Bus Load Inference and Crowding Performance Evaluation through Disaggregate Analysis of Fare Transaction, Vehicle Location, and Passenger Count Data

Abstract

Comfort is an important aspect of the transit passenger experience. Crowding can significantly decrease passenger comfort and disrupt service delivery, causing passenger travel times to increase and even resulting in passengers being unable to board an arriving vehicle. This research explores the use of automatically collected vehicle location data, fare transaction data, and passenger origin-destination inference to measure crowding on buses. Three model components are involved: scaling vehicle trip-level origin-destination transfer data, measuring crowding as perceived by passengers through performance measures defined for this purpose, and determining the sources of crowding. The latter is important to identify the most effective means of addressing crowding in each case. The models are tested on data from the Massachusetts Bay Transportation Authority, and examples of graphical applications already being used by planners are presented.

TD 2673 - Tendências e desigualdades da mobilidade urbana no Brasil i: o uso do transporte coletivo e individual

“…Esses sistemas podem incluir, por exemplo, dados sobre custos de tarifas e nível de oferta dos serviços de transporte público organizados em formato General Transit Feed Specifi cation (GTFS), 19 o monitoramento desses serviços a partir de sensores de GPS, pesquisas de contagem volumétrica e pesquisas com usuários. O uso combinado de informações de bilhetagem eletrônica com dados de GPS, por exemplo, permite que sejam feitas de maneira semiautomática e em larga escala estimativas de lotação de veículos (Sánchez-Martínez et al, 2018;Arbex e Cunha, 2020).…”

The impact of information on transportation systems with strategic customers

“…Therefore, they continuously monitor the number of passengers in their facilities and this information can be passed to the customers. Several papers have begun to study the importance of providing passengers with crowding information using empirical approaches (see, e.g., Sánchez‐Martínez et al., 2018; Wang et al., 2021). They note that if real‐time crowding information is provided to the passengers, then it will have additional benefits in improving the service level.…”