Analysis and Prediction of Regional Mobility Patterns of Bus Travellers Using Smart Card Data and Points of Interest Data

Understanding Regional Mobility Patterns Using Car-Hailing Order Data and Points of Interest Data

“…However, large-scale datasets and issues of sparsity and high-dimensionality may distort the results [15]. Moreover, car-hailing order data exhibit spatiotemporal dependence, and the temporal mobility profile is the result of all regional data properties combined [16]. Specifically, the majority of trips depart from residential areas during morning peak hours, whereas the central business district (CBD) is the main source of passengers during afternoon peak hours [17].…”

Combining ITS and optimization in public transportation planning: state of the art and future research paths

“…The suitability of the Density-Based Scanning Algorithm with Noise (DBSCAN) for mining temporal and spatial travel patterns has also been recognized in the respective literature [64,65], while modified versions of the algorithm have been devised to improve performance [66] and estimate residence and workplace locations of users [67]. As a general note, bi-level clustering procedures have been employed to treat the spatial and temporal nature of ITS data [68].…”

“…Furthermore, in most studies, clustering methods are mostly applied to isolate spatial and temporal clusters and in some cases, statistics are utilized to estimate spatio-temporal relationships. Qi et al [68] pointed out that spatial or temporal travel patterns are incomplete, as the dimensions of time and space cannot be treated separately and proposed a suitable, three-step methodology to discern regional mobility patterns using ITS data. Finally, the increased computational complexity of clustering methods renders them inapplicable for large-scale real-world transit networks.…”

“…In this context, Kieu et al [70] devised a spatial clustering algorithm to generate user clusters with similar spatial and behavioral features and highlighted its superior performance over existing methods. As a final remark, the growing research attention towards the application of unsupervised methods [68,69] and spatial analytics [67,70] highlights the potential contribution of these methods in the field of activity detection.…”

“…The latter integrate data mining methods, regression models and visualization techniques to assist in performance monitoring, predict and evaluate potential impact of different transit strategies and provide a more comprehensive understanding of network dynamics overall. Unsupervised machine learning tools can be employed to classify activities based on AFC data without any preconceptions on activity types [55], identify mobility patterns [68,69] or detect performance issues for which no prior knowledge exists [88]. Data-driven optimization models may be employed following automated data cleaning and processing and optimized design parameters can be readily available to planners, operators and administrative staff.…”