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Mitigating traffic jams is a critical step for the betterment of the urban transportation system, which comprises a large number of interconnected routes to form an intricate network. To study the distinct features and complexities of vehicular flow, a network consisting of multiple-loops with a single intersection is considered a directed weighted graph. The governing equations for individual loop densities are derived using the principle of mass conservation, considering a data-driven flow-density relationship. Firstly, we examine the stability of a double-loop network and explore the traffic behavior using the macroscopic fundamental diagram (MFD) for different sets of parameters. Utilizing popular techniques of nonlinear dynamics, the existence of bistability, bifurcations, oscillations, and switching dynamics is demonstrated in the case of the triple-loop network. Further, bistability is characterized by plotting the basin of attraction diagram for the coexisting attractors. Our study reveals that an increase in the number of loop lines enriches the dynamical properties of vehicular traffic flow. It is observed that depending upon the initial density configuration, a loop network can show various phases, namely free flow, stop-and-go traffic jams, and loop congestion. Additionally, we show the presence of period-2 orbits in the case of the quadruple-loop system. [ABSTRACT FROM AUTHOR]

How to free a road from vehicle traffic as efficiently as possible and in a given time, in order to allow for example the passage of emergency vehicles? We are interested in this question which we reformulate as an optimal control problem. We consider a macroscopic road traffic model on networks, semi-discretized in space and decide to give ourselves the possibility to control the flow at junctions. Our target is to smooth the traffic along a given path within a fixed time. A parsimony constraint is imposed on the controls, in order to ensure that the optimal strategies are feasible in practice. We perform an analysis of the resulting optimal control problem, proving the existence of an optimal control and deriving optimality conditions, which we rewrite as a single functional equation. We then use this formulation to derive a new mixed algorithm interpreting it as a mix between two methods: a descent method combined with a fixed point method allowing global perturbations. We verify with numerical experiments the efficiency of this method on examples of graphs, first simple, then more complex. We highlight the efficiency of our approach by comparing it to standard methods. We propose an open source code implementing this approach in the Julia language. [ABSTRACT FROM AUTHOR]

This work proposes and investigates a solution approach to the urban air transportation network optimization problem, considering the perspectives of different stakeholders, including societal interests. Given logistic hub positions and a set of optimized paths connecting them pairwise, we aim for a Pareto-optimal and three-dimensional air corridor network structure. This work demonstrates a way to merge the given paths into a network and provides a framework to optimize the network further regarding multiple objectives. It proposes three objective functions that evaluate the network from the economic perspectives of network providers and users and the city residents’ social point of view. Using geospatial data from Frankfurt, Germany, we conducted different experiments including and excluding the social objective function under a varying input set of pre-optimized paths. Our analysis showed that taking social aspects into account results in traffic networks whose increase in social acceptance far outweighs the extra monetary costs. We conclude that it is beneficial to integrate social criteria into optimization problems when the solutions obtained are the basis for decisions in the area of conflict between the economy and human welfare.

Electric vehicles (EVs) are a promising solution to urban environmental challenges, but their unplanned growth could strain both the road and electric networks. To address this issue, this paper introduces the Combined Model of Road Transport and Electric Distribution Network (CoRTED), a framework for strategically deploying EV charging stations considering the dynamics of both networks. The CoRTED model minimizes EV user travel costs, power loss, and voltage deviation. The model employs a bi-level optimization approach with PSO-DS algorithm for station deployment and combined convex optimization (CCO) for traffic network equilibrium. Grid economic operation is formulated as AC OPF and solved using a primal-dual interior point method. Validation is performed using a modified Nguyen-Dupius RTN and IEEE 33 bus EDN, demonstrating CoRTED’s effectiveness in integrated network planning for EV infrastructure. Due to the increase in power demand by EVs, the loss and voltage deviation are increased to 4.679 MW and 24.23%, respectively. The proposed model reduces the losses and voltage deviation by 4.487 MW and 1.24%. Also, the total travel cost on the traffic network is reduced by 1.9% as the charging costs at each charging station are reduced.

This paper develops a risk-averse-based framework for optimizing the operation of an integrated power, gas, and traffic (PGT) network with an application to a typical PGT network in downtown Edmonton, the forefront of Canada’s transition to electric vehicles and sustainable urban travel options. The developed non-probabilistic framework provides decision-makers with various secure options to avoid worst-case scenarios and promote social and environmental benefits. The integration of different energy systems allows operators to pursue optimal strategies in critical situations, such as facility outages, maintaining the system within a secure operational range without resorting to expensive workarounds. The proposed algorithm and integrated structure can select optimal travel routes to minimize gas-emission effects and locate charging options to reduce electric vehicle users’ travel time. It can mitigate challenges posed by distributed generator outages and roadway closures. The numerical results from implementing the framework on different case studies and the solar-based PGT network of Edmonton indicate its feasibility and effectiveness.

Aiming at the lack of an anti-clogging ability index in the road network traffic evaluation index, an anti-clogging ability index was proposed to measure the anti-clogging ability of urban road traffic network: Κ-anti-clogging coefficient, which is used to measure the shortest path between any pair of starting and ending points on the urban road traffic network. After the current edge of the shortest path is blocked, the shortest path is selected from the current node of the shortest path. If the current edge of the shortest path is blocked again, the selection continues until the shortest path to the ending point is selected. In the case of unrecoverable congestion, the properties of the anti-clogging coefficient vector on any origin-destination pair, a path, and the whole traffic network are analysed, and the algorithm of the anti-clogging coefficient and its complexity are given. Finally, an example analysis is carried out using a local traffic network in a city.

Software-Defined Networking (SDN) has emerged as a promising paradigm to enhance network control and management by decoupling the planes. With SDN, the centralized controller plays a critical role in managing network resources and traffic flows. Throughout the most recent couple of years, networks turned out to be more imaginative for developing different applications with the help of SDN. Network traffic analysis is a vital task in understanding network behaviour, identifying anomalies, and optimizing network performance. To deal with the load of changes in the networking industry, there is an extraordinary requirement for a productive SDN controller to work on the usage of network resources for a better presentation of the network. Therefore, the proposed approach leverages the RYU controller, an open-source SDN controller framework, to collect and analyse network traffic data. By utilizing RYU's capabilities, we can dynamically monitor and capture network traffic statistics, such as bandwidth, throughput, packet counts, and Round trip time (RTT). These statistics provide valuable insights into network performance, and traffic patterns. By leveraging real-time traffic analysis, we can dynamically adjust routing paths, and allocate network resources efficiently. Hence, the proposed work assesses the development of SDN architecture through a network topology and then, implementation of RYU controller has been done to evaluate various network performance parameters. To evaluate the effectiveness of our approach, we conduct experiments using a simulated SDN environment. We compare the performance parameters of our traffic analysis techniques with traditional methods and showcase the advantages of utilizing SDN and the Ryu controller for network traffic analysis. The results demonstrate that our approach provides accurate and timely insights into network traffic behaviour, facilitating efficient network management. In conclusion, this study highlights the significance of network traffic analysis in SDN environments and demonstrates the effectiveness of the Ryu controller for extracting valuable insights from network traffic data. [ABSTRACT FROM AUTHOR]

With the increasing penetration rate of electric vehicles, the spatiotemporal coupling relationship between the power distribution network and traffic network is stronger than ever before. Under the dynamic wireless charging mode, traffic jam charging is introduced and the dynamic loading process of traffic flow is described using a cellular transmission model. The charging load is related to traffic flow and serves as a bond between the power distribution network and traffic network. The traffic flow achieves balanced allocation under dynamic user equilibrium conditions, and cooperatively optimizes the power flow of the power distribution network in conjunction with charging loads. Numerical analysis shows that this model can accurately depict the congestion situation during peak travel periods, and alleviate traffic congestion and distribution network voltage out of range. [ABSTRACT FROM AUTHOR]

In a series of urban analyses used in the development of urban plans, there is also a traffic analysis. It is aimed at assessing the impact of the capacity of planned contents on the transport network of the narrower and wider area. In this paper, a large city project for the Belgrade waterfront area was selected as a case study and the analysis done as a transport study for the planning process for this location. Traffic analysis deals with the assessment of how much load planned purposes (housing, business, commercial, public facilities, etc.) will generate and how this will affect the existing and planned road network, standing out approach with six basic phases used in the research and description of the differences between three conceptualized network designs. For these purposes, the appropriate software is used, which for the set network load parameters at different times of the day creates scenarios of the number of vehicles and dominant directions of movement and indicates the shortcomings of the network that needs to be adapted to the requirements of users (0 –with modest attractiveness based on previous plans for the area, 1-3 for different network designs in the peak hours). This type of analysis with its results provides an input or planning the spatial organization of content and connections within the area and with a wider environment.

Bandwidth-based traffic signal coordination has long been an effective technique to make traffic flows within a network more efficient, smoother, and safer. Existing network bandwidth optimization models mainly focus on maximizing the bandwidth under NEMA phasing. The network bandwidth maximization under other typical phasing schemes, namely the split or mixed phasing, has not been intensively studied. To address this, a group of bandwidth-based network traffic signal coordination models is proposed for different types of traffic networks. All the proposed models can simultaneously optimize the key signal control variables, namely the phase sequences, offsets, and common cycle times. Additionally, all the developed models are formulated as mixed-integer linear programming problems, which guarantees that the global optimal solutions can be obtained using the branch-and-bound algorithm. The results of the presented index, which is defined as the absolute difference of the interference variables obtained through theoretical solutions and time–space diagrams, indicate that all the proposed models are correct. Furthermore, simulation results demonstrate that split phasing can significantly reduce the average delay time and the average number of stops compared with the existing NEMA phasing.

Signal optimisation is essential in traffic engineering. The traffic light control timings should be set as optimum. However, it is challenging because the traffic network is a non-polynomial problem. In this study, the problem is evaluated from a unique perspective. The primary idea is to remove the crossings of the intersections. A Hamiltonian cycle algorithm has been used to design the network. So, vehicles are only able to join or split. Thus, no control mechanism is needed that delays or interrupts the flow. The suggested algorithm and intersection design were tested on Allsop and Charlesworth's widely used sample network. Findings were compared with the literature in the form of delay calculated using the Highway Capacity Manual 2010 formula. The suggested network's delay is calculated to be 98.17% and 95.45% less than the original network and recently published study-based delay, respectively. As a result, Hamiltonian roads seem sustainable in time and fuel consumption and could be used for future designs. [ABSTRACT FROM AUTHOR]

During the excavation of tunnels, many hazards caused by the fracture zone and groundwater seepage are often encountered. Studying the surrounding rock’s stability in the fracture zone of the mountain’s water-rich tunnels is thus crucial. The rock mass’s seepage and stress fields were studied by using the seepage theory of rock mass and Darcy’s rule in the experiment, and a hydraulic coupling model of porous continuum was constructed according to the coupling relationship between them. After that, the experiment used FLAC3D software to conduct three-dimensional numerical analysis of the tunnel, and analyzed the surrounding rock’s stability under different conditions. The findings demonstrate that the model’s error rate is just 0.16% and that the surrounding rock’s stability is influenced by the excavation plan, main support, and grade of the surrounding rock. Therefore, the model considers various factors for tunnel excavation and can be applied to the prediction of actual mountain tunnel construction. During the excavation of tunnels, many hazards caused by the fracture zone and groundwater seepage are often encountered. Studying the surrounding rock’s stability in the fracture zone of the mountain’s water-rich tunnels is thus crucial. The rock mass’s seepage and stress fields were studied by using the seepage theory of rock mass and Darcy’s rule in the experiment, and a hydraulic coupling model of porous continuum was constructed according to the coupling relationship between them. After that, the experiment used FLAC3D software to conduct three-dimensional numerical analysis of the tunnel, and analyzed the surrounding rock’s stability under different conditions. The findings demonstrate that the model’s error rate is just 0.16% and that the surrounding rock’s stability is influenced by the excavation plan, main support, and grade of the surrounding rock. Therefore, the model considers various factors for tunnel excavation and can be applied to the prediction of actual mountain tunnel construction. [ABSTRACT FROM AUTHOR]

Abstract Outage recovery is important for reducing the economic cost and improving the reliability of a distribution system (DS) in extreme weather and with equipment faults. Previous studies have separately considered network reconfiguration (NR) and dispatching mobile power sources (MPS) to restore the outage load. However, NR cannot deal with the scenario of an electrical island, while dispatching MPS results in a long power outage. In this paper, a resilient outage recovery method based on co-optimizing MPS and NR is proposed, where the DS and traffic network (TN) are considered simultaneously. In the DS, the switch action cost and power losses are minimized, and the access points of MPSs are changed by carrying out the NR process. In the TN, an MPS dispatching model with the objective of minimizing power outage time, routing and power generation cost is developed to optimize the MPSs’ schedule. A solution algorithm based on iteration and relaxation methods is proposed to simplify the solving process and obtain the optimal recovery strategy. Finally, numerical case studies on the IEEE 33 and 119-bus systems validate the proposed resilient outage recovery method. It is shown that the access point of MPS can be changed by NR to decrease the power outage time and dispatching cost of MPS. The results also show that the system operation cost can be reduced by considering power losses in the objective function.

With the realization of the "dual carbon" goal, urban public transport with an increasing proportion of new energy vehicles will become the key subject to achieve the carbon emission reduction goal. Under the new background of deep coupling between transport networks and power grids, it is of great significance to study the carbon-trading mode of urban public transport participation in promoting the development of new energy vehicles and improving the operating efficiency and low-carbon level of the "energy-transport" system. In this paper, based on blockchain technology, a framework for urban public transportation networks to participate in carbon trading is established to solve the current problems of urban public transportation's insufficient motivation to reduce emissions, lax operation strategy and lack of carbon-trading matching mechanisms. Finally, Hyperledger Fabric was selected as the simulation platform, and we simulated the model through the calculation example. The results show that the proposed scheme can effectively improve the operating efficiency of urban public transport and reduce its operating costs and carbon emissions. In addition, policy recommendations on carbon price, carbon quota and penalties are proposed to improve the institutional system of the carbon-trading market. [ABSTRACT FROM AUTHOR]

Road traffic networks are chaotic and highly complex systems. In this paper, we introduce a dynamic gravity model that characterizes the behaviors of the O-D (origin-destination) traffic, such as equilibrium, period-doubling, chaos, and fractal in discrete time. In cases where the original cost function is used, the trip distribution model might degenerate into an all-or-nothing problem without the capacity constraints. To address this shortcoming, we propose substituting the original cost function with an improved conical volume-delay function. This new function retains some of the properties of the original cost function, and its parameters have the same meaning as those in the original function. Our analysis confirms that the double-constrained dynamic gravity model successfully characterizes complex traffic behavior because of the improved conical volume-delay function. Our analysis further shows that the three-parameter bifurcation diagram based on the period characteristics provides deep insight into the actual state of the road traffic networks. Investigating the properties of the model solutions, we further show that the new model is more effective in addressing the all-or-nothing problem. [ABSTRACT FROM AUTHOR]

Castells proposed that "space of flow" has multiple layers, and considered that transportation infrastructure is the carrier of its formation. However, few studies have focused on whether the infrastructure behind factor flows can provide sufficient support. In this paper, the basic supporting role of traffic networks in regional factor flows is examined. Firstly, we use traffic-connection data, enterprise-investment data and human-flow data to build a traffic network, enterprise-association network and inter-city-trip network, respectively. Then, we construct fitting models of the traffic network and the other two networks from two aspects: centrality and connection degree. Finally, this paper analyzes the standard residuals of the fitting results and looks for outliers that are greater than 1.65 or less than −1.65. Through outliers, we can find out where the traffic network cannot support the inter-city factor flows, and where the traffic network is too developed. The conclusions are as follows: First, the immaterial capital flows are still affected by the connectivity of transport facilities. With an improvement in traffic conditions, the capital links between cities can be enhanced correspondingly. Therefore, cities can gather more capital. Second, the attraction and radiation power of high-grade cities in terms of both human and capital flows are far beyond the traffic condition. They also motivate the neighboring cities to form a scale of capital- and human-flow connection that exceeds the average traffic conditions of the whole province. By analyzing the relationship between factor flows and infrastructure, and identifying mismatched networks, this paper will be helpful in many regards. It can provide guidance for Zhejiang Province in formulating transportation strategies. In addition, the conclusions can also provide decision-making support for optimizing regional infrastructure construction. [ABSTRACT FROM AUTHOR]

Urban rail transit is characterized by large traffic volume, fast speed, safety, punctuality, environmental protection, energy conservation and land use. Giving priority to the development of urban public transport system with rail transit as the backbone has become the consensus of transport development in all countries. In order to optimize the economic benefits of the urban rail transit network route planning, the paper proposes the urban rail transit network route planning considering the characteristics of peak traffic flow. Firstly, the stable state of passenger flow is divided according to the response type of passenger flow to the peak traffic flow state. Secondly, based on the passenger flow response probability, a route planning model aiming at maximizing passenger flow and minimizing operating costs is established. Finally, the model is solved by multi-level simplified search algorithm, and the optimal planning scheme is obtained. Case study shows that the route planning designed by this method can effectively enhance economic benefits. [ABSTRACT FROM AUTHOR]

Internet traffic monitoring is to measure and analyze the network bottlenecks to manage the online data are transferring processes efficiently. Various tools have been developed by using internet traffic measurement and internet traffic analysis tools, such as Hadoop. Activity measurement and adaptive examination represent the dynamics of information exchange. On the other hand, information exchange and dynamics measure movement in light of the system assets that can be accessed depending on the characteristics of the exchanged information. The main aim of this work is to apply scalable features of internet traffic measurement and analysis using Hadoop to understand the effects of these features on the speed of transferring data. This gives a new vision or opportunity to dynamically adapting the most suitable traffic measurement and analysis feature according to network capabilities and environment. This research employs Hadoop/Map Reduce as scalable internet traffic measurement and analysis tools. The simulation was conducted by using five personal computers; one as a server and four virtual computers as network nodes. Each computer has 2GB memory and 100GB storage. Five types of data segmentation are utilized 10 MB, 40MB, 64MB, 200MB, and500MB. The speed of the network is calculating in a megabit per second (Mbs) based upon the network speed on the number of allocated PCs (100 Mbs/4). The simulation is conducted to test the data transfer time based on various selections of network capabilities such as transferring extensive data through a network of medium and heavy usage. [ABSTRACT FROM AUTHOR]

Yulong Chen,1,2 Zhizhu Lai3 1Key Research Institute of Yellow River Civilization and Sustainable Development & Collaborative Innovation Center on Yellow River Civilization of Henan Province, Henan University, Kaifeng, Henan, People’s Republic of China; 2The College of Environment and Planning, Henan University, Kaifeng, Henan, People’s Republic of China; 3School of Geography and Environmental Engineering, Gannan Normal University, Ganzhou, Jiangxi, People’s Republic of ChinaCorrespondence: Zhizhu Lai, School of Geography and Environmental Engineering, Gannan Normal University, 1 South Shida Road, Rongjiang New District, Ganzhou, Jiangxi, 341000, People’s Republic of China, Tel +86 15821120157, Email laizhizhu80@163.comPurpose: The location of emergency medical service (EMS) facilities is a basic facility location problem. Many scholars have examined this kind of problem, but research on the location of EMS facilities in rural areas is still lacking. Different from urban areas, the location optimization of EMS facilities in rural areas must consider the accessibility of roads. The objective of this study conducted the optimal locations of new EMS stations and construction/upgrading of transfer links aiming to improve the medical emergency efficiency of mountain rural areas.Methods: Three multi-objective models were constructed to examine the effects of varying assumptions (suppose existing roads cannot be upgraded, existing roads can be upgraded, and existing roads can be upgraded and new roads can be constructed) about minimizing the population considered uncovered (response time from the residential to the EMS station less than or equal to 0.5 h), time spent traveling from the residential area to the EMS station, construction costs for building new emergency facilities, and costs for improving or building new roads. Furthermore, we developed an improved multi-objective simulated annealing algorithm to examine the problem of optimizing the design of rural EMS facilities.Results: We tested the models and algorithm on the Miao Autonomous County of Songtao, Guizhou Province, China. According to the actual situation of the case area, the models and algorithm were tested with the assumption that only three new EMS stations would be constructed. The number of people not covered by EMS stations decreased from 30.7% in Model 1 to 22% in Model 2, and then to 18.9% in Model 3.Conclusion: Our study showed that the traffic network had a significant impact on the location optimization of EMS stations in mountainous rural areas. Improving the traffic network conditions could effectively improve the medical emergency efficiency of mountain rural areas.Keywords: emergency medical service station, multi-objective simulated annealing algorithm, the problem of facility location design, traffic network

Time-aware spatial keyword queries in traffic networks ( $TSKQT$ ) aim to retrieve top- $k$ result objects based on the ranking score considering spatial proximity, textual relevance, and temporal similarity simultaneously. However, due to inappropriate query parameter settings, the query result set may contain one or more objects not expected by the user. To improve the usability of query results, we propose and study the why question on time-aware spatial keyword queries in the traffic network ( $WhyTSKQT$ ) for the first time. Specifically, a hybrid index structure, $TTG$ - $tree$ , is proposed to effectively organize the traffic network information and the positional, textual, and temporal information of objects. Moreover, several pruning strategies are presented to filter out massive objects irrelevant to the query. By analyzing the keywords contained by original result objects and studying the relationship between the time intervals of why objects and the query time interval, we can reasonably construct the candidate query keyword set and query time set to form the candidate refined queries, so as to minimize the refined queries to be evaluated. In addition, several optimization techniques are proposed to further speed up the acquisition of the lowest-cost refined query. Finally, extensive experiments are carried out on two traffic networks to verify the efficiency of the proposed method.

The rapid development and adoption of automatic identification systems as surveillance tools have resulted in the widespread application of data analysis technology in maritime surveillance and route planning. Traditional, manual, experience-based route planning has been widely used owing to its simplicity. However, the method is heavily dependent on officer experience and is time-consuming. This study aims to extract shipping routes using unsupervised machine-learning algorithms. The proposed three-step approach: maneuvering point detection, waypoint discovery, and traffic network construction was used to construct a maritime traffic network from historical AIS data, which quantitatively reflects ship characteristics by ship length and ship type, and can be used for route planning. When the constructed maritime traffic network was compared to the macroscopic ship traffic flow, the Symmetrized Segment-Path Distance (S S P D) metric returned lower values, indicating that the constructed traffic network closely resembles the routes ships transit. The result indicates that the proposed approach is effective in extracting a route from the maritime traffic network. [ABSTRACT FROM AUTHOR]

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With the increasing penetration rate of electric vehicles, the spatiotemporal coupling relationship between the power distribution network and traffic network is stronger than ever before. Under the dynamic wireless charging mode, traffic jam charging is introduced and the dynamic loading process of traffic flow is described using a cellular transmission model. The charging load is related to traffic flow and serves as a bond between the power distribution network and traffic network. The traffic flow achieves balanced allocation under dynamic user equilibrium conditions, and cooperatively optimizes the power flow of the power distribution network in conjunction with charging loads. Numerical analysis shows that this model can accurately depict the congestion situation during peak travel periods, and alleviate traffic congestion and distribution network voltage out of range.

Traffic parameter characteristics in congested road networks are explored based on traffic flow theory, and observed variables are transformed to a uniform format. The Gaussian mixture model is used to reconstruct route trajectories based on data regarding travel routes containing only the origin and destination information. Using a bi-level optimization framework, a Bayesian traffic demand estimation model was built using route trajectory reconstruction in congested networks. Numerical examples demonstrate that traffic demand estimation errors, without considering a congested network, are within ±12; whereas estimation demands considering traffic congestion are close to the real values. Using the Gaussian mixture model's technology of trajectory reconstruction, the mean of the traffic demand root mean square error can be stabilized to approximately 1.3. Traffic demand estimation accuracy decreases with an increase in observed data usage, and the designed iterative algorithm can predict convergence with 0.06 accuracy. The evolution rules of urban traffic demands and road flows in congested networks are uncovered, and a theoretical basis for alleviating urban traffic congestion is provided to determine traffic management and control strategies. [ABSTRACT FROM AUTHOR]

Outage recovery is important for reducing the economic cost and improving the reliability of a distribution system (DS) in extreme weather and with equipment faults. Previous studies have separately considered network reconfiguration (NR) and dispatching mobile power sources (MPS) to restore the outage load. However, NR cannot deal with the scenario of an electrical island, while dispatching MPS results in a long power outage. In this paper, a resilient outage recovery method based on co-optimizing MPS and NR is proposed, where the DS and traffic network (TN) are considered simultaneously. In the DS, the switch action cost and power losses are minimized, and the access points of MPSs are changed by carrying out the NR process. In the TN, an MPS dispatching model with the objective of minimizing power outage time, routing and power generation cost is developed to optimize the MPSs' schedule. A solution algorithm based on iteration and relaxation methods is proposed to simplify the solving process and obtain the optimal recovery strategy. Finally, numerical case studies on the IEEE 33 and 119-bus systems validate the proposed resilient outage recovery method. It is shown that the access point of MPS can be changed by NR to decrease the power outage time and dispatching cost of MPS. The results also show that the system operation cost can be reduced by considering power losses in the objective function. [ABSTRACT FROM AUTHOR]

Predicting urban traffic flow is a challenging task, due to the complicated spatio-temporal dependencies on traffic networks. Urban traffic flow usually has both short-term neighboring and long-term periodic temporal dependencies. It is also noticed that the spatial correlations over different traffic nodes are both local and non-local. What’s more, the traffic flow is affected by various external factors. To capture the non-local spatial correlations, we propose a Dilated Attentional Graph Convolution (DAGC). The DAGC utilizes a dilated graph convolution kernel to expand the nodes’ receptive field and exploit multi-order neighborhood. Technically, the lower-order neighborhood corresponds to local spatial dependencies, while the higher-order neighborhood corresponds to non-local spatial dependencies between nodes. Based on DAGC, a Multi-Source Spatio-Temporal Network (MS-Net) is designed, which suffices to integrate long-range historical traffic data as well as multi-modal external information. MS-Net consists of four components: a spatial feature extraction module, a temporal feature fusion module, an external factors embedding module, and a multi-source data fusion module. Extensive experiments on three real traffic datasets demonstrates that the proposed model performs well on both the public transportation networks, road networks, and can handle large-scale traffic networks in particular the Beijing bus network which has more than 4,000 traffic nodes. [ABSTRACT FROM AUTHOR]

• The coupling relationship of IN-PG-TN are studied. • The charging and discharging characteristics of PHEV are analyzed. • The space-time transfer and charging and discharging model of PHEV is established. • A double-layer DDPG energy balance support model considering PHEV is established. This paper proposes an energy balance support strategy of plug-in hybrid electric vehicle (PHEV) in microgrid considering the coupling of information network-power grid-traffic network (IN-PG-TN) is proposed. Firstly, the coupling characteristics of IN-PG-TN are analyzed, and an IN-PG-TN interaction model considering PHEV access is established. Next, the energy charging and discharging characteristics of PHEV are analyzed. Combined with the consideration of traffic flow and the mobility characteristics of PHEV, the time-space scheduling characteristic model of PHEV under traffic network is established. On this basis, a double-layer deep deterministic policy gradient (DDPG) energy balance support model considering PHEV is established. The upper DDPG management model aims at the minimum operation cost of microgrid, and the lower DDPG management model aims at the optimal energy management of PHEV. A modified IEEE 33-node model is established for simulation analysis of a microgrid in eastern Inner Mongolia, China. It is verified that the energy balance support strategy of microgrid considering PHEV proposed in this paper can effectively optimize the economic benefits of microgrid and the fuel economy of the PHEV itself. [ABSTRACT FROM AUTHOR]

The understanding of congestions contributes to the development of effective traffic management strategies. The propagation of congestions from a motorway section to the neighboring ones results in correlations. Here, we study symmetrized time-lagged correlation matrices and show how their spectral properties reveal congestion durations. We first carry out an empirical analysis of velocities for two local motorway networks, then set up a numerical simulation for indicator time series of traffic phases, and further propose a simplified, analytical model capturing various scenarios of congestion durations. Our empirical analysis reveals a transition behavior for the dominant eigenvalue as function of time lags, reflecting changes in traffic dynamics. Furthermore, both the numerical simulations and the analytical model disclose a nonlinear relation between the spectral transition and the congestion duration. • Statistics of time-lagged correlations in motorway traffic thorough analyzed. • Transition occurs in the spectral properties as time lag increases. • Clear nonlinear relation between spectral transition and congestion duration. • A model numerically simulated in much detail. • Very simple analytical model already captures some salient features. [ABSTRACT FROM AUTHOR]

Critical links have the characteristics of carrying large traffic flow and quickly affecting the operational performance of the road network. Existing regional traffic congestion coordination control methods do not consider the impact of critical links. To solve this issue, this paper proposes a regional traffic congestion coordination control method based on critical links. First, the median, gravitational index, and traffic sharing ratio are selected as the identification indexes of critical links in urban networks. Second, the road network vulnerability is assessed, and road link criticality is calculated based on joint entropy theory and multi-scale factor synthesis of multi-metric identification results. Then, the regional signal control range is determined, and a multi-stage signal control optimization model is developed. Finally, the optimal traffic signals for intersections are obtained by minimizing the pressure at the maximum phase of the road link and maximizing the capacity of critical links. To demonstrate the effectiveness of the proposed methods, two benchmarks (i.e., the MAXBAND method and existing traffic signals) are compared. The results show that (1) in terms of boundary control, the average vehicle delay at the boundary intersection of the road network is 34.62 s, an increase of 7.49 s and 5.03 s compared to the benchmark methods, respectively. (2) In terms of internal control, the average delay of the road network based on the proposed method is 87.54 s, which is reduced by 16.38 % and 28.73 % compared with the benchmark methods, and the queue lengths of the critical links are reduced by 21.47 % and 34.15 %, respectively. (3) The control process of the proposed method enables the number of vehicles within the road network to be maintained near the optimal carrying capacity, which gives the road network an efficient operating efficiency. [ABSTRACT FROM AUTHOR]

• A coordinated restoration model is proposed for power-gas-transportation distribution systems. • Repair crews and energy hubs are simultaneously optimized to enhance post-disaster resilience. • Spatial interdependency between power-gas systems and transportation systems is considered to ensure the feasibility of the obtained restoration decisions. • Modified Boolean logic equations are proposed to represent the spatial interdependency. Recently, extreme events have caused widespread blackouts over energy systems. In addition, the increasing interactions between power distribution networks and natural gas networks bring new challenges to the fast recovery of energy systems. Mobile emergency resources, such as repair crews (RCs), play an essential role in the restoration. However, power distribution networks and gas networks have geographical correlations with transportation systems in urban areas, therefore, there exists spatial interdependency between infrastructural systems. This property results in the dependency between RCs' routings and road states in transportation systems. To this end, this paper proposes a sequential restoration model for power-gas-transportation distribution networks. The post-disaster restoration model contains the dispatch of both RCs and fixed resources. The spatial interdependency is modeled by modified Boolean logic equations to obtain feasible recovery schedules in practice. Convex relaxations and linearization methodologies are utilized to transform the proposed model into a mixed-integer second-order cone programming (MISOCP) model. Simulations are carried out on 33–14–20 and 136–20–39 bus coupled systems. Numerical results indicate that the proposed restoration strategy could increase the post-disaster resilience index by 19.5 % in 33-bus power system, 8.7 % in 136-bus power system, 25.6 % in 14-node gas system, and 27.2 % in 20-node gas system, respectively. [ABSTRACT FROM AUTHOR]

An accurate and reliable forecast for traffic flow is regarded as one of the foundational functions in an intelligent transportation system. In this paper, a new model for traffic flow forecasting, named EnGS-DGR, is designed based on ensemble learning of graph convolutional network (GCN), sequence-to-sequence (Seq2Seq) learning model, and dynamic graph reconfiguration (DGR) algorithm. At the first stage, instead of employing entire nodes in the traffic network, the DGR algorithm is proposed to reconstruct the traffic graph topology consisting of traffic nodes with tight correlation under a specific forecasting interval, where the degree of correlation among the traffic nodes is quantized from the perspective of multi-view clustering. At the second stage, GCN-Seq2Seq integration strategy is introduced to extract the data of the spatio-temporal dependence and forecast traffic flow. We applied the proposed EnGS-DGR to two different datasets from the highways of Los Angeles County and of California's Bay Area; the simulation results show that the proposed EnGS-DGR is superior to other eight popular models for traffic flow forecasting in terms of three common performance metrics. [ABSTRACT FROM AUTHOR]

The performance of transit signal priority (TSP) with conflicting priority requests highly depends on the serving sequence of multiple TSP requests. A series of existing methods have been developed to determine the priority level of requests. However, most of these methods focused on isolated intersections or a small number of intersections, which are not applicable to complex, dynamic and nonlinear urban traffic networks. In this regard, we propose a multi-agent TSP control method at the network level considering conflicting priority requests. Fuzzy inference is used to manage signal control. We further develop a specific control algorithm. The performance of the proposed method is verified by a case study with a sizeable traffic network with 20 intersections and 49 links. Simulation results demonstrate that the proposed method outperforms other three benchmarking methods under different traffic demands and bus departure frequencies. It is worth-noting that the improvement becomes more notable with the increase of traffic demands and the reduction of bus departure frequencies. [ABSTRACT FROM AUTHOR]

Software-defined networking (SDN) is a new approach that overcomes the obstacles which are faced by conventional networking architecture. The core idea of SDN is to separate the control plane from the data plane. This idea improves the network in many ways, such as efficient utilization of resources, better management of the network, reduced cost, innovation with new evolution, and many others. To manage all these changes, there is a great need for an efficient controller to improve the utilization of resources for the better performance of the network. The controller is also responsible for the analysis and monitoring of real-time data traffic. There is a great need for a high-performance controller in networking industries, data centres, academia, and research due to the tremendous growth of distributed processing-based real time applications. Therefore, it is crucial to investigate the performance of an open-source controller to provide efficient traffic routing, leading to improved utilization of resources for the enhanced performance metrics of the network. The paper presents an implementation of SDN architecture using an open-source RYU SDN controller for the network traffic analysis. The proposed work evaluates the performance of SDN architecture based custom network topology for a node to node performance parameters such as bandwidth, throughput and roundtrip time, etc. The simulation results exhibit an improved performance of the proposed work in comparison to the existing default network topology for SDN. [ABSTRACT FROM AUTHOR]

The node selecting problem of traffic network is a significant issue and is difficult to be solved. In this paper, an artificial slime mold method is proposed to help us solve the problem. First, the chief components of an artificial slime mold are introduced to simulate the foraging behavior of a true slime mold, including external food sources, plasmodium, myxamoeba, nucleus, and nutrients. Then the learning mechanism of nutrient concentration for the artificial slime mold is illustrated, though there is no brain or neuron in its body. After that, the node selecting approach is described according to the propagation capabilities of nodes. Second, the algorithm flow is designed to show how to solve this kind of complex selecting problem. The algorithm flow to select important traffic nodes by artificial slime mold is composed of 4 main steps, including initialization, food searching, feeding, and selecting for output. Third, a comprehensive example is designed and derived from references to certificate that the proposed artificial slime mold can help us select important traffic nodes by their generated traffic topologies. The contributions of this paper are important both for traffic node selecting and artificial learning mechanism in theoretical and practical aspects.

With the realization of the “dual carbon” goal, urban public transport with an increasing proportion of new energy vehicles will become the key subject to achieve the carbon emission reduction goal. Under the new background of deep coupling between transport networks and power grids, it is of great significance to study the carbon-trading mode of urban public transport participation in promoting the development of new energy vehicles and improving the operating efficiency and low-carbon level of the “energy-transport” system. In this paper, based on blockchain technology, a framework for urban public transportation networks to participate in carbon trading is established to solve the current problems of urban public transportation’s insufficient motivation to reduce emissions, lax operation strategy and lack of carbon-trading matching mechanisms. Finally, Hyperledger Fabric was selected as the simulation platform, and we simulated the model through the calculation example. The results show that the proposed scheme can effectively improve the operating efficiency of urban public transport and reduce its operating costs and carbon emissions. In addition, policy recommendations on carbon price, carbon quota and penalties are proposed to improve the institutional system of the carbon-trading market.

Road traffic networks are chaotic and highly complex systems. In this paper, we introduce a dynamic gravity model that characterizes the behaviors of the O-D (origin-destination) traffic, such as equilibrium, period-doubling, chaos, and fractal in discrete time. In cases where the original cost function is used, the trip distribution model might degenerate into an all-or-nothing problem without the capacity constraints. To address this shortcoming, we propose substituting the original cost function with an improved conical volume-delay function. This new function retains some of the properties of the original cost function, and its parameters have the same meaning as those in the original function. Our analysis confirms that the double-constrained dynamic gravity model successfully characterizes complex traffic behavior because of the improved conical volume-delay function. Our analysis further shows that the three-parameter bifurcation diagram based on the period characteristics provides deep insight into the actual state of the road traffic networks. Investigating the properties of the model solutions, we further show that the new model is more effective in addressing the all-or-nothing problem.

Logistics networks are becoming more complex and interconnected. Guaranteeing the performance of the entire system when a part of the network is disrupted (e.g. due to excessive demands and extreme weather conditions) is one of the important issues. However, how much transportation resources should be allocated to which part of the network while maintaining efficiency is an open question. In this paper, we propose a novel metric, the substitutability centrality, which quantifies how much each transport link in the network contributes to the robustness of the system against disruptions. This metric is compelling in the following aspects: (1) it is intuitively interpretable; (2) it does not require simulation or optimization calculations; and (3) it takes into account changes in transportation routes of delivery due to disruptions. Furthermore, as a proof of concept, we demonstrate a simple case study, in which capacity allocation based on the proposed metric can maintain high performance of the system against various types of disruptions. We also found that this approach might not be effective for further increasing the robustness of networks that have many bypass routes.

Frequent occurrence of urban rainy weather, especially rainstorm weather, affects transportation operation and safety, so it is essential that effective intervention measures to recover disordered traffic be adopted and then analyzed for their influence on the dynamic network. Therefore, models and algorithm to show dynamic traffic flow of traffic network in rainy weather are a fundamental need and have drawn great interest from governments and scholars. In this paper, innovative content contains a travel cost function considering rainfall intensity; considering the travel cost function, a dynamic traffic assignment model based on dynamic rainfall intensity is built. Then a corresponding algorithm is designed. Moreover, this study designs three scenarios under rainfall and analyzes the influence of the rainfall on an example network. The results show that rainfall has a significant effect on traffic flow. The finding proved the proposed models and algorithm can express the development trend of path flow rate on a dynamic network under rainfall. [ABSTRACT FROM AUTHOR]

The rapid development and adoption of automatic identification systems as surveillance tools have resulted in the widespread application of data analysis technology in maritime surveillance and route planning. Traditional, manual, experience-based route planning has been widely used owing to its simplicity. However, the method is heavily dependent on officer experience and is time-consuming. This study aims to extract shipping routes using unsupervised machine-learning algorithms. The proposed three-step approach: maneuvering point detection, waypoint discovery, and traffic network construction was used to construct a maritime traffic network from historical AIS data, which quantitatively reflects ship characteristics by ship length and ship type, and can be used for route planning. When the constructed maritime traffic network was compared to the macroscopic ship traffic flow, the Symmetrized Segment-Path Distance (SSPD) metric returned lower values, indicating that the constructed traffic network closely resembles the routes ships transit. The result indicates that the proposed approach is effective in extracting a route from the maritime traffic network.

The spatial partitioning problem of urban traffic network sub-regions has been mainly researched in static mode by considering traffic conditions at a certain time. Nevertheless, traffic flow has strong dynamic characteristics running in urban network; it is necessary to divide the sub-regions according to the correlation of traffic flow in spatiotemporal dimension. Dynamic partitioning is of great significance for capturing the evolution of traffic sub-region and designing the peripheral control strategies in real time. In this paper, a large heterogeneous traffic network is divided into several uniform sub-regions by analyzing the temporal similarity and spatial heterogeneity of the link characteristics to reveal the evolution process of traffic flow. Firstly, a similarity model is defined by the analysis of link autocorrelation based on Moran's I index, and the slow coherency theory combining with α -Cut theory (called α -SC) is implemented to establish the framework of static partitioning. Secondly, a fine-tuning procedure is designed to maintain the homogeneity and compactness of the sub-regions. Finally, the links with high heterogeneity in the sub-regions are identified by heuristic algorithm and then iteratively adjusted, merged and cut to realize the dynamic updating of sub-regions. A case study using the real data set of Farmers Branch city is conducted to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Traffic parameter characteristics in congested road networks are explored based on traffic flow theory, and observed variables are transformed to a uniform format. The Gaussian mixture model is used to reconstruct route trajectories based on data regarding travel routes containing only the origin and destination information. Using a bi-level optimization framework, a Bayesian traffic demand estimation model was built using route trajectory reconstruction in congested networks. Numerical examples demonstrate that traffic demand estimation errors, without considering a congested network, are within ±12; whereas estimation demands considering traffic congestion are close to the real values. Using the Gaussian mixture model’s technology of trajectory reconstruction, the mean of the traffic demand root mean square error can be stabilized to approximately 1.3. Traffic demand estimation accuracy decreases with an increase in observed data usage, and the designed iterative algorithm can predict convergence with 0.06 accuracy. The evolution rules of urban traffic demands and road flows in congested networks are uncovered, and a theoretical basis for alleviating urban traffic congestion is provided to determine traffic management and control strategies.

Nowadays, parking spaces are scarce resources in urban cities. Travelers often spend too much time looking for available parking spaces, which increases travel time of travelers and results in additional traffic congestion. With the innovation and application of intelligent parking technology, parking spaces can be booked in the system in advance through mobile phone, which will greatly reduce the time for drivers to cruise and search for parking spaces. Targeted at the serious waste of parking resources, traffic congestion caused by too intensive parking demand in time and space, a parking allocation model considering the ability of dynamic and static traffic conversion is established with the goal of minimizing the total travel time of the travelers. Based on the dynamic traffic distribution model, considering the constraints of capacity of the road link, parking lot entrance and the number of the parking spaces in parking lot, the dynamic and static traffic is combined by considering the parking lot connection section as a conversion link to be added into the traffic network. And the solution method based on particle swarm optimization is proposed. Experimental results on a case (Beijing Chaoyang Joy City and surrounding parking lots) show that our parking allocation model works satisfactorily by effectively reducing the travel time of travelers and increase customer arrivals in shopping centers. From the point of view of traffic managers, the model can make the parking occupancy of all parking lots more balanced, which indicates that the model can help to better coordinate the available parking resources. In summary, the model proposed in this paper can not only divert the flow beyond the capacity of the road facilities at the connection of the parking lot, but also balance the utilization rate of the surrounding parking resources and reduce the dynamic traffic pressure, it is a proper way to develop the sustainable transportation. [ABSTRACT FROM AUTHOR]

The main purpose of the paper is to propose a traffic flow assignment method with matched selection of a multi-level road traffic network structure. Firstly, road connection 'design' parameters of traffic pricing are introduced, equilibrium conditions of users by traffic flow assignment are improved, design/pricing problem models of traffic network are constructed and surrounding semi-differentiable conditions of the reference point are considered and sensibility analysis methods of traffic equilibrium are designed. Then, sensitivity analysis is implemented combined with traffic flow assignment (TAPAS) output based on paired selection, it is shown how to use TAPAS to find a very precise equilibrium solution effectively and use the directional derivative. [ABSTRACT FROM AUTHOR]

Traffic flow prediction is a challenging task while most existing works are faced with two main problems in extracting complicated intrinsic and extrinsic features. In terms of intrinsic features, current methods don’t fully exploit different functions of short-term neighboring and long-term periodic temporal patterns. As for extrinsic features, recent works mainly employ hand-crafted fusion strategies to integrate external factors but remain generalization issues. To solve these problems, we propose a meta-learning based multi-source spatio-temporal network (Meta-MSNet). The Meta-MSNet is designed with an encoder-decoder structure. The encoder captures neighboring temporal dependencies while the decoder extracts periodic features. Furthermore, two meta-learning based fusion modules are designed to integrate multi-source external data both on temporal and spatial dimensions. Experiments on three real-world traffic datasets have verified the superiority of the proposed model. [ABSTRACT FROM AUTHOR]

A number of mathematical methods have been developed to make temporal signal analyses based on time series. However, no effective method for spatial signal analysis, which are as important as temporal signal analyses for geographical systems, has been devised. Nonstationary spatial and temporal processes are associated with nonlinearity, and cannot be effectively analyzed by conventional analytical approaches. Fractal theory provides a powerful tool for exploring spatial complexity and is helpful for spatio-temporal signal analysis. This paper is devoted to developing an approach for analyzing spatial signals of geographical systems by means of wave-spectrum scaling. The traffic networks of 10 Chinese cities are taken as cases for positive studies. Fast Fourier transform (FFT) and ordinary least squares (OLS) regression methods are employed to calculate spectral exponents. The results show that the wave-spectrum density distribution of all these urban traffic networks follows scaling law, and that the spectral scaling exponents can be converted into fractal dimension values. Using the fractal parameters, we can make spatial analyses for the geographical signals. The wave-spectrum scaling methods can be applied to both self-similar fractal signals and self-affine fractal signals in the geographical world. This study has implications for the further development of fractal-based spatiotemporal signal analysis in the future. [ABSTRACT FROM AUTHOR]

Coordinated evolution is a process with complexity, temporality, spatiality, and continuity. The existed methods cannot relevantly satisfy and measure the degree of coordinated evolution in real conditions. Aiming at solving the coordinated evolution problems for the urban traffic network, the information complexity must be evaluated, this paper uses the multi-dimensional connection number for compressing the factors of traffic network. Firstly, the basic characteristics of traffic network are analysed on the definition of traffic information complexity. The traffic network measurement model is established based on the information entropy, and the coordinated evolution process of the multi-layer urban traffic network is analysed for defining the ordered parameters of the traffic network. Then the coordinated measurement model for the multi-layer traffic network is constructed by the ordered parameters. In addition, we set up a coordinated evolution model according to the proposed estimation criteria of the ordered parameters and the theory of the multi-dimensional connection numbers. The case analysis shows that the order degree of Hangzhou traffic network is 0.7929, which approaches to 1 as while the comprehensive coordinated index of Hangzhou multi-layer traffic network is 0.3323, which clearly and intuitively gives a measurement value for the multi-layer urban traffic network. The result is also effectively verified the validity of the proposed models.

An accurate and reliable forecast for traffic flow is regarded as one of the foundational functions in an intelligent transportation system. In this paper, a new model for traffic flow forecasting, named EnGS-DGR, is designed based on ensemble learning of graph convolutional network (GCN), sequence-to-sequence (Seq2Seq) learning model, and dynamic graph reconfiguration (DGR) algorithm. At the first stage, instead of employing entire nodes in the traffic network, the DGR algorithm is proposed to reconstruct the traffic graph topology consisting of traffic nodes with tight correlation under a specific forecasting interval, where the degree of correlation among the traffic nodes is quantized from the perspective of multi-view clustering. At the second stage, GCN-Seq2Seq integration strategy is introduced to extract the data of the spatio-temporal dependence and forecast traffic flow. We applied the proposed EnGS-DGR to two different datasets from the highways of Los Angeles County and of California’s Bay Area; the simulation results show that the proposed EnGS-DGR is superior to other eight popular models for traffic flow forecasting in terms of three common performance metrics.