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Md Asim - One of the best experts on this subject based on the ideXlab platform.

  • implementation of online Shortest Path Computation based on live traffic index lti
    International Journal of Research, 2017
    Co-Authors: Ayesha Iffat Basheer, Md Asim
    Abstract:

    The online Shortest Path problem aims at computing the Shortest Path based on live traffic circumstances. This is very important in modern car navigation systems as it helps drivers to make sensible decisions. To our best knowledge, there is no efficient system/solution that can offer affordable costs at both client and server sides for online Shortest Path Computation. Unfortunately, the conventional client-server architecture scales poorly with the number of clients. A promising approach is to let the server collect live traffic information and then broadcast them over radio or wireless network. This approach has excellent scalability with the number of clients. Thus, we develop a new framework called live traffic index (LTI) which enables drivers to quickly and effectively collect the live traffic information on the broadcasting channel. An impressive result is that the driver can compute/update their Shortest Path result by receiving only a small fraction of the index. The experimental study shows that LTI is robust to various parameters and it offers relatively short tune-in cost (at client side), fast query response time (at client side), small broadcast size (at server side), and light maintenance time (at server side) for online Shortest Path problem. The existing systems were infeasible to solve the problem due to their prohibitive maintenance time and large transmission overhead. LTI-TD is a novel solution for Online Shortest Path Computation on Time Dependent Network.

Venkateswara N Rao - One of the best experts on this subject based on the ideXlab platform.

Ayesha Iffat Basheer - One of the best experts on this subject based on the ideXlab platform.

  • implementation of online Shortest Path Computation based on live traffic index lti
    International Journal of Research, 2017
    Co-Authors: Ayesha Iffat Basheer, Md Asim
    Abstract:

    The online Shortest Path problem aims at computing the Shortest Path based on live traffic circumstances. This is very important in modern car navigation systems as it helps drivers to make sensible decisions. To our best knowledge, there is no efficient system/solution that can offer affordable costs at both client and server sides for online Shortest Path Computation. Unfortunately, the conventional client-server architecture scales poorly with the number of clients. A promising approach is to let the server collect live traffic information and then broadcast them over radio or wireless network. This approach has excellent scalability with the number of clients. Thus, we develop a new framework called live traffic index (LTI) which enables drivers to quickly and effectively collect the live traffic information on the broadcasting channel. An impressive result is that the driver can compute/update their Shortest Path result by receiving only a small fraction of the index. The experimental study shows that LTI is robust to various parameters and it offers relatively short tune-in cost (at client side), fast query response time (at client side), small broadcast size (at server side), and light maintenance time (at server side) for online Shortest Path problem. The existing systems were infeasible to solve the problem due to their prohibitive maintenance time and large transmission overhead. LTI-TD is a novel solution for Online Shortest Path Computation on Time Dependent Network.

Zhiguo Gong - One of the best experts on this subject based on the ideXlab platform.

  • towards online Shortest Path Computation
    IEEE Transactions on Knowledge and Data Engineering, 2014
    Co-Authors: Leong Hou U, Man Lung Yiu, Hong Jun Zhao, Zhiguo Gong
    Abstract:

    The online Shortest Path problem aims at computing the Shortest Path based on live traffic circumstances. This is very important in modern car navigation systems as it helps drivers to make sensible decisions. To our best knowledge, there is no efficient system/solution that can offer affordable costs at both client and server sides for online Shortest Path Computation. Unfortunately, the conventional client-server architecture scales poorly with the number of clients. A promising approach is to let the server collect live traffic information and then broadcast them over radio or wireless network. This approach has excellent scalability with the number of clients. Thus, we develop a new framework called live traffic index (LTI)which enables drivers to quickly and effectively collect the live traffic information on the broadcasting channel. An impressive result is that the driver can compute/update their Shortest Path result by receiving only a small fraction of the index. Our experimental study shows that LTI is robust to various parameters and it offers relatively short tune-in cost (at client side), fast query response time (at client side), small broadcast size (at server side), and light maintenance time (at server side)for online Shortest Path problem.

Yongsheng Sang - One of the best experts on this subject based on the ideXlab platform.

  • Shortest Path Computation using pulse coupled neural networks with restricted autowave
    Knowledge Based Systems, 2016
    Co-Authors: Yongsheng Sang
    Abstract:

    Finding Shortest Paths is an important problem in transportation and communication networks. This paper develops a Pulse-Coupled Neural Network (PCNN) model to efficiently compute a single-pair Shortest Path. Unlike most of the existing PCNN models, the proposed model is endowed with a special mechanism, called on-forward/off-backward; if a neuron fires, its neighboring neurons in a certain forward region will be excited, whereas the neurons in a backward region will be inhibited. As a result, the model can produce a restricted autowave that propagates at different speeds corresponding to different directions, which is different from the completely nondeterministic PCNN models. Compared with some traditional methods, the proposed PCNN model significantly reduces the Computational cost of searching for the Shortest Path. Experimental results further confirmed the efficiency and effectiveness of the proposed model.

  • a modified pulse coupled neural network for Shortest Path Computation
    2010
    Co-Authors: Yongsheng Sang, Y I Zhang
    Abstract:

    Shortest Path Computation is a classical combinatorial optimization problem. Neural networks, as a class of effective optimization approaches, have been used for processing Path optimization problems for a long time. Pulse Coupled Neural Network (PCNN) is a very different neural network, which has also been proposed to compute Shortest Paths in recent years. In some existing PCNN models, the Computational complexity is closely related to the length of actual Shortest Path. As a result, these PCNNs suffer from high Computational cost for very long Paths. This paper proposed a new PCNN model, called Dual Source PCNN (DSPCNN), which can improve the Computational efficiency of PCNNs for Shortest Path problems. Two Autowaves produced by the DSPCNN: one comes from the Source neuron, and the other comes from the Goal neuron. Once the Autowaves from the two firing sources meet, the DSPCNN stops, and then the Shortest Path is found by backtracking the two Autowaves. Experimental results show that the DSPCNN is more efficient than some existing PCNNs.