Monash University, Institute of Transport Studies: World Transit Research (WTR)
Not a member yet
11112 research outputs found
Sort by
On the prediction of intermediate-to-long term bus section travel time with the Burr mixture autoregressive model
No full textTravel time is an essential indicator for trip planning, transportation service planning, and operation. This study aims to propose a novel Burr mixture autoregressive (BMAR) model for the intermediate-to-long term period of bus section travel time prediction, which is useful for bus service and schedule planning. The BMAR model exhibits greater flexibility, allowing it to effectively capture the multi-peak and non-peak, non-linear with heteroscedasticity characteristics of travel time. The model is trained and well-validated with 6-month bus section travel time data collected via the automatic vehicle location system. Results show that the BMAR model gives promising results in travel time point and interval prediction, especially for the higher degree of variability and irregular pattern of travel time observed on urban roads and highways. The reliability ratio index derived from the BMAR model could be used to measure the bus service reliability and aid in the bus scheduling of maintenance activities
Demand-driven integrated train timetabling and rolling stock scheduling on urban rail transit line
No full textThis paper aims to simultaneously optimise train timetabling and rolling stock scheduling in a bidirectional urban rail transit line. A novel variable train composition strategy is adopted to respond to time-dependent passenger demand by allowing modular train units to split and couple with each other at depots. We mathematically model this problem in an arrival time revision framework, simplifying the problem structure and rendering the model solved by a commercial solver. In particular, the proposed model considers two realistic first-in-first-out rules to capture passenger transportation accurately in the oversaturated urban rail transit system. The efficiency of the proposed strategy and model is validated on an illustrative example and a real-world instance in the Yizhuang Line of Beijing Subway. Results show that adopting the variable train composition strategy can save 63.6% and 44.6% of total passenger waiting time compared to the current fixed train composition strategy with eight and six units
Measuring the transit benefits of accessibility with the integration of transit systems
No full textTransit systems create various benefits for society and the economy, which are commonly reflected in their accessibility. However, transit accessibility is not necessarily distributed fairly over space, resulting in spatial disparities in transit benefits. This study empirically examines the spatial distribution of transit benefits using accessibility measures specifically designed for public transit systems. To achieve this, a series of standardised transit benefit accessibility measures are developed and applied to the integrated transit systems in Seoul, South Korea, where bus and subway systems are seamlessly integrated via smartcard technology. Our measures assess the distributional impacts of transit integration. Our analysis revealed that transit benefits of accessibility have improved significantly in most areas of Seoul due to the integration of transit systems. However, these benefits have been realised as spatially disparate in Seoul. The findings stress the need for more equitable public transit policies to mitigate the identified spatial disparities
Pandemic transit: examining transit use changes and equity implications in Boston, Houston, and Los Angeles
No full textWhile the COVID-19 pandemic upended many aspects of life as we knew it, its effects on U.S. public transit were especially dramatic. Many former transit commuters began to work from home or switched to traveling via private vehicles. But for those who continued to work outside the home and could not drive—who were more likely low-income and Black or Hispanic—transit remained an important means of mobility. However, most transit agencies reduced service during the first year of the pandemic, reflecting reduced ridership demand, increasing costs, and uncertain budgets. To analyze the effects of the pandemic on transit systems and their users, we examine bus ridership changes by neighborhood in Boston, Houston, and Los Angeles from 2019 to 2020. Combining aggregated stop-level boarding data, passenger surveys, and census data, we identify associations between shifting travel patterns and neighborhoods. We find that early in the pandemic, neighborhoods with more poor and non-white households lost proportionally fewer riders; however, this gap between high- and low-ridership-loss neighborhoods shrank as the pandemic wore on. We also model ridership change controlling for multiple factors. Ridership in Houston and LA generally outperformed Boston, with built environment and demographic factors accounting for some of the observed differences. Neighborhoods with high shares of Hispanic and African American residents retained more riders in the pandemic, while those with higher levels of auto access and with more workers able to work from home lost more riders, all else equal. We conclude that transit’s social service role elevated during the pandemic, and that serving travelers in disadvantaged neighborhoods will likely remain paramount emerging from it
A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit
No full textOn-demand app-based shared mobility services have created new opportunities for complementing traditional fixed-route transit through transit agencies’ efforts to incorporate them into their service provision. This paper presents one of the first studies that rigorously examine riders’ responses to a pilot aimed at providing such a transit-supplementing service. The study conducts latent class analysis on riders of the Via to Transit program, a mobility pilot in the Seattle region where on-demand service was offered to connect transit riders to light rail stations. The analysis identifies three distinct rider groups with heterogenous responses to the on-demand service: (1) riders who previously used private cars or ride-hailing; (2) riders who were pedestrians and bikers but switched likely because of safety concern; (3) mostly socio-economically disadvantaged riders who previously relied on the bus, but switched to the new service for the convenience and speed. These results point to rich transportation policy implications, which can inform decision-making by public transit agencies as they are exploring alternative ways to deliver the mobility services
Travel behaviour of shared mobility users: a review of empirical evidence
No full textIn recent decades, shared mobility has gained prominence as a sustainable alternative in transport, yet a comprehensive understanding of its effects on travel behaviour remains limited. This paper provides a narrative review of quantitative empirical studies, focusing on car-sharing and bike-sharing, and revisits the magnitude of the effects on four indicators: public transport use, active transport use, auto dependence, and auto ownership. Both cross-sectional and longitudinal perspectives are considered, examining variances in trip characteristics. Shared mobility users tend to rely less on private vehicles and increase cycling, with varying effects on transit use and walking. Car-sharing typically replaces private vehicles for non-commuting trips, while bike-sharing mainly competes with rather than complements public transport, especially for shorter commutes. The longitudinal effects of shared mobility appear more limited than those observed in cross-sectional analyses, indicating that shared mobility can potentially lead to a positive trend in travel mode shifts over time, albeit slowly. Additionally, this study highlights differences in shared mobility outcomes between Australia and other global contexts, exploring potential reasons for these discrepancies. Integrating shared mobility and other transport paradigms requires long-term strategies to shape travel behaviour towards multimodality, offering a continuum of choices covering most daily trips without private vehicles
Handling uncertainty in train timetable rescheduling: A review of the literature and future research directions
No full textExternal and internal factors can cause disturbances or disruptions in daily train operations, leading to deviations from official timetables and passenger delays. As a result, efficient train timetable rescheduling (TTR) methods are necessary to restore disrupted train services. Although TTR has been a popular research topic in recent years, the uncertain characteristics of railways have not been sufficiently addressed. This review first identifies the primary uncertainties of TTR and examines their impacts on both TTR and passenger routing during disturbances or disruptions. It finds that only a few uncertainties have been investigated, and the existing solution methods do not adequately meet practical requirements, such as considering the dynamic nature of disturbances or disruptions, which is crucial for real-world applications. Therefore, the review highlights problems associated with TTR uncertainties that need urgent attention and suggests promising methodologies that could effectively address these issues as future research directions. This review aims to help practitioners develop improved automatic train-dispatching systems with better train-rescheduling performance under disturbances or disruptions compared to current systems
Inferring unable-to-board commuters for overcrowded buses using smart card data
No full textAs public transportation faces increasing ridership demand, metrics such as the number of passengers denied boarding become important for measuring the service quality of transit systems. Many studies in the past have used automated fare collection (AFC) (also known as smart card data) and automated vehicle location data to infer the probability distributions for commuters that experience unable-to-board (UTB) events in metro systems, but few have studied UTB events for buses. In this paper, we demonstrate that the probability distribution of UTB commuters inferred from AFC data can be modelled by a truncated binomial distribution under certain assumptions. This model is then validated against synthetic UTB events generated using simulations and against actual UTB events recorded from ground surveys. Finally, we apply our model on real AFC data of commuters in the Singapore bus network to serve as a case study. Our method enables transport planners and operators to identify bus stops and time intervals where overcrowding and UTB events is prevalent, so that appropriate measures can be taken to mitigate such occurrences
Developing an agent-based microsimulation for predicting the Bus Rapid Transit (BRT) demand in developing countries: A case study of Dhaka, Bangladesh
No full textBus Rapid Transit (BRT) has been widely recognised as an affordable and effective mass transport system that can solve various mobility issues in countries that are unable to afford rail-based mass transit options. However, it is extremely challenging to predict the demand for the first BRT service in a city of a developing country with a weak public transport system using aggregate models, given the radical difference in the level of service between the BRT and the existing modes. Further, there can be substantial changes in the activity and travel patterns in a city after the introduction of the BRT which simpler disaggregate level analysis tools are unable to predict. Agent-based simulation tools, which are the state-of-the-art tools for simulating complex travel behaviour, are hence more appropriate for predicting the network conditions after the introduction of a new BRT system. But the application of such simulation tools has been primarily limited to developed countries where the transport landscape and the travel behaviour are very different from the developing countries. To address this gap, this paper presents a demand forecasting model for BRT and integrates it into an activity-based micro-simulation tool in the context of Dhaka, the capital of Bangladesh and one of the fastest growing megacities in the world. The model was developed based on an existing multi-agent, activity-based, travel demand simulator (MATSim). The MATSim implementation in the context of Dhaka focused on two aspects: (1) implementing behaviour models in MATSim to reflect the mode choice in the presence of the proposed BRT (2) integrating multiple data sources (including stated-preference data) for calibrating the mode choice and other components of MATSim to realistically mimic the travel behaviour in the city. Once calibrated, different access scenarios for BRT were simulated using MATSim, and the sensitivity of the outputs to different modelling assumptions is tested. Results from the simulation showed that the marginal utility of travel time, travel cost, and pricing structure of BRT significantly influenced BRT travel demands. Also, BRT demand was found to be the highest (25% of the total trips) in the scenario with multi-modal access/egress connections. While such direct model outputs presented in this paper will be useful for the planners to maximise the ridership of the proposed BRT, the calibrated simulator will be also useful for the evaluation of other innovative transport modes in the context of Dhaka in the future
The app or the cap? Which fare innovation affects bus ridership?
No full textTechnology advancements in the last two decades have changed several aspects of public transit service, particularly related to fares. Transit agencies seek to benefit from new technologies to improve the customer experience by launching mobile fare payment applications (“apps”) and adopting more sophisticated fare policies such as fare capping (“caps”). However, there is a limited understanding of the impacts of these two fare innovations on bus ridership. Therefore, this study seeks to quantify the impacts of mobile fare payment applications and fare capping policies (both daily and monthly) on bus ridership. Staggered difference-in-difference techniques were used to evaluate system-level bus ridership for the 50 largest transit agencies in the United States. This approach considers the effect on multiple treated units that adopted apps or caps at different times; it also considers heterogeneity of the treatment effect between treated agencies and over time. The results suggest that the launch of mobile fare payment applications and the adoption of daily fare capping policies did not have significant impacts on system-level ridership. On the other hand, monthly fare capping policies were associated with significant ridership gains. Transit systems that adopted monthly fare capping policies for more than one year experienced an average increase in annual bus ridership ranging from 3.6% to 4.1%; these results were heterogenous and increased over time. These findings can help to inform transit agencies across the United States as they consider different strategies to increase ridership and reverse recent bus ridership declines. Perhaps most important, the staggered difference-in-difference methodology used in this study could potentially be applied to evaluate a wide range of transportation technology and policy innovations with staggered (gradual) rollouts