Targeted Social Media Data Analytics for Real-Time Congestion
Detection in Transport Networks
Yikai Gong, Richard O. Sinnott
Department of Computing and Information Systems
University of Melbourne, Melbourne, Australia
Contact Author: rsinnott@unimelb.edu.au
Social media such as Twitter and Facebook are popular, public and real-time in nature.
Twitter in particular and its support for geo-location of Tweets offers a unique possibility to
capture real-time information on the road transport network. Whilst many researchers have
explored the content of Tweets as the basis for a range of phenomenon, few have explored
targeted geo-location of Tweets as the basis for identification of transport issues. We show
how identification of temporal and geospatial clusters of Tweets on major roads can be used
to identify traffic jams and congestion more generally. The specific contribution of this
paper is the development of novel algorithms that harvest Tweets along road transport
networks and their subsequent analysis and visualization. To achieve this, the work has
used the definitive geospatial data from Australia available through the Public Sector
Mapping Agency (PSMA). This data includes national-level data on, amongst other things,
Australia’s road network system. We show patterns of congestion based on the road travel
patterns of commuters in the city of Melbourne. The work is generalizable to other road
transport networks.
Keywords: social media, GIS, transport, data mining.
1. Introduction
Social media is a global phenomenon and now a daily and regular part of people’s lives. Twitter is
a prime example of social media. Twitter is a microblogging site launched in 2006. It has been
widely used since then. It receives around 340 million tweets per day (Twitter, 2012). It allows
users to write messages (Tweets) of up to 140 characters. Often Tweets are used to express an
individual’s thoughts, ideas and feelings. Given this, much of this data is regarded as
inconsequential and often seen as noise or junk on the Internet. However Tweets often include
metadata such as the location (latitude/longitude) of the location of the Tweeter at the time the
Tweet is made. The volume of Tweets being made combined with the geospatial information
offers possibilities to mine such information to show patterns or phenomenon across the
population and society more generally. This can be related to what is trending at any given time or
establishing the positive or negative sentiment on particular topics (Feldman, 2013). Typically
these forms of analysis are based upon the Tweet contents and use of natural language processing
approaches (Kouloumpis, 2011).
One unexplored area of Twitter is its use for real-time transport issues. Whilst the content of
individual Tweets themselves cannot always be guaranteed to be accurate and/or unbiased,
clusters of Tweets in space and time from independent Tweeters increases the believability of the
information. The premise of this paper is that correlations of temporal and geospatial clusters of
Tweets arising from the road network can be used as a real-time indicator of transport isues and
congestion. To explore this, novel algorithms for harvesting Twitter data were delivered that
focused on harvesting Tweets only along the roads and streetnetworks of Australia. The road data
itself is obtained from the Public Sector Mapping Agency (PSMA – www.psma.com.au) – the
definitive geospatial data provider for Australia. We describe the approach for harvesting Tweets
from road networks and illustrate this in a case study for Melbourne that shows congestion arising
on major thoroughfares through the day.
2. Background and Related Work
With the uptake of social media and ability to capture location-based information, it is not
surprising that studies have witnessed a ‘spatial turn’ during the past few years (Morley, 2006),
(Dörning, 2009). Social media, led by MySpace, Facebook, Twitter, LinkedIn, Flickr amongst
many other offerings, are based around Web 2.0 technologies. Increasingly such systems provide
location-based information that has moved social media from cyberspace to real place (Sui, 2011).
The volume, velocity, variety and veracity of social media data also mean that it possesses many
of the typical characteristics of big data (Manyika, 2011) and data science (Hey, 2010). The tools
to analyse and understand such big data are increasingly demanded and developed – especially
tools that allow real-time patterns to be extracted from noisy and sometimes-contradictory data.
A range of works on social media analysis has been undertaken. Sentiment analysis is one of
the most extensively researched areas with over 7000 related articles (Feldman, 2013). The most
popular approaches for sentiment analysis are: subjective lexicon where a list of words labeled as
positive, negative or neutral are given, the N-gram model where a group of N words are given as a
training data set, and machine learning used where classification is performed using a set of
features extracted from the text (Kaur & Gupta, 2013). (Kouloumpis, 2011) investigated
micro-blogging and its use for classification of Twitter data. They used supervised machine
learning systems and included three different corpora for training data sets: emoticons, hashtags
and manually labeled data. Since micro-blogging data is often terse and abbreviated, the features
and techniques used in other natural language processing approaches are often not applicable.
Twitter has been used to explore a range of scenarios and application domains. Hotel
recommendation systems were explored by (Gräbner, Zanker, Fliedl, & Fuchs, 2012) and (Kasper
& Vela, 2011); movie rankings were explored by (Oghina, 2012); disaster and emergency
response systems using Twitter were explored by (Muralidharan, 2011), (Bruns, 2012); political
sentiment analysis and use of Twitter for prediction of elections was explored by (Conover, 2011);
(Zhao, 2011) explored the use of Twitter for sport and use of spectators for tracking and capturing
moments from sporting events; (Signorini, 2011) explored the use of social media data to better
understand and track information related to pandemics and emerging infectious diseases; (Gerber,
2014) explored the use of Twitter data for prediction of crime in US cities, whilst (Yanai, 2009)
explored eating habits of individuals based on photographic evidence of food/meals posted on
Twitter. The majority of these systems are based upon extracting information from the contents of
Tweets (the Tweet text) and applying language processing techniques for sentiment classification.
Other users have explored real-time global trending information (Mathioudakis, 2010) and
identification of relationships between individuals and organisations based around the follower
relationships (Cha, 2010), (Chandra, 2011)
With regards to transport and traffic related research based on social media, various studies
have followed similar approaches. (Kosala, 2012) uses nature language processing to identify
targeted keywords in Tweets and correlates them with transport events, e.g. accidents that may
have taken place. They also analyze the confidence level of traffic information. An algorithm was
proposed to estimate the event confidence level based on the timeliness of the information and
how many people Tweet about the same event in a similar time period.
Similar to the above study, (Wanichayapong, 2011) extracts traffic information from Tweets
using syntactic analysis and then classifies the results into two categories: Point and Line. Point
data refers to a single accident/event on the road, e.g. a car crash, whilst Line data refers to a
traffic status along certain streets including a start point and end point on a map over a given time
period. This can be used to identify congestion along roads for example. The most interesting part
of their work in relation to the work described here is the Line data classification. They use a
classification algorithm to build connections/relationships among traffic events identified from
Tweets and attempt to provide an explanation of what kind of real traffic events can be
represented by those relationships. Their work depends upon large collections of often
non-relevant Tweets however. Ideally far more targeted harvesting of important and related
Tweets would be used as natural language processing not perfect and the resultant analyses will
almost always include erroneous Tweets, e.g. Tweets containing the terms “car accident” could
relate to historic incidents, incidents in other locations, or indeed statements from films/books.
(Ishino, 2012) built a system providing transportation information during a disaster in Japan
(an earthquake). They mainly focus on Tweets created by people during disasters and show how
they can be used to help victims find evacuation routes through machine learning approaches.
Their work on prediction services for events/accidents is relevant here.
Outside of Twitter and social media data sets, many transport researchers use Sydney
Coordinated Adaptive Traffic System (SCATS) data (Lowrie, 1990) for road traffic data (Clement
1997), (Mazloumi, 2009). SCATS, is an intelligent transportation system developed in Sydney,
Australia. SCATS supports the dynamic timing of signal phases at traffic signals. The system uses
sensors at each traffic signal to detect vehicle presence in each lane and pedestrians waiting to
cross at a given site. The vehicle sensors are typically installed within the road pavement.
Information collected from vehicle sensors allows SCATS to calculate and adapt the timing of
traffic signals in the network. In Australia, the majority of signalized intersections are SCATS
operated (around 11,000). However a challenge with use of SCATS data is that not all roads have
the required technology and it is primarily only available at intersections. SCATS data can also be
extremely large, e.g. data from a single intersection can include results on millions of cars that
have passed over it. Such data is often aggregated to provide statistics on the number of
cars/lorries over given intersections, but such solutions do not lend themselves to real-time
transport analysis and congestion identification.
Other solutions for transport/traffic modeling are often based around use of transport
simulators. (Hall, 1980), (Yin, 2013), (Monteil, 2014), (Schreckenberg, 2014) are typical
examples. One key issue is that the accuracy of information of such systems is not guaranteed to
reflect the true transport issues of cities. Thus for example, the day-day commuting patterns can
fluctuate greatly for a given city based on a variety of factors: real-time transport incidents,
sporting events, flooding, bush fires, with information on the accurate population using the roads
at that time amongst many more real-world situations that can and do arise across the road
transport networks.
What are required are solutions that deal with large-scale information reported from the road
networks that are real-time and accurately reflect the current patterns of flow across the network.
Furthermore the road networks themselves continue to evolve with new roads, traffic signals and
speed limits for roads occurring on a frequent basis. This is especially so in Australia and cities
such as Melbourne with the increasing population. By 2020 it is expected that 50% of the
population of Australia will live in either Melbourne or Sydney. Combining Twitter data and
current road network data offers many desirable features. However hitherto the accuracy and
reliability of Twitter-based approaches such as natural language processing and/or targeted
content retrieval has not enabled this.
3. Methodology and Data
3.1 PSMA data
The most accurate road network data for Australia is from the Public Sector Mapping Authority
(PSMA). PSMA offer a range of products: housing and land-use information; features of interest,
e.g. railways, hospitals; a national Gazeteer (address geocoding system) amongst many others. Of
direct relevance here is the road network topology for Australia. This data is license protected but
available for research use from the Australian Urban Research Infrastructure Network (AURIN www.aurin.org.au) a federally funded project exploring the current and future challenges facing
the cities of Australia. Figure 1 shows a typical record from the PSMA street network in AURIN.
It can be seen as a segment/polyline of a street include start point coordinates and end point
coordinates. This data includes specification of the road/street characteristics including its name,
the number of lanes, width, speed, height and weight limits, as well as the geographic topology of
the street itself.
Figure 1. PSMA street data example from AURIN
The PSMA data and the street network is national in coverage and regularly updated.
3.2 Harvesting Tweets along Street Networks
Twitter supports two programmatic Representational State Transfer (REST) based interfaces for
accessing Twitter data: a Streaming API and a Search API. The former is used for Tweets that are
pushed to the end user clients whilst latter is used for requesting specific Tweets. Twitter supports
the specification of geospatial coordinate systems when harvesting Twitter– typically given as
bounding boxes or circles with a given centre/radius for Twitter harvesting, e.g. only Tweets for
Melbourne. This can be Tweets that include particular text, from a particular user (@) or
containing a particular hashtag (#). Twitter harvesting is commonly done at an aggregated level
and will include Tweets from within a given bounding box.
The novelty of this work is harvesting tweets made (only) along streets by utilizing the PSMA
road network data. Figure 2 shows a sketch about how a targeted Twitter harvester works. A
PSMA pre-processor was developed for retrieving relevant data from the PSMA street network
dataset (in Javascript Object Notation (JSON) format) that is then passed to target Twitter Street
Harvesters. The Street Harvesters calculate a range of centroids used as the basis for Tweet
harvesting locations based on the diameter of the road and the centre of the road as indicated in
the black area in Figure 2. Each of these centroids are used for querying the Twitter Search API
for Tweets in that region (centroid).
Figure 2. Harvesting tweets along streets (black centroids represent Twitter request areas)
To scale the system to cover the complete street network, each street segment in PSMA contains
coordinates including a start point and an end point. To tackle this, each segment is split into
several sub-cells with equal distances based upon the diameter of the road (as obtained from
PSMA). The formula used to calculate the distance between latitude/longitude points is given in
Figure 3.
Figure 3. Formula for calculating the distance between geographic coordinates
Figure 4 shows the algorithm used to query tweets along the street network. The actual behavior
when harvesting Tweets for many thousands of centroids along entire street networks using this
approach is like driving a car at a specified speed over the streets in a given area/district. The
primary constraint of this approach is that each query can only contain a single centroid. Although
the efficiency of a single harvester is inefficient (since it only requests Tweets within a circle of
diameter ~8m), through use of Cloud infrastructure it is quite possible to scale this solution to use
multiple processes/nodes to harvest tweets in a coordinated and scalable manner. Further
refinements to this algorithm can also be supported, e.g. increasing the amount of harvesting for
roads that have increased volumes of traffic and where increased accuracy in the real-time data
harvesting is required.
READ coordinates[], interval
FOR i=0 to maximum index of coordinates
start_point = coordinates[i]
end_point = coordinates[i+1]
CALCULATE distance from start_point to end_point
num_of_gaps = integer(distance/interval + 1)
latitude_gap = (start_point.latitude - end_point.latitude)/num_of_gaps
longitude_gap = (start_point.longitude - end_point.longitude)/number_of_gaps
FOR j=0 to num_of_gaps
sample_latitude = start_point.latitude + latitude_gap * j
sample_longitude = start_point.longitude + longitude_gap * j
WRITE sample_latitude, sample_longitude
END FOR
END FOR
Figure 4. Algorithm used to Harvest Tweets along Streets
Each collected Tweet has a label added by the Twitter Harvester including metadata regarding the
Tweet including the query details, the street name, etc. Figure 5 shows an example label of one of
the collected Tweets. In this example, the label shows that this Tweet was harvested on Monash
Freeway (one of the motorways around Melbourne) and provides the associated bounding box
details. Such metadata provides rich information that can be used for visualization.
Figure 5. Harvester label added to a given Tweet
3.3 Cloud-based Deployment
This system was designed and deployed on the National eResearch Collaboration Tools and
Resurces (NeCTAR) Research Cloud (https://www.nectar.org.au/). NeCTAR provides access to
over 30,000 servers for academic researchers across Australia. Figure 6 shows the architecture of
the software systems underpinning this harvesting and analysis systems. A series of Twitter
harvesters act as feeds into the system and a group of web services are used for the associated
data analytics and subsequent visualisation. A browser-based client is used for front-end control
and visualization of results.
The Tweets themselves are stored within a noSQL database: CouchDB. This allows use of
algorithms such as MapReduce, which facilitate big data processing of the results using Cloud
resources. It is important to note that the system supports a Software-as-a-Service (SaaS) model
of Cloud utilization. Thus many harvesters can be dynamically deployed across the Cloud. This
allows for scalability and flexibility. Many more harvesters can be deployed in real-time at peak
periods for example, e.g. rush hour or when major events are taking place. Each harvester can be
parameterized with the geo-location of the road networks that are to be harvested and the time
period over which the harvesting is to occur.
Figure 6. Software structure of the system
The Cloud infrastructure here utilized 3 virtual machines running Ubuntu v12.04 (amd64). Each
of these VMs had 8GB RAM, 2 virtual CPUs and 10Gb local disk. A further 250Gb of volume
storage was attached to these VMs and used to host the CouchDB instance.
4. Visualization and Analysis
Web clients are responsible for data/analysis and visualization in this project. The user interface
(UI) uses Bootstrap-based JavaScript libraries. The Google Map API is used for visualization of
geo-located tweets on the map (shown with different markers). A variety of statistical review
pages are available for charting tweets on a particular date or on a particular day of week.
Relevant data is pulled from CouchDB via Ajax. A user can use this service to identify rush hour,
and/or incidents that may take place around the city of Melbourne. For the real-time service page,
a web socket channel is established and used for receiving Tweets in (near) real-time from Twitter
(using the Streaming API). As well as harvesting tweets from the road network, the system also
allows capturing of Tweets that refer to transport issues more generally, e.g. Tweets containing the
terms “delay, queue, accident” etc. A JavaScript message handler tags traffic relevant tweets and
traffic irrelevant Tweets in different colors. Users are also allowed to highlight relevant Tweets
via a custom keywords filter.
4.1 Twitter Transport View based on Date
Figure 7 illustrates one of the statistical pages. It allows the user to pick a particular date and
identify what happened on that day through a group of charts and data overlaid on a map. Tweets
made on streets are shown on the map. Green markers represent traffic relevant tweets and red
markers represent traffic irrelevant markers, i.e. tweets made on the road network but not related
to transport. Markers can be updated according to a specified time period in the right panel, e.g.
when a user wants to identify where people tweeted during rush hour.
Figure 7. Viewing Tweets on the Streets of Central Melbourne
Figure 8 shows the changing pattern of Tweets made on streets together with traffic relevant
tweets in Melbourne and the associated time line. This information can be used to help find a time
period in which potential traffic events/jams might occur across the road network as a whole or
across particular sections. When this happens, the user can update the markers on the map to see
the detailed events in more detail. As can be seen, there is a general increase (spike) in the number
of Tweets on the roads of Melbourne from 6am (when rush hour commences). This is typical of
all weekdays. Similarly there are spikes in the number of Tweets from 4pm onwards. Again this is
typical for a given weekday in Melbourne.
Figure 8. Patterns of Tweets on Melbourne Road Networks through the Day
A key aspect of the work was to identify Tweets on major roads (motorways) leading into/from
Melbourne. Thus it is quite possible that Tweeters (drivers/passengers) can Tweet whilst
stationary at traffic lights, i.e. without being in a traffic jam. This is/should not be the case for
motorways however. Figure 9 shows Tweets made on the major motorways around Melbourne on
a particular day.
Figure 9. Charting Tweets on major Thoroughfares in to/out from Melbourne
4.2 Twitter Transport View based on Days of the Week
Benchmarking the typical commuting patterns is important for planning purposes. Figure 10
provides a statistical view of Tweets on given days of the week. In this view, users can determine
the time during the week when people were tweeting about traffic and/or posting Tweets whilst on
the Melbourne streets. From this it is possible to identify times/days of the week that traffic jams
happen in Melbourne. Such information can be used for example to identify whether new
transport policies are having any effect, e.g. introduction of new traffic light signaling or toll
payments on the motorways of Melbourne.
Figure 10. Statistical view on day of week
4.3 Real-Time Monitoring
Most importantly, Twitter can provide near real-time information. Figure 11 shows the results of
the real-time monitoring service. If a user posts a Tweet with a GPS location it can be marked on
the map in real-time (as shown in Figure 11). A custom keywords filter is used for highlighting
Tweets based on a users interest. Traffic relevant Tweets are marked in green, traffic irrelevant
Tweets are marked in red, relevant Tweets are marked in purple. For those tweets without location
information, they will be appended to an information board in this page (not displayed).
Figure 11. Real time service page
Using such information to guide and shape the transport and commuting patterns of the
population of major cities such as Melbourne offers new opportunities to transport modeling, and
most importantly to shape the behavior of commuters. Knowing that traffic jams are occurring on
a given day of the week might result in increased use of public transport for example.
5. Conclusions and Future Work
This work has developed and explored a novel platform for Twitter data and its use for
transport/traffic patterns. The work provides targeted Twitter harvesting and visualization, which
is a completely novel approach. The focus here has been related to traffic in/around the city of
Melbourne, however the system and approach is generic. It is currently being extended to other
cities of Australia. In undertaking this work, 7.2million Tweets from around Melbourne were
harvested and processed on the NeCTAR Research Cloud.
The platform itself provides a range of analysis functions based on keyword searches and
matches based on (literally!) the road network itself. Future work includes improving the analysis
system including algorithms that automatic the process for identification of real-time congestion
issues and development of algorithms for the ‘best’ possible route according to the traffic
situation at that time. Here the best might include the fastest (according to the speed limit on the
roads as obtained from PSMA data) or the shortest – in both cases influenced by real-time
information on the current road status as determine by clusters of Tweets. The proposal for
alternative transport routes could also be delivered to end users (Tweeters) in real-time. They may
also choose to retweet this information to their friends/followers who may face similar
commuting patterns. This changing the behavior of populations is quite possible with social
media since so many individuals are actively using the technologies.
The best route might also be the safest route based on absence of reported incidence over
given time periods. Through work on AURIN, accident blackspots across Victoria is available
from VicRoads from 2007-2012 as shown in Figure 12. Similarly data on transport volumes are
available from VicRoads. Thus Twitter data and it’s analysis should not be seen as independent
and isolated form of data set, but rather it provides a complementary and rich set of resources to
augment existing data on transport phenomenon.
Figure 12. Traffic Accident BlackSpots (Black Centroids) in Melbourne (Data from AURIN, VicRoads)
We recognize that Tweets made from the centre of a road/street may well be made from
passengers in cars (or taxis) or in the case of Melbourne, from people in trams that travel down
the centre of roads. Whilst it is possible to filter Tweets made from people in trams, e.g. such
Tweets often refer to trams directly. The majority of cars in Australia are predominantly single
occupancy. Whilst for a single Tweet this may not necessarily be a valid assumption, by the
geospatial and temporal clustering of multiple Tweets from different individuals, the likelihood
that there are drivers Tweeting whilst in a given traffic jam increases greatly. This has been
specifically considered in the work since individual Tweets are often untrustworthy and hence it is
the aggregation and clustering of Tweets on road networks that is important.
Twitter is a social media and a further refinement to this work is to identify patterns of
commuting between individuals that can result in changing social patterns also. Car sharing is a
phenomenon that can help to alleviate many of the challenges faced on the road networks of cities
caused by the growing population. Identifying relationships between Tweeters and their
following/followed relationships could potentially be used to identify such car sharing
opportunities. However this idea and the work that has been illustrated is at the boundaries of
privacy and indeed legality. It is currently illegal to use a mobile phone in Australia whilst driving
(unless hands-free devices are used). The question of whether it should be allowed to Tweet
whilst stationary in a traffic jam pose new questions that will ultimately require consideration and
legislation to decide upon. The mapping of individual Tweets also poses questions that are at the
heart of privacy. The systems shown here explicitly use the geospatial information of a Tweet and
display this on maps, however this visualization is for demonstration purposes only. Knowing that
there are traffic jams on given stretches of road based on Twitter data would be sufficient without
knowing the precise Tweet location and/or the Tweeter who made this known.
Natural language processing and analysis can also be used to identify the content and
sentiment of Tweets. One assumes that individuals in traffic jams would have an increased
proportion of negative sentiment, however quantifying this and analyzing how such sentiment
changes through the day would be a further extension to the work.
The application itself can be extended in further ways. Firstly augmenting the harvesters for all
cities and motorways of Australia. Targeting specific routes based on increasing population and
commuting corridors is a further extension. Thus considerable population growth is occurring in
North West Melbourne and many ongoing discussions are occurring regarding the challenges
caused by the current and future planned road networks to cope, and factoring in other transport
solutions. The use of Twitter and targeted harvesting algorithms provides a direct and
immediately applicable solution to obtain extensive information that can guide such discussions.
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