=Paper=
{{Paper
|id=None
|storemode=property
|title=A Prototype for Semantic based Diagnosis of Road Traffic Congestions
|pdfUrl=https://ceur-ws.org/Vol-914/paper_47.pdf
|volume=Vol-914
|dblpUrl=https://dblp.org/rec/conf/semweb/SbodioLS12
}}
==A Prototype for Semantic based Diagnosis of Road Traffic Congestions==
https://ceur-ws.org/Vol-914/paper_47.pdf
A Prototype for Semantic based Diagnosis of Road
Traffic Congestions
Marco Luca Sbodio, Freddy Lecue, Anika Schumann
IBM Research - Ireland
Damastown Industrial Estate, Dublin, Ireland
{(firstname.lastname)@ie.ibm.com}
Abstract. Retrieving the causes of road traffic congestions in quasi real-time is
an important task that will enable city managers to get better insight into traffic
issues and thus take appropriate corrective actions in a timely way. Our work,
accepted at ISWC 2012, tackles this problem by integrating and reasoning over
a variety of heterogeneous data sources including data streams. In this paper we
present an initial prototype of our work for the city of Dublin, Ireland.
1 Introduction
Consider the case of city planning in anticipation of large events (for a example Re-
public of Ireland World Cup qualifier match in Croke Park, Dublin), or in reaction to
unplanned events (for example a mob assembling in the Dublin Docklands area). By
integrating and correlating partial observations from multiple data sources, we could
infer that bad weather, coupled with a large number of people assembling in one area
of the city on a normal working day, coupled with a lack of public parking, led to traffic
chaos that was widely reported in the media, driving strong negative sentiment towards
the handling of such events. Whilst such an analysis is a useful tool for understanding
“what went wrong” and “what were the causes” after the event, our work is the first
one that can compute causes of such unexpected situations in quasi real-time; other
works focus on detecting, visualizing and analyzing traffic congestions [1]. We achieve
this by exploiting semantic representations of historical data (such as traffic congestions
data) and feeding them into an AI diagnosis approach. The work is described in a paper
accepted at the In-Use track of ISWC 2012 [2].
In this paper we present an initial prototype 1 of our approach that we have de-
veloped for the city of Dublin and describe the data sets that we have semantically
encoded.
2 Diagnosing Road Traffic Congestions
We start by briefly describing our diagnosis approach [2] shown in Figure 1. First, its
heterogeneous input data (see next section for their details) are integrated using seman-
tic web technologies. This then allows AI diagnosis techniques to compute off-line a
1
see video at http://www.youtube.com/watch?v=xT5dPpnayZI
diagnoser representing historical observations over a time window and their explana-
tions (for example Canal street was congested in 2012, May 1st at 6:00pm because of
a concert event in Aviva stadium and road works in Bath avenue). Finally, quasi real-
time diagnosis consists in combining semantic matching and AI diagnosis techniques
for (i) retrieving ”similar” causes (e.g., roads with heavy traffic of same duration) with
”similar” conditions (e.g., nearby sport events) which have appeared in the past and (ii)
interpreting them in the real-time context.
Historic Road Traffic Congestion
Semantic Pure AI
Historic Diagnosis
Road Network Diagnoser Matching Diagnosis
Diagnosis Report
Computation
Real−Time Diagnosis
Source of Causes Real−Time Road Traffic Congestion
Section
Fig. 4.1
1. Overview Section
of the Semantics-Augmented Diagnosis 4.2
Approach.
3 Data Sets
Table 1 lists the heterogeneous data sets considered in our scenario. Most of these data
sources are public, and many are provided by Dublin City Council through dublinked.ie2
web site, and hosted at IBM.
The Dublin Bus Data, encoded according to the SIRI standard (note a in Table 1),
is transformed into a real-time stream (information about 1000 buses updated every 20
seconds) that is persisted into CSV files (one file per day). Each SIRI record (line in a
CSV file) contains information about the current position (latitude and longitude) of a
vehicle, its line number, its direction along the bus line, if the bus is in congestion, and
if the bus is at a stop point. Information about bus lines and stops is given separately
through other CSV files (such information is static, or at least it changes very rarely). We
have developed a simple EL++ ontology to represent SIRI data. The actual SIRI records
(lines from the CSV files) are modeled as instances of the class VehicleAtomicUpdate,
which has a property for each field of a SIRI record. Based on a history of 217 SIRI
data files (approximately 26 GB), referring to 217 days in 2011 (approximately 122
MB a day), 44.7% of the SIRI records generates 8 triples/record and 47.2% generate
11 triples/record (1 triple to define the type of the RDF resource, and 10 triples to
specify the SIRI properties); the other records generate either 9 or 10 triples/record.
The varying number of triples per record is due to some missing fields. The instances
of VehicleAtomicUpdate with missing properties are nevertheless useful to estimate the
number of buses in a bounding box in a certain time window (latitude, longitude, and
timestamp are always available in SIRI records).
City Events were captured through Eventful and EventBrite web sites (notes e, f in
Table 1). An average of 187 events a day (i.e., same days as those captured for SIRI data)
have been described using some LOD vocabularies e.g., DBpedia, Talis. In addition we
2
http://dublinked.ie/
Table 1. Data Sets
Data Source Provider Format Size
Dublin Buses Data Stream: vehicle data Dublin City Council SIRIa(XML) 4-5 GB/day
(GPS location, line number, delay, ...) (private)
Wunderground for Dublin: real-time Wundergroundb (pub- CSV 0.05-1.5 GB/day
weather information lic)
Road & Weather Conditions NRAc(public) CSV 0.1 GB/day
Road Works & Maintenance Dublinkedd(public) CSV 0.01 GB/day
Events in Dublin Eventbritee and XML 0.001-0.05 GB/fay
Eventfulf(public)
DBPedia DBPediag(publifc) RDF 3.5 × 106 concepts
h
Dublin roads: list of road types, junc- Linkedgeodata (pub- RDF 0.1 GB
tions and GPS coordinates lic)
a
SIRI (Service Interface for Real Time Information) is a standard for exchanging real-time
information about public transport services and vehicles - http://siri.org.uk
b
http://www.wunderground.com/weather/api
c
NRA - National Roads Authority http://www.nratraffic.ie/weather
d
http://www.dublinked.ie/datastore/datastore.php
e
https://www.eventbrite.com/api
f
http://api.eventful.com
g
http://dbpedia.org
h
http://linkedgeodata.org
enriched the events description with EL++ GCIs to capture their categories, which are
used for computing not only fined grained matching between historical and new events,
but also for computing the diagnosis report. Each event has been described on average
through 26 RDF triples.
Similarly an average of 51 Road Works and Maintenance 3 records a day have
also been enriched through 16 RDF triples each. An EL++ enrichment of this raw
data ensures that historical and new records can be matched for diagnosis and reporting
purposes. We also injected 14, 316 EL++ GCIs (6 RDF triples each) to describe 4772
Roads and their Interconnections 4 .
The Core Static Ontology, which is used for representing SIRI, events, road works,
road weather and Dublin weather data, is composed of 67 concepts with 24 role descrip-
tions (25 concepts subsume the 42 remaining ones with a maximal depth of 4). Finally,
a History of 217 days of the Traffic Congestion Information was computed based on
buses data streams (encoded by more than 1 × 109 RDF triples). Information about past
events, road works, weather information and road conditions was stored as 1.1 × 106
RDF triples.
3
CSV sample in http://www.dublinked.ie/datastore/metadata064.php
4
CSV sample in http://www.dublinked.ie/datastore/metadata125.php
4 Prototype
Our initial prototype analyses data of Dublin buses, and displays congested roads on an
interactive map (see figure 2(a)). Based on our quasi real-time diagnosis component, the
system displays also explanations of selected road traffic congestions (see figure 2(b)).
The explanation contains both data about its accuracy, and information extracted from
the data source of the identified cause (road maintenance in the case of figure 2(b)).
(a) Detection of traffic congestion
(b) Diagnosis of traffic congestion
Fig. 2. Screenshots of Automated Traffic Diagnosis Prototype
References
1. Biem, A., Bouillet, E., Feng, H., Ranganathan, A., Riabov, A., Verscheure, O., Koutsopoulos,
H.N., Moran, C.: Ibm infosphere streams for scalable, real-time, intelligent transportation
services. In: SIGMOD. (2010) 1093–1104
2. Lécué, F., Schumann, A., Sbodio, M.: Applying semanticweb technologies for diagnosing
road traffic congestions. In: to appear in ISWC. (2012)