Sorawit Narupiti

Development of high accuracy congestion prediction algorithm using series of camera detectors

This paper proposes a high accuracy algorithm to predict short-term traffic congestion in highway using patterns of microscopic traffic variable, including speed and its standard deviation from series of camera detectors installed at the location before observing point. The performance of the proposed algorithm is compared with a single camera detector. The result from simulation data shows the prediction accuracy of the proposed algorithm that utilizing data from series of detectors is twice that of single detector with zero false alarm rate. The proposed algorithm also performs well when applied to real-world data that show an increase of prediction accuracy by approximately fifty percent while achieve very low false alarm rate.

Development of the inner city following-lane changing model and meandering model of motorcycles

As in many Asian cities, Bangkok motorcycle riders tend to ride along a traffic lane with a car or other motorcycles. They also meander through spaces between vehicles at intersections. These behaviors should have impact on the traffic throughputs and vehicle queuing times. Since motorcycles accounts for 20% of all Bangkok vehicles, this impact should be sizable. We, therefore, developed an inner city motorcycle behavior model, based on the city riders. In the following-lane changing model, a motorcycle could share a traffic lane with another motorcycle or car. At traffic lights, meandering model would simulate motorcycles slipping through spaces between vehicle queues. The model was developed on Aimsun NG. Real traffic data used to develop the model was obtained from observations and video-recording. The recorded videos were analyzed to get a probability of each motorcycle lane changing pattern and a probability of each motorcycle meandering pattern. Simulation results indicate that the errors in travel time and speed are within 8 percents, and the error in time in queue is within 8 percents of the test data.