Monash University, Institute of Transport Studies: World Transit Research (WTR)
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How Has the Paris Rail Public-Transportation Network Recovered After the COVID-19 Pandemic? Applying a Mixture of Regressions Model
No full textThrough a combination of regulations, fear of contagion, and changes in travelers’ habits, the COVID-19 pandemic affected the mobility of public-transit ridership worldwide. To understand the longer-term effects of the pandemic on public-transit ridership, we focus on the case of Paris, France, thanks to an open 5 year record of entries into more than 500 stations. To deal with the large volume of data, we use a statistical model that performs clustering and segmentation simultaneously while incorporating many exogenous variables, such as the day of the week or lockdowns, to account for their effect on the number of entries. We carry out an in-depth analysis of the results for the segments and clusters. Examining and comparing the regression coefficients across clusters and consecutive segments allows us to draw per-cluster and per-segment conclusions. We show that the number of weekday trips decreased in most clusters and that the reduction in weekly variations is proportional to the share of weekday trips in the volume of entries before the pandemic. In addition, we characterize the changes in the weekly profile: Thursday was replaced by Tuesday as the day with the highest ridership; because of teleworking, Friday became the least crowded weekday in clusters with strong differences between weekdays and weekends, while the lowest ridership weekday remains Monday in the other clusters
Who buys public transport accessible housing? Residential sorting in the Oslo region
No full textThis article investigates whether public transport (PT) accessibility is related to socioeconomic residential sorting in the Oslo region. There is widespread concern that PT services contribute to gentrification and exclusionary processes, although empirical evidence for this is limited. This study uses detailed dwelling transaction data and estimates conditional logit models on residential purchases among different income groups. The results show that the effect of PT accessibility on dwelling selection varies as the life course progresses. Families with children and older households display lower effects of PT accessibility on residential choice than young, childless households. At the same time, PT accessibility increases the likelihood of dwelling purchase more for high-income households than for households with a lower income. These results confirm that PT accessibility is a valued and limited residential asset, one that households with more economic resources are better able to obtain
Roadmap for transitioning India’s privately owned bus fleets to electric fleets
No full textThis study employs the Best-Worst Method (BWM) and Fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) to identify effective strategies and key drivers for electrifying bus fleets among Indian private operators. Among strategies, our analysis highlights battery leasing and gross cost contracting as favourable, while outright purchase as unfavourable. Our study implies that aggregating the demand of small and fragmented private operators is essential for widespread fleet electrification. Similarly, the depots must be flexible enough to accommodate and enable overnight charging for electric buses. Besides, increasing the share of renewable energy in the electricity mix and the reliability of electricity at depots are crucial. The technological advancements, the reliability of electric buses, and reduced battery costs can facilitate a smoother transition. Furthermore, supportive government policies are critical for accelerating the transition to electric fleets
A lifeline for the disconnected: A longitudinal study of a cable car\u27s impact on accessibility, satisfaction, and leisure activities
No full textUrban transport using aerial cable systems has emerged as a solution to connect neighborhoods that are traditionally difficult to access. This study investigates the impact of a new cable car line on accessibility, public transport satisfaction, and participation in leisure activities within a peripheral, low-income, and highly segregated community in Bogotá, Colombia. In addition to a pre-cable car assessment conducted in 2018, three follow-up assessments were carried out between 2019 and 2023, comparing a treatment area with a control area. The treatment area consists of households located in neighborhoods surrounding the cable car corridor, while the control area includes households in the vicinity of a future cable car line in an area with similar geographical and socioeconomic characteristics. Household surveys (N = 6376) were conducted in both groups before and after the cable car line\u27s inauguration, with 2052 surveys collected at baseline, 1679 during the first follow-up, 1419 in the second, and 1226 in the third. This study represents the first application of a large experimental-control group design around cable cars on a global scale. Despite the significance of this intervention, few transport studies have evaluated its impact using panel data over time. The panel design offers an opportunity to observe causal links between the project and the observed outcomes. The results indicate a significant increase in accessibility, satisfaction with public transport, and participation in leisure activities among the treatment group following the implementation of the cable car. However, women experienced a smaller increase in accessibility and expressed lower satisfaction with public transport, potentially due to their caregiving roles and concerns about safety and harassment. These findings underscore the important social and potential economic impacts that transport interventions can have on populations. Public investments of this nature are typically challenging to evaluate through social control trials, but the knowledge generated from this type of experimental evaluation could be highly valuable for policymakers
The effects of face consciousness on young travelers’ intention to adopt mobility as a service (MaaS): A case study in Shanghai, China
No full textDespite widespread interest in mobility as a service (MaaS), there is a lack of evidence regarding the potential impact of cultural values on its adoption intention. In this paper, we identify face consciousness as a key cultural differentiator in understanding the intention of young Chinese travelers under 40 to adopt MaaS. Based on 329 online survey samples in Shanghai, an extended technology acceptance model (TAM) was established to analyze the direct and indirect effects of perceived ease of use (PEU), perceived usefulness (PU), individual innovativeness (IN), subjective norms (SN) and face consciousness (FC) on MaaS adoption intention (AI). The results show that perceived ease of use, perceived usefulness, individual innovativeness and subjective norms have a significant positive impact on MaaS adoption intention, and face consciousness indirectly affects MaaS adoption intention through the mediating effect of subjective norms. Finally, this study discusses implications for market strategies and policy measures
Pathways to sustainable transportation in G-20 countries: Unveiling the role of green technology, green energy, green finance and digital economy using panel data and machine learning analyses
No full textAchieving sustainability has become a global priority, with environmental sustainability gaining significant attention from policymakers due to its critical importance. While numerous studies have empirically examined the relationship between CO2 emissions and green initiatives, they often overlook sectoral differences. Since different sectors contribute unequally to carbon emissions, the environmental effectiveness of green solutions may vary across sectors. This study addresses this research gap by focusing on the transport sector, a major contributor to global CO2 emissions. This study investigates the role of green technology, green energy, green finance, digital economy, economic growth, and urbanization on transport sector CO2 emissions in G-20 economies from 2002 to 2022. Using panel quantile regression, the results reveal that green energy and economic growth reduce transport emissions in lower quantiles; while green energy, green finance, and urbanization enhance environmental quality in upper quantiles. However, green technology is associated with higher transport emissions across all quantiles. Moreover, we employ a simple regression tree model, a machine learning approach, to identify which countries are winners and losers in terms of predicted transport emissions. Our results predict a 13.72 % increase in transport emissions across G-20 nations, with Japan, Australia, Saudi Arabia, the United States, Argentina, Russia, South Africa, South Korea, France, Brazil, India, and Italy among the most affected. These findings recommends shifting to non-motorized vehicles and public transportation systems that enhance transport efficiency and mitigate environmental degradation through green transportation
Assessing complementary and competing interactions between transit and shared transportation modes
No full textShared transportation modes have been widely introduced in cities over recent years. However, their interactions with existing transit systems, i.e., whether they complement or compete with them, are still unclear. This paper addresses this issue by exploring the relationships between two shared modes (bikesharing and free-floating carsharing) and traditional public transit (subway and bus) using several passive data streams from Montreal. A novel terminology is first proposed to define different types of interactions based on their impacts on the ridership of each mode in the short or long term. Building on this conceptual framework, a rule-based algorithm is developed to classify individual bikesharing and carsharing trips into distinct groups of potential complementary or competing relationships with transit. The spatial and temporal distributions of trips in different categories are then analyzed. Finally, the causal impacts of competing bikesharing trips on daily route-level transit ridership are assessed using a fixed effects difference-in-differences model. The results reveal that daily ridership on transit lines that would have been used in the absence of bikesharing may have been reduced by 1.3 % for every 100 bikesharing trips. However, the shared modes can also complement transit in time (when the service is closed), space (outside the service area or in the first/last mile connectivity), or when the service is unsatisfying (less direct or slower). Competition is most evident in the city center, at peak times, but can also help to relieve the most congested transit lines, and thus turn into a positive interaction in the long term
Coverage vs frequency: Is spatial coverage or temporal frequency more impactful on transit ridership?
No full textTransit ridership has long been studied, and the findings are elucidated by Taylor and Fink (2003) when they say, “to sum, transit ridership is largely, though not completely, a product of factors outside the control of transit managers.” Other than transit fare price, few studies have looked with much scrutiny at the factors that are within the purview of transit agencies. Transit service provision has been found to affect ridership, but “service provision” is often nebulously defined, shedding little light onto how transit managers can best provide service that will create returns in the form of transit ridership. This study examines the effects of spatial coverage and temporal frequency on transit ridership to determine just which lever is most effective. We use a cross-sectional study design with 152 regions around the United States. We employ structural equation modeling (SEM) to explain complex relationships that exist among interrelated variables. We find that both factors are strong predictors of transit ridership, with service frequency having a larger impact
An adaptive OD flow clustering method to identify heterogeneous urban mobility trends
No full textOrigin-Destination (OD) flow, as an abstract representation of the object\u27s movement or interaction, has been used to reveal the movement patterns of human activities and the coupling process of the human-land system. As a developing spatial analysis method, OD flow clustering can be used to identify the dominant trends and spatial structures of urban mobility. However, urban flow exhibits universal heterogeneity, which is mainly manifested in irregular shapes, uneven distribution, and obvious scale differences. The existing methods are constrained by specific spatial scales and sensitive parameter settings, making it difficult to reveal heterogeneous urban mobility patterns within travel OD data. In this paper, we propose an OD flow analysis method that integrates spatial statistics and density clustering. This method can determine parameter values from datasets without manual intervention and adaptively identify multi-scale mixed OD flow clusters. In the simulation experiment, the proposed method accurately detects all preset OD clusters with less noise. It outperforms the baseline methods in terms of Silhouette Coefficient, V-measure, and Fowlkes Mallows index. As a case study, this method is applied to OD data from Chengdu, China, extracting 63 representative flow clusters and revealing the trends of heterogeneous urban mobility across different lengths and densities for public transit optimization
