“…For instance, the authors proved that a bus with 4 doors get a dwell time 17% lower that a bus with 3 doors. Similarly, Fernandez et al (2014) studied the passenger saturation flow (S) through public transport doors in the Human Dynamics Laboratory (LDH) at Universidad de los Andes. The authors showed that S could reach a range between 0.9 and 2.0 pass/s m, depending on the width of the door, the height of the step, and the passenger density.…”
“…For instance, the authors proved that a bus with 4 doors get a dwell time 17% lower that a bus with 3 doors. Similarly, Fernandez et al (2014) studied the passenger saturation flow (S) through public transport doors in the Human Dynamics Laboratory (LDH) at Universidad de los Andes. The authors showed that S could reach a range between 0.9 and 2.0 pass/s m, depending on the width of the door, the height of the step, and the passenger density.…”
“…Different from the laboratory experiments done by Dameen et al [33], Fernandez et al [34], and Fujiyama et al [35], another behaviour observed at PAMELA was that the capacity of the train doors will not only depend on the door widths but also on the ratio R. If the value of R increases, then the number of lanes of flow for those passengers alighting will decrease. This was only presented in the crowded situations, due to the high number of passengers boarding and alighting (reaching more than 4 pass/m 2 ).…”
Section: Discussion and Future Workmentioning
confidence: 62%
“…In such a case, the alighting rate is 1.6 pass/s. Nonetheless, [34] only considered passenger alighting, and Fujiyama et al [35] stated that for a bidirectional flow (boarding and alighting) the station and vehicle should be designed with a vertical gap of 50 mm, reaching a maximum flow of 1.42 passengers per second. Moreover, Karekla and Tyler [36] proposed a model to predict the dwell time based on laboratory experiments, in which a small vertical gap can reduce the dwell time by 8%.…”
Section: Journal Of Advanced Transportationmentioning
The objective of this work was to study the effect of the ratio between passengers boarding and alighting on the passengers’ behaviour at metro stations. A mock-up of a vehicle and the relevant portion of the platform was built to run a series of simulation experiments at University College London’s Pedestrian Accessibility and Movement Environment Laboratory (PAMELA). Different scenarios were tested based on the next generation London Underground trains. The scenarios were classified according to different load conditions. Four types of behaviour are described. In most cases boarding is first, and passengers compete for space to enter the train. In the case of alighting, first passengers are faster than the rest of alighters due to the space available on the platform as boarding passengers give way to those who are getting off the train. In addition, alighters form lanes of flow depending on the number of passengers waiting to board the train on the platform. With respect to the train, if the density inside the train is higher than 4 passengers per square metre, then the flow at the doors starts to decrease. More experiments are needed to study the relationship between platform density and boarding and alighting time.
“…El tercer grupo, que corresponde al de mayor foco de investigación y que se enfoca en la configuración del bus y el esquema de operación, ha encontrado que el tiempo de parada tiene relación con el número, ancho y altura de las puertas, método de pago, presencia de escalones (Sun et al, 2014;Fernández, 2015;Tirachini, 2015), así como con los períodos de operación durante el día y la tipología vehicular (El-Geneidy y Vijayakumar, 2011).…”
Mediante un estudio comparativo por observación se analizan en un diseño factorial los tiempos de ascenso por pasajero en rutas urbanas de la ciudad de Bogotá. A través de mediciones en terreno de los tiempos de detención de los buses en distintos paraderos de la ciudad, dando prioridad a los casos donde suceden ascensos de usuarios exclusivamente, dos tratamientos fueron involucrados en el experimento, la tipología vehicular con tres niveles (bus pequeño, bus mediano y bus grande) y la ocupación del bus con dos niveles (pocos y muchos pasajeros de pie). Se encontró que los datos originales no siguen una distribución normal y por tanto la variable respuesta fue objeto de transformación para estabilizar la varianza. Sobre los datos transformados, mediante un análisis de varianza de dos factores, se probó que tanto las diferentes tipologías vehiculares que operan en el transporte público de la ciudad como la ocupación del bus, tienen efectos en el tiempo de ascenso por pasajero. Con comparaciones múltiples se encontró que los buses pequeños, para bajos y altos niveles de ocupación, presentan tiempos de ascenso por pasajero significativamente diferentes en comparación con los buses medianos y grandes, y que la ocupación del bus genera diferencias significativas en la variable respuesta sólo para el bus mediano y grande. El modelo resultante fue un diseño factorial sin interacción el cual produjo una explicación del 46% de variabilidad de los datos transformados
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