Optimizing Traffic Signals for Non-Uniform Arrivals Using Sparse Probe Data

With the field of traffic signal control progressing towards more adaptive, and computationally efficient models, the integration of sparse travel time data and advanced optimization techniques enhances the intersection performance. This paper presents a novel optimization framework for traffic control using sparse travel time data, with a field penetration rate as low as 10%. The paper focuses on developing mathematical models that can handle the variability in arrival rates both within and across signal cycles. By utilizing delay polygons, we accurately model and minimize the total intersection delay caused by non-uniform vehicle arrivals and signals, leading to optimal signal timing solutions. The model effectively prioritizes queue dissipation for highly saturated phases while minimizing overall intersection delay. The algorithm accommodates variations in phase-wise delays across cycles, indirectly reflecting changes in traffic demand. Additionally, the sample-based design demonstrates performance comparable to volume-based dynamic design in terms of average delay, average speed, and total travel time across cycles. With the innovative use of sample re-identification data obtained through various sensor technologies, the proposed algorithm is capable of delivering optimal control of time varying traffic demand with minimal data input.