Motorways and Urban Mobility - International Road Federation

15
INTELLIGENT TRANSPORT SYSTEMS
The project was implemented through funds made available
by the Government of India’s Planning Commission and a
small Traffic Management Centre (TMC) was established in
the Traffic Engineering Safety Division of CRRI. This took the
form of an ITS control room housing the instrumentation
set up, which enabled the online transfer of raw video
images from the four cameras through 2 Mbps dedicated
data circuit lines. Other equipment included the converters,
server and analyser for processing the data, using Citilog
software. In practice, the TMC verifies the processed data
obtained from the images transmitted by the cameras
and flashes corresponding messages to Variable Message
Signs (VMS) installed at strategic locations. A typical
demonstration of the instrumentation set up is shown in
Figure 3.
Figure 3: Instrumentation Setup of the Video Incident
Detection System (VIDS) for Advanced Traffic Management
System (ATMS)
Reliability analysis
The enumeration of traffic flow on the study stretch was
first arrived at by manual counting from the CCTV footage.
The types of incidents occurring on the study stretch were
similarly observed manually.
The CCTV observed traffic flow and incidents (by reference
to peak and off-peak hours) were then compared with the
system data. By applying statistical approaches like chisquare
tests to compare the observed and system data,
the effectiveness of VIDS for automatic enumeration of
traffic data and incident data collection under Indian
traffic conditions could be determined. Figure 5 shows the
Goodness-of-Fit plot for data collected respectively from
| IRF BULLETIN SPECIAL EDITION: URBAN MOBILITY
CCTV observation and the system deployed on the NH-2
using Fixed Camera 1. From the plot, it can be observed
that the system is capable of counting vehicles to up to
ninety five percent accuracy.
Figure 4:
Glimpse of the incidents detected using Video Based System on
NH-2 Top: detection of congested condition due to slow down.
Bottom: detection of vehicle travelling in wrong direction.
As alluded above, the incident information was collected
separately for peak and off-peak hours. To gauge the
effectiveness of the system, the total number of field
observed incidents was compared with those detected
by the system. The number of false alarms (i.e. incidents
which were reported by the system but which were not
observed on CCTV) was recorded. Using this approach, the
performance level of the video based incident detection
system was then assessed by measuring the following
parameters:
- Mean detection time
- Incident detection rate
- False alarm frequency