This document describes a project to aggregate data from various sources about events and traffic conditions, and visualize that data to help explain abnormal traffic patterns. Data is collected from APIs providing information about scheduled events, weather, traffic incidents, and real-time traffic flow. The data is stored in a database and can be visualized on a map interface, allowing users to search for events within a given location and time range. The goal is to help analyze reasons for congestion and support future traffic prediction and analysis.

1. Submitted By: Chandrasekar Hariharan & Vaikunth Sridharan
MENTOR: PRAMOD ANANTHARAMAN
CITY TRAFFIC EVENT
AGGREGATION AND
VISUALIZING SERVICE
WRIGHT STATE UNIVERSITY
CS 7800 WEB INFORMATION SYSTEMS

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`City Traffic Event
Aggregation and
Visualizing Service
Goal:
Visualize Events responsible for abnormal
traffic pattern on a given traffic scenario.
1. Abstract:
In this project, we will analyze various traffic data
sources and event data sources to better explain
the reason for any particular congestion. Data
sources are split up in to two kinds, traffic data and
event data. Congestion factor, traffic speed and
link volume are three significant attributes which
better describes traffic variations. Weather data,
Active & Scheduled events data, etc. are some
data sources which gives the events that occur at
traffic variations in terms of those attributes.
Collecting this information from various sources,
aggregating the same, updating it on database,
further creating a visualization approach for city
events for better describing abnormal traffic
behavior and using the same for further research
analysis are the key elements of this project.
2. Introduction:
Recently traffic has enormously increased in city
areas, making it tedious and drawn-out for the
public to commute [1]. Average commute rate on
bay area is 30 percent longer than it was a year
and half ago [2]. They drivers in bay area spend
more time stuck in traffic than commuting. The
classical theory of traffic flow describes two traffic
phases: the uncongested phase or free flow phase,
and the congested phase [3]. Joint measurements
of speed and flow gathered in a learning phase,
are useful in validating the traffic flow data results
[3]. There is an inverse proportion between speeds
of a traffic flow and congestion [3].
With the help of existing traffic flow data,
predicting traffic for the next 15 to 30 minutes is
80 percent accurate [4]. Constructing a knowledge
base using incident reports, weather data, event
scenarios nearby, can serve as useful source of
information for learning, constructing traffic
models and predicting traffic flow in addition to
flow data [5].
Based on the information from the above data
sources, in this project, we develop aggregation
Figure 1: Individual speed variance in traffic flow [3]
Figure 2: Required data sources for analysis [4]

3.  City Traffic Event Aggregation and Visualizing Service
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and visualization mechanisms from the existing
data sources. The data sources selected,
aggregation and visualization architecture are
explained below.
3. Objectives
The goal of this project is to aggregate data from
diversified data sources based on location and
timestamp to visualize spatial and thematic data.
List of objectives to accomplish the suggested
goal is listed below:
3.1 Finding Possible Data sources
Social tweets, popular events nearby, weather in a
location, traffic incidents in a network are key data
sources which affect the traffic flow in a given
traffic link [5]. Reliability, credibility, data accuracy
are some key factors in selecting the right data.
Based on all these factors, we have chosen five
API’s which offers reliable services and accurate
information regarding the data requested. The
sources are Eventful, Eventbrite, Openweathermap
and Here maps flow and incident rest API’s.
3.2 Aggregating Data sources
These data sources collected from the above
mentioned API’s are aggregated together by time
and location. At any given instance and any given
location, aggregated table gives the values of all
the events possibly occurred which has an impact
on the traffic. In this project, we aim to aggregate
the data sources and give the results for further
investigation. Learning, forecasting the traffic and
finding the cause of an incident are out of scope.
By aggregating the data, user can search for all the
events bound to a timeline, thereby our data
aggregated, can serve as the data source for many
traffic related predictions using statistical
inferences or machine learning techniques.
3.3 Storing Thematic Data
The data obtained from the server is stored in the
database for every particular time interval. Incident
data, flow data and weather data are subjected to
change every now and then. Hence storing this
data for future analysis is a must. Writing a shell
script, which fetches the data from the API and
updating the database for particular time interval
is this objective.
3.4 Visualizing a Scenario
Visualizing the data obtained from various data
sources in the maps is this objective. The user
selects the location and corresponding timeline
for obtaining the events occurred. This request is
forwarded to the server, which in turn returns the
values collected from the server according to
user’s request. For every time range, the events
may vary. At a timeline, user can graphically view
what traffic incidents have happened, the events
occurred at that similar time when the incidents
happened. This data can be used for future
analysis to obtain any relation of parameters
collected.
4. Data Source Description
4.1 Scheduled Event Sources:
4.1.1 Eventful API
This API has world’s largest collection of events,
which ranges from local to global events. Venues
and events can be searched in this API. It uses REST
based calling scheme. A private application key
has to be requested to obtain data from this data
source.

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Input Parameters:
1. Location latitude
2. Location longitude
3. From date and time
4. To date and time
Output Parameters:
Figure 3: Eventful Response Parameters
4.1.2 Eventbrite API
This website allows peoples to create and attend
events in around 190 countries. Tickets for most
of the events which occurs in big cities can be
bought through this website. This website gives
an approximation of attendance present for an
event. With this as a data source, information
about popular events occurring near the area can
be retrieved for analysis.
Input Parameters:
1. Location latitude
2. Location longitude
3. From date and time
4. To date and time
Output Parameters:
Figure 4: Eventbrite Response Parameters
4.2 Active Event Sources:
4.2.1 Open Weather API
This API gives us information about weather of a
location. Weather has major impact on traffic
conditions and incidents which could possibly
happen in road. It has also been observed that
during severe snow conditions the traffic demand
also drops significantly and the congestion on
the freeway disappears [6]. Based on the
research carried out [6], there is a significant drop
in traffic volume in snowy conditions but there
was observed a significant increase in traffic
congestion. Dampen conditions significantly
contribute to traffic congestion in peak areas [6].
Increased traffic congestion and decreased traffic
volume was observed in city areas during wet
traffic conditions [6]. Hence it is important to
consider weather data for analyzing traffic.
Input Parameter: Location

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Output Parameter:
Figure 5: Open weather data Response
Parameters
4.2.2 Here Incident API
Constructions and Accidents play a major role in
affecting the flow of traffic thereby contributing to
the increased traffic congestion. Accident rate is a
complex characteristic of “road-vehicle-driver-
road environment” system [7]. From the research
[7], we could conclude that there is a direct
relationship between accident rate and traffic
volume.
From the figure 6, it can be observed that there is
a significant decrease in traffic volume when there
is increased accident density. Hence monitoring
accident data for traffic accidents gives us insights
on abnormal traffic.
Figure 6: Relationship between traffic volume and
accidents [8]
Construction events occurring in a location will
increase the traffic congestion by making it jam-
packed on a link, as it will reduce the number of
routes to destination [8]. Hence monitoring
construction events is necessary for analyzing the
traffic flow. Here API gives information about
traffic accidents and construction around a
location.
Input Parameters: Location & Bounding Box
Output Parameters:
Figure 7: Here Incidents data Response

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4.3 Flow Data Sources:
4.3.1 Here Flow API
From the research made [4], traffic flow data
accounts for 80 percent of traffic prediction. This
flow data is collected from various sensors kept
at many spots in the city. Here API collects this
flow data and sends it to the person who
requests via REST calls.
Input Parameters: Location & Bounding Box
Output Parameters:
Figure 8: Here Flow API data response
Terms and Explanation
 PC- Point Location Code
 DE - Description of Road
 QD -Queuing direction. '+' or '-'. Note
this is the opposite of the travel direction
in the fully qualified ID, for example for
location 107+03021 the QD would be '-'.
 LE - Length of the stretch of road
 JF - Jamming Factor
 CN- Confidence, an indication of how the
speed was determined. -1.0 road closed.
1.0=100% 0.7-100% Historical Usually a
value between .7 and 1.0.
 FF - The free flow speed on this stretch of
road.
 SP - Speed (based on UNITS) capped by
speed limit
 SU-Speed (based on UNITS) not capped
by speed limit
4.3.2 511 Data
This data source gives real time traffic information
about San Francisco Bay Area. Incident,
construction details, information about a link, real
time updates on traffic data are gathered from this
information source. From this data source, we
obtain the speed vs volume parameters which
could be a sensitive information in traffic
prediction.
Input Parameters: Location and Timestamp
Output Parameters:
Figure 9: 511 Data response

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System Architecture
Complete Architecture
Below described is the complete system
architecture. Events from multiple event sources
are recorded. They are aggregated and visualized
based on time.
Data sources are classified into Event sources and
Traffic sources. Traffic data sources provide real
time traffic information and flow information in an
area. Event sources provide information about the
events which are occuring in a particular area.
Collecting data from these datasources, storing it
locally in a MySQL database dynamically for future
analysis is one main theme of this project.
JSON, a lightweight data-interchange format, is
used to fetch information from data source
providers. User, environmental and traffic sensitive
information over a thematic timeline is retrieved
and stored.
User selects his timeline and searches for all the
evens that have occured in that particular timeline.
Map based visualization is facilitated whereby user
can delve around the screen to locate the
corresponding event pushpin and perform
historical data analysis on events which have
occurred in the past.
Server, which handles requests from the user to
fetch the values from the database, filters the
search according to user’s input and delivers the
required event in JSON format back to handle.
Javascript handles the data returned from the
server effectively and plots the event values on the
map. Latitude, Longitude and event description
are main fields which will plots the correct event
description on to pushpin.
Figure 10: System Architecture for Event Data Aggregation & Visualization

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A script which continuosly stores the events from
API calls to the database executes as a cron job in
the server. This cron job collects information about
a particular event, parses the information and
stores the required parameters on to the database.
Front-End Description
In this Project, we have used many Java script
plug-ins such as JQuery UI, Raphael, Moment, and
Here API. User sends his request via AJAX calls to
the server, requesting for data. Server gives the
data requested in JSON format to the user. Push
pins and text are plotted based on the response
obtained from the server. Bootstrap css framework
is used extensively to adapt this website for mobile
devices. By this user interface, the user can easily
identify the events tagged in a location. Scrollable
timeline panel gives user the ease of seeing the
events based on time.
Back-End Description
Server fetches the data from the event provider via
API calls. Obtained JSON string is parsed and
updated in to the database. This sequence
happens as a cron job thereby updating the events
dynamically on a specified time interval. The
location and timestamp is set as primary key, these
two parameters distinguishes an event from all
other events. Recording Latitude and Longitude
data is very useful, for obtaining accurate
information about a location and for plotting the
event in the map. MYSQL is used as the database
to store all the active and scheduled events.
Implementation
HTML, CSS, JAVASCRIPT are three primary
languages used for developing visualization. We
have used PHP in apache server to process the
request from the clients and dynamically update
the database about current events.
User supplies AJAX request to the server
requesting for events which happened with in a
date range. Server searches for events which are
within the date range in the database. It returns
user’s request with the events recorded. The server
returns the events obtained to the user in a JSON
format. List of POPO (Plain Old PHP Objects)
objects retrieved from the database are converted
to json format. JSON response is sent back to the
user to visualize events.
Cron jobs are scheduled to retrieve the data from
API and insert in to the MYSQL database. Traffic
information and event information are
dynamically requested to the respective data
sources. Response from the data sources are
parsed from the PHP server and are updated in to
the database.
Below is the diagram of outline of system’s
implementation.
Figure 11: System Outline

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Snapshots:
Figure 12: User Interface Snap Shot
Figure 13: SERVER (PHP) CODE

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Conclusion:
We have implemented the proposed architecture
to store and visualize the events based on a
timeline. This collection of events, can be used to
predict future traffic based on a statistical analysis,
alert the individuals about a link which is crowded
and can be used to find root cause analysis for
events. As traffic congestion is a major problem in
crowded cities, this tool can be used as an effective
way to visualize ongoing event information and
take better decisions based on the same.
Instructions to execute
Back end:
1. Install XAMPP control panel tool by Apache for
using MySQL and Apache Server.
2. After installation, copy the PHP Folder
(containing PHP files) to <XAMPP
Directory>/htdocs/
3. Open your XAMPP Control Panel > Start
Apache & MySQL Servers.
4. You can now execute the PHP files from the
browser by typing
http://localhost/<PHPFolderName>. Remember
that this is the Folder which you had copied to
htdocs before.
Figure 14: Database Snapshot

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Front end:
1. Copy the Client side folder to the same
XAMPP directory <XAMPP
Directory>/htdocs/
2. The Client Side User Interface is now
accessible in
http://localhost/<ClientFolderName>.
Keep in mind that this is the same folder
where you had copied and pasted both
your Client Side Folder and PHP Folder.
The directory location should be like the
above hierarchy.
References
1. http://research.microsoft.com/en-
us/projects/clearflow/default.aspx
2. http://www.fox10phoenix.com/story/278
42935/traffic-analytics-company-says-
average-bay-area-commute-times-are-
increasing
3. http://bayen.eecs.berkeley.edu/sites/defa
ult/files/conferences/Blandin_Salam_Baye
n_TRB12.pdf
4. http://venturebeat.com/2015/04/03/how
-microsofts-using-big-data-to-predict-
traffic-jams-up-to-an-hour-in-advance/
5. Horvitz, E. J., Apacible, J., Sarin, R., & Liao,
L. (2012). Prediction, expectation, and
surprise: Methods, designs, and study of a
deployed traffic forecasting service. arXiv
preprint arXiv:1207.1352.
6. http://www.d.umn.edu/cs/thesis/lalit_noo
kala_ms.pdf
7. http://www.balticroads.org/downloads/2
5BRC/25brc_d1_pakalnis_1.pdf
8. http://www.dot.state.mn.us/d3/projects/i
nterregionalconnection/pdfs/final/Chapt
er9.pdf
9. https://www.eventbrite.com/
10. http://lexington.eventful.com/events
11. https://developer.here.com/rest-apis
12. http://openweathermap.org/

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