Short-Term Forecasting of Passenger Demand under On-Demand Ride Services: A Spatio-Temporal Deep Learning Approach

Short-term passenger demand forecasting is of great importance to the on-demand ride service platform, which can incentivize vacant cars moving from over-supply regions to over-demand regions. The spatial dependences, temporal dependences, and exogenous dependences need to be considered simultaneously, however, which makes short-term passenger demand forecasting challenging. We propose a novel deep learning (DL) approach, named the fusion convolutional long short-term memory network (FCL-Net), to address these three dependences within one end-to-end learning architecture. The model is stacked and fused by multiple convolutional long short-term memory (LSTM) layers, standard LSTM layers, and convolutional layers. The fusion of convolutional techniques and the LSTM network enables the proposed DL approach to better capture the spatio-temporal characteristics and correlations of explanatory variables. A tailored spatially aggregated random forest is employed to rank the importance of the explanatory variables. The ranking is then used for feature selection. The proposed DL approach is applied to the short-term forecasting of passenger demand under an on-demand ride service platform in Hangzhou, China. Experimental results, validated on real-world data provided by DiDi Chuxing, show that the FCL-Net achieves better predictive performance than traditional approaches including both classical time-series prediction models and neural network based algorithms (e.g., artificial neural network and LSTM). This paper is one of the first DL studies to forecast the short-term passenger demand of an on-demand ride service platform by examining the spatio-temporal correlations.Comment: 39 pages, 10 figure

A study on mutual information-based feature selection for text categorization

Feature selection plays an important role in text categorization. Automatic feature selection methods such as document frequency thresholding (DF), information gain (IG), mutual information (MI), and so on are commonly applied in text categorization. Many existing experiments show IG is one of the most effective methods, by contrast, MI has been demonstrated to have relatively poor performance. According to one existing MI method, the mutual information of a category c and a term t can be negative, which is in conflict with the definition of MI derived from information theory where it is always non-negative. We show that the form of MI used in TC is not derived correctly from information theory. There are two different MI based feature selection criteria which are referred to as MI in the TC literature. Actually, one of them should correctly be termed "pointwise mutual information" (PMI). In this paper, we clarify the terminological confusion surrounding the notion of "mutual information" in TC, and detail an MI method derived correctly from information theory. Experiments with the Reuters-21578 collection and OHSUMED collection show that the corrected MI method’s performance is similar to that of IG, and it is considerably better than PMI

A NOVEL FRAMEWORK BASED ON THE IMPROVED JOB DEMANDS-RESOURCES (JD-R) MODEL TO UNDERSTAND THE IMPACT OF JOB CHARACTERISTICS ON JOB BURNOUT FROM THE VIEW OF EMOTION REGULATION THEORY

Background: It has been suggested that individual job characteristics have a significant impact on job burnout, and the process is subject to the regulation of demographic variables. However, the influence path of job characteristics on job burnout is still a "black box". Subjects and methods: On the basis of a systematic literature review by employing Pub Med, Science Direct, Web of Science, Google Scholar, CNKI and Scopus for required information with the several keywords "Job burnout", "Emotion regulation", "Personality traits", and "Psychological stress", in this study, an improved mine rescue workers-oriented job demands-resources (JD-R) model was put forward. Then, a novel analysis framework, to explore the impact of job characteristics on job burnout from the view of emotion regulation theory, was proposed combining the personality trait theory. Results: This study argues that job burnout is influenced by job demands through expressive suppression and by job resources through cognitive reappraisal respectively. Further more, job demands and job resources have the opposite effects on job burnout through the "loss-path" caused by job pressure and the "gain-path" arised from job motivation, respectively. Extrovert personality traits can affect the way the individual processes the information of work environment and then how individual further adopts emotion regulation strategies, finally resulting in indirectly affecting the influence path of mine rescue workers\u27 job characteristics on job burnout. Conclusions: This present study can help managers to realize the importance of employees\u27 psychological stress and job burnout problems. The obtained conclusions provide significant decision-making references for managers in intervening job burnout, managing emotional stress and mental health of employees

Spatiotemporal assessment of PM_2.5-related economic losses from health impacts during 2014–2016 in China

Background: Particulate air pollution, especially PM2.5, is highly correlated with various adverse health impacts and, ultimately, economic losses for society, however, few studies have undertaken a spatiotemporal assessment of PM2.5-related economic losses from health impacts covering all of the main cities in China. Methods: PM2.5 concentration data were retrieved for 190 Chinese cities for the period 2014–2016. We used a log-linear exposure–response model and monetary valuation methods, such as value of a statistical life (VSL), amended human capital (AHC), and cost of illness to evaluate PM2.5-related economic losses from health impacts at the city level. In addition, Monte Carlo simulation was used to analyze uncertainty. Results: The average economic loss was 0.3% (AHC) to 1% (VSL) of the total gross domestic product (GDP) of 190 Chinese cities from 2014 to 2016. Overall, China experienced a downward trend in total economic losses over the three-year period, but the Beijing–Tianjin–Hebei, Shandong Peninsula, Yangtze River Delta, and Chengdu-Chongqing regions experienced greater annual economic losses. Conclusions: Exploration of spatiotemporal variations in PM2.5-related economic losses from long-term health impacts could provide new information for policymakers regarding priority areas for PM2.5 pollution prevention and control in China

一带一路倡议下的白中城市间经济合作模式——以重庆市白罗斯风情小镇项目为例

To explore the pattern of economic cooperation between cities of Belarus and China, this paper analyzed a joint economic cooperation project between cities of two countries - Chongqing Belarusian Style Town, and concluded that the economic cooperation between cities of the two countries serves the national development strategy of both sides and develops multi-field cooperation based on the sister-city partnership with economic focus