References of "Tampère, Chris"
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See detailQuasi-dynamic traffic assignment with spatial queueing, control and blocking back
Smith, Mike; Huang, Wei; Viti, Francesco UL et al

in Transportation Research. Part B, Methodological (2019)

This paper introduces a steady-state, fixed (or inelastic) demand equilibrium model with explicit link-exit capacities, explicit bottleneck or queueing delays and explicit bounds on queue storage ... [more ▼]

This paper introduces a steady-state, fixed (or inelastic) demand equilibrium model with explicit link-exit capacities, explicit bottleneck or queueing delays and explicit bounds on queue storage capacities. The model is a quasi-dynamic model. The link model at the heart of this quasi-dynamic equilibrium model is a spatial queueing model, which takes account of the space taken up by queues both when there is no blocking back and also when there is blocking back. The paper shows that if this quasi-dynamic model is utilised then for any feasible demand there is an equilibrium solution, provided (i) queue storage capacities are large or (ii) prices are used to help impose capacity restrictions; the prices either remove queueing delays entirely or just reduce spatial queues sufficiently to ensure that blocking back does not occur at equilibrium. Similar results, but now involving the P0 control policy (introduced in Smith (1979a, 1987)) and two new variations of this policy (i.e., the spatial P0 control policy, and the biased spatial P0 control policy) are obtained. In these results, the control policies allow green-times to vary in response to prices as well as spatial queueing delays. These three policies are also tested on a small simple network. In these tests, the biased spatial version of P0 is much the best in reducing equilibrium delays (on this simple network). The paper further illustrates how the spatial queueing model works on simple networks with different merge models; it is demonstrated that equilibrium may be prevented by certain (fixed ratio) merge models. It is also shown in this case that equilibrium may be imposed on just the controlled area itself by a variety of (merge model, gating strategy) combinations. Opportunities for developing such combined gating and merging control strategies are finally discussed. [less ▲]

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See detailA global optimization heuristic for the decomposed static anticipatory network traffic control problem anticipatory network traffic control problem
Rinaldi, Marco UL; Tampére, Chris; Viti, Francesco UL

in Transportation Research Procedia (2017)

Developing traffic control strategies taking explicitly into account the route choice behavior of users has been widely recognized irregularities in the solution space shape, such as non-convexity and non ... [more ▼]

Developing traffic control strategies taking explicitly into account the route choice behavior of users has been widely recognized irregularities in the solution space shape, such as non-convexity and non-smoothness. In this work, we propose an extended as a very challenging problem. Furthermore, the inclusion of user behavior in optimization based control schemes introduces strong decomposition scheme for the anticipatory traffic control problem, based upon our previous contributions, which aims at i) reducing irregularities in the solution space shape, such as non-convexity and non-smoothness. In this work, we propose an extended the computational complexity of the problem by approaching it in a controller-by-controller fashion, and ii) internalizing specific decomposition scheme for the anticipatory traffic control problem, based upon our previous contributions, which aims at i) reducing constraints in the objective function, guiding the optimization process away from non-significant minima, such as flat regions. the computational complexity of the problem by approaching it in a controller-by-controller fashion, and ii) internalizing specific Through two small scale test networks and different, randomly chosen initial points, we compare how the proposed extension constraints in the objective function, guiding the optimization process away from non-significant minima, such as flat regions. influences optimization results with respect to our previously developed decomposed approach, as well as centralized schemes. Through two small scale test networks and different, randomly chosen initial points, we compare how the proposed extension influences optimization results with respect to our previously developed decomposed approach, as well as centralized schemes. [less ▲]

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See detailAn iterative learning approach for anticipatory traffic signal control on urban networks
Huang, Wei; Viti, Francesco UL; Tampere, Chris

in Transportmetrica B: Transport Dynamics (2017), 5(4), 407-430

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See detailA PAS-state constrained optimization formulation for the anticipatory network traffic control problem
Rinaldi, Marco UL; Tampère, Chris

Scientific Conference (2016, September)

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See detailImproving the accuracy of OD estimation from traffic counts employing a partial observabillity maximizing methodology
Rinaldi, Marco UL; Fakhraeiroudsari, Farzad; Viti, Francesco UL et al

Scientific Conference (2016, January)

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See detailAn iterative learning approach for anticipatory traffic signal control on urban networks
Huang, Wei; Viti, Francesco UL; Tampere, Chris

in Transportmetrica B: Transport Dynamics (2016)

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See detailA dual control approach for repeated anticipatory traffic control with estimation of network flow sensitivity
Huang, Wei; Viti, Francesco UL; Tampere, Chris

in Journal of Advanced Transportation (2016)

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See detailRepeated anticipatory network traffic control using iterative optimization accounting for model bias correction
Huang, Wei; Viti, Francesco UL; Tampere, Chris

in Transportation Research. Part C : Emerging Technologies (2016)

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See detailAn Optimization-Based Iterative Learning Method for Anticipatory Network Traffic Control
Huang, Wei; Ma, Wanjing; Viti, Francesco UL et al

in Proceedings of the 16th COTA International Conference of Transportation Professionals (2016)

Detailed reference viewed: 55 (3 UL)