Integrating Data Mining and Microsimulation Modelling to Reduce Traffic Congestion: A Case Study of Signalized Intersections in Dhaka, Bangladesh

Abstract

A growing body of research has applied intelligent transportation technologies to reduce traffic congestion at signalized intersections. However, most of these studies have not considered the systematic integration of traffic data collection methods when simulating optimum signal timing. The present study developed a three-part system to create optimized variable signal timing profiles for a congested intersection in Dhaka, regulated by fixed-time traffic signals. Video footage of traffic from the studied intersection was analyzed using a computer vision tool that extracted traffic flow data. The data underwent a further data-mining process, resulting in greater than 90% data accuracy. The final data set was then analyzed by a local traffic expert. Two hybrid scenarios based on the data and the expert’s input were created and simulated at the micro level. The resultant, custom, variable timing profiles for the traffic signals yielded a 40% reduction in vehicle queue length, increases in average travel speed, and a significant overall reduction in traffic congestion.

Noncommunicable Diseases, Park Prescriptions, and Urban Green Space Use Patterns in a Global South Context: The Case of Dhaka, Bangladesh

“…Furthermore, residents are increasingly exposed to traffic pollution that could have been absorbed by green spaces and vegetation land covers, were they preserved from development [54,55]. Dhaka is arguably one of the least livable cities in the world based on its decreasing amount of green space, increased air pollution, and traffic congestion [26,29,[56][57][58].…”

Traffic Flow Catastrophe Border Identification for Urban High-Density Area Based on Cusp Catastrophe Theory: A Case Study under Sudden Fire Disaster

“…Traffic flow catastrophe border identification can help engineers, planners, and emergency managers identify the approximate time or position it would take for traffic flow to manage or control in disaster-affected area. In addition, advanced traffic signal control systems can have tremendous impact on the movement of traffic flow in urban high-density areas under sudden disasters, facilitates emergency evacuation and response in disaster relief and mitigation [51,52]. However, most of these studies have not considered the systematic integration of traffic data collection intervals when optimizing control target and signal timing [52].…”

“…In addition, advanced traffic signal control systems can have tremendous impact on the movement of traffic flow in urban high-density areas under sudden disasters, facilitates emergency evacuation and response in disaster relief and mitigation [51,52]. However, most of these studies have not considered the systematic integration of traffic data collection intervals when optimizing control target and signal timing [52]. The present simulation-based study tries to provide a technical understanding regarding which the data collection interval provides better results in identifying the catastrophe border.…”

Modeling Traffic Congestion in Developing Countries using Google Maps Data

“…They have either done traffic simulation with vehicle counts or forecasting of traffic speed from GPS data. In [23], they have collected traffic data by manual counts employing local experts in several junctions and used traffic simulation at the micro level to create optimized signal timings for a congested intersection. Roy et al collected traffic volume data manually in peak periods of the morning(9:00-10:00 AM) and collected geometric and control data for four intersections of Dhaka city and simulated the data using VISSIM optimization of existing traffic signals [24].…”

“…S.M. Labib et al collected data with manual counts by deploying local surveyors for their case study area: Sheraton hotel junction in Dhaka, Bangladesh [23]. Another group collected traffic data by manually counting the vehicles at Science Laboratory Intersection in peak periods of the morning (9:00 -10:00 AM) [24].…”