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.

Assessment of transit quality of service with occupancy load factor and passenger travel time measures

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.

How transit route passenger load and distance can together influence quality of service

This paper investigates quality of service and resource productivity implications of transit route passenger loading and travel distance. Weekday Automatic Fare Collection data for a premium radial bus route in Brisbane, Australia, is used to investigate correlation between load factor and distance factor. Relationships between boardings and transit work indicate that distance factor generally increases with load factor. Time series analysis is then presented by examining each direction on an hour by hour basis. Inbound correlation is medium to strong across the entire span of service and strong for daytime services up to 19:30, while outbound correlation is strong across the entire span. Passengers tend to be making longer distance, peak direction commuter trips under the least comfortable conditions under stretched peak schedules than off-peak. Therefore productivity gains may be possible by adjusting fleet utilization during off-peak times. Weekday profiles by direction are established for a composite load-distance factor. A threshold corresponding to standing passengers on the Maximum Load Segment reveals that on-board loading and travel distance combined are more severe during the morning inbound peak than evening outbound peak, although the sharpness of the former suggests that encouraging shoulder peak travel during the morning would be more effective than evening peak. Further research suggested includes: consideration of travel duration factor, relating noise within hour to Peak Hour Factor, profiling load-distance factor across a range of case studies, and relating load-distance factor threshold to line length.

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

Measuring route passenger load diversity for capacity and quality of service assessment

This paper develops theory that quantifies transit route passenger-relative load factor and distinguishes it from occupancy load factor. The ratio between these measures is defined as the load diversity coefficient, which as a single measure characterizes the diversity of passenger load factor between route segments according to the origin-destination profile. The relationship between load diversity coefficient and route coefficient of variation in occupancy load factor is quantified. Two tables are provided that enhance passenger capacity and quality of service (QoS) assessment regarding onboard passenger load. The first expresses the transit operator’s perspective of load diversity and the passengers’ perspective of load factor relative to the operator’s, across six service levels corresponding to ranges of coefficient of variation in occupancy load factor. The second interprets the relationships between passenger average travel time and each of passenger-relative load factor and occupancy load factor. The application of this methodology is illustrated using a case study of a premium radial bus route in Brisbane, Australia. The methodology can assist in benchmarking and decision making regarding route and schedule design. Future research will apply value of time to QoS measurement, reflecting perceived passenger comfort through crowding and average time spent aboard. This would also assist in transit service quality econometric modeling.

Measuring route passenger load diversity for capacity and quality of service assessment

This poster introduces Passenger Relative Load Factor for a route or individual bus service as a capacity and quality of service measure, distinguishing it from Occupancy Load Factor. It introduces Load Diversity Coefficient as the ratio of Passenger Relative Load Factor to Occupancy Load Factor, and relates Load Diversity Coefficient to Coefficient of Variation in Occupancy Load Factor. It qualifies the operator’s and passengers’ perspectives of load factor based on Coefficient of Variation in Occupancy Load Factor along a route. A case study using weekday Automatic Fare Collection (AFC) data on a premium bus line in Brisbane, Australia illustrates the methodology. The compendium paper also qualifies the operator’s and passengers’ perspectives of these load factors along with Passengers’ Average Travel Time for capacity and quality of service assessment.

Short-Term Load Forecast Of A Low Loadfactor Power System For Optimization Of Merit Order Dispatch Using Adaptive Learning Algorithm

Short term load forecasting is one of the key inputs to optimize the management of power system. Almost 60-65% of revenue expenditure of a distribution company is against power purchase. Cost of power depends on source of power. Hence any optimization strategy involves optimization in scheduling power from various sources. As the scheduling involves many technical and commercial considerations and constraints, the efficiency in scheduling depends on the accuracy of load forecast. Load forecasting is a topic much visited in research world and a number of papers using different techniques are already presented. The accuracy of forecast for the purpose of merit order dispatch decisions depends on the extent of the permissible variation in generation limits. For a system with low load factor, the peak and the off peak trough are prominent and the forecast should be able to identify these points to more accuracy rather than minimizing the error in the energy content. In this paper an attempt is made to apply Artificial Neural Network (ANN) with supervised learning based approach to make short term load forecasting for a power system with comparatively low load factor. Such power systems are usual in tropical areas with concentrated rainy season for a considerable period of the year

Investigation of the relationship between load and loss factors for a Brazilian electric utility

With the considerable increase of the losses in electric utilities of developing countries, such as Brazil, there is an investigation for loss calculation methodologies, considering both technical (inherent of the system) and non-technical (usually associated to the electricity theft) losses. In general, all distribution networks know the load factor, obtained by measuring parameters directly from the network. However, the loss factor, important for the energy loss cost calculation, can only be obtained in a laborious way. Consequently, several formulas have been developed for obtaining the loss factor. Generally, it is used the expression that relates both factors, through the use of a coefficient k. Last reviews introduce a range of factor k within 0.04 - 0.30. In this work, an analysis with real life load curves is presented, determining new values for the coefficient k in a Brazilian electric utility. © 2006 IEEE.

A statistical analysis of loss factor to determine the energy losses

Given that the total amount of losses in a distribution system is known, with a reliable methodology for the technical loss calculation, the non-technical losses can be obtained by subtraction. A usual method of calculation technical losses in the electric utilities uses two important factors: load factor and the loss factor. The load factor is usually obtained with energy and demand measurements, whereas, to compute the loss factor it is necessary the learning of demand and energy loss, which are not, in general, prone of direct measurements. In this work, a statistical analysis of this relationship using the curves of a sampling of consumers in a specific company is presented. These curves will be summarized in different bands of coefficient k. Then, it will be possible determine where each group of consumer has its major concentration of points. ©2008 IEEE.

Analysis of efficiency and rationality in a sugar-alcohol mill using hyperboloid a-load and potency

Brazil is the world's largest producer of sugar cane and which in the state of São Paulo concentrate the greatest amount of sugar cane field of the country. The sugar-alcohol sector has the capacity to produce sufficient thermal and electrical energy to be used in their process of production and commercialize of surplus in electricity distribution network. Therefore it is necessary to evaluate the energy efficiency and rationality within the mill. Accordingly this research proposed analyze the sugar-alcohol mill's sectors globally and individually, located in the west center of the São Paulo state, using the valuation methodology employed by the Agência Nacional de Energia Elétrica (ANEEL) in the industries that do not have systems of cogeneration. In this analysis, the hyperboloids of load and potency were applied based on the indexes of potency factor and load factor that allow estimate the efficiency and rationality. © 2013 IEEE.

Investigation of the Impact of Dual-Lane Axle Spacing on Lateral Load Distribution, 2016

The spacing of adjacent wheel lines of dual-lane loads induces different lateral live load distributions on bridges, which cannot be determined using the current American Association of State Highway and Transportation Officials (AASHTO) Load and Resistance Factor Design (LRFD) or Load Factor Design (LFD) equations for vehicles with standard axle configurations. Current Iowa law requires dual-lane loads to meet a five-foot requirement, the adequacy of which needs to be verified. To improve the state policy and AASHTO code specifications, it is necessary to understand the actual effects of wheel-line spacing on lateral load distribution. The main objective of this research was to investigate the impact of the wheel-line spacing of dual-lane loads on the lateral load distribution on bridges. To achieve this objective, a numerical evaluation using two-dimensional linear elastic finite element (FE) models was performed. For simulation purposes, 20 prestressed-concrete bridges, 20 steel bridges, and 20 slab bridges were randomly sampled from the Iowa bridge database. Based on the FE results, the load distribution factors (LDFs) of the concrete and steel bridges and the equivalent lengths of the slab bridges were derived. To investigate the variations of LDFs, a total of 22 types of single-axle four-wheel-line dual-lane loads were taken into account with configurations consisting of combinations of various interior and exterior wheel-line spacing. The corresponding moment and shear LDFs and equivalent widths were also derived using the AASHTO equations and the adequacy of the Iowa DOT five-foot requirement was evaluated. Finally, the axle weight limits per lane for different dual-lane load types were further calculated and recommended to complement the current Iowa Department of Transportation (DOT) policy and AASHTO code specifications.