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

This paper investigates: - correlation between transit route passenger loading and travel distance - its implications on quality of service (QoS) and resource productivity. It uses Automatic Fare Collection (AFC) data across a weekday on a premium bus line in Brisbane, Australia. A composite load-distance factor is proposed as a new measure for profiling transit route on-board passenger comfort QoS. Understanding these measures and their correlation is important for planning, design, and operational activities.

Generalized aggregate Quality of Service computation for composite services

This article addresses the problem of estimating the Quality of Service (QoS) of a composite service given the QoS of the services participating in the composition. Previous solutions to this problem impose restrictions on the topology of the orchestration models, limiting their applicability to well-structured orchestration models for example. This article lifts these restrictions by proposing a method for aggregate QoS computation that deals with more general types of unstructured orchestration models. The applicability and scalability of the proposed method are validated using a collection of models from industrial practice.

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.

Effects of transit quality of service characteristics on daily bus ridership

This study investigates whether an Australian city’s suburbs having high transit Quality of Service (QoS) are associated with higher transit ridership than those having low transit QoS •We explore how QoS measures including service frequency, service span, service coverage, and travel time ratio, along with implicit environmental predictors such as topographic grade factor influence bus ridership •We applied Multiple Linear Regression (MLR) to examine the relationship between QoS and ridership •Its outcomes enhance our understanding of transit user behavior, which is informative to urban transportation policy, planning, and provision

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.

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.

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.

Aggregate quality of service computation for composite services

This paper addresses the problem of computing the aggregate QoS of a composite service given the QoS of the services participating in the composition. Previous solutions to this problem are restricted to composite services with well-structured orchestration models. Yet, in existing languages such as WS-BPEL and BPMN, orchestration models may be unstructured. This paper lifts this limitation by providing equations to compute the aggregate QoS for general types of irreducible unstructured regions in orchestration models. In conjunction with existing algorithms for decomposing business process models into single-entry-single-exit regions, these functions allow us to cover a larger set of orchestration models than existing QoS aggregation techniques.

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

Urban transit quality of service : user perception and behaviour

Despite its potential multiple contributions to sustainable policy objectives, urban transit is generally not widely used by the public in terms of its market share compared to that of automobiles, particularly in affluent societies with low-density urban forms like Australia. Transit service providers need to attract more people to transit by improving transit quality of service. The key to cost-effective transit service improvements lies in accurate evaluation of policy proposals by taking into account their impacts on transit users. If transit providers knew what is more or less important to their customers, they could focus their efforts on optimising customer-oriented service. Policy interventions could also be specified to influence transit users’ travel decisions, with targets of customer satisfaction and broader community welfare. This significance motivates the research into the relationship between urban transit quality of service and its user perception as well as behaviour. This research focused on two dimensions of transit user’s travel behaviour: route choice and access arrival time choice. The study area chosen was a busy urban transit corridor linking Brisbane central business district (CBD) and the St. Lucia campus of The University of Queensland (UQ). This multi-system corridor provided a ‘natural experiment’ for transit users between the CBD and UQ, as they can choose between busway 109 (with grade-separate exclusive right-of-way), ordinary on-street bus 412, and linear fast ferry CityCat on the Brisbane River. The population of interest was set as the attendees to UQ, who travelled from the CBD or from a suburb via the CBD. Two waves of internet-based self-completion questionnaire surveys were conducted to collect data on sampled passengers’ perception of transit service quality and behaviour of using public transit in the study area. The first wave survey is to collect behaviour and attitude data on respondents’ daily transit usage and their direct rating of importance on factors of route-level transit quality of service. A series of statistical analyses is conducted to examine the relationships between transit users’ travel and personal characteristics and their transit usage characteristics. A factor-cluster segmentation procedure is applied to respodents’ importance ratings on service quality variables regarding transit route preference to explore users’ various perspectives to transit quality of service. Based on the perceptions of service quality collected from the second wave survey, a series of quality criteria of the transit routes under study was quantitatively measured, particularly, the travel time reliability in terms of schedule adherence. It was proved that mixed traffic conditions and peak-period effects can affect transit service reliability. Multinomial logit models of transit user’s route choice were estimated using route-level service quality perceptions collected in the second wave survey. Relative importance of service quality factors were derived from choice model’s significant parameter estimates, such as access and egress times, seat availability, and busway system. Interpretations of the parameter estimates were conducted, particularly the equivalent in-vehicle time of access and egress times, and busway in-vehicle time. Market segmentation by trip origin was applied to investigate the difference in magnitude between the parameter estimates of access and egress times. The significant costs of transfer in transit trips were highlighted. These importance ratios were applied back to quality perceptions collected as RP data to compare the satisfaction levels between the service attributes and to generate an action relevance matrix to prioritise attributes for quality improvement. An empirical study on the relationship between average passenger waiting time and transit service characteristics was performed using the service quality perceived. Passenger arrivals for services with long headways (over 15 minutes) were found to be obviously coordinated with scheduled departure times of transit vehicles in order to reduce waiting time. This drove further investigations and modelling innovations in passenger’ access arrival time choice and its relationships with transit service characteristics and average passenger waiting time. Specifically, original contributions were made in formulation of expected waiting time, analysis of the risk-aversion attitude to missing desired service run in the passengers’ access time arrivals’ choice, and extensions of the utility function specification for modelling passenger access arrival distribution, by using complicated expected utility forms and non-linear probability weighting to explicitly accommodate the risk of missing an intended service and passenger’s risk-aversion attitude. Discussions on this research’s contributions to knowledge, its limitations, and recommendations for future research are provided at the concluding section of this thesis.

Transit Capacity and Quality of Service Manual (TCQSM) applications for education

Relevant Education Contexts, Examples of TCQSM Applicability to Undergraduate Disciplines, Why Teach with the TCQSM?, TCQS Teaching Tools, Theory Curriculum Example: Examination Question, Problem Based Learning Example: Senior Year Semester Team Project, Honors Dissertation Example Topics, Where to From Here?

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.

Effects of transit quality of service characteristics on daily bus ridership

This study explores how explicit transit quality of services (TQoS) measures including service frequency, service span, and travel time ratio, along with implicit environmental predictors such as topographic grade factor influence bus ridership using a case study city of Brisbane, Australia. The primary hypothesis tested was that bus ridership is higher within suburbs with high transit quality of service than suburbs that have limited service quality. Using Multiple Linear Regression (MLR) this study identifies a strong positive relationship between route intensity (bus-km/h-km2) and bus ridership, indicating that increasing both service frequency and spatial route density correspond to higher bus ridership. Additionally, travel time ratio (in-vehicle transit travel time to in-vehicle auto travel time) is also found to have significant negative association with ridership within a suburb, reflecting a decline in transit use with increased travel time ratio. Conversely, topographic grade and service span are not found to exert any significant impact on bus ridership in a suburb. Our study findings enhance the fundamental understanding of traveller behaviour which is informative to urban transportation policy, planning and provision.

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.