A Day-to-Day Stochastic Traffic Flow Assignment Model Based on Mixed Regulation

The analysis of complex networks has so far revolved mainly around the role of nodes and communities of nodes. However, the dynamics of interconnected systems is often focalized on edge processes, and a dual edge-centric perspective can often prove more natural. Here we present graph-theoretical measures to quantify edge-to-edge relations inspired by the notion of flow redistribution induced by edge failures. Our measures, which are related to the pseudo-inverse of the Laplacian of the network, are global and reveal the dynamical interplay between the edges of a network, including potentiallynon-localinteractions. Our framework also allows us to define the embeddedness of an edge, a measure of how strongly an edge features in the weighted cuts of the network. We showcase the general applicability of our edge-centric framework through analyses of the Iberian power grid, traffic flow in road networks, and theC. elegansneuronal network.

Path sets size, model specification, or model estimation: Which one matters most in predicting stochastic user equilibrium traffic flow?

The increasing prevalence of electric vehicles (EVs) calls for the effective planning of the charging infrastructure. In this study, a multi-objective, multistage collaborative planning model is proposed for the coupled EV charging station infrastructure and power distribution network. The planning model aims to minimize the investment and operation costs of the distribution system while maximize the annually captured traffic flow. The uncertainties of EV charging loads are modeled for three different types of charging stations. The FISK's stochastic traffic assignment model is utilized to model realistic traffic flows. And a new class of volume-delay functions, conical congestion functions, is employed to overcome the shortcomings of the conventional Bureau of Public Roads function. The multi-objective evolutionary algorithm based on decomposition (MOEA/D) algorithm is applied to find the nondominated solutions of the proposed collaborative planning model. Finally, simulations based on a 54-node distribution system are conducted to validate the effectiveness of the proposed method.

This paper first analyzes link and route travel time distributions in a degradable road network by assuming a uniform distribution on the capacity of each link. It is postulated that travelers, by acquiring route travel time variability from past experiences, make route decisions based on a travel time budget, which is the summation of the mean route travel time and the safety margin of the travel time. This paper then formulates a novel travel time budget-based stochastic user equilibrium traffic assignment model with multiple user classes and elastic demand as a variational inequality problem. Finally, a numerical example of a small road network is presented to illustrate the properties of the proposed model.

Co-evolutionary traffic signal control using reinforcement learning for road networks under stochastic capacity

Dynamic multi-scale spatial-temporal graph convolutional network for traffic flow prediction

This paper develops a method for estimating carbon‐emission specific road networks, considering the presence of electric vehicles (EVs). A mixed equilibrium traffic assignment model is set up to obtain the traffic volume for each link in the network, where oil‐fueled vehicles (OFVs) prioritizing travel time minimization, while EVs also consider charging station locations and battery charge state in route selection. A carbon‐emission estimation method is then developed, which is calculated by three parameters of traffic volume, average speed, and the road category. A case study is carried out using two networks. It is found that the travel time of the road network has increased by 27%, because EVs tend to choose paths containing charging stations. The route selection of EVs is affected by perceived risk, safe electric quantity, and expected charging electricity. EVs can reduce carbon dioxide emissions but not energy consumption for road network. In addition, it was found that the location of charging stations has a significant impact on traffic flow. After optimizing the location of charging stations, the total travel time, total carbon emissions, and balance of charging station utilization indicators in the transportation network have all relatively decreased. Among them, the total travel time has decreased by 0.2%, the total carbon emissions have decreased by 1.85%, and the balance of charging station utilization has decreased by 0.95%. The research is helpful for determining the locations of charging piles and designing road networks, and it is also helpful for estimating the traffic flow and carbon emissions.

With the development of China economy, more and more Special Planned Events (SPEs), such as Olympic Games, Expo, Vocal concert, have been held in many metropolitans, which may bring huge pressures to city's normal transportation under traffic control. In this paper, a traffic flow assignment model based on regional transportation routes controlled for Planned Special Events was set up, and the heuristic algorithm was given in detail. Then, we applied the model and its algorithm to the Tianjin sport centre, and three scenarios of route controlled in its road network were analyzed respectively and the appropriate scheme was recommended according to results from the model.

In this paper, a stochastic traffic assignment model for networks is proposed for the study of discrete dynamic Bayesian algorithms. In this paper, we study a feasible method and theoretical system for implementing traffic engineering in networks based on Bayesian algorithm theory. We study the implementation of traffic assignment engineering in conjunction with the network stochastic model: first, we study the Bayesian algorithm theoretical model of control layer stripping in the network based on the discrete dynamic Bayesian algorithm theory and analyze the resource‐sharing mechanism in different queuing rules; second, we study the extraction and evaluation theory of traffic assignment for the global view obtained by the control layer of the network and establish the Bayesian algorithm analysis model based on the traffic assignment; subsequently, the routing of bandwidth guarantee and delay guarantee in the network is studied based on Bayesian algorithm model and Bayesian algorithm network random traffic allocation theory. In this paper, a Bayesian algorithm estimation model based on Bayesian algorithm theory is constructed based on network random observed traffic assignment as input data. The model assumes that the roadway traffic distribution follows the network random principle, and based on this assumption, the likelihood function of the roadway online traffic under the network random condition is derived; the prior distribution of the roadway traffic is derived based on the maximum entropy principle; the posterior distribution of the roadway traffic is solved by combining the likelihood function and the prior distribution. The corresponding algorithm is designed for the model with roadway traffic as input, and the reliability of the algorithm is verified in the arithmetic example.

Traffic flows are key parameters for design, channelization, and signal control of the intersection. Thus, it is important to study how to correct the output of the assignment model so it can be used for the intersection design. This paper uses the assigned and observed traffic flows of some intersections as the input/output factors to train the neural network to obtain an intersection traffic forecast model. In order to train the neural network, the number and location of the sampled intersections are determined, and traffic flows in the intersection are collected. Then, the surveyed and assigned data neural network is trained. Finally, the accuracy of the trained model is examined.

Traffic congestion in urban areas is posing many challenges, and a traffic flow model that accurately predicts traffic conditions can be useful in responding to them. With the limitation of infrastructure and the difficulty for real world experiment, traffic simulation is a good tool for the validation and future potential applications of the traffic flow model. This research presents a stochastic traffic flow model which uses a stochastic partial differential equation to describe the evolution of traffic flow on the highway. The stochastic model is calibrated and validated by real traffic data on a highway and is proved to have better predictive power than the deterministic model. Also, a microscopic traffic simulation is built and calibrated by the same highway data, and the validation result shows that the simulation is capable of representing the real traffic. By using traffic simulation to further explore the capability of the stochastic model, the validation demonstrated that the prediction works well at most locations, and the interpolation error may be improved by considering the influence of ramps and the change in the number of lanes.

Road traffic bottleneck analysis for expressway for safety under disaster events using blockchain machine learning

In preventing large-scale congestion caused by urban road traffic attacks, both random and deliberate attack methods were employed, with the robustness of the traffic system analyzed using a cascading failure model. The details of the proposed methods are, firstly, a road network model is constructed based on a Geographic Information System (GIS), and the traffic flow data of a provincial city during peak traffic hours is obtained by Python. Then, the flow data of Origin-Destination (OD) pairs for the road network under normal operation is obtained using the OD estimation module in TransCAD, the urban transportation network is modeled based on the actual traffic flow distribution. Subsequently, random attacks and deliberate attacks based on traffic volume were conducted on the road network. The traffic flow of the attacked segments was reallocated according to a cascading failure model, with the robustness of the urban road network assessed using topological and traffic flow indicators. The results indicate that, in the robustness analysis considering cascading failures, deliberate attacks have a more significant impact on high-traffic segments compared to random attacks with the same number of attack steps. Therefore, future urban traffic management should focus on ensuring the efficiency of high-traffic road segments, and prevent it from becoming ineffective due to excessive traffic flow.

** † ‡ Development of a decision support system that uses real-time track data to estimate statistical parameters describing the stochastic traffic flow is described. Modern statistical decision theory is applied to optimize traffic flow. An advanced estimation algorithm provides the parameter estimates based on queuing network models of traffic flow. A hypothesis testing approach is developed for triggering traffic flow management initiatives in the terminal area, and a stochastic quadratic programming methodology is advanced to achieve flow control objectives such as runway load balancing. The use of this methodology is demonstrated using multi-day track data in the San Francisco terminal area. It is shown that the methodology can correctly identify the need for restricting the traffic flow into the terminal area, and provide decision support to balance the traffic flow at the runways under uncertain traffic flow conditions. The present approach can be extended to the creation of decision support tools for a wide variety of stochastic air traffic flow control situations.

At present, the urban traffic system is faced with the problems of congestion and low efficiency, and the traditional methods have certain limitations when dealing with the complex urban road network. This paper aims to explore a new method of capacity Optimization of Road Networks in a Smart City environment and proposes a method based on a Graph Neural network, named “Smart City Road optimization Graph Neural Network” (SCRO-GNN). SCRO-GNN first collects and preprocesses multi-source data, including road network data, traffic flow, accident records, environmental factors, etc. The key node and edge features, including the number of lanes at the intersection, the traffic flow of the section, and the adjacency matrix of the road network are defined to characterize the structure of the road network. Then, the graph neural network model is constructed and trained to predict the traffic flow of different sections and evaluate the road capacity by using the graph structure of the road network. This paper tests the performance of SCRO-GNN on real road networks in multiple cities. The results show that, compared with the traditional traffic flow prediction model, SCRO-GNN can significantly improve the prediction accuracy, especially when dealing with the highly complex urban road network structure. Based on these predictions, the proposed optimization strategy performed well in reducing traffic congestion and improving the efficiency of road use. The research in this paper not only demonstrates the potential of graph neural networks in smart city road network optimization, but also provides a new direction for future traffic system research. The successful implementation of the SCRO-GNN approach is expected to provide more efficient and intelligent solutions for urban traffic management and planning.