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.
Consider the case of city planning in anticipation of large events (for a example
Republic 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 [
In this paper we present an initial prototype 1 of our approach that we have
developed for the city of Dublin and describe the data sets that we have semantically
encoded.
We start by briefly describing our diagnosis approach [
3
Data Sets diagnoser representing historical observations over a time window and their explanations (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 realtime 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.
Diagnosis Computation
Section 4.1 Section 4.2
Fig. 1. Overview of the Semantics-Augmented Diagnosis Approach. 2 http://dublinked.ie/
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 weather information lic) Road & Weather Conditions NRAc(public) CSV Road Works & Maintenance Dublinkedd(public) CSV Events in Dublin Eventbritee and XML
Eventfulf(public) DBPedia DBPediag(publifc) RDF 3:5 Dublin roads: list of road types, junc- Linkedgeodatah(pub- RDF 0.1 GB tions and GPS coordinates lic) 106 concepts 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 descriptions (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
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