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1. Statistical Metamodeling of Dynamic Network Loading

   Song, Wenjing; Han, Ke; Wang, Yiou Friesz; del Castillo, Enrique (January 2018, Transportation research. Part B, Methodological)

   Dynamic traffic assignment models rely on a network performance module known as dynamic network loading (DNL), which expresses flow propagation, flow conservation, and travel delay at a network level. The DNL defines the so-called network delay operator , which maps a set of path departure rates to a set of path travel times (or costs). It is widely known that the delay operator is not available in closed form, and has undesirable properties that severely complicate DTA analysis and computation, such as discontinuity, nondifferentiability, nonmonotonicity, and computational inefficiency. This paper proposes a fresh take on this important and difficult issue, by providing a class of surrogate DNL models based on a statistical learning method known as Kriging . We present a metamodeling framework that systematically approximates DNL models and is flexible in the sense of allowing the modeler to make trade-offs among model granularity, complexity, and accuracy. It is shown that such surrogate DNL models yield highly accurate approximations (with errors below 8%) and superior computational efficiency (9 to 455 times faster than conventional DNL procedures such as those based on the link transmission model). Moreover, these approximate DNL models admit closed-form and analytical delay operators, which are Lipschitz continuous and infinitely differentiable, with closed-form Jacobians. We provide in-depth discussions on the implications of these properties to DTA research and model applications. 

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2. Effects of Air Quality on Housing Location: A Predictive Dynamic Continuum User-Optimal Approach

   https://doi.org/10.1287/trsc.2021.1116  

   Yang, Liangze; Wong, S. C.; Ho, H. W.; Zhang, Mengping; Shu, Chi-Wang (September 2022, Transportation Science)

   Recent decades have seen increasing concerns regarding air quality in housing locations. This study proposes a predictive continuum dynamic user-optimal model with combined choice of housing location, destination, route, and departure time. A traveler’s choice of housing location is modeled by a logit-type demand distribution function based on air quality, housing rent, and perceived travel costs. Air quality, or air pollutants, within the modeling region are governed by the vehicle-emission model and the advection-diffusion equation for dispersion. In this study, the housing-location problem is formulated as a fixed-point problem and the predictive continuum dynamic user-optimal model with departure-time consideration is formulated as a variational inequality problem. The Lax-Friedrichs scheme, the fast-sweeping method, the Goldstein-Levitin-Polyak projection algorithm, and self-adaptive successive averages are adopted to discretize and solve these problems. A numerical example is given to demonstrate the characteristics of the proposed housing-location choice problem with consideration of air quality and to demonstrate the effectiveness of the solution algorithms. 

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3. Diversity Routing to Improve Delay-Jitter Tradeoff in Uncertain Network Environments

   Rezaee, Arman; Chan, Vincent (July 2019, IEEE International Conference on Communications)

   In this paper we propose a novel approach to deliver better delay-jitter performance in dynamic networks. Dynamic networks experience rapid and unpredictable fluctuations and hence, a certain amount of uncertainty about the delay-performance of various network elements is unavoidable. This uncertainty makes it difficult for network operators to guarantee a certain quality of service (in terms of delay and jitter) to users. The uncertainty about the state of the network is often overlooked to simplify problem formulation, but we capture it by modeling the delay on various links as general and potentially correlated random processes. Within this framework, a user will request a certain delay-jitter performance guarantee from the network. After verifying the feasibility of the request, the network will respond to the user by specifying a set of routes as well as the proportion of traffic which should be sent through each one to achieve the desired QoS. We propose to use mean-variance analysis as the basis for traffic distribution and route selection, and show that this technique can significantly reduce the end-to-end jitter because it accounts for the correlated nature of delay across different paths. The resulting traffic distribution is often non-uniform and the fractional flow on each path is the solution to a simple convex optimization problem. We conclude the paper by commenting on the potential application of this method to general transportation networks. 

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4. Bilevel optimization of a housing allocation and traffic emission problem in a predictive dynamic continuum transportation system

   https://doi.org/10.1111/mice.13007  

   Yang, Liangze; Wong, S. C.; Ho, H. W.; Shu, Chi‐Wang; Zhang, Mengping (April 2023, Computer-Aided Civil and Infrastructure Engineering)

   Abstract In recent decades, the effects of vehicle emissions on urban environments have raised increasing concerns, and it has been recognized that vehicle emissions affect peoples’ choice of housing location. Additionally, housing allocation patterns determine people's travel behavior and thus affect vehicle emissions. This study considers the housing allocation problem by incorporating vehicle emissions in a city with a single central business district (CBD) into a bilevel optimization model. In the lower level subprogram, under a fixed housing allocation, a predictive dynamic continuum user‐optimal (PDUO‐C) model with a combined departure time and route choice is used to study the city's traffic flow. In the upper level subprogram, the health cost is defined and minimized to identify the optimal allocation of additional housing units to update the housing allocation. A simulated annealing algorithm is used to solve the housing allocation problem. The results show that the distribution of additional housing locations is dependent on the distance and direction from the CBD. Sensitivity analyses demonstrate the influences of various factors (e.g., budget and cost of housing supply) on the optimized health cost and travel demand pattern. 

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5. Service Function Chain Placement in Cloud Data Center Networks: a Cooperative Multi-Agent Reinforcement Learning Approach

   Gao Lynn; Chen Yutian; Tang Bin (January 2021, 11th EAI International Conference on Game Theory for Networks (GameNets 2021))

   Service function chaining (SFC), consisting of a sequence of virtual network functions (VNFs) (i.e., firewalls and load balancers), is an effective service provision technique in modern data center networks. By requiring cloud user traffic to traverse the VNFs in order, SFC im- proves the security and performance of the cloud user applications. In this paper, we study how to place an SFC inside a data center to mini- mize the network traffic of the virtual machine (VM) communication. We take a cooperative multi-agent reinforcement learning approach, wherein multiple agents collaboratively figure out the traffic-efficient route for the VM communication. Underlying the SFC placement is a fundamental graph-theoretical prob- lem called the k-stroll problem. Given a weighted graph G(V, E), two nodes s, t ∈ V , and an integer k, the k-stroll problem is to find the shortest path from s to t that visits at least k other nodes in the graph. Our work is the first to take a multi-agent learning approach to solve k- stroll problem. We compare our learning algorithm with an optimal and exhaustive algorithm and an existing dynamic programming(DP)-based heuristic algorithm. We show that our learning algorithm, although lack- ing the complete knowledge of the network assumed by existing research, delivers comparable or even better VM communication time while taking two orders of magnitude of less execution time. 

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