Research on Optimization for Passenger Streamline of Hubs

AbstractThis paper proposes an optimization model for passenger streamline to promote the organization of hub management. Passengers are divided into two different categories, namely familiar type and unfamiliar type. Then the different route choice behaviors of these two types are analyzed. The graph theory is employed to abstract the hub network. The system cost is taken as the optimization objective, and then an optimization design model for passenger streamline is built. To find a solution, we adopt a traversal search algorithm to enumerate all the possible schemes, and then choose the scheme with the minimum system cost. Finally, a simple case is taken to verify the validity of the proposed model

A computation method on time-dependent accessibility of urban rail transit networks for the last service

Urban rail transit networks seldom provide 24-hour service. The last train is the latest chance for passengers. If passengers arrive too late to catch the last train, the path becomes inaccessible. The network accessibility thus varies depending on the departure time of passenger trips. This paper focuses on the computation method on the time-dependent accessibility of urban rail transit networks in order to facilitate the itinerary planning of passengers. A label setting algorithm is first designed to calculate the latest possible times for Origin–Destination (O–D) pairs, which is the latest departure times of passengers from the origins such that the destinations can be reach successfully. A searching approach is then developed to find the shortest accessible path at any possible departure times. The method is applied in a real-world metro network. The results show that the method is a powerful tool in solving the service accessibility problem. It has the ability to allow passengers to plan an optimal itinerary. Comparison analysis indicates that the proposed method can provide exact solutions in much shorter time, compared with a path enumeration method. Extensive tests on a set of random networks indicate that the method is efficient enough in practical applications. The execution time for an O–D pair on a personal computer with 2.8 GHZ CPU and 4GB of RAM is only 1.2 s for urban rail transit networks with 100 transfer stations

Patent Partner Recommendation in Enterprise Social Networks

It is often challenging to incorporate users ’ interactions into a recommendation framework in an online model. In this paper, we propose a novel interactive learning framework to formulate the problem of recommending patent partners into a factor graph model. The framework involves three phases: 1) candidate generation, where we identify the potential set of collaborators; 2) candidate refinement, where a factor graph model is used to adjust the candidate rankings; 3) interactive learning method to efficiently update the existing recommendation model based on inventors ’ feedback. We evaluate our proposed model on large enterprise patent networks. Experimental results demonstrate that the recommendation accuracy of the proposed model significantly outperformsseveralbaselinesmethodsusingcontentsimilarity, collaborative filtering and SVM-Rank. We also demonstratetheeffectivenessandefficiencyoftheinteractivelearning, which performs almost as well as offline re-training, but with only 1 percent of the running time

Cross-domain collaboration recommendation

Interdisciplinary collaborations have generated huge impact to society. However, it is often hard for researchers to establish such cross-domain collaborations. What are the patterns of cross-domain collaborations? How do those collaborations form? Can we predict this type of collaborations? Cross-domain collaborations exhibit very different patterns compared to traditional collaborations in the same domain: 1) sparse connection: cross-domain collaborations are rare; 2) complementary expertise: cross-domain collaborators often have different expertise and interest; 3) topic skewness: cross-domain collaboration topics are focused on a subset of topics. All these patterns violate fundamental assumptions of traditional recommendation systems. In this paper, we analyze the cross-domain collaboration data from research publications and confirm the above patterns. We propose the Cross-domain Topic Learning (CTL) model to address these challenges. For handling sparse connections, CTL consolidates the existing cross-domain collaborations through topic layers instead of at author layers, which alleviates the sparseness issue. For handling complementary expertise, CTL models topic distributions from source and target domains separately, as well as the correlation across domains. For handling topic skewness, CTL only models relevant topics to the cross-domain collaboration. We compare CTL with several baseline approaches on large publication datasets from different domains. CTL outperforms baselines significantly on multiple recommendation metrics. Beyond accurate recommendation performance, CTL is also insensitive to parameter tuning as confirmed in the sensitivity analysis

Social Action Tracking via Noise Tolerant Time-varying Factor Graphs

Users’behaviors(actions)inasocialnetworkareinfluencedbyvarious factors such as personal interests, social influence, and global trends. However, few publications systematicallystudy how social actions evolve in a dynamic social network and towhat extent different factors affect the user actions. In this paper, we propose a Noise Tolerant Time-varying Factor Graph Model (NTT-FGM) for modeling and predicting social actions. NTT-FGM simultaneously models social network structure, user attributes and user action history for better prediction of the users ’ future actions. More specifically, a user’s action at time t is generated by her latent state at t, which is influenced by her attributes,herownlatentstateattimet−1andherneighbors’ states attimetandt−1. Basedonthisintuition,weformalizethe social action tracking problem using the NTT-FGM model; then present an efficient algorithm to learn the model, by combining the ideas from both continuous linear system and Markov random field. Finally, we present a case study of our model on predicting future social actions. We validate the model on three different types ofreal-worlddatasets. Qualitatively,ourmodelcandiscover interestingpatternsofthesocialdynamics. Quantitatively,experimental resultsshowthattheproposedmethodoutperformsseveralbaseline methods for social actionprediction. Categories andSubject Descriptor