Modeling traffic flow interrupted by incidents

Baykal‐Gürsoy, Melike; Xiao, Weihua; Özbay, Kaan

doi:10.1016/j.ejor.2008.01.024

Cited by 103 publications

(59 citation statements)

References 35 publications

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“…Baykal-Gursoy and Xiao [5], Baykal-Gursoy, Xiao and Ozbay [6], D'Auria [8]), where the system alternates randomly between two phases: phase However, there is a fundamental distinction between the current process and the ones mentioned above: our system is not a work-conserving one, whereas all the above processes are. Specifically, when a switch from phase J = 1 to phase J = 0 occurs, it always brings the system to state (0, 0), flushing all present customers out of the system.…”

Section: Balance Equations and Generating Functionsmentioning

confidence: 96%

Queues with system disasters and impatient customers when system is down

Yechiali

2007

Queueing Syst

122

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Consider a system operating as an M/M/c queue, where c = 1, 1 < c < ∞, or c = ∞. The system as a whole suffers occasionally a disastrous breakdown, upon which all present customers (waiting and served) are cleared from the system and lost. A repair process then starts immediately. When the system is down, inoperative, and undergoing a repair process, new arrivals become impatient: each individual customer, upon arrival, activates a random-duration timer. If the timer expires before the system is repaired, the customer abandons the queue never to return. We analyze this model and derive various quality of service measures: mean sojourn time of a served customer; proportion of customers served; rate of lost customers due to disasters; and rate of abandonments due to impatience.

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Section: Balance Equations and Generating Functionsmentioning

confidence: 96%

Queues with system disasters and impatient customers when system is down

Yechiali

2007

Queueing Syst

122

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“…They also show that for very small value(s) of traffic intensity, there exists a direct relationship between the departure size and the utility function of the traffic junction. Allowing a very small number(s) of cars (5,6) to depart at the turn of the green cycle phase reveals that traffic build up occurs over longer cycle times in order to clear when compared to a greater number(s) of departing cars. When the light turns green, and cars depart from the queue, the study has shown that the number of cars leaving the service point may not necessarily be constant.…”

Section: Resultsmentioning

confidence: 99%

“…Excess demand for road space, irregular occurrences such as traffic accidents, vehicle disablements, spilled loads and hazardous materials were identified by Baykal-Gursoy et al [5] as some causes of road flow reduction.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Under the impact of the interruption, all servers work at lower efficiency until the interruption is cleared. Varying the parameters with respect to the expected number of vehicles in the system (as given in [5]), the impact of each parameter of E(X) was presented. The steady-state performance measure (expected number of vehicles in the system) was presented for m/m/2 and m/m/3 in [3] where they stated that "The analysis of the M/MSP/C queue with n server states clearly indicates that explicit solutions for the general case would be difficult to obtain''.…”

Section: Literature Reviewmentioning

confidence: 99%

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Traffic flow model at fixed control signals with discrete service time distribution

Igbinosun¹,

Omosigho²

2016

Croat. Oper. Res. Rev.

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Abstract.Most of the models of road traffic flow at fixed-cycle controlled intersection assume stationary distributions and provide steady state results. The assumption that a constant number of vehicles can leave the system during the green phase is unrealistic in real life situations. A discrete time queuing model was developed to describe the operation of traffic flow at a road intersection with fixed-cycle signalized control and to account for the randomness in the number of vehicles that can leave the system. The results show the expected queue size in the system when the traffic is light and for a busy period, respectively. For the light period, when the traffic intensity is less than one, it takes a shorter green cycle time for vehicles to clear up than during high traffic intensity (the road junction is saturated). Increasing the number of cars that can leave the junction at the turn of the green phase reduces the number of cycle times before the queue is cleared.

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“…The model can accurately predict the queue length caused by traffic incident. M Baykal-Gu¨rsoy et al 5 proposed a method to calculate the queue length caused by traffic incident on the road using M/M/C queuing model and studied the change of vehicle queue length under different traffic incident conditions. By learning the method of determining the research scope of traffic impact assessment, a study found that the methods for determining the scope of traffic incident mainly include road enclosure method, plume model method, and spherical extrapolation method.…”

Section: Introductionmentioning

confidence: 99%

Determination of the scope of traffic incident

Zheng

Xia

2017

Advances in Mechanical Engineering

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The scope of traffic incidents impacts the performance of traffic incident impact assessment. The inadequate impact scope of the traffic incident will weaken the performance of the traffic incident impact assessment and increase the cost of travel time and payment. This article mainly analyzes road enclosure method, plume model method, and spherical extrapolation method to determine the scope of impact of traffic incident. Advantages and limitations of these three models are presented. Based on these analyses, some improvements are proposed on the basis of the existing situation.

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Modeling traffic flow interrupted by incidents

Cited by 103 publications

References 35 publications

Queues with system disasters and impatient customers when system is down

Queues with system disasters and impatient customers when system is down

Traffic flow model at fixed control signals with discrete service time distribution

Determination of the scope of traffic incident

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