Traffic Data Collection and Visualization Tool for Knowledge Discovery Using Google Maps

Traffic jam is increasingly aggravating in almost every urban area. Traffic forecast, traffic modeling, visualization can help to provide appropriate route and time for traveling and thus provides a significant impact on traffic jam reduction. For traffic forecasting, modeling and visualization, city-wide traffic data collection and analysis are needed, which is still challenging in many aspects. This paper aims to develop a tool for acquiring and processing traffic data from Google Maps that can be used for forecasting, modeling, and visualization. Dhaka city is used as a case study since there is no infrastructure available for traffic data collection. The traffic flow intensity of the road is analyzed to determine the congestion of the road. The flow intensity is used for traffic modeling, visualization, traffic prediction and many more.

Download Full-text

Computer vision techniques for traffic data collection and analysis

Transportation Research Part A Policy and Practice ◽

10.1016/s0965-8564(97)88277-8 ◽

1997 ◽

Vol 31 (1) ◽

pp. 59-60

Keyword(s):

Computer Vision ◽

Data Collection ◽

Traffic Data ◽

Traffic Data Collection ◽

Data Collection And Analysis

Download Full-text

Enhancing unsupervised video-based vehicle tracking and modeling for traffic data collection

Canadian Journal of Civil Engineering ◽

10.1139/cjce-2019-0087 ◽

2020 ◽

Vol 47 (8) ◽

pp. 982-997

Author(s):

Mohamed H. Zaki ◽

Tarek Sayed ◽

Moataz Billeh

Keyword(s):

Data Collection ◽

Video Analysis ◽

Three Dimensional ◽

Ground Truth ◽

Traffic Data ◽

Ground Truth Data ◽

The Road ◽

Traffic Data Collection ◽

Bounding Boxes ◽

Automated Video Analysis

Video-based traffic analysis is a leading technology for streamlining transportation data collection. With traffic records from video cameras, unsupervised automated video analysis can detect various vehicle measures such as vehicle spatial coordinates and subsequently lane positions, speed, and other dynamic measures without the need of any physical interconnections to the road infrastructure. This paper contributes to the unsupervised automated video analysis by addressing two main shortcomings of the approach. The first objective is to alleviate tracking problems of over-segmentation and over-grouping by integrating region-based detection with feature-based tracking. This information, when combined with spatiotemporal constraints of grouping, can reduce the effects of these problems. This fusion approach offers a superior decision procedure for grouping objects and discriminating between trajectories of objects. The second objective is to model three-dimensional bounding boxes for the vehicles, leading to a better estimate of their geometry and consequently accurate measures of their position and travel information. This improvement leads to more precise measurement of traffic parameters such as average speed, gap time, and headway. The paper describes the various steps of the proposed improvements. It evaluates the effectiveness of the refinement process on data collected from traffic cameras in three different locations in Canada and validates the results with ground truth data. It illustrates the effectiveness of the improved unsupervised automated video analysis with a case study on 10 h of traffic data collection such as volume and headway measurements.

Download Full-text

A traffic data collection and analysis method based on wireless sensor network

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-019-1628-5 ◽

2020 ◽

Vol 2020 (1) ◽

Cited By ~ 2

Author(s):

Huan Wang ◽

Min Ouyang ◽

Qingyuan Meng ◽

Qian Kong

Keyword(s):

Wireless Sensor Network ◽

Data Collection ◽

Sensor Network ◽

Traffic Flow ◽

Wireless Sensor ◽

Traffic Data ◽

Flow Data ◽

Analysis Method ◽

Traffic Data Collection ◽

Data Collection And Analysis

AbstractWith the rapid development of urbanization, collecting and analyzing traffic flow data are of great significance to build intelligent cities. The paper proposes a novel traffic data collection method based on wireless sensor network (WSN), which cannot only collect traffic flow data, but also record the speed and position of vehicles. On this basis, the paper proposes a data analysis method based on incremental noise addition for traffic flow data, which provides a criterion for chaotic identification. The method adds noise of different intensities to the signal incrementally by an improved surrogate data method and uses the delayed mutual information to measure the complexity of signals. Based on these steps, the trend of complexity change of mixed signal can be used to identify signal characteristics. The numerical experiments show that, based on incremental noise addition, the complexity trends of periodic data, random data, and chaotic data are different. The application of the method opens a new way for traffic flow data collection and analysis.

Download Full-text