1. The entire decision horizon is divided into several stages. Each stage is 50 to
100 seconds long.
2. In each stage, the signal must be changed once and at most three signal
changes can be made. There could be many different signal change scenarios
in each stage. For each scenario, the resulted delay is calculated.
3. For each stage, the OSCS algorithm is applied. The optimal signal change
scenario is determined independently for each stage. As shown in Figure 8,
the inputs to any intermediate stage include queues of all approaches at the
end of previous stage, current signal status, and the last signal change. For all
feasible signal change scenarios, their corresponding delays are evaluated and
compared. The signal change scenario with the lowest delay value is stored
and used as the optimal solution for the current stage. The resulted queues,
signal status, and the last signal change information are passed on to the next
stage for further computation.
20
In his research, Gartner first presented an isolated intersection traffic control
example using a dynamic programming approach, later named as OPAC-1 (41), that was
similar to DYPIC (29). Gartner discussed that though this dynamic programming
approach can guarantee global optimality, it is not suitable for real time applications due
to the excessive computation time and the requirement of exact traffic arrival data.
Based on OPAC-1, Gartner proposed a simplified control algorithm using Optimal
Sequential Constrained Search (OSCS) algorithm instead of dynamic programming (8).
The resulted new control method was referred to as OPAC-2. The OSCS algorithm
requires less computation time but can produce results that are close to the optimal ones
produced by the dynamic programming approach. However, the OSCS algorithm is less
straightforward compared with the dynamic programming approach. To implement the
OSCS algorithm, there are three steps to follow