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Smartphones and relevant mobile service have greatly influenced people’s daily lives, and intense smartphone users can be commonly seen at transit stops/stations, playing with their smartphones while waiting for buses/trains. This research presents a holistic perspective for analyzing the effects of smartphone usage on transit passengers, which also considers the effects at a psychological level. Such effects may be manifested as the reduction of perceived waiting time at stops/stations against the negative emotionality induced by long waiting, such as boredom and tediousness, so as to result in improved travel experience. An on-site survey is designed and implemented over the bus system in Taipei, Taiwan, to collect the revealed responses of bus passengers in regard to waiting time perception and smartphone usage, particularly for travel-irrelevant mobile service. The survey data are modeled and analyzed in both numerical and verbal representation of perceived waiting time by using a multiple linear regression model and a cumulative proportional odds logistic model, respectively. A finite mixture model is further employed to investigate the potential heterogeneity of waiting time perception related to using smartphones for travel-irrelevant mobile service. The analysis results highlight that travel-irrelevant smartphone usage may lead to the reduction of perceived waiting, and the effect can be more significant for young passengers and those without receiving bus arrival information, especially when waiting time is prolonged. Such findings can contribute to the comprehensive consideration of passenger behavior in transit system planning and associated information/service provision strategies.

A novel tool wear predictive model was developed based on the current signals in this study. The system adapts to different part geometry with accurate prediction of the tool wear during the operation. The current sensor was utilized presenting a practical and better choice for tool wear monitoring which is inexpensive and no need to be attached to the working table or spindle. To avoid interruptions during the machining process, the tool wear was only measured at the end of the operation. The Long Short-Term Memory model was used to develop the tool wear prediction system. The tool wear prediction results indicate 23.92% and 36.41% average error for all the testing samples after 1/3 of the operations for profiling and straight turning, respectively. When the tool wear prediction was carried out after 2/3 of the operations, excellent results are observed with 6.15% error for profiling and 9.44% error for straight turning. The prediction results at the end of the operation shows 0.18% and 0.68% error for profiling and straight turning. The performance of the model using the current sensor shows that the model can predict the tool wear with less than 10% error after 2/3 of the turning operation without interfering with the turning process.

Facing the rapidly growing popularity of bike-sharing systems worldwide, this study explores the trip characteristics of using public bikes at the level of zonal Origin-Destination (O-D) pairs, particularly with respect to the effect of pricing scheme change. Based on the transaction records of Youbike, a bike-sharing system in Taipei, Taiwan, which contain data before and after an increase of the rental fee (as the cancellation of a free-usage period for the first 30-minute), the associated demand-side responses are studied in the context of multivariate analysis. Two regression models are developed thereupon to investigate the effect of the change on the O-D demand associated various land-use patterns. A multiple linear regression model is developed for direct before-after analysis on the variation of zonal O-D demand. A finite mixture model is further constructed, which identifies three usage groups with different levels of sensitivity toward the price change. Both models suggest that the pricing scheme change have greater impact on short-distance trips and O-D pairs where alternative transit services are provided; these affected trips can be generally related to frequent users. Such research findings enable better understanding of the usage pattern of bike-sharing systems, which can benefit the associated planning and operation. They also imply the trade-off between the perspective of encouraging greener mobility and other managerial principles regarding public transportation and government subsidy. Keywords: Bike sharing system, Pricing scheme, Land use pattern, User-pays principle, Before-after analysis