Monash University, Institute of Transport Studies: World Transit Research (WTR)
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The influence of working from home and underlying attitudes on the number of commuting and non-commuting trips by workers during 2020 and 2021 pre- and post-lockdown in Australia
No full textSince the start of 2020, we have seen major changes in the way communities operate. Mobility behaviour has been drastically impacted by work from home (WFH) and by lockdowns and restrictions in different jurisdictions. This study investigates the influence of WFH and different lockdown patterns on commuting and non-commuting trips in Australia by workers between early 2020 and late 2021. The data includes three waves of data collection to represent different lockdown periods. A multiple discrete–continuous extreme value (MDCEV) model is estimated to represent the number of one-way trips undertaken weekly with different purposes (commuting, work-related, education, shopping, personal business/social recreation), and by different modes (car, public transport, active modes). Explanatory variables include socioeconomic characteristics, location, the time period during the pandemic (i.e., waves). In addition, latent variables were included representing underlying attitudes such as satisfaction towards life or concern about the use of public transport – which might certainly play an important role in understanding individual weekly travel behaviour decisions. The model structure has the advantage that it estimates commuting and non-commuting activity together, allowing for a substitution effect between them. The results suggest that across all waves and jurisdictions, respondents who WFH more are more likely to have a higher number of shopping trips and personal business/social recreation trips, perhaps substituting these trips in replacement of their lesser commuting trips. Interestingly, all other influences held constant, individuals who are more concerned about the use of public transport are more likely to undertake commuting trips by all modes, more likely to do shopping trips, and less likely to undertake personal business/social recreation trips – suggesting they are prioritising essential trips rather than social/personal trips and perceive the risk of COVID-19 to be higher due to this travel
Artificial intelligence for improving public transport: a mapping study
No full textThe objective of this study is to provide a better understanding of the potential of using Artificial Intelligence (AI) to improve Public Transport (PT), by reviewing research literature. The selection process resulted in 87 scientific publications constituting a sample of how AI has been applied to improve PT. The review shows that the primary aims of using AI are to improve the service quality or to better understand traveller behaviour. Train and bus are the dominant modes of transport investigated. Furthermore, AI is mainly used for three tasks; the most frequent one is prediction, followed by an estimation of the current state, and resource allocation, including planning and scheduling. Only two studies concern automation; all the others provide different kinds of decision support for travellers, PT operators, PT planners, or municipalities. Most of the reviewed AI solutions require significant amounts of data related to the travellers and the PT system. Machine learning is the most frequently used AI technology, with some studies applying reasoning or heuristic search techniques. We conclude that there still remains a great potential of using AI to improve PT waiting to be explored, but that there are also some challenges that need to be considered. They are often related to data, e.g., that large datasets of high quality are needed, that substantial resources and time are needed to pre-process the data, or that the data compromise personal privacy. Further research is needed about how to handle these issues efficiently
Public transport mode choice behavior of different-income passengers during large-scale public health incidents
No full textPublic transport ridership has been hit hard by the COVID-19 pandemic in many countries. Investigating passengers\u27 public transport mode choice behavior during large-scale public health incidents can uncover the major influential factors and help propose policies and strategies to reduce the pandemic transmission and recover the public transport revenue. This study develops an integrated choice and latent variables (ICLV) model by income based on structural equation model to model passengers\u27 public transport choice behavior during the normalized stage of the pandemic. The model considers passengers\u27 socioeconomic attributes, travel attributes, and attitude-perception attributes, and can appropriately capture passengers\u27 psychological latent attributes. Taking Beijing China as an example, we collect some revealed preference survey data online. The modeling results show that the risk perception as a mediator variable has a significant impact on mode preference. Moreover, the convenience of public transport has the largest influence on risk perception. These findings suggest that risk perception and the convenience of public transport play a major role in passengers\u27 mode choice behavior. In addition, the impacts of the various influential factors on the public transport mode choices are significantly different across different income groups. Further, the ICLV model can achieve better performance and is superior to the traditional Multinomial Logit model. The modeling framework can help propose targeted and instructive strategies during the normalized stage of the pandemic by uncovering the major influential factors in passengers’ public transport mode choices, which is applicable to similar pandemics in the future
Optimizing bikeshare service to connect affordable housing units with transit service
No full textAffordable housing and transit accessibility have long been focal points for housing and urban development agencies at all levels, from national to regional. However, these elements are often considered and planned for independently, leading over time to an expanding spatial gap between affordable housing and transit services. This separation creates issues of mobility equity. Fortunately, bikeshare, an emerging mobility solution, provides the potential to bridge this spatial gap between affordable housing communities and transit services. To leverage this, we designed a bikeshare station (BSS) location optimization tool that aims to alleviate the impacts of this spatial divide through bikeshare services. Specifically, we developed a multimodal agent-based modeling (ABM) and simulation framework aimed at enhancing accessibility to a variety of destinations. To solve this optimization problem, we developed a genetic algorithm to determine the optimal locations for BSS. There are three main conclusions from this research. First, the strategic positioning of BSS will enhance accessibility for affordable housing community (AHC) residents by reducing average transit travel time and walk distance, or increasing the number of destinations accessible by transit. This underscores the advantages of combining bikeshare and transit systems to cater to the mobility requirements of AHC residents, especially for non-work-related trips in suburban areas. Second, the relationship between transit and bikeshare is twofold: complementation and substitution. In instances where a lengthy trip involves multiple transit transfers and significant waiting time, bikeshare could replace some transit trips. Lastly, but most importantly, this study advises a forward-looking approach for governments and operators to avoid the waste of public resources when planning bikeshare systems. The tool developed by this research can facilitate this forward-looking approach in practice
Behavioral differences between young adults and elderly travelers concerning the crowding effect on public transit after the COVID-19 pandemic
No full textAfter the COVID-19 pandemic, passengers\u27 concerns about crowding on public transit vehicles has increased due to their fear of being infected. Since WHO has announced that the elderly are the most vulnerable to COVID-19, it is important to understand the impact of crowding on the elderly and provide policy implications on public transit vehicles. The aim of this study is to analyze the behavioral differences between young adults and the elderly concerning the crowded conditions on public transit with a stated preference survey in Seoul metropolitan area. Crowding multipliers are estimated to quantify the impedance on crowding based on the mixed logit model. The results indicate that the elderly are more sensitive to crowding on public transit vehicles than younger people, and subway passengers are affected more by crowding than passengers who are using other modes of transit. In addition, it is confirmed that the preference of the elderly for public transit varies according to gender, income, and the purpose of travel. These analysis results can provide implications for setting new policy directions to improve the public hygiene and comfort of elderly people when they use public transit in the post-COVID-19 world
Resilience assessment of subway system to waterlogging disaster
No full textThe subway system is experiencing significant waterlogging challenges due to climate change and urbanization, and the enclosed underground structure makes this issue worsen. Identification of subway system waterlogging resilience (SSWR) and the development of improvement measures are critical. We proposed a “Stability-Resistance-Recovery” assessment framework of the SSWR based on the system performance curve. An integrated index system was established, which defined and quantified several indexes to capture unique characteristics of subway systems. The system used inundation results simulated by InfoWorks ICM model as trigger for waterlogging, providing accurate reflection of inundation situations around subway stations. A case study in Beijing identified the stability and resistance as the leading factors affecting the SSWR, with water bodies surface percentage, evening peak departure time interval, and population density having the strongest impacts. Subway system exhibited a relatively low level of waterlogging resilience, with 51.9% of stations indicating very low or low levels. Stations at medium SSWR were dispersed throughout the areas neighboring stations at very low or low SSWR. Stations at very high or high SSWR were minimal and scattered in peripheral areas. This study provides a widely applicable index system for the SSWR and helps decision-makers devise the improvement measures
Risk mitigation in urban bus concession contracts: Overcoming uncertainties with a real options model
No full textIn this paper, we analyzed a risk-sharing mechanism that can be used in urban bus public service contracts (PSC) to deal with demand uncertainty in the upcoming years. This mechanism is similar to a collar option: an MRG (Minimum Revenue Guarantee) combined with a revenue cap. We applied this mechanism to a case study, the Lisbon Metro Area bus contracts. We compared the chosen model (a gross cost contract) with a simulated scenario modeled as a collar option. To develop this scenario, we model the flexibilities applying Real options Analysis and the project uncertainty as a Geometric Brownian Motion (GBM). The calculations were performed using Monte Carlo simulation. The results show that, compared to the gross cost, the collar option model allows the private operator to benefit from a potential demand increase. In the case of a downturn, the loss is shared between the two sides, reducing the contract cost borne by the government. Furthermore, from a public policy perspective, contrary to the gross cost model, this model may incentivize the private partner to improve service quality as a strategy to increase demand and, consequently, revenues. In addition, if demand evolves in a different trend, the financial rebalance is made automatically, reducing litigation costs for both sides
Framework for evaluating online public opinions on urban rail transit services through social media data classification and mining
No full textUrban rail transit (URT) service quality assessments are pivotal for transport authorities to gauge passenger preferences and refine operational strategies. Online public opinion offers a vast pool of data at a reduced acquisition cost compared to traditional survey methods. However, current research lacks effective methodologies for classifying and interpreting extensive social media data (SMD) related to URT services. This study presents a comprehensive framework tailored to efficiently classify and mine public opinion on URT services from social media platforms. Leveraging data from ten Chinese cities with extensive URT networks, a domain-specific lexicon is semi-automatically constructed by integrating official documents (standards, policies, and annual reports) and high-frequency online terms. Additionally, a text classification algorithm based on this lexicon is proposed. Subsequently, sentiment, semantic, and timeline analyses are conducted on the classified texts to extract public opinion. Importantly, many manual steps employed in this study can be avoided when extended to other application scenarios. Therefore, this study contributes to the advancement of SMD processing efficiency in the URT domain and holds promise for broader applications in the fields of transportation management and policy-making
Analyzing spatiotemporal distribution patterns of metro ridership: Comparison between common-class and business-class carriage service
No full textUnderstanding differentiated services is pivotal for enhancing the appeal and diversity of the metro system, yet this facet has received relatively scant attention in existing literature. To bridge this research gap, our analysis delves into the business-class services offered by the metro and compares them with the common-class offerings. First, we illustrate the spatial and temporal patterns of business- and common-class ridership across stations and hours. Second, we construct two-stage geographically weighted regression models to identify key determinants and their spatiotemporally heterogeneous effects, focusing on land-use patterns, demographic considerations, and intermodal transfer modes. Leveraging one-week smart card data collected in Shenzhen from May 13th to 17th, 2019, our findings underscore the following aspects: (1) Spatial and temporal variations in business-class ridership across stations are linked to diverse land-use configurations. (2) Bus-metro transfers, business establishments, medical facilities, and the proportion of young commuters contribute significantly to the business-class ridership. (3) The emergence of business-class trip is weakly associated with bike-sharing activities but has a strong correlation with bus transfers. (4) Business establishments and medical facilities exhibit nuanced impacts on business-class travel, with excessive aggregation leading to unintended consequences. These insights offer valuable policy implications for fostering the development of business-class services in the metro systems of other Chinese cities
Post-pandemic transit commute: Lessons from focus group discussions on the experience of essential workers during COVID-19
No full textPublic transit services, which provide a critical lifeline for many essential workers, were severely interrupted during the COVID-19 pandemic. As institutions gradually return to normal in-person operations, it is critical to understand how the pandemic affected essential workers\u27 commute and what it will take to ensure the effective recovery of transit ridership and enhance the long-term resiliency and equity of public transportation systems for those who need it the most. This study used focus group discussions with essential workers who were pre-pandemic transit riders to understand how the COVID-19 pandemic has impacted their commute perceptions, experiences, motives, and challenges and explore the potential changes in their travel behavior post-pandemic. We used NVivo 12 Pro to conduct a thematic analysis of the transcripted discussion data and examined patterns of commute mode change with respect to participants\u27 attributes, including job type, home location, and gender. The results show that public transit had multiple reliability and frequency challenges during the pandemic, which resulted in most participants switching away from public transportation. With the increased availability of hybrid remote work and pandemic-related parking policies, driving emerged as a safer and more affordable commute mode for many pre-pandemic transit riders, rendering transit services less efficient for those who continued to rely on it. Planning for post-COVID resilient and reliable mobility requires a major rethinking of providing an efficient and effective transport system and a more fundamental approach to long-term public transport policy. To recover transit ridership, transit agencies need to ensure transit service availability and provide reliable transit information through smartphone apps. Similarly, transit agencies need to coordinate with other employers to provide free or heavily subsidized transit passes, to facilitate the recovery of transit demand effectively