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The selfish routing of network flow often considers a single objective, namely travel time or travel distance, and optimisation models are often guided by the principle of user equilibrium (UE). A more challenging approach is to consider multiple objectives simultaneously, as for example distance, travel time and pollution. In this paper we present a bi-criteria problem whereby the road users selfish objective of minimising their travel time is at odds with the objective of minimising the overall fuel consumption of all road users. The approach taken is to manipulate “free” parameters, namely speed limits, in an attempt to coerce the road users into behaviour which helps the latter objective. Motivated by the work done on the Price of Anarchy (PoA) into classifying the suboptimality of equilibrium with respect to the minimum total travel time we look to classify the equilibrium solutions with respect to a weighted sum model of the minimum total travel time and overall fuel consumption. Our results show that small changes to these “free” parameters can result in solutions which Pareto dominate other solutions. Whilst our measure for the suboptimality of equilibrium solutions can highlight the inefficiency of a network configuration itself, it does not allow insight into the overall quality of the solution when compared with other network configurations.

In Website Fingerprinting (WF) attack, a local passive eavesdropper utilizes network flow information to identify which web pages a user is browsing. Previous researchers have demonstrated the effectiveness of WF attacks under a strong Single Page Assumption: the network flow extracted by the adversary belongs to a single web page. In reality, the assumption may not hold because users tend to open multiple tabs simultaneously (or within a short period of time) so that their network traffic is mixed. In this paper, we propose an automated multi-tab Website Fingerprinting attack that is able to accurately classify websites regardless of the number of simultaneously opened pages. Our design is powered by two innovative designs. First, we develop a split point classification method to dynamically identify the split point between the first page and its subsequent pages. As a result, the network traffic before the split point is solely generated for the first page. Then, we propose a new chunk-based WF classifier to infer the websites based on the initial chunk of clean traffic. For both classifiers, we apply automated feature selection to select a concise yet representative feature set. We perform extensive evaluations using SSH and Tor-based datasets to demonstrate the effectiveness of our system components and the integrated system.

The purpose is to solve the security problems of the Cooperative Intelligent Transportation System (CITS) Digital Twins (DTs) in the Deep Learning (DL) environment. The DL algorithm is improved; the Convolutional Neural Network (CNN) is combined with Support Vector Regression (SVR); the DTs technology is introduced. Eventually, a CITS DTs model is constructed based on CNN-SVR, whose security performance and effect are analyzed through simulation experiments. Compared with other algorithms, the security prediction accuracy of the proposed algorithm reaches 90.43%. Besides, the proposed algorithm outperforms other algorithms regarding Precision, Recall, and F1. The data transmission performances of the proposed algorithm and other algorithms are compared. The proposed algorithm can ensure that emergency messages can be responded to in time, with a delay of less than 1.8s. Meanwhile, it can better adapt to the road environment, maintain high data transmission speed, and provide reasonable path planning for vehicles so that vehicles can reach their destinations faster. The impacts of different factors on the transportation network are analyzed further. Results suggest that under path guidance, as the Market Penetration Rate (MPR), Following Rate (FR), and Congestion Level (CL) increase, the guidance strategy's effects become more apparent. When MPR ranges between 40% ~ 80% and the congestion is level III, the ATT decreases the fastest, and the improvement effect of the guidance strategy is more apparent. The proposed DL algorithm model can lower the data transmission delay of the system, increase the prediction accuracy, and reasonably changes the paths to suppress the sprawl of traffic congestions, providing an experimental reference for developing and improving urban transportation.

Ridepooling services require efficient optimization algorithms to simultaneously plan routes and pool users in shared rides. We consider a static dial-a-ride problem (DARP) where a series of origin-destination requests have to be assigned to routes of a fleet of vehicles. Thereby, all requests have associated time windows for pick-up and delivery, and may be denied if they can not be serviced in reasonable time or at reasonable cost. Rather than using a spatial representation of the transportation network we suggest an event-based formulation of the problem, resulting in significantly improved computational times. While the corresponding MILP formulations require more variables than standard models, they have the advantage that capacity, pairing and precedence constraints are handled implicitly. The approach is tested and validated using a standard IP-solver on benchmark data from the literature. Moreover, the impact of, and the trade-off between, different optimization goals is evaluated on a case study in the city of Wuppertal (Germany).

The notion of age of information (AoI) has become an important performance metric in network and control systems. Information freshness, represented by AoI, naturally arises in the context of caching. We address optimal scheduling of cache updates for a time-slotted system where the contents vary in size. There is limited capacity for the cache for making updates. Each content is associated with a utility function that depends on the AoI and the time duration of absence from the cache. For this combinatorial optimization problem, we present the following contributions. First, we provide theoretical results of problem tractability. Whereas the problem is NP-hard, we prove solution tractability in polynomial time for a special case with uniform content size, by a reformulation using network flows. Second, we derive an integer linear formulation for the problem, of which the optimal solution can be obtained for small-scale scenarios. Next, via a mathematical reformulation, we derive a scalable optimization algorithm using repeated column generation. In addition, the algorithm computes a bound of global optimum, that can be used to assess the performance of any scheduling solution. Performance evaluation of large-scale scenarios demonstrates the strengths of the algorithm in comparison to a greedy schedule. Finally, we extend the applicability of our work to cyclic scheduling.

In an observational study, one is given disjoint samples of treatment units and control (untreated) units, and the goal is to compare outcomes between the two samples in order to estimate a treatment effect. A complication is that the treatment and control units often differ on important pre-treatment attributes, and these differences, referred to as covariate imbalance, can bias the estimate. One method to correct for covariate imbalance is to select a subset of the control sample that has minimum imbalance with respect to the treatment sample, and then use this control subset for estimating the treatment effect. While this optimization problem is NP-hard in general, certain special cases can be solved efficiently. Specifically, the variant of this optimization problem with one covariate is easy to solve, the variant with three or more covariates is NP-hard, and the variant with two covariates is solvable in polynomial time. We present several network flow formulations for the problem of minimizing imbalance on two nominal covariates. First, we present a minimum cost network flow formulation for solving the problem with the constraint that the control subset must have the same size as the treatment sample. We then derive an improved maximum flow formulation. For alternate size restrictions on the control subset, we use a proportional imbalance objective which leads to non-integral supplies and demands in the preceding network flow formulations. We then derive an alternate minimum cost network flow formulation that ensures integrality and solves the proportional imbalance problem in polynomial time.

We present H-TD²: Hybrid Temporal Difference Learning for Taxi Dispatch, a model-free, adaptive decision-making algorithm to coordinate a large fleet of automated taxis in a dynamic urban environment to minimize expected customer waiting times. Our scalable algorithm exploits the natural transportation network company topology by switching between two behaviors: distributed temporal-difference learning computed locally at each taxi and infrequent centralized Bellman updates computed at the dispatch center. We derive a regret bound and design the trigger condition between the two behaviors to explicitly control the trade-off between computational complexity and the individual taxi policy's bounded sub-optimality; this advances the state of the art by enabling distributed operation with bounded-suboptimality. Additionally, unlike recent reinforcement learning dispatch methods, this policy estimation is adaptive and robust to out-of-training domain events. This result is enabled by a two-step modelling approach: the policy is learned on an agent-agnostic, cell-based Markov Decision Process and individual taxis are coordinated using the learned policy in a distributed game-theoretic task assignment. We validate our algorithm against a receding horizon control baseline in a Gridworld environment with a simulated customer dataset, where the proposed solution decreases average customer waiting time by 50% over a wide range of parameters. We also validate in a Chicago city environment with real customer requests from the Chicago taxi public dataset where the proposed solution decreases average customer waiting time by 26% over irregular customer distributions during a 2016 Major League Baseball World Series game.

On-demand ride services, e.g., taxi companies, ridesourcing/transportation network companies (TNCs), play a vital role in nowadays transportation modes. The relocation/repositioning of idle vehicles is an important operational problem for service providers. With the recent success of deep learning, a large and growing body of literature emerged on learning-based transportation problems, including the relocation problem. In this work, we study the idle vehicle relocation problem to achieve better drivers' profits and customers' satisfaction. In detail, we formulate the learning problem on real-world road networks while recent repositioning works under a deep reinforcement learning scheme formulate the environment as grids. Meanwhile, a graph formulation enables learning with Graph Neural Network (GNN), which shows promising results in recent transportation studies. We empirically show that preserving the road network structure and learning the data representation with GNN contribute to improving the repositioning decision policy. Our simulation experiments are conducted on two real-world datasets, in New York City and Austin, respectively. The results show our formulation's advantages in terms of reductions of order rejection rates and passenger waiting time, and increasing of vehicle occupancy rates and driver revenues.

Hazardous materials (hazmat) shipments are integral to any modern society, and railroad is the primary transportation mode in North America. Given the catastrophic nature of rail hazmat incidents, every effort should be made to mitigate risk. This study investigates a novel risk mitigation tool, i.e., subsidy, to induce railroad operators to take alternate routes that are away from high-risk portions of the network such as population centers. To this end, a bi-level optimization program is developed that captures both the risk perspective of the leader (i.e., government) and the cost concerns of the follower (i.e., railroad operator). Model complexity necessitated the development of a customized solution technique, and the resulting methodology was used to study problem instances generated on the realistic infrastructure of a railroad operator in Midwest United States. Analyses underscore the significance of using subsidy as a risk mitigation tool and in ensuring a fairer distribution of risk, in demonstrating how significant improvement in risk can be achieved by offering modest subsidy, and in defining the threshold level of the subsidy budget beyond which no improvement should be expected.

Robustness is one of the most important performance criteria for any rail transit network (RTN), because it helps us enhance the efficiency of RTN. Several studies have addressed the issue of RTN robustness primarily from the perspectives of given rail network structures or static distributions of passenger flow. An open problem that remains in fully understanding RTN robustness is how to take the spatio-temporal characteristics of passenger travel into consideration, since the dynamic passenger flow in an RTN can readily trigger unexpected cascading failures. This paper addresses this problem as follows: (1) we propose a two-layer rail transit network (TL-RTN) model that captures the interactions between a rail network and its corresponding dynamic passenger flow network, and then (2) we conduct the cascading failure analysis of the TL-RTN model based on an extended coupled map lattice (CML). Specifically, our proposed model takes the strategy of passenger flow redistribution and the passenger flow capacity of each station into account to simulate the human mobility behaviors and to estimate the maximum passenger flow appeal in each station, respectively. Based on the smart card data of RTN passengers in Shanghai, our experiments show that the TL-RTN robustness is related to both external perturbations and failure modes. Moreover, during the peak hours on weekdays, due to the large passenger flow, a small perturbation will trigger a 20% cascading failure of a network. Having ranked the cascade size caused by the stations, we find that this phenomenon is determined by both the hub nodes and their neighbors.

Demand uncertainties are inevitable in transportation networks. The transit priority network design problem over more than a decade of development has been solved under deterministic conditions. This paper proposes a model to find the optimal transit priority scheme in a multimodal transportation network under uncertain demand. This model is formulated as a risk-based bi-level optimization problem. At the upper-level, a risk measure of expected social cost is minimized subject to a chance constraint on total travel time with a user-specified confidence level and a budget constraint. At the lower-level, a mode choice, a traffic user equilibrium assignment, and a transit assignment are applied. An ant colony algorithm is utilized to solve this complex design problem. Numerical results using a real world middle-size city network empirically demonstrate that the demand uncertainty has a significant impact on the solution and the proposed model is applicable to realistic networks.

Well-designed multi-modal transportation networks are crucial for our connected world. For instance, the excessive construction of railway tracks in China, at speeds up to 350 km/h, makes it necessary to consider the interaction of rail with air transportation for network design. In this study, we propose a model for an air/rail multi-modal, multiple allocation hub location problem with uncertainty on travel demands. Our model is unique in that it integrates features from the existing literature on multi-modal hub location problem (including hub-level capacities, link capacities, direct links, travel cost and time, transit costs and uncertainty), which have not been considered simultaneously, given its high computational complexity. We formulate this model with O(n⁴) variables and show that the implementation of a Benders decomposition algorithm is inherently hard, because of the cubic number of variables in the master problem. Furthermore, we derive an iterative network design algorithm and additional improvement strategies: MMHUBBI which resolves a restricted problem by the solver CPLEX and MMHUBBI-DIRECT which re-designs the transportation network by a heuristic. Our evaluation on real-world dataset for Chinese domestic transportation shows that MMHUBBI provides a significant speed-up on all instances, compared to using CPLEX, while obtaining near-optimal solutions. MMHUBBI-DIRECT further reduces the runtime/memory usage but provides solutions with worse quality. We believe that our study contributes towards the design of more realistic multi-modal hub location problems.

Repair scheduling and routing and logistics support are interdependent and critical for post-disaster distribution system restoration (PDSR), which is also influenced by the geographical characteristics of outage area. Hence, we develop a co-optimization model for the PDSR with logistics support and geographical characteristics. A hybrid improved bacterial colony chemotaxis algorithm is proposed to solve the model, in which A* algorithm is employed to route repair crews and material delivery in the transportation network considering geographical characteristics, and an improved bacterial colony chemotaxis algorithm is proposed to determine the repair scheduling and material allocation in the distribution system. Different scale of distribution system instances with different damage levels and different geographical characteristics are used to demonstrate the effectiveness of the proposed methodology.

This paper investigates the idea of introducing learning algorithms into parking guidance and information systems that employ a central server, in order to provide estimated optimal parking searching strategies to travelers. The parking searching process on a network with uncertain parking availability can naturally be modeled as a Markov Decision Process (MDP). Such an MDP with full information can easily be solved by dynamic programming approaches. However, the probabilities of finding parking are difficult to define and calculate. Learning algorithms are suitable for addressing this issue. We propose an algorithm based on Q-learning, where a unique property of the parking searching MDP and the topology of the underlying transportation network are incorporated and utilized to improve its performance. This modification allows us to reduce the size of the learning problem dramatically, and thus the amount of data required to learn the optimal strategy. Numerical experiments conducted on a toy network with fixed parking probabilities show that the proposed learning algorithm outperforms the original Q-learning algorithm and three greedy heuristics in terms of the quality of the approximated optimal solution as well as the amount of training data required. Our numerical experiments on a real network with time-dependent underlying probabilities show that effective searching strategies can be achieved by the proposed algorithm, even though the learning algorithms treat the parking probabilities as constant during each exploration-exploitation cycle. The results again demonstrate that the proposed modified Q-learning algorithm significantly outperforms the original Q-learning with the same amount of training data. The results also provide insights into how the length and the split of the exploration-exploitation cycle affect the effectiveness of the proposed learning algorithm.

Ride-hailing systems (RHSs) provide passengers with convenient and flexible mobility services, have played an important role in modern urban transportation. With the limited vehicles, RHSs wish to optimize the dispatch of vehicles to requests with the objective of serving as many requests as possible. To address such a city-scale vehicle dispatch problem with thousands of vehicles and requests in each epoch, existing algorithms always take a tradeoff between effectiveness (i.e., real-time) and efficiency (i.e., service rate), such as ignoring future demands to guarantee real-time or solving a complex combinatorial optimization to improve service rate. To guarantee the service rate in a real-time fashion, this paper proposes two novel network flow-based vehicle dispatch algorithms. A network flow-based algorithm (NFBA) is provided to deal with offline scenarios. By constructing the vehicle-shareability network, a min-cost flow is built to find the optimal dispatch of vehicles to requests. To improve the request service rate in real-time, an efficient multi-sample multi-network flow-based algorithm (MNFBA) is proposed for the online scenarios. Each min-cost flow is utilized for a sample of future requests, and online vehicle dispatch policy is averaged over these flows. Extensive simulations based on real-world trip datasets in New York City are conducted. The experimental results show that compared to the benchmarks, our proposed algorithm can generate the dispatch of vehicles to requests within seconds, but can greatly increase the daily request service rate.

Benefiting from dense connections in data center networks (DCNs), load balancing algorithms are capable of steering traffic into multiple paths for the sake of preventing traffic congestion. However, given each path’s time-varying and asymmetrical traffic state, this may also lead to worse congestion when some paths are overutilised. Especially in the two-tier hybrid optical/electrical DCNs (Hoe-DCNs), the port contentions and large-grained optical packets of the fast optical switch (FOS) require the top-of-rack (TOR) switch to have microsecond-level load balancing capability for microburst traffic. This paper establishes a leaf-spine Hoe-DCN model to illustrate the principal characteristic of dynamic load balancing in TOR switches for the first time. Moreover, we propose the dynamic distributed multi-path (DDMP) load balancing algorithm that relies on dynamic hashing computing for network flow distribution in DCNs, which dynamically adjusts traffic flow distribution at microsecond level according to the inverse ratio of the buffer occupancy. The simulation results show that our proposed algorithm reduces the TOR-to-TOR latency by 15.88% and decreases the packet loss by 22.06% compared to conventional algorithms under regular load conditions, which effectively improves the overall performance of the Hoe-DCNs. Moreover, our proposed algorithm prevents more than 90% packet loss under low load conditions.

This study examines the impact of carpool on network traffic in a highly idealized futuristic world, where all travelers are willing to participate in carpool arranged by a Transportation Network Company. We build a parsimonious carpool model that focuses on the trade-off between inconvenience costs and travel cost savings. Underlying the model is a nonlinear bipartite matching problem that seeks to maximize commuters’ welfare. By assuming the congestion effect is negligible, we derive several useful analytical results. When the inconvenience cost is less than the median trip valuation of a rider, the platform could always achieve an almost perfect match while maximizing commuters’ welfare, which corresponds to a 50% reduction in vehicular traffic flow. In the case of perfect match, if there is an even number of travelers, we propose a pricing policy that possesses all desired properties of the Vickrey-Clark-Groves (VCG) policy – a benchmark truthful policy for achieving socially optimal solution – but runs a lower deficit. Otherwise, we show the VCG policy always generates a profit. If the inconvenience cost is too high, the perfect match is no longer socially optimal, but the VCG policy still yields a positive profit. Solutions from numerical experiments generally agree with the analytical results. They also suggest that matching across O-D pairs occurs only when it has a significantly lower inconvenience cost than matching within, an unlikely event in reality. Moreover, when cross O-D matching does become prevalent, it leads to higher vehicle miles travelled, hence worse congestion. Thus, from the point of view of traffic management, cross O-D carpool should not be encouraged.

Dynamic wireless power transfer (DWPT), as an emerging technology of coupling the electrified transportation network and power grid, is regarded as a powerful solution for electric vehicles (EVs) promotion. In this article, we propose an intelligent path planning strategy for each individual EV by combining the urban electrified transportation network, power grid, and DWPT. Based on the Floyd algorithm, we ensure the timeliness of path planning by updating the link weights in real time. The EV intelligent path planning strategy comprehensively considers the travel time, charging energy, charging cost, and user convenience. In addition, to ensure that the grid bus voltage has the lowest deviation rate while considering the bus load limit, we propose a multi-stage two-layer optimization algorithm to determine the charging power of each link. The upper layer uses the particle swarm optimization, and the decision variable is the charging power of each link; the lower layer calculates the voltage deviation rate by solving the alternating current optimal power flow model. Finally, the simulation results demonstrate that the intelligent path planning strategy proposed in this article can effectively guarantee a good comprehensive performance in terms of travel time, charging energy, charging cost, and user convenience.

In the periods following sales on online shopping carnivals, demand for express transportation is expected to precipitously surge within a short time, potentially causing express system overload and delivery delays. In this study, we address a service network design problem of a hub-and-spoke structured express transportation network during periods of surging demands. We consider a new strategy of upgrading existing spokes into temporary hubs to expand the network capacity with low costs. We propose a two-stage bi-objective robust model based on a time-space network to integrate the spoke-upgrading selection, service-scheduling, and vehicle-routing for designing an express service network that is both efficient and economic. To solve the model, we develop an enhanced column-and-constraint generation algorithm. Finally, a case study of a real express logistics company in China is demonstrated to validate the effectiveness of the proposed strategy and to offer decision support in real problems.

This paper presents the properties of the minimum mean cycle-canceling algorithm for solving linear programming models. Originally designed for solving network flow problems for which it runs in strongly polynomial time, most of its properties are preserved. This is at the price of adapting the fundamental decomposition theorem of a network flow solution together with various definitions: that of a cycle and the way to calculate its cost, the residual problem, and the improvement factor at the end of a phase. We also use the primal and dual necessary and sufficient optimality conditions stated on the residual problem for establishing the pricing step giving its name to the algorithm. It turns out that the successive solutions need not be basic, there are no degenerate pivots, and the improving directions are potentially interior in addition to those on edges. For solving an m × n linear program, it requires a pseudo-polynomial number O ( n Δ ) of so-called phases, where Δ depends on the number of rows and the coefficient matrix.

Energy demand and supply vary from area to area where an unbalanced load may occur and endanger the system security constraints and cause significant differences in the locational marginal price (LMP) in the power system. With the increasing proportion of local renewable energy (RE) sources in microgrids that are connected to the power grid and the growing number of electric vehicle (EV) charging loads, the imbalance will be further magnified. In this paper, we first model the EV charging network as a cyber-physical system (CPS) that is coupled with both the transportation networks and the smart grids. Then we propose an EV charging station recommendation algorithm. With a proper charging scheduling algorithm deployed, the synergy between the transportation network and the smart grid can be created. The EV charging activity will no longer be a burden for power grids, but a load balancing tool that can transfer energy between the unbalanced distribution grids. The proposed system model is validated via simulations. The results show that the proposed algorithms can optimize the EV charging behaviors, reduce charging costs, and effectively balance the regional load profiles of the grids.

Car ferry services constitute an important part of the transportation network in several parts of the world, especially in areas with limited alternative modes of transport. A central problem facing decision makers is the socially optimal capacity of ferry services. However, the literature has not examined all the decision variables the are relevant to decision makers in a simultaneous framework, only partially. We add to the literature by treating all the relevant decisions variables in a simultaneous framework, which enables a more complete representation of optimal capacity, than partial frameworks. Our proposed methodology also includes the cost of not being able to board the first arriving departure, which is an essential cost in the case of car ferries with too low capacity. We apply the methodology to a case study of three major ferry crossings in Norway. Results indicate that a too large capacity is provided. Thus, local policy makers should consider revising the current service levels. Other policy makers may enact better decisions based on the findings we provide. Sensitivity test suggests that the method used to estimate the number of users not being able to board due to capacity concerns may be improved. This, however, does not alter our main conclusion.

Reducing uplift payments has been a challenging problem for most wholesale markets in the U.S. The main difficulty comes from the discrete decision-making and non- linear objective function in the unit commitment (UC) problem. Recently, the convex hull pricing approach has shown promises to reduce the uplift payments, in which efficient algorithms are available when i) the convex hull description for each unit and ii) the convex envelope for the objective function are available. Following this framework, in this paper, we provide network-flow-based compact extended formulations for each UC considering ramping constraints, different initial statuses, and maximum start-up restrictions. Meanwhile, by using a piece-wise linear approximation of a quadratic cost curve in the objective function, we show that the corresponding piece-wise linear convex envelope converges to the quadratic envelope as the number of pieces increases in the convex hull pricing problem. The final computational experiments on a revised IEEE 118-bus system indicates the effectiveness of the proposed approach. The trade-off between the cost-saving and computational time improvement is also reported as a reference for further usage.

The increasing prevalence of electric vehicles (EVs) has intensified the coupling between power distribution networks (PDNs) and transportation networks (TNs) in both temporal and spatial dimensions. In order to accurately model the coupled network, this paper studies the dynamic network equilibrium to capture the temporally-dynamic interactions between PDNs and TNs. This equilibrium encapsulates the driver’s choices of route, departure time, and charging location, and the electricity price. In the TN, a dynamic traffic model with point queues is proposed to describe the spatial and temporal evolution of traffic flows that is congruent with established user equilibrium choices. In particular, the queues formed at charging stations are, for the first time, modeled by a point queue. In the PDN, the electricity prices are accurately determined from a second-order conic program with a guarantee of strong duality. After theoretically proving the existence of an equilibrium solution, an improved fixed-point algorithm based on extrapolation is also proposed to solve the equilibrium problem efficiently. Numerical results show that the dynamic network equilibrium can be efficiently solved to capture the temporally variant nature of traffic flows, queues, and electricity prices.

The emergence of new business and technological models for urban-related transportation has revealed the need for transportation network companies (TNCs). Most research works on TNCs optimize the interests of drivers, passengers and the operator assuming vehicle resources remain unchanged, but ignore the optimization of resource utilization and satisfaction from the perspective of flexible and controllable vehicle resources. In fact, the load of the scene is variable in time, which necessitates flexible control of resources. Drivers wish to effectively utilize their vehicle resources to maximize profits. Passengers desire to spend minimum time waiting and the platform cares about the commission they can accrue from successful trips. In this paper, we propose an adaptive intelligent cruise guidance and vehicle resource management model to balance vehicle resource utilization and request success rate, while improving platform revenue. We propose an advanced deep reinforcement learning (DRL) method to autonomously learn the statuses and guide the vehicles to hotspot areas where they can pick orders. We assume the number of online vehicles in the scene is flexible and the learning agent can autonomously change the number of online vehicles in the system according to the real-time load to improve effective vehicle resource utilization. An adaptive reward mechanism is enforced to control the importance of vehicle resource utilization and request success rate at decision steps. Simulation results and analysis reveal that our proposed DRL-based scheme balances vehicle resource utilization and request success rate at acceptable levels while improving the platform revenue, compared with other baseline algorithms.

Hydrogen has shown great potential in the renewable power integration and urban mobility decarbonization like the hydrogen fuel cell vehicles (HFCVs). The HFCV refueling as an essential hydrogen load is of great flexibility. Considering the HFCV demand response, this paper studies the integrated electric power and hydrogen system (IPHS) operation. First, the HFCV refueling load model is formulated with its routing on the transportation network considered. Second, the optimal IPHS operation model is developed in which the electric power operation, tube-trailer based hydrogen delivery and HFCV refueling are coordinated. Third, a Lagrangian Relaxation (LR) based method is developed to solve the proposed model efficiently, which corresponds to a price-based demand response mechanism for HFCVs. Compared with existing works on IPHS, the influence of transportation networks is delicately analyzed on both HFCV refueling and hydrogen delivery. The case studies have proven the effectiveness of the proposed method and demonstrated that the overall operation cost is decreased via the proper guidance of HFCV refueling. The HFCV demand response shows great potential in exploring the synergy of energy and transportation systems.

The replacement of hydrogen with conventional fossil fuel has been viewed as a feasible route to the ZERO emission transportation. Nowadays, the water electrolysis method has become mature and efficient enough to produce large-scale of hydrogen, and the condensation and reliquefaction technologies also greatly reduce the hydrogen loss via pipeline transmission, which both motivate the applications of hydrogen as a fuel. In this study, a combined power-hydrogen network is planned to supply the power demand of residential blocks and the charging/fueling demand for electrical vehicles and hydrogen vehicles, respectively. However, the joint scheduling of power-hydrogen network is challenging due to the non-linearity and non-convexity inherited from the hydraulic dynamics of hydrogen transmission. To resolve this problem, a convex-concave based sequential convex approximation method is proposed. A revised 37-bus system is used to demonstrate the validity of proposed method.

Traffic forecasting problem remains a challenging task in the intelligent transportation system due to its spatio-temporal complexity. Although temporal dependency has been well studied and discussed, spatial dependency is relatively less explored due to its large variations, especially in the urban environment. In this study, a novel graph convolutional network model, Multi-Weight Traffic Graph Convolutional (MW-TGC) network, is proposed and applied to two urban networks with contrasting geometric constraints. The model conducts graph convolution operations on speed data with multi-weighted adjacency matrices to combine the features, including speed limit, distance, and angle. The spatially isolated dimension reduction operation is conducted on the combined features to learn the dependencies among the features and reduce the size of the output to a computationally feasible level. The output of multi-weight graph convolution is applied to the sequence-to-sequence model with Long Short-Term Memory units to learn temporal dependencies. When applied to two urban sites, urban-core and urban-mix, MW-TGC network not only outperformed the comparative models in both sites but also reduced variance in the heterogeneous urban-mix network. We conclude that MW-TGC network can provide a robust traffic forecasting performance across the variations in spatial complexity, which can be a strong advantage in urban traffic forecasting., Comment: 11 pages, 7 figures, Accepted to IEEE Transactions on Intelligent Transportation Systems (2020)

We develop an event-driven Receding Horizon Control (RHC) scheme for a Mobility-on-Demand System (MoDS) in a transportation network where vehicles may be shared to pick up and drop off passengers so as to minimize a weighted sum of passenger waiting and traveling times. Viewed as a discrete event system, the event-driven nature of the controller significantly reduces the complexity of the vehicle assignment problem, thus enabling its real-time implementation. Simulation results using actual city maps and real taxi traffic data illustrate the effectiveness of the RH controller in terms of real-time implementation and performance relative to known greedy heuristics.

For a general production network, state-of-the-art methods for constructing sparse flexible designs are heuristic in nature, typically computing a proxy for the quality of unseen networks and using that estimate in a greedy manner to modify a current design. This paper develops two machine learning-based approaches to constructing sparse flexible designs that leverage a neural network to accurately and quickly predict the performance of large numbers of candidate designs. We demonstrate that our heuristics are competitive with existing approaches and produce high-quality solutions for both balanced and unbalanced networks. Finally, we introduce a novel application of process flexibility in healthcare operations to demonstrate the effectiveness of our approach in a large numerical case study. We study the flexibility of linear accelerators that deliver radiation to treat various types of cancer. We demonstrate how clinical constraints can be easily absorbed into the machine learning subroutine and how our sparse flexible treatment networks meet or beat the performance of those designed by state-of-the-art methods.

In this work, we advance the security promise of split manufacturing through judicious handling of interconnects. First, we study the cost-security trade-offs underlying for split manufacturing, which are limiting its adoption. Next, aiming to resolve these concerns, we propose three effective and efficient strategies to dedicatedly lift nets to higher metal layers. Towards this end, we design custom “elevating cells” and devise procedures for routing blockages. All our techniques are employed in a commercial-grade computer-aided design (CAD) framework. For our security analysis, we leverage various state-of-the-art attacks (network flow-based attack, routing-congestion-aware attack, and deep learning-based attack), established metrics (CCR, OER, and HD), and advanced metrics (percentage of netlist recovery and mutual information). Our extensive experiments show that our scheme provides superior protection. Simultaneously, we induce reasonably low and controllable overheads on power and performance, without any silicon area costs. Besides, we support higher split layers, which helps to alleviate concerns on the practicality of split manufacturing.

Electric vehicles (EVs) endow great potentials for future transportation systems, while efficient charge scheduling strategies are crucial for improving profits and mass adoption of EVs. Two critical and open issues concerning EV charging are how to minimize the total charging cost (Objective 1) and how to minimize the peak load (Objective 2). Although extensive efforts have been made to model EV charging problems, little information is available about model properties and efficient algorithms for dynamic charging problems. This paper aims to fill these gaps. For Objective 1, we demonstrate that the greedy-choice property applies, which means that a globally optimal solution can be achieved by making locally optimal greedy choices, whereas it does not apply to Objective 2. We propose a non-myopic charging strategy accounting for future demands to achieve global optimality for Objective 2. The problem is addressed by a heuristic algorithm combining a multi-commodity network flow model with customized bisection search algorithm in a rolling horizon framework. To expedite the solution efficiency, we derive the upper bound and lower bound in the bisection search based on the relationship between charging volume and parking time. We also explore the impact of demand levels and peak arrival ratios on the system performance. Results show that with prediction, the peak load can converge to a globally optimal solution, and that an optimal look-ahead time exists beyond which any prediction is ineffective. The proposed algorithm outperforms the state-of-the-art algorithms, and is robust to the variations of demand and peak arrival ratios.

The cyber physical deep coupling makes power systems face more risks under small probability and high risk typhoon disasters. Resilience describes the ability of cyber physical power system (CPPS) withstanding extreme disasters and re sum ing normal operation. To improve the resilience assessment and analysis method of CPPS, first, a CPPS resilience assessment framework that considers the space time metrics of disasters and the interactions of information systems and power grids is proposed, including fault scenarios extraction, response and recovery analysis, quantitative assessment of resilience. Second, f rom the perspective of the geographical coupling between OPGW and transmission lines and the control coupling between automatic generati on control system (AGC), substation automation system (SAS) and power system, the interaction of information flow and energy flow during the failure period is analyzed. The network flow theory is used to establish an information network traffic model to de scribe the operating status of the information system at each stage. On this basis, a mixed integer linear programming model for DC optimal power flow considering the information network constraints and a multi stage bi level model for cyber physical colla borative recovery are established. Finally, we take the IEEERTS 79 system as an example to show that the proposed method can improve the quantization accuracy comparing with the assessment method of the conventional power system, and evaluate the enhanceme nt of typical measures at different stages.

Network flow formulations are among the most successful tools to solve optimization problems. Such formulations correspond to determining an optimal flow in a network. One particular class of network flow formulations is the arc flow, where variables represent flows on individual arcs of the network. For N P -hard problems, polynomial-sized arc flow models typically provide weak linear relaxations and may have too much symmetry to be efficient in practice. Instead, arc flow models with a pseudo-polynomial size usually provide strong relaxations and are efficient in practice. The interest in pseudo-polynomial arc flow formulations has grown considerably in the last twenty years, in which they have been used to solve many open instances of hard problems. A remarkable advantage of pseudo-polynomial arc flow models is the possibility to solve practical-sized instances directly by a Mixed Integer Linear Programming solver, avoiding the implementation of complex methods based on column generation. In this survey, we present theoretical foundations of pseudo-polynomial arc flow formulations, by showing a relation between their network and Dynamic Programming (DP). This relation allows a better understanding of the strength of these formulations, through a link with models obtained by Dantzig-Wolfe decomposition. The relation with DP also allows a new perspective to relate state-space relaxation methods for DP with arc flow models. We also present a dual point of view to contrast the linear relaxation of arc flow models with that of models based on paths and cycles. To conclude, we review the main solution methods and applications of arc flow models based on DP in several domains such as cutting, packing, scheduling, and routing.

This paper models autonomous ridesharing — multiple travelers simultaneously riding one shared autonomous vehicle (SAV) — in a network equilibrium setting with mixed SAV and human-driven vehicle (HV) traffic. We make two major methodological contributions. First, a novel one (SAV)-to-many (riders) matching is proposed to characterize the waiting times of an SAV and multiple travelers who share rides in the SAV during online matching, which is a nontrivial generalization of the one-to-one matching without ridesharing. Our matching characterization considers the possibilities of a traveler matched with an SAV starting from the same origin, whereto the SAV moved unoccupied as a result of either pickup or relocation, or with an in-service SAV that goes through the traveler's origin. Second, a section-based formulation for SAV ridesharing user equilibrium is introduced to characterize the SAV traveler flow, which accommodates the possibility that an SAV traveler's itinerary (OD pair) is different from that of the serving SAV and other travelers in the SAV. Unlike the existing link and route based ridesharing formulations, the notion of section both prevents undesired traveler en-route transfer(s) and allows travelers of multiple ODs to share rides, meanwhile respecting the SAV seat capacity constraint. In addition to the above two methodological contributions, the optimal SAV fleet size, fare, routing, and allocation (to in-service, pickup, and relocation states) decisions of a transportation network company (TNC) are formulated. The TNC decisions anticipate traveler reactions as characterized by a new multimodal autonomous ridesharing user equilibrium (MARUE), which is put forward with a proof of its existence and finds the endogenous market shares and road congestion effects of SAV/HV. Original insights are obtained from model implementation, including substantial systemwide benefit of ridesharing, marginal benefit of relocation in the presence of ridesharing, and diminishing economies of SAV size.

Post-disruption restoration of critical infrastructures (CIs) often faces uncertainties associated with the required repair tasks and the related transportation network. However, such challenges are often overlooked in most studies on the improvement of CI resilience. In this paper, two-stage risk-averse and risk-neutral stochastic optimization models are proposed to schedule repair activities for a disrupted CI network with the objective of maximizing system resilience. Both models are developed based on a scenario-based optimization technique that accounts for the uncertainties of the repair time and the travel time spent on the underlying transportation network. Given the large number of uncertainty realizations associated with post-disruption restoration tasks, an improved fast forward algorithm based on a wait-and-see solution methodology is provided to reduce the number of chosen scenarios, which results in the desired probabilistic performance metrics. To assess the risks associated with post-disruption scheduling plans, a conditional value-at-risk (CVaR) metric is incorporated into the optimization models through a scenario reduction algorithm. The proposed restoration framework is applied to the French RTE electric power network with a DC power flow procedure, and the results demonstrate the added value of using the stochastic optimization models incorporating the travel times related to repair activities. It is essential that risk-averse decision-making under uncertainty largely impacts the optimum schedule and the expected resilience, especially in the worst-case scenarios.

In the global maritime transportation network, the on-time performance of cargo transportation depends largely on the service capacity and accessibility of seaports. When opportunities for infrastructure expansions are not available, seaport congestion mitigation may require effective scheduling of the vessel traffic in the port waters. Although existing works on vessel traffic scheduling focus on minimizing vessel delays, this paper studies a novel vessel traffic scheduling problem that aims to address the inter-shipping line equity issue. We develop a lexicographic optimization model that accounts for two conflicting performance measures: efficiency, which favors minimizing total vessel delay; and equity, which favors balancing the impacts of delays fairly among shipping lines. Our model allows the port operator to quantify the efficiency-equity tradeoff and make the best vessel traffic scheduling decisions. For solving the model, we develop an effective two-stage solution method in which the first stage solves two single-objective models to obtain the maximum system efficiency and equity, whereas the second stage trades between efficiency and equity and seeks the best compromise between the two conflicting objectives. We apply our model and solution method on instances generated from the operational data of the Port of Shanghai. Our computational results show that an efficiency-oriented model can lead to highly inequitable traffic plans, whereas inter-shipping line equity can be achieved at only mild losses in efficiency, indicating that the consideration of inter-shipping line equity can lead to satisfactory service at both the vessel level and the shipping line level.

We study the design of large-scale group testing schemes under a heterogeneous population (i.e., subjects with potentially different risk) and with the availability of multiple tests. The objective is to classify the population as positive or negative for a given binary characteristic (e.g., the presence of an infectious disease) as efficiently and accurately as possible. Our approach examines components often neglected in the literature, such as the dependence of testing cost on the group size and the possibility of no testing, which are especially relevant within a heterogeneous setting. By developing key structural properties of the resulting optimization problem, we are able to reduce it to a network flow problem under a specific, yet not too restrictive, objective function. We then provide results that facilitate the construction of the resulting graph and finally provide a polynomial time algorithm. Our case study, on the screening of HIV in the United States, demonstrates the substantial benefits of the proposed approach over conventional screening methods. Summary of Contribution: This paper studies the problem of testing heterogeneous populations in groups in order to reduce costs and hence allow for the use of more efficient tests for high-risk groups. The resulting problem is a difficult combinatorial optimization problem that is NP-complete under a general objective. Using structural properties specific to our objective function, we show that the problem can be cast as a network flow problem and provide a polynomial time algorithm.

A fundamental characteristic of energy markets is their vulnerability to the presence of market power, which is that one or more suppliers can influence market prices. This vulnerability is related to a number of factors: low price sensitivity of demand, inflexibility of supply, limited abilities to store energy, and restricted capacities of the transportation network. These characteristics are discussed in Sect. 9.3. First, Sect. 9.2 discusses the general conditions and welfare consequences of the presence of firms using market power. Afterwards, Sect. 9.4 discusses how the presence and use of market power in energy markets can be monitored. Finally, Sect. 9.5 discusses a number of regulatory options to address the market failure of market power.

Reliability has been widely used as a potential indicator of the performance assessment for several real-life networks. Focus on a multistate transportation network in tourism (MTN), this study evaluates the reliability of the MTN as a basis for investigating the influence of transit time. Reliability is the probability to fulfill transportation demand under the given time threshold and budget limitation and evaluated at various levels of transit times. An algorithm, which employs the boundary points and recursive sum of disjoint products technique, is proposed to evaluate the MTN reliability. According to the obtained results, this paper analyzes the influence of transit times on MTN reliability. Particularly, this paper discusses and provides some suggestions about the appropriate transit time to maintain reliability. Decision-makers in the tourism industry also can predetermine the significant drops of reliability to improve the relevant transit times. Besides, the proposed investigation is indicated and proved through an illustrative example and a practical case.

The reconfiguration algorithms have been extensively investigated to ensure the reliability and stability for the processor arrays with faults. It is important to reduce the power consumption, capacitance and communication costs in the processors by reducing the interconnection length of the VLSI array. This paper discusses the reconfiguration problem of the high-performance VLSI processor array under the row and column rerouting constraints. A novel method, making use the idea of network flow, is proposed in this paper. Firstly, a network flow model of the VLSI processor array is constructed, such that the high-performance VLSI target array can be obtained by utilizing the minimal cost flow algorithm. Secondly, we propose a new strategy for bottleneck row selection in the logical array using the minimum cut technique, which can find a more suitable bottleneck row. Finally, we conducted reliable experiments to clearly reveal the efficiency of the new rerouting scheme and algorithm in reducing the number of long interconnects. The experimental results show that, for a host array with size of 256×256, the number of long interconnects in the subarray can be reduced by up to 79.22% and 55.88% without performance penalty for random faults with density of 1% and 25% respectively, when compared with state-of-the-art. In addition, the proposed scheme improves existing algorithm in terms of subarray size. On a 256×256 host array with 25% faulty density, the average improvement in subarray size is up to 3.77% compared with state-of-the-art.

This paper proposes a satellite handover strategy for the huge low earth orbit (LEO) satellite constellation. As the increasing of the constellation scale and the number of user terminals (UTs), the satellite handover becomes more complicated. Thus, we formulate the network-flows model of the satellite handover, and the handover UTs can access the satellites according to the flow matrix. In the proposed network-flows model, the weighted edges are determined by the requests of UTs and the quality of satellite services. The multiple matching between the satellites and UTs can be determined by calculating the minimum cost and maximum flow of the network-flows. Besides, we propose a modified handover strategy based on network-flows (HSNF) algorithm to enhance the algorithm performance by preventing infinite loop. Finally, the simulations show the effectiveness and superiority of the proposed strategy.

Network flows are the basic components of the Internet. Considering the serious consequences of abnormal flows, it is crucial to provide timely anomaly detection in dynamic communication networks. To obtain accurate anomaly detection results in dynamic networks, supervision from experts is highly demanded. However, to obtain high-quality ground truth of abnormal flows, we suffer from two major problems: (1) limited labor resources: experts with the latest domain knowledge are much fewer than the large number of flows; and (2) dynamic environment: considering the new abnormal patterns (i.e., new attacks) and continuously changing network structures, it requires timely supervision to adaptively update the parameters. To tackle these problems, we propose HADDN, a novel bandit framework for periodic-updated anomaly detection in dynamic communication networks. We formulate the task as a bandit problem, where by interactions, supervision is offered by human experts to provide the ground truth to a fraction of flows. We construct semi-parametric expected rewards to optimize the estimation of flows' abnormality in limited interactions. Also, we utilize feature-based clusters and structural correlations to make connections between historical flows and new flows to improve both efficiency and accuracy of abnormality estimation. What's more, we provide two implementations for the semi-parametric expected reward of the proposed HADDN with theoretical proof. Experimental evaluations on public datasets demonstrate the substantial improvement of our proposed approaches compared to state-of-art anomaly detection methods.

In this paper, we consider a flow network that is described by a digraph, each edge of which can admit a flow within a certain interval, with nonnegative end points that correspond to lower and upper flow limits. We propose and analyze a distributed iterative algorithm for solving the so-called feasible circulation problem, which consists of computing flows that are within the given intervals at each edge and balance the total in-flow and the total out-flow at each node. Unlike previously proposed distributed algorithms that required bidirectional communication between pairs of nodes that share an edge in the flow network, the algorithm we propose can operate over any communication network, assuming the corresponding digraph that describes it is strongly connected. The proposed algorithm allows the nodes to asymptotically compute (with a geometric rate that depends on the specifics of the given flow network and communication topology) a solution to the feasible circulation problem, as long as such a solution exists. An important special case of the setting studied in this paper is the case where the digraph of the flow network matches the digraph of the communic

Winter urban traffic issues and performance present critical problems in large cities and metropolitan areas. In urban areas, there is a critical need for efficient methods for snow removal while considering the impact on the transportation infrastructure of a city. Several proposals and approaches on modeling snow removal that heuristically deals with finding solutions to this wideopen problem have been studied and published in recent years. In this paper, we developed a new mathematical model that uses the Just-In-Time (JIT) method to optimize a transportation problem. The paper’s main objective is to design a model for establishing efficient truck routes for snow removal by optimizing cost and time, which implicitly minimizes the impact on a city’s transportation infrastructure. We applied the network flow problem for snow removal to minimize time and cost of cleaning urban streets just in time. We ran several simulations of the models using the MATLAB®.

In this letter, we make a case for in-network intelligence in programmable data planes (PDPs) by taking the first steps toward running distributed Artificial Neural Networks (ANNs) in programmable switches. The main novelty of our research lies in distributing the neurons of an ANN into multiple switches instead of running an entire ANN in a single device. The many advantages of this approach include wider network flow visibility and better resource usage across switches and links. We discuss the research challenges involved in expressing neuron logic for PDPs, mapping neurons to switches, and enabling neuron communication. To tackle these challenges, we introduce PDP programming constructs for performing neuron computation, formalize an optimization model for neuron placement, and tailor in-band telemetry for neuron inter-communication using production flows. Results obtained with a P4 implementation evidence that our approach improves network management tasks while keeping their provisioning overhead similar to a baseline.

Transportation network companies (TNCs) offer a ride-splitting option for ridesourcing trips, allowing users to share the vehicle with others at a lower fare. While encouraging shared rides has environmental benefits, little is known about how price affects the decision to share. Using TNC trip data from Chicago, we investigate the temporal and spatial distribution of authorized ride-splitting trips in 2019. We found that the willingness to share TNC trips differed across neighborhoods with different demographics, socioeconomic status, and built environment characteristics. The willingness to share was related to price and trip duration. We estimate logistic regression and random forest models to determine the marginal price and time effects on the decision to share. The results indicate the probability of authorizing a ride-splitting trip is highly elastic to the price per mile and the random forest model had better predictive accuracy than the logistic model. Additionally, we examine the importance and marginal effects of total price and trip duration. We use two data preprocessing methods to address rounding errors in the price and demonstrate the robustness of the results. Policy implications for increasing shared trips are discussed based on the findings.