74,190 research outputs found

    The Spatial Variability of Vehicle Densities as Determinant of Urban Network Capacity

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    Due to the complexity of the traffic flow dynamics in urban road networks, most quantitative descriptions of city traffic so far are based on computer simulations. This contribution pursues a macroscopic (fluid-dynamic) simulation approach, which facilitates a simple simulation of congestion spreading in cities. First, we show that a quantization of the macroscopic turning flows into units of single vehicles is necessary to obtain realistic fluctuations in the traffic variables, and how this can be implemented in a fluid-dynamic model. Then, we propose a new method to simulate destination flows without the requirement of individual route assignments. Combining both methods allows us to study a variety of different simulation scenarios. These reveal fundamental relationships between the average flow, the average density, and the variability of the vehicle densities. Considering the inhomogeneity of traffic as an independent variable can eliminate the scattering of congested flow measurements. The variability also turns out to be a key variable of urban traffic performance. Our results can be explained through the number of full links of the road network, and approximated by a simple analytical formula

    Hop-Based dynamic fair scheduler for wireless Ad-Hoc networks

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    In a typical multihop Ad-Hoc network, interference and contention increase when flows transit each node towards destination, particularly in the presence of cross-traffic. This paper observes the relationship between throughput and path length, self-contention and interference and it investigates the effect of multiple data rates over multiple data flows in the network. Drawing from the limitations of the 802.11 specification, the paper proposes a scheduler named Hop Based Multi Queue (HBMQ), which is designed to prioritise traffic based on the hop count of packets in order to provide fairness across different data flows. The simulation results demonstrate that HBMQ performs better than a Single Drop Tail Queue (SDTQ) scheduler in terms of providing fairness. Finally, the paper concludes with a number of possible directions for further research, focusing on cross-layer implementation to ensure the fairness is also provided at the MAC layer. © 2013 IEEE

    Multi-Gated Perimeter Flow Control of Transport Networks

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    This paper develops a control scheme for the multi-gated perimeter traffic flow control problem of urban road networks. The proposed scheme determines optimally distributed input flows (or feasible entrance link green times) for a number of gates located at the periphery of a protected network area. A macroscopic model is employed to describe the traffic dynamics of the protected network. To describe traffic dynamics outside of the protected area, we augment the basic state-space model with additional state variables to account for the queues at store-and-forward origin links at the periphery. We aim to equalise the relative queues at origin links and maintain the vehicle accumulation in the protected network around a desired point, while the system's throughput is maximised. The perimeter traffic flow control problem is formulated as a convex optimal control problem with constrained control and state variables. For real-time control, the optimal control problem is embedded in a rolling-horizon scheme using the current state of the whole system as the initial state as well as predicted demand flows at entrance links. A meticulous simulation study is carried out for a 2.5 square mile protected network area of San Francisco, CA, including fifteen gates of different geometric characteristics. Results demonstrate the efficiency and equity properties of the proposed approach to better manage excessive queues outside of the protected network area and optimally distribute the input flows

    Bayesian inference for origin-destination matrices of transport networks using the EM algorithm

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    Information on the origin-destination (OD) matrix of a transport network is a fundamental requirement in much transportation planning. A relatively inexpensive method for updating an OD matrix is to draw inference about the OD matrix based on a single observation of traffic flows on a specific set of network links, where the Bayesian approach is a natural choice for combining the prior knowledge about the OD matrix and the current observation of traffic flows. The existing approaches of Bayesian modeling of OD matrices include using normal approximations to Poisson distributions, which leads to the posterior being intractable even under some simple special cases, and using Markov chain Monte Carlo simulation, which incurs extreme demand of computational efforts. In this article, through the EM algorithm, Bayesian inference is reinvestigated for a transport network for estimating the population means of traffic flows, reconstructing traffic flows, and predicting future traffic flows. It is shown that the resultant estimates have very simple forms with minimal computational costs

    A network traffic flow model for motorway and urban highways

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    The research reported in this paper develops a network level traffic flow model (NTFM) which is applicable for both motorway and urban roads. It forecasts the traffic flow rates, queue propagation at the junctions and travel delays through the network. NTFM uses sub-models associated with all road and junction types which comprise the highway. The flow at any one part of the network is obviously very dependent upon the flows at all other parts of the network. To predict the two-way traffic flow in NTFM, an iterative simulation method is executed to generate the evolution of dependent traffic flows and queues. To demonstrate the capability of the model it is applied to a small case study network and a local Loughborough-Nottingham highway network. The results indicate that NTFM is capable of identifying the relationship between traffic flows and capturing traffic phenomena such as queue dynamics. By introducing a reduced flow rate on links of the network then the effects of strategies employed to carry out roadworks can be mimicked

    Computer supported estimation of input data for transportation models

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    Control and management of transportation systems frequently rely on optimization or simulation methods based on a suitable model. Such a model uses optimization or simulation procedures and correct input data. The input data define transportation infrastructure and transportation flows. Data acquisition is a costly process and so an efficient approach is highly desirable. The infrastructure can be recognized from drawn maps using segmentation, thinning and vectorization. The accurate definition of network topology and nodes position is the crucial part of the process. Transportation flows can be analyzed as vehicle’s behavior based on video sequences of typical traffic situations. Resulting information consists of vehicle position, actual speed and acceleration along the road section. Data for individual vehicles are statistically processed and standard vehicle characteristics can be recommended for vehicle generator in simulation models

    A characteristic particle method for traffic flow simulations on highway networks

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    A characteristic particle method for the simulation of first order macroscopic traffic models on road networks is presented. The approach is based on the method "particleclaw", which solves scalar one dimensional hyperbolic conservations laws exactly, except for a small error right around shocks. The method is generalized to nonlinear network flows, where particle approximations on the edges are suitably coupled together at the network nodes. It is demonstrated in numerical examples that the resulting particle method can approximate traffic jams accurately, while only devoting a few degrees of freedom to each edge of the network.Comment: 15 pages, 5 figures. Accepted to the proceedings of the Sixth International Workshop Meshfree Methods for PDE 201

    Application of network traffic flow model to road maintenance

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    The study shows how the evolution of two-way traffic flows on a local highway network can be predicted over time using a network-level traffic flow model (NTFM) to model both urban and motorway road networks. After a brief review of the main principles of the NTFM and its associated sub-models, the paper describes how a maintenance worksite can be modelled using a roadwork-node sub-model and a network solution routine in the NTFM. In order to model the two-way traffic flow in the road network, an iterative simulation method is used to generate the evolution of dependent traffic flows and queues. The NTFM has been applied to model the traffic characteristics and the effects of maintenance activities on the local Loughborough–Nottingham highway network. The study has demonstrated that the methodology is useful in selecting various worksite arrangements in order to reduce the effects of maintenance on road users

    Inferring Traffic Flow Characteristics from Aggregated-flow Measurement

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    In the Internet, a statistical perspective of global traffic flows has been considered as an important key to network operations and management. Nonetheless, it is expensive or sometime difficult to measure statistics of each flow directly. Therefore, it is of practical importance to infer unobservable statistical characteristics of individual flows from characteristics of the aggregated-flows, which are easily observed at some links (e.g., router interfaces) in the network. In this paper, we propose a new approach to such inference problems based on finding an inverse function from (observable) probabilities of some states on aggregated-flows to (unobservable) probabilities of some states on flows on a discrete state model, and provide a method inferring arrival rate statistics of individual flows (the OD traffic matrix inference). Our method is applicable to cases not covered by the existing normal-based methods for the OD traffic matrix inference. We also show simulation results on several flow topologies, which indicate potential of our approach
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