Monash University, Institute of Transport Studies: World Transit Research (WTR)
Not a member yet
11112 research outputs found
Sort by
The relationship between the degree of ethnic enclaves and travel mode choices
No full textThis article aims to highlight how the degree of ethnic enclaves plays an important role in travel mode choices according to racial/ethnic groups in the Atlanta metropolitan areas in the US. This study finds that ethnic enclaves play a different role in travel mode choices according to the degree of ethnic enclaves, racial/ethnic groups, and travel purposes. For example, white people in low enclaves are more likely to take public transit for work (0.390), and black people in high enclaves are more apt to use household carpool, inter-household carpool, public transit, and walk/bike for school (1.545, 1.725, 1.205, 1.659, respectively) and public transit and walk/bike for other purposes (0.273 and 0.233, respectively). Asian people in high enclaves are more inclined to take household carpool for leisure (3.480), and Hispanic people in low enclaves are more likely to use inter-household carpool for shopping (6.377)
What is the market potential for on-demand services as a train station access mode?
No full textOn-demand mobility services (FLEX) are often proposed as a solution for the first/last mile problem. We study the potential of using FLEX to improve train station access by means of a three-step sequential stated preference survey. We compare FLEX with the bicycle, car and public transport for accessing two alternative train stations. We estimate a joint access mode and train station choice model. Estimating a latent class choice model with different nesting structures, we uncover four distinct segments in the population. Two segments (∼50%) with a lower Willingness-to-Pay seem to be more likely to take-up FLEX. Ex-urban car drivers seem to be the most likely segment to adopt FLEX, showing great, since members of this segment are currently frequent users of the private car. Our case study also shows that while FLEX competes primarily with public transport when accessing local stations, it competes primarily with car for reaching distant stations
Fare revenue forecast in public transport: A comparative case study
No full textThis paper presents results from a case study of fare revenue prediction in public transportation in Berlin using machine learning and time series analysis. Our work aims to aid in the implementation of automated revenue controlling and data-driven decision support within existing controlling processes.
We generate forecasts based on fare revenue data for different product segments aggregated on a monthly basis. Additionally, we model exogenous effects using data publicly available.
The results were obtained using a variety of methods including regression methods as well as autoregressive methods and exponential smoothing. Among others, SARIMAX, MLR, LASSO and Ridge were applied.
We evaluate the predictive quality of each method and compare them. Where appropriate, we apply automatic feature selection to improve performance.
Our findings, alongside a discussion of their interpretability, can serve as recommendations for practitioners, supporting them in choosing appropriate methods and suitable exogenous variables to reliably predict the fare revenues of different products
Optimizing sustainable urban mobility: A comprehensive review of electric bus scheduling strategies and future directions
No full textPublic transportation is in a new era of electrification, with battery-powered electric buses emerging for sustainable urban mobility. However, their operational characteristics, including prolonged charging times and limited travel distances, have introduced complex scheduling challenges that demand innovative solutions. This review paper explores the electric bus scheduling problem (EBSP), comprehensively analyzing state-of-the-art research in this rapidly evolving field. The literature is categorized based on structural and procedural aspects. In the structural section, the different structures of defining EBSP have been explained. Moreover, mathematical models, their associated constraints, and objective functions are examined. The procedural section reviews various solution methodologies based on the underlying objective functions. The review reveals potential avenues for future research, such as investigating multi-depot and multi-vehicle configurations, incorporating charge scheduling and partial charging, developing robust schedules to account for uncertainty, and applying advanced techniques like machine learning and hyper-heuristics to optimize electric bus transportation efficiency
On the dynamic vulnerability of an urban rail transit system and the impact of human mobility
No full textUrban rail transit (URT) plays a pivotal role in facilitating human mobility within urban environments. It is significant to understand its vulnerability, i.e., the variation in capacity and demand when confronted with unexpected events, particularly operational disruptions. Although the network topology is generally fixed, the hourly-changing travel demand greatly impacts the actual vulnerability of a URT system. Unfortunately, few existing studies consider the combined influence of dynamic travel demand and network topology. To fill the gap, this paper proposes a network vulnerability assessment method with the joint consideration of static network topology and dynamic travel demand. This method includes a defined reasonable path, an accessibility-based identification of station importance with time-varying passenger demand, and a new dynamic vulnerability delay index considering affected travel demand. An empirical analysis was carried out by taking the URT system of Beijing, China as an example, and the impact of the more realistic multiple consecutive station failures in a URT system is also examined. Results show that the distribution of high-importance stations indeed varies with the time of day, affected by both static topology and hourly-changing passenger flow. When the disturbance of operation delay occurs, the impact of high-importance stations on the network vulnerability changes nonlinearly with the increase of delayed travel demand. Some stations that serve as bridges and are visited by large passenger flows have the greatest impact on network vulnerability. Network performance degradation is obviously segmented and stratified in the case of interval continuous failure. The disruption between different lines is the main cause of network performance degradation, and some high-importance stations within the lines act as catalysts to accelerate the performance degradation. The proposed method not only offers a valuable reference for quantifying network vulnerability arising from fluctuations in passenger mobility but also introduces a novel vulnerability evaluation index to the URT system
Nonlinear impact of built environment on people with disabilities’ metro use behavior
No full textEnsuring equal access to public transportation for people with disabilities is crucial for social equity and sustainable urban development. The physical characteristics and travel preferences of people with disabilities may lead to unique spatial and temporal characteristics of metro use behavior and make them more likely to be influenced by built environment characteristics. We tested this hypothesis by comparing metro travel behavior in Wuhan, China, for people with and without disabilities. The results showed that people with disabilities take more and shorter metro trips than people without disabilities. The relative importance of built environment features in influencing metro ridership displays notable distinctions between people with disabilities and those without, as indicated by the outcomes of the gradient boosting decision tree (GBDT) model. For metro ridership among people with disabilities, street density, the number of medical facilities, and the building plot ratio are the top three influencing variables on weekdays, while the number of shopping centers, medical facilities, and recreational facilities are the top three on weekends. Additionally, these variables exhibit clear nonlinear associations on metro ridership for both groups. The findings offer new insights for providing a better travel environment for people with disabilities and thus promoting transportation equity
Investigating urban mobility through multi-source public transportation data: A multiplex network perspective
No full textThe integration of multi-source and diverse spatio-temporal travel data provides a comprehensive insight into urban mobility. Using data from Shenzhen\u27s public transportation system, this study presents an analytical framework based on multiplex networks to examine variations in multi-mode public transportation usage (metro, bus, taxi, and shared bike) and their correlation with the built environment. This framework encompasses the analysis of network topological characteristics, centrality, and communities. The examination of network topological characteristics reveals that the multiplex transportation network exhibits high global accessibility and local connectivity. Network centrality analysis, focusing on weighted outdegree centrality, captures the patterns of public transportation ridership. Centrality modeling, employing the light gradient boosting machine, demonstrates a nonlinear relationship between ridership and the built environment. Factors including population density, residential land use percentage, entertainment service density, restaurant density, and metro station density consistently exhibit positive correlations with ridership across different times of the day. The community structure analysis, using consensus community detection, indicates that distinct urban areas exhibit clustering behavior based on public transportation demand patterns, forming distinct communities that closely align with the functional zoning of urban planning. These findings could provide valuable insights for the strategic planning of transportation services and the built environment
Navigating public transport during a pandemic: Key lessons on travel behavior and social equity from two surveys in Tehran
No full textThis study examines the impact of the Covid-19 pandemic on the mobility system in Tehran, Iran, and provides lessons and insights to enhance readiness for similar circumstances in the future. Two cross-sectional surveys were conducted in Tehran, before and towards the end of the pandemic. The study utilizes descriptive analyses and econometric modeling to investigate the frequency of use of different modes of transportation during the two periods and the changes in the willingness to use these modes due to the pandemic, in relation to sociodemographic and socioeconomic factors and pandemic-related measures. The findings indicate that public transport, and by extension collective line taxis, experienced a decline in their modal share while private car share increased particularly as passengers. Contrary to the globally observed trend, the ridership of active modes remained stable, while motorbike usage doubled during the same period. Gender and income were found to strongly influence behavior during the pandemic. Women and the elderly were less likely to use public transport, while users who continued to use public transport were primarily low-income men with little to none telecommuting options. The study recommends context-specific policy interventions including investing in ICT and active transport infrastructure, as well as promoting electric mobility, primarily for two-wheelers
Unsupervised Learning for Public Transport Delay Pattern Analysis
No full textTo analyze inherent and diverse patterns within line-based public transport daily delay occurrences, we introduce a data-driven exploratory analysis focused on the spatial-temporal distribution of these delays. Our approach relies on the utilization of the image pattern recognition technique and k-means clustering algorithm. We extract daily punctuality information from the automatic vehicle location data for a singular public transport route. This information is then translated into a visual representation through aggregated daily delay distribution profile images, offering insights into the spatial and temporal distribution of delays. The delay distribution finds expression in the arrangement of pixels within these profile images. The essence of these images is further distilled through image pattern recognition using the neural network architecture of ResNet50. Employing the k-means algorithm, we cluster these images based on their similarity, revealing five distinct daily delay patterns. The analysis of these patterns offers insight into their unique characteristics, yielding noteworthy outcomes. These findings hold the potential to provide public transport operators with an enriched comprehension of the dynamics of delays occurring on a specific line
Blind classification of e-scooter trips according to their relationship with public transport
No full textE-scooter services have multiplied worldwide as a form of urban transport. Their use has grown so quickly that policymakers and researchers still need to understand their interrelation with other transport modes. At present, e-scooter services are primarily seen as a first-and-last-mile solution for public transport. However, we demonstrate that of e-scooter trips are either substituting it or covering areas with little public transportation infrastructure. To this end, we have developed a novel data-driven methodology that autonomously classifies e-scooter trips according to their relation to public transit. Instead of predefined design criteria, the blind nature of our approach extracts the city’s intrinsic parameters from real data. We applied this methodology to Rome (Italy), and our findings reveal that e-scooters provide specific mobility solutions in areas with particular needs. Thus, we believe that the proposed methodology will contribute to the understanding of e-scooter services as part of shared urban mobility