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The stability of the transportation system refers to the structural stability of the system. When the system structure is unstable, local or global bifurcation phenomena will occur, which is one of the main reasons for nonlinear traffic phenomena such as congestion. To truly understand the internal mechanism of the formation of these phenomena, it is necessary to analyze the bifurcation of traffic flow. In this paper, the Hopf bifurcation control of a modified viscous macroscopic traffic flow model is studied by using the linear state feedback method, which changes the characteristics of the bifurcation phenomenon of the dynamic system and obtains the required dynamic behavior of the system. First, we can convert the original traffic model into the nonlinear ordinary differential form suitable for bifurcation analysis, solve the equilibrium point of the system, and carry out phase plane analysis. Then, the linear state feedback term is added and the corresponding controlled system is generated, the existence and type of Hopf bifurcation and the existence of saddle node bifurcation are proved. Numerical simulation results show that the analysis and control of Hopf bifurcation in the traffic model are well realized in this paper. [ABSTRACT FROM AUTHOR]

Urban road traffic network is becoming increasingly complex. The increasing travel demand has exceeded facility supply of transportation system. Thus, a scientific way of transportation can solve the problem of traffic congestion. If some central intersections are congested, the road network could easily lose its travel efficiency and further lead to cascading failure. In this regard, from the viewpoint of complex networks, this paper provides a cascading failure mitigation strategy for the urban road traffic network. A load redistribution strategy is represented which can redistribute congestion load reasonably. Random and intentional failures are simulated with MATLAB on Barabási and Albert (BA) scale-free network. The indices related to complex network of different methods are compared on BA network and part of the real road network in Baoding City. The load redistribution strategy in this paper is implemented combined with user equilibrium assignment, then the travel time related to urban road network is compared with another method on the Sioux Falls network. The simulation results suggest that when the capacity parameter reaches the threshold, further increase has little effect on the mitigation time. Comparisons of other mitigation approaches verify the effectiveness of our approach in lessening cascading failure caused by traffic congestion. The increase in total network travel time and additional delay by reason of load redistribution is acceptable. [ABSTRACT FROM AUTHOR]

Network topology is the basic for the development of traffic management and control. In a road network, bayonets with installation of surveillance facilities are key components to recognize traffic congestion from time to time. Therefore, identifying the essential bayonets in a road network becomes one of the most efficient ways to alleviate traffic congestion for traffic engineers and transport department. To do so, this paper aims to propose a novel sorting algorithm based on similarity measurements and traffic flow information to efficiently identify key bayonets in road networks. Our research results show that by analyzing the bayonet data in a fixed period of time in a medium-sized city of China, we have successfully identified the location of key bayonet points. Most of these key bayonet points are closed to residential areas and important traffic stations. The rank of these bayonet points can help the city managers better understand the topological characteristics of the road network as well as the propagation of congestion so as to make the traffic policies or control strategies for traffic congestion alleviation. [ABSTRACT FROM AUTHOR]

Based on the new macroscopic two-velocity difference model, this paper analyzes the linear stability of the new model and studies the nonlinear bifurcation theory. First, the linear stability analysis method is used to study the stability conditions of the shock wave in the model. Then, considering the long wave model in the coarse-grained scale, the reduced perturbation method is used to analyze the characteristics of the traffic flow in the metastable region, and the solitary wave solution of the Korteweg-de Vries (KdV) equation in the metastable region is derived. In addition, by using the bifurcation analysis method, the type, and stability of the equilibrium solution are discussed and the existing conditions of the saddle-node bifurcation are proven. Then, taking the saddle-node bifurcation as the starting point, we draw the density space-time diagram and phase plane diagram of the system. It is proven that the newly proposed model can describe complex traffic phenomena such as stop-and-go and sudden changes in stability, which is of great help to solve traffic congestion. [ABSTRACT FROM AUTHOR]

Most of the conventional cellular automaton (CA) traffic flow models have two shortcomings: unlimited deceleration capabilities and incapability of reproducing the synchronized flow in the three-phase traffic flow theory. Based on an original deceleration CA model that emphasizes limited mechanical capabilities and human overreaction as the origin of congested traffic states, this paper proposes a new deceleration CA model where the slow-to-start rules are incorporated. For periodic boundary conditions, one also finds that the present model can reproduce well the three different phases of traffic flow (free flow, synchronized traffic flow, wide moving jam) as well as two first-order phase transitions (the transitions from free flow to synchronized flow and from synchronized flow to wide moving jam) among them. Compared to the original deceleration CA model, one notes that the phase transition from the synchronized flow to wide moving jam becomes distinguishable. Furthermore, the present model can reliably reproduce most empirical findings including synchronized flow with different slopes, the so-called pinch effect, and the time-headway distribution of free flow and so on. Importantly, the synchronized flow with different slopes is supported by spatiotemporal diagrams and the statistical distribution of velocity and so on. For open boundary conditions, the present model can reproduce the spatiotemporal diagrams of well-known five patterns including moving synchronized flow pattern, localized synchronized flow pattern, widening synchronized flow pattern, dissolving general pattern and general pattern. [ABSTRACT FROM AUTHOR]

Vehicular Ad-Hoc Network is one of the most growing research fields and has become a promising topic for researchers. Day by day, vehicle density increases, causing very serious issues like road accidents and traffic jams. These issues can be minimized by a periodic exchange of awareness messages among the vehicles. Traffic density increases transmission of awareness messages, which causes channel congestion and degrades safety services, so congestion control schemes must ensure that the channel load should be below a particular threshold, boost the quality of services and minimize information loss and delay in the network. This paper proposes a machine learning-based risk-aware congestion control (MLB-RACC) scheme which is an efficient congestion control scheme based on the modified k -means machine learning algorithms. MLB-RACC technique processes the input data and helps to improve the quality of services in the automotive industry. It works in three phases: detecting congestion, clustering of vehicles and controlling the broadcasting of awareness messages for decreasing the channel load. MLB-RACC scheme works by grouping the nearest vehicles and the number of groups or clusters is decided on the basis of transmission range/transmission power. The group member selects adaptive transmission rate by looking at the channel busy ratio (CBR). In this technique, message generation rate is controlled at two levels: first is at the clustering level and second is during the checking of CBR. The scheme is implemented through MATLAB, NS2 and the SUMO platform and provides evidence for the minimization of information loss by analyzing the throughput, packet loss and end-to-end delay in comparison to ordinary decentralized congestion control (DCC) technique. [ABSTRACT FROM AUTHOR]

In the past decade, the number of cars in China has significantly raised, but the traffic jam spree problem has brought great inconvenience to people's travel. Accurate and efficient traffic flow prediction, as the core of Intelligent Traffic System (ITS), can effectively solve the problems of traffic travel and management. The existing short-term traffic flow prediction researches mainly use the shallow model method, so they cannot fully reflect the traffic flow characteristics. Therefore, this paper proposed a short-term traffic flow prediction method based on one-dimensional convolution neural network and long short-term memory (1DCNN-LSTM). The spatial information in traffic data is obtained by 1DCNN, and then the time information in traffic data is obtained by LSTM. After that, the space-time features of the traffic flow are used as regression predictions, which are input into the Fully-Connected Layer. In the end, the corresponding prediction results of the current input are calculated. In the past, most of the researches are based on survey data or virtual data, lacking authenticity. In this paper, real data will be used for research. The data are provided by OpenITS open data platform. Finally, the proposed method is compared with other road forecasting models. The results show that the structure of 1DCNN-LSTM can further improve the prediction accuracy. [ABSTRACT FROM AUTHOR]

Learning a fast global model that describes the observed phenomenon well is a crucial goal in the inherently distributed Vehicular Networks. This global model is further used for decision-making, which is especially important for some safety-related applications (i.e., the altering of accident and warning of traffic jam). Most existing works have ignored the network overhead caused by synchronizing with neighbors, which inevitably delays the time for agents to stabilize. In this paper, we focus on developing an asynchronous distributed clustering algorithm to learn the global model, where cluster models, rather than raw data points, are shared and updated. Empirical experiments on a message delay simulator show the efficiency of our methods, with a reduced convergence time, declined network overhead and improved accuracy (relative to the standard solution). This algorithm is further improved by introducing a tolerant delay. Compared to the algorithm without delay, the performance is improved significantly in terms of convergence time (by as much as 47%) and network overhead (by around 53%) if the underlying network is geometric or regular. [ABSTRACT FROM AUTHOR]

Wireless sensor networks congestion occurs easily due to its centralized traffic pattern. Normally, mono-sync wireless sensor network experiences multiple traffic flow congestion in the dense environment of the network, which leads to excess energy consumption and severe packet loss. To overcome these issues, a congestion detection and alleviation mechanism using cluster based heuristic optimized hierarchical routing protocol is proposed in this paper. Here, congestion detection and alleviation utilize the features of sensor nodes. The congestion is categorized into two types: (i) node level congestion and (ii) link level congestion. The node level congestion is detected by assessing the buffer utilization and the interval amid the consecutive data packets. The link level congestion is considered with computing link usage utilizing back-off step of round robin carrier sense multi-access with collision avoidance. Congestion detection and alleviation reactively affected node/link through cuckoo hosted rider search multi-hop routing algorithm. It has two phases: the cluster head selection and multi-path routing. Cluster head selection is performed through Taylor multiple random forest kernel fuzzy C-means clustering algorithm and multi-path routing is performed through cuckoo hosted rider search multi-hop routing algorithm. The simulation of the proposed method is done in network simulator tool. Here, the performance metrics, like packet delivery ratio, delay, energy consumption, packet drop, overhead, network lifetime and throughput are calculated. The experimental outcomes of the proposed technique shows 11.6%, 18.4% and 28.1% lower delay, 78.2%, 65.4% and 52.6% higher packet delivery ratio, and 29.2%, 37.4% and 40.8% lower packet drop compared with the existing methods, like congestion detection and alleviation using multi-attribute decision-making in optimization-based hybrid congestion alleviation routing protocol in wireless sensor networks, congestion detection and alleviation using hybrid K-means with greedy best first search algorithms in packet rate reduction utilizing adaptive weight firefly algorithm ant colony optimization based routing protocol in wireless sensor networks and congestion detection and alleviation using multi-input time on task optimization algorithm for altered gravitational search algorithm routing protocol in wireless sensor networks. [ABSTRACT FROM AUTHOR]

Personalized path-finding allows a user to search for a travel path that can visit its several regions of interest (ROIs). Existing independent searching methods may bring many users to ROIs simultaneously, such as popular attractions in scenic, and thus induce traffic jams. Therefore, it is necessary to find a personalized travel path considering the load balance of traffic flow. However, it is challenging to ensure load balancing on the road and ROIs while allowing each user to visit their ROIs. In this paper, we propose a personalized path-finding method with a multi-agent path-finding (MAPF) framework. Based on the MAPF, it allows users to find the path independently, and we designed a reward to guide the agent simultaneously to navigate to the destination and ROIs for personalized travel. We also improve the architecture of MAPF to guide the agents in learning the load balance through a centralized value network. We evaluated the algorithm with up to 1024 agents on randomly generated road network graphs and compared it against state-of-the-art MAPF planners. We also trained the agent using imitation learning and validated our framework on real-world datasets. Experimental results show that our algorithm is efficient and better than the existing algorithms. [ABSTRACT FROM AUTHOR]

With the rapid growth of the global economy, the global car ownership is also increasing year by year, which has caused a series of problems, the most prominent of which is traffic congestion and traffic accidents. In order to solve the traffic problem, all countries are actively studying the intelligent transportation system, and one of the important research contents of the intelligent transportation system is vehicle detection. Vehicle detection based on vision is to capture vehicle images in the driving environment through a camera, and then use computer vision recognition technology for vehicle detection and recognition. Although computer vision recognition technology has made great progress, how to improve the detection accuracy of the image to be detected is still an important content of visual recognition technology research. Intelligent vehicle visual robust detection and identification of methods of research to reduce the growing incidence of traffic accidents, improve the existing road traffic safety and transportation efficiency, alleviate the degree of driver fatigue problem are of great significance. This paper considers the intelligent vehicle environmental awareness of the key technology to the goal of robust detection and recognition based on machine vision problems for further research. The particle filter is used to extract the local energy of the image to realize the fast segmentation of the region of interest (ROI). In order to further verify the ROI, a measure learning method based on multi-core embedding is proposed, and the semantic classification of ROI is realized by integrating the color, shape and geometric features of ROI. Experimental results show that the algorithm can effectively eliminate false sexy ROI interest, and the algorithm is robust to complex background, illumination changes, perspective changes and other conditions. [ABSTRACT FROM AUTHOR]

As the core of reproducing the real nonlinear phenomena of traffic congestion, the investigation of car-following models, which take into account human factors, has consistently assumed a central role within the domain of transportation science. Nevertheless, it is noteworthy that prior studies have invariably employed a fixed value for driver reaction time to stimuli, whereas it is imperative to recognize the dynamic nature of this parameter, closely associated with the instantaneous vehicle speed. To address this issue, this paper constructs a dynamic sensitivity coefficient (DSC), and further develops a nonlinear human factoring car-following model to reveal the relation between the reaction time and the current speed. Firstly, the linear critical stability condition and the mKdV equation of the model are derived by the linear stability analysis and kink–antikink solution analytic method, respectively. Then, the numerical experiments are conducted to demonstrate that the proposed model is more practical and can reproduce the real driving behavior and phenomena. Finally, calibration and validation results exhibit the actual vehicle trajectory can be simulated well. [ABSTRACT FROM AUTHOR]

Ride hailing services, such as Uber, Lyft, and Grab, have become a major transportation mode in the last decade. The number of current passenger requests is one of the important factors for such services routing and pricing algorithms. Therefore, predicting future passenger request for ride hailing services can boost the efficiency of the service for both drivers and riders by pre-planning the allocation of vehicles and avoiding traffic congestions. Demand forecasting for ride hailing services relies upon the spatial and temporal correlations of its features. The existing literatures mostly divide the target area into rectangular grids (based on the longitude and latitude), consider only adjacent grids for spatial correlation, and calculate demand for each grid independently. An individual grid can contain different regions with high and low demand or have a major part of it outside the land area, which obscures the granularity and precision of estimations and predictions. This paper attempts to mitigate the limitations of grid-based methods by estimating and predicting ride hailing service demand between geographic regions as pickup and destination zones. For predicting demand, a convolutional neural network is integrated with a recurrent autoencoder network to best capture the spatial–temporal correlations of features, including time of the day, month, year, weekend, holiday, pickup zone, destination, and demand. In our experiments, we forecast the demand for each pickup–destination pair for the next day at a certain hour by observing the demands over the past 2 weeks during the same hour in the New York City hire vehicle data set. Using the same model (CNN-biLSTM-AE) to predict demand for geographical regions, it achieved an R 2 of 0.984, while predicting demand for cells in the grid achieved an R 2 0.545. While using the geographical regions instead of grids for partitioning the space, we compared our deep learning model with LSTM, CNN, CNN-LSTM, and LSTM-AE models and observed an improvement in R 2 from 0.632 to 0.767 and an improvement in RMSE from 20.53 to 16.33 against CNN. [ABSTRACT FROM AUTHOR]

One dominant aspect of cities is transport and massive passenger mobilization which remains a challenge with the increasing demand on the public as cities grow. In addition, public transport infrastructure suffers from traffic congestion and deterioration, reducing its efficiency. In this paper, we study the capacity of transport in 33 worldwide metro systems under the accumulation of damage. We explore the gradual reduction of functionality in these systems associated with damage that occurs stochastically. The global transport of each network is modeled as the diffusive movement of Markovian random walkers on networks considering the capacity of transport of each link, where these links are susceptible to damage. Monte Carlo simulations of this process in metro networks show the evolution of the functionality of the system under damage considering all the complexity in the transportation structure. This information allows us to compare and classify the effect of damage in metro systems. Our findings provide a general framework for the characterization of the capacity to maintain the transport under failure in different systems described by networks. [ABSTRACT FROM AUTHOR]

In actual transportation systems, the response time of drivers to the stimulus from the preceding vehicle varies at different speeds. The traditional car-following model cannot capture the heterogeneity of drivers for the response time is regarded as a constant. This paper designed a sigmoid function to describe the relation between the response time of drivers and the current speed. Then, a new hydrodynamic lattice model is developed by introducing the proposed nonlinear function. The model is analyzed by using Fourier series, and the linear stability criterion is derived. Numerical experiments are conducted in a ring road. Simulation results show that the evolution of density waves occurring in actual traffic is reproduced well. Finally, the model is calibrated and verified with the real data, and the simulation results are quantitatively consistent with the detector data. [ABSTRACT FROM AUTHOR]

Large and expanding cities suffer from a traffic congestion problem that harms the environment, travelers, and the economy. This paper aims to predict short term traffic congestion on a road section of expressway in Delhi city. For this purpose, we first propose a traffic congestion index based on traffic speed and flow. Clustering techniques and the Greenshield's model were used for the derivation of the congestion index. Using this congestion index, congested time intervals of each day and each location of a weekday were identified. This study also introduces a feature series long short-term memory neural network (FSLSTMNN), which links a long short-term memory (LSTM) layer to each feature. It is trained using the many heterogeneous traffic features data collected in Delhi city for the next five minutes of traffic flow and speed prediction. FSLSTMNN achieved the good capability to learn feature series data. We also trained several traditional and deep-learning models using the same traffic data. The FSLSTMNN reduces mean absolute error 12.90% and 17.13%, respectively, in speed and traffic flow prediction compared to the second good-performance long short-term memory neural network (LSTMNN). Finally, traffic congestion is predicted classwise (light, medium, and congested) using the developed congestion index and traffic speed and flow predicted by the FSLSTMNN. Predicted results are consistent with the measured field data. Study results confirm that the developed congestion index and FSLSTMNN can be used successfully to predict traffic congestion. [ABSTRACT FROM AUTHOR]

The essence of traffic congestion is a kind of bifurcation behavior incurred by the loss of stability in the traffic flow from the perspective of system stability. Therefore, researching the bifurcation characteristics of traffic flow can provide some new methods for relieving the traffic congestion. In this paper, we study the bifurcation dynamic behavior and the bifurcation conditions of traffic flow based on Kühne's continuum traffic flow model. We discussed the types and stabilities of the equilibrium solutions and proved the existence conditions of some bifurcation of the model. Then various bifurcations of the system are found by numerical simulation and the stability catastrophic behaviors initiated by some bifurcation in traffic flow are investigated. Combined with the measured data, we study the actual traffic flow bifurcation phenomena and analyze the internal reasons for the stability catastrophic behavior of traffic flow passed by the bifurcation point. The nonlinear characteristics and formation mechanism of traffic flow phenomena are revealed from a new perspective. It may help to design the corresponding control schemes for preventing and alleviating traffic congestion. [ABSTRACT FROM AUTHOR]

In this paper, we study the limitations imposed on the transcription process by the presence of short ubiquitous pauses and crowding. These effects are especially pronounced in highly transcribed genes such as ribosomal genes (rrn) in fast growing bacteria. Our model indicates that the quantity and duration of pauses reported for protein-coding genes is incompatible with the average elongation rate observed in rrn genes. When maximal elongation rate is high, pause-induced traffic jams occur, increasing promoter occlusion, thereby lowering the initiation rate. This lowers average transcription rate and increases average transcription time. Increasing maximal elongation rate in the model is insufficient to match the experimentally observed average elongation rate in rrn genes. This suggests that there may be rrn-specific modifications to RNAP, which then experience fewer pauses, or pauses of shorter duration than those in protein-coding genes. We identify model parameter triples (maximal elongation rate, mean pause duration time, number of pauses) which are compatible with experimentally observed elongation rates. Average transcription time and average transcription rate are the model outputs investigated as proxies for cell fitness. These fitness functions are optimized for different parameter choices, opening up a possibility of differential control of these aspects of the elongation process, with potential evolutionary consequences. As an example, a gene's average transcription time may be crucial to fitness when the surrounding medium is prone to abrupt changes. This paper demonstrates that a functional relationship among the model parameters can be estimated using a standard statistical analysis, and this functional relationship describes the various trade-offs that must be made in order for the gene to control the elongation process and achieve a desired average transcription time. It also demonstrates the robustness of the system when a range of maximal elongation rates can be balanced with transcriptional pause data in order to maintain a desired fitness. [ABSTRACT FROM AUTHOR]

Travel time reliability plays a key role in bus scheduling and service quality. Owing to various stochastic factors, buses often suffer from traffic congestion, delay and bunching, which leads to disturbances of travel time. Automatic vehicle location (AVL) could record the spatiotemporal information of buses, making it possible to understand the status of bus service. In this paper, we specifically analyze the statistical characteristics of travel time based on historic AVL data. Moreover, a Kalman filter-LSTM deep learning is proposed to estimate bus travel time. Numerical tests indicate that the travel time of bus routes shows a left-skewed and right-tail pattern with a good fit of the lognormal distribution. The bus service reliability fluctuates largely in the peak hours, especially the morning peak. Bus bunching and large bus time headway easily occur, and once it occurs, it will continue until destination. The Kalman filter-LSTM model outperforms the ensemble learning methods to predict travel time. This study could provide implications for transit schedule optimization to improve the bus service quality. [ABSTRACT FROM AUTHOR]

LTE-Advanced (LTE-A) offloading is concerned about alleviating traffic congestion for the LTE-A network, which includes the core network and the radio access network (RAN). Due to the scarcity of the radio resource, offloading for the LTE-A RAN is more critical, for which an efficient way is to integrate Wi-Fi with LTE-A to form a heterogeneous RAN environment. An LTE-A UE (User Equipment) with the wi-fi interface can therefore access the Internet via an LTE-A base station of Evolved Node B (eNB) or a wi-fi Access Point (AP). In this paper, wireless network selection for UEs with delay-sensitive traffic in the heterogeneous RAN of LTE-A and wi-fi is addressed. Based on the queueing model of M/G/1, a novel network selection and offloading scheme, namely Delay-Sensitive Network Selection and Offloading (DSO), is proposed. The average system time at LTE-A eNBs and wi-fi APs calculated according to M/G/1 is used for network selection as well as offloading operations in DSO. The benefit of DSO in terms of satisfying the delay budget of UEs and load balancing is demonstrated by the simulation study. [ABSTRACT FROM AUTHOR]

The high growth of vehicular travel in urban areas, in particular, requires a traffic control system that optimizes traffic flow efficiency. Traffic congestion can also occur by large de-lays in Red Light etc. The delay in lighting is difficult to code and does not rely on real traffic density. It follows that traffic controls are simulated and configured to better meet this rising demand. So, in order to avoid the traffic control problem, the Adaptive Intelligent Traffic Light control system (AITLCS) has been proposed based on OpenCV and Image processing technique. The system proposed is designed to ensure smooth and efficient traffic flow for daily life as well as emergency and public transportation safety. Based on the road density instead of the levels set the proposed system provides the timing for the traffic light signal so that a highly loaded side switched on over long periods compared with the other lanes. It can also be used at an intersection with traffic signs, which controls the traffic light signal at the intersection. If timers are smart to predict the exact time, the system is more efficient because it reduces the time spent on unintended green signal significantly. With the help of OpenCV software, this paper aims to have a signal management SMART solution that will be cost-effective at the end. The system consists of a camera facing a lane taking pictures of the route we want to travel and then the density of the pedestrian and vehicle is taken and compared with each image employing image processing. Such images are processed effectively to learn the density of traffic. [ABSTRACT FROM AUTHOR]

With the high development of economy and the continuous growth of the population of major cities in the world, the problem of traffic jams has become a serious problem that local governments must solve. In addition, facing the impact of global warming, how to save energy and reduce carbon emission is another critical problem that local governments need to face. An effective smart bus APP can enhance people's satisfaction with the use of the APP while it can increase their willingness to take the bus, thereby reducing the number of people driving their own cars. It is of some help to alleviate the problem of urban traffic congestion. At the same time, it also reduces the total amount of carbon emitted from cars so as to achieve the effects of energy saving and carbon reduction. Therefore, this paper, through questionnaires, gathered opinions from passengers who used the bus APP and established a performance evaluation model of the bus APP. This model can assist the designer or management engineer of the bus APP to find out the service items of the bus APP that need to be improved, in order to make the bus APP more intelligent and efficient to use. [ABSTRACT FROM AUTHOR]

As the key component of wireless network-on-chip (WiNoC), wireless router is required to handle a large number of data packets which could cause network congestion. The congestion not only reduces the network performance, but also results in additional power consumption. In this paper, a low-power WiNoC with congestion-aware wireless node is proposed. Firstly, flit counter unit and address resolution unit are added in wireless interface (WI), which can sense the congestion information and destination address information of each wireless node dynamically. Secondly, through the proposed congestion judgment algorithm, the congestion judgment unit in global network can judge the priority of each wireless node pair and set the highest-priority wireless node pair to use the channel resources preferentially. The mechanism can effectively alleviate the congestion of wireless nodes and ensure the stability of system performance. In addition, a sleep mechanism is utilized to switch off WI which fails in the wireless channel competition. Experimental results show that the proposed scheme can effectively improve the performance in terms of packets' transmission latency and network throughput. [ABSTRACT FROM AUTHOR]

In this paper, the focus is on the role of the municipality, as an enabler of a collaboration between freight forwarders and the municipality in which the consolidation of goods is considered as a means for goods flow improvement in urban freight transportation. We present a cost allocation model that is based on solution concepts from cooperative game theory, for allocating the operational costs associated with the collaboration. It is assumed that the municipality is willing to carry some cost to ensure a stable collaboration for the potential benefits received, e.g., reduced traffic congestion in the city. The model is applied to some illustrative examples, and the cost allocation results are discussed. It is shown that the role of the municipality may be decisive in achieving a stable collaboration between the freight forwarders, and further that the municipality does not necessarily need to contribute to covering the costs. [ABSTRACT FROM AUTHOR]

In urban traffic systems, alleviating traffic congestion originated from left-turn/U-turn lanes at highly demanded intersections plays an important role in enhancing the traffic efficiency. For a typical intersection, the layout with double left-turn (DLT) lanes is a relatively common geometric feature. However, the effect of DLT scheme on the performance of the entire traffic system is not clear. This paper compares the performance of urban traffic system with different lane function schemes with extended cellular automaton (CA) model considering the car-following, lane-changing, intersection-traversing and route-choice behavior of vehicles. Using the flow-density relation, three macroscopic parameters (average trip completion rate, average traffic flow and average velocity) are examined to evaluate the system performance. With the wide application of DLT scheme, the vehicles can be distributed more evenly among the lanes. As a result, the traffic flow and the trip completion rate will be higher, especially at high density. Case studies of realistic Hefei city network and Sioux Falls networks are also carried out. The results show that the performance of traffic network will be better if more intersections adopt the DLT scheme. [ABSTRACT FROM AUTHOR]

The congestion caused by ramp overflow has an essential impact on the energy consumption of autonomous vehicles. It is urgent to solve the energy waste of ramp overflow on connected and autonomous vehicles and improve the energy utilization rate. Aiming at the traffic congestion caused by ramp overflow, this paper obtains the data of autonomous vehicles and traffic flow based on vehicle road cooperative perception, analyzes the impact mechanism of vehicle energy consumption, and summarizes the energy consumption modes of autonomous vehicles into three categories. Second, an energy consumption evaluation framework is proposed based on the CSANS strategy. This strategy can make up for the deficiency of constructing neighborhoods within European distance, find important influencing variables on connected and autonomous vehicles' energy consumption, and accurately capture the manifold characteristics of energy consumption data. Finally, flow control is carried out from the macro perspective of traffic engineering to optimize ramp overflow's impact on autonomous vehicles' energy consumption. Through multiple groups of experiments, it has been found that it can effectively reduce the energy consumption of autonomous vehicles. [ABSTRACT FROM AUTHOR]

Multiplex networks have proven to be valuable tools for modeling and analyzing real complex system. Extensive work has been done on the traffic dynamics on multiplex networks, but there remains a lack of sufficient attention towards studying routing strategies for the purpose of suppressing epidemic spreading. In this paper, the impact of global awareness routing (GAR), improved global awareness routing (IGAR), and improved active routing (IAR) strategies on traffic-driven epidemic spreading are investigated. Our findings indicate that in the case of infinite node-delivery capacity and no traffic congestion in the network, adjusting routing parameters can effectively suppress epidemic spreading. In this context, these three strategies show better abilities on the multiplex network built by WS or ER model to minimize the density of infected nodes, thus contributing to the overall inhibition of the epidemic spread. However, in the multiplex network constructed by BA model, GAR strategy has a promoting effect on epidemic spreading compared with the shortest routing strategy. In addition, by controlling traffic flow, limiting node delivery capabilities can contain outbreaks. Our results suggest that adopting appropriate routing strategies in multiplex networks can play a proactive role in controlling epidemic spreading. This is crucial for formulating effective prevention and control measures and improving public health security. [ABSTRACT FROM AUTHOR]

Automated material handling systems (AMHS) have been widely used in semiconductor manufacturing. However, the performance of AMHS heavily hinges on vehicle fleet sizing, which is a complex yet crucial problem. For example, a small fleet size may increase the average wait time, but a large fleet size can also result in traffic congestion. This tradeoff is difficult and can be further exacerbated by profound uncertainty in the manufacturing process. In the literature, the existing models are focused on improving the mean-based performance of AMHS, where the resulting optimal vehicle fleet size is fixed, lacking the ability and flexibility to respond to the changes and/or special requirements that suddenly come up in the manufacturing process. Another drawback with the existing models is that they are not able to characterize the upside/downside risks associated with the resulting vehicle fleet size. This paper, motivated by a real project, presents a novel quantile-based decision model to fill the gap. The adjustment of α values in the proposed decision model allows for agile vehicle fleet sizing according to the production situations, resulting in the satisfactory performance of AMHS. We develop a simulation optimization solution method, called ES-AMHS in short, to enable the efficient derivation of the optimal vehicle fleet size. A comprehensive numerical analysis is conducted to evaluate the efficiency and efficacy of the solution method. Finally, an empirical study in cooperation with a wafer fab in Taiwan is presented to show the practical usefulness of this methodology in a real-world setting. [ABSTRACT FROM AUTHOR]

This paper put forward a new discrete-time lattice hydrodynamic model with a steady flow control strategy. The controlled discrete-time lattice hydrodynamic model is theoretically studied by the discrete-time Lyapunov stability theory, and a sufficient stable condition to suppress traffic congestion is given in the form of linear matrix inequality. Also, the traffic flow evolution rules under different conditions of control gain are exhibited through numerical simulation, and the stabilization effect of the steady flow control strategy on traffic flow is verified intuitively. [ABSTRACT FROM AUTHOR]

One frequently observed congested traffic flow pattern is wide moving jam (WMJ), in which the average vehicle speed is very low and the density is very high. In some recent studies, variable speed limits (VSL) were proposed as effective measures to eliminate or abate the influence of jam waves. However, in most of these studies, the stochastic features of driving behaviors and the resulting uncertainty of traffic flow dynamics were not fully considered. In this paper, we use cellular automaton (CA) model-based simulations to test the performances of different VSL control strategies and apply the three-phase traffic theory to further analyze the obtained results. Based on the simulation results, we got two novel findings. Firstly, we observed seven, instead of the previously assumed six, states of traffic flow in the evolution process of WMJ, when VSL were applied. Secondly and more importantly, we found that inappropriate speed limit may induce new WMJ and exaggerate congestions in two ways: one way corresponds to an F → J transition and the other corresponds to an F → S → J transition. Based on these findings, the appropriate lower bound of VSL was finally discussed in this paper. [ABSTRACT FROM AUTHOR]

In this paper, an overview of different analytic queueing models for traffic on road networks is presented. In the literature, it has been shown that queueing models can be used to adequately model uninterrupted traffic flows. This paper gives a broad review on this literature. Moreover, it is shown that the developed published methodologies (which are mainly single node oriented) can be extended towards queueing networks. First, an extension towards queueing networks with infinite buffer sizes is evaluated. Secondly, the assumption of infinite buffer sizes is dropped leading to queueing networks with finite buffer sizes. The impact of the buffer size when comparing the different queueing network methodologies is studied in detail. The paper ends with an analytical application tool to facilitate the optimal positioning of the counting points on a highway. [ABSTRACT FROM AUTHOR]

In this paper, two definitions about traffic congestion are proposed. We describe the traffic congestion spreading with the SEIR model of a complex small-world network. In addition, the relationships among the congestion factor, the number of average infection, the average recover rate and the infection rate are given by simulations in general traffic congestion conditions. [ABSTRACT FROM AUTHOR]

Traffic status recognition and classification is an important prerequisite for traffic management and control. Based on the idea of weight optimal, a weighted fuzzy c-means clustering method for improving the accuracy of traffic classification is proposed in this study to ease traffic congestion. First, since there are many indexes that affect the traffic flow state classification, three commonly used indexes namely, volume, speed and occupancy are chosen as the main parameters for the traffic flow state classification in this paper. Second, in order to quantitatively analyze the influence degree of different traffic flow parameters on traffic flow state division, based on the principle of weight optimization, the objective function of weight optimization is established. Then the weight of each attribute index is obtained by using the branch and bound algorithm. Finally, since the traditional fuzzy c-means clustering method will not consider the influence of different traffic flow parameter weights on the traffic flow state classification results, the classification effect needs to be further improved. A fuzzy weighted c-means classification method which uses weighted Euclidean distance instead of Euclidean distance is proposed to classify the traffic flow states. Based on the same traffic flow data sample on the same road section, the traffic state classification results with different methods show that it is helpful to improve the traffic flow state classification accuracy by weighting the clustering index. Because the influence of different parameters on the traffic flow state classification is considered in the process of clustering, it is more conducive to improve the classification accuracy. Moreover, it can provide more accurate classification information for traffic control and decision making. [ABSTRACT FROM AUTHOR]

In this paper, an extended car-following model is proposed to simulate traffic flow with consideration of incorporating the effects of driver's memory and mean expected velocity field in ITS (i.e. intelligent transportation system) environment. The neutral stability condition of the new model is derived by applying the linear stability theory. Compared with the optimal velocity model and the full velocity difference model, the stability region of the new model can be significantly enlarged on the phase diagram, and the anticipating motion information of more vehicles ahead can further enhance traffic stability. Furthermore, the mean expected velocity field effect plays a more important role than that of driver's memory effect in improving the stability of traffic flow. Nonlinear analysis is also conducted by using the reductive perturbation method, and the mKdV equation near the critical point is obtained to describe the evolution properties of traffic density waves. Numerical simulation results show that the coupling effect of driver's memory and the mean expected velocity field can suppress the traffic jam effectively, which is in good agreement with the analytical result. [ABSTRACT FROM AUTHOR]

Embedded distributed multimedia applications based on the use of on-chip networks for communication and messages exchange requires specific and enhanced quality of service (QoS) management. To reach the desired performances at the application level, the network-on-chip (NoC) router should implement per flit handling strategy with wide granularity. This purpose requires an enhanced internal architecture that ensures from one hand a specific management according to a service classification and from the other hand, it enhances the routing process. In this context, this paper proposes a new mechanism for QoS management in NoC. This mechanism is based on the use of central memory where flits are in-queued according to their class of service. This scheme enables an optimal flit scheduling phase and provides more capabilities to drop low important flits when the router shows congestion state symptoms. The paper presents, also, a protocol structure that fills with this architecture and introduces a signaling mechanism to make efficient the QoS management through the proposed architecture. The circuit performances and its adaptability to achieve QoS with low power processing and high bandwidth in on chip multiprocessor systems will be studied in this paper. [ABSTRACT FROM AUTHOR]

Variable speed limit (VSL) is well-known for the ability to eliminate freeway wide moving jam (WMJ). Different VSL maneuvers have been proposed and proved to be useful theoretically or empirically. However, the coordination of VSL still needs to be analyzed in order to cater for different traffic conditions encountered on the roadway. In this paper, we execute a study on freeway VSL method based on the three-phase traffic theory and Cellular Automata (CA) based simulations. Results indicate that VSL is a promising method in solving freeway WMJs, but in some cases, it induces new congestions due to improper configurations and the stochastic feature of traffic flow. Therefore, in this paper, we propose an optimal coordination model of VSL including five main principles. The purpose of our model lies on two aspects, to ensure the elimination of WMJ and to avoid the emergence of new traffic jams. By analyzing the traffic dynamics, we first present several theoretical principles of VSL configuration with respect to the WMJ elimination. Second, we study the relation between VSL effectiveness and the traffic uncertainty through CA model-based simulations and some empirical principles are raised. [ABSTRACT FROM AUTHOR]

In 2004, Yang and co-workers proposed the extraction of bridge frequencies from the dynamic response of a moving test vehicle [Y. B. Yang, C. W. Lin and J. D. Yau, Extracting bridge frequencies from the dynamic response of a passing vehicle, J. Sound Vib. 272 (2004) 471-493] and verified the technique by a field test [C. W. Lin and Y. B. Yang, Use of a passing vehicle to scan the bridge frequencies - An experimental verification, Eng. Struct. 27(13) (2005) 1865-1878]. This technique was extended to construction of mode shapes [Y. B. Yang, Y. C. Li and K. C. Chang, Constructing the mode shapes of a bridge from a passing vehicles: A theoretical study, Smart Struct. Syst. 13(5) (2014) 797-819] and damage identification of bridges. It was referred to as the indirect method for bridge measurement because no vibration sensors are needed for installation on the bridge, but it only requires one or few vibration sensors on the test vehicle. When compared with the conventional direct method that relies fully on the response of the bridge fitted with vibration sensors, the advantage of the indirect method is clear: mobility, economy, and efficiency. Over the past years, many research studies were conducted along the lines of the indirect method for bridge measurement. Significant advances have been made on various aspects of application. This paper represents a state-of-the-art review of the related research works conducted worldwide. Comments and recommendations will be made at proper places, while concluding remarks including future research directions will be presented at the end of the paper. [ABSTRACT FROM AUTHOR]

The congestion control of traffic is one of the most important studies in complex networks. In the previous congestion algorithms, all the network traffic is assumed to have the same priority, and the privilege of traffic is ignored. In this paper, a privilege and common traffic congestion control routing strategy (PCR) based on the different priority of traffic is proposed, which can be devised to cope with the different traffic congestion situations. We introduce the concept of privilege traffic in traffic dynamics for the first time and construct a new traffic model which taking into account requirements with different priorities. Besides, a new factor is introduced by the theoretical derivation to characterize the interaction between different traffic routing selection, furthermore, is related to the network throughput. Since the joint optimization among different kinds of traffic is accomplished by PCR, the maximum value of can be significantly reduced and the network performance can be improved observably. The simulation results indicate that the network throughput with PCR has a better performance than the other strategies. Moreover, the network capacity is improved by 25% at least. Additionally, the network throughput is also influenced by privilege traffic number and traffic priority. [ABSTRACT FROM AUTHOR]

To alleviate traffic congestion, a variety of route guidance strategies have been proposed for intelligent transportation systems. A number of strategies are introduced and investigated on a symmetric two-route traffic network over the past decade. To evaluate the strategies in a more general scenario, this paper conducts eight prevalent strategies on an asymmetric two-route traffic network with different slowdown behaviors on alternative routes. The results show that only mean velocity feedback strategy (MVFS) is able to equalize travel time, i.e. approximate user optimality (UO); while the others fail due to incapability of establishing relations between the feedback parameters and travel time. The paper helps better understand these strategies, and suggests MVFS if the authority intends to achieve user optimality. [ABSTRACT FROM AUTHOR]

This paper investigates the degree of congestion and efficiency in complex traffic networks, by introducing congestion effects, which can be described by flow-based link cost functions. Different network topologies including random networks, small-world networks and scale-free networks are explored. The impact of different distributions of capacity and origin-destination traffic demand on the degree of congestion and efficiency in complex networks is mainly studied. A phase transition from free flow state to traffic jams can be uncovered. The relationship between congestion and efficiency in complex networks is also discussed. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to develop a model capable of simulating traffic jams in slow axonal transport. Slowing of slow axonal transport is an early sign of some neurodegenerative diseases. Axonal swellings observed near the end stage of such diseases may be an indication of traffic jams developing in axons that cause the slowing down of slow axonal transport. Traffic jams may result from misregulation of microtubule-associated proteins caused by an imbalance in intracellular signaling or by mutations of these proteins. This misregulation leads to a decay of microtubule tracks in axons, effectively reducing the number of "railway tracks" available for molecular-motor-assisted transport of intracellular organelles. In this paper, the decay of microtubule tracks is modeled by a reduction of the number density of microtubules in the central part of the axon. Simulation results indicate that the model predicts the build-up of the bell-shaped concentration wave, as the wave approaches the bottleneck (blockage) region. This increase in concentration will likely plug the bottleneck region resulting in a traffic jam that would hinder the slow axonal transport. [ABSTRACT FROM AUTHOR]

The difference between the optimal current difference and the actual current difference will be used as the correction item. The dynamic multiple current information about the front lattice will be considered. A modified lattice traffic hydrodynamics model is established by considering the downstream traffic conditions in the two-lane system. Through the stability analysis, it is found that the downstream traffic condition can be added as a correction term to increase the stability of the system. The area of the stable region on the phase diagram is enlarged by the derived stability. The mKdV equation, which can describe density wave, is derived by nonlinear analysis. Finally, the phase diagram of stability condition in linear analysis and the kink wave diagram of mKdV equation in nonlinear analysis are obtained by numerical simulation, which verifies the theoretical derivation of this paper. The results show that in the two-lane traffic flow expansion model, considering the downstream traffic conditions can effectively suppress traffic jams and make the traffic flow stable. [ABSTRACT FROM AUTHOR]

We consider a class of variational inequality problems with linear constraints, where the mapping is unknown and the system is an oracle. The capacitated traffic congestion pricing problem of transportation is such an application, and many classical methods cannot deal with this class of problems. Note that the cost of the observation (observe the exact solution of the subproblem) is very expensive. It is important to get an inexact solution instead of an exact solution, especially when the iteration is far from the solution set. In this paper, we propose a modified inexact prediction–correction method. Under the mild condition that the underlying mapping is strongly monotone, we prove the global convergence. Some numerical examples are presented to illustrate the efficiency of the inexact strategy. [ABSTRACT FROM AUTHOR]

Traffic congestion is now nearly ubiquitous in many urban areas. The improvement of road infrastructure is an effective way to ease traffic congestion, especially the key road links. So, it is a fundamental and important step to identify the key link for improving transportation efficiency. However, most approaches in the current literature use simulated data and need many assumption conditions. The result shows the low comprehensibility and the bad exactitude. This paper provides a new identification method of key links for urban road traffic network (URTN) based on temporal-spatial distribution of traffic congestion. The method involves identifying congestion state, computing time distribution of congestion state and determining key road link. By the cluster analysis of the history field data of URTN, the threshold to determine the traffic congestion of each link can be obtained. Then the time-interval of the traffic congestion can be computed by median filtering. At last, the time-interval coverage is defined and used to determine the target road link whether it is a key road link or not. The method is validated by a real-world case (Beijing road traffic network, BRTN). The result shows the feasibility and accuracy. [ABSTRACT FROM AUTHOR]

The route guidance systems (RGS) are efficient in alleviating traffic congestion and reducing transit time of transportation networks. This paper studies the effects of RGS on performance of variably weighted small-world networks. The properties of the average shortest path length, the maximum degree, and the largest betweenness, as important indices for characterizing the network structure in complex networks, are simulated. Results show that there is an optimal guided rate of RGS to minimize the total system cost and the average shortest path length, and proper RGS can reduce the load of the node with maximum degree or largest betweenness. In addition, we found that the load distribution of nodes guided by RGS decay as the power laws which is very important for us to understand and control traffic congestion feasible. [ABSTRACT FROM AUTHOR]

The spread of traffic congestion is related to the rate of infection and the recovery rate. In this paper, we describe the traffic congestion spread with SIR model of a complex network. From the point of the complex network, the spread of the traffic congestion with different parameters are simulated. By simulation, we find that the behavior of the traffic system is tightly related to the average rate of infection, the average recovery rate and the topological properties of traffic network. [ABSTRACT FROM AUTHOR]

In this paper, a GIS-based method was developed to extract the real-time traffic information (RTTI) from the Google Maps system for city roads. The method can be used to quantify both congested and free-flow traffic conditions. The roadway length was defined as congested length (CL) and free-flow length (FFL). Chengdu, the capital of Sichuan Province in the southwest of China, was chosen as a case study site. The RTTI data were extracted from the Google real-time maps in May 12-17, 2013 and were used to derive the CL and FFL for the study areas. The Multifractal Detrended Fluctuation Analysis (MFDFA) was used to characterize the long-term correlations of CL and FFL time series and their corresponding multifractal properties. Analysis showed that CL and FFL had demonstrated time nonlinearity and long-term correlations and both characteristics differed significantly. A shuffling procedure and a phase randomization procedure were further integrated with multifractal detrending moving average (MFDMA) to identify the major sources of multifractality of these two time series. The results showed that a multifractal process analysis could be used to characterize complex traffic data. Traffic data collected and methods developed in this paper will help better understand the complex traffic systems. [ABSTRACT FROM AUTHOR]

The proper setting of traffic signals at signalized intersections is one of the most important tasks in traffic control and management. This paper has evaluated the four-phase traffic signal plans at a four-leg intersection via cellular automaton simulations. Each leg consists of three lanes, an exclusive left-turn lane, a through lane, and a through/right-turn lane. For a comparison, we also evaluate the two-phase signal plan. The diagram of the intersection states in the space of inflow rate versus turning ratio has been presented, which exhibits four regions: In region I/II/III, congestion will propagate upstream and laterally and result in queue spillover with both signal plans/two-phase signal plan/four-phase signal plan, respectively. Therefore, neither signal plan works in region I, and only the four-phase signal plan/two-phase signal plan works in region II/III. In region IV, both signal plans work, but two-phase signal plan performs better in terms of average delays of vehicles. Finally, we study the diagram of the intersection states and average delays in the asymmetrical configurations. [ABSTRACT FROM AUTHOR]

This paper proposes a new full velocity difference model considering the driver's heterogeneity of the disturbance risk preference for car-following theory to investigate the effects of the driver's heterogeneity of the disturbance risk preference on traffic flow instability when the driver reacts to the relative velocity. We obtain traffic flow instability condition and the calculation method of the unstable region headway range and the probability of traffic congestion caused by a small disturbance. The analysis shows that has important effects the driver's heterogeneity of the disturbance risk preference on traffic flow instability: (1) traffic flow instability is independent of the absolute size of the driver's disturbance risk preference coefficient and depends on the ratio of the preceding vehicle driver's disturbance risk preference coefficient to the following vehicle driver's disturbance risk preference coefficient; (2) the smaller the ratio of the preceding vehicle driver's disturbance risk preference coefficient to the following vehicle driver's disturbance risk preference coefficient, the smaller traffic flow instability and vice versa. It provides some viable ideas to suppress traffic congestion. [ABSTRACT FROM AUTHOR]

Online 3D games require fast and efficient user interaction support over the network environments, and the networking support is usually implemented by the use of a network game engine. The network game engine should minimize the network delay and mitigate the network traffic congestion. To minimize the network traffic between game users, a client-based prediction (dead reckoning (DR) algorithm) is used. Each game entity uses the algorithm to estimate its own movement as well as the others'. In case the estimation error exceeds the threshold, the entity sends an UPDATE packet which includes velocity, position and the like to other entities. As the estimation accuracy is increased, each entity can minimize the transmission of the UPDATE packet. In this paper, a Kalman filter-based approach is proposed in order to improve the prediction accuracy and an adaptive Kalman gain control in order to minimize the number of UPDATE packets to distant devices. The BZFlag game was used in the experiment in order to verify the proposed approach and the results have shown that it is possible to increase prediction accuracy and reduce the network traffic by 12%. [ABSTRACT FROM AUTHOR]