Modeling the capacity and level of service of urban transportation networks

Yang, Hai; Bell, M.G.; Meng, Qiang

doi:10.1016/s0191-2615(99)00024-7

Cited by 155 publications

(104 citation statements)

References 17 publications

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“…The relationship between the link flow and route flow is represented in equation (13), where d pq a, r represents the link-route incidence indicator, which equals 1 if link a is on route r from p to q and 0 otherwise. All the variables must be nonnegative, that is, constraints (14)- (16).…”

Section: Upper-level Problemmentioning

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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Section: Upper-level Problemmentioning

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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“…The capacity of a network indicates the maximum attainable throughput of the given network 8 . In the conventional network, the capacity problem is very easy to state: if a network has capacities on arc flows, how to send as much flow as possible from an origin node to a destination node without exceeding the capacity of any arc.…”

Section: General Statementsmentioning

confidence: 99%

Hybrid Intelligent Algorithm for Determining Network Capacity with Transportation Time Reliability Constraints

Wang¹,

Li²,

Wang³

et al. 2011

IJCIS

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Motivated by a problem in the commodity transportation, a mathematical model is developed to calculate capacity of single-commodity network when the time reliability levels of transporting commodity between origin-destination pairs are constrained. We use a hybrid intelligent algorithm, in which genetic algorithm is embedded with Monte Carlo simulation to solve the optimization model. In the hybrid intelligent algorithm, the genetic algorithm is used to report the best path flow solutions and the Monte Carlo simulation is to check the feasibility of the chromosomes of genetic algorithm. With a computational experiment, the fact that network capacity decreases with the increase of the transportation time reliability level is validated. The efficacies of the developed procedures are examined by comparing the computational times of solving algorithm with that of previous work.

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“…Meanwhile, the researchers made a longterm effort to model and estimate the maximum throughput of transportation networks. The achievements include maxflow min-cut theorem [1], incremental assignment approach [2], and later bilevel programming models [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

“…But it assumes that the O-D distribution is totally variable, which may produce unrealistic results that cause the trip productions at some origins below their current levels. Furthermore, Yang et al [4] suggested that the new increased O-D demand pattern should be variable in both level and distribution, while the current travel demand is fixed. Later, Yang's model was also referred to as the practical capacity by Kasikitwiwat and Chen [5].…”

Section: Introductionmentioning

confidence: 99%

Robust Evaluation for Transportation Network Capacity under Demand Uncertainty

Jiang

Cheng

et al. 2017

Journal of Advanced Transportation

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As more and more cities in worldwide are facing the problems of traffic jam, governments have been concerned about how to design transportation networks with adequate capacity to accommodate travel demands. To evaluate the capacity of a transportation system, the prescribed origin and destination (O-D) matrix for existing travel demand has been noticed to have a significant effect on the results of network capacity models. However, the exact data of the existing O-D demand are usually hard to be obtained in practice. Considering the fluctuation of the real travel demand in transportation networks, the existing travel demand is represented as uncertain parameters which are defined within a bounded set. Thus, a robust reserve network capacity (RRNC) model using min-max optimization is formulated based on the demand uncertainty. An effective heuristic approach utilizing cutting plane method and sensitivity analysis is proposed for the solution of the RRNC problem. Computational experiments and simulations are implemented to demonstrate the validity and performance of the proposed robust model. According to simulation experiments, it is showed that the link flow pattern from the robust solutions to network capacity problems can reveal the probability of high congestion for each link.

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Modeling the capacity and level of service of urban transportation networks

Cited by 155 publications

References 17 publications

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

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

Hybrid Intelligent Algorithm for Determining Network Capacity with Transportation Time Reliability Constraints

Robust Evaluation for Transportation Network Capacity under Demand Uncertainty

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