Monash University, Institute of Transport Studies: World Transit Research (WTR)
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How does artificial intelligence usage affect the safety behavior of bus drivers? A double-edged sword study
No full textThis study aims to provide a framework for understanding the mechanism by which artificial intelligence (AI) usage affects the safety behavior of bus drivers through cognitive appraisal theory. We examined data from 555 bus drivers at three-time points. Our findings indicate that AI usage is positively related to both safety self-efficacy and job insecurity, which in turn are linked to safety behavior. Safety self-efficacy and job insecurity mediate the relationship between AI usage and safety behavior. Additionally, we found that trait resilience moderates the positive relationship between AI usage and safety self-efficacy, as well as the relationship between AI usage and job insecurity. Furthermore, trait resilience moderates the indirect effect of AI usage on safety behavior through safety self-efficacy and job insecurity. The results suggest that AI usage has two faces, both enhancing and impairing the safety behavior of bus drivers. These findings are crucial for management theory and practice
Electric bus fleet charging management: A robust optimisation framework addressing battery ageing, time-of-use tariffs, and energy consumption uncertainty
No full textThe large-scale adoption of electric buses offers sustainable and reliable transportation, but it poses challenges in designing appropriate charging strategies to accommodate the operation requirements of the fleets. The optimisation of those strategies is crucial to avoid disrupting daily operations due to insufficient energy for trips, aiming to minimise operational costs and grid overload because of coincident peak demand. This work introduces a robust optimisation model to provide solutions accounting for uncertainties in energy consumption, enabling operators to establish cost-effective and resilient charging plans. The model includes features such as battery ageing, time-of-use tariffs, vehicle-to-grid (V2G), and operational constraints. We propose a reformulation approach to solve the model and deal with its computational complexity. An illustrative case study is conducted using real-world data from a mid-sized city in Portugal. The findings indicate significant cost reductions through coordinated charging, with deterministic and robust models achieving 37 % and 12 % reductions, respectively, compared to a business-as-usual charging scenario. Further, V2G activities generate additional revenue, also emphasizing the importance of considering degradation costs to reduce battery capacity fade. Additionally, the effectiveness of the robust approach in addressing energy consumption uncertainty is demonstrated, offering operators a flexible method to adapt to various operational contexts and improve bus transportation service reliability
Does transit-oriented development (TOD) influence perceived safety and mode choice?
No full textTransit-oriented development (TOD) is an established urban planning principle for increasing public transport (PT) use. However, whether TOD enhances perceived safety and increases PT use remains an open question. This study analyzes the link between mode choice, perceived safety, and TOD dimensions on a large dataset covering the Greater Copenhagen area in Denmark. Using survey data and site observations, we first estimate multiple linear regression models to show which TOD dimensions enhance individuals’ perceived safety at train stations. Then, using large-scale travel survey data encompassing 21,844 trips between 2009 and 2018, including various user socioeconomic variables, we estimate a mode choice model in which TOD score and perceived safety are used as explanatory variables. Our results provide empirical evidence showing that the safety dimension of TOD significantly increases perceived safety and that perceived safety at both the home and activity ends of the trip influences the likelihood of an individual choosing PT. Only a higher TOD score at the activity end significantly increases PT use, whereas park-and-ride lots at the activity end reduce it and make cycling less attractive at both trip ends. Distance to the nearest stations/stops and service headway have a significant influence on mode choice at both ends of the trip. These results indicate that dense urban development around stations supports PT use and cycling more strongly than allocating space to park-and-ride lots. Our results are important for policymakers seeking to use TOD guidelines to increase individuals’ perceived safety and PT use in cities
The affects and emotions of everyday commutes in Kolkata: shaping women’s public transport mobility
No full textPublic transport inherently involves encounters with other people. For women, negotiating everyday overcrowded, unsafe, and unreliable conditions is a major barrier to accessing public transport mobility that triggers emotions. Using qualitative research methods – in-depth interviews and visual surveys – this study delves beyond understanding the barriers and looks at the affective realm to comprehend how affects and emotions shape accessibility, acceptability, and affordability of public transport for women in Kolkata. The disruptive affects of overcrowded, unsafe, and unreliable conditions produce emotional ordeals, increase travel time and costs, and restrict mobility. The sense of despair that emerges compels women to adjust, accept, and even opt out of overcrowded, unsafe, and unreliable public transport more often than not. This paper argues that affects, emotions, reactions, and consequences are entangled and impact the accessibility, acceptability, and affordability of public transport. The contribution of this paper lies in bringing to the fore the need for feminist inquiries into gendered mobility inequalities and the role of affects and emotions therein
Disrupted intermodality: Examining adaptation strategies to public transport e-scooter bans in Barcelona
No full textElectric scooters (e-scooters) have changed urban mobility by offering a dynamic solution to the critical “first and last mile” problem, connecting individuals from their homes to public transport and their final destinations. Despite their growing popularity, e-scooters navigate through a landscape of shifting legal frameworks, highlighting the urgency for policies that not only harness their potential but also address their inherent challenges. This study aims to shed light on the intermodal practices and demographics of e-scooters users in Barcelona, explores the potential impacts of regulatory changes on established transport habits, and assesses the adaptability of users to changing transportation options. Through a self-reported survey of 311 private e-scooter users, we find a notable prevalence of young men from lower socioeconomic backgrounds engaging in intermodal travel, primarily for employment purposes. To better understand how e-scooter riders integrate the device in their daily mobility strategies, we introduce the Intermodality Ratio (IR). A Generalized Linear Model (GLM) is then used to identify key demographic, socioeconomic, and geographic predictors of the IR, revealing place of residence as the most significant factor influencing intermodal behavior. Finally, we analyze participants’ anticipated behavioral shifts in response to the upcoming ban using a Multinomial Logistic Regression (MLR) model, which explores the sociodemographic factors affecting the likelihood of adopting alternative transport strategies. These findings contribute to the limited understanding of e-scooter utilization and intermodal practices, particularly within the context of public transit, offering insights into how transport policies can more effectively accommodate emerging mobility solutions
Unveiling coopetition dynamics between shared mobility and public transport: A game-theoretic approach
No full textThe liberalization of the transport market and advancements in real-time information technologies have prospered various shared mobility services, such as ridesourcing and carsharing. The emergence of these services complicates the relationships between them and public transport, as they often compete and cooperate simultaneously. This study develops a game-theoretic model to unveil these interactions using a multi-leader single-follower framework. In this framework, operators set their service rates as leaders, while travelers are assigned to services based on a logit model, which influences the profitability of both operators. The public transport operator may also subsidize travelers who use shared mobility service to access first- or last-mile trips, referring to as the bundle services. We reformulate the resulting nonlinear, nonconvex problem into a standard convex bilevel model by using outer linear approximations and applying KKT conditions to replace the lower-level problem. An iterative algorithm is developed to solve the game-theoretical model, complemented by an optimization-based bound tightening technique to enhance solution efficiency and accuracy. Our findings show that smaller operators, limited by budget constraints, are more likely to cooperate in bundle services for longer distances but tend to compete for shorter distances. In contrast, larger operators strategically alternate between competition and cooperation based on market conditions. Furthermore, well-designed subsidies in the bundle services can incentivize cooperation between shared mobility and public transport, benefiting both operators and travelers
A real-time prediction framework for energy consumption of electric buses using integrated Machine learning algorithms
No full textAn accurate prediction of energy consumption in electric buses (EBs) can effectively reduce driving range anxiety and facilitate bus scheduling. Existing studies have not provided real-time predictions based on distance traveled using integrated machine learning methods. This study proposes a framework for predicting EB energy consumption, which is primarily divided into energy consumption estimation, kinematic feature prediction, and energy consumption prediction. The framework begins by fusing high-resolution real-world EB data with weather and road information, from which five types of influencing factors are extracted for different driving distances. An eXtreme Gradient Boosting (XGBoost) model is developed to evaluate feature importance and estimate the energy consumption rate (ECR). The SHapley Additive explanation (SHAP) method is then used to analyze the factors affecting the ECR. To predict important kinematic characteristics, spatial and temporal characteristics are captured using Long Short-Term Memory (LSTM) and a fully connected neural network. Finally, the predicted kinematic characteristics and the XGBoost model are combined to enable real-time prediction of the ECR. The results indicate that estimation and prediction accuracies gradually improve with increased driving distance. The mean absolute error of average ECR decreases from 43.9 % for 100 m to 7.5 % for 16 km. Temperature, bus stop density, and peak periods emerge as the most significant external factors after 8 km. This framework shows an improvement of over 10 % in most scenarios compared with other models in the literature, enabling individual forecasts of energy consumption currently in transit and aiding in the calculation of remaining battery-supported distance
Biosignal-based attention monitoring for evaluating train driver safety-relevant tasks
No full textThis study explores the impact of biosignal-based attention monitoring on train driver performance in the context of smartphone usage, a critical factor influencing railroad safety. The persistent problem of smartphone distractions, which severely impair situational awareness and contribute to accidents, necessitates innovative solutions to enhance operational safety. To address this issue, this study develops an electroencephalogram (EEG)-based system for detecting smartphone usage in train drivers and analyzing its effects on cognitive performance. A full-type train simulator was used to replicate real-world train operations, where EEG data were collected from 25 participants under two experimental conditions: (1) train driving with smartphone usage, and (2) train driving without smartphone usage. A deep learning-based classification model, utilizing Long Short-Term Memory (LSTM) networks, was developed to analyze EEG signals and detect smartphone-related cognitive impairments. The model achieved an accuracy of 85.6% in distinguishing smartphone usage states, demonstrating its effectiveness in detecting cognitive changes associated with smartphone distractions. Furthermore, the findings indicate that smartphone usage leads to a 1.4x increase in response time to critical situations, significantly impacting reaction times and error rates. Unlike traditional behavior-based monitoring methods, this study pioneers an objective, real-time EEG-based smartphone usage detection system, offering a proactive strategy for accident prevention in railroad operations. By integrating deep learning with biosignal analysis, this research contributes to the advancement of real-time safety monitoring systems, providing new insights into human performance assessment in high-risk environments
Exploring the impact of built environment on crash risks at transportation hubs
No full textThis study investigates the impact mechanism of the built environment surrounding transportation hubs on crash risks (CR). Three buffer zones (300 m, 500 m, and 800 m) are defined as the spatial analysis units, and Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), and Multiscale Geographically Weighted Regression (MGWR) are utilized in this study. The results reveals that the 800 m buffer zone provides deeper insights into the factors affecting CR related to the built environment surrounding transportation hubs. Additionally, MGWR demonstrates superior performance in explaining the built environment’s impact on CR compared to the other two methods, with an explanation rate of 83.7 %. To reduce CR near transportation hubs, rationally planning the surrounding land use layout and reducing population density per unit area are recommended. Moreover, the density of road networks surrounding airports and railway stations should be kept at a lower level to reduce CR. The findings of this study contribute to a deeper understanding of the relationship between the built environment surrounding transportation hubs and crashes, providing planning guidance and creating a friendly environment surrounding transportation hubs