Monash University, Institute of Transport Studies: World Transit Research (WTR)
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Towards a history of transit etiquette: the development of orderly boarding practices in Tokyo
No full textThis article is a call for the historical study of transit etiquette: the behavioural expectations that guide the mundane conduct of transport users. It identifies the formation of contemporary protocols of transit etiquette as a productive line of scholarly inquiry by taking the transformation of (de)boarding behaviours in Tokyo between the 1880s and the 1960s as a case study. Zooming in on urban railways in the Japanese capital, it describes the processes through which (de)boarding practices grew more elaborate in character and more narrowly defined in terms of the spatio-temporal location at which they could be legitimately exercised. It examines three groups of factors that contributed to this process: “software” and “hardware” interventions in transport operations as well as their broader historic context. Simultaneously, it cautions against linear narratives of consistent improvement by stressing the contradictions of this process. The article contributes to mobility studies by calling attention to the malleability and socio-technical construction of the norms that guide mundane mobility practices. It provides a provisional template for subsequent historical accounts of transit etiquette, and argues that such studies can empower research on mobilities and transport to contribute to wider debates about (in)civility and the organisation of urban life
Multi-sequence spatio-temporal feature fusion network for peak-hour passenger flow prediction in urban rail transit
No full textThis research addresses the challenge of predicting URT station passenger flow during peak hour. The Multi-Sequence Spatio-Temporal Feature Fusion Network Model (MSSTFFN) based on trend decomposition is introduced to capture complex spatio-temporal correlations. This model combines seasonal trend decomposition, graph convolutional neural networks, and modified Transformer networks. The MSSTFFN model is evaluated using actual data from Hangzhou City. The results indicate that, in comparison to the baseline model, this model consistently delivers the best prediction results across various datasets as well as prediction tasks. It exhibits exceptional and consistent performance in prediction sub-tasks involving different input and prediction step combinations, highlighting its advanced, robust, and versatile nature. Through micro-comparisons of specific prediction results for different types of stations, the practical application value is verified. Furthermore, through the design of ablation experiments and testing on various datasets, the contribution value of the features and model’s generalization capability are validated
Energy intensity, GHG and pollutant emissions of freight and passenger rail applications in the United States
No full textThis research establishes a baseline for the energy intensity and emissions of freight and passenger rail applications, focusing on diesel locomotives. We quantified the diesel energy intensity for freight rail (in Btu/revenue ton-mile), and both diesel and electric energy intensity for passenger rail (in Btu/passenger-mile) using publicly available data. Emissions of HC, CO, NOX, and PM emissions from diesel locomotives were analyzed based on real world emissions testing of in-use locomotives and compared to EPA standards. Additionally, greenhouse gas (GHG) emissions from freight and passenger rails were compared to those from other transportation modes, including heavy-duty trucks, transit buses, and freight and passenger aircrafts, using a well-to-wheels (WTW) approach. Results show that freight rail powered by diesel engines produces lower WTW emissions when compared to competing freight transportation modes such as trucks and aviation. In contrast, passenger rail exhibits higher overall WTW emissions compared to other passenger transportation modes (e.g. cars, transit buses, aviation), and greater variability primarily due to the fluctuations in passenger load factors
Optimal design of bimodal hierarchical transit systems: Tradeoffs between costs and CO2 emissions
No full textTo investigate the environmental implications of a collaborative approach between fixed-route transit and demand-responsive transit, this paper studies a bimodal hierarchical transit system, with a specific focus on the impact of CO2 emissions. In this system, demand-responsive transit serves regions with lower demand, while fixed-route transit extends its service to the central business district. Subsequently, a continuous approximation model is formulated, optimizing critical network design parameters such as stop spacing, line spacing, and headway. This optimization process balances passengers\u27 time costs, agency expenditures, and environmental considerations. A series of comparative analyses are conducted to assess the influence of incorporating emission-related costs into the network design. The inclusion of environmental factors in the design process results in an approximate 8.3% reduction in CO2 emissions. Furthermore, numerical case studies are presented, encompassing a broader spectrum of key design parameters, including the value of time and variations in demand. The findings also demonstrate that the proposed hierarchical transit system exhibits lower CO2 emissions when compared to the paired-line system
Valuation of stochastic occupancy levels and public transport policy options during the COVID pandemic
No full textThis study aims to evaluate passengers’ valuations of occupancy levels and public transport policy options during the COVID-pandemic. An important objective of the research is to understand how passengers value uncertainty in the occupancy level for their trip.
We estimate a mixed logit model, using stated choice experiments among 195 respondents. One of the features of the stated choice experiments is a simple representation of probabilities for different occupancy levels.
Our results suggest that the highest occupancy level dominates passengers’ choices, regardless of probability. This implies that respondents have a strong aversion to high occupancy rates, even at low probabilities. In terms of policy options, we find that respondents value blocked seats positively, which is consistent with the aversion to high occupancy rates. The obligation to wear face masks and reserving seats for travelers in vital professions are also valued positively.
Blocked seats, obligatory face masks and reserving seats for vital professions are viable policy instruments in a future pandemic. Moreover, the strong aversion to high occupancy rates may also be relevant for public transport policy in times without pandemic. Further research could be aimed at testing the relevance and order of magnitude of this finding in the post-COVID era
How does the perception of safety on paratransit influence usage and willingness to pay
No full textThis paper investigates how the perception of safety influences the decision on using paratransit in Cochabamba, Bolivia and how safety or safety improvements influence the willingness to pay (WTP) for paratransit, a service provided by many private organizations. Previous studies have already documented that paratransit in South America has a problem with insecurity, such as harassment, robbery, and other crimes. This research also shows that paratransit users in Cochabamba have in many cases already experienced crimes in traffic as well. Examining survey data by a logistic regression produced fairly robust results and makes it possible to predict and understand who will or will not pay for more safety. Many respondents indicated that safety impacts their decision to use paratransit. If the city of Cochabamba chooses to increase its transport fares for this purpose, policy-makers should differentiate fares according to social-demographic criteria, which have a major influence on residents\u27 willingness to pay
Exploring spatiotemporal characteristics of ride-hailing ridership connecting with metro stations: A comparative analysis of holidays, weekdays, and weekends
No full textRide-hailing services offer practical solutions for addressing “first- and last-mile” connectivity challenges at metro stations. While previous research has explored the spatiotemporal patterns of metro station-based ride-hailing ridership (MBRR) on weekdays and weekends, it has largely overlooked the unique dynamics of holiday periods. Furthermore, the influence of the built environment on first-mile MBRR (FM-MBRR) and last-mile MBRR (LM-MBRR) has received insufficient attention. To address these gaps, this study investigates the characteristics of MBRR across regular weekdays, weekends, Valentine\u27s Day, and the Spring Festival. We employed ordinary least squares (OLS) and spatial lag regression (SLR) models to analyze the impact of the built environment on MBRR at the station level. Using data from Shenzhen, our findings reveal that: 1) Metro station-based ride-hailing is predominantly used for accessing metro stations, with FM-MBRR consistently exceeding LM-MBRR. 2) The Spring Festival results in a decrease in MBRR, while Valentine\u27s Day exhibits an increase in post-work activity and nighttime MBRR. 3) On Valentine\u27s Day, travel distance positively influences FM-MBRR, reflecting longer ride-hailing trips for holiday-related activities. During the Spring Festival, tourist attractions significantly influence both FM-MBRR and LM-MBRR, highlighting the role of tourism in shaping holiday mobility patterns. These findings provide valuable insights for integrating ride-hailing services with metro systems, emphasizing the need to account for holiday-specific dynamics and local built environment characteristics in urban transportation planning
Time-dependent associations between accessibility to tram stops, proximity to tram tracks, and property prices: From construction to operation
No full textResearch on how accessibility to tram stops and proximity to tram tracks affect property prices has been limited. Additionally, the time-dependent effects of the tram system and its effects at different price levels remain underexplored. This study fills these gaps by analyzing the relationship between Chengdu Tram Line 2 and nearby property prices. Using a before-and-after treatment-control design and a dataset of 33,150 property transactions over six years, it applies multilevel hedonic price, difference-in-differences (DID), and quantile regression models to investigate the association between accessibility to tram stops, proximity to tram tracks, and property prices during various phases (e.g., construction and operation phases). Our findings are listed below. First, the positive influence of accessibility to tram stops only becomes significant during the operation phase. Specifically, property prices within 800 m of tram stops are 1.4 % higher than those farther away. Second, price penalties induced by proximity to tram tracks persist throughout the construction and operation phases. Third, the impact of accessibility to tram stops varies significantly across different price levels. Specifically, buyers of low-priced properties are more willing to pay a premium for accessibility to tram stops, whereas purchasers of high-end properties prefer greater distances from tram tracks to avoid nuisances. The results highlight the time-dependent accessibility benefits and negative externalities linked to tram services. Finally, policy implications, such as measures to alleviate the disturbances caused by tram tracks, are discussed
Exploring urban railway station-based attractiveness considering demographic-specific demands: Case study of Odakyu line, Japan
No full textUrban railways play a crucial role in meeting the commuting demands of rapidly growing megacities. In alignment with United Nations\u27 sustainable development goals (SDGs), integrating sustainable, convenient, and high-quality living environments around railway stations presents a significant challenge for urban planners and railway operators. This study introduces an innovative method for quantifying railway station attractiveness by analysing surrounding Points of Interest. Using a major railway line within the Tokyo metropolitan area as a case study, the research extends traditional gravity-based models to account for demographic-specific demands, illuminating the dynamic interactions between stations and diverse passenger groups. The findings reveal a disparity between high ridership levels and station attractiveness, emphasizing the importance of addressing the specific needs of various demographic segments. Accessibility emerges as a critical factor influencing attractiveness, with notable variations across different groups, reflecting their unique priorities and behaviours. These insights highlight the need for targeted strategies to enhance station attractiveness, catering to key demographic groups such as individuals with accessibility requirements and tourists. By prioritising passenger-focused services and improving station environments, this approach contributes significantly to the sustainable development of megacities, reinforcing their commitment to SDGs
An advanced learning environment and a scalable deep reinforcement learning approach for rolling stock circulation on urban rail transit line
No full textRolling stock circulation is the process of assigning rolling stocks to a set of predetermined train trips with fixed departure and arrival times. This paper considers mathematical models, solving approaches, and numerical experiments for hypothesized and real-world cases of rolling stock circulation, in which two end-point depots of an urban rail transit line are involved. The objective aims to minimize the total number of rolling stocks in utilization, to balance the workload of the utilized rolling stocks, and to balance the numbers of rolling stocks available at each depot at the beginning and the end of the planning horizon. To achieve the goals, a multi-commodity flow model and a deep reinforcement learning framework for the rolling stock circulation problem are proposed, accommodating the use of multiple types of rolling stocks, of which the former is a non-linear integer programming model. The multi-commodity flow model is solved by the CP Optimizer embedded in ILOG CPLEX and a custom-developed Ant Colony Optimization algorithm, serving as the exact and heuristic benchmarks respectively. The rolling stock circulation problem is innovatively modeled as a Markov decision process within the deep reinforcement learning framework, incorporating an advanced learning environment. This environment is designed by embedding state definition, constraint detection, and reward assignment, enabling effective interaction with the agent. A proximal policy optimization algorithm with a proximal policy update mechanism and adaptive policy-learning rates is adopted to solve the proposed problem. Numerical experiments on hypothesized and real-world cases illustrate the effectiveness of the proposed deep reinforcement learning method for rolling stock circulation. Compared to the benchmark approach, deep reinforcement learning can improve the solution quality with the problem scale increasing, which proves the adaptiveness to applications with complex environments and large state spaces and shows the strong potential to generalize across problems with different scales