Monash University, Institute of Transport Studies: World Transit Research (WTR)
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Im/mobilising bus travel as an infrastructure of care: student experiences in a mid-size city
No full textMany universities are transforming campuses by responding to globally significant, locally specific, economic, political, and social imperatives. Some are implementing urban and regional transformations in higher education delivery to increase student access and diversity. Their success can depend upon infrastructures provided by other parties. Public transport is an example. Transit accessibility and equity affect quality of life, livelihoods, life course, and liveability in cities. Growing numbers of international studies consider factors shaping student travel to and from university campuses by public transport; fewer address local socio-spatial experiences of travel. Informed by debates about differential accessibility of suburban and city campuses, we examined student experiences at an Australian regional university undergoing transformation. We report on a study assessing multiple trips to and from two campuses to five destinations. Rich insights were drawn from experiences of antisocial behaviour, vulnerability, and sub-optimal service provision and reveal why some students think public transport is a mode of last resort. Universities and their stakeholders need to know more about student experiences of mobility. Such knowledge could inform tailored transport interventions and universities’ willingness to encourage public transport providers to view their services as infrastructures of care
Visualizing ventilation in the bus: Addressing risk perception in public transport passengers
No full textBackground This study evaluated the effectiveness of a communicative intervention in addressing passengers’ increased risk perception of getting infected with COVID-19 in public buses and thereby improving travel-related well-being after the pandemic. Method A pre-registered quasi-experimental field-study (N = 306) was conducted in two public bus line bundles. The intervention consisted of visualizing the fresh air supply in the bus via tinsel garlands. Findings The intervention successfully increased passengers’ awareness of fresh air supply in the bus (in the intervention group as compared to the control group; d = 0.25). However, this awareness did neither reduce passengers’ risk perception, nor did it increase their subjective well-being in the bus. An explorative analysis identified crowding, and general COVID-19 risk perception as major predictors of risk perception on-board. Conclusion The study revealed first-hand, real-time insights in bus passengers’ risk perception, travel well-being and their major predictors during the out fading COVID-19 pandemic
Unveiling the drivers of modal switch from motorcycles to public transport in Southeast Asia
No full textMost studies on commuter and public transport mode choice rely on theoretical frameworks that draw boundaries between the utility function, social-ecological system, demographic and socio-economic attributes, and psychological theory. While models predicting the shift towards public transport are commonly applied in developed countries, there is a lack of similar studies that construct these models in developing countries. In addition, in some developing countries, the motorcycle is one of the main private motorised travel modes. Therefore, the modal shift from motorcycles to public transport in Southeast Asia to reduce private mobility is one of the important topics to examine. This paper presents a systematic literature review, utilising a comprehensive search strategy across electronic databases and reputable sources to identify directed acyclic graphs (DAGs), revealing factors influencing the choice and intention to shift to public transport. Data synthesis from selected studies highlights the intrinsic and extrinsic variables influencing public transport use, motorcycle use, and the modal shift to public transport. The study also proposes a theoretical framework for the modal shift in usage from motorcycles to public transport
Predicting passenger satisfaction in public transportation using machine learning models
No full textEnhancing the understanding of passenger satisfaction in public transportation is crucial for operators to refine transit services and to establish and elevate quality standards. While many researchers have tackled this issue using diverse tools and methods, the prevalent approach involves surveys with discrete choice models or structural equations. However, a common limitation of these models lies in their inherent assumptions and predefined relationships between dependent and independent variables.
To address these limitations, we introduce a novel perspective by harnessing machine learning (ML) models to gauge and predict passenger satisfaction. ML models are advantageous when dealing with complex, non-linear relationships and massive datasets, and do not rely on predefined assumptions. Thus, in this paper, we evaluate four ML models for the prediction of ratings of the quality of transit service. These models were calibrated using data from the Transantiago bus system in Chile.
Among the ML models, the Random Forest model emerges as the most effective, showcasing its ability to analyze and predict passengers’ satisfaction levels. We delve deeper into its capabilities by examining the impact of three pivotal variables on passengers’ score ratings: waiting time, bus occupation, and bus speed. The Random Forest model is able to capture threshold values for these variables that significantly influence or have no effect on passenger preferences
Quantifying weather-induced unreliable public transportation service in cold regions under future climate model scenarios
No full textClimate change, particularly in cold regions, significantly challenges public transportation systems. This study conducts a comprehensive analysis of weather patterns and public transit reliability in the context of climate change impacts. Leveraging advanced modeling techniques, including a ridge regression model for snow water equivalent data estimation and a long short-term memory (LSTM) based on recurrent neural network, the study aims to assess the reliability trends of the rapid transit system under various climate scenarios. The findings reveal that climate change in general increases weather-related delays in the Toronto transit system. The number of short delays decreased accordingly due to changes in winter temperatures but exacerbated long delays as the number of weather extremes increased. The LSTM model performed effectively in predicting delays, especially for the rapid transit system sensitive to weather variations. This study emphasizes the need for robust planning and interventions to increase the resilience of transit systems against climate change and highlights the importance of the integration of climate and extreme weather considerations into transportation management
Vehicle pricing considering EVs promotion and public transportation investment under governmental policies on sustainable transportation development: The case of Norway
No full textThe present research investigates the advancement of sustainable transportation by promoting electric vehicles, expanding public transit, and implementing tax increases on fossil fuels and automobiles, via crucial strategies. The present study has examined a market with a fossil fuel vehicle (FFV) manufacturer, an electric vehicle manufacturer, and the government. The manufacturers\u27 pricing strategies are geared towards optimizing profits, whereas the government\u27s objectives for transportation are centered on attaining sustainability across economic, environmental, and social dimensions. The government has considered two strategies, one taxation-based and the other non-taxation-based, to achieve its objectives. In the taxation-based strategy, fixed taxes on fossil-fuel vehicles and fossil fuels are considered, while in the non-taxation-based strategy, subsidies for purchasing electric vehicles and the number of buses purchased for public transportation are predefined. Based on this, eight scenarios with different values for the predefined factors of each strategy have been defined to attain their respective objectives. Game theory has been utilized to determine an optimal solution based on Norwegian data, owing to the interconnected decision-making among stakeholders. By analyzing the findings, managers can enhance their decision-making capabilities. The results suggest that appropriately adjusting subsidies, taxes, and bus numbers within specific thresholds is crucial for mitigating air pollution and achieving other objectives. The assertion that electric cars are a definitive solution may not be accurate, as it depends on the source of the generated electricity. While excessive subsidies in non-renewable energy countries can lead to increased pollution, even in Norway, where most electricity is renewable, rising demand for electric vehicles contributes to air pollution due to increased production. In countries where electricity generation is not primarily from renewable resources, the growth of electric vehicles further exacerbates pollution. Simultaneous increases in fuel taxes and tariffs on fossil fuel vehicles negatively impact the demand for fossil fuel vehicles and government revenues. Therefore, it is recommended that an increase in fuel taxes be implemented to optimize both government revenues and social welfare. This approach safeguards public revenues, promotes societal well-being, and mitigates air pollution
Evaluating the 15-minute city paradigm across urban districts: A mobility-based approach in Hamilton, New Zealand
No full textThis study explores the ‘15-minute city’ concept in Hamilton, New Zealand, focusing on challenges related to car dependency and urban sprawl. Triggered by the greater emphasis on sustainable urban environments following the global pandemic, the research employs a mobility-based approach to assess the model\u27s applicability across various urban districts. Geographic Information System (GIS) mapping is used to identify ‘liveable areas’ in business, residential, and industrial districts where essential services are accessible within three different thresholds of 5, 10, and 15 min walking distance. This tiered approach offers a detailed view of urban accessibility, highlighting the practicality and varying implementation levels of the 15-minute city concept across diverse urban areas. Geolocated mobile phone data from 88,660 residents is analysed with a focus on ‘inflow’ and ‘outflow’ travel distances at both city and district levels. While the results reveal the practical challenges of implementing the ‘15-minute city’ paradigm, they also show partial alignment of Hamilton\u27s urban fabric with the paradigm, offering scope for adjustments to better suit the city\u27s specific characteristics and residents\u27 behaviours. The study highlights opportunities for enhancing the diversity and accessibility of amenities and improving public transportation and alternative transport options, all key factors for sustainable urban development. This adaptable methodology serves as a valuable reference for other cities in developing strategies for sustainable living. The study concludes that while Hamilton shows potential for transformation, a nuanced and locally focused approach is crucial. These insights contribute to the current new urbanist literature by providing a comprehensive city-district perspective, extending the discourse to include distances beyond the ‘15-minute city’ and highlighting areas where further urban planning or intervention is necessary
Enhancing accessibility through rail transit in congested urban areas: A cross-regional analysis
No full textThe efficacy of urban rail transit in reducing the impact of road congestion is the subject of debate in policy and academic communities. Despite huge challenges arising from multifaceted factors such as data limitation, the recently released annual average congestion data enables us to empirically analyze the effect of urban rail transits in terms of their contribution to the average accessibility under road congestion. This paper presents a framework to evaluate the accessibility improvement benefit of urban rail transit under road congestion from a mean-field perspective. A cross-regional analysis in 43 Chinese cities demonstrates that the rail transit networks enhance the potential accessibility and contour accessibility by up to 5.07% and 12.09%, respectively. Besides, the accessibility improvement benefit largely depends on the road congestion level, rail network size, and layout of rail network. Furthermore, simulations on randomly generated rail transit networks indicate that the population coverage and proximity to roads are two structural determinants on the performance of rail transit. The proposed framework, together with the empirical findings, provides insights for city authorities to plan urban rail networks
Evaluating the impacts of supply-demand dynamics and distance decay effects on public transit project assessment: A study of healthcare accessibility and inequalities
No full textPrevious studies evaluating the impacts of transport interventions on accessibility to healthcare have largely overlooked competition among patients for limited resources and the tendency to use healthcare located closer to them (i.e., distance decay effects). This study aims to demonstrate how overlooking supply-demand dynamics and distance decay effects can distort the evaluation of a new public transit service\u27s impacts on healthcare accessibility and inequalities. Specifically, using a new bus rapid transit (BRT) service in Columbus, Ohio, USA, as an example, we compare three types of measures: 1) cumulative-opportunity, 2) multimodal two-step floating catchment area (2SFCA), and 3) multimodal generalized 2SFCA (G2SFCA) accessibility metrics. To understand the similarities in results obtained from different accessibility measures, we use the Pearson correlation coefficients of standardized accessibility change scores. Further, we leverage an inequality index, the Palma ratios, to examine how inequality assessments can be sensitive to the choice of accessibility measure. The correlation analysis reveals notable differences among the three measures. This indicates that neglecting supply-demand dynamics and distance decay effects can lead to differences in results, potentially distorting transit project evaluations and misleading stakeholders. Moreover, although overall conclusions about inequalities are largely consistent, we observed nuanced and statistically significant differences in Palma ratios when derived from cumulative-opportunity metrics compared to the multimodal 2SFCA and multimodal generalized 2SFCA measures. Our findings underscore the importance of considering supply-demand dynamics and distance decay effects for a more realistic and accurate assessment of new transit service\u27s impacts on healthcare accessibility and inequalities
Fuel and infrastructure options for electrifying public transit: A data-driven micro-simulation approach
No full textElectric vehicles (EVs) have been widely introduced into the bus fleet while the short driving range and long charging time for battery electric buses (BEBs) are the two main barriers. Thus, bus operators are considering alternatives. Hydrogen buses (HBs) could be a promising option because of their longer driving range and shorter refueling time compared to BEBs. However, introducing HBs would be costly and thus it remains unclear which fuel option (hydrogen or electricity) is more feasible for an electrified public transit system. In response, this study proposed a data-driven micro-simulation approach to compare the system cost and level of service (i.e., the delay time of deviating from the timetable caused by charging events) with different fuel options (electricity or hydrogen) for electrifying public transit, using real-world bus operation information extracted from a GPS bus trajectory dataset in Shenzhen, China. The results suggested that the charging demands of BEBs tended to be concentrated in the central and northwest areas of the city while the refueling demands of HBs were more evenly distributed in not only the center but also the southwest and northeast areas. These resulted in different layouts of charging/hydrogen stations accordingly. Furthermore, given almost the same level of service to maintain, the system cost of the HB scenario could be 48.2% higher than that of the BEB scenario. Therefore, BEBs tended to be more economically feasible in Shenzhen