Network-level Optimization Research Articles

Bus Route Sketching: A Multimetric Analysis from the User’s and Operator’s Perspectives

The purpose of this study is to develop an optimal bus route search algorithm that considers both the user’s and supplier’s perspectives. The process of providing bus route service involves route network design, route allocation, and operation and management in sequence. Among these, establishing the optimal rationality for route network design in practical applications is challenging, and route modifications often occur during the operation process. To minimize these practical difficulties, this study proposes the Bus Route Sketch (BRS) methodology. This methodology, designed for network-level optimization, distinguishes itself from existing bus route setting methodologies by minimizing travel costs while taking user needs into account. This study yielded positive results, with the evaluation score improving from 8.83 to 9.50 from the supplier’s perspective and from 7.13 to 9.89 from the user’s perspective. This BRS methodology, developed to suit both route planning and operation processes, is expected to be utilized in the practical evaluation, adjustment, and design of bus routes.

A network-level management system to mitigate the global warming potential of road pavements

ABSTRACT This study details a new, network-level optimization tool aimed at supporting transportation agencies in their efforts to reduce the global warming potential of their road pavement infrastructure. Through a two-stage bottom-up algorithm that integrates with a comprehensive cradle-to-grave life cycle assessment, the proposed tool learns optimal management policies for individual pavement sections and uses that information to guide network-level allocation choices. Through a realistic case study based on data made available by a state department of transportation, this study demonstrates that the proposed modelling approach identifies management strategies expected to reduce the global warming potential of a pavement network by up to 4.8% over 20 years relative to a more traditional, reactive management approach. The resulting model presented in this paper can support agencies in achieving ambitious targets to reduce the global warming potential of their paved infrastructure systems.

A Joint Trajectory Planning and Signal Control Framework for a Network of Connected and Autonomous Vehicles

The advancement of the Intelligent Transport Systems (ITS) and the emerging Connected and Automated Vehicles (CAVs) technology are acknowledged to hold a great potential to mitigate challenging problems in the current transportation networks. Particularly, a proper traffic control strategy with a precise vehicular movement control scheme can alleviate the congestion and improve the safety and efficiency of the traffic. This paper proposes a novel bi-level control framework that combines a design of traffic signal timings at a network level, and a detailed trajectory control policy for individual vehicles at a link-level within a network of CAVs. We develop a group-based longitudinal trajectory planning scheme to coordinate vehicular movements at the lower level of our framework while abiding by the signal operations along with end-to-end vehicle routing decisions from the upper network level optimization. This joint and mutual interaction between the two different control strategies in the urban signalized corridors is complex and can significantly affect the overall network’s performance, nevertheless has not been explored previously in the literature. The proposed framework enables such studies where we derive an efficient algorithm that iteratively solves the mixed-integer linear programming (MILP) and linear programming models in each link at the lower and over the network at the upper levels, respectively. Numerical results show the effectiveness of the proposed joint control framework in network performance regarding the average travel time, queue formation and dissipation across the network.

How to Use In-Band Network Telemetry Wisely: Network-Wise Orchestration of Sel-INT

As a promising network monitoring technique, in-band network telemetry (INT) helps to visualize networks in a fine-grained and real-time manner. Meanwhile, to address the overheads of INT, people have proposed a few selective INT (Sel-INT) approaches that only select a portion of packets in each flow to insert INT fields and distribute different types of INT fields over the selected packets. In this paper, we study how to use Sel-INT wisely in a network such that the tradeoff between monitoring accuracy/coverage and INT overheads can be balanced well. Specifically, we try to orchestrate the Sel-INT schemes of flows in both network- and flow-levels. For the network-level optimization, we model it as an INT planning problem in which the Sel-INT schemes of flows should be determined to maximize the information gain of INT as well as minimize the bandwidth overheads of INT. We formulate an integer linear programming (ILP) model to tackle the problem, prove its <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {N} \mathcal {P}$ </tex-math></inline-formula> -hardness, and leverage Lagrangian relaxation to design a polynomial-time approximation algorithm for it. The flow-level optimization considers a dynamic network environment, and we propose to combine deep learning (DL) based traffic prediction with Sel-INT, such that the Sel-INT scheme of each individual flow can be updated timely and adaptively. We implement the proposal in a small but real network testbed and experimentally demonstrate self-adaptive orchestration of Sel-INT with it.

Analysis of long-term maintenance decision for asphalt pavement based on analytic hierarchy process and network level optimization decision

&lt;abstract&gt; &lt;p&gt;In order to effectively mitigate the deterioration of pavement and roadbed, the need for extensive repairs and costly reconstruction ought to be minimized. Hence, this study introduces a novel approach towards long-term preservation of asphalt pavement, which conducts in-depth research on pavement maintenance decision-making using the decision tree method. The selection of appropriate decision-making indicators is based on their respective significance and the actual maintenance requirements, from which a comprehensive decision model for asphalt pavement maintenance is developed. By employing the Analytic Hierarchy Process (AHP) and a network-level optimization decision-making approach, this study investigates the allocation of maintenance decisions, structural preservation, optimal combinations of maintenance strategies, and fund allocation schemes. The result is the development of a project-level and network-level structural preservation decision optimization method. Furthermore, a decision-making module is designed to accompany this method, facilitating the visualization of comprehensive data and decision-making plans. This module enhances the effectiveness and efficiency of the decision-making process by providing a user-friendly interface and a clear presentation of data-driven insights and decision outcomes. The case study clearly proved the applicability and rationality of the long-term preservation strategy of structures based on intelligent decision-making, which laid the foundation for the sustainable development of pavement maintenance and development.&lt;/p&gt; &lt;/abstract&gt;

Extremum-Seeking Adaptive-Droop for Model-Free and Localized Volt-VAR Optimization

In an active power distribution system, Volt-VAR optimization (VVO) methods are employed to achieve network-level objectives such as minimization of network power losses. The commonly used model-based centralized and distributed VVO algorithms perform poorly in the absence of a communication system and with model and measurement uncertainties. In this paper, we proposed a model-free local Volt-VAR control approach for network-level optimization that does not require communication with other decision-making agents. The proposed algorithm is based on extremum-seeking approach that uses only local measurements to minimize the network power losses. To prove that the proposed extremum-seeking controller converges to the optimum solution, we also derive mathematical conditions for which the loss minimization problem is convex with respect to the control variables. Local controllers pose stability concerns during highly variable scenarios. Thus, the proposed extremum-seeking controller is integrated with an adaptive-droop control module to provide a stable local control response. The proposed approach is validated using IEEE 4-bus and IEEE 123-bus systems and achieves the loss minimization objective while maintaining the voltage within the pre-specific limits even during highly variable DER generation scenarios.

Network-Level Optimization for Unbalanced Power Distribution System: Approximation and Relaxation

The nonlinear programming (NLP) problem to solve distribution-level optimal power flow (D-OPF) poses convergence issues and does not scale well for unbalanced distribution systems. The existing scalable D-OPF algorithms either use approximations that are not valid for an unbalanced power distribution system, or apply relaxation techniques to the nonlinear power flow equations that do not guarantee a feasible power flow solution. In this paper, we propose scalable D-OPF algorithms that simultaneously achieve optimal and feasible solutions by solving multiple iterations of approximate, or relaxed, D-OPF subproblems of low complexity. The first algorithm is based on a successive linear approximation of the nonlinear power flow equations around the current operating point, where the D-OPF solution is obtained by solving multiple iterations of a linear programming (LP) problem. The second algorithm is based on the relaxation of the nonlinear power flow equations as conic constraints together with directional constraints, which achieves optimal and feasible solutions over multiple iterations of a second-order cone programming (SOCP) problem. It is demonstrated that the proposed algorithms are able to reach an optimal and feasible solution while significantly reducing the computation time as compared to an equivalent NLP D-OPF model for the same distribution system.

Element-Level Optimization Module for Bridge Planning and Programming

The overall objective of this research is to support state departments of transportation with their decision-making processes and transitions to performance management and performance-based planning and programming mandated by the Moving Ahead for Progress in the 21st Century Act. Accomplishing this objective requires a systematic multiobjective optimization methodology. This research proposes such a methodology, referred to as an “element-based multiobjective optimization” (EB-MOO) methodology, which produces optimal or near-optimal sets of short- and long-term intervention strategies detailed at the bridge element level for planning and programming. The methodology currently focuses on the bridge asset class and consists of five modules: (1) data processing, (2) improvement, (3) element-level optimization (ELO), (4) bridge-level optimization (BLO), and (5) network-level optimization (NLO) modules. This paper details the ELO module, specifically: the basic framework of underlying processes and concepts, the alternative feasibility screening process, optimization problem types and mathematical formulations, and the heuristic algorithm used to solve the ELO problems. The paper also includes an illustrative example using a prototyping tool developed to implement EB-MOO methodology. The example presents several ELO problems under unconstrained scenarios. The implementation demonstrated the module’s capability in producing optimal or near-optimal ELO solutions, recommending element intervention actions, predicting performance, and determining funding requirements for the specified improvement type and program year. The broader EB-MOO methodology uses the ELO results as inputs for the BLO and NLO modules.

Bridge-Level Optimization Module for Planning and Programming

The paper briefly introduces an element-based multi-objective optimization (EB-MOO) methodology to support state departments of transportation with their decision-making process, asset management, and performance-based transportation planning and programming. The methodology focuses on the bridge asset class and consists of five modules: (i) data processing, (ii) improvement, (iii) element-level optimization (ELO), (iv) bridge-level optimization (BLO), and (v) network-level optimization (NLO) modules. These five modules jointly produce short- and long-term intervention strategies detailed at the bridge element level for planning and programming. The paper focuses on the BLO module, specifically: the basic framework of underlying processes and concepts, the optimization problem types and mathematical formulations, and the heuristic algorithm to solve the BLO problems. A prototyping tool is developed to implement these five modules of the EB-MOO methodology, test concepts, prove effectiveness, and demonstrate potential benefits. The paper also includes an illustrative example using the prototyping tool. The example consists of the BLO problems under different budget and/or performance scenarios. The implementation proves the module’s capability in producing a diverse set of Pareto optimal or near-optimal solutions, recommending set of element intervention actions and timings, predicting performance, and determining budget requirements for the entire program period. The BLO results associated with the recommended solutions serve as the fundamental inputs for the NLO module. Nevertheless, the BLO module can be used independently, providing a systematic process for the development of bridge improvement/preservation programs detailed at the element level.

Element-Based Multi-Objective Optimization Methodology Supporting a Transportation Asset Management Framework for Bridge Planning and Programming

The U.S. Moving Ahead for Progress in the 21st Century Act (MAP-21) mandates the development of a risk-based transportation asset management plan and the use of a performance-based approach in transportation planning and programming. This paper introduces a systematic element-based multi-objective optimization (EB-MOO) methodology integrated into a goal-driven transportation asset management framework to improve bridge management and support state departments of transportation with their transition efforts to comply with these MAP-21 requirements. The methodology focuses on the bridge asset class and is structured around five modules: data processing, improvement, element-level optimization, bridge-level optimization, and network-level optimization modules. It relies on a leading-edge forecasting model, three separate screening processes (i.e., the element deficiency, alternative feasibility, and solution superiority screening processes) to overcome computer memory and processing time limitations, and a simulation arrangement to generate life-cycle alternatives (series of improvement actions). Additionally, the EB-MOO methodology consists of three levels of optimization assessment based on the Pareto optimality concept: element-level, bridge-level, and network-level (following either a top-down or bottom-up approach). A robust metaheuristic genetic algorithm handles the different multi-objective optimization problems. A prototyping tool was developed for the implementation of the methodology through several examples of unconstrained and constrained (by budget, performance, or both) scenarios. Results reveal the capability of the methodology to generate Pareto optimal or near-optimal solutions, predict performance, and determine funding requirements and short- and long-term intervention strategies detailed at the bridge-element level for planning and programming. The EB-MOO methodology can also be expanded to accommodate other asset classes or modes.

Optimization Model of Network‐Level Pavement Maintenance Decision considering User Travel Time and Vehicle Fuel Consumption Costs

The maintenance management decisions of network‐level asphalt pavements have long been a challenge to highway agencies, and a great amount of factors have been involved. In this study, a network‐level optimization method was established by integrating the maintenance benefits into the zero‐one programming optimization model. An optimized performance evaluation method of asphalt pavement was proposed which contains 11 different kinds of combinations. The benefit model quantifies the cost savings of user travel time and vehicle fuel consumption to the pavement condition index (PCI) and ride quality index (RQI), respectively. Based on the simplified evaluation method as well as the quantified maintenance benefit model, an optimization model was established by employing the zero‐one programming. This optimization model aimed to maximize the improvements/price ratio of pavement maintenance for the whole pavement network. The calculation results present the optimal strategies of maintenance for every road section in the network. The applicability of the newly proposed model was validated by a case study. The methodology developed in this study helps to offer guidelines to highway agencies in managing and making decisions about network‐level pavement maintenance.

Effective Network Level Optimization for Bridges Maintenance

The objective of this paper is to present an effective new methodology to optimize the maintenance costs of bridges stock. Optimization takes place at the network level and not in a project level (bridge by bridge). The dynamics of passage between bridges condition state (from 1 to 5) is achieved by the Markov chains probabilistic method. The Markov transition matrix is determined either by ratios of total areas and areas degraded annually, or by the resolution of an optimization problem. In the latter case, the nonlinear optimization algorithm SQP (Sequanciel Quadratic Programming) is developed. A bridge maintenance matrix is introduced in the calculation of the repair cost. The originality of our approach is to parameterize this matrix by introducing the different optimization variables of the problem. Finally, the cost function to be optimized annually is calculated and optimized by a genetic algorithm. This cost function represents the cost of maintaining the entire asset.

Optimizing NFV Chain Deployment in Software-Defined Cellular Core

Today's cellular core relies on a few expensive and dedicated hardware racks to connect the radio access network and the egress point to the Internet, which are geographically placed at fixed locations and use the specific routing policies. This inelastic architecture fundamentally leads to increased capital and operating expenses, poor application performance and slow evolution. The emerging paradigm of Network Function Virtualization (NFV) and Software Defined Networking (SDN) bring new opportunities for cellular networks, which makes it possible to flexibly deploy service chains on commodity servers and fine-grained control the routing policies in a centralized way. We present a two-stage optimization framework Plutus. The network-level optimization aims to minimize the service chain deployment cost, while the server-level optimization requires to determine which Virtualized Network Function (VNF) should be deployed onto which CPU core to balance the CPU processing capability. We formulate these two problems as two optimization programs and prove their hardness. Based on parallel multi-block ADMM, we propose a (δ, 2)-bicriteria approximation algorithm and a learning-based algorithm to address two cases whether the flow information and the resource consumption can be known as a priori, respectively. Large-scale simulations and DPDK-based OpenNetVM platform show that Plutus can reduce the capital cost by 84% and increase the throughput by 36% on average.

An Indoor Positioning and Navigation System Using Named Data Networking

Named data networking (NDN) is a new and promising Internet architecture, which aims to replace the current transmission control protocol/Internet protocol (TCP/IP)-based Internet. The NDN internet architecture has introduced several benefits in numerous applications. An NDN-based indoor positioning and navigation system can further optimize existing localization technologies with reduced the server load and faster response time. In this study, an NDN-based approach for the existing Wi-Fi fingerprinting-based indoor positioning and navigation was investigated. Among the many features of NDN, the network-level caching can reduce the computational load and response time of the localization and navigation server. The theoretical analysis of the runtime complexity shows that the NDN's network-level caching performance is better than those of the conventional algorithms. In this paper, naming methods for different services and a server-side algorithm for handling the NDN requests are proposed. The real-world implantation and testing results show a better overall performance than that of TCP/IP. This network-level optimization for indoor positioning and navigation opens new opportunities because it can be combined with other application-level optimization techniques for more efficient indoor positioning and navigation in the future.

Airfield Infrastructure Management Using Network-Level Optimization and Stochastic Duration Modeling

This paper proposes a facility-specific modeling approach to plan maintenance and rehabilitation (M&amp;R) activities on a network of airport runway pavement facilities. The objective of the modeling approach is to minimize system M&amp;R cost while recommending M&amp;R activities for each runway pavement facility over a planning horizon. To do so, pavement condition forecast is derived from estimating stochastic duration models which capture the inherent uncertainty and dynamics in pavement deterioration and impacts of exogenous factors. Building on the pavement condition forecast, a network optimization-based M&amp;R planning framework is developed which accounts for the interdependence of M&amp;R activities among facilities as reflected in (1) the requirement for aggregate pavement performance and (2) simultaneous implementation of a major M&amp;R action on connected facilities. The budget constraint is also respected. The M&amp;R planning framework with the stochastic duration model-based pavement condition forecast is applied to Chicago O’Hare International Airport. It is found that the proposed approach leads to much reduced M&amp;R cost compared to the state-of-the-practice which does not consider the interdependence of M&amp;R activities among different pavement facilities. On the other hand, accounting for the simultaneous implementation of a major M&amp;R action on connected facilities would substantially increase M&amp;R cost.

A Learning-Based Thermal-Sensitive Power Optimization Approach for Optical NoCs

Optical networks-on-chip (NoCs) based on silicon photonics have been proposed as emerging on-chip communication architectures for chip multiprocessors with large core counts. However, due to the thermal sensitivity of optical devices used in optical NoCs, on-chip temperature variations cause significant thermal-induced optical power loss, which would counteract the power advantages of optical NoCs. To tackle this problem, in this work, we propose a learning-based thermal-sensitive power optimization approach for mesh- or torus-based optical NoCs in the presence of temperature variations. The key techniques proposed include an initial device-setting and thermal-tuning mechanism that is a device-level optimization technique, and a learning-based thermal-sensitive adaptive routing algorithm that is a network-level optimization technique. Simulation results of an 8x8 mesh-based optical NoC show that the proposed initial device-setting and thermal-tuning mechanism confines the worst-case thermal-induced optical energy consumption to be on the order of tens of pJ/bit, by avoiding significant thermal-induced optical power loss caused by temperature-dependent wavelength shifts. Besides, it shows that the learning-based thermal-sensitive adaptive routing algorithm is able to find an optimal path with the minimum estimated thermal-induced optical power consumption for each communication pair. The proposed routing has a greater space for optimization, especially for applications with more long-distance traffic.

Network-level Optimization of Bus Stop Placement in Urban Areas

Bus stops provide accessibility to public transit service, whereas they also influence the efficiency of mobility due to the extra dwell time. The decision of bus stop locations is a tradeoff between access coverage and mobility. This paper formulates a biobjective optimization model of bus stop placement problem at the network level. Two objectives are pertinent to the improvement of mobility: minimization of total dwell time at stops and minimization of total number of bus stops. Access coverage is constrained to ensure a certain level of accessibility. The issue of stop congestion and its effect on road traffic flow is also considered in the model. The model is applied to a case study of the bus network in urban areas of Yancheng, China. The results show that the proposed model can be efficiently solved by CPLEX to obtain Pareto optimal solutions for real-case problems.

Network-level optimization method for road network maintenance programming based on network efficiency

To maintain their capacity, transportation infrastructures are in need of regular maintenance and rehabilitation. The major challenge facing transportation engineers is the network-level policies to maintain the deteriorating roads at an acceptable level of serviceability. In this work, a quantitative transportation network efficiency measure is presented and then how to determine optimally network-level road maintenance policy depending on the road importance to the network performance has been demonstrated. The examples show that the different roads should be set different maintenance time points in terms of the retention capacities of the roads, because the different roads play different roles in network and have different important degrees to the network performance. This network-level road maintenance optimization method could not only save lots of infrastructure investments, but also ensure the service level of the existing transportation system.

Implementation of Preventive Maintenance in Network-Level Optimization

Application of appropriate preventive maintenance treatments, at the right time, extends the service life of pavements, resulting in benefits to road users through increased ride comfort and safety and to road agencies through the reduction of future maintenance costs and improvement of the network condition. Preventive maintenance treatments usually have been applied on the highly trafficked sections of the Serbian national road network, but they have not been applied on the low-volume part of the network. This study used the Serbian low-volume roads network as a case study to identify appropriate preventive maintenance treatments for low-volume roads, model these into a pavement management system, and assess the potential benefits of their application compared with the current practice. The World Bank's RONET (road network evaluation tools) model, designed to assess the current characteristics of road networks and their future performance according to various levels of interventions (and budgets), was selected for this study. The model was modified for the study to incorporate use of preventive maintenance treatments. Modifications included adjustment of the pavement deterioration curves to incorporate a slower deterioration rate resulting from the application of crack sealing and pothole patching and surface dressings or thin overlays while the pavement was still in good condition. The results indicate that the use of preventive maintenance treatments would result in increased net benefits and a substantial reduction in future road agency costs compared with the maintenance scenario without preventive maintenance.

Methodology for safety improvement programming using constrained network-level optimization

This paper develops and implements a decision support framework that prescribes and prioritizes cost-effective safety improvements at deserving locations under funding constraints, economic and comprehensive crash cost methods, at rural two-lane highway sections. The framework is demonstrated using Indiana’s 7700-mile rural two-lane network. For sections that are both deficient and hazardous under unconstrained budget and economic crash cost method, it was determined that $55M is needed. Assuming a safety budget of $1M/year over a 5-year period, it was found that 170–180 crashes (translating to $8–15M) could be prevented. If the annual budget is increased to $2M/year over a 5-year period, 244 crashes (translating to $12–26M) can be prevented. Monetary amounts are in Year 2000 constant dollars. Overall, the results suggest that perpetual increases in highway safety improvements spending are not likely to be accompanied by a commensurate reduction of crashes at rural two-lane highways. In other words, there is a ceiling to the effectiveness of engineering safety countermeasures, and therefore non-engineering countermeasures such as safety education and enforcement must be sought to complement the engineering efforts at rural two-lane highways.