Planning signal timing for oversaturated networks should minimize traffic delays, satisfy constraints on maximum queue lengths, and allow for time-varying demands. Gazis (1964) and Gazis and Potts (1965) were the first to systematically address signal timing for oversaturated conditions through the use of a semigraphical approach, which identifies phase switching policies to manage queue dispersion and the total delay in the network by minimizing green time loss during the oversaturated period (Green, 1968). Michalopoulos and Stephanopoulos (1977, 1978) developed a two-stage timing method which identifies the optimal switch-over point at which the timing strategies for undersaturated and oversaturated conditions should be interchanged. This work was extended to identify smoother switching strategies through modeling the discrete nature of cycles (Chang and Lin, 2000). The two-stage timing method was then coupled with TRANSYT-7F in an integrated approach, where TRANSYT-7F identifies signal timings for undersaturated intersections, and the two-stage model, for oversaturated intersections (Chang and Sun, 2004). While these approaches focus on switching between under- and oversaturated timing strategies, a second set of studies focuses on the identification of optimal cycle lengths and green times for oversaturated conditions alone. Lieberman and Chang (2005) used a mixed-integer linear programming approach for this problem, and heuristic optimization methods have also been successfully applied (Saito and Fan, 2000; Varia and Dhingra, 2004; Sun et al., 2006; Teklu et al., 2007; Maher, 2008). A GA approach was used to minimize total delay through identifying phase sequences and proportions of green times (Foy et al., 1992). GA is a heuristic search algorithm belonging to a class of algorithms known as evolutionary algorithms (Holland, 1975) that are based loosely on the process of natural evolution. GAs have been applied in various disciplines of civil engineering such as construction engineering (Al-Bazi and Dawood, 2010; Cheng and Yan, 2009), transportation engineering (Vlahogianni et al., 2007; Lee and Wei, 2010), highway engineering (Kang et al., 2009), design optimization (Adeli and Cheng, 1994a, 1994b; Hung and Adeli, 1994; Adeli and Kumar, 1995a, 1995b; Sarma and Adeli, 2000a, 2000b, 2001, 2002; Kim and Adeli, 2001; Mathakari et al., 2007), structural control (Jiang and Adeli, 2008), and environmental pollution (Martínez-Ballesteros et al., 2010). Hadi and Wallace (1993) developed a hybrid approach that couples a GA with the TRANSYT-7F program. The GA optimizes cycle length, phase sequence, and offsets, and TRANSYT-7F is used to optimize green splits. Park et al. (1999) developed a GA-based method to identify cycle lengths, green splits, offsets, and phase sequences. GA was also used in a study performed to capture the critical operational issues at signalized intersections and remove the blocking effects of different lane groups in oversaturated conditions (Liu and Chang, 2011). Another signal control methodology is formulated as a quadratic programming problem to minimize and balance the link queues, therefore minimizing the risk of queue spillback (Aboudolas et al., 2010).
To better facilitate the use of a GA-based approach to control oversaturated conditions, the signal timing optimization model was reformulated to include in the objective function the total number of vehicles processed by the network during the oversaturated period (Abu-Lebdeh and Benekohal, 1997, 2000). New models for estimating the capacities of oversaturated arterials were developed based on the capacities of individual intersections, vehicle queue lengths, and offsets. The GA was applied to coordinate signals to maximize throughput, and results demonstrated a control strategy that avoided queue spillback and de facto red. This strategy was extended to coordinate oversaturated signals along an arterial that crosses multiple, parallel coordinated arterials (Girianna and Benekohal, 2002a, 2004). The research presented here utilizes this new objective function and compares the use of ACO to the performance of the previously reported GA-based approach.