1 research outputs found
Large-Scale Traffic Signal Offset Optimization
The offset optimization problem seeks to coordinate and synchronize the
timing of traffic signals throughout a network in order to enhance traffic flow
and reduce stops and delays. Recently, offset optimization was formulated into
a continuous optimization problem without integer variables by modeling traffic
flow as sinusoidal. In this paper, we present a novel algorithm to solve this
new formulation to near-global optimality on a large-scale. Specifically, we
solve a convex relaxation of the nonconvex problem using a tree decomposition
reduction, and use randomized rounding to recover a near-global solution. We
prove that the algorithm always delivers solutions of expected value at least
0.785 times the globally optimal value. Moreover, assuming that the topology of
the traffic network is "tree-like", we prove that the algorithm has near-linear
time complexity with respect to the number of intersections. These theoretical
guarantees are experimentally validated on the Berkeley, Manhattan, and Los
Angeles traffic networks. In our numerical results, the empirical time
complexity of the algorithm is linear, and the solutions have objectives within
0.99 times the globally optimal value