Observation of bus ridership in the aftermath of the 2011 Floods in Southeast Queensland, Australia

The 2011 floods in Southeast Queensland had a devastating impact on many sectors including transport. Road and rail systems across all flooded areas of Queensland were severely affected and significant economic losses occurred as a result of roadway and railway closures. Travellers were compelled to take alternative routes because of road closures or deteriorated traffic conditions on their regular route. Extreme changes in traffic volume can occur under such scenarios which disrupts the network re-equilibrium and re-stabilisation in the recovery phase as travellers continuously adjust their travel options. This study explores how travellers respond to such a major network disruption. A comprehensive study was undertaken focusing on how bus riders reacted to the floods in Southeast Queensland by comparing the ridership patterns before, during and after the floods. The study outcomes revealed the evolving reactions of transit users to direct and indirect impacts of a natural disaster. A good understanding of this process is crucial for developing appropriate strategies to encourage modal shift of automobile users to public transit and also for modelling of travel behaviours during and after a major network disruption caused by natural disasters.

Impact of rain on daily bus ridership : a Brisbane case study

Weather is one of the most significant elements affecting transit ridership on a daily basis. Until now, there has been limited focus in the literature investigating this issue. Adverse weather conditions impact travellers in choosing travel mode and route, travel schedule, and trip making itself. This paper explores the relationship between adverse weather and transit ridership by analysing the correlation between daily bus ridership and daily precipitation for a three-year period from 2010 to 2012. It is observed from the analysis that wet weather has varying impacts on daily bus ridership. Overall, rainfall negatively affects the daily bus ridership in this region. Morning peak-hours and weekend ridership were found more sensitive to rain than entire day’s ridership and weekdays. The study also found a negative correlation between the morning-peak precipitation level and the daily bus ridership, which suggests that a small amount of morning peak-hours rain reduces a significant amount bus ridership for the whole day. The analysis also confirms that summer rain has the most significant effect on ridership compared with the other three seasons. The study findings will contribute to enhancing the fundamental understanding of traveller behaviours, particularly mode choice behaviour under adverse weather conditions.

Adverse weather effects on bus ridership

This study focuses on weather effects on daily bus ridership in Brisbane, given bus’ dominance in this city. The weather pattern of Brisbane varies by season according to its sub-tropical climate characteristics. Bus is prone to inclement weather condition as it shares the road system with general traffic. Moreover, bus stops generally offer less or sometimes no protection from adverse weather. Hence, adverse weather conditions such as rain are conjectured to directly impact on daily travel behaviour patterns. There has been limited Australian research on the impact of weather on daily transit ridership. This study investigates the relationship between rainy day and daily bus ridership for the period of 2010 to 2012. Overall, rainfall affects negatively with varying impacts on different transit groups. However, this analysis confirmed a positive relationship between consecutive rainy days (rain continuing for 3 or more days). A possible explanation could be that people may switch their transport mode to bus to avoid high traffic congestion and higher accident potentiality on rainy days. Also, Brisbane’s segregated busway (BRT) corridor works favourably towards this mode choice. Our study findings enhance the fundamental understanding of traveller behaviour, particularly mode choice behaviour under adverse weather conditions.

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.

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

Adverse weather effects on bus ridership

This study focuses on the effects of weather on daily bus ridership in Brisbane, given the dominance of buses in that city. The weather pattern of Brisbane varies by season according to its subtropical climate characteristics. Bus operation is affected by inclement weather conditions, as buses share the road system with general traffic. Moreover, bus stops generally offer little, or sometimes no, protection from adverse weather. Hence, adverse weather conditions such as rain are thought to directly impact on daily travel behaviour patterns. There has been limited Australian research on the impact of weather on daily transit ridership. This study investigates the relationship between rainy days and daily bus ridership for the period 2010 to 2012. Overall, rainfall has a negative effect, with varying impacts on different transit groups. However, this analysis confirmed a positive relationship between consecutive rainy days (rain continuing for 3 or more days). A possible explanation could be that people switch their transport mode to bus to avoid high traffic congestion and higher accident potentiality on rainy days. Also, Brisbane’s segregated busway corridor works favourably towards this mode choice. The findings of our study enhance the fundamental understanding of traveller behaviour, particularly mode-choice behaviour, under adverse weather conditions.

Investigating variation in bus ridership rates within localised areas of a sub-tropical city

This thesis makes a significant contribution to knowledge and understanding of 'Human Travel Behaviour' in relation to transportation research. It holds some important merits that have not been proposed before. It develops a new, comprehensive and meaningful relationship that includes bus transit ridership change due to weather variables, seasonality and transit quality of service within a single daily ridership rate estimation model. The research incorporated both temporal and spatial influences on ridership within a modelling structure, named as the Nested Model Structure. It provides a complete picture of ridership variation across the sub-tropical city of Brisbane, Australia.

Effects of transit quality of service characteristics on daily bus ridership

A case study of Brisbane, the capital city of Queensland, Australia, explored how explicit measures of transit quality of service (e.g., service frequency, service span, and travel time ratio) and implicit environmental predictors (e.g., topographic grade factor) influenced bus ridership. The primary hypothesis tested was that bus ridership was higher in suburbs with high transit quality of service than in suburbs with limited service quality. Multiple linear regression, used to identify a strong positive relationship between route intensity (bus-km/h-km2) and bus ridership, indicated that both increased service frequency and spatial route density corresponded to higher bus ridership. Additionally, the travel time ratio (i.e., the ratio of in-vehicle transit travel time to in-vehicle automobile travel time) had a significant negative association with suburban ridership: transit use declined as travel time ratio increased. In contrast, topographic grade and service span did not significantly affect suburban bus ridership. The study findings enhance the fundamental understanding of traveler behavior, which is informative to urban transportation policy, planning, and provision.

Understanding the effects of complex seasonality on suburban daily transit ridership

Fluctuations in transit ridership pattern over the year have always concerned transport planners, operators and researchers. Predominantly, metrological elements have been specified to explain variability in ridership volume. However, the outcome of this research points to new direction to explain ridership fluctuation in Brisbane. It explored the relationship between daily bus ridership, seasonality and weather variables for a one-year period, 2012. Rather than segregating the entire year’s ridership into the four calendar seasons (summer, autumn, spring, and winter), this analysis distributed the yearly ridership into nine complex seasonality blocks. These represent calendar season, school/university (academic) period and their corresponding holidays, as well as other observant holidays such as Christmas. The dominance of complex seasonality over typical calendar season was established through analysis and using Multiple Linear Regression (MLR). This research identified a very strong association between complex seasonality and bus ridership. Furthermore, an expectation that Brisbane’s subtropical summer is unfavourable to transit usage was not supported by the findings of this study. A nil association of precipitation and temperature was observed in this region. Finally, this research developed a ridership estimation model, capable of predicting daily ridership within very limited error range. Following the application of this developed model, the estimated annual time series data of each suburb was analysed using Fourier Transformation to appreciate whether any cyclical effects remained, compared with the original data.

Estimating annual ridership and operating expense for fixed route bus systems in small urban areas /

Jan. 1979.

Variation in the walking time to bus stop by the degree of transit captivity

Measures of transit accessibility are important in evaluating transit services, planning for future services and investment on land use development. Existing tools measure transit accessibility using averaged walking distance or walking time to public transit. Although the mode captivity may have significant implications on one’s willingness to walk to use public transit, this has not been addressed in the literature to date. Failed to distinguish transit captive users may lead to overestimated ridership and spatial coverage of transit services. The aim of this research is to integrate the concept of transit captivity into the analysis of walking access to public transit. The conventional way of defining “captive” and “choice” transit users showed no significant difference in their walking times according to a preliminary analysis. A cluster analysis technique is used to further divide “choice” users by three main factors, namely age group, labour force status and personal income. After eliminating “true captive” users, defined as those without driver’s licence or without a car in respective household, “non-true captive” users were classified into a total of eight groups having similar socio-economic characteristics. The analysis revealed significant differences in the walking times and patterns by their level of captivity to public transit. This paper challenges the rule-of-thumb of 400m walking distance to bus stops. In average, people’s willingness to walk dropped drastically at 268m and continued to drop constantly until it reached the mark of 670m, where there was another drastic drop of 17%, which left with only 10% of the total bus riders willing to walk 670m or more. This research found that mothers working part time were the ones with lowest transit captivity and thus most sensitive to the walking time, followed by high-income earners and the elderly. The level of captivity increases when public transit users earned lesser income, such as students and students working part time.

Optimization models for selecting bus stops for accessibility improvements for people with disabilities

Bus stops are key links in the journeys of transit patrons with disabilities. Inaccessible bus stops prevent people with disabilities from using fixed-route bus services, thus limiting their mobility. The Americans with Disabilities Act (ADA) of 1990 prescribes the minimum requirements for bus stop accessibility by riders with disabilities. Due to limited budgets, transit agencies can only select a limited number of bus stop locations for ADA improvements annually. These locations should preferably be selected such that they maximize the overall benefits to patrons with disabilities. In addition, transit agencies may also choose to implement the universal design paradigm, which involves higher design standards than current ADA requirements and can provide amenities that are useful for all riders, like shelters and lighting. Many factors can affect the decision to improve a bus stop, including rider-based aspects like the number of riders with disabilities, total ridership, customer complaints, accidents, deployment costs, as well as locational aspects like the location of employment centers, schools, shopping areas, and so on. These interlacing factors make it difficult to identify optimum improvement locations without the aid of an optimization model. This dissertation proposes two integer programming models to help identify a priority list of bus stops for accessibility improvements. The first is a binary integer programming model designed to identify bus stops that need improvements to meet the minimum ADA requirements. The second involves a multi-objective nonlinear mixed integer programming model that attempts to achieve an optimal compromise among the two accessibility design standards. Geographic Information System (GIS) techniques were used extensively to both prepare the model input and examine the model output. An analytic hierarchy process (AHP) was applied to combine all of the factors affecting the benefits to patrons with disabilities. An extensive sensitivity analysis was performed to assess the reasonableness of the model outputs in response to changes in model constraints. Based on a case study using data from Broward County Transit (BCT) in Florida, the models were found to produce a list of bus stops that upon close examination were determined to be highly logical. Compared to traditional approaches using staff experience, requests from elected officials, customer complaints, etc., these optimization models offer a more objective and efficient platform on which to make bus stop improvement suggestions.

Diesel bus emissions of submicrometer particles measured in a tunnel study

The emission factors of a bus fleet consisting of approximately three hundreds diesel powered buses were measured in a tunnel study under well controlled conditions during a two-day monitoring campaign in Brisbane. The number concentration of particles in the size range 0.017-0.7 m was monitored simultaneously by two Scanning Mobility Particle Sizers located at the tunnel’s entrance and exit. The mean value of the number emission factors was found to be (2.44±1.41)×1014 particles km-1. The results are in good agreement with the emission factors determined from steady-state dynamometer testing of 12 buses from the same Brisbane City bus fleet, thus indicating that when carefully designed, both approaches, the dynamometer and on-road studies, can provide comparable results, applicable for the assessment of the effect of traffic emissions on airborne particle pollution.

Airborne particulate matter at a bus station : concentration levels and governing parameters

Traffic emissions are an important contributor to ambient air pollution, especially in large cities featuring extensive and high density traffic networks. Bus fleets represent a significant part of inner city traffic causing an increase in exposure to general public, passengers and drivers along bus routes and at bus stations. Limited information is available on quantification of the levels, and governing parameters affecting the air pollution exposure at bus stations. The presented study investigated the bus emissions-dominated ambient air in a large, inner city bus station, with a specific focus on submicrometer particles. The study’s objectives were (i) quantification of the concentration levels; (ii) characterisation of the spatio-temporal variation; (iii) identification of the parameters governing the emissions levels at the bus station and (iv) assessment of the relationship between particle concentrations measured at the street level (background) and within the bus station. The results show that up to 90% of the emissions at the station are ultrafine particles (smaller than 100 nm), with the concentration levels up to 10 times the value of urban ambient air background (annual) and up to 4 times the local ambient air background. The governing parameters affecting particle concentration at the station were bus flow rate and meteorological conditions (wind velocity). Particle concentration followed a diurnal trend, with an increase in the morning and evening, associated with traffic rush hours. Passengers’ exposure could be significant compared to the average outdoor and indoor exposure levels.