Economics-Based Optimization of Unstable Flows

As an example for the optimization of unstable flows, we present an economics-based method for deciding the optimal rates at which vehicles are allowed to enter a highway. It exploits the naturally occuring fluctuations of traffic flow and is flexible enough to adapt in real time to the transient flow characteristics of road traffic. Simulations based on realistic parameter values show that this strategy is feasible for naturally occurring traffic, and that even far from optimality, injection policies can improve traffic flow. Moreover, the same method can be applied to the optimization of flows of gases and granular media.Comment: Revised version of ``Optimizing Traffic Flow'' (cond-mat/9809397). For related work see http://www.parc.xerox.com/dynamics/ and http://www.theo2.physik.uni-stuttgart.de/helbing.htm

A intelligent particle swarm optimization for short-term traffic flow forecasting using on-road sensor systems

On-road sensor systems installed on freeways are used to capture traffic flow data for short-term traffic flow predictors for traffic management, in order to reduce traffic congestion and improve vehicular mobility. This paper intends to tackle the impractical time-invariant assumptions which underlie the methods currently used to develop short-term traffic flow predictors: i) the characteristics of current data captured by on-road sensors are assumed to be time-invariant with respect to those of the historical data, which is used to developed short-term traffic flow predictors; and ii) the configuration of the on-road sensor systems is assumed to be time-invariant. In fact, both assumptions are impractical in the real world, as the current traffic flow characteristics can be very different from the historical ones, and also the on-road sensor systems are time-varying in nature due to damaged sensors or component wear. Therefore, misleading forecasting results are likely to be produced when short-term traffic flow predictors are designed using these two time-invariant assumptions. To tackle these time-invariant assumptions, an intelligent particle swarm optimization algorithm, namely IPSO, is proposed to develop short-term traffic flow predictors by integrating the mechanisms of particle swarm optimization, neural network and fuzzy inference system, in order to adapt to the time-varying traffic flow characteristics and the time-varying configurations of the on-road sensor systems. The proposed IPSO was applied to forecast traffic flow conditions on a section of freeway in Western Australia, whose traffic flow information can be captured on-line by the on-road sensor system. These results clearly demonstrate the effectiveness of using the proposed IPSO for real-time traffic flow forecasting based on traffic flow data captured by on-road sensor systems

A rolling-horizon quadratic-programming approach to the signal control problem in large-scale congested urban road networks

The paper investigates the efficiency of a recently developed signal control methodology, which offers a computationally feasible technique for real-time network-wide signal control in large-scale urban traffic networks and is applicable also under congested traffic conditions. In this methodology, the traffic flow process is modeled by use of the store-and-forward modeling paradigm, and the problem of network-wide signal control (including all constraints) is formulated as a quadratic-programming problem that aims at minimizing and balancing the link queues so as to minimize the risk of queue spillback. For the application of the proposed methodology in real time, the corresponding optimization algorithm is embedded in a rolling-horizon (model-predictive) control scheme. The control strategy’s efficiency and real-time feasibility is demonstrated and compared with the Linear-Quadratic approach taken by the signal control strategy TUC (Traffic-responsive Urban Control) as well as with optimized fixed-control settings via their simulation-based application to the road network of the city centre of Chania, Greece, under a number of different demand scenarios. The comparative evaluation is based on various criteria and tools including the recently proposed fundamental diagram for urban network traffic

TRA-950: A DYNAMIC PROGRAMMING APPROACH FOR ARTERIAL SIGNAL OPTIMIZATION IN A CONNECTED VEHICLE ENVIRONMENT

Within the Connected Vehicle (CV) environment, vehicles are able to communicate with each other and with infrastructure via wireless communication technology. The collected data from CVs provide a much more complete picture of the arterial traffic states and can be utilized for signal control. Based on the real-time traffic information from CVs, this paper enhances an arterial traffic flow model for arterial signal optimization. Then a dynamic programming optimization model is created to solve the signal optimization application. A real-world arterial corridor is modeled in VISSIM to validate the algorithms. This approach is shown to generate good results and may be superior to well-tuned fixed-time control

Joint Energy Efficient and QoS-aware Path Allocation and VNF Placement for Service Function Chaining

Service Function Chaining (SFC) allows the forwarding of a traffic flow along a chain of Virtual Network Functions (VNFs, e.g., IDS, firewall, and NAT). Software Defined Networking (SDN) solutions can be used to support SFC reducing the management complexity and the operational costs. One of the most critical issues for the service and network providers is the reduction of energy consumption, which should be achieved without impact to the quality of services. In this paper, we propose a novel resource (re)allocation architecture which enables energy-aware SFC for SDN-based networks. To this end, we model the problems of VNF placement, allocation of VNFs to flows, and flow routing as optimization problems. Thereafter, heuristic algorithms are proposed for the different optimization problems, in order find near-optimal solutions in acceptable times. The performance of the proposed algorithms are numerically evaluated over a real-world topology and various network traffic patterns. The results confirm that the proposed heuristic algorithms provide near optimal solutions while their execution time is applicable for real-life networks.Comment: Extended version of submitted paper - v7 - July 201

A MapReduce-based nearest neighbor approach for big-data-driven traffic flow prediction

In big-data-driven traffic flow prediction systems, the robustness of prediction performance depends on accuracy and timeliness. This paper presents a new MapReduce-based nearest neighbor (NN) approach for traffic flow prediction using correlation analysis (TFPC) on a Hadoop platform. In particular, we develop a real-time prediction system including two key modules, i.e., offline distributed training (ODT) and online parallel prediction (OPP). Moreover, we build a parallel k-nearest neighbor optimization classifier, which incorporates correlation information among traffic flows into the classification process. Finally, we propose a novel prediction calculation method, combining the current data observed in OPP and the classification results obtained from large-scale historical data in ODT, to generate traffic flow prediction in real time. The empirical study on real-world traffic flow big data using the leave-one-out cross validation method shows that TFPC significantly outperforms four state-of-the-art prediction approaches, i.e., autoregressive integrated moving average, Naïve Bayes, multilayer perceptron neural networks, and NN regression, in terms of accuracy, which can be improved 90.07% in the best case, with an average mean absolute percent error of 5.53%. In addition, it displays excellent speedup, scaleup, and sizeup

Modeling and Probing Strategy for Intelligent Transportation System Utilizing Lagrangian Traffic Data.

Traffic flow model that provides accurate traffic prediction can be beneficial for traffic congestion management. Macroscopic traffic flow models were used in the past to incorporate probe vehicle data and to provide real-time traffic information, but the data collection has not been done efficiently. Also, prediction of traffic state, especially for unexpected traffic jam, is needed to compensate latency in data processing and to provide advance warning to the driver. The objective of this dissertation is to develop an analytical tool which predicts congested highway traffic by utilizing macroscopic traffic flow model and strategically collecting data from probing vehicles with real-time update. First, Newtonian relaxation method is used to incorporate probing data into the LWR model in Eulerian coordinates for traffic status estimation. An investigation of probe vehicle deployment optimization is used to reveal the trade-off between the quality of traffic estimation and the probing cost. Synthetic data is used for numerical experiment, and Genetic algorithm is used to solve the optimization problem. The result indicates that optimal deployment of probe vehicle can reduce probing cost and estimation error by efficient usage of probe vehicles. It is possible to decrease probing data for congested traffic with negligible degradation on the quality of traffic estimation. Second, a stochastic Lagrangian macroscopic traffic flow model is formulated which update the prediction of model parameters and traffic state with unscented Kalman filter in real-time. The proposed probing method tracks vehicles in pairs and utilizes loop detector data for additional information as needed. The model is validated with two sets of empirical data to demonstrate its capability of providing short-term prediction and using model parameter to detect traffic jam i advance. An adaptive probing scheme is presented to show that adjusting probing cell size based on the variance from stochastic model can improve the prediction accuracy. This dissertation proposed a stochastic Lagrangian traffic flow model with the capability of traffic prediction and traffic jam detection, and also demonstrated the benefit of using adaptive probing. Future research interests include performance bounds investigation, optimization of adaptive probing, traffic information distribution, and probing commercial vehicle with optimal operation.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133332/1/kcchu_1.pd