Inferring passenger types from commuter eigentravel matrices

Abstract

A sufficient knowledge of the demographics of a commuting public is essential in formulating and implementing more targeted transportation policies, as commuters exhibit different ways of traveling-including time in the day of travel, the duration of travel, and even the choice of transport mode. With the advent of the Automated Fare Collection system (AFC), probing the travel patterns of commuters has become less invasive and more accessible. Consequently, numerous transport studies related to human mobility have shown that these observed patterns allow one to pair individuals with locations and/or activities at certain times of the day. However, classifying commuters using their travel signatures is yet to be thoroughly examined.Here, we contribute to the literature by demonstrating a procedure to characterize passenger types (Adult, Child/Student, and Senior Citizen) based on their three-month travel patterns taken from a smart fare card system. We first establish a method to construct distinct commuter matrices, which we refer to as eigentravel matrices, that capture the characteristic travel routines of individuals. From the eigentravel matrices, we build classification models that predict the type of passengers traveling. Among the models explored, the gradient boosting method (GBM) gives the best prediction accuracy at 76%, which is 84% better than the minimum model accuracy (41%) required vis-à-vis the proportional chance criterion. In addition, we find that travel features generated during weekdays have greater predictive power than those on weekends. This work should not only be useful for transport planners, but for market researchers as well. With the awareness of which commuter types are traveling, ads, service announcements, and surveys, among others, can be made more targeted spatiotemporally. Finally, our framework should be effective in creating synthetic populations for use in real-world simulations that involve a metropolitan's public transport system.

Identifying Public Transit Commuters Based on Both the Smartcard Data and Survey Data: A Case Study in Xiamen, China

“…The heterogeneity of public transit riders was analyzed by Langlois et al [22] through four-week smartcard data, and 11 travel patterns were generated. Legara and Monterola [16] developed a new classification method with promising accuracy by using the concept of eigentravel matrices which captured a commuter's characteristic travel routine, while, in operational-level studies, precise performance indicators such as schedule adherence, the number of transfers, and vehicle-kilometers on a public transit network were discussed [1,10,13]. Among them, the study of origindestination (OD) and interchange inference was a hot topic and received much research attention.…”

“…Fortunately, Automatic Fare Collection (AFC) systems have been widely implemented in China as a more efficient way of 2 Journal of Advanced Transportation managing fare over the manual collection method [12]. At the same time, smartcard data can provide more abundant and higher quality travel data with less cost, through which travel patterns could be analyzed based on precise observations of individuals' smartcard usage in a time period [10,[13][14][15][16][17][18].…”

Smart transportation and analytics

“…Nuzzolo and Comi 2016), and inference of transportation-related activities using smart-card data (e.g. Legara and Monterola 2017;Ma et al 2013;Medina 2016;Pelletier et al 2011;Yang et al 2017) to name a few. This special issue of Transportmetrica B: Transport Dynamics aims to address four key aspects of the timely topic of 'smart transportation and analytics' and present the recent advance of research in the area.…”

Crowd intelligence for sustainable futuristic intelligent transportation system: a review

“…The level of improvement of autonomy is important for transportation services. The artificial intelligence and machine learning techniques when combined with crowdsourced data produce services such as real‐time traffic monitoring [12, 13], traffic prediction, travel time prediction [14, 15], travel activity tracking [16, 17]. These services combinedly form another level of the autonomy in the transportation system.…”