# Predicting the impact of urban change in pedestrian and road safety

Cristina Bustos  Internet Interdisciplinary Institute (IN3), Universitat
Oberta de Catalunya, Barcelona, Catalonia, Spain  Daniel Rhoads  Internet
Interdisciplinary Institute (IN3), Universitat Oberta de Catalunya, Barcelona,
Catalonia, Spain  Agata Lapedriza  Internet Interdisciplinary Institute (IN3),
Universitat Oberta de Catalunya, Barcelona, Catalonia, Spain  Javier Borge-
Holthoefer  Internet Interdisciplinary Institute (IN3), Universitat Oberta de
Catalunya, Barcelona, Catalonia, Spain  Albert Solé-Ribalta  Internet
Interdisciplinary Institute (IN3), Universitat Oberta de Catalunya, Barcelona,
Catalonia, Spain

###### Abstract

Increased interaction between and among pedestrians and vehicles in the
crowded urban environments of today gives rise to a negative side-effect: a
growth in traffic accidents, with pedestrians being the most vulnerable
elements. Recent work has shown that Convolutional Neural Networks are able to
accurately predict accident rates exploiting Street View imagery along urban
roads. The promising results point to the plausibility of aided design of safe
urban landscapes, for both pedestrians and vehicles. In this paper, by
considering historical accident data and Street View images, we detail how to
automatically predict the impact (increase or decrease) of urban interventions
on accident incidence. The results are positive, rendering an accuracies
ranging from 60 to 80%. We additionally provide an interpretability analysis
to unveil which specific categories of urban features impact accident rates
positively or negatively. Considering the transportation network substrates
(sidewalk and road networks) and their demand, we integrate these results to a
complex network framework, to estimate the effective impact of urban change on
the safety of pedestrians and vehicles. Results show that public authorities
may leverage on machine learning tools to prioritize targeted interventions,
since our analysis show that limited improvement is obtained with current
tools. Further, our findings have a wider application range such as the design
of safe urban routes for pedestrians or to the field of driver-assistance
technologies.

## Introduction

Despite significant public investment in their prevention, traffic fatalities
remain a leading cause of death around the world [1]. Accordingly, the past
several years have seen more and more cities publicly committing to the Vision
Zero initiative [2], a global standard originally promoted by the Swedish
parliament, which aims at setting a goal of 0 yearly traffic fatalities. With
vast amounts of data about cities currently available, automated analysis has
been recognized as a crucial tool to help urban planners in decision tasks [3,
4, 5, 6]. While the link between the growth of urban open data and new
computational techniques such as Deep Learning has been pointed out by several
authors [7, 8, 9, 10, 11, 12, 13], much work can still be done in leveraging
these new technologies towards the end of achieving the ambitious goals of
Vision Zero.

A crucial step in this direction is to better understand the structural and
dynamic properties of pedestrian, bike and urban networks that cause
accidents. Of course, a large amount of factors impact accident incidence in
urban environments, including driver [14] and pedestrian distraction [15],
traffic network structure [16, 17, 18], demographic variables (socio-economic
status, race, gender) [19], and more. Among these, built environment features,
such as the organization and complexity of the visual stimuli at particular
locations, are important, but relatively understudied at large scales. Many
studies in transportation planning and traffic safety have analyzed the
characteristics of particular urban intersections and street segments, in
relation to crash data, in order to extrapolate which features contribute to
accident incidence [15, 19, 20, 21, 22]. Up to now, due to lack of available
(or open) data and tools for automated analysis, these studies have mostly
been constrained either to the limited features available from planimetric map
data [16, 17, 23], or to detailed, but time-consuming, descriptions of
particular settings collected by hand [20, 21]. However, the rise of new Big
Data sources [24, 13, 25, 26] related to urban environments and transportation
has boosted the development of new techniques [10, 27, 26, 28] and the
combination of complementary tools existing in different fields [29, 5], such
as complex systems [18, 30].

In previous work [10], with the aim of clarifying the relation between
pedestrian and vehicle accidents and built environment features, we compiled
and analyzed a large dataset of street-level images obtained from Google
Street View [31] and the crowd-sourced Mapillary [32] collection. These
datasets provide valuable fine-grained information on the location and
organization of urban objects, and even dynamic information in terms of
existing mobile objects (pedestrians, bicycles, cars and public transport).
The Deep Learning model in [10], taking as inputs (1) driver point-of-view
images, and (2) geotagged historical accident data, shows notable performance
in predicting accident rates at a very high spatial resolution (approx. 15
meters). The high levels of accuracy achieved seem to indicate that the
presence, absence, or particular arrangement of urban objects can indeed
affect accident rates. Further, simple and well-organized scenes seem to be
related to lower accident rates. The causal plausibility of this uncovered
correlation can be supported by extensive literature on attention processes
[15, 33, 34, 35, 36], which confirm that complex scenes are harder to process
(in terms of division of attention) by drivers and pedestrians, which could
logically lead to an increase in driver’s stress [37] or accident rates [10].

The urban environment is constantly changing as a response to shifting
mobility patterns, new construction, or government intervention. Several
studies have already taken advantage of street-level imagery to automate the
analysis of urban change [7, 38, 39, 8], road safety assessment [9, 40],
perceived risk [41], urban demographics [42] and others [13, 13], but much
remains to be done to understand and design the physical substrate necessary
for safe and efficient pedestrian and vehicle navigation networks [43, 44, 45,
18]. In this work, leveraging our recent advances in the prediction of vehicle
and pedestrian hazard, we address the prediction of the impact urban change
may have on accident rates in the city of Barcelona, Catalunya. We initially
motivate our work with a detailed analysis of the evolution of accident rates
and, subsequently, we show that our automated methods are able to predict,
with sufficient accuracy, how such structural changes and interventions will
affect accident rates. We, finally, combine our hazard estimation with network
theory to put in contrast pedestrian safety with sidewalk and road demand.

The rest of the paper is organized as follows. Section 1 describes (1) the
collection, curation and construction of the different datasets that are used
to draw and test predictions; (2) the Deep Learning architecture employed; and
(3) a brief overview of traffic flow estimation for pedestrians and vehicles.
Section 2 is devoted to the results. Section 2.1 analyzes the temporal
evolution of accident incidence in the city of Barcelona from different
perspectives. Section 2.2, describes how we predict the impact of urban change
on accident rates. Section 2.3 describes the way we combine the hazard index
estimation with network descriptors to assess the effective risk of
pedestrians and vehicles navigating the city. Finally, in Section 3, we
provide the discussion and conclusions of our work.

##  1 Data and methods

In the following subsections we describe the different datasets and machine
learning tools that we use throughout the rest of the paper.

###  1.1 Pedestrian and vehicle accident dataset

To study the temporal evolution of accident rates, historical accident records
for the city of Barcelona during the period from 2010 to 2017 were downloaded
from the city government’s open data portal [46]. All data points are
geolocated with the GPS coordinates recorded by the local police. Aside from
location, we can further distinguish between accidents involving both a
vehicle and a pedestrian (simply ‘pedestrian’, or $P$, onwards), and vehicle-
to-vehicle accidents (simply ‘vehicle’, or $V$, onwards). In order to capture
the temporal change in accident frequency, each of these groups (pedestrian
and vehicle) were further divided according to whether the accidents occurred
during the range of years 2010 - 2013, or the range 2014 - 2017 (inclusive).
The number of accidents by year and by category is shown in below in Table 1.
Note that the total number of accidents increased by about 9.5% between the
two periods. In detail, the total number of pedestrian and vehicle accidents
increased by 4.3% and 10.2% respectively. Even from this most basic data
analysis, it is clear that these traffic safety statistics are moving in the
wrong direction. We will provide an extensive study of this dataset in Sec.
2.1.

| 2010-2013 | 2014-2017 | Entire period
---|---|---|---
Pedestrian Accidents ($P$) | 4478 | 4672 (+4.3%) | 9150
Vehicle Accidents ($V$) | 32031 | 35328 (+10.2%) | 67359
Total | 36509 | 40000 | 76509
Table 1:  Accident dataset disaggregated by accident type and the period in
which the accident occurred.

###  1.2 Street view dataset

To construct our Street view dataset, a total of 177K of unique locations from
Barcelona’s street network, obtained from OpenStreetMap [47], were queried
from Google StreetView (GSV) [31] to obtain imagery of two years: 2010 and
2017. The querying process resulted in 138K images corresponding for 2010 and
177K images for 2017, resulting in a total of 316K unique images and 138K
images pairs from the same location in different time periods, we needed to
discard several images from 2017 since no street view image exist in the 2010.
These years were selected to provide sufficient temporal distance to capture a
large range of urban interventions and changes. Although an exact match
between camera viewpoints is nearly impossible, a selection of image pairs
across time were checked visually and found to correspond very closely. Image
locations along a given street segment are, on average, separated from each
other by about 15 meters. GSV images are available for the full 360 degree
panorama surrounding the camera. According to the objective of our work, we
limited our queries to images facing directly down the direction of traffic,
considering two-way streets when necessary. The result of this process is a
homogeneous dataset of StreetView image pairs from both periods of study, at
every location across the city (Figure 1 provides some examples).

Figure 1: Illustrative examples of urban interventions that have affected
accident rates. Panel A shows a location where the number of vehicle accidents
rose from 4 (period 2010-2013) to 10 (period 2014-2017), as new temporary
signage and a denser traffic pattern were introduced. Panel B shows a location
where the number of pedestrian accidents fell from 9 (period 2010-2013) to 2
(period 2014-2017), in conjunction with the placement of a new crosswalk and a
walkable median zone.

The collected images contain GPS locations in their metadata, which, in
combination with the historical accident data described in 1.1, allows us to
assign each street image a binary accident category (“safe” vs. “dangerous”)
according to its proximity to the geo-located accident records. We categorize
a point as “dangerous” if one or more accidents have occurred with a 50 meter
radius of its location. Otherwise, the point is categorized as “safe”. Table 2
provides a detailed description of the dataset. It is important to put special
emphasis to the high resolution of our tagged image dataset, which can provide
information about accident rates and hazard every 15 meters along all the
city’s road network.

|  | 2010-2013 | 2014-2017
---|---|---|---
Barcelona Accidents | Total | Accident | No accident | Accident | No accident
Pedestrian Accidents ($P$) | 316329 | 50945 | 87739 | 62108 | 115537
Vehicle Accidents ($V$) | 316329 | 101572 | 37112 | 134546 | 43099
Table 2: Dataset resulting from the combination of urban images and historical
accident data. The table shows the number of locations where we observed at
least one accident (“Accident”), and the number where no accidents were
observed (“No Accident’). Our data distinguishes between vehicle-vehicle
accidents (“Vehicle accidents”) and vehicle-pedestrian accidents (“Pedestrian
accidents”).

###  1.3 Hazard Index Estimation

Deep Learning tools for image classification allowed us to develop a hazard
index for pedestrians and vehicles (“Hazard Index”) for each StreetView image
in the dataset [10], adapting a well-known convolutional neural network called
Residual Neural Network (ResNet-50-v2) [48]. The adaptation includes the
removal of the connections from the last layer of the ResNet model, and their
replacement with a new, fully-connected layer (with Softmax activation
function) and two outputs, according to our number of classes.

Once the ResNet is trained and ready to draw predictions regarding both
accident-related categories (’dangerous’ or ’safe’), we define our Hazard
Index (H) as the probability that an input image is classified as ’dangerous’
by the ResNet. Thus, for each street-level input image, the classifiers
deliver a value $H$ in the range of [0,1]. This value corresponds to the
output of the Softmax activation function of the last fully connected layer of
the ResNet architecture. When $H\approx 1$, the location corresponding to the
image is considered to be dangerous. On the contrary, when $H\approx 0$, the
corresponding location is considered to be safe. Accordingly with the defined
accident types ($V$ and $P$), we train our ResNet to estimate two subtypes of
Hazard Index: $H^{v}$ and $H^{p}$, corresponding to the Hazard Indices for
vehicle-to-vehicle and vehicle-to-pedestrian accidents, respectively.
Therefore, we end up training 2 models in total over the full datasets, which
includes the entire period 2010-2017.

###  1.4 Modeling pedestrian and road traffic

It is important highlighting that the described Hazard Index does not
explicitly consider the pedestrian and cars volumes navigating the network:
Section 2.3 addresses these issues. For sake of consistency, in the following
we describe the fundamentals we use to estimate vehicle and pedestrian
traffic. For a detailed description of the methods and available source code
we refer the reader to [49].

To model pedestrian traffic on top of the sidewalk network of Barcelona, we
adapted the method designed by Yang et al. [50], which assumes a gravitational
analogy wherein those locations of the network containing the most services
(i.e. having the most mass) are the most attractive to pedestrians. Upon this
assumption we can estimate pedestrian Origin-Destination (OD) matrices
considering: geographic population data obtained from government Open Data
portals, point-of-interest (POI) data from Foursquare [51, 52] (a location-
based social network), and real pedestrian flow samples provided by TC Group
Solutions (https://www.tc-street.com/). After some numerical calculations,
each entry, $o_{ij}$, of the resulting OD matrix (of size $N\times N$ , where
$N$ is the number of nodes in the city’s sidewalk network) represents the
amount of people, per time step, who begin a journey departing from location
$i$ to destination $j$. As in [53, 54, 55], we subsequently fed with our OD
matrix to the calculation of the edge betweenness centrality upon Barcelona’s
sidewalk network to obtain the expected amount of pedestrians that traverse
each network edge per time step. That is, the effective edge betweenness of
the pedestrian network, $E_{ij}^{P}$.

The estimation of the road traffic is obtained following a similar procedure,
in which the OD matrix is assumed to be all-to-all and the effective edge
betweenness of the road network network, $E_{ij}^{V}$, is obtained considering
Barcelona’s road network, weighted by traversal time (edge distance divided by
allowed road speed).

##  2 Results

###  2.1 Temporal evolution of pedestrian and vehicle safety

(A) | (B)
---|---
|
Figure 2: Mean change in number of accidents by neighborhood, disaggregated
into vehicle (left) and pedestrian (right) accidents. Besides a general trend
of increased accident incidence in the center of the city, the patterns for
the two types of accidents seem to show little correlation, either positive or
negative.

Barcelona’s commitment to Vision Zero was formalized in 2017 [56]. Since then,
the city government has implemented several strategies to reduce traffic
accidents, including the definition and implementation of a Superblock system
[57], and the use of the so-called tactical urbanism [58]. These initiatives
have led to frequent and steady changes in the visual and structural makeup of
urban scapes. Some of these interventions have been conducted modifying the
urban landscape locally, in order to provide separate areas for vehicle
traffic and pedestrian flows. In general, less interaction between pedestrians
and vehicles should imply less probability of accident encounters. But, at the
same time, increasing levels of population density and traffic flows
contribute to the continued conflicting relationship between pedestrians and
motorized mobility.

(A) | (B)
---|---
|
(C) | (D)
|
Figure 3: Panels A and B show the variation in the number of accidents,
between the two periods of study (2010-2013 and 2014-2017), for vehicle-
vehicle and vehicle-pedestrian at each of the 138K image locations of our
dataset. Plots display the absolute variation. Positive values indicate an
increase in the number of observed accidents. Inset plots show the change (in
terms of distribution median) of the number of accidents for points that had
$\geq x$ accidents in the period 2014-2017. Panels C and D analyze these
variations disaggregated by accident counts on the period 2014-2017 ($x$
axis). Each corresponding box plot represents the distribution of accident
counts from the earlier period (2010-2013, $y$ axis). Distribution median and
quantiles are represented. Accordingly, when the mean falls below the 1:1
diagonal, these points saw an average increase in accident counts across the
two periods. Otherwise, they saw an average decrease. Insets of panels C and
D, show the number of points in the dataset with a given number of accidents
($x$-value) in the 2014-2017 period.

In order to untangle this complicated web of temporal cause and effect, we
first provide a descriptive analysis of the evolution of urban accidents
involving pedestrians, vehicles, and a combination of both. Figure 2 shows
that the evolution in the number of accidents does not seem to follow any
particular spatial pattern. Increases and reductions are distributed almost
homogeneously across the city. Additionally, we observe that there is very
limited correlation between where vehicle-vehicle and vehicle-pedestrian
accidents occur, most likely reflecting the differences between pedestrian and
vehicle mobility dynamics. Panels A and B of Fig. 3 and Table 1 evidence
quantitatively that accident incidence for both pedestrians and vehicles has
increased over the course of the two periods in absolute number, but also, in
some districts, on a point-by-point basis. Aside from this slight average
increase, we also observe that the distributions of the variation in accident
incidence is quite broad, see panels A and B of Fig. 3. Some locations have
experienced a major improvement, while others a major deterioration. In panels
C and D of Fig. 3 we examine the evolution in accident incidence disaggregated
by the number of accidents in the period 2014 to 2017. It is interesting to
note that, on average, pedestrian safety has deteriorated independently of the
present situation. In contrast, for vehicle-vehicle accidents, although the
trend is similar for areas with fewer accidents, heavily affected points (more
than 50 accidents observed) have, on average, seen an improvement in safety.
Inset on panels A and B of Fig. 3 provide statistics on the variation of
accident incidence when considering locations with a number of accidents
greater than a given threshold (x-axis value). Over all, the average behavior
indicates that interventions from local authorities seem to be insufficient to
contain road safety, and the large deviation observed also indicates that the
problem seems to require a multi perspective analysis to prioritize
interventions.

The preceding analysis provides a global picture of the situation, but more
work is needed to uncover the connections in the evolution of accident
incidence over time at particular locations, and how this relates to
interventions and changes in the built environment.

###  2.2 Predicting the impact of urban interventions on pedestrian and
vehicle safety

The advantage of using automated tools to aid in the design of safe urban
environments comes from those tools being able to inform whether a location
will increase or decrease accident incidence as a result of a urban
intervention or change in its physical layout. Currently, this process is
usually done by relying on specific field knowledge (e.g. urban planers,
municipalities, etc.), or through a posteriori statistical analyses of the
effects of interventions. However, both methods have their own downsides. On
the one hand, specific knowledge requires time and experience, which is harder
to obtain as cities grow; on the other, statistical analyses require accidents
to occur, which, besides being undesirable, does not allow to assess the
safety of those locations where accidents do not occur that often.

In this section we will locally address this issue in an automated way.
Specifically, we assess whether the difference between the Hazard Index
estimated from Street View images across both periods of study for the same
location is a good proxy to predict the empirical change in accident incidence
at that location. Recall that the trained neural network is only fed with
Street View images: no information about traffic, pedestrian density or time
is provided. Thus, differences in the Hazard prediction are obtained solely
considering the physical or structural change in the scene.

Type |  | Incr.
---
Acc.
| Incr. Acc.
---
&
Incr. Hzrd
| Incr acc
---
&
Incr. Hzrd $\pm 0.05$
| Decr.
---
Acci
| Decr. Acc
---
&
Decr. Hzrd
| Decr. acc
---
&
Decr. Hzrd $\pm 0.05$
Full dataset
$P$ | 30230 | 18533 (61%) | 21842 (72%) | 30672 | 18557 (60%) | 22125 (72%)
$V$ | 58230 | 36372 (62%) | 45673 (78%) | 48486 | 23085 (48%) | 32926 (68%)
Restricted dataset
$P$ | 12630 | 9060 (71 %) | 10661 (84%) | 12975 | 9156 (70 %) | 10884 (84 %)
$V$ | 36827 | 23313 (63 %) | 31154 (85%) | 31729 | 15536 (49%) | 24265 (76%)
Table 3: Evaluation of the correlation between the increment/reduction of the
number accidents with the increase/decrease of the Hazard Index. For each of
the well estimated image-pairs in which the number of accidents has increased
(or decreased), we counted those in which the Hazard Index also increase (or
decreased). Columns including $\pm 0.05$ provides the same statistics. These
results provides us with information about the quality in detecting large
variations on accident incidents, or else in detecting no variations. Results
labelled as Full dataset, regard the analysis of the 138k image pairs we could
obtain for Barcelona. Results labelled as Restricted dataset, regards results
obtained considering the subset of images correctly classified by the method
in [10].

Results in Table 3 show the performance we obtain in predicting the increase
or decrease of accident rates. Results considering all data available show
that we can predict an increase or decrease on accident rates with an accuracy
of 60%, which increase to 70% if we consider a small tolerance on the
prediction. Since we know a priori that error rates in predicting dangerous
vs. not dangerous images should be approximately 24% per image [10]. We could
estimate that in 42% of image pair either at least one of the images will be
wrongly classified. To understand how this affects our predictions we also
evaluated an scenario in which only the set of image pairs where both images
could be classified correctly in a pre-processing step using the method in
[10] are used. Accuracies in this scenario raise to 70% and 80%, which is
promising.

Overall results in Table 3 show that method works good in practically all
scenarios analyzed, except in predicting the decrease in accident rates for
cars.

Figure 4 explores these results with higher detail. We see that, on average,
the larger the increase (decrease) in the Hazard Index (lower-right and upper
left corners of the plot) the larger the increase (decrease) in accident
incidence. This implies that, beyond predicting in a binary fashion whether or
not there will be a variation in accident incidence, the Hazard Index is able
to provide information about the magnitude of such changes. To complement the
previous results, Fig. 5 shows that the presence of some objects related to
larger accident rates become more predominant in increasingly dangerous
locations. This provides more evidence for our hypothesis that the increasing
frequency of some urban objects may be directly related to accident incidence.

(A) | (B)
---|---
|
Figure 4: Comparative analysis of the observed change in the number of
accidents and the change predicted by the Hazard Index for the same location
across the two periods of study. The plot has been assembled considering, for
each location, the number of accidents and the Hazard Index prediction from
the first (2010-2013) and second (2014-2017) periods. The color of each
hexagonal bin represents the change in number of accidents for all locations
with particular Hazard Index pairs, $(x,y)$ coordinates representing the
different periods of study. Red tones indicate that accidents increased, and
blue tones accidents decreased. Panel A shows the results for vehicle-
pedestrian accidents, and panel B shows those for vehicle-vehicle accidents.
(A)
---
(B)
Figure 5: Comparative analysis of urban object occupancy variation with
respect to variation of accident rates. For each pair of images, taken in the
same place, we calculated the difference between the number of accidents and
then clustered all pairs of image according this difference. Subsequently, we
assessed which objects gained or lost importance with respect to accident rate
variations. Specifically, being $v(i)\in\mathbb{R}^{C}$, a vector containing
information of the relative fraction of object category $C$ in image $i$. The
mean difference is calculated $(v(i)_{m}-v(i)^{\ell})/N$ , where $m$ refers to
images with more accidents, $\ell$ to less dangerous images, and $N$ the
number of pair-images with this condition. Panel A presents the results for
pedestrians and Panel B for vehicles. To obtain the object occupancy, we first
segmented all images on our dataset using the Inplace-ABN
(DeepLabV3+WideResNet-38) implementation [62], already trained with the
Mapillary Vistas dataset [63]. Once segmented, object occupancy, in percentage
of pixels, was obtained for each object in each image.

###  2.3 Assessing the risk evolution of sidewalks and road networks

The analysis provided in the previous sections does not explicitly include
information regarding the demand of the corresponding transportation networks:
sidewalks and roads. Although very informative about the intrinsic hazard of
the scene, disregarding the network perspective and demand, our localized
predictions may be insufficient to quantify the effective hazard population
experience on their daily mobility and, consequently, useless for efficient
prioritization of urban interventions. Note, for instance, that a location
with large hazard index but with practically no traffic (pedestrians or
vehicles) may not be too problematic. At the same time, a relatively less
dangerous location with numerous trajectories passing through may result in a
large accident rate. In this section, we combine the hazard index of sidewalks
and road segments with network features to provide a practical analysis of
urban safety.

(A) | (B)
---|---
|
Figure 6:  Correlation between Hazard Index and network centrality measures,
across periods and accident type. Panel A shows the results regarding
betweenness centrality and Panel B regarding closeness centrality.

We start by examining the relation between the estimated Hazard Index, and
centrality (considering here two flavours: closeness [59] and effective edge
betweenness [60]) of the corresponding sidewalk and road networks. Betweenness
centrality is representative of the traffic demand on a transportation
networks when correctly fitted with real data, see Sec. 1.4 and [44].
Closeness centrality, a purely structural measure to describe network nodes,
is useful to determine the geometric centrality of a network. Since our hazard
index is related to the edges of the network, we construct an edge version of
closeness centrality, $C_{ij}$, by averaging the closeness of an edge’s
terminal nodes.

Results in Fig. 6 evidence a clear positive relation between betweenness
centrality and the Hazard Index, meaning that sidewalks and roads with high
demand are also the most dangerous ones. Results regarding closeness
centrality are qualitatively equivalent for vehicles and pedestrians.

We finally assess how the Hazard Index has varied across periods with respect
to the network centrality measures. We do not observe a clear prioritization
of interventions neither in terms of hazard nor traversal demand, see Fig. 7
panel A. Additionally, Fig. 7 panel B shows the distribution of the locations
with larger variation (the largest 5% increase or decrease) of the Hazard
Index contrasted by its betweenness value. Distribution of improvements of the
Hazard Index is homogeneously spread over the urban landscape. This seems to
indicate as well an homogeneous distribution of interventions across
districts. In turn, deterioration of safety also seems to be homogeneously
distributed. Fig. 8 shows some illustrative examples of locations with
different betweenness values that experienced a large variation of Hazard
Index (increase or decrease). Finally, it is worth mentioning that
transportation demand (betweenness) is poorly related to the registered urban
interventions, either decreasing or increasing the Hazard Index.

(A) | (B)
---|---
|
Figure 7:  (A) Correlation plot with the variation of the Hazard Index, across periods, with respect to the betweenness centrality of the same network segment. (B) Location over the map of Barcelona of the sidewalk segments with larger variation (increase or decrease) of Hazard Index. These points correspond to those segments with Hazard Index above or below $2\sigma$ with respect to the average variation. (A) | (B)
---|---
|
(C) | (D)
|
Figure 8: Examples of urban scenes with different effective betweenness values
with large increase or decrease of Hazard Index across periods. For each pair
of images, left side one regards the period 2010-2013 and right side one to
the period 2014-2017. Images on the top row (panels A and B) regard images
obtained from pedestrian areas with large betweenness, which contrast with the
images corresponding to low betweenness areas seen in the bottom panels
(panels C and D). Left panels (A and C) correspond to images where we observed
a decrease in the hazard index and right panels (B and D) a large increase.
Panel A represents a location where pedestrian accident rates reduce from 4 to
1 (hazard reduction from x to y). In the original urban setup a park on the
left side of the image was accessible by crossing the road from a non-
authorized area, which probably was encouraging pedestrians to do so. We see
that the urban actuation has modified the area to physically forbid pedestrian
crossings, adding, additionally, a bike lane. In panel B, we see a location
where pedestrian accident rated have increase from 2 to 10. We can see
different interventions that may have triggered such increase. We highlight
two. The first regarding the fence protecting pedestrians have been removed
and the second regarding the reduction of distances between sidewalks crossing
the big road (small pedestrian island has been introduced), which may
encourage pedestrians to rapidly cross the road while traffic lights in read.
Panel C shows a low pedestrian betweenness that have increased accident rates
from 2 to 5, see that several services has been made accessible in the area,
augmenting probably the affluence of pedestrians [6]. Finally, Panel C shows a
region with low pedestrian betweeness where accident rates have been reduced
from 4 to 0. See that interventions have been addressed to separate sidewalk
from the road network.

##  3 Discussion, limitations and conclusions

As cities grow, their management becomes increasingly difficult. Many actors
(urban planners, data scientists, local administration, etc.) need to
coordinate to make sense of urban data, to design and prioritize interventions
in order to reach a kind of ”urban efficiency” understood in a broad manner
(efficient, sustainable and equitative mobility, no traffic accidents,
economic growth, etc.). At the same time, many tasks may be automated to some
degree by means of urban sensors and big data analysis. In line with other
recent advances which take profit of new and extensive data-sets [18, 29, 6,
41], machine learning [4, 5, 10, 5, 43] and their combination with complex
networks tools [61, 28, 30, 49, 19, 16], our work focuses on the automated
assessment and improvement of urban traffic safety, for both pedestrian and
vehicles.

Here, taking these conclusions further, we have shown that our methods are
sensitive enough to predict the effect urban interventions on safety, for both
pedestrians and vehicles. This finding opens the door to the automated and
simulated assessment of the effects of specific interventions in the built
environment, potentially allowing planners and stakeholders to gain insights
on the effectiveness of proposed actuations.

Clearly, other factors not considered here may have an impact on the evolution
of accident rates: changes in vehicle or pedestrian flows, new services in
particular areas, or simply different weather conditions. While our deep
learning pipeline can be easily modified to include other kinds of data, the
results we present in this paper show that visual information alone is enough
to achieve high levels of accuracy. This is important, since driver point-of-
view images (like those we use) are readily available at low cost,
particularly in light of new open providers of imagery, such as Mapillary
[32]. This means that no costly data acquisition phases are needed to obtain
predictions for practically any city world-wide.

We hope that, encouraged by the good accuracy we have obtained, our work can
stimulate the development of more sophisticated approaches to the problem of
automatically proposing improvements in the urban built environment, for
example with the use of Generative Adversarial Networks to produce virtual,
plausible alternatives to target scenes (a kind of automatic “safe-
ification”). These techniques could be further trained to estimate the
potential cost of possible interventions, also considering the trade-off
between cost and gains in safety, in order to aid urban planners in the
prioritization of urban interventions.

Finally, we would also like to stress the importance of our work in
contributing to the design of pedestrian friendly and sustainable cities. The
design of efficient [45], enjoyable [11], and safe cites remains a challenge
with international attention, listed in the Sustainable Development Goals of
United Nations. Within the development of Goal 11 (Sustainable cities and
communities), some of the objectives rely on the definition/characterization
and analysis of pedestrian paths, from the amount of existing services (or
points-of-interest)[50], to the width of sidewalks [44]. The automatic
characterization of path segments is crucial to the automated design of these
routing strategies [43, 45]. Our work, with its ability to draw very detailed
(microscopic) safety predictions, contributes to one of those urban aspects
that have historically been considered important: pedestrian safety.

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