Estimating the minimum delay optimal cycle length based on a time-dependent delay formula

Zakariya, Ahmed Y.; Rabia, Sherif I.

doi:10.1016/j.aej.2016.07.029

Cited by 30 publications

(31 citation statements)

References 1 publication

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“…In contrast, an excessively large cycle length provides relatively longer green splits for one or all approaches, which again increases average delay for vehicles waiting idly in queues in other directions. This general pattern of cycle length vs. delay is consistent with previous studies [85,86]. Figure 7a,b present cycle lengths vs. average vehicle relationship for Intersection-I and Intersection-II, respectively.…”

Section: Methods Validationsupporting

confidence: 89%

Intelligent Intersection Control for Delay Optimization: Using Meta-Heuristic Search Algorithms

et al. 2020

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Traffic signal control is an integral component of an intelligent transportation system (ITS) that play a vital role in alleviating traffic congestion. Poor traffic management and inefficient operations at signalized intersections cause numerous problems as excessive vehicle delays, increased fuel consumption, and vehicular emissions. Operational performance at signalized intersections could be significantly enhanced by optimizing phasing and signal timing plans using intelligent traffic control methods. Previous studies in this regard have mostly focused on lane-based homogenous traffic conditions. However, traffic patterns are usually non-linear and highly stochastic, particularly during rush hours, which limits the adoption of such methods. Hence, this study aims to develop metaheuristic-based methods for intelligent traffic control at isolated signalized intersections, in the city of Dhahran, Saudi Arabia. Genetic algorithm (GA) and differential evolution (DE) were employed to enhance the intersection’s level of service (LOS) by optimizing the signal timings plan. Average vehicle delay through the intersection was selected as the primary performance index and algorithms objective function. The study results indicated that both GA and DE produced a systematic signal timings plan and significantly reduced travel time delay ranging from 15 to 35% compared to existing conditions. Although DE converged much faster to the objective function, GA outperforms DE in terms of solution quality i.e., minimum vehicle delay. To validate the performance of proposed methods, cycle length-delay curves from GA and DE were compared with optimization outputs from TRANSYT 7F, a state-of-the-art traffic signal simulation, and optimization tool. Validation results demonstrated the adequacy and robustness of proposed methods.

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Section: Methods Validationsupporting

confidence: 89%

Intelligent Intersection Control for Delay Optimization: Using Meta-Heuristic Search Algorithms

et al. 2020

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“…However, until now, this formula has been limited to calculate the optimal cycle from historical data for a pre-timed traffic signal control. There are many modified versions of Webster minimum delay optimal cycle formula [4]. Although this method has been decades old, it is still applicable to modern systems.…”

Section: The Related Workmentioning

confidence: 99%

“…The basic principle of this approach is that at the end of each cycle the optimal cycle length for the next cycle is calculated as in (1). This formula is suggested in [4] and referred to as a modified version of Webster's optimal cycle formula…”

Section: A the Calculation Of Cycle Lengthmentioning

confidence: 99%

Fuzzy Logic and Webster’s Optimal Cycle Based Decentralized Coordinated Adaptive Traffic Control Method

Ali

Durdu

Çeltek

et al. 2020

ELEKTRON ELEKTROTECH

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Traffic control systems for an urban traffic management play an important role in reducing congestion and the negative effects of social and economic aspects. In this paper, the coordinated control method for an arterial road network is proposed. The proposed method is based on fuzzy logic and Webster optimum cycle formula. It is a cyclic method, which means that all-feasible phases at the intersection are get at least a minimum green signal during each cycle. These minimum green times can be used for pedestrian crossing purposes. This method eliminates the starvation that occurs at minor roads due to the non-cyclic strategy. The proposed method is investigated in both coordinated and isolated circumstances. It is compared with non-optimized fixed time control and the cyclic backpressure strategy suggested in the literature. The cyclic backpressure strategy was selected due to its similarity with our proposed method. Based on the obtained results, the adaptive fuzzy logic and Webster based coordinated method outperforms the other methods in terms of the average of waiting time, travel time, travel speed, and queue lengths. In addition, the result achieved from a coordinated situation slightly superior that obtained from isolated situation, which means the proposed method provides good performance both in an isolated and coordinated situations.

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“…Lost time estimation methods that has been distinguished by USA, Japan, Germany and Australia is equal to the sum of the intergreen times (I) [7]. An increasing of lost time may effects on the increasing of optimal cycle length, and as a consequences, the intersection delay increases [8].…”

Section: Introductionmentioning

confidence: 99%

Measuring Impact of Traffic Parameters in Adaptive Signal Control through Microscopic Simulation

Aminzal¹,

Rohani²

2020

IJACSA

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This paper aims to exploit the traffic parameters setting in adaptive traffic control. In this study, is known as Dynamic Timing Optimiser (DTO). DTO is an online algorithm, uses real-time optimisation in estimating cycle length according to fluctuations arrival flow registered from the detector. DTO cycle time estimation is also incorporated with preset parameters including saturation flow rate (s) and lost time (L). However, these traffic flow parameters commonly inputted as one deterministic value which adopted for the whole day. For example, presumed constant of saturation flow rate (s) do not accurately represent an actual oversaturated condition. The effects of employing inaccurate saturation flow rate (s) lead to the underestimation of cycle length. Therefore, a set of parameters value is applied and tested encompass of default value and adjusted value that implied a heaviest traffic condition through microscopic simulation. This resulted in outcomes of intersection performance in terms of intersection delay, travel time and throughput. According to simulation result, saturation flow rate (s) parameters show a great influence in cycle length optimisation compared to lost time (L) parameter. Employing a realistic saturation flow rate (s) while inputting parameters in DTO according to real traffic conditions contribute to a less intersection delay. In addition, the study revealed that a longer lost time (L) configured in the signal system, a longer cycle length generated by DTO algorithm. As predicted, high delay occurs during long cycle length yet benefited in allowing a higher throughput.

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Estimating the minimum delay optimal cycle length based on a time-dependent delay formula

Cited by 30 publications

References 1 publication

Intelligent Intersection Control for Delay Optimization: Using Meta-Heuristic Search Algorithms

Intelligent Intersection Control for Delay Optimization: Using Meta-Heuristic Search Algorithms

Fuzzy Logic and Webster’s Optimal Cycle Based Decentralized Coordinated Adaptive Traffic Control Method

Measuring Impact of Traffic Parameters in Adaptive Signal Control through Microscopic Simulation

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