=Paper=
{{Paper
|id=Vol-2518/paper-FOMI4
|storemode=property
|title=An Ontology-Based Standard for Transportation Planning
|pdfUrl=https://ceur-ws.org/Vol-2518/paper-FOMI4.pdf
|volume=Vol-2518
|authors=Megan Katsumi,Mark Fox
|dblpUrl=https://dblp.org/rec/conf/jowo/KatsumiF19
}}
==An Ontology-Based Standard for Transportation Planning==
https://ceur-ws.org/Vol-2518/paper-FOMI4.pdf
An Ontology-Based Standard for
Transportation Planning
Megan KATSUMI a,1 and Mark FOX b
a University of Toronto Transportation Research Institute, University of Toronto,
Canada
b Mechanical & Industrial Engineering, University of Toronto, Canada
Abstract. Transportation planning is concerned with the development of infras-
tructure, definition of policies, and other activities based on a plan to meet future
transportation needs. Transportation planning activities must consider a range of
factors such as land use and land value, capacity of the transportation network, and
public safety. The analyses performed in support of transportation planning activ-
ities must therefore utilize and combine a wide range of information. In order to
effectively perform these analyses the data must be correctly combined. This re-
quires a clear understanding of the terms used to describe the data, as well as how
they relate to one another; the semantic integration of this data is just one of sev-
eral clear applications for ontologies in the transportation planning domain. In this
paper, we make the case for an ontology-based standard to support transportation
planning. Toward this, we present a suite of ontologies which is currently proposed
as a standard for transportation planning data. We give an overview of the ontol-
ogy’s contents, highlighting some key concepts, and describing the intended use of
the standard.
Keywords. ontology, standards, transportation, planning, urban informatics
1. Introduction
The importance of standards is well-recognized in many areas of transportation such as
Intelligent Transportation Systems (ITS), however standards are decidedly less preva-
lent in the area of transportation planning. Despite this, we observe that transportation
planning stands to benefit considerably from the implementation and adoption of stan-
dards. Standards for transportation planning would support the integration of collected
data thereby making data more easily accessible and reusable. This would also ensure
that data used in a particular analysis could easily be reused by other researchers to better
understand and analyse results obtained throughout the community. Similarly, a standard
representation of simulation models and their output would serve both sharing and reuse
of models between research groups, but would support a better understanding and evalu-
ation of the model results. While transportation planning activities make use of existing
integration efforts, what is truly needed is a standard specifically targeted to the domain
of transportation planning.
1 Corresponding Author: Megan Katsumi, University of Toronto, 5 King’s College Rd. Toronto, ON M5S
3G8, Canada; E-mail: katsumi@mie.utoronto.ca. Copyright c 2019 for this paper by its authors. Use permitted
under Creative Commons License Attribution 4.0 International (CC BY 4.0).
In this paper we motivate the need for an ontology-based standard for transporta-
tion planning. We provide an outline of the requirements for such a standard, and subse-
quently present the Transportation Planning Suite of Ontologies (TPSO), a set of ontolo-
gies designed to address these requirements.
2. Motivation
Standards for transportation data are traditionally specified as data models. They rely on
detailed documentation, or tools such as UML in order to convey the intended use of the
standard. Unfortunately even in the most detailed efforts, these approaches are unable to
completely eliminate ambiguity. This ambiguity undermines the efforts of the standards,
as it leaves room for misinterpretation and thus misuse of the standards. It also poses
a challenge for the task of identifying correspondences with other standards. Instead,
we propose the use of ontologies as a mechanism for the specification of standards, in
particular for the specification of a standard for transportation planning data. The use
of an ontology enables an explicit specification of semantics, thereby removing the risk
of ambiguity in the standard. In addition, the representation supports a more precise
mapping with other standards, as the mappings themselves may be defined using the
vocabulary of the ontology in the same formal logic.
3. Related Work
The transportation planning standard described here is distinct from existing standard
efforts both in terms of its formalism and its scope. Many existing standards in the trans-
portation domain are focused on very specific components of transportation data; for ex-
ample GML [1] which focuses only on geographic information. Those standards that do
have a broader scope are focused on standardizing operational transportation data and
activities; for example, Transmodel [2] focuses on capturing public transport data. While
there is certainly overlap between these topics, no standard precisely captures the scope
required for transportation planning. Furthermore, none of the existing standards are for-
malized with a well-defined semantics, so the work described here is novel in that sense
as well. While there is a considerable body of work that looks at applying ontologies
within the transportation domain (a detailed survey is presented in [3]), at the time of this
writing no efforts have addressed transportation planning data.
4. Requirements for a Transportation Planning Standard
The goal of the standard is to capture the concepts underlying data that are relevant
for transportation planning. Not only do these entities include travel activities, they in-
clude various aspects of the urban system. The concepts required for this may be divided
into three categories: land use, agents, and infrastructure. Other aspects of transportation
planning, such as surveys and simulations are out of scope. The purpose of the standard
is to facilitate the consistent representation of data regarding the concepts involved in
transportation planning. Representation of the use and provenance of this data are impor-
tant but would be most effectively addressed in specialized standards, such as a standard
for the representation of simulation data.
4.1. Land Use
The classification of land use is a fundamental aspect of transportation planning. His-
torical studies and simulations investigate the relationship between land use and trans-
portation behaviour in order to support decision making. The capture of land use in-
formation requires the representation of parcels of land and their associated classifica-
tion(s). Different classifications may be used, according to various systems. In addition
to these classification systems, individual buildings and parking areas and the locations
that they occupy must be represented in order to capture land use at a more detailed level
of granularity.
4.2. Agents
Another important aspect of transportation planning is the representation of behaviour
in the urban domain. Data is collected and generated regarding travel behaviour in the
urban system to answer important questions regarding the travel demand and capacity
within a system. This includes changes in the demographics of the population and the
resulting impact on travel behaviour. A standard for transportation planning must support
the representation of agents in order to capture this behaviour of interest. This includes
concepts such as persons and the trips they take, but also more detailed attributes such as
their occupations and the households they are members of.
4.3. Infrastructure
A final key area of transportation planning that must be considered is infrastructure. The
transportation infrastructure provides the context required for a more complete represen-
tation of travel activities. At its core, this amounts to a representation of the transporta-
tion system such that trips from one point to another may be distinguished by the paths
travelled. Consideration of various modes, such as walking, driving, and public transit
must also be considered. A standard for transportation planning must support the rep-
resentation of this system and the various modes it supports. This includes vehicles and
public transit, as well as various attributes of the transportation network: costs associated
with travel, attributes of the various links in the network such as allowed modes, speed
detected from sensors and so on.
5. Ontologies for a Transportation Planning Standard
The goal of the TPSO is to serve as a specification for a standard for transportation plan-
ning data. The use of an ontology to specify the standard will facilitate interoperability
as it provides a vocabulary that data may be encoded in or mapped to, for example via
Ontology-Based Data Access as presented in [4]. The formal semantics of the TPSO
may also be leveraged to support verification of the data via consistency checking. It will
also augment the TPSO as a standard, making the terms and their relation to one-another
explicit. Finally, the TPSO is also intended to support the identification of mappings with
other standards; ontology alignments may be specified in order to precisely identify the
relationship between the TPSO and other ontologies, and even data standards lacking a
formal semantics may be interpreted by the TPSO with the specification of mappings
much like those R2RML models defined for relational data.
The TPSO includes ontologies to define the range of concepts involved in transporta-
tion planning activities. The ontology design was initially driven by the data consumed
and produced by transportation planning modules developed at the UofT Transportation
Research Institute [5], and in use by the City of Toronto and other cities in the Greater
Toronto Area. This initial ontology was then refined by reviewing the transportation plan-
ning literature and feedback and data provided by subject matter experts from the trans-
portation planning domain. The ontology was tested for consistency and evaluated based
on how well it captured and communicated data across the sub-ontologies.
The division of the ontologies was designed based on groupings of concepts that
could be semantically self-sufficient. To date, the TPSO focuses solely on the transporta-
tion of people as opposed to goods, however industrial transportation (i.e. freight) is an
important factor that should be considered in transportation planning activities and will
be included in future extensions of the TPSO.
OWL2 is the proposed representation language for the formalization of the TPSO.
Primarily, this decision is owing to its role as the de facto standard for the Semantic
Web, and the resulting popularity and available tool support. Using OWL2 ensures that
the standard will be supported by Semantic Web technologies, such as Ontology-Based
Data Access tools and triple stores. Nevertheless, we acknowledge that there are other
advantages, increased expressivity in particular, that may motivate the use of other logical
languages such as first-order logic. This will be a point of discussion for future work.
Where possible, the TPSO has reused existing ontologies to promote interoperability
with other data sets. The Time 2 , iContact 3 , GeoSPARQL 4 , LBCS [6], SSN 5 , and Or-
ganization 6 ontologies have been reused via imports, and select terms have been reused
from schema.org, the Units of Measure Ontology [7]. While the reuse of existing ontolo-
gies will facilitate some interoperability, we anticipate the eventual need for ontology
alignments to be identified with other work in overlapping domains.
5.1. Foundations
Beyond the domain-specific subjects that are clearly identified the requirements, there
are fundamental concepts that are necessary to formulate an accurate representation of
the domain. These concepts are defined in a set of foundational ontologies, so-named be-
cause they provide a reusable foundation for the development of other ontologies in the
transportation domain. This will become clear in the sections that follow as we will de-
scribe how the foundational ontologies are reused in various ways in order to capture the
required domain-specific concepts. The clear definition and uncoupling of the founda-
tional concepts makes the fundamental commitments of the TPSO clear and accessible to
potential adopters. It also ensures interoperability and consistency in the representation
of key concepts such as time and location.
2 https://www.w3.org/TR/owl-time/
3 http://ontology.eil.utoronto.ca/icontact.owl
4 http://www.opengis.net/ont/geosparql
5 https://www.w3.org/TR/vocab-ssn/
6 http://ontology.eil.utoronto.ca/tove/organization.owl
The TPSO defines eight ontologies to define the following foundational concepts:
time7 , mereology8 , spatial location9 , units of measure10 , change11 , activities12 , recur-
ring events13 , resources14 , and observations15 . Although this collection of ontologies res-
onates strongly with the concepts often associated with the so-called upper ontologies, it
is important to emphasize that it is not the intent of this work to present these ontologies
as an upper ontology.
Rather than commit to a particular upper ontology, this collection of ontologies was
identified through a domain-first approach wherein only concepts that were identified
as foundational or generic in the context of transportation planning are included in the
foundational ontologies. These were reused from existing ontologies where possible, and
otherwise developed from-scratch. Upper ontologies were found to operate at a level
of abstraction that was too high for direct use in this ontology. It is our position that
this design decision achieves a clearer presentation of the scope and semantics of the
ontology by avoiding the inclusion of unnecessary, high-level concepts.
A key commitment that is made in the foundational ontology is the representation
of change over time. Many of the concepts identified in the urban system ontologies are
subject to change. For example, a Vehicle will have one location at one time, and another
location at a later time; it may have only one passenger at one time, and four passen-
gers at a later time. The Change Ontology serves as to facilitate a consistent approach
to formalizing change over time throughout the TPSO. The Change Ontology adopts an
approach similar to 4-D perspective was proposed by [8], based on the design pattern
presented by [9]. The 4-D view was chosen as it was found to provide a more natural rep-
resentation in OWL, in contrast with the 3-D view which requires the use of the so-called
N-ary relations approach [10]. The differences between the two approaches are discussed
in greater detail in [9]. The ontology introduces the division of concepts that are subject
to change into invariant and variant classes; we refer to these as TimeVaryingConcept
and Manifestation classes, respectively. By distinguishing between these class types and
recognizing the properties that are (and aren’t) subject to change, the ontology supports
the capture of both the static and dynamic aspects of a particular entity. A class that is
subject to change is defined as a type of TimeVaryingConcept (e.g. Vehicle may be a sub-
class of TimeVaryingConcept). The TimeVaryingConcept itself is invariant and defined
by properties that do not change over time. As per [11], we view TimeVaryingConcepts
as perdurants (things that occur over time, i.e. processes). A TimeVaryingConcept has
Manifestations that demonstrate their changing (variant) properties over time. Different
types (subclasses) of TimeVaryingConcept may be defined based on the Manifestations
that are part of them.
7 http://ontology.eil.utoronto.ca/icity/Time/
8 http://ontology.eil.utoronto.ca/icity/Mereology/
9 http://ontology.eil.utoronto.ca/icity/SpatialLoc/
10 http://ontology.eil.utoronto.ca/icity/OM/
11 http://ontology.eil.utoronto.ca/icity/Change/
12 http://ontology.eil.utoronto.ca/icity/Activity/
13 http://ontology.eil.utoronto.ca/icity/RecurringEvent/
14 http://ontology.eil.utoronto.ca/icity/Resource/
15 http://ontology.eil.utoronto.ca/icity/Observations/
5.2. Land Use
The TPSO defines three ontologies to capture the concepts of land use required for trans-
portation planning: the Land Use Classification, Building, and Parking ontologies.
5.2.1. Land Use Classification16
The Land Use Ontology provides the necessary concepts to describe a particular classifi-
cation(s) applied to some parcel of land. Currently, the Land Use Ontology includes clas-
sifications from Land-Based Classification Standards (LBCS), Canada Land Use Moni-
toring Plan (CLUMP), and Agriculture and Agri-Food Canada (AAFC); it may easily be
extended to capture other classification systems as required. A goal of future work will be
to define the classifications in greater detail such that any relationships between classifi-
cations in different systems may be inferred. The Land Use Ontology imports the Spatial
Location ontology; in particular, a Parcel is defined as a type of spatial Feature. Parcels
have some location which may be described as a geometry, they may also be related to
other parcels (or arbitrary spatial Features) by the spatial relations such as containment,
contact, overlaps, and so on.
5.2.2. Building17
The Building ontology defines the concepts to capture information about individual
buildings, thus describing land use from a different perspective and at a finer level of
granularity than typical land use classifications. The Building Ontology also reuses the
Spatial Location ontology in order to capture the location of a building. While we expect
the address of a building to remain constant its exact location may change over time, as
in the case of a remodelling or extension. The possibility is supported by the Building
Ontology, which also reuses the Change Ontology and captures the location as a variant
property. Other attributes of a building are captured for transportation planning purposes,
such as the type of building, units contained in a building, their monetary value, and so
on. The Mereology Ontology is used to capture the disaggregate parts of a building, and
the Units of Measure Ontology is required to capture attributes required for land value
considerations, such as sale prices and square footage.
5.2.3. Parking18
Like the Building Ontology, the Parking Ontology allows for a representation of land
use at a finer level of detail. In addition to characterising land use, the representation of
parking facilities is important for studies considering travel activities as parking often
contributes to the route and modes chosen to journey to a particular destination. Various
aspects of parking are addressed in the ontology; beyond the location of a parking facil-
ity, the ontology captures concepts related to its policies such as a price and/or allowable
periods. The ontology also uses the Mereology Ontology in order to describe a decom-
position of parking facilities into parking areas. This is needed in order to capture cases
where groups of parking spaces may have different policies within the same lot. Some
studies may also require a representation of parking areas at an individual level in order
to capture parking use and availability.
16 http://ontology.eil.utoronto.ca/icity/LandUse
17 http://ontology.eil.utoronto.ca/icity/Building
18 http://ontology.eil.utoronto.ca/icity/Parking
5.3. Agents in Transportation Planning
In the context of transportation planning, the key agents of interest are persons, house-
holds and organizations. The behaviour of each of these agents may be of interest for cer-
tain analyses and studies. Travel behaviour is of major importance, though other activi-
ties such as job changes (joining or leaving organizations) and household changes (join-
ing or leaving a household) are also relevant. The TPSO defines five ontologies to cap-
ture the required concepts for agents in transportation planning: the Person, Household,
Organization, Contact, and Trip ontologies.
5.3.1. Person19
Persons are key agents in transportation planning. It is the combination of decisions of
persons in the population that result in changes to travel behaviour. For example, consider
a person’s decision to change places of employment. Among other things, this change
will likely impact their daily travel behaviour. The Person Ontology enables the repre-
sentation of persons and their attributes of interest. Factors such as a person’s age, in-
come, and place of residence are defined as properties of a person. The Change Ontol-
ogy is used to specify which attributes may change over time (e.g. income), and which
attributes are constant (e.g. date of birth).
5.3.2. Household20
The behaviour of a household may be represented by the collective activities of its mem-
bers. The Household Ontology supports the representation of Households, Families, and
Dwelling Units - all of which are distinct, though closely related concepts. This ontology
is described in greater detail in Section 6.
5.3.3. Organization21
Organizations provide another source of influence on the behaviour in the urban sys-
tem. An organization is defined broadly as some group of individuals, typically having
some shared goal(s). Organizations such as schools and businesses are particularly im-
portant in transportation planning as they determine regular travel patterns for much of
the population. The Organization Ontology introduces the concepts of Students and Em-
ployees who are members of certain types of Organizations. Using the Spatial Location
ontology, the Organization ontology captures the location of a person’s work or school.
The Change Ontology is used to support the representation of variable attributes of an
organization, such as its location and members.
5.3.4. Contact22
Contact information is relevant for a range of concepts in the transportation domain.
For example, a building may have some associated address, similarly a person or an
organization may have some contact address (or phone number, email address, and so
19 http://ontology.eil.utoronto.ca/icity/Person
20 http://ontology.eil.utoronto.ca/icity/Household
21 http://ontology.eil.utoronto.ca/icity/Organization
22 http://ontology.eil.utoronto.ca/icity/Contact
on). Note that a person’s contact address may differ from their place of residence. Rather
than define these attributes separately for persons, organizations, and so on, it makes
sense to capture the general concepts assocated with contact information in a separate
ontology. The iContact ontology23 , is reused to provide the core concepts necessary to
define this type of information. The Contact Ontology extends this representation and
uses concepts from the Spatial Location ontology in order to associate an address with a
location. It also uses the Recurring Event Ontology to introduce a more specific definition
of hours of operation (introduced in iContact) as a specialization of a RecurringEvent.
5.3.5. Trip24
Apart from activities that change the demographics of the population, trips are one of
the most important activities performed by agents from the perspective of transportation
planning. The Trip Ontology leverages the Activity and Person ontologies to define the
concept of a Trip as is a kind of Activity wherein a Person(s) is transported from one
location to another via some mode(s). As with activities, trips may have participants;
they may also be described with specializations of the has participant property to cap-
ture drivers and passengers. The Trip Ontology also introduces the concepts of a Trip
Segment and a Tour. Trip Segments allow for the division of a Trip into smaller parts,
whereas a Tour represents a collection of Trips that begins and ends at the same loca-
tion. Ontologies that describe the Urban Infrastructure (in the following section) are also
utilized to capture the path that a trip takes in the context of the transportation network
(road or rail lines, for example).
5.4. The Urban Infrastructure
Representation of the urban infrastructure and its attributes is an important requirement
in order to provide context for transportation planning activities. The TPSO defines five
ontologies to capture the required concepts: the Transportation System, Public Transit,
Vehicle, Travel Cost, and Trip Cost ontologies.
5.4.1. Transportation System25
In the planning domain, the transportation planning system is a socio-technical system in
the sense that it combines user behaviour with the physical aspects of the transportation.
The term transportation system refers to a subset of the “transportation planning system”,
composed of an abstract transportation network and the physical assets that implement
it.
The Transportation System Ontology models the transportation network separately
from the physical infrastructure. The constraints on the flow of traffic are something that
is applied to the physical infrastructure – the two are seen as distinct concepts. Although
some constraints may be consequences of the physical infrastructure, such as flow con-
straints imposed by the size of the lane that an arc accesses, this is a specific relationship
that is captured as opposed to conflating the two types of concepts. For example, there
is nothing to stop a vehicle from going the wrong way on a road, except for the flow
23 http://ontology.eil.utoronto.ca/icontact.owl
24 http://ontology.eil.utoronto.ca/icity/Trip
25 http://ontology.eil.utoronto.ca/icity/TransportationSystem
of traffic that is imposed on the system (and these constraints may change with time).
This results in the identification of two key concepts: the Transportation Network (di-
rected graphs that represent the possible flow of traffic), and the Transportation Complex
(physical features where transportation occurs).
A Transportation Network is made up of Arcs and Nodes. Both Nodes and Arcs may
have implicit locations based on the infrastructure they access, however unlike the infras-
tructure classes, Nodes and Arcs are not Spatial Things. These parts of the Transporta-
tion Network are tied to the physical infrastructure by relating an Arc to a Transporta-
tion Complex (some road segment or rail, for example) that it “accesses”. The Change
ontology is reused to represent the changes that may occur to nodes, arcs, and physical
aspects of the transportation network. The Transportation System Ontology also extends
the Observations ontology in order to define system-specific concepts regarding the ob-
servations of interest that may be made about the system. For example, loop detectors
are defined as sensors that are used to observe traffic flow metrics.
5.4.2. Public Transit26
The Public Transit Ontology provides a representation of transit agencies and the services
they provide. The Route is a key concept in this domain; the Transportation System
ontology is used to describe Routes in detail based on the paths in the transportation
network. The Transit ontology also provides a representation for route schedules and,
using the Trips ontology, individual transit trips. This allows for the representation of the
actual trips of transit vehicles on particular routes, as well as the trips taken by people
via transit (i.e. on multiple segments of some routes).
5.4.3. Vehicle27
The Vehicle Ontology enables a representation of various types of vehicles that enable
transit. Using the Change Ontology, the Vehicle Ontology distinguishes between static
and variable vehicle attributes. Static attributes include characteristics such as their ca-
pacity, vintage. Variable attributes include characteristics such as location, which is cap-
tured using the Spatial Location Ontology.
5.4.4. Travel Cost28
The Travel Cost Ontology uses the Transportation System Ontology to introduce the con-
cept of costs associated with accessing and travelling in some transportation network(s).
These may take the form of direct costs such as tolls and fares. There may also be non-
monetary costs associated with travel such as pollution and travel time. Costs are asso-
ciated with Network access, but also with individual Arcs. The ontology supports a rep-
resentation of travel costs that may vary depending on mode of access and time of day.
It is important to clarify that Travel Costs define the costs associated with accessing the
transportation system; a travel cost is a property of an arc or its network. These costs are
distinguished from other costs that are dependent on situational factors such as time of
day, or age of traveller. These costs vary between individual trips and are captured by the
Trip Costs Ontology (described subsequently).
26 http://ontology.eil.utoronto.ca/icity/PublicTransit
27 http://ontology.eil.utoronto.ca/icity/Vehicle
28 http://ontology.eil.utoronto.ca/icity/TravelCost
5.4.5. Trip Cost29
In contrast with travel costs, the Trip Cost Ontology defines costs that are specific to a
particular trip. Using the Trip ontology, the Trip Cost ontology introduces the concept
of a cost that is related to a Trip, Trip Segment, or Tour. These Trip Costs may take the
form of direct costs such as those presented in the Travel Cost Ontology, but there may
also be non-monetary costs associated with travel over different arcs such as pollution
and travel time. Trip Costs capture these indirect costs that may vary between individual
trips.
6. Households: A Deep Dive
Rather than attempt present all of the axioms of the TPSO in detail in a single paper
we opt to take a closer look at one of the key ontologies in the TPSO: the Household
Ontology. The aim of this section is to provide a more detailed example of the breadth
and depth of the TPSO.
The Household Ontology introduces three key concepts: households, dwelling units,
and families. A Household is considered to be a group of people who reside at the same
place. A Household may or may not be comprised of Persons from the same Family, and
some Persons may be members of multiple Households (as is the case for children of a
divorce with shared custody). A Household’s membership may vary without affecting its
identity. However, the dwelling that is occupied by a Household may not change; it is
part of the identify of the household. In the context of transportation planning, if a person
moves to a new residence, this is considered to be a new household – even if the members
of the household remain unchanged. In order to capture these changes, we reuse concepts
from the Change ontology (prefixed c:) to define two distinct perdurant and manifestation
types of Household. As described in [9], this also involves the introduction of definitions
to ensure that Household perdurants have only Household objects as manifestations, and
that there must exist some such manifestations; a similar constraint holds for the inverse
relationship. The Person ontology (prefixed p:) is reused to provide the semantics for the
Person class.
HouseholdPD v c:TimeVaryingEntity (1)
HouseholdPD ≡ ∃c:hasMani f estation.Household (2)
u ∀c:hasMani f estation.Household
HouseholdPD v= 1occupies.DwellingUnit (3)
Household v c:Mani f estation (4)
Household ≡ ∃c:mani f estationO f .HouseholdPD (5)
u ∀c:mani f estationO f .HouseholdPD
Household v ∃hasHouseholdMember.p:Person (6)
u ∀hasHouseholdMember.p:Person
29 http://ontology.eil.utoronto.ca/icity/TripCost
Dwelling Units are Building Units that are occupied by Households. The Building On-
tology (prefixed b:) is reused to introduce the notion of a Building Unit. The main dis-
tinction between a Building Unit and a Dwelling Unit is that a Building Unit may exist
without being occupied. The location of a Dwelling Unit is part of its identity, however
other attributes such as its occupants and its value are subject to change. The Spatial Lo-
cation ontology (prefixed sloc:) is used to capture the location, and the Units of Measure
ontology (prefixed om:) is used to capture the monetary value of a unit. As with the other
classes, this is represented using the TimeVaryingEntity and Manifestation classes from
the Change ontology, as follows:
DwellingUnitPD v c:TimeVaryingEntity (7)
DwellingUnitPD ≡ ∃c:hasMani f estation.DwellingUnit (8)
u ∀c:hasMani f estation.DwellingUnit
DwellingUnitPD v b:BuildingPD (9)
DwellingUnitPD v ∀sloc:hasLocation.sloc:Feature (10)
DwellingUnit v c:Mani f estation (11)
DwellingUnit ≡ ∃c:mani f estationO f .DwellingUnitPD (12)
u ∀c:mani f estationO f .DwellingUnitPD
DwellingUnit v b:Building (13)
DwellingUnit v= 1occupiedBy.Household (14)
DwellingUnit v ∀hasValue.om:MonetaryValue (15)
The basic definition of a Family is simply a group of persons who are related to one
another. The rationale is that this concept may be extended with more specific subclasses
of Family (e.g. immediate family or extended family) as required. Similar to the above
concepts, in the Household Ontology a Family is defined using variant and invariant
classes to capture the possible changes in membership. Although it is possible to capture
the intended semantics of a ‘related to’ property that holds between two people, this
property cannot be used to fully define Family as something for which all of its family
members are related to one another. This is one of several examples where it was not
possible to completely capture the detailed semantics of the domain in OWL. It is the
goal of the TPSO to specify the semantics as precisely as possible within OWL; the
limitations that are encountered and their potential consequences will be carefully re-
examined in future stages of this work.
Households play a major role in transportation planning in determining the demand
for transportation over the planning horizon. The planning community puts significant
effort into developing and validating household-based demand models. These demand
models determine how households evolve over time, (for example: births, deaths, chil-
dren moving out) in order to generate the trips that members of a household take on a
given day. For example, the decision to drive depends not only on the time of day and
destination, it depends on the travel of other members of the household. Another house-
hold member may already be using the only available vehicle, or may require a ride
somewhere thereby impacting the resulting trip itself. Despite this, there is no widely
accepted definition of a household. This means that what is identified as a household in
one data set may differ from the semantics of a household used in some other analysis.
The Household Ontology introduces the fundamental terms that are necessary to define
a Household. These terms may then be used to align and distinguish between the varied
interpretations of a Household.
7. Conclusion
This paper motivates the need for ontology-based standards for transportation planning
and provides an overview an artefact designed to address this need: the TPSO. The TPSO
reuses a significant number of existing ontologies to better facilitate semantic interop-
erability. Its design was motivated by transportation planning work encountered in the
iCity-ORF project [5], and it is evidence of the breadth and depth required to represent
the data used in the transportation planning domain.
Future work includes the continued refinement of the TPSO ontologies, as well as
the development of a first-order logic version or selected first-order logic extensions. As
noted in the discussion of the Household Ontology, some detailed semantics are beyond
the expressive abilities of OWL. Rather than concealing this semantics in the ontology’s
documentation, the development of formal, detailed extensions that capture this seman-
tics is desired. This will serve as an unambiguous reference to support the semantics of
the TPSO standard, and may also be used to provide support for advanced reasoning
tasks.
We gratefully acknowledge support provided by the Ontario Ministry of Research
and Innovation through the ORF-RE program.
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