MATLAB Based on embedded System
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 methods found in the literature, the probabilistic approach based on the higher probability provides estimates of the associated probability with each prediction.

MATLAB Based on embedded System
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.
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

MATLAB Based on embedded System 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
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

MATLAB Based on embedded System
IMAGE
PROCESSING 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
CCTV
CAMERA
SIGNAL
PROCESSING
RF
TRANSMITTER
MATLAB
ATMEL
AT89S52
HT12E
ENCODER IC
PC

MATLAB Based on embedded System
RECEIVER
RF
RECEIVER
HT12D
DECODER IC
ATMEL
AT89S52
16*2 LCD

MATLAB Based on embedded System
MATERIALS DIAGRAM

MATLAB Based on embedded System

MATLAB Based on embedded System
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