Monash University, Institute of Transport Studies: World Transit Research (WTR)
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Factors facilitating and hindering the implementation of digital sleep coaching for bus drivers
No full textImproving fatigue management is critical to the occupational safety of professional drivers. We aimed to identify the factors that facilitated or hindered the implementation of digital sleep coaching in bus companies and to explore bus drivers’ experiences with it. Two bus companies implemented coaching for bus drivers. Using a mixed methods design, we collected data through two workshops (n = 30 and n = 27) attended by key personnel from the organisations and through questionnaires to the drivers (n = 30). Implementation was facilitated by, for example, the flexible participation and multichannel information of coaching, and hindered by restrictions on social support due to the COVID-19 pandemic, and lack of interest and inspiring examples. On average, the drivers rated the appropriateness and the feasibility of coaching as good. However, further development could lead to wider dissemination. It would also be important to involve the key people in the organisations and stakeholders more in supporting the implementation
Impact of a First-In–First-Out Rule on the Merge of a Metro Line with a Junction
No full textOn a metro line with a junction, the merge of the line is a key point for the operation. This paper proposes a discrete event traffic model for a metro line with a junction. It is based on the line’s existing signaling system and determines trains’ departure times at all line nodes. Furthermore, the model estimates trains’ average headway and frequency. We apply our model to Paris metro line 13, which is currently not operated with a FIFO (first-in–first-out) rule on the merge. Thus, we seek to evaluate the effect of a FIFO rule on the nominal frequency. Compared to the current line operation, we also test the FIFO rule as a control strategy for daily disturbances. At the steady state, we show that the train frequency is maximized regardless of the distribution of the trains on the three parts of the line (the central part and two branches). Then, we study the train frequency in two disturbed situations that often occur on a metro line. We show that the FIFO rule reduces the impact of these disturbances in time and intensity. This research is motivated by the Paris metro operator, as the considered line is expected to be fully automated in the coming years. The operator wants to better understand the potential benefits of new operating rules and how operating protocols can improve the throughput and reliability of the system. This work is the first step in seeing frequency evolution on a line with a junction
Artificial Intelligence Aided Crowd Analytics in Rail Transit Station
No full textCrowd analysis and management is a key area of study for transit agencies seeking to optimize their operations and to facilitate safety risk management activities. Key features of crowd analytics include passenger flow volume, crowd density, and walking speed. This study proposes a generalized artificial intelligence (AI)-based crowd analytics model framework for rail transit stations, by analyzing and visualizing crowd analysis data from video records of high-density crowds. Specifically, we propose a generalized AI-aided methodological framework (AI-Crowd) for calculating flow volume, crowd density, and walking speed. You Only Look Once (YOLO) and Deep SORT are integrated into the model framework to detect and track each individual’s dynamic position. Camera calibration is utilized to transform detected trajectories into a real-world coordinate system. Methods for calculating crowd dynamic metrics are formulated based on the data. To validate the model framework, several video records from a platform scenario at a major rail transit station are used. The model’s pedestrian counting accuracy can reach 95% and the fundamental diagrams of density–speed are shown to be consistent with empirical studies. Further crowd analysis of a stair scenario and a transferring passage scenario using the proposed model framework shows some differentiations in walking behavior. The methodology has further practical applications, such as monitoring social distancing
Life-Cycle analysis of economic and environmental effects for electric bus transit systems
No full textElectric buses play a crucial role in reducing the carbon footprint. This study evaluates the life cycle costs (LCCs) and environmental impacts of three e-bus transit systems: stationary charging, battery swapping, and dynamic wireless charging. A mixed-integer nonlinear optimization problem is formulated to determine the optimal design parameters for the charging infrastructure, bus fleet size, and battery capacity for each e-bus transit system considering battery degradation. Taking Guangzhou’s Bus Rapid Transit (BRT) system as an example, a sensitivity analysis of the optimized solution is conducted. The LCC analysis framework is extended to BRT systems in 38 cities globally. The results indicate the superiority of battery swapping in most cases, while stationary charging and dynamic wireless charging are more competitive in cases with long circuit lengths and high service frequencies. Dynamic wireless charging becomes the best option when charging infrastructure is shared with other bus lines or private cars
Navigate through the haze: Wildfire smoke exposure and Metrorail ridership
No full textAdverse weather events significantly impact the operations of urban transportation systems and change human travel behaviors. Over the decades, wildfires have emerged as a pressing concern due to their increased frequency and intensity, yet the relationship between wildfire smoke and public transportation usage remains largely unexplored. Leveraging high-resolution daily wildfire-driven PM2.5 concentration estimates and station-level Metrorail ridership data in the Washington Metropolitan Area spanning 2012–2019, we examine the effects of wildfire smoke exposure on Metrorail usage. We find that wildfire smoke exposure results in a 0.8% increase in Metrorail ridership on weekdays and a more pronounced 3.7% rise on weekends. Additionally, we show a stronger response in Metrorail ridership to wildfire smoke during off-peak hours compared to peak hours, with the most substantial increase observed during the winter. Our heterogeneity analysis further suggests that a lack of vehicle ownership and higher reliance on walking and public transportation are key factors leading to increased Metrorail ridership. Collectively, these results highlight the need for proactive service adjustments and effective communication strategies to accommodate the potential shifts in human travel behaviors and Metrorail ridership on days exposed to wildfire smoke
Bus fleet decarbonization under macroeconomic and technological uncertainties: A real options approach to support decision-making
No full textFleet owners deal with various and critical uncertainties while planning bus fleet replacement, encompassing macroeconomic and technological factors, such as fuel and electricity prices, energy consumption, purchasing costs of electric batteries and powertrains, and bus salvage value upon resale. This paper proposes a Real Option-based framework to evaluate investment choices in different bus technologies over time (diesel, hybrid electric, compressed natural gas, and full-electric), where the proposed options usually involve replacing existing diesel fleets with alternatively powered buses. In particular, we provide a multi-option least squares Monte Carlo simulation to help fleet owners in making cost-effective investment decisions, resulting in potential cost savings of up to 10% of the annual total cost of ownership. By applying the proposed approach to different bus route types (inner-city, urban, suburban, commuter), the results show how the optimal investment choices for fleet replacement change over time. Initially, diesel buses or, at most, compressed natural gas buses are more cost-effective compared to electric technology. However, as electric technology becomes increasingly competitive, internal combustion buses are phased out. Inner-city and urban routes are better suited for the electric transition, whereas the transition may progress more slowly on other route types.
Finally, we show that, under technological and macroeconomic uncertainties, the duration of contracts can strongly affect the investment decisions, in absence of specific guarantees on the new fleet
Estimation of schedule preference and crowding perception in urban rail corridor commuting: An inverse optimization method
No full textThis paper introduces an inverse optimization method to uncover commuters’ schedule preference and crowding perception based on aggregated observations from smart card data for an urban rail corridor system. The assessment of time-of-use preferences typically involves the use of econometric models of discrete choice based on detailed travel survey data. However, discrete choice models often struggle with potential endogeneity issues in behavioral observations when estimating individual samples from massive transit data with limited exogenous identifying information. This motivates us to employ an equilibrium modeling approach to capture the dynamism hidden in commuters’ departure time decision-making from aggregations. Assuming user optimality in observed choices, an inverse optimization method is proposed to find a set of preference parameters in the stochastic user equilibrium-based morning commuting model with heterogeneous commuters so that the resulting equilibrium pattern best approximates the observed departure rate distribution over time. The proposed inverse optimization problem can be formulated by a bi-level programming model and a sensitivity analysis-based solution framework is further designed for model estimation. Lastly, the smart card data and train timetable data from the rail corridor along the Beijing Subway Batong Line are synthesized for a case study to estimate commuters’ departure time choice preferences during morning peak periods, as well as to validate the robustness and practicality of the proposed method
A quantified planning method of local public transport services for expanding residents’ activity opportunities
No full textThe purpose of public transport is to expand activity opportunities of residents. Many public transport planning methods focus on the needs of residents. However, residents can adapt to the environment and form limited needs in areas with low public transport service levels, such as rural areas. Therefore, it is important to focus on activity opportunities (various states of people (being) and actions (being able)) rather than needs. The authors constructed a method for local public transport planning that focuses on activity opportunities; however, the variables and solutions of the model were abstract and thus did not reach the stage of practical application. Therefore, this study aimed to put into practical use a supporting method for local public transport planning. This method consists of a “measurement model for activity opportunity,” in which a given bus service and the ability to use it are variables, and an “evaluation model for planning alternatives” that incorporates a social relationship function and disparity principle. Through a case analysis in a rural area to which this method was applied, its usefulness was verified, and it was confirmed that it can contribute to public transport planning to increase activity opportunities
Resilience optimization of bus-metro double-layer network against extreme weather events
No full textThe resilience of bus and metro systems to extreme weather events is a critical concern in urban planning, given their growing complexity and interconnectivity. Traditional studies often simplify or overlook the interdependency between different transportation modes, focusing on recovery strategies for a single mode to enhance system resilience. This study proposes an integrated resilience assessment framework for bus and metro systems, conceptualized as a Bus-Metro Double-Layer Network (B-M DLN). The framework considers both network structure and system function to accurately evaluate the B-M DLN resilience. A resilience optimization model for B-M DLN based on Genetic Algorithm (GA) is established to suggest the optimal recovery sequence of damaged stations, emphasizing the importance of station repair time, node strength, and node degree in recovery prioritization. Through a case analysis of Xi’an City, China, the B-M DLN shows significantly enhanced resilience when applying the optimal recovery strategy, especially in large-scale failure scenarios
Comparative analysis of deep-learning-based models for hourly bus passenger flow forecasting
No full textAn efficient transportation system is conducive to maintaining traffic flow and safety. Passenger flow forecasting (PFF), an area of traffic forecasting, is a key part of the efficient transportation system. In recent years, deep-learning-based models have led to extensive research on the different conditions in this field. Hence, model determination is the most suitable for a specific application would be a key advantage. To address this issue, a comparative analysis of nine typical deep network approaches, including recurrent neural network (long short-term memory (LSTM) and gated recurrent unit (GRU)), bidirectional-based RNN (BiLSTM and BiGRU), convolutional neural network (CNN) (CNN1D and CNN2D), convolutional LSTM, and hybrid network (CNN1D-LSTM and CNN1D-GRU), for hourly PFF has been conducted. This comparison utilized two datasets with hourly records of bus lines from Guangzhou, China. The comparison results show that the bidirectional-based models were slightly better than other candidates in terms of the values of the root-mean-square error, determination coefficient, and Theil coefficient, and had a lower individual error distribution than the others, both numerically and proportionally. Furthermore, the bidirectional-based models were different from the other models in terms of the Friedman test (a special case is that the BiLSTM and LSTM had no significant difference for Line 10 application). Besides, the results from model structure aspect indicated that the bidirectional-based models achieved better performance with stable and reliable model structure (measurement index: posterior error distribution) and less computational complexity (measurement index: number of floating-point operations and parameters). It is concluded that the bidirectional-based deep learning models are the preferential choice for hourly PFF