Narrow Results

Passengers’ boarding time interval is of great significance for analysis of collective mobility behaviors. In this paper, we empirically investigate the boarding time interval of mobility behaviors based on three large-scale reservation records of passengers traveling by three different types of transportation from a travel agency platform, namely airplane, intercity bus and car rental. The statistical results show that similar properties exist in the passengers’ mobility behaviors, for example, there are similar burstiness $〈B〉=0$ and memory $〈M〉=-0.5$ for different time interval distribution, which indicates that the passengers’ mobility behaviors are periodical. Furthermore, we present a probability model to regenerate the empirical results by assuming that the passengers’ next boarding time interval will generate between a short time of 1–7 days with probability $p$ and a random long time with probability $1-p$. The simulation results show that the presented model can reproduce the burstiness and memory effect of the boarding time interval when $p=0.6$ for three empirical datasets, which suggests the periodical behaviors with the probability $p$. This work helps in deeply understanding the regularity of human mobility behaviors.

Price promotion and product recommendation are important tactics to gain market share in the e-commerce context. To increase sales and enhance promotion profits, practitioners often recommend non-discounted products in the promotional campaign for a product. However, no analytical model is hitherto available to jointly optimize the price discount for the promoted product and the product portfolio to recommend. This paper provides a probability model to complete the task. The proposed model motivates customers through an attractive price discount for the promoted product, while simultaneously encouraging customers to purchase the non-discounted products through the recommendation system. The numerical studies show that the proposed method attains higher profits than do conventional methods. Finally, we offer managerial insights and provide useful guidelines to help e-tailers to make the most profitable online promotional decisions.

This paper proposes a probability-interval mixed uncertainty model considering parametric correlations and a corresponding structural reliability analysis method. First of all, we introduce the sample correlation coefficients to express the correlations between different kinds of uncertain variables including probability and interval variables. Then dependent parameters are transformed into independent ones through a matrix transformation. A reliability analysis model is put forward, and an efficient method is built to obtain the reliability index or failure probability interval of the structure. Finally, four numerical examples are provided to verify the validity of the method.

Correct conditioning of geological models to well data is important as it has a major impact on facies simulation. In this paper, we performed a simulation of lithofacies using a multiple-point facies (MPF) method conditioned to probability models, which applied successfully to a subsurface reservoir modeling in the Bohai Bay Basin, China. Probability models, from four lithofacies proportion maps transformed from Average Negative Amplitude cube to lateral trends and Vertical proportion curves to vertical constrains, are simulated from probability logs of lithofacies generated from drilling data by using the compositional kriging method. These probability models even give an exact percentage for every lithofacies probability and then improve the accuracy of the model grid description. Later, different parameter settings lead to the generation of a set of three-dimensional models. Several tests show that the model generated by the MPF method with strong conditioning to probability models can more accurately describe the distribution of sand bodies and interbedded intervals, and provide the best match for both the lithofacies proportion of well logs and the actual production performance. The error in estimating the amount of geological reserves is less than 5%. More than 90% of the wells show an excellent match with the water, with an error of less than 5%. The approach proposed in this paper provides a systematic method for reservoir evaluation, especially in offshore fields with widely spaced wells such as the Bohai Bay Basin.