Reserve capacity of a signal-controlled road network

Wong, S. C.; Yang, Hai

doi:10.1016/s0191-2615(97)00002-7

Cited by 212 publications

(131 citation statements)

References 7 publications

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“…This network was introduced by Wong and Yang (1997) and has two O-D pairs, 7 links and 6 nodes, of which nodes 2 and 5 are signal-controlled intersections. The current O-D demand from nodes 1 to 6 is 18 veh/min, from nodes 3 to 4 is 6 veh/min.…”

Section: Results For Simple Networkmentioning

confidence: 99%

Mixed network design using hybrid scatter search

Khooban¹,

Farahani

Miandoabchi³

et al. 2015

European Journal of Operational Research

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This research proposes a bi-level model for the mixed network design problem (MNDP). The upper level problem involves redesigning the current road links' directions, expanding their capacity, and determining signal settings at intersections to optimize the reserve capacity of the whole system.The lower level problem is the user equilibrium traffic assignment problem. By proving that the optimal arc flow solution of the bi-level problem must exist in the boundary of capacity constraints, an exact line search method called golden section search is embedded in a scatter search method for solving this complicated MNDP. The algorithm is then applied to some real cases and finally, some conclusions are drawn on the model's efficiency.

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Section: Results For Simple Networkmentioning

confidence: 99%

Mixed network design using hybrid scatter search

Khooban¹,

Farahani

Miandoabchi³

et al. 2015

European Journal of Operational Research

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“…Functions (18) and (19) satisfy the CN principle of AVs. Functions (31)-(33) satisfy the logit-based mode choice.…”

Section: The Lower-level Problemmentioning

confidence: 99%

Optimal Signal Design for Mixed Equilibrium Networks with Autonomous and Regular Vehicles

Jiang

2017

Journal of Advanced Transportation

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A signal design problem is studied for efficiently managing autonomous vehicles (AVs) and regular vehicles (RVs) simultaneously in transportation networks. AVs and RVs move on separate lanes and two types of vehicles share the green times at the same intersections. The signal design problem is formulated as a bilevel program. The lower-level model describes a mixed equilibrium where autonomous vehicles follow the Cournot-Nash (CN) principle and RVs follow the user equilibrium (UE) principle. In the upper-level model, signal timings are optimized at signalized intersections to allocate appropriate green times to both autonomous and RVs to minimize system travel cost. The sensitivity analysis based method is used to solve the bilevel optimization model. Various signal control strategies are evaluated through numerical examples and some insightful findings are obtained. It was found that the number of phases at intersections should be reduced for the optimal control of the AVs and RVs in the mixed networks. More importantly, incorporating AVs into the transportation network would improve the system performance due to the value of AV technologies in reducing random delays at intersections. Meanwhile, travelers prefer to choose AVs when the networks turn to be congested.

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“…Wong and Yang 12 first incorporated the reserve capacity concept into a signal-controlled road network. The reserve capacity is defined as the largest multiplier applied to a given OD demand matrix that can be allocated to a network, so the solution is significantly affected by the predetermined OD matrix.…”

Section: Modelsmentioning

confidence: 99%

Alternative network robustness measure using system-wide transportation capacity for identifying critical links in road networks

Jiang

Cheng

2017

Advances in Mechanical Engineering

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In transportation planning works, critical links identification is helpful to evaluate the vulnerable parts of the designed network schemes. A new capacity-based network robustness index is presented for identifying critical links and evaluating the transportation system performance. It uses the change of the total network capacity as an evaluation measure. The advanced practical network capacity model is employed to estimate the throughput of transportation system. A sensitivity analysis based algorithm is also developed to solve the practical capacity model efficiently. The capacity-based network robustness index identifies the links which play critical roles in the transportation capacity of the whole network. The capacity-based network robustness index is an effective supplementary to the existing network evaluation indices. The experiments demonstrate the differences between the capacity-based network robustness index and the efficiencybased network robustness index. The capacity-based network robustness index can be used as a practical measure in the situations where the network robustness index is not significant.

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Reserve capacity of a signal-controlled road network

Cited by 212 publications

References 7 publications

Mixed network design using hybrid scatter search

Mixed network design using hybrid scatter search

Optimal Signal Design for Mixed Equilibrium Networks with Autonomous and Regular Vehicles

Alternative network robustness measure using system-wide transportation capacity for identifying critical links in road networks

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