A Deep Spatio-Temporal Fuzzy Neural Network for Passenger Demand Prediction

In spite of its importance, passenger demand prediction is a highly challenging problem, because the demand is simultaneously influenced by the complex interactions among many spatial and temporal factors and other external factors such as weather. To address this problem, we propose a Spatio-TEmporal Fuzzy neural Network (STEF-Net) to accurately predict passenger demands incorporating the complex interactions of all known important factors. We design an end-to-end learning framework with different neural networks modeling different factors. Specifically, we propose to capture spatio-temporal feature interactions via a convolutional long short-term memory network and model external factors via a fuzzy neural network that handles data uncertainty significantly better than deterministic methods. To keep the temporal relations when fusing two networks and emphasize discriminative spatio-temporal feature interactions, we employ a novel feature fusion method with a convolution operation and an attention layer. As far as we know, our work is the first to fuse a deep recurrent neural network and a fuzzy neural network to model complex spatial-temporal feature interactions with additional uncertain input features for predictive learning. Experiments on a large-scale real-world dataset show that our model achieves more than 10% improvement over the state-of-the-art approaches.Comment: https://epubs.siam.org/doi/abs/10.1137/1.9781611975673.1

Social Capital, Socioeconomic Status and Self-efficacy

This study internalized social capital on the basis of traditional study of the influence of economic factors on self-efficacy, and studied the relationship among the family socio-economic status, social capital and self-efficacy. Based on the theoretical analysis, with first-hand data collection and using multiple regression models, the paper studied the intermediate effect of social capital in the relationship between the socioeconomic status and self-efficacy. We draw on the following conclusions: (1) The family socio-economic status as well as all its dimensions (father’s degree of education, mother’s degree of education, the total annual income of the family, father’s occupation, mother’s occupation) is significantly positively related to social capital and all the dimensions of its proxy variable (peer support, kinship support and general support of others); (2) There is a significant positive correlation between the family socio-economic status as well as all its dimensions and self-efficacy; the socio-economic status, with its dimensions, is the predictive variable of self-efficacy; (3) Social capital, with dimensions of its proxy variable, is positively correlated with self-efficacy and has predictive effect on self-efficacy (4) Social capital plays a significant intermediate role between socio-economic status and self-efficacy, and the mediating effect size is about 51.75%

Agreeableness, Extraversion, Stressor and Physiological Stress Response

Based on the theoretical analysis, with first-hand data collection and using multiple regression models, this study explored the relationship between agreeableness, extraversion, stressor and stress response and figured out interactive effect of agreeableness, extraversion, and stressor on stress response. We draw on the following conclusions: (1) the interaction term of stressor (work) and agreeableness can negatively predict physiological stress response; (2) the interaction term of stressor (health) and agreeableness can negatively predict physiological stress response; (3) the interaction term of stressor (family) and agreeableness can negatively predict physiological stress response; (4) the interaction term of stressor (social) and agreeableness can negatively predict physiological stress response; (5) the interaction term of stressor (work) and extraversion can negatively predict physiological stress response; (6) the interaction term of stressor (health) and extraversion can negatively predict physiological stress response; (7) the interaction term of stressor (family) and extraversion can negatively predict physiological stress response; (8) the interaction term of stressor (social) and extraversion can negatively predict physiological stress response

ECGSC: Elliptic Curve based Generalized Signcryption Scheme

Signcryption is a new cryptographic primitive that simultaneously fulfills both the functions of signature and encryption. The definition of generalized signcryption is proposed in the paper firstly. Generalized signcryption has a special feature that provides confidentiality or authenticity separately under the condition of specific inputs. So it is more useful than common ones. Based on ECDSA, a signcryption scheme called ECGSC is designed. It will be equivalent to an AtE(OTP$,MAC) encryption scheme or ECDSA when one of party is absent. A third party can verify the signcryption text publicly in the method of ECDSA. Security properties are proven based on Random Oracle mode: confidentiality (CUF-CPA), unforgeability (UF-CMA) and non-repudiation. Compared with the others, ECGSC presents a 78% reduction in computational cost for typical security parameters for high level security applications

The Effects of Weather on Passenger Flow of Urban Rail Transit

Predicting passenger flow on urban rail transit is important for the planning, design and decision-making of rail transit. Weather is an important factor that affects the passenger flow of rail transit by changing the travel mode choice of urban residents. This study aims to explore the influence of weather on urban rail transit ridership, taking four cities in China as examples, Beijing, Shanghai, Guangzhou and Chengdu. To determine the weather effect on daily ridership rate, the three models were proposed with different combinations of the factors of temperature and weather type, using linear regression method.   The large quantities of data were applied to validate the developed models.  The results show that in Guangzhou, the daily ridership rate of rail transit increases with increasing temperature. In Chengdu, the ridership rate increases in rainy days compared to sunny days. While, in Beijing and Shanghai, the ridership rate increases in light rainfall and heavy rainfall (except moderate rainfall) compared to sunny days. The research findings are important to understand the impact of weather on passenger flow of urban rail transit. The findings can provide effective strategies to rail transit operators to deal with the fluctuation in daily passenger flow

Study of Financial Literacy and Interpersonal Influence on Self-Evaluation Bias-An Empirical Analysis with Chinese Sample

Based on the theoretical analysis, with first-hand data collection and using multiple regression models, this study explored the relationship between financial literacy, interpersonal influence and self-evaluation bias and figured out interactive effect of financial literacy, interpersonal influence on self-evaluation bias. We draw on the following conclusions: (1) Three financial literacy factors (sophisticated financial literacy, basic financial literacy and numeracy) entered into the regression equation on self-evaluation bias, with a predictive power of 14.8%. (2) The interaction term of financial literacy and coworkers/classmates’ influence can negatively predict self-evaluation bias. (3) The interaction term of financial literacy and family members’ influence can negatively predict self-evaluation bias

Study of Risk Preference, Investment Experience and Interpersonal Influence-An Empirical Analysis with Chinese Sample

This study internalized investment experience on the basis of study of the influence of interpersonal influence on risk preference, and studied the relationship among the interpersonal influence, investment experience and risk preference. Based on the theoretical analysis, with first-hand data collection and using multiple regression models, the paper studied the intermediate effect of investment experience in the relationship between the interpersonal influence and risk preference. We draw on the following conclusions: ① Investment experience plays a significant intermediate role between friends’ influence and risk preference, and the mediating effect size is about 57.61%. ② Investment experience is a full intermediate variable between family’s influence and risk preference. ③ Investment experience plays a significant intermediate role between coworkers/classmates’ influence and risk preference, and the mediating effect size is about 55.09%

Stochastic Optimization of Coupled Power Distribution-Urban Transportation Network Operations with Autonomous Mobility on Demand Systems

Autonomous mobility on demand systems (AMoDS) will significantly affect the operation of coupled power distribution-urban transportation networks (PTNs) by the optimal dispatch of electric vehicles (EVs). This paper proposes an uncertainty method to analyze the operational states of PTNs with AMoDS. First, a PTN operation framework is designed considering the controllable EVs dispatched by AMoDS as well as the uncontrollable driving behaviors of other vehicle users. Then, a bi-level power-traffic flow (PTF) model is proposed to characterize the interaction of power distribution networks (PDNs) and urban transportation networks (UTNs). In the upper level, a social optimum model is established to minimize the operating cost of PDNs and UTNs embedded with controllable EVs. In the lower level, a stochastic user equilibrium (SUE) model is established to minimize the operating cost of uncontrollable EVs and gasoline vehicles (GVs) in UTNs. Finally, a probabilistic PTF analysis method is developed to evaluate PTN operations under environmental and human uncertainties. A regional sensitivity analysis method is proposed to identify the critical uncertainties and quantify the impacts of their distribution ranges on PTN operations. The effectiveness of the proposed method is verified by the PTN consisting of a 21-bus PDN and a 20-node UTN.Comment: 10 pages, 13 figure