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.

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

scholarly journals Exploring the Spatial-Temporal Relationship between Rainfall and Traffic Flow: A Case Study of Brisbane, Australia

2020 ◽  
Vol 12 (14) ◽  
pp. 5596 ◽  
Author(s):  
Yanmin Qi ◽  
Zuduo Zheng ◽  
Dongyao Jia
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Transportation Network ◽  
Central Business District ◽  
Weather Conditions ◽  
Inclement Weather ◽  
Real Time Traffic ◽  
Policy Analysts ◽  
Business District ◽  
The Impact

The impact of inclement weather on traffic flow has been extensively studied in the literature. However, little research has unveiled how local weather conditions affect real-time traffic flows both spatially and temporally. By analysing the real-time traffic flow data of Traffic Signal Controllers (TSCs) and weather information in Brisbane, Australia, this paper aims to explore weather’s impact on traffic flow, more specifically, rainfall’s impact on traffic flow. A suite of analytic methods has been applied, including the space-time cube, time-series clustering, and regression models at three different levels (i.e., comprehensive, location-specific, and aggregate). Our results reveal that rainfall would induce a change of the traffic flow temporally (on weekdays, Saturday, and Sunday and at various periods on each day) and spatially (in the transportation network). Particularly, our results consistently show that the traffic flow would increase on wet days, especially on weekdays, and that the urban inner space, such as the central business district (CBD), is more likely to be impacted by inclement weather compared with other suburbs. Such results could be used by traffic operators to better manage traffic in response to rainfall. The findings could also help transport planners and policy analysts to identify the key transport corridors that are most susceptible to traffic shifts in different weather conditions and establish more weather-resilient transport infrastructures accordingly.

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

scholarly journals Investigation of a Promoted You Only Look Once Algorithm and Its Application in Traffic Flow Monitoring

2019 ◽  
Vol 9 (17) ◽  
pp. 3619 ◽  
Author(s):  
Chang-Yu Cao ◽  
Jia-Chun Zheng ◽  
Yi-Qi Huang ◽  
Jing Liu ◽  
Cheng-Fu Yang
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
High Performance ◽  
Intelligent Transportation System ◽  
Weather Conditions ◽  
Recall Rate ◽  
Model Parameters ◽  
Data Set ◽  
Flow Problems ◽  
Flow Monitoring

We propose a high-performance algorithm while using a promoted and modified form of the You Only Look Once (YOLO) model, which is based on the TensorFlow framework, to enhance the real-time monitoring of traffic-flow problems by an intelligent transportation system. Real-time detection and traffic-flow statistics were realized by adjusting the network structure, optimizing the loss function, and introducing weight regularization. This model, which we call YOLO-UA, was initialized based on the weight of a YOLO model pre-trained while using the VOC2007 data set. The UA-CAR data set with complex weather conditions was used for training, and better model parameters were selected through tests and subsequent adjustments. The experimental results showed that, for different weather scenarios, the accuracy of the YOLO-UA was ~22% greater than that of the YOLO model before optimization, and the recall rate increased by about 21%. On both cloudy and sunny days, the accuracy, precision, and recall rate of the YOLO-UA model were more than 94% above the floating rate, which suggested that the precision and recall rate achieved a good balance. When used for video testing, the YOLO-UA model yielded traffic statistics with an accuracy of up to 100%; the time to count the vehicles in each frame was less than 30 ms and it was highly robust in response to changes in scenario and weather.

Forecasting Model of Short-Term Traffic Flow Based on Cloud Model

2011 ◽  
Vol 94-96 ◽  
pp. 38-42
Author(s):  
Qin Liu ◽  
Jian Min Xu
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Cloud Model ◽  
Prediction Method ◽  
Absolute Error ◽  
Short Term ◽  
Guangzhou City ◽  
Time Prediction ◽  
Real Time Traffic ◽  
Absolute Percent Error

In order to improve the prediction precision of the short-term traffic flow, a prediction method of short-term traffic flow based on cloud model was proposed. The traffic flow was fit by cloud model. The history cloud and the present cloud were built by historical traffic flow and present traffic flow. The forecast cloud is produced by both clouds. Then, combining with the volume of the short-term traffic flow of an intersection in Guangzhou City, the model was calculated and simulated through programming. Max Absolute Error (MAE) and Mean Absolute percent Error (MAPE) were used to estimate the effect of prediction. The simulation results indicate that this prediction method is effective and advanced. The change of the historical and real time traffic flow is taken into account in this method. Because the short-term traffic flow is dealt with as a whole, the error of prediction is avoided. The prediction precision and real-time prediction are satisfied.

scholarly journals Macroscopic Traffic Flow Control Via State Estimation

2015 ◽  
Vol 15 (5) ◽  
pp. 5-16
Author(s):  
H. Abouaïssa ◽  
H. Majid
Keyword(s):  
Flow Control ◽  
State Estimation ◽  
Traffic Flow ◽  
Traffic Control ◽  
Traffic Density ◽  
Traffic State ◽  
Loop Detectors ◽  
Traffic State Estimation ◽  
Estimation And Control ◽  
And Control

Abstract The studies presented in this paper deal with traffic control in case of missing data and/or when the loop detectors are faulty. We show that the traffic state estimation plays an important role in traffic prediction and control. Two approaches are presented for the estimation of the main traffic variables (traffic density and mean speed). The state constructors obtained are then used for traffic flow control. Several numerical simulations show very promising results for both traffic state estimation and control.

scholarly journals A Novel Approach of Traffic Density Estimation Using CNNs and Computer Vision

2021 ◽  
Vol 5 (4) ◽  
pp. 80-84
Author(s):  
Luong Anh Tuan Nguyen ◽  
Thanh Xuan Ha
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Traffic Flow ◽  
Density Estimation ◽  
Traffic Density ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Modern Life ◽  
Traffic Jam ◽  
Novel Approach

In modern life, we face many problems, one of which is the increasingly serious traffic jam. The cause is the large volume of vehicles, inadequate infrastructure and unreasonable distribution, and ineffective traffic signal control. This requires finding methods to optimize traffic flow, especially during peak hours. To optimize traffic flow, it is necessary to determine the traffic density at each time in the streets and intersections. This paper proposed a novel approach to traffic density estimation using Convolutional Neural Networks (CNNs) and computer vision. The experimental results with UCSD traffic dataset show that the proposed solution achieved the worst estimation rate of 98.48% and the best estimation rate of 99.01%.

Real-Time Traffic Measurement from Single Loop Inductive Signatures

2002 ◽  
Vol 1804 (1) ◽  
pp. 98-106 ◽  
Author(s):  
Seri Oh ◽  
Stephen G. Ritchie ◽  
Cheol Oh
Keyword(s):  
Real Time ◽  
The United States ◽  
Traffic Data ◽  
Traffic Surveillance ◽  
Vehicle Class ◽  
Traffic Measurement ◽  
Single Loop ◽  
Loop Detectors ◽  
Real Time Traffic ◽  
Traffic Data Collection

Accurate traffic data acquisition is essential for effective traffic surveillance, which is the backbone of advanced transportation management and information systems (ATMIS). Inductive loop detectors (ILDs) are still widely used for traffic data collection in the United States and many other countries. Three fundamental traffic parameters—speed, volume, and occupancy—are obtainable via single or double (speed-trap) ILDs. Real-time knowledge of such traffic parameters typically is required for use in ATMIS from a single loop detector station, which is the most commonly used. However, vehicle speeds cannot be obtained directly. Hence, the ability to estimate vehicle speeds accurately from single loop detectors is of considerable interest. In addition, operating agencies report that conventional loop detectors are unable to achieve volume count accuracies of more than 90% to 95%. The improved derivation of fundamental real-time traffic parameters, such as speed, volume, occupancy, and vehicle class, from single loop detectors and inductive signatures is demonstrated.

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