Dynamic Intersection Signal Control Optimization (DISCO) is a dynamic traffic signal control optimization software for network-wide junctions in urban areas.
Regional Coordination
DISCO is developed based on the meso-level Cell Transmission Model (CTM) with fast simulation speed where the traffic flow characteristics of dynamic, spatial queuing and stochastics are well captured. The advantage of fast simulation enables optimisation of signal timing plans from a single isolated junction, to coordination of multiple junctions in a region in one go.
Cloud computing and parallel computing via API
The API coding environment of DISCO empowers the applications of cloud computing and parallel computing for users with various speed and resource considerations.
AI-engine and Learning-based methods for signal optimization
AI-engine and Learning-based methods for signal control optimisation
New optimisers are continually added to DISCO for realisations of the novel research effort in AI-engine and learning-based methods. The API coding environment also supports user-defined optimiser.
Based on the pioneer research effort on the cell transmission model for traffic signal control by Professor Hong K. Lo (Lo, 1999; 2001), the first demo version of DISCO was developed by Professor Lo and his team at the Hong Kong University of Science and Technology (HKUST) in the early 2000s. The team members included Mr. Elbert Chang, Mr. Joe Chan, Mr. Eric Hung, and Dr. Andy Chow.
The first demo version of DISCO software provided explicit illustrations of simulating traffic flow through CTM given an existing signal timing plan, and offered options for optimizing a new timing plan using a genetic algorithm. It provided the basic input and output user interface for setting up intersections and signal plans, and presented a traffic animation display. Targeting small networks for demonstration and research purposes, the first version is far from an application for large-scale network operation.
With the subsequent research effort by Professor Lo and collaborators on speeding up optimization (Lo et al., 2001; Lo and Chow, 2002; Lo and Chow, 2004; Chow and Lo, 2007) and addressing the stochastic traffic demand property (Lo, 2006; Ma, An, Lo, 2016; Huang, Li, Lo, 2018; Li, Huang, and Lo, 2018), a solid theoretical foundation of a traffic signal control software was established for real-life practical applications.
DISCO software and the peripheral modules were applied in Hong Kong, including the new signal placement of Pok Oi Roundabout and Tai Tam Road adaptive traffic signal control.
History of DISCO
-
Lo, H.K. 1999. A novel traffic signal control formulation. Transportation Research Part A, 33(6), 433-448.
-
Lo, H.K. 2001. A cell-based traffic control formulation: strategies and benefits of dynamic timing plans. Transportation Science, 35(2), 148-164.
-
Lo, H.K., Chow, H.F. 2002. Adaptive traffic control system: control strategy, prediction resolution, and accuracy. Journal of Advanced Transportation: Intelligent Transportation Systems Special Issue, 36 (3), 323-347.
-
Lo, H.K. 2006. A reliability framework for traffic signal control. IEEE Transactions on Intelligent Transportation Systems, 7(2), 250-260.
-
Lo, H.K., Chang, E., Chan, Y. C. 2001. Dynamic network traffic control. Transportation Research Part A, 35(8), 721-744.
-
Lo, H.K., Chow, A., 2004. Control strategies for oversaturated traffic. ASCE Journal of Transportation Engineering, 130(4), 466-478.
-
Li, L.,Huang, W., Lo, H.K. 2018. Adaptive coordinated traffic control for stochastic demand. Transportation Research Part C, 31-51.
-
Ma, W., An, K., Lo, H.K., 2016. Multi-stage stochastic program to optimize signal timings under coordinated adaptive control. Transportation Research Part C, 72, 342-359.
-
Chow, A.H.F., Lo, H.K. 2007. Sensitivity analysis of signal control with physical queuing: Delay derivatives and an application. Transportation Research Part B, 41(4), 462-477.
-
Huang, W., Li, L., Lo, H.K. 2018. Adaptive traffic signal control with equilibrium constraints under stochastic demand. Transportation Research Part C, 394-413.