Measuring Control Delay at Signalized Intersections

Quiroga, Cesar; Bullock, Darcy M.

doi:10.1061/(asce)0733-947x(1999)125:4(271)

Cited by 77 publications

(39 citation statements)

References 4 publications

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“…Thereafter, deceleration, stop and acceleration delays were estimated by using Eqs. (1), (2) and (3), respectively (Quiroga and Bullock, 1999) and the resulting delay times are presented in Table 2.…”

Section: 5mentioning

confidence: 99%

Characterisation of nanoparticle emissions and exposure at traffic intersections through fast–response mobile and sequential measurements

Goel

Kumar

2015

Atmospheric Environment

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Section: 5mentioning

confidence: 99%

Characterisation of nanoparticle emissions and exposure at traffic intersections through fast–response mobile and sequential measurements

Goel

Kumar

2015

Atmospheric Environment

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“…In this signal system, the longest cycle length (and the one that is used on Friday afternoons) is 116 seconds. 2 Without getting into details of the control plan, let us assume, for simplicity, that the absolute minimum green time for any arterial movement is 40 seconds. A motorist with exceptionally poor luck who is the first vehicle to stop on red at every signal would encounter an increase in travel time of approximately (116 -40) 6 4 5 304 seconds, or 5.1 minutes.…”

Section: Processing and Visualizing Raw Datamentioning

confidence: 99%

Roadway System Assessment Using Bluetooth-Based Automatic Vehicle Identification Travel Time Data

Day¹,

Brennan²,

Hainen³

et al. 2012

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ROADWAY SYSTEM ASSESSMENT USING BLUETOOTH-BASED AUTOMATIC VEHICLE IDENTIFICATION TRAVEL TIME DATAThis monograph is an exposition of several practice-ready methodologies for automatic vehicle identification (AVI) data collection systems. This includes considerations in the physical setup of the collection system as well as the interpretation of the data. An extended discussion is provided, with examples, demonstrating data techniques for converting the raw data into more concise metrics and views. Examples of statistical before-after tests are also provided. A series of case studies were presented that focus on various real-world applications, including the impact of winter weather on freeway operations, the economic benefit of traffic signal retiming, and the estimation of origin-destination matrices from travel time data. The technology used in this report is Bluetooth MAC address matching, but the concepts are extendible to other AVI data sources.

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“…In 1997, control delay replaced stopped delay. The relationship between control delay and stopped delay was explored (17,18), and a generalized delay model was built and validated (19,20).…”

Section: Literature Reviewmentioning

confidence: 99%

Signalized Intersection Level of Service Incorporating Safety Risk

Zhang

Prevedouros

2003

Transportation Research Record: Journal of the Transportation R

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A methodology that quantifies potential conflicts between left-turning vehicles and opposing through vehicles and pedestrians is presented. The methodology was based on and designed to be compatible with the Highway Capacity Manual 2000. A model was developed to combine delay and safety to get a comprehensive level-of-service indicator, the delay and safety index (DS). A case study of two intersections shows that if potential conflict is not considered, the signal timing plan with permitted left turns delivers a better level of service than that with protected left turns. However, if potential conflict is considered, the level of service under protected left-turn phasing is better than that under permitted left-turn phasing, according to the DS, when the safety weight factors exceed a certain value. The proposed method models the trade-off between safety and efficiency explicitly and considers both vehicle-to-vehicle and vehicle-to-pedestrian conflicts associated with left turns.

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Measuring Control Delay at Signalized Intersections

Cited by 77 publications

References 4 publications

Characterisation of nanoparticle emissions and exposure at traffic intersections through fast–response mobile and sequential measurements

Characterisation of nanoparticle emissions and exposure at traffic intersections through fast–response mobile and sequential measurements

Roadway System Assessment Using Bluetooth-Based Automatic Vehicle Identification Travel Time Data

Signalized Intersection Level of Service Incorporating Safety Risk

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