A dynamic evacuation network optimization problem with lane reversal and crossing elimination strategies

Xie, Chi; Lin, Dung Ying; Waller, S. Travis

doi:10.1016/j.tre.2009.11.004

Cited by 175 publications

(109 citation statements)

References 28 publications

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“…The departure response curve has been assumed to follow many different distributions. Some examples are instantaneous departure (Lewis 2001;Chen and Zhan 2004;Chiu et al 2006), a Uniform distribution (Liu et al 2006;Yuan et al 2006), a Rayleigh distribution (Tweedie et al 1986), a Poisson distribution (Cova and Johnson 2002), a Weibull distribution (Jonkman 2007;Lindell 2008) or sigmoid curve (Kalafatas and Peeta 2009;Xie et al 2010). The Weibull distribution and sigmoid curve are most often used and claimed to be most realistic.…”

Section: Sequential Travel Demand Modelmentioning

confidence: 99%

A review on travel behaviour modelling in dynamic traffic simulation models for evacuations

2011

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Dynamic traffic simulation models are frequently used to support decisions when planning an evacuation. This contribution reviews the different (mathematical) model formulations underlying these traffic simulation models used in evacuation studies and the behavioural assumptions that are made. The appropriateness of these behavioural assumptions is elaborated on in light of the current consensus on evacuation travel behaviour, based on the view from the social sciences as well as empirical studies on evacuation behaviour. The focus lies on how travellers' decisions are predicted through simulation regarding the choice to evacuate, departure time choice, destination choice, and route choice. For the evacuation participation and departure time choice we argue in favour of the simultaneous approach to dynamic evacuation demand prediction using the repeated binary logit model. For the destination choice we show how further research is needed to generalize the current preliminary findings on the location-type specific destination choice models. For the evacuation route choice we argue in favour of hybrid route choice models that enable both following instructed routes and en-route switches. Within each of these discussions, we point at current limitations and make corresponding suggestions on promising future research directions.

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Section: Sequential Travel Demand Modelmentioning

confidence: 99%

A review on travel behaviour modelling in dynamic traffic simulation models for evacuations

2011

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“…Furthermore, the constraints (27)- (29) limit the traffic routing in multiple ways. (27) ensures that the number of traffic flows streaming into cell j is not exceeding the number of available lanes in cell j. Hence merging processes at junctions due to traffic flows from different directions are not allowed any more.…”

Section: Consideration Of Lanes and Limiting Traffic Flowsmentioning

confidence: 99%

“…However, there might exist some situations where these constraints, especially constraint (27) and (32) might result in no feasible solution if the number of lanes is relatively low and many junctions exist. As an example, imagine a t-junction with three one-lane streets whereas two of these streets are deadlocks.…”

Section: Consideration Of Lanes and Limiting Traffic Flowsmentioning

confidence: 99%

“…As an example, imagine a t-junction with three one-lane streets whereas two of these streets are deadlocks. Now, these two (deadlock) streets have to use the (non-deadlock) street in order to be evacuated, but suchlike situations are forbidden by (27) and (32). In such cases, these constraints have to be relaxed and a new constraint i∈I y i jt ≤ Q jt for all cells j ∈ I and periods t ∈ T must be added in order to ensure that traffic flow capacities are not exceeded.…”

Section: Consideration Of Lanes and Limiting Traffic Flowsmentioning

confidence: 99%

“…There are numerous publications utilizing the basic ideas of the CTM, e.g. [4,10,15,23,26,27,31]: Chiu 407 et al [4], Kalafatas & Peeta [10], Liu et al [15], Shen et al [23], Tuydes & Ziliaskopoulos [26] and Xie et al [27] presented optimization models based on the Cell-Transmission equations for evacuation planning which were applied to networks of different size. The seminal work of Ziliaskopoulos [31] contains a general model for dynamic traffic assignment problems.…”

Section: Introductionmentioning

confidence: 99%

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Extended cell-transmission-based evacuation planning in urban areas

Kimms

Maassen

2011

Pesqui. Oper.

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ABSTRACT.A well-known traffic simulation model bases on the idea to represent street sections by socalled cells where vehicles are streaming from one cell to another in order to give a realistic image of traffic flows. Its basic ideas were picked up recently to formulate an optimization model for evacuation planning purposes. We will address three major weaknesses of the recent model, namely the usage of a fixed single cell size, missing consideration of lanes as well as missing limitations for traffic flows and show how to overcome them. Stimulated by this, we formulate the extended Cell-Transmission-Based Evacuation Planning Model and a new optimization model to facilitate this model in terms of choosing the optimum cell size. Additionally, we will present an iterative simulation-based optimization approach in order to evaluate and to enhance results in a more realistic environment. This approach takes advantage of a standalone vehicle reallocation optimization model, additional traffic flow limitations as well as a state-of-the-art microscopic traffic simulation. We will demonstrate the effectiveness of this approach in a real-world example.

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