Reserve Capacity for a Road Network Under Optimized Fixed Time Traffic Signal Control

Ceylan, Halim; Bell, M.G.

doi:10.1080/15472450490437780

Cited by 47 publications

(40 citation statements)

References 3 publications

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“…1 Computational results are summarized in Table 1 for a comparative analysis at two distinct initial tolls. Three earlier well-known methods in solving the network design problem are also considered: the sensitivity analysis method (SAB) proposed by Yang & Yagar (1995), the Genetic Algorithm (GA) proposed by Ceylan & Bell (2004), and recently proposed Generalized Projected Subgradient (GPS) method by Chiou (2007). As it seen in Table 1, the SPSS approach improved the minimal toll revenue at two distinct initial tolls nearly by 18% and 16% while the SAB method only did by 8% and 6%.…”

Section: Numerical Calculationsmentioning

confidence: 99%

Optimization of Multi-Tiered Supply Chain Networks with Equilibrium Flows

Chiou¹

2008

Supply Chain

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Section: Numerical Calculationsmentioning

confidence: 99%

Optimization of Multi-Tiered Supply Chain Networks with Equilibrium Flows

Chiou¹

2008

Supply Chain

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“…Later, Yang's model was also referred to as the practical capacity by Kasikitwiwat and Chen [5]. In summary, although unrealistic, the reserve capacity model is more easy-to-use 2 Journal of Advanced Transportation and has been adopted widely in many researches [7][8][9][10]. The ultimate capacity and practical capacity model are more practical but have more parameters to be calibrated when applied, and the formulated models are still difficult to solve [11].…”

Section: Introductionmentioning

confidence: 99%

Robust Evaluation for Transportation Network Capacity under Demand Uncertainty

Jiang

Cheng

et al. 2017

Journal of Advanced Transportation

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As more and more cities in worldwide are facing the problems of traffic jam, governments have been concerned about how to design transportation networks with adequate capacity to accommodate travel demands. To evaluate the capacity of a transportation system, the prescribed origin and destination (O-D) matrix for existing travel demand has been noticed to have a significant effect on the results of network capacity models. However, the exact data of the existing O-D demand are usually hard to be obtained in practice. Considering the fluctuation of the real travel demand in transportation networks, the existing travel demand is represented as uncertain parameters which are defined within a bounded set. Thus, a robust reserve network capacity (RRNC) model using min-max optimization is formulated based on the demand uncertainty. An effective heuristic approach utilizing cutting plane method and sensitivity analysis is proposed for the solution of the RRNC problem. Computational experiments and simulations are implemented to demonstrate the validity and performance of the proposed robust model. According to simulation experiments, it is showed that the link flow pattern from the robust solutions to network capacity problems can reveal the probability of high congestion for each link.

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“…Results emphasized that the increase on the reserve capacity is not affected subsequently with the increase in information of the road users. Ceylan and Bell [9] developed a novel approach to calculate reserve capacity of a signalized network. In their study, optimum values of offsets, common cycle time, and the green times are investigated using genetic algorithm by taking into account stochastic user equilibrium link flows.…”

Section: Introductionmentioning

confidence: 99%

Reserve Capacity Model for Optimizing Traffic Signal Timings with an Equity Constraint

Başkan¹,

Ozan²

2017

Highway Engineering

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This paper represents a solution algorithm for optimizing traffic signal timings in urban road networks by considering reserve capacity with an equity constraint. It is well known that the variation of signal timings in a road network may cause an inequity issue with regard to the travel costs of road users travelling between origin-destination (O-D) pairs. That is, the users may be influenced differently by changing traffic signal timings. In this context, the bilevel programming model is proposed for finding reserve capacity for signalized road networks by taking into account the equity issue. In the upper level, the reserve capacity is maximized with an equity constraint, whereas deterministic user equilibrium problem is dealt in the lower level. In order to solve the proposed model, a heuristic solution algorithm based on harmony search combined with a penalty function approach is developed. The application of the proposed model is illustrated for an example road network taken from a literature.

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Reserve Capacity for a Road Network Under Optimized Fixed Time Traffic Signal Control

Cited by 47 publications

References 3 publications

Optimization of Multi-Tiered Supply Chain Networks with Equilibrium Flows

Optimization of Multi-Tiered Supply Chain Networks with Equilibrium Flows

Robust Evaluation for Transportation Network Capacity under Demand Uncertainty

Reserve Capacity Model for Optimizing Traffic Signal Timings with an Equity Constraint

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