=Paper=
{{Paper
|id=Vol-3813/paper8
|storemode=property
|title=Application of Multi-Instance Counterfactual Explanation in Road Safety Analysis
|pdfUrl=https://ceur-ws.org/Vol-3813/8.pdf
|volume=Vol-3813
|authors=Andre Artelt,Andreas Gregoriades
|dblpUrl=https://dblp.org/rec/conf/att/ArteltG24
}}
==Application of Multi-Instance Counterfactual Explanation in Road Safety Analysis==
https://ceur-ws.org/Vol-3813/8.pdf
Application of Multi-Instance Counterfactual Explanation
in Road Safety Analysis
André Artelt1,2 , Andreas Gregoriades3
1
Bielefeld University, Germany
2
University of Cyprus, Cyprus
3
Cyprus University of Technology, Cyprus
Abstract
Road accidents cause millions of fatalities worldwide and impose a significant economic burden on societies.
To address this problem, road safety researchers have mainly applied statistical or machine learning methods
to predict accident occurrences and identify the causes of such crashes, but to the best of our knowledge,
no work has explored how to optimally address the causes of such crashes to minimise accidents’ severity.
Recently, eXplainable AI (XAI) techniques have been applied in transportation to evaluate the effect of accidents’
contributing factors. Limited work however delved into optimum ways to reduce accident severity by minimising
changes needed to road network’s infrastructure using XAI. In this work, we apply counterfactual explanations, a
popular XAI technique, to road accident data, to identify optimal changes to infrastructure to improve road safety,
by converting severe accidents into minor accidents. Traditionally, counterfactual explanations are used for
single instances(accidents), which is not appropriate to this problem since the goal is to find actionable changes
to the road infrastructure to convert as many severe accidents to non-severe accidents. Thus, our proposed
methodology is based on multi-instance explanations. The proposed methodology is evaluated in a case study
with real accident data from Cyprus.
Keywords
Road Safety, XAI, Counterfactual Explanations
1. Introduction
According to the World Health Organisation (WHO), approximately 1.4 million accidents occur each
year worldwide leaving millions of people injured. Road accidents constitute the eighth leading cause
of death worldwide and this number is likely to increase if this problem is not addressed effectively.
Predominantly research on road safety utilizes classical statistical techniques such as Logistic, Poisson,
and Negative binomial regression [1, 2]. These methods undoubtedly provided insights; however, the
fundamental characteristics of accident data often result in methodological limitations that cannot be
accounted for. Recently, research has indicated that machine learning (ML) techniques outperformed
conventional statistical methods by offering superior prediction and greater accuracy due to their ability
to work with massive amounts of multidimensional and noisy data, while also being able to address
generalizability, as reported by Iranitalab et al. (2017). With the increased availability of data from
various sources such as Internet of Things devices, installed in the road network, connected vehicles,
and naturalistic driving studies, machine learning is becoming a key methodology in transportation.
During road safety analysis, traffic accident data is used to develop models to predict and understand
the causes of accidents through the identification of relationships among contributing factors and the
outcome of the models. Contributing factors can be combinations of properties relating to the road
infrastructure, driving behavior, and environmental conditions. These are considered independent
variables and the outcome (accident occurrence or severity) is the dependent variable. Predicting an
accident before it happens gives the chance to policymakers to take precautionary measures to minimise
or prevent accidents from happening. In the machine learning domain, researchers use techniques such
ATT’24: Workshop Agents in Traffic and Transportation
$ aartelt@techfak.uni-bielefeld.de (A. Artelt); andreas.gregoriades@cut.ac.cy (A. Gregoriades)
https://andreartelt.github.io/ (A. Artelt)
0000-0002-2426-3126 (A. Artelt); 0000-0002-7422-1514 (A. Gregoriades)
© 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
as eXplainable AI (XAI) [3], and/or causality analysis to investigate the contributing factors that lead
to accidents. The latter approaches use multivariate statistical models to evaluate the effects of the
contributing factors. However, statistical relationships are not always causal and could be the result
of chance, bias, confounding variables, or others. A cause can be an action or event that changes the
outcome that would not have changed otherwise. A cause is therefore a necessary precondition for
an event such as an accident. Causal relationships are usually characterised by regularity, meaning
the same cause always produces the same result [4]. Causal relationships also are linked to the notion
of counterfactual, which explains what would have happened if the cause was not present. Thus, to
infer a causal relationship, it is always necessary to establish the counterfactual. In scientific analysis,
the most popular approach to do so is to conduct a controlled experiment where treatment is applied
at random, and the control group shows what would have happened if the action/treatment was not
introduced (counterfactual). Road safety experiments are difficult to perform, with most studies being
observational, thus the task of establishing the counterfactual is similar to controlling the confounding
factors.
In transportation, one of the main approaches to improve safety is by addressing one or more causes or
risk factors that are associated with accident occurrence. The traffic safety literature highlights different
modifications to either the policy or road infrastructure to improve safety. For instance, infrastructural
modifications could be, changes in horizontal curvature of roads, shoulder widths, the width of the
median that separate lanes, etc. Such modifications, however, are usually introduced after analysing
the problem, designing a solution, simulating it, and then applying it, without having any guarantees
of the effect that these changes can make. Therefore, most of such projects fail to meet their goals.
Additionally, such modifications are usually introduced without knowing the optimum degree of change
(for example speed limit) to archive the desired effect. Optimisation has been applied in transportation
to minimise the budget required to improve safety as reported in work by [5]. Limited work however
addressed the problem of optimising infrastructural changes to reduce fatalities or accident severity.
Explainable AI (XAI) [3] is an approach aiming to explain black-box machine learning models and
the reasons they come up with decisions through intuitive and human-understandable explanations.
The need for explainability is not new since it addresses the question "why" a system behaves the
way it does. The term XAI however has been recently coined by DARPA and it is used in a variety
of domains where machine learning is applied. For instance, in transportation, two of the main XAI
techniques used to extract knowledge from prediction models are Shapley Additive exPlanation (SHAP)
and Lime [6]. These approaches, however, do not offer recommendations on how to achieve the desired
results. Counterfactual explanations [7] ("counterfactuals" for short) on the other hand are designed for
this purpose and thus became a popular technique for explaining black-box models. However, their
application in the traffic safety domain is missing, making this work one of the first works that apply
counterfactual explanations to road safety.
The goal of this work is the maximisation of road safety (reducing severe accidents) by minimising
the infrastructural changes to a road network to improve safety. This is achieved by explaining using
counterfactual explanations a machine learning model trained to predict accident severity using histori-
cal accident data of the specific road network. Counterfactuals, however, usually provide explanations
for single instances (accidents) rather than a group of cases. Moreover, counterfactual explanations are
applied on numerical variables (e.g. road width, speed limit, etc.), even though categorical variables
(e.g. type or intersection) are a key type of features in different domains, including transportation.
The approach proposed herein addresses these two problems by finding counterfactuals that satisfy
multiple instances(accidents) simultaneously, characterised by both numerical and categorical features.
Therefore, the method provides policymakers with recommendations that not only indicate the factors
that contribute to the problem but also provide them with information regarding the degree of change
to these factors to archive the desired effect. Therefore, the method can potentially minimise the costs
of satisfying stakeholders’ goals.
The remainder of this work is organised as follows: Section 2 elaborates on the background of coun-
terfactual explanations, the related literature, and the motivation behind the proposed counterfactual
methodology. The next section introduces the methodology (Section 3) and elaborates on its application
in a road safety case study using accident data (Section 4). Finally, we summarize and discuss future
directions (Section 5).
2. Counterfactual Explanations
A counterfactual explanation [7] ("counterfactual" for short) states how to change a given instance such
that the output of the model for this instance changes in a specific way (towards a desired outcome).
The popularity of counterfactual explanations comes from the fact that they are very similar to the way
humans explain situations [8] and that they provide precise and actionable recommendations that can
be directly applied in the real world [7].
To be useful in practice, a counterfactual must not only be feasible (i.e. valid) but also as simple as
possible - e.g. not too many recommendations or big changes [7]. Considering these two aspects, the
computation of a counterfactual ⃗𝛿 cf for a given case ⃗𝑥orig can be formally phrased as an optimization
problem that minimises the modifications to the attributes of ⃗𝑥orig so that the classifier ℎ(·) changes its
prediction to the desired output 𝑦cf [7, 9]:
Definition 1 (Counterfactual Explanation). Assume a prediction function ℎ : 𝒳 → 𝒴 is given. Computing
a counterfactual explanation ⃗𝛿 cf ∈ 𝒳 for a given instance ⃗𝑥orig ∈ 𝒳 is phrased as the following optimization
problem:
⃗ orig ⊕ ⃗𝛿 cf ), 𝑦cf + 𝐶 · 𝜃(𝛿
⃗ cf ) (1)
(︀ )︀
arg min ℓ ℎ(𝑥
⃗𝛿 cf ∈ 𝒳
where ℓ(·) denotes a loss function that penalizes deviation of the output ℎ(𝑥 ⃗ orig ⊕ ⃗𝛿 cf ) from the requested
⃗
output 𝑦cf , 𝜃(·) implements the cost of 𝛿 cf – i.e. prefer “simple, cheap & easy to execute” explanations –,
and 𝐶 > 0 denotes the regularization strength.
In order to not make any assumptions on the data domain, we use the symbol ⊕ to denote the
application/execution of the counterfactual ⃗𝛿 cf to the original instance ⃗𝑥orig . While in the case of real
and integer numbers (e.g. 𝒳 = R𝑑 ) this reduces to the translation (i.e. (𝑥 ⃗ cf )𝑖 = (𝑥 ⃗ cf ) , in the
⃗ orig )𝑖 + (𝛿 𝑖
case of categorical features it denotes a substitution – i.e. (𝑥 ⃗
⃗ cf )𝑖 = (𝛿 cf )𝑖 .
Note that Definition 1 constitutes a non-causal approach - i.e. no causal model of the world is included.
There exists an entirely different line of research on counterfactuals utilizing structural causal models
to incorporate causal knowledge [10]. However, in practice, such causal models are usually not known
and have to be estimated from data or carefully specified with the help of domain experts. However,
experts are not easily available, thus in this work, we only consider a non-causal approach.
There exists a wide variety of methods for computing counterfactual explanations – i.e. solving
the optimization problem. The model-agnostic methods utilize gradient-based optimization methods
that can be applied on any black box model [11, 12, 13], while the model-specific methods use details
of the specific model’s architecture to calculate recommendations [9]. An important limitation of
counterfactual explanations is the fact that they are missing uniqueness. This means that there usually
exists more than one possible explanation which raises the question of which one to pick - usually, the
"simplest" explanation is picked.
2.1. Multi-instance Counterfactual Explanations
In many real-world applications of counterfactuals, one is interested in gaining knowledge about a
set or group of instances instead of a single instance, which is the usual case. For instance, in any
organization, the human resource department is interested in minimizing employees’ attrition, since
this causes several problems. To understand the cause of attrition and deploy appropriate (global)
countermeasures, an organization needs to consider all cases (employees intending to leave) and find a
change (maybe increase employee salary) that will guarantee the retention of as many employees as
possible [14, 15].
To find a common feasible recommendation for a group of instances (i.e. employees), counterfactual
explanations have been recently extended towards multi-instance counterfactual explanations (also
called group-counterfactuals) [14, 15, 16, 17]. A multi-instance counterfactual states what to change on
a group level (e.g. increasing some specific attribute for all instances in the group by the same amount)
such that the outcome for this group of instances changes simultaneously in some desired way. Like
counterfactual explanations, the computation of a multi-instance counterfactual explanation ⃗𝛿 cf for a
set of cases 𝒟 can be formalized as an optimization problem:
Definition 2 (Multi-instance Counterfactual Explanation). Let ℎ : 𝒳 → 𝒴 denote a prediction function,
and let 𝒟 be a set of labeled instances with the same prediction 𝑦 ∈ 𝒴 under ℎ(·) – i.e. ℎ(𝑥 ⃗ 𝑖 ) = 𝑦 ∀𝑥
⃗ 𝑖 ∈ 𝒟.
We are looking for a single change ⃗𝛿 cf ∈ R𝑑 that, if applied to the instances in 𝒟, changes as many of their
predictions to some requested output 𝑦cf ∈ 𝒴.
We call all pareto-optimal solutions ⃗𝛿 cf to the following multi-objective optimization problem multi-
instance counterfactuals:
⃗ cf ) , ℓ(ℎ(𝑥
⃗ 1 ⊕ ⃗𝛿 cf ), 𝑦cf ), . . . , ℓ(ℎ(𝑥
⃗ |𝒟| ⊕ ⃗𝛿 cf ), 𝑦cf ) (2)
(︀ )︀
min 𝜃(𝛿
⃗𝛿 cf ∈ 𝒳
where 𝜃(·) denotes the cost of the counterfactual, and ℓ(·) denotes a suitable loss function penalizing
deviations from the requested outcome 𝑦cf – suitable loss functions might be the mean-squared error or
cross-entropy loss, while the cost 𝜃(·) might be implemented by a 𝑝-norm.
Note that in contrast to normal counterfactuals (Definition 1), multi-instance counterfactuals (Defi-
nition 2) require multiple constraints – one constraint for each case in the set 𝒟. Similar to normal
counterfactuals, one could merge all constraints into a single objective which would then enable the
use of general gradient-based or black-box methods for solving the optimization problem.
A major challenge in the computation of multi-instance counterfactuals is that there might be no
feasible solution – i.e. it might be impossible to find a single change ⃗𝛿 cf that is feasible for all instances
in 𝒟. Therefore, one might either want to relax the constraints and compute a change ⃗𝛿 cf that is feasible
for as many as possible instances in ⃗𝛿 cf , or find a grouping/clustering of 𝒟 such that there exists a
change ⃗𝛿 cf for each of the groups that is feasible for all instances within this sub-group – here the
additional challenges of finding such sub-groups arise. We denote the percentage of instances for which
the explanation is feasible as accuracy.
Unlike counterfactual explanations (Definition 1), multi-instance counterfactuals (Definition 2) is
a novel concept and consequently, existing work on this is rather limited. For instance, the work
by [15] applies multi-instance counterfactuals to the employee attrition problem but only considers a
linear classifier. One of the latest works such as [14] proposes a counterfactual explanation tree, which
assigns counterfactual explanations to a learned decision tree that assigns samples to groups – this
method is only applicable if an automatic clustering into sub-groups is needed. Besides that, most
existing work for multi-instance counterfactuals can be interpreted as summarizing or aggregating local
counterfactuals. In the work of [16], multi-instance counterfactuals are generated by first computing
individual counterfactuals and then selecting those that maximizes the cover of a given set of instances.
Similarly, [18] tries to obtain a global explanation by simply aggregating local explanations. However,
these methods cannot guarantee high accuracy because they consider all instances separately.
3. Multi-instance Counterfactuals for Improving Road Safety
Herein we propose a methodology that utilizes multi-instance counterfactuals to analyze road accident
records and compute suggestions on how to improve road safety – the only assumption we make is
that the collected road accident records are labeled with accident severity. The proposed methodology
consists of three main steps as illustrated in Figure 1:
1. Train a binary classifier using road accident data to predict the severity of accidents.
Accidents Multi-instance
Accident Classifier Clustering
Records Counterfactuals
Generate
Train classifier to predict Cluster accidents into recommendations how to
serverity of accidents groups improve road safety
Figure 1: Proposed methodology for reducing accident severity.
2. (Optional:) Cluster all severe accidents into groups of similar characteristics.
3. Compute a multi-instance counterfactual explanation for each group - i.e. what to change to turn
a severe accident into a non-severe (slight).
We interpret the computed multi-instance counterfactuals as potential suggestions on how road
safety can be improved. Note that the grouping in the second step of the methodology is optional
because one could simply consider all severe accidents as one large group. However, depending on
the use case a more refined grouping might allow the computation of suggestions for more specific
questions: For instance, depending on the nature of the collected data, it might be possible to group
road accidents based on their location (e.g. rural area vs. urban) or timing of the accident (day, night).
In this way, one could generate more specific recommendations (e.g. area/time specific) for certain
types of accidents.
4. Empirical Case Study
We empirically evaluate our proposed methodology (see Section 3) in a case study using real-world
accident data.
4.1. Data
The original data consists of accidents that occurred in Nicosia, Cyprus during 2007 - 2012. After
merging and cleaning the data, 9829 cases were left each consisting of 58 attributes. To ensure that the
recommendations to be made are actionable, we only consider 12 attributes that could be changed in
practice, in contrast to attributes that cannot be changed, such as the age of drivers. Examples of mutable
attributes are the road width, speed limit, traffic control, pedestrians crossing, the existence of a median
in the road, etc. The accident type attribute is converted into a binary variable by combining fatal with
severe accidents and labelling them as severe, and slight(minor) with property damage accidents and
labelling these as non-severe accidents.
4.2. Implementation
Because the data mainly consists of numerical (e.g. integer) and categorical attributes, most standard
machine learning models are not suitable. Thus, in this work, we decided to use a tree-based classifier
(i.e. Xgboost [19]) that can handle such categorical attributes without transforming them. The classifier
is trained to predict the severity of a given accident using different hyperparameters that have been
tuned to improve the model’s performance such as depth of trees, number of estimators, learning rate,
and scale-pos-weight to address data imbalance. The classifier achieves an average F1-score of approx.
80% on the test data – i.e. we split the data into train (70%) and test data (30%).
We consider three groups of accidents (among the severe accidents only) in our experiments: All
Severe accidents; Severe accidents in rural areas; and Severe accidents in urban areas. These groupings
can be changed depending on the case study. For instance, a transportation engineer might use only
accidents at a specific location in the road network (black spot). Table 1 shows different multi-instance
recommendations for each of these groups. The column multi-instance counterfactuals show the
changes that need to be made so that the severe accidents are converted to non-severe. The variables
in parenthesis refer to the property of the infrastructure and the number next to each one defines the
type and degree of change. Therefore for the case (Traffic-control, 3) which is a categorical variable, it
denotes that the type of traffic control needs to change to traffic-light (this refers to the number 3) for
all of these accidents so that these change to non-severe. Similarly for continuous variables such as
’speed’ the recommended change is defined as a number with a sign indicating positive or negative
change. In the case of speed limit, most recommendations indicate a reduction in speed limit which
abides to road safety literature [20, 21].
Because in this case study the data also include non-continuous attributes, existing methods for
computing multi-instance counterfactuals cannot be applied out of the box. To address this issue, we
built an evolutionary algorithm for computing multi-instance counterfactuals that are guaranteed to
adhere to the specific attribute ranges and yield feasible solutions. The evolutionary (i.e. genetic)
algorithm treats all variables as discrete and iteratively mutates and merges (cross-over) candidate
solutions until convergence. To guarantee the feasibility of the final multi-instance counterfactual ⃗𝛿 cf ,
we construct the set of feasible changes for each feature of numerical variables as follows – assuming
non-negativity which can be achieved by adding a constant:
⃗ 𝑗 )𝑖 } and 𝑢𝑖 = 𝛽𝑖 − max{(𝑥
𝑙𝑖 = 𝛼𝑖 − min{(𝑥 ⃗ 𝑗 )𝑖 } (3)
𝑗 𝑗
where 𝛼𝑖 and 𝛽𝑖 denote the maximum and minimum feasible value of the 𝑖-th feature, and the final
set of feasible changes is then given as [𝑙𝑖 , 𝑢𝑖 ]. These sets are used when computing mutations in our
evolutionary algorithm of existing individuals during the optimization. As an objective, we use the
zero-norm (i.e. setting p=0 in the p-norm) - by this, we aim to minimize the number of suggested
changes. Together with the constraints, this yields the following optimization problem:
(︁ )︁
arg min ‖𝛿 ⃗ cf ‖0 , ℓ(ℎ(𝑥
⃗ 1 + ⃗𝛿 cf ), 𝑦cf ), . . . , ℓ(ℎ(𝑥
⃗ |𝒟| + ⃗𝛿 cf ), 𝑦cf ) (4)
⃗𝛿 cf
The final algorithm is given in Algorithm 1.
4.3. Results
To generate reliable results and avoid recommendations that are based on a bad train-test data split,
the experiments are conducted using a 3-fold cross-validation. Thus, we generate three multi-instance
explanations for each accident groups (i.e. all severe accidents, severe accidents in urban areas, and
rural areas) as shown in Table 1.
Besides listing the explanations in Table 1 (tuple of attribute and recommended change), we also
added a column for the accuracy of the explanation which refers to the percentage of instances for
which the explanation correctly changes their prediction (Table 1). From the results, we observe that
the generated multi-instance counterfactuals are almost always feasible (the accuracy is close to one).
This demonstrates that our implemented evolutionary algorithm is able to compute feasible solutions
with high reliability.
The results from the multi-instance counterfactuals show the required changes to infrastructure
to convert severe accidents into non-severe. The variables that are considered important and thus
are part of the recommended changes are the Traffic Control, which takes the values traffic signals,
roundabout, police, stop sign and none; Road Width, stating the width in meters; Speed Limit in Km/h;
Pedestrian Crossing type, that takes values: zebra crossing, pedestrian traffic signal crossing, and
pelican crossing; Constriction variable that takes the values: one-way, two-way bridge, none; and Brake
Algorithm 1 Multi-instance Counterfactuals for Road-safety Analysis
Input: Set of labeled (severe vs. non-severe) accidents 𝒟 = {(𝑥 ⃗ 𝑖 , 𝑦𝑖 )}, hyper-parameter 𝑁 denoting
the number of evolutionary steps
Output: Recommendations (i.e. multi-instance counterfactual explanation) ⃗𝛿 cf
1: Split data 𝒟 into train 𝒟train and test 𝒟test set ◁ Repeat in k-fold cross validation
2: Fit XGBoost classifier ℎ(·) to 𝒟train
3: Consider all accidents ⃗ 𝑥𝑖 ∈ 𝒟test with ℎ(𝑥 ⃗ 𝑖 ) = "severe" – create 𝒟severe ◁ Severe accidents only
4: (Group accidents and pick group of interest) ◁ Optional
5: Compute feature bounds {[𝑙𝑖 , 𝑢𝑖 ]} Eq. (3) on 𝒟severe
6: ◁ Evolutionary algorithm for computing a multi-instance counterfactual
⃗ cf 𝑗 } = random_init({[𝑙𝑖 , 𝑢𝑖 ]})
7: {𝛿 ◁ Random initial population of solutions – respect feature
bounds!
8: for N iterations do
9: Evaluate fitness of each ⃗𝛿 cf 𝑗 using Eq. (4)
10: Select best ⃗𝛿 cf 𝑗 for next generation
11: Apply random mutations considering feature bounds {[𝑙𝑖 , 𝑢𝑖 ]}
12: Apply cross-over to create next generation of solutions {𝛿 ⃗ cf 𝑗 }
13: end for
14: (︁ ◁ Select best solution as the)︁final recommendation ⃗𝛿 cf
15: ⃗
𝛿 cf = arg min ‖𝛿 ⃗ cf ‖0 , ℓ(ℎ(𝑥
⃗ 1 + ⃗𝛿 cf ), 𝑦cf ), . . . , ℓ(ℎ(𝑥
⃗ |𝒟severe | + ⃗𝛿 cf ), 𝑦cf )
⃗ cf }
{𝛿 𝑗
Grouping Accuracy ↑ Multi-instance counterfactual
(’TRAFFIC_CONTROL’, ’3’), (’SPEED_LIMIT’, ’-83’),
All 1.0
(’PEDESTRIAN_CROSSING’, ’2’)
(’TRAFFIC_CONTROL’, ’3’), (’CONJUNCTION_TYPE’, ’1’),
All 0.968
(’SPEED_LIMIT’, ’-15’), (’PEDESTRIAN_CROSSING’, ’2’)
(’ROAD_WIDTH’, ’+60’), (’BREAK_LANE_WIDTH’, ’+8’),
All 1.0
(’SPEED_LIMIT’, ’-23’), (’PEDESTRIAN_CROSSING’, ’1’)
(’TRAFFIC_CONTROL’, ’1’), (’SPEED_LIMIT’, ’-6’),
Urban 1.0
(’PEDESTRIAN_CROSSING’, ’2’)
(’TRAFFIC_CONTROL’, ’2’), (’ROAD_WIDTH’, ’+41’),
Urban 1.0
(’BREAK_LANE_WIDTH’, ’+11’), (’ROAD_DESCR’, ’1’)
(’TRAFFIC_CONTROL’, ’2’), (’SPEED_LIMIT’, ’-39’),
Urban 0.96
(’PEDESTRIAN_CROSSING’, ’2’), (’LIGHTING’, ’1’)
(’TRAFFIC_CONTROL’, ’1’), (’SPEED_LIMIT’, ’-78’),
Rural 1.0
(’PEDESTRIAN_CROSSING’, ’2’)
(’TRAFFIC_CONTROL’, ’3’), (’CONSTRUCTION’, ’2’),
Rural 1.0
(’SPEED_LIMIT’, ’-90’), (’BUS_STOP’, ’-1’), (’LIGHTING’, ’1’)
(’TRAFFIC_CONTROL’, ’2’), (’ROAD_WIDTH’, ’+48’),
Rural 1.0
(’SPEED_LIMIT’, ’-18’)
Table 1
Multi-instance counterfactual explanations from the conducted case study.
Lane, that describes the width of the footway/shoulder in meters. The recommended changes mainly
highlight the importance of traffic control type and speed limit, factors that are known in the literature
to affect road safety [20, 21]. Additional changes include an increase in road width and break lane width,
recommendations which are also consistent with the literature [22].
To validate our approach we used SHAP (SHapley Additive exPlanations) [23], an XAI model-agnostic
method, to identify which features are influencing mostly accident type. The SHAP summary plot in
Figure 2 shows that the road width, break lane width, and traffic control are key features along with
Figure 2: SHAP results show similar features contributing to accident type.
conjunction type and speed limit. These results verify that our method uses these key features to make
counterfactual recommendations. The limitation of SHAP however is that it does not indicate which
combinations of features must change and by how much so that the accidents are converted from severe
to non-severe.
5. Summary & Future Research
In this work, we proposed a methodology for analyzing road accident data and using XAI to generate
suggestions on how to improve road safety. Our methodology requires training a classifier to predict
the severity of accidents (severe, non-severe) and then recommending changes to the input of a set
of severe accidents using multi-instance counterfactual explanations, so that the predictions of the
model change to non-severe accidents. The approach enable us to compute a global recommendation
on how to reduce the severity of accidents by converting all severe accidents into non-severe using
minimum alterations to chargeable features (referring to infrastructure), and by doing this improve
road safety. We also conducted an empirical case study on real-world data, which showed that our
proposed methodology computes reasonable recommendations that abide with the literature.
Despite the promising results, some aspects need further investigation:
• We observed that the suggested changes are often large (for example the speed limit), and it
is not clear how plausible those changes would be in practice. We think this is mainly due to
the fact that the designed evolutionary algorithm does not have any distributional knowledge
about the data - i.e. which variable values or combinations are more often observed in the real
world. Such distributional information might be used as part of the objective function, thus,
automatically punishing large changes that would be costly to implement. Currently, we are
working on an extension where the evolutionary algorithm is given distributional information
which it utilizes when generating random mutations, new individuals, and cross-overs. By this,
we hope to improve the quality of the recommended infrastructural changes significantly.
• In the presented case study, we either considered all severe accidents as one large group or
manually split them into two sub-groups based on a location attribute from the dataset. While
the computation of multi-instance counterfactuals worked well for both groups (large group, and
two smaller sub-groups), it remains unclear if other interesting or beneficial (for the generated
suggestions) groupings exist. An automatic clustering might not only reveal interesting clusters
within the accident data set but also give rise to better and more specific suggestions on how to
improve road safety. Currently, we are investigating how to cluster cases into groups such that
the resulting multi-instance counterfactuals are as simple as possible.
Acknowledgments
This research was supported by the Ministry of Culture and Science NRW (Germany) as part of the
Lamarr Fellow Network. This publication reflects the views of the authors only.
References
[1] A. Kassu, M. Hasan, Factors associated with traffic crashes on urban freeways, Trans-
portation Engineering 2 (2020) 100014. URL: https://www.sciencedirect.com/science/article/pii/
S2666691X20300154. doi:https://doi.org/10.1016/j.treng.2020.100014.
[2] J. S. Madushani, R. K. Sandamal, D. Meddage, H. Pasindu, P. A. Gomes, Evaluating expressway traffic
crash severity by using logistic regression and explainable & supervised machine learning classi-
fiers, Transportation Engineering 13 (2023) 100190. URL: https://www.sciencedirect.com/science/
article/pii/S2666691X23000301. doi:https://doi.org/10.1016/j.treng.2023.100190.
[3] A. Adadi, M. Berrada, Peeking inside the black-box: a survey on explainable artificial intelligence
(xai), IEEE access 6 (2018) 52138–52160.
[4] R. Elvik, Assessing causality in multivariate accident models, Accident Analysis & Prevention
43 (2011) 253–264. URL: https://www.sciencedirect.com/science/article/pii/S0001457510002514.
doi:https://doi.org/10.1016/j.aap.2010.08.018.
[5] C. B. Byaruhanga, H. Evdorides, A budget optimisation model for road safety infrastructure coun-
termeasures, Cogent Engineering 9 (2022) 2129363. doi:10.1080/23311916.2022.2129363.
[6] M. Gregurić, F. Vrbanić, E. Ivanjko, Towards the spatial analysis of motorway safety in the
connected environment by using explainable deep learning, Knowledge-based systems 269 (2023)
110523.
[7] S. Wachter, B. Mittelstadt, C. Russell, Counterfactual explanations without opening the black box:
Automated decisions and the gdpr, Harv. JL & Tech. 31 (2017) 841.
[8] R. M. J. Byrne, Counterfactuals in explainable artificial intelligence (xai): Evidence from human
reasoning, in: Proceedings of the Twenty-Eighth International Joint Conference on Artificial
Intelligence, IJCAI-19, International Joint Conferences on Artificial Intelligence Organization, 2019,
pp. 6276–6282. URL: https://doi.org/10.24963/ijcai.2019/876. doi:10.24963/ijcai.2019/876.
[9] A. Artelt, B. Hammer, On the computation of counterfactual explanations–a survey, arXiv preprint
arXiv:1911.07749 (2019).
[10] A.-H. Karimi, G. Barthe, B. Balle, I. Valera, Model-agnostic counterfactual explanations for
consequential decisions, in: International Conference on Artificial Intelligence and Statistics,
PMLR, 2020, pp. 895–905.
[11] S. Verma, V. Boonsanong, M. Hoang, K. E. Hines, J. P. Dickerson, C. Shah, Counterfactual explana-
tions and algorithmic recourses for machine learning: A review, arXiv preprint arXiv:2010.10596
(2020).
[12] I. Stepin, J. M. Alonso, A. Catala, M. Pereira-Fariña, A survey of contrastive and counterfactual
explanation generation methods for explainable artificial intelligence, IEEE Access 9 (2021)
11974–12001.
[13] R. Guidotti, Counterfactual explanations and how to find them: literature review and benchmarking,
Data Mining and Knowledge Discovery (2022) 1–55.
[14] K. Kanamori, T. Takagi, K. Kobayashi, Y. Ike, Counterfactual explanation trees: Transparent and
consistent actionable recourse with decision trees, in: International Conference on Artificial
Intelligence and Statistics, PMLR, 2022, pp. 1846–1870.
[15] A. G. Andre Artelt, "how to make them stay?": Diverse counterfactual explanations of employee
attrition, in: Proceedings of the 25th International Conference on Enterprise Information Systems,
ICEIS 2023, Volume 1, Prague, Czech Republic, SCITEPRESS, 2023, pp. 532–538. URL: https:
//doi.org/10.5220/0011961300003467. doi:10.5220/0011961300003467.
[16] G. Warren, M. T. Keane, C. Gueret, E. Delaney, Explaining groups of instances counterfactu-
ally for xai: A use case, algorithm and user study for group-counterfactuals, arXiv preprint
arXiv:2303.09297 (2023).
[17] Z. Huang, M. Kosan, S. Medya, S. Ranu, A. Singh, Global counterfactual explainer for graph neural
networks, in: Proceedings of the Sixteenth ACM International Conference on Web Search and
Data Mining, 2023, pp. 141–149.
[18] D. Ley, S. Mishra, D. Magazzeni, Global counterfactual explanations are reliable or efficient, but
not both (2023).
[19] T. Chen, C. Guestrin, Xgboost: A scalable tree boosting system, in: Proceedings of the 22nd acm
sigkdd international conference on knowledge discovery and data mining, 2016, pp. 785–794.
[20] A. Vadeby, Åsa Forsman, Traffic safety effects of new speed limits in sweden, Accident Anal-
ysis & Prevention 114 (2018) 34–39. URL: https://www.sciencedirect.com/science/article/pii/
S0001457517300532. doi:https://doi.org/10.1016/j.aap.2017.02.003, road Safety on
Five Continents 2016 - Conference in Rio de Janeiro, Brazil.
[21] M. Papageorgiou, C. Diakaki, V. Dinopoulou, A. Kotsialos, Y. Wang, Review of road traffic control
strategies, Proceedings of the IEEE 91 (2003) 2043–2067. doi:10.1109/JPROC.2003.819610.
[22] P. Pokorny, J. K. Jensen, F. Gross, K. Pitera, Safety effects of traffic lane and shoulder widths
on two-lane undivided rural roads: A matched case-control study from norway, Accident Anal-
ysis & Prevention 144 (2020) 105614. URL: https://www.sciencedirect.com/science/article/pii/
S0001457520303006. doi:https://doi.org/10.1016/j.aap.2020.105614.
[23] A. B. Owen, C. Prieur, On shapley value for measuring importance of dependent inputs, SIAM/ASA
Journal on Uncertainty Quantification 5 (2017) 986–1002. doi:10.1137/16M1097717.