Open Access
Fang, Fang
Graduate Program:
Civil Engineering
Doctor of Philosophy
Document Type:
Date of Defense:
April 16, 2004
Committee Members:
  • Lily Elefteriadou, Committee Chair/Co-Chair
  • Konstadinos Goulias, Committee Member
  • Martin T Pietrucha, Committee Member
  • Natarajan Gautam, Committee Member
  • Andrew Scanlon, Committee Member
  • Signal Optimization
  • Diamond Interchanges
  • Dynamic Programming
  • Traffic Simulation
  • Adaptive Signal Control
  • ITS (Intelligent Transportation Systems)
  • Real-time Signal Control
The signalization of two closely spaced intersections in interchanges presents a major challenge in providing efficient traffic operations within the highway system. In current practice, PASSER III is the only existing signal optimization model for diamond interchanges. It optimizes the pre-timed signal plan based on off-line demand and cannot adapt itself to fluctuating demand situations. The two most popular adaptive signal control systems (i.e., OPAC and RHODES) have some limitations: OPAC cannot guarantee a globally optimum solution, both systems cannot be applied to optimize phase sequence, and the arrival patterns used for optimization horizons may not be reliable. Therefore, this research develops a methodology and a corresponding implementation algorithm using dynamic programming (DP) to provide optimal signal control of diamond interchanges in response to real-time traffic fluctuations. The problem is formulated as to find a phase sequencing decision with a phase duration that makes a pre-specified performance measure minimized over a finite horizon that rolls forward. The problem is solved by DP forward value iterations method. The optimization performance measure can be, for example, delay, queue length, number of stops, or any combination of these. A horizon of 10 seconds is divided into an integral number of intervals, each having 2.5 seconds. The optimal signal switches over each 2.5-second interval are found for each horizon. The optimization process proceeds one horizon after another and is based on the advanced vehicle information obtained from loop detectors set back a certain distance from the stop-line. A dynamic model of future vehicular detections, arrivals and departures is developed at the microscopic level in this study to estimate the traffic flows at the stop-line for each horizon. The DP algorithm is coded in C++ language and dynamically linked to AIMSUN, a stochastic micro-simulation package, which is used for evaluation of the developed methodology. AIMSUN simulates a signalized diamond interchange instrumented with loop detectors that can provide vehicle counts and speeds to the DP algorithm. Based on this, the algorithm calculates the optimal phase sequence and the duration of each horizon, and passes them back to AIMSUN, which subsequently controls the interchange in real time. To enable the algorithm to implement practical scenarios, a so-called majority rolling technique was also developed. A sensitivity analysis using simulation results is conducted to study the characteristics of the DP algorithm. The results have shown that queue length and storage ratio defined performance measures are the best ones in minimizing system delays. A general rule of choosing the weight of an approach is that a larger weight applied for approaches having more demand. The study has also demonstrated the benefits of using dynamic weights without manually requiring the changing of weights. Dynamic weights can reduce system delay by 36 percent ?49 percent than fixed weights when the demand varies unpredictably every 15 minutes and is unbalanced. Moreover, the real-time DP algorithm has revealed the capability to accommodate various demand situations. The real-time DP algorithm has also been compared to two off-line optimization packages: PASSER III and TRANSYT-7F. The optimized pre-timed signal plans from TRANSYT-7F and PASSER III are implemented in AIMSUN, and the results are compared to those from the DP algorithm. The simulation has exhibited that the real-time adaptive signal algorithm is superior to PASSER III and TRANSYT-7F in handling demand fluctuations for medium to high flow scenarios when the field demand is increased from the one used in off-line optimization. The performance of the three algorithms is almost identical if the simulation demand is the similar to off-demand situation and dose not vary much.