=Paper=
{{Paper
|id=Vol-2181/paper-07
|storemode=property
|title=Towards Semantically Annotated Data-Driven Methodologies for Composite Metric Development
in Traffic Incidents
|pdfUrl=https://ceur-ws.org/Vol-2181/paper-07.pdf
|volume=Vol-2181
|authors=Daniel Mejia
|dblpUrl=https://dblp.org/rec/conf/semweb/Mejia18
}}
==Towards Semantically Annotated Data-Driven Methodologies for Composite Metric Development
in Traffic Incidents==
https://ceur-ws.org/Vol-2181/paper-07.pdf
Towards Semantically Annotated Data-Driven
Methodologies for Composite Metric Development in
Traffic Incidents
Daniel M. Mejia1
1
The University of Texas at El Paso, El Paso, TX 79968, USA
dmmejia2@miners.utep.edu
Abstract. At the core of Smart Cities solutions, both in fundamental and applied
research, is the use of technology to improve quality of life of city residents.
Measuring the improvement that specific solutions bring normally requires data
collection and analytics before and after the implementation of such solutions.
This work involves traffic incidents, where data is available for use, but there is
a lack of a comprehensive understanding on safety and efficiency metrics. We
believe that methodologies for modeling and evaluating semantically annotated
data can be a driving factor for further understanding real-world scenarios in a
city. By using a data-driven ontology, it is expected that new information can be
represented, manipulated and used. Data-driven ontologies can enable the crea-
tion of metrics for use by a wide variety of stakeholders, from domain experts to
city residents. This work focuses on the creation of semantically annotated data-
driven indicators that are maintainable, changeable, and transferable amongst cit-
ies with similar data.
Keywords: Smart Cities, Semantically Annotated Data-Driven Modeling, On-
tology, Interdisciplinary Research, Linked Data
1 Problem Statement
Smart Cities encompasses the notion of incorporating technology into the everyday lives
of residents within a city. The need for Smart Cities stems from rapid urbanization of
both large metropolitan and smaller urban areas; it is expected that by 2050 over 70%
of the world population will live in a city [1]. Throughout the world there have been
many different types of indicators that have been created, but none of them are widely
used beyond the city or country that initiates them. Many of the countries that have
adapted any sort of metrics to measure city performance use the idea of a triple bottom
line, which has a focus on environmental, economic, and social issues [2]. Since Smart
Cities have more than one specific domain focus of research, there are indicators to
represent transportation, energy consumption, water use, health, economy, land usage,
and several more areas [3].
2
Since the late 1990s and early 2000s there has been a pattern of developing indicators
and metrics for transportation systems. Based on work done by Mihyeon [4], the United
States Department of Transportation (USDOT) has a mission to “serve the United
States by ensuring a fast, safe, efficient, accessible and convenient transportation sys-
tem… and enhance the quality of life of the American people.” Through the enhance-
ment of the quality of life, it is crucial for indicators to be made standard so that it can
be measured beyond its initial context. Current metrics are captured through the collec-
tion of data such as number of crashes per year or number of injuries per year; however,
there are no metrics that are driven by the data itself to determine what it actually means
in practice.
In the area of Smart Mobility, safety is the cornerstone of a majority of its indicators.
There are many factors that relate to indicators with respect to traffic incidents, includ-
ing the severity of the incident. Severity indicators are shown by describing the effects
of the incident that occurred, such as injury severity, fatalities, or damage [5]. Further-
more, the frequency of incidents on the roadway and its location also play a role in
safety indicators [6]. There are additional indicators that can also be considered to be
safety related including blood alcohol concentrations (BAC) of motorists as well as to
consider if they were under the influence of drugs [7]. Safety indicators on roadways
must also consider drivers age, speed, seatbelt use, weather indicators [8][9], vehicle
types [10], and time of day.
2 Relevancy
In the city of El Paso, Texas there have been more than 60,000 traffic incidents since
2014 [11]. The traffic incidents range from minor fender benders to multiple fatalities
in a single incident. Throughout the world, and especially the United States, depart-
ments of transportation are working towards providing safe roadways and travel for
those who use its roads. The development of semantically annotated data-driven met-
rics is beneficial to providing a larger composite understanding of safety and efficiency
in the roadways. Department of transportations throughout the United States as well as
road users will be able to use the metric that is developed. The knowledge that is gained
through the transformation from data will allow for policy makers to understand what
is truly occurring on roadways and a way to use that information to make improvements
with respect to safety and efficiency.
This work will enhance the way that data is cleaned, tracked, and used for improved
interoperability and ad-hoc analysis. These improvements promote transferability
amongst similar and different domains; it also provides a foundation to inform city res-
idents, policy makers and other stakeholders on the advantages of using Linked Data
for sharing and consuming city-generated data.
3
3 Related Work
Data relevant to every city activity we may be interested in can be found. Understanding
and measuring the data that is found for comparison and growth is critical to the im-
provement of technology and society. Meadows [12] writes, “Indicators are natural,
everywhere, part of everyone’s life… Indicators arise from values (we measure what
we care about), and they create values (we care about what they measure)”.
I believe that indicators and measurements are key for fostering services that im-
prove quality of life for city residents. Measuring indicators is a promising way to un-
derstand progress and makes it useful for comparisons [13][14]. Cheu [15] writes that
some of the common indicators found that relate to traffic describe specific events, in-
cluding, but not limited to:
• Average travel time (in minutes)
• Average Speed (MPH)
• Average Delay (in sec/person, sec/Vehicle)
• Travel time reliability (index)
• Number of crashes per year
• Number of injuries or fatalities per year
Of these indicators, none of them describe traffic in a way that would be relevant for
everyday commuters. It does not describe what caused the incidents, nor if there is a
possibility that the geographic location played a factor with it.
3.1 Sustained Indicators Over Time
Work being done by Lazaroiu et al. [16] aims to develop a sustainability indicator
of Smart Cities by addressing the economy, mobility, environment, people, living con-
ditions, and governance. Each of these areas have sub-areas that are associated to it,
from which are given a set of different possible weighted indicators. These types of
indicators may appear appropriate to determine some type of metric for a city, but since
they are given based on the opinion of a set of individuals, it is not necessarily an ac-
curate representation of the city. Similarly, one of the most accepted indicators in the
world is the ISO 37120:2014 sustainable development of communities – indicators for
city services and quality of life [17]. The ISO 37120:2014 is the first of its kind for city
standards; particularly on a global scale. The standards presented by the ISO are signif-
icant advances in the way that Smart Cities are measured, but the issue lies in its over-
whelming generalization including, but not limited to: number of public transportation
trips and number of two-wheeled motorized vehicle per capita, which do not necessarily
describe safety or efficiency [18].
As cities grow, policy makers are making decisions that are data driven [18]. The
United States Department of Transportation has issued a five-year Research, Develop-
ment and Technology Strategic Plan that describes their research and development
4
priorities. Throughout the world at least 1.2 million people are killed each year due to
road incidents [19] and up to 50 million additional people suffer injuries [6]; of all the
deaths, more than half of them are between 15-44 years old [19].
For USDOT, the four main focuses are to promote safety, improve mobility, improve
infrastructure, and preserve the environment [20]. In the United States, the problems
with rapid urbanization are developing quickly. It is predicted that over the next 30
years the population will increase by 70 million and the economy will double [20]. With
rapid growth, it is critical to examine the way that safety and mobility can be improved
and measured.
According to the work by Hoornweg et al. [21], there are 12 major characteristics
that an indicator has: must have a clear objective, be relevant to the objectives, be meas-
urable and replicable, statistically representative of the city, comparable and standard-
ized, potential to predict, effective, economical, interrelated to society, consistent and
sustainable.
3.2 Indicator Significance
The Federal Highway Administration (FHWA) is attempting to address national
highway challenges over the next five years. The goals that the FHWA are looking
toward improving based on [20] are:
• Highway safety
• Improving mobility of people and goods
• Maintaining infrastructure integrity
• Enhancing system performance
• Promoting environmental sustainability
• Preparing for the future
For indicators to be considered a true indicator, it must be measurable [18]. Indica-
tors are derived from facts and reveal new information [7] by nature. The transfor-
mation of qualitative data into useful quantitative is the key way to make a standard
way to understand what is truly going occurring on the roadways. I believe that the key
to transforming individual data sets into useful knowledge is developing a significant
indicator. Indicators without a significance is just another arbitrary number of some
event or set of situations.
4 Research Questions
The proposed work aims to address the following research questions:
Q1. What do semantically annotated data-driven models contribute to the under-
standing of traffic incidents by different groups of stakeholders?
5
Q2. How can methodologies for transformation of data to knowledge through se-
mantic-based models, services, and practices instill trust into transportation
indicators?
Q3. How can very large decoupled data-sets be used to create quantifiable, stand-
ard metrics that are both reusable and comparable to other geographic loca-
tions, with respect to traffic incidents? (i.e. compare one city to another)
Q4. How can cleaned data-sets and developed semantic-based metrics be used to
make predictions about recurrent traffic incidents?
5 Hypotheses
H1. By providing contextualization to a real-world scenario it expected that the
semantic-based, data-driven models create more comprehensive metrics that
will advance the understanding in traffic incidents from the perspective of dif-
ferent groups of stakeholders.
H2. The proposed methodologies will provide a standardization on the process
for domain experts to explore alternative data sets. By introducing a standard
method, domain experts will be ensured that the processing of data is accu-
rate and may be useful for producing metrics necessary for them and society
at large.
H3. Large decoupled data-sets is expected to provide the foundation of a data-
driven semantic annotated focus of Smart City research. Through semanti-
cally annotated data and data that is not immediately recognized to be related
to the domain, it is expected that weighted values can be incorporated to de-
scribe the importance of each data point and understand the effect of inci-
dents occurring throughout the city.
H4. Given developed metrics created from data, new information is expected to
be predicted by providing possible real-world scenarios to attempt to under-
stand ways to prevent incidents and improve Smart Mobility. In addition,
formally described metrics can be transferred between regions and allow
side-to-side comparison.
6 Preliminary Results
I have developed a semantic-based framework for monitoring freight performance in
the borderland area of El Paso. This area poses a unique challenge given its unique
position as a border with Mexico [9]. By using an ontology as a high-level data model,
my previous work showed that heterogeneous data sets have relationships that may be
useful in the development in traffic safety and efficiency metrics. This idea leverages
6
the foundation of Linked Open Data that is critical for data-driven research. Fig. 1
shows the way linked-data is used in this research along with several data points that is
used in developing an indicator. Fig. 1 is similar to other ontologies such as VEACON
[22] which will be used to help expand this knowledge graph.
Fig. 1. Semantically annotated data-driven ontology model of Smart Cities and the Smart Mo-
bility focus area [23].
From the implementation perspective, I have created multipurpose parsers that are
able to handle different csv file inputs from multiple sources. The parsers clean and
process the raw data into a numerical form (which will be used for future machine
learning) then gives a JSON output of the cleaned data.
I have begun developing weights to the data points for the beginning stages of com-
posite safety and efficiency indices for traffic incidents. The raw JSON files have been
uploaded into NoSQL databases for preliminary competency questions. This research
shows promise in taking large heterogeneous data sets, linking them together, then
transforming it into data that can be queried, thus knowledge can be captured from it.
The critical composite indicator is computed in the following way: for every incident
individual, take the summation of all of its attribute weights and divide by the maximum
possible weight that can be obtained (the absolute worst case); then multiple the entire
value by 100. This ensures that the absolute worst case possible will be equal to 100
and that the best case (no incident) is equal to 0. The range of possible values are be-
tween 0 and 100. Values 0-20 are considered to be a minor incident, 20.1 – 40 are
considered to be major, 40.1 – 50 are considered to be severe. Values that are greater
7
than 50.1 are considered to be extreme because it will likely include physical injury or
death. Incidents in general can quickly move from being minor to extreme based on
circumstances of the event. Fig. 2 shows two incidents with various data points from
the accident.
Fig. 2. Data set with computed critical composite index for each, respectively.
This work has led to early contributions in understanding incidents in the city of El
Paso, but can be expanded to other cities throughout Texas and the United States. This
work can be expanded by taking additional data sets and link them together for a refined
indicator. The work contributes not only to the Smart Cities research but as well as
Computer Science.
The methodology being used lays new ground in the way that semantic modeling
can be used for linking heterogenous data as well as providing use for that data. Fur-
thermore, the parsers that have been developed provide the state of the art in cleaning
similar data sets for non-domain experts. The techniques used to create the parsers and
JSON writers are independently created so that they can be run without needing the
other, with exception to the data serving as an input. This methodology provides tech-
niques to take theoretical knowledge graphs and models and transform them using rel-
evant data into useful information that can be interpreted.
7 Approach
The proposed research is being done by a bottom-up data driven approach that fo-
cuses on data that has been collected from incidents and provided by official sources
(e.g., government agencies). The data provides a historical reference as well as a prov-
enance trace that enable users to trust the data. By establishing a way to connect heter-
ogeneous data from the primary data model, I can begin establishing the effectiveness
of this method compared to a top-down approach (where a model is made prior to data
collection).
The proposed approach becomes iterative, where data, relationships, computations,
and analysis can be made on what data is available. This allows for the answering of
the research questions and hypotheses step by step and determine the quality of the
approach at each stage of development for enhanced insight. Evaluation can
8
furthermore be expressed by understanding the way the critical composite index is de-
veloped and modified for improved understanding by domain experts and everyday
users.
The proposed approach follows the idea that data drives the transformation of cities
into Smart Cities. Smart Cities solutions come from observations, but this approach
takes it to a deeper level where data drives the way we understand specific incidents in
a city by providing a model to develop measurements from the Linked Data.
8 Evaluation Plan
Success will be measured two-fold: by developing a comprehensive methodology
that can be analyzed iteratively and by developing metrics that is useful for domain
experts as well as everyday road users. The ontologies and framework will be devel-
oped and evaluated iteratively first from the original data model until the final imple-
mentation that computes the metrics. The original data model comes directly from the
data points that are being used and will be evaluated on the basis of correctness. Domain
experts in the field will evaluate the usefulness by its ability to answer competency
questions, willingness to adopt the metrics, and the trust that is instilled by the metrics.
Metrics will also be evaluated based on their ability to represent specific focuses
such as safety or efficiency of road movement. The metrics developed will be compared
to current metrics that have been already identified. Although a majority of the available
metrics already developed are not representative of safety or efficiency at a composite
level, they will be used to as a way to compare and analyze the validity of the developed
metrics.
9 Reflections
The approach that is being taken by this work is data-driven. This idea stems from the
foundation that data provides accurate facts. As a result of having data as the driving
factor behind an entire methodology to develop a model representative of traffic inci-
dents as well as the development of a metric it provides a foundation to enable access
to knowledge bases by rendering the knowledge in different modalities (i.e. natural
language text and raw data).
Acknowledgments
I would like to acknowledge my Ph.D. faculty advisor Dr. Natalia Villanueva-Rosales
for her insight and support with this research. This work used resources from Cyber-
ShARE Center of Excellence, which is supported by National Science Foundation
grant number HRD-0734825.
9
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