| Ei Ei Mon, Hideya Ochiai, Chaiyachet Saivichit and Chaodit Aswakul, "Bottleneck Based Gridlock Prediction in Urban Road Network Using Long Short-Term Memory," Electronics 2020, 9, 1412. https://doi.org/10.3390/electronics9091412 |
Bottleneck Based Gridlock Prediction in Urban Road Network Using Long Short-Term Memory |
| C. Eosanurak, N. Wongtrakoon, E. Mon and C. Aswakul, "Computer simulation study of vehicle type classification using machine learning techniques with mobile phone location data," accepted for SUMO User Conference 2020, 2020. |
Computer Simulation Study of Vehicle Type Classification Using Machine Learning Techniques with Mobile Phone Location Data |
| Soe Ye Htet and Choadit Aswakul, "Design and Implementation of Outdoor Fault-Tolerant In-Band Software-Defined Wireless Mesh Network with Observable Node Reachability," submitted for journal, 2020. |
Design and Implementation of Outdoor Faulty-Tolerant In-Band Software-Defined Wireless Mesh Network with Observable Node Reachability |
| Phoo Phoo Thet Lyar Tun and Chaodit Aswakul, "Design and Preliminary Functionality Test of Road Network Traffic Monitoring System Based on Indoor SDWMN In-Band Architecture", submitted for conference, 2020. |
Design and Preliminary Functionality Test of Road Network Traffic Monitoring System Based on Indoor SDWMN In-Band Architecture |
| Source Code and Dataset from Link Quality Measurement |
Link Quality Measurement |
| Phoo Phoo Thet Lyar Tun, Chaodit Aswakul and JongWon Kim, "Prototyping a Small-Scaled Resilient Software-Defined Wireless Mesh Network with Dual-band Data and Out-of-band Control Planes," presented at APAN50: Asia Pacific Advanced Network Conference Meeting 50, Hong Kong, 3-7 August 2020. |
Prototyping a Small-Scaled Resilient Software-Defined Wireless Mesh Network with Dual-band Data and Out-of-band Control Planes |
| Ei Ei Mon, Hideya Ochiai, Chaiyachet Saivichit and Chaodit Aswakul, "Recurrent and Non-recurrent Congestion Based Gridlock Detection on Chula-SSS Urban Road Network," EPiC Series in Computing, vol. 62, pp. 158-171, 2019, url = https://easychair.org/publications/paper/vxbj, doi = 10.29007/cxkb. |
Recurrent and Non-recurrent Congestion Based Gridlock Detection on Chula-SSS Urban Road Network |
| Thanapapas Horsuwan and Chaodit Aswakul, "Reinforcement Learning Agent under Partial Observability for Traffic Light Control in Presence of Gridlocks," EPiC Series in Computing, vol. 62, pp. 29-47, 2019, url = https://easychair.org/publications/paper/nsT5, doi = 10.29007/bdgn. |
Reinforcement Learning Agent under Partial Observability for Traffic Light Control in Presence of Gridlocks |
| Ei Ei Mon, Hideya Ochiai, Chaiyachet Saivichit and Chaodit Aswakul, "Traffic Anomaly Classification by Support Vector Machine with Radial Basis Function on Chula-SSS Urban Road Network," Proceedings of ICFCC 2019 Conference, 27th February – 1st March, 2019, 2019, Yangon, Myanmar. |
Traffic Anomaly Classification by Support Vector Machine with Radial Basis Function on Chula-SSS Urban Road Network |
| A. M. Htut, S. Y. Htet, K. Leevangtou, K. Kawila and C. Aswakul, "Testbed design of near real-time wireless image streaming with Apache Kafka for road traffic monitoring," 33rd International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC), pp. 188-191, 2018. |
Testbed design of near real-time wireless image sequence streaming cloud using Apache Kafka for road traffic monitoring application |
| Ei Ei Mon, Hideya Ochiai, Patrachart Komolkiti & Chaodit Aswakul, "Real-world sensor dataset for city inbound-outbound critical intersection analysis," Scientific Data. 2022 Jun 21;9(1):357. https://www.nature.com/articles/s41597-022-01448-6 |
Real-world sensor dataset for city inbound-outbound critical intersection analysis |
| Ei Ei Mon, Hideya Ochiai & Chaodit Aswakul, "Application of Traffic Light Control in Oversaturated Urban Network Using Multi-Agent Deep Reinforcement Learning," IEEE Access. 2024 May 6. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10521498 |
Application of Traffic Light Control in Oversaturated Urban Network Using Multi-Agent Deep Reinforcement Learning |