Traditionally, offset optimization for coordinated traffic signals is based on average travel
times between intersections and average traffic volumes at each intersection, without consideration
of the stochastic nature of field traffic. Using the archived high-resolution traffic
signal data, in this paper, we develop a data-driven arterial offset optimization model
which will take two well-known problems with vehicle-actuated signal coordination into
consideration: the early return to green problem and the uncertain intersection queue
length problem. To account for the early return to green problem, we introduce the concept
of conditional distribution of the green start times for the coordinated phase. To handle the
uncertainty of intersection queue length, we adopt a scenario-based approach that generates
optimal offsets using a series of traffic demand scenarios as the input to the optimization
model. Both the conditional distributions of the green start times and traffic demand
scenarios can be obtained from the archived high-resolution traffic signal data. Under different
traffic conditions, queues formed by side-street and main-street traffic are explicitly
considered in the derivation of intersection delay. The objective of this offset optimization
model is to minimize total delay for the main coordinated direction and at the same time it
considers the performance of the opposite direction. Due to the model complexity, a
genetic algorithm is adopted to obtain the optimal solution. The proposed methodology
was tested on a major arterial (TH55) in Minnesota. The results from the field implementation
show that the proposed model can reduce travel delay of coordinated direction significantly
without compromising the performance of the opposite approach
Traditionally, offset optimization for coordinated traffic signals is based on average traveltimes between intersections and average traffic volumes at each intersection, without considerationof the stochastic nature of field traffic. Using the archived high-resolution trafficsignal data, in this paper, we develop a data-driven arterial offset optimization modelwhich will take two well-known problems with vehicle-actuated signal coordination intoconsideration: the early return to green problem and the uncertain intersection queuelength problem. To account for the early return to green problem, we introduce the conceptof conditional distribution of the green start times for the coordinated phase. To handle theuncertainty of intersection queue length, we adopt a scenario-based approach that generatesoptimal offsets using a series of traffic demand scenarios as the input to the optimizationmodel. Both the conditional distributions of the green start times and traffic demandscenarios can be obtained from the archived high-resolution traffic signal data. Under differenttraffic conditions, queues formed by side-street and main-street traffic are explicitlyconsidered in the derivation of intersection delay. The objective of this offset optimizationmodel is to minimize total delay for the main coordinated direction and at the same time itconsiders the performance of the opposite direction. Due to the model complexity, agenetic algorithm is adopted to obtain the optimal solution. The proposed methodologywas tested on a major arterial (TH55) in Minnesota. The results from the field implementationshow that the proposed model can reduce travel delay of coordinated direction significantlywithout compromising the performance of the opposite approach
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