Effects from usage of pre-trip information and passenger scheduling strategies on waiting times in public transport: an empirical survey based on a dedicated smartphone application

Abstract

Waiting times are important indicators of the degree of travel time optimisation and other behavioural traits among public transport (PT) passengers. As previous studies have shown, the level and usage of pre-trip information regarding schedule or realtime departures are important factors that influence the potential to realise travel time savings by enabling PT passengers to optimise waiting times. Most empirical evidence regarding the revealed PT travel behaviour concerning information levels is based on manual interviews or traditional travel surveys, in which there is a risk that the actual context of where and when the choice of departure time was made is not taken into account. This paper reports the results of a travel survey based on a dedicated smartphone application applied in a field study in a Swedish mid-size urban and regional context. Context-aware notification prompting was used to allow respondents to state their use of pre-trip information as well as whether they had pre-planned their trip and how contingent planning aids were used for time optimisation. The implications on passenger waiting times of the use of information regarding departure times by passengers were emphasised during analyses of the resulting data, along with personal characteristics, in which auxiliary sources such as timetable data and Automatic Vehicle Location were utilised to determine ground truth trip trajectories and trip-contextual factors. The results indicate the significance of having access to pre-trip information, especially for long trips above one hour's duration, in order to pre-plan and thereby optimise waiting times. In addition, the use and source of pre-trip information differ among age and gender groups. Trip purpose and time of day to some extent determine waiting times and choice of trip optimisation strategy (arrival or departure time).

Shared public transport within a physical internet framework: Reviews, conceptualization and expected challenges under COVID-19 pandemic

“… Empirical Improved operations of the demand-responsive PT systems by increasing the service quality and their ridership related to joint-leisure-trips via timetable rescheduling. [ 7 ] (Berggren et al, 2019) Sweden Urban PT Commuters Scheduling strategies on waiting times of PT. Empirical Improved operations by indicating the significance of having access to pre-trip information, especially for long trips above one hour's duration, to pre-plan and thereby optimize waiting times.…”

“…Actually, city dwellers could get a set of common vehicles using PT such as buses, trams, and trains that operate at regular times, on fixed paths, with affordable costs. Over time, researches have based on planners' previous knowledge and local communities' requirements to investigate PT travel behavior [ 7 , 9 ], operations of demand-responsive [ 6 , 8 ], and scheduling the fleet size that connects metropolitan and residential areas [ 4 , 5 ]. PT has played, subsequently, a major role in relieving traffic congestion; though, there are few studies that focus on PT applications, emergent technologies, and potential future policy scenarios [ 3 ].…”

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“…In general, smartphone data could be an alternative to endogenous data sources (e.g., AVL and AFC) as it could enable the option of 'tracking and tracing' passengers ( [144]), especially in cases where there are no GPS data available (which is the case in many developing countries, e.g., because of missing infrastructure). Moreover, as for AFC, it could enable estimating the waiting times ( [145]). However, one of the issues, which appears with such an emerging data usage as in social media, is privacy ( [146]).…”

On understanding the impacts of shared public transportation on urban traffic and road safety using an agent-based simulation with heterogeneous fleets: a case study of Casablanca city

“…Actually, city dwellers could get a set of common vehicles using PT such as bus, tram, and train that operate at regular time on fixed paths with affordable costs. Over time, researches have based on planners' previous knowledge and local communities' requirements to investigate PT travel behavior (Berggren et al 2019;Ngoc et al 2017), operations of demand-responsive (Gkiotsalitis and Stathopoulos 2016;Huang et al 2020), and schedauling the fleet size that connects metropolitan and residential areas (Manser et al 2020;Liu et al 2019). PT has played, subsequently, a major role in relieving traffic congestion.…”