Monash University, Institute of Transport Studies: World Transit Research (WTR)
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Effects of built environment on metro ridership at a microscopic scale: a case study of Xi’an, China
No full textFew studies have examined the relationship at the microscopic spatial scale. In this study, multiple sources of data including mobile phone signal data, automatic fare collection system data, geo-information data, and street-view image data are combined to measure metro ridership and built environment at the plot or block scale. The Random Gradient Boosting Decision Tree was used to explore relationship between the built environment and ridership. The results show the following: (1) the relationship between built environment and ridership shows different types of curves. (2) The path distance to the metro station and the visual perception of road space have more significant impacts on ridership than road network density. (3) The location of the grid also affects grid-level metro ridership. The results suggest that planners should consider the locational factors, pay attention to the different effective thresholds of different variables on ridership and the longitudinal landscaping of non-motorized urban roads
Revealing latent trajectories of (intended) train travel during and after COVID-19
No full textThis study investigates whether the decline in public transit ridership is a temporary phenomenon or indicative of a structural shift in travel patterns and attitudes. We estimate a latent class trajectory model using data from a comprehensive and large-scale survey administered by the Dutch national train operator conducted at eight different points in time after the onset of the pandemic. Six latent trajectories in train use and stated future intentions to use the train are revealed, showing different ‘recovery’ pathways. Whereas low-educated frequent commuters travel almost as much as before, highly educated frequent commuters and mixed-purpose travellers still travel much less, even in the last wave when all restrictions are lifted. The results indicate that travellers belonging to these classes have structurally changed their behaviour. The shift to working from home is more pronounced than the shift to private car use
The impacts of optimization approaches on BEB system configuration in transit
No full textBattery-electric buses (BEBs) are considered suitable technology for transit to tackle climate change and promote environmentally friendly mobility solutions. However, the systemic configuration of BEBs in transit requires sophisticated planning efforts due to contradictory objectives and decisions. The optimal design of a BEB transit system is often approached from various perspectives, leading to different system configurations and distinct impacts on the electricity grid. Towards that end, this study develops three BEB system configuration optimization models, including minimizing capital costs, electricity costs, and greenhouse gas (GHG) emissions. All three models inform the optimal charging system configuration, BEBs battery capacity, and BEBs charging schedule for a general hub-and-spoke transit network. The proposed models are applied to a case study of the Belleville City, Ontario, Canada, bus transit network. The results demonstrate that BEB system configuration and GHG emissions vary significantly according to the optimization perspective. Moreover, the findings emphasize the importance of using the energy storage system to reduce electricity costs and GHG emissions
Analyzing the impact of fare-free public transport policies on crowding patterns at stations using crowdsensing data
No full textFully or partially fare-free public transport (FFPT) is a measure to make public transport (PT) more attractive and affordable. Cities worldwide are experimenting with a variety of fare discount policies, which will lead to spatiotemporal changes in mobility patterns, including crowding in PT stations. However, the response of PT stations to these policies can vary due to the heterogeneity in their surrounding built environment and other factors. Non-conventional data sources could be used to better model and understand these spatiotemporal dynamics. In this study, we propose a three-step methodological framework to understand the impact of FFPT or extreme fare discount interventions on PT demand patterns, specifically focusing on crowding patterns in PT stations, using crowdsensing data. First, we design a busyness-based similarity measure that leverages the histogram method to capture changes in crowding patterns. Then, we employ a Gaussian Mixture Model (GMM) to cluster PT stations based on their crowding pattern deviations at different stages of policy implementation. This clustering step enables the identification of distinct station types based on their response to the policy. Finally, we train a LightGBM model to learn the relationship between crowding pattern changes and the spatial–temporal characteristics of PT stations, using the busyness-based station types identified by GMM as labels. We apply our methodology to a public transport experiment in Germany during the summer of 2022 when the country introduced a monthly “9-EUR” ticket valid on local and regional PT nationwide. The clustering results show three station types: unaffected, mildly stimulated, and intensely stimulated stations. Furthermore, the classifier indicates that the station’s location, activity options near the station, and population within the adjacent area of the station, and the crowding patterns under normal operations (before policy implementation) play a significant role in the heterogeneity of the 9-EUR ticket’s impact. The insights gained from our study can help planners better understand and manage the crowding at PT stations during fare interventions
Optimal fare and headway for a demand adaptive paired-line hybrid transit system in a rectangular area with elastic demand
No full textDemand adaptive paired-line hybrid transit systems that integrate fixed- and flex-route transit have emerged in the last decade and attracted increasing attention because of their potential to improve accessibility for passengers. To facilitate the operation of such a hybrid transit system, this study develops a model to determine the optimal fare and headways associated with fixed- and flex-route transit along a rectangular corridor. Compared with existing literature, the novelty of this study lies in designing the fare structure while simultaneously considering demand elasticity and passenger behaviour. A continuous approximation modelling approach is employed to derive the agency’s and travellers’ cost components. Using these, a nonlinear programming optimisation model is formulated to minimise the total user cost subject to the agency’s nonnegative revenue constraints and passengers’ route choice behaviour, which is characterised as a path-size logit model. Numerical experiments are performed using a stylised network to examine the properties of the model, in which the solution is obtained by combining a brute force method that enumerates headway and fare combinations and an iterative method that determines equilibrated passenger choices. The results show that as potential demand density increases, fare and headway fluctuate and drop, while the percentage of passengers choosing to ride on flex-route transit increases. In addition, there may be an optimal maximum offset distance, which is defined as the width of the corridor to be covered by the transit system, when the potential demand density is low, leading to minimum user cost and maximum travel demand within the service area
Resilience-based post-disaster repair strategy for integrated public transit networks
No full textThe resilience of public transit systems has become a critical issue due to their essential role in urban mobility. However, the challenges of repair selection and sequencing in integrated public transportation networks (IPTN) have not been fully explored. Therefore, we propose a bi-level resilience-based repair strategy optimisation (RRSO) model to investigate the optimisation of resilience in IPTN. To consider trip selection and account for cascade failure processes, a lower-level traffic assignment model was integrated into a single-layer RRSO model. In the traffic assignment model, perceived travel time reflects the traffic impedance of public transportation. Additionally, to assess the performance of the RRSO model under emergency repair conditions, random variables were introduced to represent uncertain factors in the traffic environment. Finally, the results demonstrate that the RRSO model outperforms standard empirical repair strategies. This study provides theoretical insights into the management of urban public transportation
Examining the socio-spatial patterns of bus shelters with deep learning analysis of street-view images: A case study of 20 cities in the U.S.
No full textPrevious studies on public transit in cities found the positive role of bus shelters in promoting bus ridership. However, a large-scale and comparative investigation of bus shelter status has yet to be conducted, leaving a significant knowledge gap. To fill this gap, this research examined the socio-spatial patterns of bus shelters in 20 small- and medium-sized cities in the United States by employing a deep learning-based computer vision analysis with large-scale street-view images. The results revealed a regional difference in the bus shelter scores (range: 30.2–52.1 %). Overall, there are more bus shelters in neighborhoods with higher population densities or higher proportions of minority populations. However, there are nine cities where neighborhoods with higher proportions of minority populations are not significantly correlated with more bus shelters, suggesting issues of mobility injustice. This study is one of the first to combine an AI method with emerging urban data to examine the socio-spatial patterns of bus shelters in cities
How job stressors and economic stressors impact public transport drivers’ performance and well-being under the health risk of the COVID-19 pandemic
No full textIntroduction: During the COVID-19 pandemic, public transport (e.g., bus and taxi) drivers encountered great stress because they needed to work to maintain the operation of the transportation system. This study proposes and empirically investigates the impacts of job stressors and economic stressors of public transport drivers on emotional exhaustion, and subsequent psychological well-being and performance under the health risk of COVID-19. The moderating effects of perceived threat and death anxiety on the relationships between stressors and emotional exhaustion are also examined. Method and Results: Using two survey samples collected from bus and taxi drivers in Taiwan, the results reveal that, except for the effect of time pressure on taxi drivers’ exhaustion, job stressors (job overload and time pressure) and economic stressors (job insecurity) positively relate to emotional exhaustion for both bus and taxi drivers. Drivers’ emotional exhaustion has negative effects on both job satisfaction and positive effects on risky driving behaviors. Perceived pandemic threat strengthens the positive influence of job insecurity on emotional exhaustion for bus drivers, while perceived pandemic threat and death anxiety weaken the negative influence of job insecurity on emotional exhaustion for taxi drivers. Practical Applications: Effective intervention strategies and policies to mitigate perceived pandemic threat and death anxiety of drivers are recommended
Optimal design of pure battery electric bus system on the grid network
No full textElectrifying bus fleets is a practical way to develop sustainable transit systems. This study develops a continuous approximation model to provide strategic-level insights. Considering conventional depot charging mode, the proposed model optimises the design parameters of a pure electric bus network such as stop spacing, headway, and batteries’ expected charging range. Numerical cases are presented over a wider range of key design parameters. Both analytical and numerical simulations are conducted to compare the results between the proposed system and the diesel bus system. Substituting electric buses for traditional diesel could save CO2 emissions from 47% to 52% and up to 8% of the total cost can be saved with electric buses, and deploying electric bus systems would bring an around 7% increase in capital cost but save 23% operation cost. On a real-world grid network in Xi’an, China, deploying electric buses can save the system cost by 7.7%
Modeling the resilience of interdependent networks: The role of function dependency in metro and bus systems
No full textOwing to the pervasive interdependency among networks and the great threat of various disasters, one of the most challenging issues is the resilience evaluation of interdependent networks. Existing studies have been conducted to analyze interdependent network resilience addressing unidirectional dependency, which triggers and propagates network failures. The loss and recovery of network functionality are complicated and important under such interdependency. Ignoring the interdependency nature of different networks would lead to incomplete or incorrect results in their resilience assessment. This paper developed a resilience assessment model for interdependent transit networks under failures. Taking the interdependency relations, network topology, flow characteristics, and demand distribution into account, the proposed methodology explicitly quantifies the impacts of varying network interdependency on the resilience of interdependent networks. The approach was applied to the interdependent metro and bus networks of Xi’an, China. Results show the resilience of interdependent networks is greatly affected by node degree heterogeneity in topology. The higher the heterogeneity of interdependent nodes, the larger the network resilience becomes. The bidirectional function dependency among networks demonstrates dominant effects on the resilience of interdependent networks if one network is disrupted. If interdependent networks have homogeneous function, the resilience of the networks would be significantly improved with the increase in interdependency. The degree of flow matching between both networks plays a particularly important role in network resilience enhancement. Findings of this study would provide practical implications for the design and planning of interdependent infrastructure systems under disasters