Monash University, Institute of Transport Studies: World Transit Research (WTR)
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Assessing Tram-Train feasibility via multicriteria approach: The case of Brescia (Italy)
No full textTram-Trains (TTs) are advanced, sustainable mobility systems that merge the best features of trams with regional railways. However, integrating heavy rail and tram operations introduces complexity in various areas, including infrastructure (track design, vehicles, power supply, and facilities) and organisational management. Ensuring compatibility often requires compromise solutions, such as adjusting the track gauges, modifying vehicle specifications, integrating power supply systems, and coordinating scheduling and operational procedures. Additionally, although this system is familiar to the transport community, there is a notable lack of studies utilising relevant planning tools and evaluation frameworks to assess the system\u27s applicability and analyse potential alternatives, thereby enabling the selection of the best option for implementation in the target context. The framework applied in this paper aims to address this gap in the existing literature. It employs a combined multi-criteria analysis approach: Analytic Hierarchy Process (AHP) to establish a hierarchy of evaluation criteria and ELimination Et Choix Traduisant la REalité (ELECTRE) to determine the best compromise alternative. Moreover, the framework benefits from the input of practitioners and academics who were involved to assign importance to evaluation criteria and Monte Carlo simulation methods to include uncertainty. This framework enables the analysis of various design options and identifies the most suitable one for implementing a TT system in the studied context, precisely the territory of Brescia (Italy). Therefore, this study shows the potential viability of an interoperable system, such as the TT, which could be applicable in the selected area. These results are helpful since they might guide city planners and practitioners and serve as a benchmark for the scientific community while evaluating the applicability of the system
Impacts of COVID-19 on the premiums of proximity to railway stations: An in-depth analysis using passenger flows
No full textAs a result of the coronavirus (COVID-19) pandemic, work and leisure patterns have changed significantly, as have commuting and travel behavior. This has led to changes in the degree of dependence and demand for mass transportation. This paper proposes that these changes decrease people\u27s demand to live near public transportation services and cause the decline of the positive externality created by public transit as measured by house prices. This paper explores the train stations of Taiwan\u27s two largest cities (Taipei and Kaohsiung) by adopting various hedonic price models to estimate the COVID-19 effect on the premiums of proximity to rail stops. We then analyze whether the changes in premiums are affected by the passenger flow at the station. This paper uses four variables to measure passenger flows: the number of total passengers on services, the net number of passengers (the number leaving the local area minus the number of arriving at the local area), and the expected numbers of commuters and non-commuters. The empirical results of this paper show that after the COVID-19 outbreak, the monetary value of the proximity of railway stations decreased significantly. However, the COVID-19 effect on different stations is heterogeneous. The post-COVID-19 decrease in the positive externality associated with proximity to a rail station is significantly affected by passenger flows. The changes in the positive externality of stations found in this study suggest the public has reduced their dependence on stations due to changes in commuting behavior. We urge the government to pay attention to whether the commuting flows and the public\u27s demand for public transportation have shifted and the use of vehicles and road transportation has risen
Enhancing public transport use: The influence of soft pull interventions
No full textPublic transport (PT) success depends on targeted interventions, ranging first from push measures that discourage car use to pull measures that encourage PT use, and second from hard measures that intervene at physical infrastructures to soft measures that intervene at psychological elements of individuals\u27 behaviors. Focusing on soft-pull policy measures, and through a scoping review of 36 publications, we categorize these measures into three overarching groups: 1) Internally motivating strategies that gradually but firmly instill pro-sustainability attitudes and norms in people\u27s mind; 2) Satisfaction increasing strategies that primarily help retain current users especially those who feel forced to use PT and secondary attract new riders by improving the service factors and modifying travelers\u27 inaccurate perceptions of the service; 3) Stimulating PT-use and car-habit disrupting strategies such as attractive incentives and tailored information that encourage auto-drivers to give PT a try and break their car-habit. This review provides an analytical evaluation of each approach, offering recommendations for policy makers and PT service providers, along with identifying research gaps and suggesting future research directions
A spatial statistical approach to estimate bus stop demand using GIS-processed data
No full textThis study integrates the fields of geography, urban transit planning, and statistical learning to develop a sophisticated methodology for predicting bus demand at the stop level. It uses a Generalized Additive Model that captures non-linear relationships and incorporates spatial dependence, improving traditional methods. It showcases a high predictive capacity with a pseudo R-squared of 0.79 during its validation, ensuring substantial explanatory power for new observations. A large number of variables, including land-use characteristics, socioeconomic factors, and transit supply, are analysed. These widely available predictors facilitate the transferability of the methodology to other urban areas. Transit supply predictor considers the number of annual trips per stop and area as well as the location of stops along the lines that serve them. GIS processing of the data allows the calculation of variables within the areas of influence of each stop, obtained by following the walkable street network. For the case study, the presence of universities, hospitals, and lodgings areas, as well as inhabitants and ratio of bus trips show a positive impact on bus demand. This geo-analysis process employs accurate disaggregated data, such as information on uses in each building, as well as methods for assigning socioeconomic information from local areas to residential buildings. This study highlights the complex relationship between the location of transit network stops, both along the bus line and in terms of geographical proximity, their transit supply, and its surrounding factors. The results indicate that there is spatial dependence for stops less than 1.15 km apart. The developed methodology provides reliable information to transit network planners for decision making. Specifically, this proposed methodology can contribute to designing new routes, optimizing stop locations, and estimating the impact of changes in the transit network or urban planning on bus demand. All these improvement measures promote sustainable urban mobility, consequently fostering environmental and social benefits
Investigating the Effects of COVID-19 Pandemic on the Perception of Residential Accessibility in Greater Toronto and Hamilton Area
No full textThis paper investigates the changes caused by the COVID-19 pandemic on households’ perceived utility of the accessibility of their residence in the Greater Toronto and Hamilton Area (GTHA). The paper considers several neighborhood and dwelling attributes and fuses those with property price data from mid-March 2019 to mid-March 2021 to analyze changes in housing trends before and during the COVID-19 pandemic. Two mixed geographically weighted regression (MGWR) models are estimated for the year before the start of the pandemic and the year during the pandemic to address spatial autocorrelation and non-stationarity in price data. The empirical models reveal new patterns in accessibility perception for some factors, including accessibility to regional subway and inter-regional rail transit. This study also contributes to the literature of MGWR modeling by assessing the capacity of the model through validation procedures. Estimation is performed on randomly selected samples from the population to compare the errors with the population-based model and a traditional hedonic price model. The findings suggest that the application of MGWR is restricted to cases where price data are abundant
Using Geographically Weighted Models to Explore Temporal and Spatial Varying Impacts on Commute Trip Change Resulting from COVID-19
No full textCOVID-19 deeply affected people’s daily life and travel behaviors. Comprehending changes in travel behavior holds significant importance, making it imperative to investigate the influential factors of sociodemographics and socioeconomics on such behavior. This study used large-scale mobile device location data at the U.S. county level in the Washington, D.C., Maryland, and Virginia (DMV) area, U.S., to reveal the impacts of demographic and socioeconomic variables on commute trip change. The study investigated the impact of these variables on commuter trips over time and space. It reflected the short- and long-term impact of COVID-19 on travel behavior via linear regression and geographically weighted regression (GWR) models. The findings indicated that counties with a higher percentage of people using walking and biking (active mode) for commuting during the initial phase of COVID-19 experienced a greater reduction in their commute trips compared with others. Conversely, for the long-term effect of COVID-19 in November 2020, we can see the impact of using active mode on trip change is not significant any more and, instead, results showed people who were using bus and rail (public mode) for commuting decreased their trips more than others. Additionally, a positive correlation was observed between median income levels and the reduction in commute trips. On the other hand, sectors that necessitated ongoing outdoor operations during the pandemic, such as manufacturing, wholesale trade, and food services, showed a substantial negative correlation with trip change. Moreover, in the DMV area, counties with a higher proportion of Democrat voters experienced less trip reduction than others. Notably, by applying the GWR and multiscale GWR models, the local spatial relationships of variables and commuting behaviors were captured. The results showed the emergence of local correlations as the pandemic evolved, suggesting a geographical impact pattern. At the onset of COVID-19, the pandemic’s impact on commuting behaviors was global. However, as time passed, travel behavior became more influenced by spatial factors and started to show localized effects
Car Ownership, Commute Distance, and Commute Mode Choice in the Dense Megacity of a Developing Country: The Direct and Indirect Role of the Built Environment
No full textDespite much having been published about the effects of the built environment (BE) on urban travel in the developed world, few articles have so far been published based on studies using a megacity in a developing country. The paper addresses the existing gaps in research by conducting a study in Dhaka, one of the densest urban areas globally. An integrated framework based on the structural equation model and discrete choice model is used to examine how individual commute mode choice behavior is influenced by the BE, as mediated by car ownership and commute distance. Three BE features—population density, street connectivity, and job-to-household ratio—have a direct and total positive association with non-motorized transport use. Although being close to bus stops does not directly affect people’s choice to take non-motorized transport, it does promote non-motorized travel in an indirect way by decreasing car ownership and commute distance. Population density, job-to-household ratio, proximity to the nearest central business district, and bus stop proximity all have a positive direct and total impact on transit use, although larger employment densities directly support automobile use over transit. Understanding how the BE affects commute distance, car ownership, and mode choice is a useful reference for the development of practical measures to reduce demand for automobiles
Segmenting transit ridership: From crisis to opportunity
No full textCrises are an opportunity to learn, and transportation is no exception. The dramatic reduction in mobility levels during COVID-19, the slow recovery of transit ridership and new trends such as remote working have raised essential questions for the future of public transport. Our work focuses on transit rider segmentation, understanding the heterogeneity of users based on their behaviour before, during, and coming out of the pandemic, and what that means for the economic and social sustainability of transit systems. We asked ourselves two main questions: (i) will people continue riding transit after COVID-19? and (ii) what are riders’ reasons behind increasing, maintaining, or decreasing public transport use? Using a two-wave survey conducted in 2020 and 2021, we assessed the motives behind future public transport use in two Canadian cities (Toronto and Vancouver). We used quantitative and qualitative methods, particularly latent class cluster analysis (LCCA), text mining, and qualitative content analysis. We identified six transit riders’ profiles, ranging from those experiencing transport poverty who rely on public transport to those more resourced users who will ride less since they can choose alternatives such as remote work, private modes, or active travel. We discuss the policy and practice implications of these results, focusing on what public transport decision-makers should prioritize to benefit disadvantaged groups and recover ridership
An influence path analytic study for the operational performance of large passenger railway stations: The China case
No full textThe knowledge development of the influence mechanism for the operational performance of large passenger railway stations (OPLPRS) is of great significance for station managers in making comprehensive management decisions, particularly in determining the decision direction under limited allocatable resources. Hence, this paper is dedicated to conducting an empirical study on the influence path analysis of OPLPRS to enrich the relevant theoretical development and better assist in the choice of decision direction at the macro level. First, a hierarchical performance component system (HPCS) with multiple groupings is developed for OPLPRS, and several hypotheses are formulated for its internal structural relationships. Then, a multi-path analytic method based on structural equation modeling (SEM-MPAM) is proposed to (1) establish and validate the conceptual structure of HPCS and measure each path coefficient inside, and (2) quantitatively analyze the direct, indirect, and compound influence paths of each performance component or group on the OPLPRS with and without considering the decision timeliness. Survey data from two representative large passenger railway stations in China is applied to the proposed SEM-MPAM to conduct the influence path analysis. The results well support the developed hypotheses and path structure, yield the influence intensity of multiple paths, and therefore identify the critical influence paths of the OPLPRS under different conditions. Finally, the practical implications gained, the applications of multi-path analytic findings in meeting different decision-making needs, and the applicability of the proposed method are discussed, which helps station managers better seek the optimal improvement of OPLPRS in practice
Cooperative bus eco-approaching and lane-changing strategy in mixed connected and automated traffic environment
No full textIn mixed traffic environments, existing bus eco-approaching and lane-changing methods fail to adequately consider the uncontrollability of human-driven vehicles and their interactions with surrounding vehicles during lane changes, often leading to increased fuel consumption and emissions. To address this issue, this paper proposes a two-stage cooperative bus motion control model during lane-changing and approaching stops: the first stage finds the optimal positions and timings for bus lane changes in a mixed traffic environment; the second stage constructs a Model Predictive Control (MPC)-based cooperative lane change controller, which couples the lateral and longitudinal movements of buses. Using Lyapunov stability theory, the stability of this controller is demonstrated. A solution algorithm that integrates the Pontryagin Minimum Principle (PMP) and the Broyden–Fletcher–Goldfarb–Shanno Sequential Quadratic Programming (BFGS-SQP) method is proposed. The proposed model is tested on real-world cases with the Simulation of Urban Mobility (SUMO). The results show that, compared to traditional strategies, the cooperative strategy improves lane-changing efficiency by 14.95 %, reduces fuel consumption by 17.24 %, and increases traffic stability by 25.21 %. Under high-traffic conditions, the proposed strategy can significantly reduce lane-changing time by 25.18 %. The higher coefficient values in the objective function drive buses to adopt more proactive actions to quickly complete the space creation process. The results in a whole bus line show the proposed method increases the lane-changing success rate by 25.42 % and reduces the waiting time by 37.35 %