Hsun-Jung Cho

A Simulated Annealing Approach to the Network Design Problem with Variational Inequality Constraints

The equilibrium network design problem can be formulated as a mathematical program with variational inequality constraints. We know this problem is nonconvex; hence, it is difficult to solve for a globally optimal solution. In this paper we propose a simulated annealing algorithm for the equilibrium network design problem. We demonstrate the ability of this algorithm to determine a globally optimal solution for two different networks. One of these describes an actual city in the midwestern United States.

Generalized honeycomb torus

Stojmenovic introduced three different honeycomb tori by adding wraparound edges on honeycomb meshes, namely honeycomb rectangular torus, honeycomb rhombic torus, and honeycomb hexagonal torus. These honeycomb tori have been recognized as an attractive alternative to existing torus interconnection networks in parallel and distributed applications. In this paper, we propose generalized honeycomb tori. The three different honeycomb tori proposed by Stojmenovic are proved to be special cases of our proposed generalized honeycomb tori. We also discuss the Hamiltonian property of some generalized honeycomb tori.

Chaos and control of discrete dynamic traffic model

Abstract This study discusses chaotic traffic flow. The discrete dynamic model proposed herein is derived from both the flow–density–speed fundamental diagram and Greenshields model. The model employs occupancy as its variable and the ratio of free flow and average speed as its control parameter. The function form of the model is equal to logistic map that bifurcates when the value of the control parameter increases. Chaotic traffic means that traffic becomes unstable and unpredictable, which is dangerous for driving. Therefore, this study considers the implementation of chaotic control in signal or ramp metering design so as to stabilize the chaotic traffic phenomena. These results are illustrated by numerical examples.

A reduction method for local sensitivity analyses of network equilibrium arc flows

A reduction method is proposed which allows standard sensitivity techniques for variational inequalities to applied to equilibrium network flow problems without additional assumptions on either the underlying network or the numbers of active paths. In particular it is shown that under mild regularity conditions, small perturbations of equilibria can be given an explicit arc-flow representation which is free of path-flow variables. It is also shown that this reduced form allows the differentiability of perturbations to be studied by standard methods. These results are illustrated by a small numerical example.

Day-to-day vehicular flow dynamics in intelligent transportation network

In this paper, a flow evolution model is developed by using the dynamical system approach for a vehicular network equipped with predicted travel information. The concerned system variables, path flow, and predicted minimal travel time of an origin-destination (OD) pair, are measured on the peak-hour-average base for each day. The time change rates of these two variables are formulated as a system of ordinary differential equations under the assumption of daily learning and adaptive processes for commuters. By incorporating the total perceived travel time loss (or saving) into the proposed models, time change rates of path flows are generated with a flow-related manner to prevent path flow dynamics from being insensible to traffic congestion which had been formulated in the existing studies. Heterogeneous models with various user adjusting sensitivities and predicted travel information are also presented. Equilibrium solutions of the proposed network dynamics satisfy the Wardrop user equilibria and are proved to be asymptotically stable by using the stability theorem of Lyapunov. The issue of existence and uniqueness of solutions is proved both on the lemma of Lipschitz condition and the fundamental theorem of ordinary differential equations. In addition, some simple examples are demonstrated to show the asymptotic behaviors of the proposed models numerically.

Ring embedding in faulty honeycomb rectangular torus

Assume that m and n are positive even integers with n ≥ 4. The honeycomb rectangular torus HReT(m, n) is recognized as another attractive alternative to existing torus interconnection networks in parallel and distributed applications. It is known that any HReT(m, n) is a 3-regular bipartite graph. We prove that any HReT(m, n) - e is hamiltonian for any edge e ∈ E(HReT(m, n)). Moreover, any HReT(m, n) - F is hamiltonian for any F = {a, b} with a ∈ A and b ∈ B where A and B are the bipartition of HReT(m, n), if n ≥ 6 or m = 2.

Hybrid shortest path algorithm for vehicle navigation

Vehicle navigation is one of the important applications of the single-source single-target shortest path algorithm. This application frequently involves large scale networks with limited computing power and memory space. In this study, several heuristic concepts, including hierarchical, bidirectional, and A*, are combined and used to develop hybrid algorithms that reduce searching space, improve searching speed, and provide the shortest path that closely resembles the behavior of most road users. The proposed algorithms are demonstrated on a real network consisting 374,520 nodes and 502,485 links. The network is preprocessed and separated into two connected subnetworks. The upper layer of network is constructed with high mobility links, while the lower layer comprises high accessibility links. The proposed hybrid algorithms are implemented on both PC and hand-held platforms. Experiments show a significant acceleration compared to the Dijkstra and A* algorithm. Memory consumption of the hybrid algorithm is also considerably less than traditional algorithms. Results of this study showed the hybrid algorithms have an advantage over the traditional algorithm for vehicle navigation systems.

A stimulus-response model of day-to-day network dynamics

A general structure of stimulus-response formula is presented to specify the interacted network dynamics under the assumption of a daily learning and adaptive travel behavior. By taking the time derivative of system variable as a response term, the evolution is formulated as a dynamic system. Issues of existence, uniqueness, and stability for the proposed differential equations are briefly discussed. Approximation of a time-varying route-choice model is derived from the addressed path-flow dynamics. Threshold effects on path-flow dynamics are encapsulated into the proposed general structure by incorporating a discontinuous stimulus term. Then, the quasi user equilibrium is achieved when all users feel indifferent between the experienced and predicted travel time provided by intelligent transportation systems, i.e., the whole system dynamics stay within a bounded range. The derived quasi user equilibrium is reduced to Wardrops user equilibrium as the threshold effects of path-flow dynamics vanish.

Hybrid models toward traffic detector data treatment and data fusion

This paper develops a data processing with hybrid models toward data treatment and data fusion for traffic detector data on freeways. hybrid grey-theory-based pseudo-nearest neighbor method and grey time-series model are developed to recover spatial and temporal data failures. Both spatial and temporal patterns of traffic data are also considered in travel time data fusion. Two travel time data fusion models are presented using a speed-based link travel time extrapolation model for analytical travel time estimation and a recurrent neural network with grey-models for real-time travel time prediction. Field data from the Taiwan national freeway no. 1 were used as a case study for testing the proposed models. Study results shown that the data treatment models for faulty data recovery were accurate. The data fusion models were capable of accurately predicting travel times. The results indicated that the proposed hybrid data processing approaches can ensure the accuracy of travel time estimation with incomplete data sets.

A support vector machine approach to CMOS-based radar signal processing for vehicle classification and speed estimation

Abstract In this work, a complementary metal-oxide semiconductor (CMOS) based transceiver with a sensitivity time control antenna is successfully implemented for advanced traffic signal processing. The collected signals from the CMOS radar system are processed with optimization algorithms for vehicle-type classification and speed determination. The high recognition rate optimization algorithms are mainly based upon the information of short setup time and different environmental installation of each sensor. In the course of optimization, a video recognition module is further adopted as a supervisor of support vector machine and support vector regression. Compared with conventional circuit-based detector systems, the developed CMOS radar integrates submicron semiconductor devices and thus not only possesses low stand-by power but also is ready for production. In the meantime, the developed algorithm of this study simultaneously optimizes the vehicle-type classification and speed determination in a computationally cost-effective manner, which benefits real-time intelligent transportation systems.