Network Traffic Model Research Articles

A dynamic shortest path algorithm based on an improved ant colony algorithm

The shortest path problem is the key problem in intelligent transportation systems. In this paper, the shortcomings of classical shortest path algorithms in solving the dynamic shortest path problem are analyzed. A dynamic traffic network model is constructed, and the characteristics of an ant colony algorithm are analyzed. According to the characteristics of the traffic network, a dynamic shortest path algorithm is proposed based on an improved ant colony algorithm. A simulation experiment showed that the algorithm proposed in this paper could effectively find the shortest path in a dynamic traffic network.

Traffic flow guidance algorithm in intelligent transportation systems considering the effect of non-floating vehicle

Based on the hypothesis that all vehicles on roads are floating vehicles equipped with vehicular terminals and upload their traffic information to the traffic control center in real time, many traffic flow guidance algorithms are proposed in order to improve the road capacity in Intelligent Transportation System. Based on this complete information scenario, the traffic control center can obtain all traffic information on roads and compute the guidance metrics according to the traffic flow guidance algorithm. The vehicle therefore can select the route with the guidance. However, due to the cost and technology limits, in short time, some vehicles cannot be equipped with vehicular terminals, which are non-floating vehicles and cannot upload their traffic information to the traffic control center. In this incomplete information scenario, the guidance effect will be affected by non-floating vehicles. In this paper, the method of estimating non-floating vehicles’ driving information according to floating vehicles’ information is introduced. With the estimation method, a new traffic flow guidance algorithm, Estimated Weighted Vehicle Density Feedback Strategy based on Weighted Vehicle Density Feedback Strategy (WVDFS) is proposed. In the simulation, the guidance effect of the new algorithm is performed based on the two-route scenario, which is the simplified model of traffic network. NaSch model is also used as the vehicle mobility model, which can simulate the vehicle motion. The simulation results show that non-floating vehicles have great negative influence on the performance of WVDFS, and the applicability of our algorithm is validated to have better performance in incomplete information scenarios. Our algorithm can provide solutions for current traffic flow guidance with incomplete information scenarios.

Synchronisation between two different networks with multi-weights and its application in public traffic network

ABSTRACTThe paper established a conventional bus traffic network and a subway network by using the method of space R based on the existing public traffic network model. Regarding these two networks as the sub-networks, this paper presented a new two-layer-coupled public traffic network with multi-weights through the transfer relationship between conventional bus lines and subway lines. Every edge of this model’s two sub-networks has one or several different property weights, and the coupling edges between two sub-networks have one weight. Based on the method of network split, the paper splits the complex networks with multi-weights into several different single-weighted two-layer-coupled public traffic networks and then investigates its global synchronisation. Finally, according to the synchronisation theory of coupling networks with multi-weights and taking ‘Lorenz system’ as the network node, some numerical examples are given to show the impact of congestion degree, passenger-flow density, transfer degree and capacity matching degree to the two-layer-coupled public traffic network balance.

Invulnerability Analysis of Traffic Network in Tourist Attraction Under Unexpected Emergency Events Based on Cascading Failure

This study aims to analyze the cascading failure process of traffic network in tourist attraction under unexpected emergency events, and discuss the measures to improve traffic network invulnerability. For that purpose, taking tourist attraction as the research object, the topology model of traffic network in tourist attraction is established based on Space L method. After different types of emergency events are simulated as different attack strategies, we discuss the spatial and temporal distribution characteristics and evolution process of tourism emergency events. The cascading failure model of traffic network based on load-capacity is constructed, then the main factors affecting the scale of dynamic cascading failures are given and their sensitivity are analyzed. Taking the Summer Palace in Beijing as an example, the invulnerability analysis of the traffic network is carried out and the node protection strategies with different network load are proposed. The study reveals that (a) the traffic network of the Summer Palace has a typical network characteristics of small world; (b) network load, node capacity, node attack strategy, adjacent nodes relationship, load distribution rule significantly affect the scale of cascading failure of the traffic network in tourist attraction, (c) when the network load coefficient $\delta , the cascading failure rate (CFR) can be effectively controlled within 0.2 to avoid large-scale cascading failure; (d) the scale of cascading failure can be effectively reduced by 22.14%, 40.91%, and 63.66%, after increasing the capacity of the nodes which are the top 5%, 10%, and 20% in the ranking of CFR, respectively.

Charging Demand Forecasting Model for Electric Vehicles Based on Online Ride-Hailing Trip Data

Electric vehicle (EV) has been popularized and promoted on a large scale because of its clean and efficient features. Charging this increasing number of EVs is expected to have an impact on the electricity grid and traffic network. Therefore, it is necessary to model and forecast the EV charging demand. Most of the existing researches have not utilized real-world traffic data to analyze the EV charging demand. Few researches have considered and analyzed the characteristics of space-time transfer of charging load in urban functional areas. As an emerging mode of transportation, however, online ride-hailing trip data provide an ideal source for analyzing traffic planning and operation. On the basis of this, a charging demand forecasting model of EVs based on a data-driven approach was presented in this paper. In this methodology, it is firstly assumed that residents’ transportation trip demand is not restricted by vehicle categories(electric or fuel vehicles). The original trip trajectory data of Didi online ride-hailing were conducted to model via data mining and fusion. And the process of data analysis included region-scope selection, spatial grid modeling, trajectory data mapping, retrieval data identification and urban functional area clustering as well as traffic network modeling. Through modeling and processing, the following regenerative feature data were obtained: functional regional division (i.e., residential areas, industrial areas, commercial areas, and public service areas), trip rule distribution (i.e., temporal distribution and spatial distribution on weekdays, weekends and holidays) and actual driving path (i.e., driving path with the shortest distance or with the minimum time-consuming). And then, considering the movable load feature of EVs, vehicles were subdivided into three kinds such as private vehicles, taxis and other public vehicles, and the single EV model with driving and charging characteristic parameters was established. Furthermore, the regeneration data obtained from modeling and analysis along with the determined single EV model were supported as data sources and model for the charging demand forecast architecture. At last, the actual urban traffic network in Nanjing, China was selected as an example to design the path planning experiments and charging demand load experiments in different scenarios. The results demonstrate that this proposed model is able to realistically simulate the actual dynamic driving process of EVs, and effectively predict the spatial-temporal distribution characteristics and load transfer trends of charging demands in different date type as well as different functional areas. The model also lays a theoretical foundation for the subsequent research on investment and construction of charging facilities, as well as charging control and charging guidance of EVs.

A Combined Forecasting Model for Satellite Network Self-Similar Traffic

Since satellite network traffic is self-similar and long-range-dependent (LRD), after analyzing current network traffic forecasting models, a satellite network traffic combined forecasting model that is based on the decomposition fruit fly optimization algorithm - extreme learning machine (FOA-ELM) is proposed. This forecasting model decomposes LRD network traffic into multiple short-range dependent (SRD) components via empirical mode decomposition (EMD), applies the FOA-ELM forecasting model to the decomposed high-frequency components, and applies the ELM forecasting model to low-frequency components. The simulation results show that the forecasting model can improve the forecasting accuracy and forecasting speed, reduce the complexity, and achieve effective and efficient forecasting of satellite network traffic.

Prediction of Network Traffic of Smart Cities Based on DE-BP Neural Network

Smart cities make full use of information technology so as to make intelligence responses to all requirements, including network and city services. This paper proposes a differential evolution back propagation (DE-BP) neural network traffic prediction model applicable for a smart cities network to predict the network traffic. The proposed approach takes the impact factor of network traffic as the input layer and the network traffic as the output layer and trains the DE-BP network with the past traffic data so as to obtain the mapping relationship between the impact factor and the network traffic and get the predicted value of the network traffic. The experimental results show that the proposed approach can accurately predict the trend of network traffic. Within the allowable error range, the predicted traffic volume is consistent with the actual traffic volume trend, and the predicted error is small.

Integrated application of network traffic and IDM models in the test laboratory analysis of autonomous vehicles and electric vehicles

The aim of the research is to develop a laboratory model-based diagnostic procedure that performs tests of the motion processes of autonomous electric vehicles in a particular city, on a transport network or track. The test consists of a laboratory based generation of the corresponding speed and steering angle signals, being in accordance with real driving and traffic conditions, which are also used in the test procedure. The procedure takes into account the real trajectory tracking process as well (Péter and Lakatos, 2017).

Traffic Accident’s Severity Prediction: A Deep-Learning Approach-Based CNN Network

In traffic accident, an accurate and timely severity prediction method is necessary for the successful deployment of an intelligent transportation system to provide corresponding levels of medical aid and transportation in a timely manner. The existing traffic accident's severity prediction methods mainly use shallow severity prediction models and statistical models. To promote the prediction accuracy, a novel traffic accident's severity prediction-convolutional neural network (TASP-CNN) model for traffic accident's severity prediction is proposed that considers combination relationships among traffic accident's features. Based on the weights of traffic accident's features, the feature matrix to gray image (FM2GI) algorithm is proposed to convert a single feature relationship of traffic accident's data into gray images containing combination relationships in parallel as the input variables for the model. Moreover, experiments demonstrated that the proposed model for traffic accident's severity prediction has a better performance.

Research on Cascading Failure Model of Urban Regional Traffic Network under Random Attacks

For better describing the network cascading failure caused by random attacks such as traffic accidents, this paper introduces the ORNL-PSerc-Alaska (OPA) model to analyze the urban regional traffic network vulnerability. Firstly, the double-layer network model is built for analyzing the complex characteristics of the regional traffic network. Secondly, the cascading failure model is built to better describe the process of the network cascading failure under random attacks. Finally, by simulating the network failure process, the vulnerability of the traffic network will be pointed out, which provides the theoretical basis for avoiding the network cascading failure under random attacks.

User equilibrium with a policy-based link transmission model for stochastic time-dependent traffic networks

Non-recurrent congestion is a major problem in traffic networks that causes unexpected delays during travels. In such a scenario, it is preferable to use adaptive paths or policies where next link decisions on reaching junctions are continuously adapted based on the information gained with time. In this paper, we study a traffic assignment problem in stochastic time-dependent networks. The problem is modeled as a fixed-point problem and existence of the equilibrium solution is discussed. We iteratively solve the problem using the method of successive averages (MSA). A novel network loading model inspired from Link transmission model is developed that accepts policies as inputs for solving the problem. This network loading model is different from the existing network loading models that take predefined paths for input flows. We demonstrate through numerical tests that solving traffic assignment problem with the proposed loading modeling scheme is more efficient as compared to solving the problem using path-based network loading models.

Construction of C-ON/OFF network traffic model based on time series

Construction of C-ON/OFF network traffic model based on time series

A route-based traffic flow model accounting for interruption factors

In this paper, we utilize route flow to propose a network traffic flow model with consideration of interruption factors. The proposed model is then used to study the effects of two typical interruption factors (e.g., bus station and accident) and the divergence effect at the shared node on each link’s traffic flow. The model is conducted under the stochastic route choice behavior in a network with an origin–destination (OD) pair and two routes, where the numerical results indicate that the proposed model can qualitatively describe some complex physical phenomena (e.g., shock, rarefaction wave, stop-and-go, jam and route divergence). The above phenomena are related to each route’s initial density, where the formation and propagation speed are affected by network structure and route choice behavior.

Bayesian-entropy Network for Information Fusion and Reliability Assessment of National Airspace Systems

The air traffic control (ATC) system is critical in maintaining the safety and integrity of the National Airspace System (NAS). This requires the information fusion from various sources. This paper introduces a hybrid network model called the Bayesian-Entropy Network (BEN) that can handle various types of information. The BEN method is a combination of the Bayesian method and the Maximum Entropy method. The Maximum Entropy method introduces constraints and is given as an exponential term added to the classical Bayes’ theorem. The exponential term can be used to encode extra information in the form of constraints. The extra information can come from human experience, historical data etc. These knowledges, once written in a mathematical format, can be incorporated into the classical Bayesian framework. The BEN method provides an alternative way to consider common data types (e.g., point observation) and uncommon data types (e.g., linguistic description for human factors) in the NAS. The reported work is demonstrated in two example problems. The first example involves an air traffic control network model and the BEN uses information from various sources to update for the risk event probability. The second example is related to the prediction of the cause of runway incursion. A network model studying different sources of error is used to make predictions of the cause of runway incursion. The training and validation data is extracted from existing accident report in the Aviation Safety Reporting System (ASRS) database. The results are compared with that of the traditional Bayesian method. It is found that the BEN can make use of the available information to modify the distribution function of the parameter of concern.

Network Traffic Flow Evolution Model Based on Disequilibrium Theory

The disequilibrium theory in economics is used to depict the network traffic flow evolution process from disequilibrium to equilibrium. Three path choice behavior criteria are proposed, and the equilibrium traffic flow patterns formed by these three criteria are defined as price regulation user equilibrium, quantity regulation user equilibrium, and price-quantity regulation user equilibrium, respectively. Based on the principle of price-quantity regulation user equilibrium, the method of network tatonnement process is used to establish a network traffic flow evolution model. The unique solution of the evolution model is proved by using Picard’s existence and uniqueness theorem, and the stability condition of the unique solution is derived based on stability theorem of nonlinear system. Through numerical experiments, the evolution processes of network traffic flow under different regulation modes are analyzed. The results show that all the single price regulation, single quantity regulation, and price-quantity regulation can simulate the evolution process of network traffic flow. Price-quantity regulation is the combination of price regulation user equilibrium and quantity regulation user equilibrium, which thus can simulate the evolution process of network traffic flow with multiple user class.

Signal Preemption Control of Emergency Vehicles Based on Timed Colored Petri Nets

This paper focuses on the use of timed colored Petri nets (TCPN) to study emergency vehicle (EV) preemption control problem. TCPN is adopted to establish an urban traffic network model composed of three submodels, namely, traffic flow model, traffic signal display and phase switch model, and traffic signal switch control model. An EV preemption optimization control system, consisting of monitoring subsystem, phase time determination subsystem, and phase switching control subsystem, is designed. The calculation method of the travelling speed of EV on road sections is presented, and the methods of determining the actual green time of current phase and the other phase are given. Some computational comparisons are performed to verify the signal preemption control strategies, and simulation results indicate that the proposed approach can provide efficient and safe running environments for emergency vehicles and minimize EV’s interference to social vehicles simultaneously.

Exploring the effectiveness of demand management policy in reducing traffic congestion and environmental pollution: Car-free day and odd-even plate measures for Bandung city in Indonesia

Traffic congestion has been a major problem in big cities around the world, not to mention several large cities in Indonesia. Bandung is the second largest metropolitan area after Jakarta in Indonesia which suffers from extreme levels of congestion. With a high number of motorcycles and large private car population, congestion in this city is ever growing worsening the environment. While the local authorities struggle to find resources to fund capital intensive capacity expansion projects, this research explores the use of cost effective demand management policy measures to reduce the congestion and pollution. This study aims at assessing two relatively under-researched demand management policy measures that restrict vehicle flows viz., car-free day and odd-even plate schemes to investigate the effect on traffic congestion and the environment. SATURN traffic network modelling software has been used to predict the route choices of vehicles. Bandung city road network and origin destination matrix have been adapted to simulate the two measures during the peak hour. As well as providing the necessary inputs to a pollutant emission estimation model, traffic network modelling output forms the basis for assessing the congestion levels. Results show that both car-free day and odd-even plate measures have unintended consequences that undermine their effectiveness which if addressed could make them highly beneficial solutions. Car-free day scheme reduces the traffic flow levels in the vicinity of scheme but diverts the vehicle flow elsewhere to other routes which may adversely affect the congestion/pollution. Odd-even plate scheme is very effective at the beginning of its implementation but the performance gradually diminishes as drivers start to adapt by buying a second vehicle or even using fake number plates.

Vulnerability Identification of Urban Road Network under Unexpected Congestion

An original method is used to analyze the topology structure of a road network and two indexes of road network performance, namely, connectivity and network efficiency, are used to evaluate road network robustness in medium measure and thus quantify the variety of urban road network vulnerability caused by unexpected congestion. Then, a variety of road network impedances is set as the vulnerability identification index and a network traffic flow model with a link queuing capacity constraint is established, considering such constraint on unexpected congestion condition. Finally, a Lagrange dual algorithm is designed to solve the model, whose accuracy is subsequently tested by an example. Results show that the model based on the vulnerability index can accurately recognize road network vulnerability. Invalid links caused by unexpected congestion lead to changes in road network impedance, thereby directly affecting the robustness of the entire road network.

Calibration and validation of a simulation-based dynamic traffic assignment model for a large-scale congested network

Deployment of a simulation-based dynamic traffic assignment (DTA) model requires calibration of a large number of demand and supply input parameters. In this paper, we present a data-intensive framework for deployment, calibration, and validation of a simulation-based DTA model of Melbourne, Australia as a large-scale congested network. The model consists of 55,719 links and 24,502 nodes and simulates almost 2.1 million commuters in a 4-h morning peak period. We propose a machine learning based technique to classify and calibrate the traffic flow fundamental diagrams against empirical data obtained from a large number of freeway loop detectors across the network. An optimization framework for estimating the time-dependent origin-destination (TDOD) demand is also presented. To enhance the quality of the TDOD demand estimation, we apply a departure time profiling technique to account for the spatial differences between OD pairs. The paper also demonstrates the impact of adaptive driving on the quality of the DTA calibration. A comparison between the simulated and observed link volumes over 1,250 locations across the entire network shows that the calibration procedure generates an approximately 30% improvement in the root mean square error (RMSE). The impact of pedestrians and cyclists on the vehicular traffic is also implicitly considered in the central business district (CBD) area to improve underestimation of the simulated travel times. Validation results suggest that the calibrated DTA model successfully replicates traffic patterns in the network and support the future applications of the model for various transportation operations and planning purposes.

Multi-objective Optimization Algorithm for Multimedia English Teaching (MOAMET) Based on Computer Network Traffic Prediction Model

In order to solve the problem of multi-objective optimization for multimedia English teaching, this paper proposes a multi-objective optimization algorithm for multimedia English teaching (MOAMET) based on computer network traffic prediction model, which is based on the computer network traffic prediction model strategy. This algorithm establishes time series for individuals correlated to same reference points, and for such time series through computer network traffic model optimizes multimedia English teaching objectives. Meanwhile, it feeds back the prediction error of the historical moment to the current prediction to improve the accuracy of the optimization, and adds disturbance in each optimized individual to increase the diversity of initial multimedia English teaching so as to speed up the convergence speed of the algorithm in the new environment. Through experiments it teats the algorithm, also makes comparison and analysis with two existing algorithms, the results show that the proposed algorithm can maintain good performance in dealing with multi-objective optimization for multimedia English teaching.