This paper presents a stochastic model for predicting freeway traffic speed based on historical data and analyzes the impact of traffic density on accident occurrences, particularly in tunnel environments. It introduces a system for tracking vehicles and detecting shock waves to enhance traffic management and reduce accidents. The proposed system aims to improve prediction accuracy and provide real-time information to drivers about traffic conditions and safety measures.

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AUTOMATIC MOVING OBJECT EXTRACTION THROUGH
AREAL-WORLD VARIABLE-BANDWIDTH NETWORK FOR
TRAFFIC MONITORING SYSTEMS
ABSTRACT
Using a stochastic approach, this paper explores and models the basic
stochastic characteristics of freeway traffic behavior under a wide range of traffic
conditions during peak Periods and then applies the models to short-term traffic
speed prediction. The speed transition probabilities are estimated from real-world
30-s speed data over a six-year period at three different locations along the 38-mi
corridor of Interstate 4 (I-4) in Orlando, FL. The cumulative negative / positive
transition probabilities and expected values are derived from the transition
probabilities and fitted using logistic and exponential models, respectively. The
expected values associated with the most likely transition of speed are then derived
from the fitted models and used for predicting speed. Each predicted speed is also
associated with a probability value, indicating the chance of observing the
occurrence of such transition. The prediction performance was compared for three
methods using the root mean square errors (RMSEs). The weighted average
method was very close to the higher probability method in most cases. For the two
probabilistic methods, the performance was slightly better for the morning peak
periods than the evening peak period or all data combined. While the prediction
performance of the probabilistic models was comparable with those of other

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methods found in the literature, the probabilistic approach based on the higher
probability provides estimates of the associated probability with each prediction.
This provides a measure of confidence in the predicted values before such
information is disseminated to the public by traffic agencies.
We have circumstantially analyzed traffic accidents in the tunnel by data
from video cameras installed in the tunnel. They are practically used for
surveillance by the traffic management. In saturated traffic, vehicles do not move
at constant speed. They repeat high and low speed over a period of time. When the
traffic density is low, vehicles run at 40 km/h. In contrast, when the traffic density
is high, vehicles are almost stalled. In this situation, rapid speed difference is
caused between low and high-density parts. Therefore rear-end accidents caused by
Speed differential are more likely to occur. In traffic jam situation, incidents occur
at a low speed about 10 km/h. It would appear that the driver’s carelessness causes
these incidents.
From the analysis, it was clear that 70% of these accidents originate in the
speed differential in saturated traffic, and the remaining 30% occurs when a driver
goes at a low speed without attention. In this paper, the system to prevent the
accident of the former type with high possibility of causing a serious accident is
constructed. It is mentioned that such a situation is tunnel, but is also noticed as a
common factor of traffic accidents in some places. Therefore this solution is
thought to be effective on any road where saturated traffic occurs.

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The system consists of three parts: vehicle tracking part, detection part and
information providing part. First, an average velocity of traffic flow and also the
average size of the vehicle is calculated from a result of the vehicle tracking part
Secondly, surveillance video cameras that have already been installed in the tunnel
are utilized to observe a condition of traffic flow. And finally depending upon the
number of vehicles and their size inside the tunnel, the remaining number of
vehicles that can pass through the tunnel can be displayed on LCD at outside the
tunnel, so that drivers could get an awareness of vehicles’ space available inside
the tunnel.
EXISTING SYSTEM
This memo defines a portion of the Management Information Base (MIB)
for use with network management protocols in the Internet community. In
particular, it describes managed objects for Traffic Engineered (TE) Tunnels; for
example, Multi-Protocol Label Switched Paths. The MIB module defined by this
memo allows one to configure TE Tunnels, to assign one or more paths to a
Tunnel, and to monitor operational aspects of the Tunnel, such as the number of
octets and Packets that have passed through the Tunnel
DISADVANTAGES
 The system does not consider the maximum size of the vehicle which can
pass through the tunnel
 It does not take images of the vehicle using the tunnel.
 It is trusted one

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PROPOSED SYSTEM
This study describes the development of a vision sensor for detecting shock
waves which is one of the main factors of accidents in highway traffic flow. The
major contributions of this research are development of vehicle tracking and
detection of shock wave in saturated traffic. Moreover, realization of a vehicle
infrastructure integration system for providing arrival Information of such
propagation to drivers is proposed. Therefore a prediction technique at the arrival
time of the propagation is integrated in the authors’ system. By using this
prediction technique and taking the error tolerance of drivers into account, the
experimental results show that prediction success rates are improved by about 5%.
ADVANTAGES
 Increase viability Data transmission.
 Fast incident detection
 Very convenient.
 Cover over all area.
 Avoiding Collision.
BLOCK DIAGRAM
TRANSMITTER
SIGNAL
PROCESSING
CCTV
CAMERA
MATLABIMAGE
ATMEL
AT89S52
RF
TRANSMITTER

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RECEIVER
HT12D
DECODER IC
ATMEL
AT89S52
16*2 LCD
RF
RECEIVER

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MATERIALS DIAGRAM

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HARDWARE REQUIREMENTS
 MICROCONTROLLER UNIT- acts as interconnection system
 RF Tx &Rx- make wireless communication b/w tunnel center and entry
 CAMERA- for capture input video stream
 PC- MATLAB code will performed
 LCD- information about the traffic in tunnel
SOFTWARE REQUIREMENTS
 KEIL COMPILER
 MATLAB