The potential of e-ticketing for public transport planning: the Piedmont region case study

Abstract

In the Piedmont region (Italy) the electronic ticketing system called BIP, is currently active across much of its territory, and thedata collected in the Province of Cuneo since the full activation of the system (2014) provide today a sound source ofinformation. Two different travel documents are available, travel passes and pay-per-use, with different validation rules: check-inonly for travel passes and check-in and check-out for pay-per-use. Data produced by this electronic ticketing system employingsmart cards allow to perform a detailed analysis of each user’s behaviour, and calculate time and space distributions of eachpassenger trip. In detail, data originating from smart card transactions allow to trace back the trip chains, establish journey originsand destinations, and produce a “travel diary” for each passenger. Based on this data, performance indicators (i.e. load factor) aswell as user mobility patterns and origin-destination matrices can be calculated in an automated and reliable way. This articlepresents a methodology for assessing the quality of the data collected when information about boarding and alighting stops isavailable from the (on board) validation system. It also presents an algorithm to assign a destination for each trip where only theboarding information is available. In the case study of the Province of Cuneo, it was found that 91% of the pay-per-use journeydata are reliable and can be used for further analysis, whereas with the use of the proposed algorithm it was possible to estimatethe destinations for 82% of the travel pass trips.DOI: http://dx.doi.org/10.4995/CIT2016.2016.1999

Exploring the potential of using real-time traveler data in public transport disturbance management

“…Oyster card in London and Access card in Stockholm). The smart card detection may occur at PTS entrance and exit (Lathia and Capra 2011;Zhao et al 2017;van der Hurk et al 2014), at vehicle entrance or on platform, waiting to board a certain vehicle (Wang et al 2011;Munizaga and Palma 2012;Ma et al 2013;Arnone et al 2016), at vehicle entrance and exit (Ma et al 2013;van Oort et al 2015;Arnone et al 2016), or when onboard via a contactless smart card (Zeller 2013) (cf. Table 2).…”

“…Normally, the date, time and card numbers are available and recorded (Pelletier et al 2011). In some cases, the boarding location is also accessible, either in the form of precise information, such as geographical position or station ID (Lathia and Capra 2011;Wang et al 2011;Munizaga and Palma 2012;Ma et al 2013;van Oort et al 2015;Arnone et al 2016), or in the form of estimated information, such as line number or vehicle ID in combination with time (Ma et al 2013). For both the estimated and precise boarding location information, some data processing and data connection activities might be needed before the traveler location data can be identified.…”

“…In this paper, we are only interested in systems that include boarding location in some form, since this information is needed to estimate the number of passengers in a vehicle, which represents the lowest level of information in Table 1. Based on the boarding location, the entire route can be predicted using one of the trip-chaining methods available in literature (Zhao et al 2007;Munizaga and Palma 2012;Arnone et al 2016). In general, these methods are designed for contact smart card systems that only require the traveler to check in but not out of a journey.…”

“…However, the collected information can also be used to estimate some of the other information types. For instance, there are several studies describing how to estimate the alighting stop based on historical data from detection at vehicle entrance or platform only (Wang et al 2011;Munizaga and Palma 2012;Ma et al 2013;Arnone et al 2016). This means that the information types may either be collected in real-time, or be estimated (predicted) based on non-real-time data, possibly in combination with other types of real-time data, see Table 2.…”

“…For instance, some studies have investigated how data retrieved from smart card systems designed for automated fare collection, can be utilized (in addition to fare collection) for traffic line planning, terminus planning, studying the transport network usage etc. (van Oort et al 2015;Arnone et al 2016;Fujiyama and Cao 2016). Furthermore, different types of sensors can also be used to collect information about travelers.…”