Deep learning approaches are widely employed for forecasting short-term travel demand to respond to real-time demand. Although it is critical for demand forecasting to be evenly distributed in the spatial and temporal views to support real-time mobility service operations, in related studies, the predictive performance of models has been evaluated only in terms of aggregated errors. Therefore, the present study was conducted to investigate the distribution of errors to explore spatiotemporal correlations. Six deep learning models with the same architecture, except for the base module, consisting of three stacked layers, were constructed. These models were used to forecast demands for a station-based bike-sharing service in Seoul, South Korea. To attain our goals, global and local Moran’s I of the errors was introduced to evaluate the spatial and temporal performances of the deep learning approaches. The results showed that the model with convolutional long short-term memory layers, which are effective at predicting spatiotemporal data, outperformed the other models in terms of aggregated performance. However, the global Moran’s I of the errors in the model reflects spatial dependency over the regions. This suggests that the best predictive performance of the model does not necessarily imply that it performs well in demand forecasting in all regions. Furthermore, cluster and outlier analyses of the errors indicated that excessive or insufficient predictions were clustered or dispersed throughout the regions. These results can be used to enhance the model by introducing the spatial correlation index into the loss function or by incorporating additional features for handling spatial correlations.
Automated driving technologies have advanced remarkably and are expected to be a part of our lives soon. Because automated driving technology does not require a driver, a significant change in future mobility services is expected. Automated driving technology is closely related to the development of public transit services as it can significantly reduce driver labor costs and provide a more comfortable in-vehicle environment. In particular, the preference for automated mobility-on-demand services that can respond in real time to the dynamic demand through automated driving technology is growing. Previous studies have compared passengers’ preferences for automated mobility-on-demand services and other transportation modes and proposed a way to enable more passengers to use automated mobility-on-demand services. However, as the number of pilot operations increases, future research will focus on ways to improve competitiveness among automated mobility-on-demand services. This study conducts a passenger preference survey based on the characteristics of automated mobility-on-demand services. In particular, changes in the in-vehicle environment and seat selection system, which differ from existing mobility-on-demand services due to automated driving technology, are investigated. The latent class modeling approach is used to classify passengers based on stated preference data collected from the survey. The estimation results show that vehicle type and seat choice system have a significant impact on passengers’ preference for automated mobility-on-demand services. In addition, considering that a high percentage of passengers do not prefer to improve autonomy in seat reservation and the in-vehicle environment, this study suggests that cost-consuming service improvement strategies are not always appropriate.
As individuals have different thoughts about automated driving (AD) vehicles, the gap between their manual driving (MD) styles and their expected AD styles would affect their willingness to ride AD vehicles. The objective of this study is to investigate the effects of personal driving styles on AD preference and the variations of these effects by travel distance. To identify latent driving styles and their effects simultaneously, we developed a hybrid choice model using sequential stated-choice data from an online survey administered to 511 respondents in South Korea in 2019. The latent driving styles were classified as impatient, over-nervous, and cautious driving styles. The results significantly show that individuals of each style prefer AD vehicles in different situations. Impatient drivers, who are concerned about travel delays, rarely worry about the driving mode if it drives them faster. Over-nervous drivers, who lack confidence in their driving skills, tend to trust AD technology, regardless of the travel distance. Cautious drivers, who are mindful of their surroundings, prefer AD when traveling relatively short distances. The elasticities of the travel time and cost show that the driving styles have impacts on the AD preference. Moreover, the results of value of time (VoT) for AD have been estimated to differ by travel distance. The results of this study have identified the effects of personal driving styles on AD preference, providing valuable insights that can be used to improve AD vehicle related policies and marketing strategies (such as implementing price discrimination by travel distance and lowering the overall price of AD vehicles), which may increase the market share of AD vehicles.
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