Stations are being converted into various living spaces that can be used for public transportation, work, commerce, and leisure. To satisfy the various requirements and expectations for functional extension, it is necessary to investigate and understand the phenomena caused by users. A methodology to cluster the characteristics of pedestrian space of a railway station through the pedestrian trajectory data collected from an actual operating station is proposed in this paper. Then the spatial usability of the movement and stay of pedestrians were defined through the results of the clustering. The procedure to cluster the indoor space characteristics of an urban railway station in this study consists of four steps: data collection, feature vector extraction, K-means clustering, and cluster characteristics analysis. A case study was conducted for the Samseong station. The results of the proposed spatial clustering analysis showed that there are several types of spaces depending on the space occupancy characteristics of pedestrians. The proposed methodology could be applied to indoor space diagnosis from the perspective of station monitoring and management. In addition, the station operator could respond flexibly to unexpected events by monitoring the indoor spaces according to whether the flow is normal or suggestive of an emergency.
Recently, there have been emerging demands for new transportation modes, such as personal rapid transit (PRT), to improve the connectivity of first and last mile travel. Advancement of ICT and growing concerns over environmental issues reinforce such demands through which specific transportation modes can satisfy the need of each individual for short-distance trips. Although PRT has received particular attention for short-distance trips, it is true that recent approaches have been developed to analyze the behavior of travelers for mid- to long-distance trips that are not relevant for short-distance trips. This study proposed a suitable approach using logistic regression models that could assist the understanding of features which determine mode choice in a short-distance trip. The mode choice for PRT in short-distance trips in this study was based on the data from the survey. After considering various factors, it was apparent that the purpose of the trip together with weather conditions impacted significantly on travelers’ mode choices to PRT in short-distance trips. Additionally, it is expected that this study will play an important initial role in analyzing emerging transportation modes that can more easily respond to new demands for short-distance trips.
An approach is presented to determine the most likely tour distributions and model behavior for investigating drayage truck movements in a coastal region. This was done by implementing a revised form of entropy maximization based on truck tours to model and better understand drayage truck tour behavior at the San Pedro Bay Ports (SPBPs) complex in Southern California. The drayage trucks at the SPBPs have features that are distinct from other commercial trucks. The tour-based entropy maximization model proposed in this paper provides an opportunity to incorporate periodically updated GPS data collected in Southern California into a large-scale tour-based model. With the dataset, four models were estimated by cargo movement: (1) year-based, (2) low period, (3) medium period, and (4) high period models. The findings were consistent with the tour patterns varying by season and by cargo movement. Furthermore, the medium period, which represented relatively steady cargo movement, indicated a better MAPE (mean absolute percent error) than did other models. This proposed approach provides a significant advantage in that the most recent touring information obtained from advanced technologies could be directly applied to the tour-based model and subsequently used to assess various strategies.
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