Workers' attitude towards bus rapid transit : considering Dhaka, Bangladesh

The Government of Bangladesh is planning to develop and implement Bus Rapid Transit (BRT) in Dhaka city. This paper presents a stated choice survey conducted to understand workers’ attitudes toward BRT in Dhaka. The survey data are analysed using a multinomial logit (MNL) model to scrutinize social and economic factors’ impact on participant’s mode choices. Analysis results reveal that males, and workers with higher age, education qualification, and income have greater tendency towards choosing BRT.

Workers' attitude towards bus rapid transit in Dhaka, Bangladesh

This study investigates: –how travel and socio-demographic attributes act on workers’ mode choice decisions in Dhaka –whether Dhaka’s commuters would choose BRT for their work trip once implemented •Very limited research exists on users’ perceptions of BRT in developing countries’ megacities •We adopted a discrete choice modelling approach •As BRT has not yet been implemented in Dhaka, we collected Stated Choice (SC) survey data including a hypothetical BRT mode to understand factors important to workers’ mode choice decisions •We compare the impact of travel factors between Dhaka and cities of developed countries

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.

Analyzing busway station potential capacity under mixed stopping and non-stopping operation

Busway stations are the interface between passengers and services. The station is crucial to line operation as it is typically the only location where buses can pass each other. Congestion may occur here when buses manoeuvring into and out of the platform lane interfere with bus flow, or when a queue of buses forms upstream of the platform lane blocking the passing lane. Further, some systems include operation where express buses do not observe the station, resulting in a proportion of non-stopping buses. It is important to understand the operation of the station under this type of operation and its effect on busway capacity. This study uses microscopic simulation to treat the busway station operation and to analyse the relationship between station potential capacity where all buses stop, and Mixed Potential Capacity where there is a mixture of stopping and non-stopping buses. First, the micro simulation technique is used to analyze the All Stopping Buses (ASB) scenario and then statistical model is tuned and calibrated for a specified range of controlled scenarios of dwell time characteristics Subsequently, a mathematical model is developed for Mixed Stopping Buses (MSB) Potential Capacity by introducing different proportions of express (or non-stopping) buses. The proposed models for a busway station bus capacity provide a better understanding of operation and are useful to transit agencies in busway planning, design and operation.

Analysing busway station capacity under mixed stopping and non-stopping operation

Busway stations are the interface between passengers and services. The station is crucial to line operation as it is typically the only location where buses can pass each other. Congestion may occur here when buses manoeuvring into and out of the platform lane interfere with bus flow, or when a queue of buses forms upstream of the platform lane blocking the passing lane. Further, some systems include operation where express buses do not observe the station, resulting in a proportion of non-stopping buses. It is important to understand the operation of the station under this type of operation and its effect on busway capacity. This study uses microscopic simulation to treat the busway station operation and to analyse the relationship between station potential capacity where all buses stop, and Mixed Potential Capacity where there is a mixture of stopping and non-stopping buses. First, the micro simulation technique is used to analyze the All Stopping Buses (ASB) scenario and then statistical model is tuned and calibrated for a specified range of controlled scenarios of dwell time characteristics Subsequently, a mathematical model is developed for Mixed Stopping Buses (MSB) Potential Capacity by introducing different proportions of express (or non-stopping) buses. The proposed models for a busway station bus capacity provide a better understanding of operation and are useful to transit agencies in busway planning, design and operation.

Traffic safety at road-rail level crossings using a driving simulator and traffic simulation

A number of Intelligent Transportation Systems (ITS) were used with an advanced driving simulator to assess its influence on driving behavior. Three types of ITS interventions namely, Video in-vehicle (ITS1), Audio in-vehicle (ITS2), and On-road flashing marker (ITS3) were tested. Then, the results from the driving simulator were used as inputs for a developed model using a traffic micro-simulation (Vissim 5.4) in order to assess the safety interventions. Using a driving simulator, 58 participants were required to drive through a number of active and passive crossings with and without an ITS device and in the presence or absence of an approaching train. The effect of driver behavior changing in terms of speed and compliance rate was greater at passive crossings than at active crossings. The difference in speed of drivers approaching ITS devices was very small which indicates that ITS helps drivers encounter the crossings in a safer way. Since the current traffic simulation was not able to replicate a dynamic speed change or a probability of stopping that varies based on different ITS safety devices, some modifications of the current traffic simulation were conducted. The results showed that exposure to ITS devices at active crossings did not influence the drivers’ behavior significantly according to the traffic performance indicators used, such as delay time, number of stops, speed, and stopped delay. On the other hand, the results of traffic simulation for passive crossings, where low traffic volumes and low train headway normally occur, showed that ITS devices improved overall traffic performance.

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

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