Performance Comparison of Filtering Techniques for Real Time Traffic Density Estimation under Indian Urban Traffic Scenario

2015 ◽  
Author(s):  
B. Dhivyabharathi ◽  
Shrikant Fulari ◽  
Rushikesh Amrutsamanvar ◽  
Lelitha Vanajakshi ◽  
Shankar C. Subramanian ◽  
...  
Keyword(s):  
Real Time ◽  
Density Estimation ◽  
Performance Comparison ◽  
Urban Traffic ◽  
Traffic Density ◽  
Real Time Traffic ◽  
Traffic Scenario ◽  
Filtering Techniques

scholarly journals A V2I-Based Real-Time Traffic Density Estimation System in Urban Scenarios

2015 ◽  
Vol 83 (1) ◽  
pp. 259-280 ◽  
Author(s):  
Javier Barrachina ◽  
Piedad Garrido ◽  
Manuel Fogue ◽  
Francisco J. Martinez ◽  
Juan-Carlos Cano ◽  
...  
Keyword(s):  
Real Time ◽  
Density Estimation ◽  
Traffic Density ◽  
Real Time Traffic ◽  
Estimation System

A MACHINE LEARNING FRAMEWORK FOR REAL-TIME TRAFFIC DENSITY DETECTION

2009 ◽  
Vol 23 (07) ◽  
pp. 1265-1284 ◽  
Author(s):  
JING CHEN ◽  
EVAN TAN ◽  
ZHIDONG LI
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Density Estimation ◽  
Heavy Traffic ◽  
Weather Conditions ◽  
Traffic Density ◽  
Model Parameters ◽  
Loop Detectors ◽  
Real Time Traffic ◽  
Flow Information

Traffic flow information can be employed in an intelligent transportation system to detect and manage traffic congestion. One of the key elements in determining the traffic flow information is traffic density estimation. The goal of traffic density estimation is to determine the density of vehicles on a given road from loop detectors, traffic radars, or surveillance cameras. However, due to the inflexibility of deploying loop detectors and traffic radars, there is a growing trend of using video-content-understanding technique to determine the traffic flow from a surveillance camera. But difficulties arise when attempting to do this in real-time under changing illumination and weather conditions as well as heavy traffic congestions. In this paper, we attempt to address the problem of real-time traffic density estimation by using a stochastic model called Hidden Markov Models (HMM) to probabilistically determine the traffic density state. Choosing a good set of model parameters for HMMs has a significant impact on the accuracy of traffic density estimation. Thus, we propose a novel feature extraction scheme to represent traffic density, and a novel approach to initialize and construct the HMMs by using an unsupervised clustering technique called AutoClass. We show through extensive experiments that our proposed real-time algorithm achieves an average traffic density estimation accuracy of 96.6% over various different illumination and weather conditions.

Real-Time Traffic Density Estimation without Reliable Side Road Data

2015 ◽  
Vol 29 (2) ◽  
pp. 04014033 ◽  
Author(s):  
T. Ajitha ◽  
L. Vanajakshi ◽  
S. C. Subramanian
Keyword(s):  
Real Time ◽  
Density Estimation ◽  
Traffic Density ◽  
Real Time Traffic ◽  
Road Data

PENGEMBANGAN ”REAL TIME TRAFFIC INFORMATION SYSTEM” BAGI PENGGUNA JALAN

2013 ◽  
Vol 12 (3) ◽  
Author(s):  
Rusmadi Suyuti
Keyword(s):  
Information System ◽  
Real Time ◽  
Travel Demand ◽  
Urban Traffic ◽  
Road User ◽  
Traffic Information ◽  
Estimation Methods ◽  
Traffic Counts ◽  
Real Time Traffic ◽  
Demand Models

Traffic information condition is a very useful  information for road user because road user can choose his best route for each trip from his origin to his destination. The final goal for this research is to develop real time traffic information system for road user using real time traffic volume. Main input for developing real time traffic information system is an origin-destination (O-D) matrix to represent the travel pattern. However, O-D matrices obtained through a large scale survey such as home or road side interviews, tend to be costly, labour intensive and time disruptive to trip makers. Therefore, the alternative of using traffic counts to estimate O-D matrices is particularly attractive. Models of transport demand have been used for many years to synthesize O-D matrices in study areas. A typical example of the approach is the gravity model; its functional form, plus the appropriate values for the parameters involved, is employed to produce acceptable matrices representing trip making behaviour for many trip purposes and time periods. The work reported in this paper has combined the advantages of acceptable travel demand models with the low cost and availability of traffic counts. Two types of demand models have been used: gravity (GR) and gravity-opportunity (GO) models. Four estimation methods have been analysed and tested to calibrate the transport demand models from traffic counts, namely: Non-Linear-Least-Squares (NLLS), Maximum-Likelihood (ML), Maximum-Entropy (ME) and Bayes-Inference (BI). The Bandung’s Urban Traffic Movement survey has been used to test the developed method. Based on several statistical tests, the estimation methods are found to perform satisfactorily since each calibrated model reproduced the observed matrix fairly closely. The tests were carried out using two assignment techniques, all-or-nothing and equilibrium assignment.  

scholarly journals The Dynamic Control of the Light Signalling Device in Real-Time

2019 ◽  
Vol 292 ◽  
pp. 03014
Author(s):  
Jan Mrazek ◽  
Lucia Duricova Mrazkova ◽  
Martin Hromada ◽  
Jana Reznickova
Keyword(s):  
Real Time ◽  
Traffic Control ◽  
Dynamic Control ◽  
Traffic Density ◽  
Light Signaling ◽  
Dynamic Traffic ◽  
Real Time Control ◽  
Real Time Traffic ◽  
Time Control ◽  
Control Dynamic

The article is focused on the issue of interval on a light signaling device. Light signaling devices operate on different systems by means of which they are controlled. The control problem is a very static setting that does not respond to real-time traffic. Important variables for dynamic real-time control are traffic density in a selected area along with average speed. These variables are interdependent and can be based on dynamic traffic control. Dynamic traffic control ensures smoother traffic through major turns. At the same time, the number of harmful CO2 emitted from the means of transport should be reduced to the air. When used in low operation, power consumption should be reduced.

scholarly journals Real-time Traffic Pattern Analysis and Inference with Sparse Video Surveillance Information

2018 ◽  
Author(s):  
Yang Wang ◽  
Yiwei Xiao ◽  
Xike Xie ◽  
Ruoyu Chen ◽  
Hengchang Liu
Keyword(s):  
Real Time ◽  
Video Surveillance ◽  
Pattern Analysis ◽  
Transition Probabilities ◽  
Urban Traffic ◽  
Third Party ◽  
Distribution Model ◽  
Traffic Patterns ◽  
Traffic Pattern ◽  
Real Time Traffic

Recent advances in  video surveillance systems enable a new paradigm for intelligent urban traffic management systems. Since surveillance cameras are usually sparsely located to cover key regions of the road under surveillance, it is a big challenge to perform a complete real-time traffic pattern analysis based on incomplete sparse surveillance information. As a result, existing works mostly focus on predicting traffic volumes with historical records available at a particular location  and may not provide a complete picture of real-time traffic patterns. To this end, in this paper, we go beyond existing works and tackle the challenges of traffic flow analysis from three perspectives. First, we train the transition probabilities to capture vehicles' movement patterns. The transition probabilities are trained from third-party vehicle GPS data, and thus can work in the area even if there is no camera. Second, we exploit the Multivariate Normal Distribution model together with the transferred probabilities to estimate the unobserved traffic patterns. Third, we propose an algorithm for real-time traffic inference with  surveillance as a complement source of information. Finally, experiments on real-world data show the effectiveness of our approach.

Real-Time Traffic Status Analysis Methods Based on Smart Light Pole for Typical Urban Traffic Scenes

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Nale Zhao ◽  
Siyuan Hao ◽  
Yizheng Wu ◽  
Jiahui Li ◽  
Keman Wu ◽  
...  
Keyword(s):  
Real Time ◽  
Urban Traffic ◽  
Real Time Traffic ◽  
Analysis Methods ◽  
Light Pole ◽  
Status Analysis
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