Transit route occupancy load factor and passenger average travel time for quality of service assessment

This paper investigates quality of service (QoS) and resource productivity implications of transit route passenger loading and travel time. It highlights the value of occupancy load factor as a direct passenger comfort QoS measure. Automatic Fare Collection data for a premium radial bus route in Brisbane, Australia, is used to investigate time series correlation between occupancy load factor and passenger average travel time. Correlation is strong across the entire span of service in both directions. Passengers tend to be making longer, peak direction commuter trips under significantly less comfortable conditions than off-peak. The Transit Capacity and Quality of Service Manual uses segment based load factor as a measure of onboard loading comfort QoS. This paper provides additional insight into QoS by relating the two route based dimensions of occupancy load factor and passenger average travel time together in a two dimensional format, both from the passenger’s and operator’s perspectives. Future research will apply Value of Time to QoS measurement, reflecting perceived passenger comfort through crowding and average time spent onboard. This would also assist in transit service quality econometric modeling. The methodology can be readily applied in a practical setting where AFC data for fixed scheduled routes is available. The study outcomes also provide valuable research and development directions.

Transit route occupancy load factor and passenger average travel time for quality of service assessment

This presentation investigates quality of service (QoS) and resource productivity implications of transit route passenger loading and travel time. It highlights the value of occupancy load factor as a direct passenger comfort QoS measure. Automatic Fare Collection data for a premium radial bus route in Brisbane, Australia, is used to investigate time series correlation between occupancy load factor and passenger average travel time. Correlation is strong across the entire span of service in both directions. Passengers tend to be making longer, peak direction commuter trips under significantly less comfortable conditions than off-peak. The Transit Capacity and Quality of Service Manual uses segment based load factor as a measure of onboard loading comfort QoS. This paper provides additional insight into QoS by relating the two route based dimensions of occupancy load factor and passenger average travel time together in a two dimensional format, both from the passenger’s and operator’s perspectives. Future research will apply Value of Time to QoS measurement, reflecting perceived passenger comfort through crowding and average time spent onboard. This would also assist in transit service quality econometric modeling. The methodology can be readily applied in a practical setting where AFC data for fixed scheduled routes is available. The study outcomes also provide valuable research and development directions.

Stochastic analysis of transit route segments' passenger load variation for capacity and quality of service assessment

This paper investigates stochastic analysis of transit segment hourly passenger load factor variation for transit capacity and quality of service (QoS) analysis using Automatic Fare Collection data for a premium radial bus route in Brisbane, Australia. It compares stochastic analysis to traditional peak hour factor (PHF) analysis to gain further insight into variability of transit route segments’ passenger loading during a study hour. It demonstrates that hourly design load factor is a useful method of modeling a route segment’s capacity and QoS time history across the study weekday. This analysis method is readily adaptable to different passenger load standards by adjusting design percentile, reflecting either a more relaxed or more stringent condition. This paper also considers hourly coefficient of variation of load factor as a capacity and QoS assessment measure, in particular through its relationships with hourly average and design load factors. Smaller value reflects uniform passenger loading, which is generally indicative of well dispersed passenger boarding demands and good schedule maintenance. Conversely, higher value may be indicative of pulsed or uneven passenger boarding demands, poor schedule maintenance, and/or bus bunching. An assessment table based on hourly coefficient of variation of load factor is developed and applied to this case study. Inferences are drawn for a selection of study hours across the weekday studied.

Stochastic analysis of transit route segments' passenger load variation for capacity & quality of service assessment

This study uses weekday Automatic Fare Collection (AFC) data on a premium bus line in Brisbane, Australia •Stochastic analysis is compared to peak hour factor (PHF) analysis for insight into passenger loading variability •Hourly design load factor (e.g. 88th percentile) is found to be a useful method of modeling a segment’s passenger demand time-history across a study weekday, for capacity and QoS assessment •Hourly coefficient of variation of load factor is found to be a useful QoS and operational assessment measure, particularly through its relationship with hourly average load factor, and with design load factor •An assessment table based on hourly coefficient of variation of load factor is developed from the case study

Assessment of the molecular structure of an intermediate member of the triplite-zwieselite mineral series: A raman and infrared study

The mineral series triplite-zwieselite with theoretical formula (Mn2+)2(PO4)(F)-(Fe2+)2(PO4)(F) from the El Criolo granitic pegmatite, located in the Eastern Pampean Ranges of Córdoba Province, was studied using electron microprobe, thermogravimetry, and Raman and infrared spectroscopy. The analysis of the mineral provided a formula of (Fe1.00, Mn0.85, Ca0.08, Mg0.06)∑2.00(PO4)1.00(F0.80, OH0.20)∑1.00. An intense Raman band at 981 cm−1 with a shoulder at 977 cm−1 is assigned to the ν1 symmetric stretching mode. The observation of two bands for the phosphate symmetric stretching mode offers support for the concept that the phosphate units in the structure of triplite-zwieselite are not equivalent. Low-intensity Raman bands at 1012, 1036, 1071, 1087, and 1127 cm−1 are assigned to the ν3 antisymmetric stretching modes. A set of Raman bands at 572, 604, 639, and 684 cm−1 are attributed to the ν4 out-of-plane bending modes. A single intense Raman band is found at 3508 cm−1 and is assigned to the stretching vibration of hydroxyl units. Infrared bands are observed at 3018, 3125, and 3358 cm−1 and are attributed to water stretching vibrations. Supplemental materials are available for this article. Go to the publisher's online edition of Spectroscopy Letters to view the supplemental file.