Network planning and design

About: Network planning and design is a research topic. Over the lifetime, 12393 publications have been published within this topic receiving 229776 citations. The topic is also known as: network design.

On the impacts of locally adaptive signal control on urban network stability and the Macroscopic Fundamental Diagram

Abstract: Urban traffic networks are inherently unstable when congested. This instability causes a natural tendency towards spatially inhomogeneous vehicle distributions and less consistent and reproducible relationships between urban traffic variables. It is important to find ways to mitigate this unstable behavior since well-defined relationships between average network flow and density – the MFD – are useful to aid network design and control. This paper examines the impacts of locally adaptive traffic signals – e.g., those that allocate green times proportionally to upstream approach densities – on network stability and the MFD. A family of adaptive signal control strategies is examined on two abstractions of an idealized grid network using an analytical model and an interactive simulation. The results suggest that locally adaptive traffic signals provide stability when the network is moderately congested, which increases average flows and decreases the likelihood of gridlock. These benefits increase with the overall adaptivity of the signals. However, adaptive signals appear to have little to no effect on network stability or the MFD in heavily congested networks as vehicle movement becomes more constrained by downstream congestion and queue spillbacks. Under these conditions, other strategies should be used to mitigate the instability, such as adaptively routing drivers to avoid locally congested regions. These behaviors are verified using more realistic micro-simulations and are consistent with other observations documented in the literature.

An optimization approach for groundwater quality monitoring network design

Abstract: The optimal sampling plan for groundwater quality monitoring is formulated as a mixed integer programming (MIP) problem. A sampling plan consists of the number and locations of sampling sites as well as the temporal sampling frequency. The MIP network problem is defined by the minimization of the variance of estimation error subject to resource and unbiasedness constraints. The mean and covariance of the spatial/temporal variable (chloride concentration measurements) are derived from the advection-dispersion equation governing mass transport. The solution for the optimal sampling proceeds in two stages: (1) parameter estimation and (2) network optimization. The MIP model was successfully tested with a network design problem in a buried valley aquifer in Butler County, Ohio. The application illustrates the role of objective function, resource constraint, mass transport processes, and hydrogeologic setting in groundwater quality monitoring network design.

A Compressive Sensing-Based Approach to End-to-End Network Traffic Reconstruction

Abstract: Estimation of end-to-end network traffic plays an important role in traffic engineering and network planning. The direct measurement of a network's traffic matrix consumes large amounts of network resources and is thus impractical in most cases. How to accurately construct traffic matrix remains a great challenge. This paper studies end-to-end network traffic reconstruction in large-scale networks. Applying compressive sensing theory, we propose a novel reconstruction method for end-to-end traffic flows. First, the direct measurement of partial Origin-Destination (OD) flows is determined by random measurement matrix, providing partial measurements. Then, we use the K-SVD approach to obtain a sparse matrix. Combined with compressive sensing, this partially known OD flow matrix can be used to recover the entire end-to-end network traffic matrix. Simulation results show that the proposed method can reconstruct end-to-end network traffic with a high degree of accuracy. Moreover, in comparison with previous methods, our approach exhibits a significant performance improvement.

Water Quality Monitoring Network Design: a Problem of Multi-Objective Decision Making

Abstract: Hydrologic data network design is a fairly complicated problem where questions as to the number of gages required, time frequencies to be selected, and benefits/costs of monitoring still remain unresolved. These issues are intensified in case of water quality variables as they are more error-prone, costly, and time consuming to sample. The basic difficulty underlying the design and evaluation of monitoring systems is the lack of an objective criterion to assess: (a) the efficiency, and (b) cost-effectiveness of a network. A statistical procedure based on the entropy principle of information theory is proposed to address the evaluation of both factors. Efficiency is measured quantitatively in terms of the information produced by a network. Similarly, benefits of monitoring are described by informative measures for an objective evaluation of cost-effectiveness. The study presented demonstrates the applicability of the entropy method in assessing the efficiency and the benefits of an existing water quality monitoring network. The method is applied for temporal and spatial features of monitoring, handled as both separate and combined problems. The results are shown in the case of the highly polluted Porsuk River in Turkey. The strengths and shortcomings of the proposed methodology are discussed, with recommendations for future research on the application of the entropy principle in network design.

Algorithmic approaches for efficient enumeration of candidate p-cycles and capacitated p-cycle network design

Abstract: We develop and test an algorithmic approach for providing p-cycle survivable transport network designs. The basic approach is to first identify a set of primary p-cycles, then to search for improvements on those cycles through various operations to create a final set of cycles of high individual and collective efficiency, before finally placing one p-cycle at a time, iteratively, until all working capacity of the network is protected. We compare the solution quality of the algorithm to optimal designs obtained with ILP methods. The primary advantage of this algorithmic approach is that it entirely avoids the step of enumerating all cycles, which is a preliminary step in both ILP and heuristic solution methods based on preselection. This method proceeds initially with no more than S "primary" p-cycles, and in the worst case will enumerate no more than S/sup 2/-N other candidate cycles during its execution, where S is the number of spans in the network and N is the number of nodes. We also find that the set of candidate cycles developed by the algorithm can themselves be used as a quite small but highly effective set of eligible cycles in an ILP design model.