patterns on pedestrian safety at the intersection level are mainly 17

through the generation of pedestrian activity as a determinant of risk exposure. To test this hypothesis, 18

collision frequency models with and without BE variables are compared. 19

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4. DATA ASSEMBLY AND DESCRIPTION 21

For this research, data collected at 509 signalized intersections in the City of Montreal, Quebec, Canada 22

are used to develop pedestrian activity and crash prediction models at signalized intersections. Data for 23

this analysis is provided by the Transport Department of Montreal (Direction des transports du 24

Montreal) and the Public Health Department (Direction de Santé Publique du Montreal). 25

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Pedestrian and traffic data 27 In this study, pedestrian and traffic volumes from 509 signalized intersections were available for the 28

year 2003. Those intersections are mainly located in the central neighbourhoods. Pedestrian volume data 29

were collected by the City of Montreal from three different periods: i) Peak morning from 7:30 a.m. to 30

8:30 a.m., ii) noon period from 12:00 p.m. to 1:00 p.m, and ii) Peak afternoon from 4:30 p.m. to 5:30 31

p.m. Traffic volumes were also available for the same intersections represented by the average annual 32


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daily traffic (AADT). These daily volumes were estimated based on 3-hour traffic counts collected on 1

the same days as the pedestrian data. The data at each intersection were collected on a weekday with 2

normal weather conditions during the spring and summer session. Some summary statistics of pedestrian 3

and AADT data at the studied intersections are presented in Table 1. 4

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Table 1: Summary statistics of pedestrian, traffic and collision frequency data 6

Variable Mean Std. Dev. Min Max

3-hour pedestrian volume 700.8 702.3 10 4434

AADT 26993.4 13152.5 3236.6 78305.9

Pedestrian collisions 1.2 1.8 0 11 7

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Vehicle-pedestrian collision data 9

All injured pedestrians for whom an ambulance was sent on the island of Montreal over a five year 10

period (1999-2003) were included in the study – see (25). The Urgences-santé Corporation (or 11

Montreal’s ambulance service) is the single source of data as it has a monopoly on the territory covered. 12

For every call made to 911 in Montreal, the caller’s address is automatically sent to Urgences-santé and 13

the location of the victim is validated over the phone. This location is instantly mapped in a GIS to 14

dispatch and guide an ambulance. Identical locations were aggregated and the Montreal hierarchical 15

street network (called geobase) was used to combine locations within a radius of 15 m of an intersection. 16

17

Built environment (BE): land use, demographics, transit and road network 18

BE variables in the vicinity of each intersection are generated using Geographic Information Systems 19

(GIS) data obtained from various sources. To take into account the impact of buffer dimension, different 20

buffer sizes were tested including 50, 150, 400 and 600 meters. Compared to the work done by (15 and 21

16), shorter buffers were explored given the fact that the area under study (central neighbourhoods in 22

Montreal) is a dense area, with a rich mix of land uses and high transit accessible due to the extensive 23

public transport system. The 50 meters buffer was used to find how an intersection’s immediate 24

surroundings affected pedestrian activity. A 150 meters buffer examines the effects of characteristics 25

within close proximity to the intersection. The 400 meters and 600 meters buffers served as a proxy for 26

how characteristics at a walking distance or neighbourhood scale affect the level of pedestrian activity at 27

a particular intersection. A list of the variables with a short definition is provided in Table 2. To extract 28

demographic data, census data at the census tract level were intersected with each buffer generated 29


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around the intersections. To classify intersections according to the number of approaches (three versus 1

four-legged intersections), a dummy variable was generated. 2

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Table 2. Built environment variables Variable Units

Land Use

Commercial m2 in thousands Residential m2 in thousands Industrial m2 in thousands Park m2 in thousands Open Space m2 in thousands Number of jobs counts in thousands Number of schools counts

Demographic

Population counts in thousands Workers counts in thousands Children counts in thousands Seniors counts in thousands

Transit system characteristics

Presence of metro station 0/1 indicator Number of bus Stops counts Km of bus route km in thousands

Road network Characteristics

Road Length (km) km in thousands

Class 1 – Expressway km in thousands

Class 2 – Primary Hi h

km in thousands

Class 3 – Secondary Hi h

km in thousands

Class 4 – Arterial Road km in thousands

Class 5 – Local Road km in thousands

Number of street segments counts

Number of intersections counts

Portion of major roads (Class 1 to Class 4)/total toad length

Average Street Length Road length/number of streets

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5. MODELING RESULTS 6

Based on the conceptual model specification defined in Eq. 1, a regression modeling analysis was then 7

carried out to investigate the relationship between BE, pedestrian activity and accident occurrence at the 8

signalized intersections. Various modeling issues were encountered including i) multicolinearity, 9

heterogeneity and the potential presence of endogeneity. To address the multicolinearity issue, an 10

exploratory analysis was first carefully done to identify serious problems of correlation between BE 11


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factors. Pedestrian activity and accident frequency models were then developed to account for 1

heterogeneity. Finally, a simultaneous two-equation model was also fitted to the data in order to test the 2

potential presence of endogeneity. 3

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Exploratory data analysis 5

As a first step, an exploratory analysis was conducted to identify high (linear and non-linear) 6

correlations between the different attributes. A correlation matrix for each buffer size was generated 7

including 50, 150, 400 and 600 meters - see Table 3. This exercise was also useful to identify the 8

potential attributes related to pedestrian activity and collision frequency. Based on these results different 9

observations were done: 10

The land use variables that are highly correlated with pedestrian activity are commercial, with 11

the highest correlation at the 50-meter buffer (r= 0.51), industrial, number of jobs and number of 12

schools at the 400-meter buffer respectively. Population and employees living in the buffer are 13

also correlated to pedestrian volumes, with the maximum correlation at the 400-meter buffer – 14

However population and employees are highly correlated between them. For the transit 15

characteristics, metro and bus stops are the ones with the highest correlations at the 150-meter 16

buffer. Among the road network features, local road length, average street length, proportion of 17

major roads and average street length are the most correlated variables with pedestrian activity at 18

the 400-meter buffer. 19

Pedestrian volume and AADT are correlated to collision frequency with a correlation of about 20

0.4. Other variables, such as commercial, number of bus stops and local road length also show 21

correlations higher than 0.25. Note however that these variables are also highly correlated to 22

pedestrian activity and/or AADT. Therefore, one should be aware that including BE variables 23

along with pedestrian and traffic volumes in the same model may generate multicolinearity. 24


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Table 3: Correlations between BE and pedestrian activity, traffic and collision frequency

Variables Buffer 50m Buffer 150m Buffer 400m Buffer 600m

A ln(P) P ADT A ln(P) P ADT A ln(P) P ADT A ln(P) P ADT Pedestrian collisions 1.00 1.00 1.00 1.00

log of pedestrian volume 0.36 1.00 0.36 1.00 0.36 1.00 0.36 1.00

Pedestrian volume 0.37 0.84 1.00 0.37 0.84 1.00 0.37 0.84 1.00 0.37 0.84 1.00

AADT 0.40 0.11 0.11 1.00 0.40 0.11 0.11 1.00 0.40 0.11 0.11 1.00 0.40 0.11 0.11 1.00

Commercial 0.32 0.49 0.51 0.06 0.24 0.42 0.46 0.05 0.11 0.36 0.35 0.02 0.07 0.35 0.34 -0.02

Residential -0.07 -0.14 -0.18 -0.20 0.05 0.01 -0.05 -0.16 0.10 0.16 0.11 -0.13 0.10 0.16 0.14 -0.10

Industrial -0.03 -0.23 -0.17 0.52 -0.07 -0.27 -0.17 0.43 -0.10 -0.34 -0.22 0.28 -0.05 -0.27 -0.17 0.24

Park -0.09 -0.13 -0.12 -0.06 -0.10 -0.15 -0.13 -0.03 -0.13 -0.16 -0.17 -0.06 -0.09 -0.12 -0.15 -0.07

Open Space -0.04 -0.08 -0.05 0.07 -0.02 -0.05 -0.01 0.09 -0.01 -0.06 -0.04 0.12 -0.02 -0.03 -0.04 0.11

Number of schools 0.00 0.00 -0.02 -0.03 -0.08 0.12 0.05 -0.01 0.09 0.31 0.22 -0.02 0.09 0.27 0.22 -0.03

Number of jobs 0.16 0.26 0.33 0.06 0.17 0.33 0.37 0.17 0.10 0.36 0.34 0.08 0.02 0.20 0.14 0.09

Population 0.10 0.38 0.29 -0.17 0.12 0.42 0.31 -0.17 0.15 0.47 0.38 -0.15 0.16 0.47 0.41 -0.12

Employees 0.06 0.39 0.29 -0.18 0.08 0.42 0.32 -0.18 0.11 0.46 0.38 -0.15 0.11 0.45 0.40 -0.13

Children 0.10 0.19 0.16 -0.13 0.12 0.20 0.15 -0.13 0.18 0.23 0.16 -0.07 0.19 0.20 0.13 -0.05

Seniors 0.09 0.24 0.14 -0.13 0.12 0.28 0.18 -0.11 0.20 0.34 0.25 -0.06 0.22 0.35 0.28 -0.03

Presence of metro station 0.07 0.17 0.28 -0.01 0.20 0.32 0.46 0.14 0.08 0.32 0.33 0.05 0.04 0.24 0.23 0.04

Number of bus Stops 0.42 0.23 0.22 0.56 0.31 0.37 0.39 0.36 0.08 0.34 0.25 0.00 0.02 0.32 0.25 -0.07

Km of bus route 0.32 0.29 0.28 0.49 0.26 0.30 0.30 0.38 0.00 0.18 0.12 0.05 -0.08 0.08 0.08 0.07

Road Length (km) 0.06 -0.08 -0.05 0.48 0.08 0.14 0.12 0.18 0.07 0.27 0.21 -0.01 0.07 0.31 0.28 -0.02

Class 1 – Expressway -0.04 -0.10 -0.05 0.08 -0.03 -0.13 -0.08 0.06 -0.06 -0.17 -0.12 0.09 -0.09 -0.19 -0.15 0.17

Class 2 – Primary Highway -0.02 -0.15 -0.07 0.31 -0.02 -0.16 -0.08 0.31 -0.08 -0.20 -0.13 0.08 -0.10 -0.21 -0.15 -0.02

Class 3 – Secondary Highway 0.10 0.07 0.07 0.12 0.05 0.10 0.12 0.06 0.01 0.17 0.19 -0.04 0.01 0.19 0.20 0.00

Class 4 – Arterial Road 0.22 0.01 -0.01 0.52 0.17 -0.01 -0.03 0.49 0.01 -0.08 -0.13 0.21 0.01 -0.02 -0.04 0.09

Class 5 – Local Road -0.26 -0.03 -0.02 -0.49 -0.10 0.15 0.10 -0.43 0.10 0.38 0.31 -0.20 0.11 0.39 0.34 -0.14

Number of street segments -0.04 -0.07 0.04 0.05 -0.01 0.02 0.06 -0.04 0.00 0.03 0.06 0.06 -0.02 0.06 0.09 0.08

Number of intersections -0.02 -0.12 -0.07 0.33 -0.02 -0.01 0.00 0.12 -0.04 0.04 0.03 0.03 -0.02 0.12 0.12 0.03

Portion of major roads 0.29 0.03 0.04 0.60 0.17 -0.06 -0.01 0.57 -0.06 -0.22 -0.19 0.25 -0.10 -0.22 -0.18 0.17

Average street length 0.11 0.00 -0.07 0.43 -0.10 -0.13 -0.05 -0.23 -0.07 -0.24 -0.14 0.06 -0.07 -0.18 -0.12 0.11

Intersection type 0.16 0.30 0.20 -0.05 0.16 0.30 0.20 -0.05 0.16 0.30 0.20 -0.05 0.16 0.30 0.20 -0.05


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Pedestrian activity model 1

The next step was to develop a prediction regression model for pedestrian activity as a function of BE 2

variables. This model corresponds to the Eq. 1(i). Given that pedestrian activity is a count variable 3

positively skewed, two different types of model settings were tested including the log-linear and count 4

data regression models such as the standard negative binomial regression model and its extensions 5

(Miranda-Moreno et al. 2006). Since the results are very similar, the analysis focuses in the log-linear 6

model. 7

A great deal of care was taken to the correlations among explanatory variables. To define the best 8

model, different combinations of explanatory variables were tested based on the exploratory analysis. 9

The goal was to minimize the correlation among explanatory variables to avoid serious problems of 10

multicolinearity. To do so, if two explanatory variables were highly correlated between them 11

(correlation greater than 0.3), the variable that had a weaker relationship (lower correlation) with 12

pedestrian activity was omitted from the model. In very few cases, best judgment was used. For 13

instance, the presence of metro stations within 400 meters had a slightly lower correlation value than the 14

number of metro stations within 150 meters. However, the number of metro stops at this buffer was 15

chosen because it had a lower correlation with the number of bus stops. To evaluate the goodness-of-fit 16

of each model, the R-squared measure was used. 17

18

Table 4 shows the parameter estimates for the log-linear model. Note that eight BE variables have 19

statistically significant effects on pedestrian activity, including population, commercial land use, number 20

of jobs, number of schools, presence of metro station, number of bus stops, percentage of major arterials 21

and average street length - a measure of connectivity, see e.g., (26). An intersection type indicator was 22

also introduced to the model to differentiate between three and four-legged intersections. An interesting 23

result is that all variables have a positive effect, except the proportion of major arterials being the only 24

factor negatively associated to pedestrian activity. The results also show that the association of six of the 25

variables happened at the 400-meter buffer - commercial land use and number of bus stops are related to 26

pedestrian activity at the 50 and 150 meters buffer respectively. 27

28

Table 4 also shows the elasticities associated to each explanatory variable calculated at the point of 29

means. From these elasticities, one can see that a 100 percent increase in the population living in the 30

proximity of an intersection (50 meters) is associated with a 30 percent increase in the pedestrian 31


14

activity. An augmentation of 100 percent of commercial area or bus stops in the vicinity of an 1

intersection also represent, respectively, a 20 percent and a nearly 40 percent increase in pedestrian 2

activity. A metro station increases the pedestrian activity by 30 percent. Interestingly, an increase of 100 3

percent in the proportion of major arterials is associated with a decrease of 20 percent in pedestrian 4

activity. These results clearly indicate that the implementation of local policies encouraging 5

densification, land use mix, transit accessibility and/or road network connectivity are expected to have 6

significant and positive impacts on pedestrian presence at the intersections under analysis. 7

8

Table 4. Log-linear model for pedestrian activity 9

+ 4-legged intersection = 1 and 3-legged intersection = 0 10 **: Statistically significant at 5%, ***: Statistically significant at 1% 11

++Standard errors given in parenthesis 12

+++Elasticities estimated at the sample mean 13

14

Pedestrian collision frequency model 15

For the modeling of pedestrian collision frequency, two negative binomial modeling settings were 16

attempted including the standard negative binomial (NB1) model with fixed dispersion parameter and 17

generalized negative binomial (NB2) allowing observed heterogeneities in the dispersion parameter. The 18

Variables Buffer Parameter estimates

Elasticities+++ Coefficient++ Sig. Intercept 4.115 (0.022) ***

Population 400m 0.071 (0.022) ** 0.30

Commercial 50m 0.192 (0.026) *** 0.20

No. jobs 400m 0.173 (0.027) *** 0.28

No. schools 400m 0.146 (0.071) *** 0.20

Metro station 400m 0.329 (0.014) *** 0.28

No. bus stops 150m 0.108 (0.261) *** 0.37

% of major arterials 400m -0.858 (0.323) ** -0.19

Ave. street length 400m 1.036 (0.076) ** 0.50

Intersection indicator+

0.348 (0.184) *** 0.29

Goodness-of-fit R2 = 0.55


15

results of both models were very similar showing the consistency of the results of different regression 1

methods. 2

3

The issue when modeling collision frequency is the high level of correlation between the BE and both 4

pedestrian activity and collisions frequency as illustrated in Table 3, - commercial land use is a good 5

example. This makes it difficult to precisely identify and isolate the effect of BE attributes on pedestrian 6

activity and/or pedestrian safety. A simple solution is to fit different collision models with and without 7

BE attributes. Accordingly, Table 5 presents first the results of a model including only pedestrian 8

activity, AADT and a dummy variable for intersection type. Then, by introducing BE attributes to the 9

previous model, different variables combinations were tested. The best model of this exercise is also 10

reported in the same table. 11

12

From the short model (without BE variables), one can see in Table 5 that both pedestrian and traffic 13

volumes are positively and statistically significant as expected. The size of these parameters is in the 14

range of those reported in the literature - e.g. see (6), (8), (10) and (18). As suspected, the effect of the 15

intersection indicator is also significant, which means that 4-legged intersections have a higher 16

frequency of accidents. From the extended model (with BE variables), one can observed that only few 17

variables are statistically significant including commercial area, number of bus stops and schools - all 18

other variables are non-significant at the 5% level. In addition, parameter estimates of pedestrian volume 19

and AADT are importantly affected (reduced) with the incorporation of these variables. However, since 20

it only improves the model fit slightly, this suggests that most of the impact of BE occurs through their 21

association with pedestrian activity and/or traffic volume. For instance, coming back to Table 5, one can 22

see that the number of bus stops in the 50m buffer is highly correlated to collision frequency and that 23

commercial land use is highly correlated to pedestrian activity. 24

25

Looking at the dimension of parameter estimates in Table 5, motor vehicle traffic is by far the major 26

determinant of pedestrian accident frequency – For instance, a regression coefficient of 1.15 is obtained 27

in the short model (NB1 model) This means that the growth in the number of collisions would be more 28

than linear with respect to AADT. In terms of elasticities, one can observe that a reduction of 100 29

percent in the current traffic condition will represent a decrease of 90-120 percent in the number of 30

pedestrian collisions. 31


16

The importance of pedestrian activity is also confirmed with a regression parameter of 0.45 in the NB1 1

model indicating that an increase of the number of collisions would be less than linear. Another 2

interesting finding is the negative sign of the number of schools. This can be related to speed limits 3

and/or some calming measures that may be applied around schools. 4

5

Table 5. Pedestrian collision frequency model 6

Variables

Parameter estimates Short model (without BE) Extended model (with BE)

NB1 GNB1 NB2 GNB2

Mean Intercept -14.790 *** -14.590 *** -11.636 *** -11.661 ***

ln AADT 1.152 *** 1.122 *** 0.906 *** 0.900 ***

ln pedestrian volume 0.447 *** 0.461 *** 0.263 *** 0.280 ***

Intersection indicator 0.404 *** 0.412 *** 0.431 *** 0.435 ***

Commercial (50m buffer) - - - - 0.145 *** 0.143 ***

No. bus stops (50m buffer) - - - - 0.160 *** 0.151 ***

No. schools (150m buffer) - - - - -0.234 *** -0.225 ***

Alpha

Constant 0.414 *** -1.027 ** 0.323 ** -0.923 *

ln AADT - 9.757 ** 8.437 -

AIC 1406.9 1413.1 1381.8 1381.4

Elasticities

ln AADT 1.15 1.12 0.91 0.90

ln pedestrian volume 0.45 0.46 0.26 0.28

Intersection indicator 0.33 0.34 0.35 0.35

Commercial 0.15 0.15

No. bus stops 0.30 0.28

No. schools -0.053 -0.051

++ 4-legged intersection = 1 and 3-legged intersection = 0 7 *: Statistically significant at 1%, 8 **: Statistically significant at 5%, 9 ***: Statistically significant at 10% 10

11


17

Finally, to test the potential correlation between the error terms in Eq. 1 (cov(P, A ) 0), a bivariate 1

Poisson regression model was also attempted (27). In this model, the two equations (pedestrian activity 2

and collision frequency) are calibrated simultaneously assuming dependency between the two outcomes. 3

Since non evidences of endogeneity were identified, the results are not reported in this paper. Although 4

parameter estimates are very similar to those previously reported, more research is needed in this 5

(statistical/methodological) issue. 6

Some policy implications 7

Different practical implications can be supported from our analysis: 8

A strong link between BE and pedestrian activity was confirmed at the intersection level. 9

However, the direct impact of the surrounding built environment (ex. land use patterns) on 10

collision frequency seems to be marginal, traffic volume and pedestrian activity being the main 11

determinants of collision occurrence. Although, intersection geometry characteristics were not 12

available, an important impact of road width, median and calming measures on safety may also 13

exist – this effect was partially captured by intersection type indicator. 14

Urban policies aiming to increase population density, land use mix, transit offer and road 15

network connectivity may have a double benefit: a direct increase in pedestrian activity and 16

indirect decrease in the risk of pedestrian collision. That is, given the fact that the risk of injury 17

to pedestrians is highly non-linear, the more pedestrian activity there is at an intersection, the 18

lower is the risk for each pedestrian – this result is visually shown in Figure 2. Note, however, 19

that with no supplementary strategies, the total number of injured pedestrians would increase 20

with pedestrian activity. 21

The more motor vehicles at intersections, the higher is the risk for each pedestrian. In addition to their 22

beneficial effect on noise and air pollution, strategies to reduce traffic volume would lower both the 23

individual risk of pedestrian collision and the total number of injured pedestrians. For instance, a 24

reduction of 30% in the traffic volume at each intersection in the area of study would greatly reduce the 25

average pedestrian risk (-50%) and the total number of injured pedestrians at those intersections (-35%) 26

(this is shown graphically in Figure 2). It is noteworthy that major roads seem to have a double negative 27

effect on pedestrians, being positively associated with traffic volume and negatively related with 28

pedestrian activity. Such results support the idea of retrofitting urban major roads into complete streets 29


18

(28), an important issue considering that in urban settings like Montreal, a majority of pedestrians are 1

injured on major roads (25). 2

3

4

Figure 2. Effect of an AADT reduction in the expected risk of pedestrian collision 5

6

Model validation 7

In order to validate the prediction models for pedestrian activity that were developed, the intersection 8

sample was randomly divided into an exploratory sample, 80 percent, and a validation sample, 20 9

percent. The exploratory sample was then used to calibrate the pedestrian activity model. This model 10

was then applied to the validation data set to estimate pedestrian volume. Observed versus estimated 11

values were then compared. As a second step, estimated and observed pedestrian volumes in the 12

exploratory sample were used to estimate two negative binomial models. According to these two 13

models, the expected collision frequency was computed using the validation sample. The prediction 14

capability of each model was then determined by comparing the estimated values with the observed 15

collision frequencies. Sample splitting was repeated 100 times to produce 100 sets of exploratory and 16

0

2

4

6

8

10

12

14

16

18

20

0 1000 2000 3000 4000 5000

Ris

k (

Col

lisi

on f

req

uen

cy/t

hou

san

ds

of p

edes

tria

ns)

Pedestrian volumes

Current situation

30 percent reduction in AADT


19

validation samples (a code in R software was developed for this purpose). To measure prediction 1

accuracy, two measures were used: the Pearson correlation (rp) and normalized root mean squared 2

deviation (NRMSD)4. The results of this validation procedure are reported in Table 6. According to 3

these results, the performance of the models seems to be acceptable. For instance, a correlation mean 4

value of 0.72 was observed between the predicted and observed pedestrian activity. In addition, the 5

collision frequency predictions based on observed and estimated pedestrian activity are very similar, 6

producing relatively low mean values of NRMSD - mean values of around 0.18 and 0.17 were obtained 7

when using predicted and observed pedestrian activity, respectively. Accordingly, when pedestrian 8

activity data is not present, practitioners can use the built environment to predict pedestrian activity and 9

incorporate it in collision prediction models. 10

11

Table 6. Validation model results 12

Model Normalized root mean squared deviation (NRMSD)

Mean* Median Min Max

Pedestrian activity 0.14 0.140 0.08 0.20

Collision frequency with estimated pedestrian volume

0.18 0.18 0.13 0.24

Collision frequency with observed pedestrian volume

0.17 0.17 0.13 0.23

Model Pearson Correlation

Mean Median Min Max

Pedestrian activity 0.72 0.73 0.54 0.88

Collision frequency with estimated pedestrian volume

0.56 0.57 0.36 0.77

Collision frequency with observed pedestrian volume

0.58 0.58 0.42 0.76

*Mean and median values are based on 100 validation random sets 13

14

4 Note that the closer to one the correlation is, the better the model predictions are. On the contrary, the closer to zero the NRMSD is, the better the validation results are.


20

1

2

CONCLUSIONS AND FUTURE WORK 3

This work aims to understand how built environment (BE) affects both pedestrian activity and collision 4

frequency. In doing so, two major contributions have been made. First, a model framework has been 5

developed to jointly analyze pedestrian activity and safety at the intersection level. This modelling 6

framework is useful for the identification of effective pedestrian safety actions, the prediction of 7

pedestrian volume when lacking pedestrian count data, and the appropriate design of new developments 8

encouraging walkability. 9

Secondly, some results based on the estimation of these models for intersections in Montreal have been 10

presented. In accordance with previous studies, these results show that some BE characteristics have a 11

powerful association with pedestrian activity including population, commercial land use, number of 12

jobs, number of schools, presence of metro station, number of bus stops, percentage of major arterials 13

and average street length. The reported influence of BE on pedestrian collisions at intersection, however, 14

seems largely mediated through pedestrian activity and traffic volume. Our study also provides some 15

additional evidence that traffic volume is the primary cause of collision frequency at the intersection 16

level. A reduction in traffic volume would be associated with great improvements in pedestrian safety. 17

Finally, an original validation procedure measured the prediction capability of our models. 18

Our future efforts will concentrate on examining the validity of these findings across a wider spectrum 19

of intersections and longer periods of pedestrian data collection. The disaggregated analysis will also 20

make it possible to include intersection geometry characteristics (ex. road width). A simultaneous 21

modeling approach will be further explored to evaluate the potential effect of error correlation and 22

improve prediction capacity. 23

24


21

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