Comparison of delay estimation models for signalised intersections using field observations in Shanghai

Wang, Zhongyu; Chen, Shiyu; Yang, Hang; Wu, Bing; Wang, Yinhai

doi:10.1049/iet-its.2014.0280

Cited by 9 publications

(6 citation statements)

References 9 publications

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“…The objective of analysing the U‐turn median opening is to improve the utilisation rate of the green time by reducing the mutual interference between the left‐turn and U‐turn movements at intersections with double left‐turn lanes. Therefore, the MNPV per unit time, which is referred to as the saturation flow rate [21–23], is taken as the objective of the optimisation model. The L value corresponding to the maximum saturation flow rate is the optimal location of U‐turn median opening.…”

Section: Optimisation Methods Of the Location Of The U‐turn Median Omentioning

confidence: 99%

Optimal location of the U‐turn at a signalised intersection with double left‐turn lanes

Sun

et al. 2018

IET Intelligent Transport Systems

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Section: Optimisation Methods Of the Location Of The U‐turn Median Omentioning

confidence: 99%

Optimal location of the U‐turn at a signalised intersection with double left‐turn lanes

Sun

et al. 2018

IET Intelligent Transport Systems

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“…Furthermore, the methods proposed in [5,7] are not designed for real-time operation, while the study in [6] focuses only on traffic delay in work zones. One of the most recent studies in [16] provides insight on the capabilities of existing models but the focus is on signalized intersections.…”

Section: Journal Of Advanced Transportationmentioning

confidence: 99%

“…. , 16 are used for training, while 2 is used for testing, and so on. Table 2 shows that the proposed algorithm achieves higher detection rate and lower false alarm rate than the benchmark algorithm.…”

Section: Assessment Using Real-world Data Collected In Thailandmentioning

confidence: 99%

A Streaming Algorithm for Online Estimation of Temporal and Spatial Extent of Delays

Hiri-O-Tappa

Thajchayapong

Chaovalit

et al. 2017

Journal of Advanced Transportation

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Knowing traffic congestion and its impact on travel time in advance is vital for proactive travel planning as well as advanced traffic management. This paper proposes a streaming algorithm to estimate temporal and spatial extent of delays online which can be deployed with roadside sensors. First, the proposed algorithm uses streaming input from individual sensors to detect a deviation from normal traffic patterns, referred to as anomalies, which is used as an early indication of delay occurrence. Then, a group of consecutive sensors that detect anomalies are used to temporally and spatially estimate extent of delay associated with the detected anomalies. Performance evaluations are conducted using a real-world data set collected by roadside sensors in Bangkok, Thailand, and the NGSIM data set collected in California, USA. Using NGSIM data, it is shown qualitatively that the proposed algorithm can detect consecutive occurrences of shockwaves and estimate their associated delays. Then, using a data set from Thailand, it is shown quantitatively that the proposed algorithm can detect and estimate delays associated with both recurring congestion and incident-induced nonrecurring congestion. The proposed algorithm also outperforms the previously proposed streaming algorithm.

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“…Murat et al [12] proposed Multiple Regression delay model to establish the relationship between cyclic vehicle queues and vehicular delays. Wang Z et al [13] compared various delay models such as HCM 2000, Shanghai adjusted, Webster model, and deterministic queuing modes. HCM 2000 model and the Shanghai adjusted model perform satisfactorily and show similar outcomes at different volume-capacity (v/c) ratios.…”

Section: Introductionmentioning

confidence: 99%

Delay models for Signalised Intersections with Vehicle Actuated Controlled system in Heterogeneous Traffic Conditions

Borigarla

Chandrasekar²,

Ramya³

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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Delay is a critical parameter used to assess the performance of signalised intersections. Non-deterministic arrival and departure rates of vehicles at intersectionseffect the determination of delay. Furthermore, driver behaviour, vehicle characteristics, and type of signal control system at intersectionwere also influencing parameters. Previous literature has offered analytical and empirical models to estimate the average delay using hourly traffic flow data and a fixed cycle time. In India, major signalised intersections on urban arterial roads are controlled with vehicle actuated controlled system. In the present study, Simpson’sone third rule was applied to estimate the overall delay by using arrival rate and departure rate of vehicles in everycycle. In addition, the current studydevelopedregression-based delay model and modified the Indo-HCM model by changing incremental delay term in themodels. Thisensuredaccurateestimation of the delays at signalised intersections in both the VAC and fixed signal control systems. The proposed modelswere statistically significant, with R2values of 0.89 and 0.51, respectively.

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Comparison of delay estimation models for signalised intersections using field observations in Shanghai

Cited by 9 publications

References 9 publications

Optimal location of the U‐turn at a signalised intersection with double left‐turn lanes

Optimal location of the U‐turn at a signalised intersection with double left‐turn lanes

A Streaming Algorithm for Online Estimation of Temporal and Spatial Extent of Delays

Delay models for Signalised Intersections with Vehicle Actuated Controlled system in Heterogeneous Traffic Conditions

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