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Abstract:The primary objective of this study was to explore the factors that influence metro-bikeshare ridership from a spatial perspective. First, a reproducible method of identifying metro-bikeshare transfer trips was derived using two types of smart-card data (metro and bikeshare). Next, a geographically weighted Poisson regression (GWPR) model was established to explore the relationships between metro-bikeshare transfer volume and several types of independent variables, including sociodemographic, travel-related, and built-environment variables. Moran's I statistic was applied to examine the spatial autocorrelation of each explanatory variable. The modeling and spatial visualization results show that riding distance is negatively correlated with metro-bikeshare transfer demand, and the coefficient values are generally lower at the edge of the city, especially in underdeveloped areas. Moreover, the density of bus, bikeshare, and other metro stations within 2 km of a metro station has different impacts on metro-bikeshare transfer volume. Travelers whose origin or destination is entertainment related tend to choose bikeshare as a feeder mode to metro if this trip mode is available to them. These results improve our understanding of metro-bikeshare transfer spatial patterns, and several suggestions are provided for improving the integration between metro and bikeshare.

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The technique to forecast available parking spaces (APSs) is the foundation theory of parking guidance information systems (PGISs). This study utilises data collected on parking availability at several off-street parking garages in Newcastle upon Tyne, England, to investigate the changing characteristics of APS. Using these baseline data the research reported here aims to build up a short-term APS forecasting model and applies the wavelet neural network (WNN) method to the PGIS problem. After selecting optimal preferences, including training set size, delay time and embedding dimension, the APS short-term forecasting model based on WNN is built and tested using the real-world dataset. By experimental tests conducted using the same dataset, WNN's prediction performance is compared with the largest Lyapunov exponents (LEs) method in the aspects of accuracy, efficiency and robustness. It is found that WNN prevails through a more efficient structure and employs, barely half of the computational cost compared to the largest LEs method, which could be significant if applied to real-time operation. Moreover, WNN enjoys a more accurate performance, for its prediction average mean square error (MSE) is 6.4 ± 3.1 (in a parking building with 492 parking lots) for workdays and 8.5 ± 6.2 for weekends, compared to the MSE of largest LEs method, 18.7 and 29.0, respectively.

The emergence of the level 3 automated vehicles (L3 AVs) can enable drivers to be completely disengaged from driving and safely perform other non-driving related tasks, but sometimes their takeover of control of the vehicle is required. The takeover of control is an important human–machine interaction in L3 AVs. However, little research has focused on investigating the effect of gender on takeover performance. In order to fill this research gap, a driving simulator study with 76 drivers (33 females and 43 males) was conducted. The participants took over control from L3 AVs, and the timing and quality of takeover were measured. The results show that although there was no significant difference in most of the measurements adopted to quantify takeover performance between female and male. Gender did affect takeover performance slightly, with women exhibited slightly better performance than men. Compared to men, women exhibited a smaller percentage of hasty takeovers and slightly faster reaction times as well as slightly more stable operation of the steering wheel. The findings highlight that it is important for both genders to recognise they can use and interact with L3 AVs well, and more hands-on experience and teaching sessions could be provided to deepen their understanding of L3 AVs. The design of the car interiors of L3 AVs should also take into account gender differences in the preferences of users for different non-driving related tasks.