Fuzzy model of vehicle delay to determine the level of service of two-lane roads

Martín, Sergio Quintero; Romana, Manuel G.; Santos, Matilde

doi:10.1016/j.eswa.2015.12.049

Cited by 29 publications

(13 citation statements)

References 22 publications

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“…Delay can be classified as uniform or random. However, methods based on the random delay concept are more rational and have been adopted by several previous studies [42,43]. Intersection delay can also be classified as stopped delay, approach delay, travel time delay, time in queue delay, and control delay.…”

Section: Delay At Signalized Intersectionsmentioning

confidence: 99%

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: Delay At Signalized Intersectionsmentioning

confidence: 99%

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

et al. 2020

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“…The direct determination of the parameters in the above delay model is a very complex task because it is affected by many factors, most of which are uncertain and inaccurate. At present, mainstream delay models such as the Webster model [22], the Miller model [7], the HCM 2000 model [23] mainly calibrate parameters indirectly through the data collected by traditional detectors. However, the data collected by the traditional detector has the disadvantages of poor reliability and low comprehensiveness, and it is not real-time, resulting in a large gap between the results and the actual situation.…”

Section: Parameter Calibration Of Vehicle Delay Modelmentioning

confidence: 99%

Vehicle Delay Model Applied to Dynamic and Static Traffic Impact Analysis of Large Parking Lots

Li-ya

Chen

Cao³

et al. 2021

Applied Sciences

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With the surge of motor vehicle ownership and land intensification, plenty of large parking lots emerge as the times demand. Although it solves the problem of insufficient parking spaces, it intensifies the interaction between dynamic and static traffic. This paper presented an impact assessment method for the interaction of dynamic and static traffic flow in parking lots. Firstly, the average vehicle delay was selected as the evaluation index. The delay effect caused by the interaction of dynamic and static traffic flow was determined according to the driving path of vehicles. Then, the average vehicle delay models of arrival vehicles, departure vehicles, and road vehicles in the parking lot were established. Finally, for the parameters difficult to determine directly in the delay model, this paper proposed a method to calibrate the model parameters by using the simulation experimental data on the VISSIM platform. The results showed that the errors of the three models are within the controllable range, and the delay model parameters had high reliability and feasibility. The delay models can provide a quantitative basis for the parking lot management department to formulate regulation strategies and realize more refined information guidance and navigation in the parking lot.

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“…The authors reviewed the last decade of literature, starting from 2004, citing for the previous period three papers: by Vlahogianni, et al [15], for short-term traffic forecasting literature and related conceptual and methodological issues up to 2003; by Adeli [16] and by Van Lint and Van Hinsbergen [17], for neural network and artificial intelligence applications to short-term traffic forecasting. Traditional methods, generally based on quantitative measurements of average time between vehicles and thresholds, fail to take into account the inherent vagueness of the driving process [18]. As a result of different conditions and driver's perception, level of service is different at the signalized and unsignalized intersections [19].…”

Section: Los Analysis For Future Traffic Growthmentioning

confidence: 99%

Design Service Volume, Capacity, Level of Service Calculation and Forecasting for a Semi-urban City

Singh¹,

Saraswat²

2019

RIA

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The purpose of this research is to assess the traffic situations of the existing system by evaluating the Level of service as a key component. Traffic survey have been carried out for the analysis of AADT, Design service volume, capacity and Leve of Service of the city road networks. By using AADT, the LOS have been estimated for the analysis of future traffic condition. Peak hour traffic survey data have been collected for the analysis of AADT and Level of Service. LOS has been converted into percentile form for the analysis of the future LOS. It has been observed that the LOS of the roads of the major traffic operations have mostly same LOS in the morning & evening peak hours which signifies that the traffic movement and the pattern of movement are same in both the traffic session of peak hours. The rapid decrease in LOS has been presented in results, which means, LOS will reduce from time to time and the unimproved existing traffic system will create a problem for motorists and pedestrians. Hence, the existing traffic systems must be evaluated in order to cope with future traffic demands and problems.

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Fuzzy model of vehicle delay to determine the level of service of two-lane roads

Cited by 29 publications

References 22 publications

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

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

Vehicle Delay Model Applied to Dynamic and Static Traffic Impact Analysis of Large Parking Lots

Design Service Volume, Capacity, Level of Service Calculation and Forecasting for a Semi-urban City

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