Investigating Potential Transit Ridership by Fusing Smartcard and Global System for Mobile Communications Data

Abstract

The public transport industry faces challenges in catering to the variety of mobility patterns and corresponding needs and preferences of passengers. Travel habit surveys provide information on overall travel demand as well as its spatial variation. However, that information often does not include information on temporal variations. By applying data fusion to smartcard and Global System for Mobile Communications (GSM) data, researchers were able to examine spatial and temporal patterns of public transport usage versus overall travel demand. The analysis was performed by contrasting different spatial and temporal levels of smartcard and GSM data. The methodology was applied to a case study in Rotterdam, Netherlands, to analyze whether the current service span is adequate. The results suggested that there is potential demand for extending public transport service on both ends. In the early mornings, right before transit operations are resumed, an hourly increase in visitor occupancy of 33% to 88% was observed in several zones, showing potential demand for additional public transport services. The proposed data fusion method was shown to be valuable in supporting tactical transit planning and decision making and can easily be applied to other origin-destination transport data.

Transit OD matrix estimation using smartcard data: Recent developments and future research challenges

“…Other heterogeneous big data such as Global System for Mobile Communications (GSM) data (White and Wells, 2002), high-frequency mobile phone location data (Calabrese et al, 2011), and social media data, e.g., Twitter data (Lee et al, 2015) are rarely explored for the validation purpose of tOD estimation (Liu and Zhou, 2019). There are studies which have loosely coupled smartcard and mobile phone data for the tOD analysis, such as Holleczek et al (2014), andRegt et al (2017) but their work doesn't directly build on the tOD estimation or validation problem.…”

“…The traditional datasets include sampled household travel surveys (Stopher and Greaves, 2007) and vehicle counts from loops or manual (Zhou et al, 2003). Advanced datasets include Bluetooth data (Behara et al, 2020), vehicle number plate recognition system (Rao et al, 2018), AVL (Guozhen et al, 2011), APC (Cats et al, 2019), mobile phone data (Regt et al, 2017), smartphone location data (Nikolic and Bierlaire, 2017), and social media data (Rashidi et al, 2017). Although the above-stated systems have a higher initial cost, it has low cost throughout their life.…”

Reliable frequency determination: Incorporating information on service uncertainty when setting dispatching headways

“…The service frequencies can be updated by modifying the dispatching headways of the respective bus services since the frequency of one bus line is inversely proportional to its dispatching headway. Bus line frequencies can be adjusted to the passenger travel needs subject to resource capacities and operational cost limitations by using information from passengers (i.e., smartcard logs (Pelletier et al, 2011;Ma et al, 2013;Munizaga and Palma, 2012;Luo et al, 2017), smartphones (Alexander et al, 2015;Gkiotsalitis and Stathopoulos, 2015;Calabrese et al, 2013;de Regt et al, 2017) and operating vehicles (Cortés et al, 2011).…”