Link-based Algorithm Research Articles

An Application of a Link-Based Optimal Pathfinding Algorithm with the Integrated Transit Network from Smart Card Systems

With the spread of information technology in the transportation field, many cities around the globe have introduced smart card systems to stimulate the use of transit services. The systems have resulted in not only the improvement of users’ convenience but also the dramatic changes of travel behaviors with transit services. One prevailing phenomenon is the user’s perception for all transit modes as one single mode in the integrated transit network because of seamless services provided by smart card systems. Although this change has brought tremendous advantages to the dynamics of transit services, it has become more complicated to understand travel behaviors in the integrated transit network which includes features of transfers, service schedule, waiting time and fares. Given that smart card systems have affluent information which can reflect such characteristics, their proper utilizations are still in question. In this sense, when compared with a single-mode network, specific approaches need to be considered to build an integrated transit network from smart card systems, and to develop a suitable model that can reflect the features of the integrated transit network. With smart card systems in the greater Seoul area, Korea, this research proposes methodological techniques which can create the integrated transit network and facilitate transfers automatically between modes. In addition, an attempt is made to verity the applicability of a link-based optimal pathfinding algorithm that has been spotlighted to consider complex travel behaviors in a transit network but has not been proven in the integrated transit network from smart card systems. The concepts of mode-based link labeling and “big node” for transfers are employed with this algorithm. From their applications, optimal transit paths are successfully searched by proposed concepts with smart card data. Given that building an integrated transit network and finding optimal paths are essential tasks for transit analysis studies, this research provides irreplaceable contributions to utilize link-based optimal pathfinding algorithms in the integrated transit network with smart card systems.

Node-Based Distributed Channel Access With Enhanced Delay Characteristics

Recent studies in wireless scheduling have shown that carrier-sense multiple access (CSMA) can be made throughput optimal by optimizing over activation rates. However, those throughput optimal CSMA algorithms were found to suffer from poor delay performance, especially at high throughputs where the delay can potentially grow exponentially in the size of the network. Motivated by these shortcomings, in this paper we propose a node-based version of the throughput optimal CSMA (NB-CSMA) as opposed to traditional link-based CSMA algorithms, where links were treated as separate entities. Our algorithm is fully distributed and corresponds to Glauber dynamics with “Block updates”. We show analytically and via simulations that NB-CSMA outperforms conventional link-based CSMA in terms of delay for any fixed-size network. We also characterize the fraction of the capacity region for which the average queue lengths (and the average delay) grow polynomially in the size of the network, for networks with bounded-degree conflict graphs. This fraction is no smaller than the fraction known for link-based CSMA, and is significantly larger for many instances of practical wireless ad-hoc networks. Finally, we restrict our focus to the special case of collocated networks, analyze the mean starvation time using a Markov chain with rewards framework and use the results to quantitatively demonstrate the improvement of NB-CSMA over the baseline link-based algorithm.

Cluster Ensemble with Link-Based Approach for Botnet Detection

Botnet detection is one of the most imminent tasks for cyber security. Among popular botnet countermeasures, an intrusion detection system is the prominent mechanism. In the past, packet-based intrusion detection systems were popular. However, flow-based intrusion detection systems have been preferred in recent years due to their ability to adapt to modern high-speed networks. A collection of flows from an enterprise network usually contains both botnet traffic and normal traffic. To classify this traffic, supervised machine learning algorithms, i.e., classifications, have been applied and achieved a high accuracy. In an effort to improve the ability of intrusion detection systems against botnets, some studies have suggested partitioning flows into clusters before applying the classifications and this step could significantly reduce the complexity of a flow set. However, the instability of individual clustering algorithms is still a constraint for botnet detection.To overcome this bottleneck, we propose a novel method that combines individual partitions to become a strong learner through the use of a link-based algorithm. Our experiments show that our cluster ensemble model outperforms existing botnet detection mechanisms with a high reliability. We also determine the balance between accuracy and computer resources for botnet detection, and thereby propose a range for the maximum duration time of flows in botnet research.

Exploring Link-based Algorithm for Web Spam Detection

Exploring Link-based Algorithm for Web Spam Detection

Modeling Stochastic Route Choice Behaviors with Equivalent Impedance

A Logit-based route choice model is proposed to address the overlapping and scaling problems in the traditional multinomial Logit model. The nonoverlapping links are defined as a subnetwork, and its equivalent impedance is explicitly calculated in order to simply network analyzing. The overlapping links are repeatedly merged into subnetworks with Logit-based equivalent travel costs. The choice set at each intersection comprises only the virtual equivalent route without overlapping. In order to capture heterogeneity in perception errors of different sizes of networks, different scale parameters are assigned to subnetworks and they are linked to the topological relationships to avoid estimation burden. The proposed model provides an alternative method to model the stochastic route choice behaviors without the overlapping and scaling problems, and it still maintains the simple and closed-form expression from the MNL model. A link-based loading algorithm based on Dial’s algorithm is proposed to obviate route enumeration and it is suitable to be applied on large-scale networks. Finally a comparison between the proposed model and other route choice models is given by numerical examples.

Unconstrained weibit stochastic user equilibrium model with extensions

This study provides an unconstrained minimization program as an alternative formulation for the multinomial weibit (MNW) stochastic user equilibrium (SUE) model that explicitly considers the heterogeneous perception variances with respect to different trip lengths under congested conditions. Qualitative properties of the unconstrained minimization program are given to establish the equivalency and uniqueness of the MNW-SUE solution. The advantage of the unconstrained minimization programming formulation is that it allows the development of a link-based algorithm, which obviates path storage and enumeration. The methodological contributions lie in the derivation of the expected perceived travel cost (or the satisfaction function) that enables the development of an unconstrained MNW-SUE minimization program and a link-based stochastic loading mechanism combined with recent advances in line search strategies in the link-based algorithm. Numerical examples are also provided to illustrate the features of the MNW-SUE model and the link-based algorithm along with several extensions for future research.

A Bootstrap Aggregating Technique on Link-Based Cluster Ensemble Approach for Categorical Data Clustering

Although attempts have been made to solve the problem of clustering categorical data via cluster ensembles, with the results being competitive to conventional algorithms, it is observed that these techniques unfortunately generate a final data partition based on incomplete information. The underlying ensemble-information matrix presents only cluster-data point relations, with many entries being left unknown. The paper presents an analysis that suggests this problem degrades the quality of the clustering result, and it presents a BSA (Bootstrap Aggregation) is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy along with a new link-based approach, which improves the conventional matrix by discovering unknown entries through similarity between clusters in an ensemble. In particular, an efficient BSA and link-based algorithm is proposed for the underlying similarity assessment. Afterward, to obtain the final clustering result, a graph partitioning technique is applied to a weighted bipartite graph that is formulated from the refined matrix. Experimental results on multiple real data sets suggest that the proposed link-based method almost always outperforms both conventional clustering algorithms for categorical data and well-known cluster ensemble techniques.

A New Link-Based Single Tree Building Algorithm for Shortest Path Searching in an Urban Road Transportation Network

The shortest-path searching algorithm must not only find a global solution to the destination, but also solve a turn penalty problem (TPP) in an urban road transportation network (URTN). Although the Dijkstra algorithm (DA) as a representative node-based algorithm secures a global solution to the shortest path search (SPS) in the URTN by visiting all the possible paths to the destination, the DA does not solve the TPP and the slow execution speed problem (SEP) because it must search for the temporary minimum cost node. Potts and Oliver solved the TPP by modifying the visiting unit from a node to the link type of a tree-building algorithm like the DA. The Multi Tree Building Algorithm (MTBA), classified as a representative Link Based Algorithm (LBA), does not extricate the SEP because the MTBA must search many of the origin and destination links as well as the candidate links in order to find the SPS. In this paper, we propose a new Link-Based Single Tree Building Algorithm in order to reduce the SEP of the MTBA by applying the breaking rule to the LBA and also prove its usefulness by comparing the proposed with other algorithms such as the node-based DA and the link-based MTBA for the error rates and execution speeds.

A Link-Based Cluster Ensemble Approach for Categorical Data Clustering

Although attempts have been made to solve the problem of clustering categorical data via cluster ensembles, with the results being competitive to conventional algorithms, it is observed that these techniques unfortunately generate a final data partition based on incomplete information. The underlying ensemble-information matrix presents only cluster-data point relations, with many entries being left unknown. The paper presents an analysis that suggests this problem degrades the quality of the clustering result, and it presents a new link-based approach, which improves the conventional matrix by discovering unknown entries through similarity between clusters in an ensemble. In particular, an efficient link-based algorithm is proposed for the underlying similarity assessment. Afterward, to obtain the final clustering result, a graph partitioning technique is applied to a weighted bipartite graph that is formulated from the refined matrix. Experimental results on multiple real data sets suggest that the proposed link-based method almost always outperforms both conventional clustering algorithms for categorical data and well-known cluster ensemble techniques.

New travel time prediction algorithms for intelligent transportation systems

Recently, travel time prediction has become a crucial part of trip panning and dynamic route guidance for many advanced traveler information and transportation management systems. Moreover, a scalable prediction system with high accuracy is critical for the successful deployment of ATIS (Advanced Travelers Information Systems) in road networks. In this paper, we propose two travel time prediction algorithms using naive Bayesian classification and rule-based classification. Both classification techniques provide a velocity class to be used for measuring travel time accurately. Our algorithms exhibit high accuracy in predicting travel time when using a large amount of historical traffic database. In addition, our travel time prediction algorithms are suitable for road networks with arbitrary travel routes. It is shown from our performance comparison, our travel time prediction algorithms significantly outperform the existing prediction algorithms, such as the link-based algorithm, the switching model, and the linear regression algorithm. In addition, it is revealed that our algorithm using naive Bayesian classification is better on the performance of mean absolute relative error than our algorithm using rule-based classification.

Flexible Multipath Search Algorithm for Multipurpose Location-Based Activities

An algorithm is developed that provides not only least-cost alternate routes but also ones that are close to the least-cost routes for users to select from multiple paths that route through points of interest. The algorithm utilizes a link-based search method rather than the traditional node-based search method. The link-based algorithm is able to use the existing transportation network by allowing U-turns and restricting left turns without modifications. The algorithm also uses relative prices rather than absolute prices in selecting paths. The relative price is the difference between the minimum price available in the market and the price that is available on a path. Introduction of relative prices enables the provision of flexible route guidance to meet the various needs of users. The algorithm developed has been applied to a virtual network, which successfully demonstrated the implementation of the multipath search algorithm for multipurpose activities.

DELAY GUARANTEE BACKUP ROUTE PLANNING FOR MULTICAST NETWORKS

This paper considers quality of service in terms of end-to-end delay in planning backup routes for multicast communications. The problem of preplanning backup routes considering both cost minimization and end-to-end delay guarantee for multicast communications in the case a single link failure is investigated. Two delay labels, the limited label and the tolerable label, are defined to evaluate the end-to-end delay requirement. Four heuristic algorithms, including two tree-based algorithms, one subtree-based algorithm, and one link-based algorithm, are proposed to determine the delay-constrained backup routes having the minimum costs. Two procedures to determine a node selection sequence, Random and Minimum-Cost, are used in the tree-based algorithm. Experimental results show that the tree-based algorithm by the Minimum-Cost sequence yields the best performance in cost minimization with guarantee of end-to-end delay.

A novel scheme for supporting integrated unicast and multicast traffic in ad hoc wireless networks

Current multicast routing protocols for mobile ad hoc networks can be classified into two categories, tree-based protocols and mesh-based protocols. Mesh-based protocols have high packet delivery ratios compared to tree-based protocols, but incur more control overhead. Though the control overhead involved in tree-based protocols is low, the performance in terms of packet delivery ratio of such protocols, decreases with increasing mobility. This is due the lack of proper tree maintenance mechanisms. We propose an efficient multicast routing protocol called preferred link-based multicast protocol that uses a preferred link approach for forwarding JoinQuery packets. This approach reduces control overhead during the forwarding of JoinQuery packets. In this approach only a subset of neighbors of a node, that are termed as preferred nodes, are made eligible for further forwarding of JoinQuery packets. These preferred nodes are selected using our preferred link-based algorithm. We conducted extensive simulation experiments to evaluate the proposed protocol and have compared our protocol with some of the existing protocols. The simulation results show that our protocol performs better than the other multicast protocols in terms of packet delivery ratio and control overhead. Further, we have extended our multicast protocol to support unicast and multicast traffic simultaneously. This unified approach for routing is analyzed in order to study the impact of unicast traffic on multicast traffic, and vice versa. Performance evaluation of the unified approach, that we term as preferred link-based unified routing, was carried out through extensive simulation experiments.

Sensitivity Analysis for Stochastic User Equilibrium Network Flows—A Dual Approach

Recently, extensive studies have been conducted on the computational methods of sensitivity analysis for the Wardropian equilibrium modeling of traffic networks and their applications. But the same problems in the context of the stochastic user equilibrium modeling seem not to have been addressed. In this paper, we present a method for sensitivity analysis for network flows at stochastic user equilibrium. Our method is developed from a dual formulation of the stochastic user equilibrium analysis. By adopting Dial's algorithm for stochastic traffic assignment, we are able to formulate a computationally efficient link-based algorithm for the sensitivity analysis. Since the Wardropian equilibrium in a traffic network is an extreme case of stochastic user equilibrium with θ → ∞, θ being a dispersion parameter in the expected utility function for stochastic route choice, the method presented here can also be used for sensitivity analysis of the Wardropian equilibrium by setting θ large enough.

Comparison of Link-Based Versus Route-Based Algorithms in the Dynamic User-Optimal Route Choice Problem

One link-based algorithm (Frank-Wolfe) and three route-route based algorithms are compared for use in the dynamic user-optimal route choice problem. Computational experience with five test networks indicates that the route-based algorithms are in general superior to the link-based algorithm in terms of execution time but slightly inferior in terms of memory requirement.