Wasan Pattara-Atikom

Estimating Road Traffic Congestion using Vehicle Velocity

This study investigates an alternative way to estimate degrees of road traffic congestion based on routine GPS measurements from main roads in urban areas of Bangkok, Thailand. We classify three levels of traffic congestion according to the weighted exponential moving averages of measured GPS speed. Human perception is used to obtain classification thresholds and evaluated the performance of the proposed method. The benefits of our proposed method over existing techniques is that it is simple, easy to understand, and compatible with existing traffic report systems in Bangkok

Estimating road traffic congestion from cellular handoff information using cell-based neural networks and K-means clustering

This research proposes alternative methods for estimating degrees of road traffic congestion by using cell dwell time (CDT) information available from cellular networks. CDT is the duration that a cellular phone remains associated to a base station between handoff events. As a phone in a vehicle travels along a road having different degrees of congestion, the value of CDT varies accordingly. Measurements of CDT were taken and classified into one of the three degrees of congestion using 1) K-means clustering algorithm and 2) backpropagation neural network. These machine-assigned classifications were then compared against human opinion to assess the accuracy. The results demonstrate the feasibility of using K-means and neural networks in classifying degrees of traffic congestion and that the neural network approach performs well for this task.

Perception-Based Road Traffic Congestion Classification Using Neural Networks and Decision Tree

In this study, we investigated an alternative technique to automatically classify road traffic congestion with travelers’ opinions. The method utilized an intelligent traffic camera system orchestrated with an interactive web survey system to collect the traffic conditions and travelers’ opinions. A large numbers of human perceptions were used to train the artificial neural network (ANN) model and the decision tree (J48) model that classify velocity and traffic flow into three congestion levels: light, heavy, and jam. The both model was then compared to the Occupancy Ratio (OR) technique, currently in service in the Bangkok Metropolitan Administration (BMA). The accuracy of ANN was more than accuracy of the J48. The evaluation indicated that our ANN model could determine the traffic congestion levels 12.15% more accurately than the existing system. The methodology, though conceived for use in Bangkok, is a general Intelligent Transportation System (ITS) practice that can be applied to any part of the world.

Classification of Road Traffic Congestion Levels from Vehicle’s Moving Patterns: A Comparison Between Artificial Neural Network and Decision Tree Algorithm

We proposed a technique to identify road traffic congestion levels from velocity of mobile sensors with high accuracy and consistent with motorists’ judgments. The data collection utilized a GPS device, a webcam, and an opinion survey. Human perceptions were used to rate the traffic congestion levels into three levels: light, heavy, and jam. We successfully extracted vehicle’s moving patterns using a sliding windows technique. Then the moving patterns were fed into ANN and J48 algorithms. The comparison between two learning algorithms yielded that the J48 model shown the best result which achieved accuracy as high as 91.29%. By implementing the model on the existing traffic report systems, the reports will cover on comprehensive areas. The proposed method can be applied to any parts of the world.

Impact of intelligent traffic information system on congestion saving in Bangkok

Traffic information is undeniably essential to every one especially for commuters. With accurate and up to date traffic information, a better traveling plan can be made, which translates to reduction in time and energy consumption. Consequently, overall society and environment can benefit from traffic information. This paper presents a study of impact of intelligent traffic information system (ITIS) on congestion saving in Bangkok. We propose to utilize both static and dynamic traffic information in order to find the least congested route. The results revealed that up to 75% congestion saving can be achieved with ITIS.

Uniform location referencing standard for road traffic data in Thailand

Traffic information such as accident and traffic congestion becomes an essential part of any commuters daily life. Reporting such information requires a geography location reference of the event. However, in practice, different providers may have different and/or mismatched scales and versions of the map which may lead to different interpretation of the event location. To alleviate the problem of inconsistency of location referencing, a standard for location referencing is needed. This paper presents a location referencing standard for disseminating road traffic information in Thailand. An empirical evaluation of accuracy such standard is also investigated. The results indicate that the standard is well suited for on-route location referencing but for off-route location referencing further exploration is required.

A multimedia-based and time-sensitive interactive web survey for road user opinion on traffic condition

Web Survey has increased in popularity because of its convenience, ease of use, low cost, and quickness in knowing the result of a survey in real time. Web survey is widely used to gather user opinions on various purposes. However, most such systems are plain-text, and sometimes with a few graphical images. This paper presents an innovative Multimedia-Based and Time-Sensitive Interactive (MTI) Web Survey to collect user opinions on the traffic condition. Open source development tools and emerging web technologies, such as PHP and AJAX, are used in implementing the system to achieve event-sensitive user opinions. This MTI web survey, though presented and illustrated by using road user opinion on traffic condition data, is a general technique that can be applied to any time-sensitive data collection. The usability testing on MTI web survey was also conducted to gain insights on usage issues and the results are discussed.