Crash Experience Warrant for Traffic Signals

Bonneson, James A; Laustsen, Kelly; Rodegerdts, Lee; Beaird, Scott

doi:10.17226/22292

Cited by 7 publications

(5 citation statements)

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“…Moreover, considering various tra c scenarios for the network to test the accuracy and reliability of the designed controller before and after modeling is illustrated in Table .1. These results are following the ndings of [51,52,53,54,55,56} who have explained the signi cant negative effects of x-time methods. The tra c volume will uctuate over time, so, designing the green time at the beginning of a phase for the present phase can be more compatible with the present tra c situation.…”

Section: Resultssupporting

confidence: 82%

Traffic Signal Prediction Based on ANFIS and Metaheuristic Algorithms Applied to a Vissim-Based Simulated Intersection

Shahkar

Oruç

Yelghi³

2023

Preprint

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Traffic signs are among the most important traffic equipment that are used in urban and non-urban areas and their purpose is to increase road volume and reduce delays while ensuring safe movement. Over the years, due to the growing trend of car production and car use, which has increased urban and road traffic, traffic signs have increased and become more diverse and efficient. One of these traffic control methods at intersections is the use of traffic signal scheduling techniques, which despite the advantages of this method has a major drawback, and that is that due to the dynamic behavior of traffic, this method has issues in predicting traffic signal timing especially in times of peak traffic. Designing appropriate green times for traffic signal lights with Adaptive Neuro-Fuzzy Inference System (ANFIS) technique in traffic signal controller is a feasible solution to tackle this issue in urban network congestion during peak hours. The capability to learn from experience is one of the specifications of ANFIS that makes these techniques appropriate to mention genuine universe challenges. ANFIS Traffic Signal Controller is used to control the traffic density of an intersection so that it can reduce the queue length and latency to the minimum optimal time expected. ANFIS Traffic Controller is an intelligent controller with automatic learning sets the appropriate green time for each phase of the traffic light at the beginning of the phase, and the system generally depends on traffic information. The controller uses metaheuristic algorithms to tune ANFIS parameters during learning time. The first part of this article concerns the simulation of an isolated intersection in a VISSIM simulator, for the generation of the new phase distribution of it (optimum cycle). In the second part, ANFIS with metaheuristic algorithms is modelized and applied to the VISSIM simulated intersection. In the modelized system, for training and testing phases, 90 samples of newly generated data sets from VISSIM, and 40 others were considered respectively. By tuning the parameters using metaheuristic algorithms, we tried to increase the accuracy of the ANFIS network prediction to demonstrate the high performance of the ANFIS network in predicting and controlling traffic in intersections. The predicting system uses real-time data to predict the signal time. Results of the analysis demonstrated that our predictor system with ANFIS-GA indicates better predicts in comparison to ANFIS-PSO and ANFIS-HS. The predictor system presented a total and Relative Mean Square Error of 2.9619 and 8.4215 in the train set and 4.2209 and 11.8501 in the test respectively. The designed prediction model in the field of complex data showed an acceptable level of reliability and flexibility.

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Section: Resultssupporting

confidence: 82%

Traffic Signal Prediction Based on ANFIS and Metaheuristic Algorithms Applied to a Vissim-Based Simulated Intersection

Shahkar

Oruç

Yelghi³

2023

Preprint

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“…Some recent studies have recommended the use of a different model form such as a log-normal model that would ensure the predicted CMF from a CMFunction would always be greater than zero. In the case of the log-normal model, it has been shown that the appropriate weight for a weighted log-normal regression model would instead be [CMF i /Var(CMF i )] ( 21 ).…”

Section: Resultsmentioning

confidence: 99%

Before–After Evaluation of the Realignment of Horizontal Curves on Rural Two-Lane Roads

Srinivasan¹,

Carter²,

Lyon³

et al. 2018

Transportation Research Record

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This study determined the crash modification factors (CMFs) associated with horizontal curve realignment using the before–after empirical Bayes method and compared the results from published CMFs from cross-sectional studies. This evaluation used data from rural, two-lane roads in California, North Carolina, and Ohio. The evaluation revealed a 68% reduction in total crashes, a 74% reduction in injury and fatal crashes, a 78% reduction in run-off-road and fixed object crashes, a 42% reduction in crashes during dark conditions, and an 80% reduction in wet-road crashes, all of which were statistically significant at the 95% confidence level. The results pertain to a range of site characteristics, the most important of which is the range of before and after degree of curve. The average degrees of curve in the before and after periods were 18.1 (with a minimum of 3.2 and a maximum of 52.1) and 6.9 (with a minimum of 0.0 and a maximum of 16.3), respectively. The average central angle of the curves was approximately 42° (with a minimum of 1 and a maximum of 117). The CMFs from this before–after evaluation are lower compared to CMFs estimated from two previous cross-sectional studies. There is a need for further research with a larger sample of sites to assess the reliability of the CMFs obtained from this before–after evaluation.

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“… a Maryland MUTCD Table 2B-1a ( 16 ) provides guidelines for conversion from stop to yield control. b Selected with consideration of the proposed crash warrant criteria for signals, NCHRP Project 07-18 ( 19 ). c Rounded calculation from the 1,000 and 1,800 units/day value using 7.8%, which is the peak-hour factor used in the economic analysis. d Value selected because (a) 1983 study in rural Michigan ( 20 ) found no statistical difference for stop-controlled and no-control intersections with major street volumes fewer than 1,000 vehicles per day and (b) the 1,000 value is less than the value selected for Yield sign control (1,800). e From NCHRP Report 320 ( 17 ). f Values currently in 2009 MUTCD with changes of vehicular volume to units. g Selected based on HCM ( 4 ) Exhibit 19-1, lowest control delay (s/veh) for Level of Service E (when v/c <=1.0). h As recommended in the Handbook for Designing Roadways for the Aging Population ( 22 ). …”

Section: Recommendationsmentioning

confidence: 99%

“… b Selected with consideration of the proposed crash warrant criteria for signals, NCHRP Project 07-18 ( 19 ). …”

Section: Recommendationsmentioning

confidence: 99%

Developing and Implementing Manual on Uniform Traffic Control Devices Criteria for Selecting Traffic Control for Unsignalized Intersections

Hawkins

Fitzpatrick

Brewer

2019

Transportation Research Record

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The 2009 United States Manual on Uniform Traffic Control Devices (MUTCD) includes guidance for the use of various types of traffic control at unsignalized intersections. Despite changes and advances in traffic engineering in recent decades, the MUTCD content related to selection of traffic control in Part 2B has seen only minor changes since 1971. The types of unsignalized traffic control addressed in the current research included no control, yield control, two-way stop control, and all-way stop control. The research team developed recommendations using information available from reviews of existing literature, policies, guidelines, and findings from an economic analysis along with the engineering judgment of the research team and panel. The researchers then developed recommended language for the next edition of the MUTCD for unsignalized intersections. This includes consideration of high-speed (rural) and low-speed (urban) conditions along with the number of legs at the intersection. Because the number of expected crashes at an intersection is a function of the number of legs, the decision on appropriate traffic control should also be sensitive to the number of legs present. The proposed language includes introductory general considerations, discusses alternatives to changing right-of-way control, and steps through the various forms of unsignalized control from least restrictive to most restrictive, beginning with no control and concluding with all-way stop control.

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Crash Experience Warrant for Traffic Signals

Cited by 7 publications

References 0 publications

Traffic Signal Prediction Based on ANFIS and Metaheuristic Algorithms Applied to a Vissim-Based Simulated Intersection

Traffic Signal Prediction Based on ANFIS and Metaheuristic Algorithms Applied to a Vissim-Based Simulated Intersection

Before–After Evaluation of the Realignment of Horizontal Curves on Rural Two-Lane Roads

Developing and Implementing Manual on Uniform Traffic Control Devices Criteria for Selecting Traffic Control for Unsignalized Intersections

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