Transit Authority Research Articles

Short-Term Passenger Flow Prediction in Urban Rail Transit based on Hybrid Deep Learning Models

Urban rail transit systems are essential for efficient, reliable, and environmentally friendly transportation in modern cities. Correct and effective short-term passenger flow prediction is crucial for optimizing operational efficiency, enhancing service quality, and ensuring passenger safety. Traditional prediction methods often fail to capture the complex, non-linear, and dynamic patterns in urban rail transit systems. This study uses a hybrid deep learning model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to predict short-term passenger flow in urban rail transit. By integrating the strengths of CNN in capturing spatial features and LSTM in modeling temporal dependencies, the hybrid model aims to improve prediction accuracy. Using historical data from Hangzhou Metro, the study demonstrates the model's effectiveness in predicting passenger flow, which reasonably has a good fit. Additionally, models with different convolutional layers show different performances. These improved predictions offer valuable insights for transit authorities, enabling them to make more informed decisions regarding train scheduling, resource allocation, and emergency response planning. By anticipating passenger demand more accurately, authorities can optimize the deployment of trains, reduce waiting times, enhance passenger comfort, and improve overall service reliability.

Something between a colleague and a tool: exploring the complex nexus between the role, responsibilities, and expectations of language assistants in multinational police settings in Kosovo (1999–2018)

ABSTRACT The United Nations Mission in Kosovo (UNMIK) and the European Union Rule of Law Mission in Kosovo (EULEX) included armed international police officers (IPOs) vested with transitional executive authorities from 1999 to 2018. IPOs initially assumed full policing responsibilities in cooperation with language assistants (LAs), until recruited Kosovo Police officers (KPOs) underwent training and integrated into multinational police teams. Drawing on twenty-three ethnographic interviews conducted between 2018 and 2021, this article explores stakeholders’ perspectives on the role and expectations of police LAs. The analysis reveals that, besides serving as communication facilitators, cultural mediators, fixers, and repositories of cultural knowledge and information, LAs transcended conventional translator and interpreter roles by proactively assuming police officers’ responsibilities, including statement-taking, assisting with reports, and proofreading. However, LAs encountered trust issues stemming from IPOs’ apprehensions about losing control due to the language gap and deviations from literalism, coupled with KPOs’ concerns about LAs’ potential influence on their performance evaluations and promotions. Moreover, IPOs from authoritarian states and militarized police backgrounds tended to perceive LAs as tools rather than equal team members. Interviews indicate that a professional LA, beyond linguistic proficiency, embodies strong interpersonal skills and professional qualities essential for fostering trust and effective communication.

Exploring attitudes and perceptions regarding a self-driving demonstration

Purpose Public opinion regarding autonomous vehicles (AV) heavily influences how quickly the technology will be implemented and adopted in the future. However, there is a dearth of empirical evaluations in the literature about riders' perceptions toward service characteristics of shared autonomous vehicles (SAVs) and their attitudes toward developing AVs. Therefore, the aim of this study is to identify attitudes, views and concerns regarding a self-driving demonstration called RAPID (Rideshare, Automation and Payment Integration Demonstration) incorporated with an already-existing on-demand ridesharing service in Arlington, Texas. Design/methodology/approach This study developed a ridership survey to collect data from those who had experience using the service at least once during the service deployment. As the RAPID service operations were restricted to the areas near the University of Texas at Arlington (UTA) campus, sample population of this study is highly skewed with all participants being affiliated with UTA. Findings Findings indicated that survey respondents positively perceived the service features, including comfort, boarding the vehicles, ride safety, booking and scheduling, vehicle speed, climate control and service cost. To complement the survey results, the authors conducted interviews and a focus group study and used conventional content analysis to gain more in-depth insights about RAPID service operations from the perspectives of users and non-users in the post-implementation period. The results indicated that geographic accessibility, service availability and trip cost were the primary concerns of the focus group participants. Originality/value This study offers critical insights into individual attitudes and perceptions toward shared AVs (SAVs) that will assist local, state and federal transit authorities and planners in formulating policies and transportation strategies to target SAV ridership when the service is more widespread.

A Simulated Annealing Approach to the Scheduling of Battery-Electric Bus Charging

With an increasing adoption of battery-electric bus (BEB) fleets, developing a reliable charging schedule is vital to a successful migration from their fossil fuel counterparts. In this paper, a simulated annealing (SA) implementation is developed for a charge scheduling framework for a fixed-schedule fleet of BEBs that utilizes a proportional battery dynamics model, accounts for multiple charger types, allows partial charging, and further considers the total energy consumed by the schedule as well as peak power use. Two generation mechanisms are implemented for the SA algorithm, denoted as the “quick” and “heuristic” implementations, respectively. The model validity is demonstrated by utilizing a set of routes sampled from the Utah Transit Authority (UTA) and comparing the results against two other models: the BPAP and the Qin-Modified. The results presented show that both SA techniques offer a means of generating operationally feasible schedules quickly while minimizing the cost of operation and considering battery health.

Accessibility enhancement of mass transit system through GIS based modeling of feeder routes

Access and ingress to and from the metro stations plays a crucial role in elevating the efficacy of transportation systems. This study presents a novel approach to optimize and integrate feeder route network with mass transit system using GIS. An initial accessibility analysis of existing feeder routes revealed only 0.12% of the total population being served. To address the accessibility challenge a comprehensive GIS based feeder route design model based on population data and road network attributes was developed. The model efficiently identified candidate feeder stops which were further subjected to a route analysis model for optimal route design. A total of 29 feeder routes with an average length of 10.2 ​km were identified by the route analysis model. The results demonstrated that the feeder routes obtained through this method exhibited exceptional coverage across the study area effectively bridging the accessibility gaps. The effectiveness of the proposed model was validated through the service area analysis and density analysis, verifying a noticeable increase in route density and substantial population served corresponding to 0.25%. which is doubled as compared to the population served existing feeder routes. The study provides an opportunity for transit authorities to design efficient feeder networks complying with better integration and accessibility.

Strategies to Optimize the Deployment of Roadway Maintenance Machines for Overnight Maintenance in Urban Rail Systems

This research investigates the effectiveness of several strategies to deploy roadway maintenance machines (RMMs) in preparation for overnight maintenance in rapid transit systems. Owing to the short windows of time available for maintenance activities in the overnight period (i.e., when revenue service is suspended), efficient deployment of RMMs is an important aspect of ensuring adequate productive time for crews at work locations. Four deployment strategies are investigated: optimizing the long-term yard storage locations of RMMs; optimizing the assignment of RMMs to work zones; optimizing the use of nonrevenue locations within the network to “preposition” RMMs closer to their work zones; and optimizing the routing and scheduling of RMMs through the network to reach the work zones. The strategies have been tested using data from the Washington Metropolitan Area Transit Authority. The results indicated that the proposed strategies reduced the time needed for RMM deployment. In the case of prepositioning, the median prepositioned RMM was deployed 23 min earlier than in the baseline (i.e., routing and scheduling alone) scenario, and the median RMM receiving a new yard storage location was deployed 16 min earlier. This was achieved without widespread negative impacts to RMMs that could not benefit from the proposed strategies for operational reasons. The results also demonstrated the potential of the routing and scheduling model as a tool to evaluate the deployment time impacts of distance-minimizing strategies, considering factors such as conflicts between RMMs and the availability of tracks to avoid disruptions to revenue service.

Short and mid-term effect of the streetcar on vehicle-vehicle (and vehicle-pedestrian) crash rate on the adjacent street

IntroductionIntroducing new public transit systems impacts the surrounding built environment, and changes in the built environment can affect travel behavior. Prior research has yet to thoroughly conduct a comprehensive exploration of the influence of new investments in public modes of transit, particularly streetcars, on motor vehicle crashes occurring on adjoining streets, considering other related factors. In particular, the difference between short-term and mid-term impacts of streetcars considering initial break-in periods has yet to be thoroughly conducted. This study focuses on the short-term and mid-term effects of the streetcar on total, injury, and pedestrian-involved vehicle crash rates on the adjacent street, considering traffic volume, traffic speeds, and traffic conflicts (transit ridership, pedestrian volume, and traffic policy). Data & MethodThis paper used the Utah Department of Transportation’s (UDOT) crash count, annual average daily traffic (AADT), iPeMS data, Utah Transit Authority’s (UTA) ridership, manually calculated pedestrian volume from Google Street View, and conducted interviews with UDOT’s experts. In the method, we used three quasi-experimental research designs: (1) before-after without a control group, (2) interrupted time series, and (3) before-after with a control group. In addition, to identify the cause of this impact, we examined multiple dimensions, including traffic volume, traffic speeds, transit ridership, pedestrian volume, and adjustments in traffic policy changes. ResultsAs a result, the establishment of the S-Line streetcar eventually led to a significant decrease in total (short: 11 %, mid: −15 %), injury (short: −9%, mid: −41 %), and pedestrian-involved (short: −25 %, mid: −43 %) crash rates on the adjacent street, especially after the streetcar was fully established (3 years after). In particular, injury and pedestrian-involved crash rates decreased significantly. Also, we found that increased drivers’ awareness and vitality of the street due to the increased transit ridership (short: 43 %, mid: 50 %), increased pedestrian volume (short: 35 %, mid: 75 %), and improvement of traffic signal on the adjacent street can be the main causes. Practical ApplicationsThe outcomes of this study are considered to help establish short-term and mid-term traffic policies that consider public transit improvements such as streetcars.

Enhancing Electric Shuttle Bus Efficiency: A Case Study on Timetabling and Scheduling Optimization

As transit authorities increasingly adopt electric buses (EBs) to mitigate air quality concerns and greenhouse gas emissions, new challenges arise in bus scheduling and timetabling. Unlike traditional buses, EBs face operational obstacles due to their shorter range and extended charging times. Existing mathematical optimization models for operation planning of traditional buses must be revised to address these unique characteristics of EBs. This study introduces a new approach to integrate timetabling and bus scheduling to enhance the level of service and minimize operational costs, using a case study of a University shuttle bus service in Montreal, Canada. The level of service will be enhanced by reducing students waiting time and improving their in-vehicle comfort through seat availability. The scheduling aspect seeks to reduce the total operational costs, which include travel, electricity consumption, and usage costs of EBs. The proposed algorithm calculates the waiting time and seat availability for different headway values and addresses the scheduling problem using a mixed-integer linear programming (MILP) model with an arc-based approach, solved using the Cplex Optimization Studio software version 12.8. A normalized weighted sum technique is then applied to select the optimal headway, balancing waiting time, seat availability, and operational costs. The effectiveness of our approach was tested through a case study of Concordia University’s shuttle bus service. Comparative analysis of the current and proposed schedules shows that our approach significantly improves service quality by decreasing waiting times and increasing seat availability while optimizing cost-effectiveness compared to the existing timetable of the Concordia shuttle bus. The proposed approach ensures a smooth transition to a fully electric transit system for shuttle bus services.

Assessing the Environmental, Economic, and Route Optimization Impacts of Implementing a Green Bus System in Urban Areas

The proliferation of e-bus has swept the globe. Many countries have begun the transformation of electric buses. Electric buses are embraced worldwide for their environmental and low-cost advantages. This work will help local governments establish a complete e-bus system.The first question mainly addresses the ecological impact that would be brought by the impact. In this work, the Ecological Impact Quantification Model (EIQM) is developed to address gaseous pollution including Greenhouse gas carbon dioxide, pollutants nitric oxide and PM2.5. The ecological effect enhancement with e-bus system is presented, and especially, the gaseous pollution caused by electricity production is approximated by the Monte Carlo simulation method. As a result, this team figures out that with the development of e-bus system, the average CO2 emission per person decreases, having a reduction of 11391g at first, and 14039g after five years.The second question mainly addresses the financial problem that the government needs to face. In this work, the Quantification of Financial Impact Model (QFIM) is developed. This team utilizes the differential equation to obtain the exact varying rate of diesel fuel and electricity fee, and finally determined the possible range of the government deficit. By synthesizing these data, this team discovers that the government could start reducing its deficit after 8 years of the transformation from diesel bus to e-bus, having deficit from $-88,412,180 to $-64,182,722.The third question addresses the 10-year plan for the selected city and 2 additional cities. This team develops Optimized Deployment Model based on Greedy Algorithm (ODGA) to optimize the transformation plan over 10 years of existing bus routes and the number of e-bus to be constructed per year. Monetary and time utility is considered and Greedy Algorithm is introduced to find out the best travelling route for e-buses. This team successfully figures out the best transformation plan of travelling routes and number of e-bus to be constructed per year to maximize the efficiency, including 5-min interval departure time and 867 buses are required to run the proposed route in Chicago. Finally, the team writes a piece of letter to Chicago Transit Authority and proposes the 10-year plan of e-bus system construction to guarantee the steady implementation of e-bus.

Robust-augmentation nonlinear dynamic inversion control of over-actuated coaxial high-speed helicopter in transition mode

As the elevator and rudder can be used actively for control, in addition to the rotors, Coaxial High-speed Helicopters (CHHs) have the problems of control redundancy and changing control authority in the transition mode. This paper presents a robust-augmentation transitioning flight control design for a CHH under the adverse conditions of parametric uncertainties and external disturbances. First, based on control characteristic analysis, an Adaptive Filtered Nonlinear Dynamic Inversion (AFNDI) controller is proposed for the angular rate to handle the effect of unknown unstructured uncertainties and external turbulence. Theoretical analysis proves that the presented angular rate controller can guarantee steady-state and transient performance. Furthermore, the attitude angle and velocity controllers are also added. Then, an Incremental-based Nonlinear Prioritizing Control Allocation (INPCA) method is designed to take into account control surface transition and changing control authority, which efficiently distributes the required moments between coaxial rotors and aero-surfaces, and avoids the control reversal problem of the yaw channel. In the proposed control architecture, the low-pass filter is introduced to alleviate the adverse influence of time delay and measurement noise. Finally, the effectiveness of the proposed controller is demonstrated through nonlinear numerical simulations, and is compared with existing methods. Simulation results show that the proposed control law can improve both capabilities of disturbance rejection and fast response, and works satisfactorily for the CHH transitioning control characteristic.

Modeling an Investment Framework for BMTA Electric Bus Fleet Development

In Thailand, diesel buses are notorious for their poor energy efficiency and contribution to air pollution. To combat these issues, battery electric buses (BEBs) have emerged as a promising alternative. However, their high initial costs have posed challenges for fleet management, especially for agencies such as the Bangkok Mass Transit Authority (BMTA). This study aims to revolutionize BEB fleet management by developing an energy model tailored to the BMTA’s needs. The methodology consists of two crucial steps: analyzing BMTA bus routes and designing fleet management and charging systems. Through this process, the study seeks to determine the maximum number of BEBs that can be operated on each route with the fewest chargers possible. The results reveal exciting possibilities. Within the city bus landscape, two out of five BMTA bus routes show potential for transitioning to BEBs, provided they meet a maximum energy requirement of 200 kWh every two rounds. This analysis identifies routes ripe for BEB adoption while considering the limitations of battery size. In the next step, the study unveils a game-changing strategy: a maximum of 13 BEBs can operate on two routes with just four chargers requiring 150 kW each. This means fewer chargers and more efficient operations. Plus, the charging profile peaks at 600 kW from 4:00 to 8:00 p.m., showing when and where the fleet needs power the most. However, the real eye-opener? Significant energy savings of THB 10.44 million per year compared to diesel buses, with an initial investment cost savings of over 37%. These findings underscore the potential for BEB fleet management to revolutionize public transportation and save money in the long run. However, there is more work to be done. The study highlights the need for real-time passenger considerations, the development of post-service charging strategies, and a deeper dive into total lifetime costs. These areas of improvement promise even greater strides in the future of sustainable urban transportation.

Study Identifies Opportunities for Intervention Improvement With Collaborative Workflows

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 212922, “The Intervention Opportunity: Why the Industry Does Not Do More and How New Collaborative Workflows With Aligned Outcomes Can Change This,” by Matthew E. Billingham, SPE, SLB; Fraser J. Proud, North Sea Transition Authority; and Pierre Ramondenc, SPE, SLB. The paper has not been peer reviewed. _ The authors write that their analysis presented in the complete paper challenges the typical way interventions have been planned and executed, both from an operational and commercial basis, and examines where room exists for significant improvement. The paper examines the case for performing interventions and aims to identify opportunities for both financial and net-zero-goal achievement. By appreciating the issues operators face when justifying and designing intervention activity, challenges can thus be addressed by proper alignment for the best outcome. The Complexity of Interventions Downhole issues can be broken into two main categories: reservoir-based bottlenecks, and well-integrity and completion hardware issues. Problems categorized as reservoir-based can manifest either in the formation or in the wellbore. For instance, scaling and sand production are processes that originate from the reservoir but must be dealt with at least partially in the wellbore. The same is true for downhole flow-assurance challenges, which often find their root cause in the reservoir. The Value of Intervention In the UK, operators complete an annual stewardship survey (also known as the UKSS) for which a large amount of data is collected, especially around wells. The average production added from many subsea intervention jobs is significant, with reperforations, for example, adding approximately 120,000 BOE/yr per intervention, a more than 300-BOEPD uplift. Clearly, opportunities exist to increase production in the UK Continental Shelf (UKCS). The question becomes whether the investment in intervention is economical. Not all operators use well interventions to their maximum potential. A large variance exists in the number of jobs an operator completes; the operators seeing the most production added generally are the ones performing the most interventions. Many operators may be capital-constrained and, thus, cannot invest in more well intervention. However, it is clear that operators that do invest consistently see good value returns. Intervention Challenges Platform/Wellsite Access. A major obstacle to performing interventions is the logistical bottleneck created by additional persons on board (POB) and deck-space limitations. This applies to operations on land as well, where lack of facilities, especially in remote locations, may cause similar bottlenecks. Asset Mindset. Another difficulty is sometimes referred to as the “asset mindset,” whereby the asset-management team might regard interventions as a high-risk operation, which jeopardizes the existing production base as opposed to gaining a potential uplift. The asset mindset looks at the intervention not so much as a chance to increase production, but as a potential risk to lose base-case existing production. However, technology leaps have been made in the provision of intervention services to ensure safe entry and exit of wells, with zero damage to the completion or formation, and provide excellent chances of a production increase.

Enhancing Public Transit System Through AI and IoT

The paper investigates the synergy between AI and IoT in revolutionizing public transit systems, emphasizing their role in addressing existing challenges and improving overall efficiency. It explores the application of IoT devices in creating a smart infrastructure that enables real-time data collection, monitoring, and management of transit operations. The research delves into the realm of predictive maintenance, showcasing how AI algorithms, powered by IoT data, can anticipate potential issues in transit vehicles. By analyzing sensor data, transit authorities can proactively address maintenance needs, minimizing downtime, reducing repair costs, and ensuring a more reliable and sustainable public transit service. Route optimization emerges as another crucial aspect of the study, highlighting how AI algorithms leverage historical and real-time data to recommend the most efficient transit routes. Factors such as traffic patterns, weather conditions, and passenger demand are considered to enhance overall system efficiency and reduce travel time for passengers. The paper introduces the concept of dynamic scheduling, illustrating how AI-driven algorithms adapt transit schedules in real-time based on changing passenger needs and external factors. This dynamic approach aims to provide more responsive services, ultimately reducing wait times and improving overall user satisfaction. Passenger information systems are explored as a pivotal component, illustrating how AI and IoT technologies enhance the passenger experience. Real-time communication through mobile apps, digital displays, and other channels ensures that passengers have accurate and timely information about arrival times, delays, and alternative routes, empowering them to make informed decisions. The researchers also delve into fare optimization, examining how AI algorithms analyze data on passenger demographics, travel patterns, and economic factors to create fair and affordable fare structures. This approach aims to encourage ridership, increase revenue, and improve the financial sustainability of public transit systems. The abstract presents a comprehensive overview of how the integration of AI and IoT technologies in public transit systems transforms urban mobility. The findings suggest that leveraging real-time data, predictive analytics, and dynamic solutions can significantly enhance the reliability, accessibility, and sustainability of public transit. As cities continue to explore innovative solutions, the abstract serves as a roadmap for developing smarter, user-friendly, and efficient urban transportation networks, ultimately contributing to improved quality of life for residents.

Just Transition in Australia – depoliticisation?

ABSTRACT We cannot and should not assume that Just Transition (JT) is a straightforward and uncontested concept. In this article, we turn a critical eye on JT, firstly by looking at the origins and development of the idea. Secondly, we will look at the theoretical frameworks that have been mobilised to underpin JT. Thirdly, we will look at the way that trade unions in Australia, both through the ACTU and individual unions, have approached the subject, as well as the approach put forward by an Australian Senate Inquiry into the future of jobs in regional Australia. We will look in particular at the way that the move to a new model of regional development is critically affecting JT. We examine the impact of this emerging model with growing demands for a National Transition Authority to drive and coordinate transitions. Our concern is that in engaging with an emerging and powerful view of regional development that combines Renewable Energy Industrial Zones, hydrogen hubs and public-private partnerships, JT is being replaced by notions of decarbonisation or even diversification that threaten to undermine the value and power of the concept.

MetRoBERTa: Leveraging Traditional Customer Relationship Management Data to Develop a Transit-Topic-Aware Language Model

Transit riders’ feedback provided in ridership surveys, customer relationship management (CRM) channels, and, in more recent times, through social media, is key for transit agencies to better gauge the efficacy of their services and initiatives. Getting a holistic understanding of riders’ experience through the feedback shared in those instruments is often challenging, mostly because of the open-ended, unstructured nature of text feedback. In this paper, we propose leveraging traditional transit CRM feedback to develop and deploy a transit-topic-aware large language model (LLM) capable of classifying open-ended text feedback to relevant transit-specific topics. First, we utilize semi-supervised learning to engineer a training dataset of 11 broad transit topics detected in a corpus of 6 years of customer feedback provided to the Washington Metropolitan Area Transit Authority. We then use this dataset to train and thoroughly evaluate a language model based on the RoBERTa architecture. We compare our LLM—MetRoBERTa—with classical machine learning approaches utilizing keyword-based and lexicon representations. Our model outperforms those methods across all evaluation metrics, providing an average topic classification accuracy of 90%. Finally, we provide a value proposition of this work demonstrating how the language model, alongside additional text processing tools, can be applied to add structure to open-ended text sources of feedback such as Twitter. The framework and results we present provide a pathway for an automated, generalizable approach for ingesting, visualizing, and reporting transit riders’ feedback at scale, enabling agencies to better understand and improve customer experience.

Understanding the Role of Public Transportation in Supporting the Care Economy in Washington, DC, USA

Women’s empowerment is a powerful engine for personal and societal economic development and well-being. Nevertheless, gender biases in physical infrastructure investments lead to negative consequences for women and children that reduce their empowerment and limit their economic benefits. Public fixed-route buses, such as those in Washington, DC, illustrate how physical transportation infrastructure has innate gender biases. These young residents likely depend on strollers to travel longer than a few blocks. The Washington Metropolitan Area Transit Authority (WMATA) runs the public transportation system in Washington, DC. In 2021, 7% of DC’s 720,000 residents were under five. WMATA maintains a fleet of approximately 1595 buses, 95% of which banned the onboarding of open strollers until recently. This ban directly limited the use of Metro buses for the caregivers of young children, primarily women. It also reduced the opportunities for these caregivers to participate in DC’s economic life. In neighborhoods dependent on buses for essential mobility, the stroller ban reduces employment, healthcare, social service, educational, and recreational offerings beyond walkable distances. This paper examines the publicly available discussions and actions that led to the updated stroller policy and offers opportunities for improving caregiver transit access in Washington, DC, and, by extension, other cities worldwide.

Towards Efficient Battery Electric Bus Operations: A Novel Energy Forecasting Framework

As the adoption of battery electric buses (BEBs) in public transportation systems grows, the need for precise energy consumption forecasting becomes increasingly important. Accurate predictions are essential for optimizing routes, charging schedules, and ensuring adequate operational range. This paper introduces an innovative forecasting methodology that combines a propulsion and auxiliary energy model with a novel concept, the environment generator. This approach addresses the primary challenge in electric bus energy forecasting: estimating future environmental conditions, such as weather, passenger load, and traffic patterns, which significantly impact energy demand. The environment generator plays a crucial role by providing the energy models with realistic input data. This study validates various models with different levels of model complexity against real-world operational data from a case study of over one year with 16 electric buses in Göttingen, Germany. Our analysis thoroughly examines influencing factors on energy consumption, like altitude, temperature, passenger load, and driving patterns. In order to comprehensively understand energy demands under varying operational conditions, the methodology integrates data-driven models and physical simulations into a modular and highly accurate energy predictor. The results demonstrate the effectiveness of our approach in providing more accurate energy consumption forecasts, which is essential for efficient electric bus fleet management. This research contributes to the growing body of knowledge in electric vehicle energy prediction and offers practical insights for transit authorities and operators in optimizing electric bus operations.

Inferring mobility of care travel behavior from transit smart fare card data

Existing research underscores substantial gender-based variations in travel behavior on public transit. Studies have concluded that these differences are largely attributable to household responsibilities typically falling disproportionately on women, leading to women being more likely to utilize transit for purposes referred to by the umbrella concept of “Mobility of Care”. In contrast to past studies that have quantified the impact of gender using survey and qualitative data, we examine a novel data-driven workflow utilizing a combination of previously developed origin, destination, and transfer inference (ODX) based on individual transit fare card transactions, name-based gender inference, and geospatial analysis as a framework to identify mobility of care trip making. We apply this framework to data from the Washington Metropolitan Area Transit Authority (WMATA). Analyzing data from millions of journeys conducted in the first quarter of 2019, the results of this study show that our proposed workflow can identify mobility of care travel behavior, both in terms of (1) detecting times and places of interest where the share of women travelers in an equally-sampled subset (on basis of inferred gender) of transit users is 10 %–15 % higher than that of men, and (2) finding women significantly more likely to exhibit a consistent accompaniment patterns with riders who are children, elderly, or people with disabilities. The workflow presented in this study provides a blueprint for combining transit origin-destination data, inferred customer demographics, and geospatial analyses enabling public transit agencies to assess, at the fare card level, the gendered impacts of different policy and operational decisions.

Cost Minimization for Charging Electric Bus Fleets

Recent attention for reduced carbon emissions has pushed transit authorities to adopt battery electric buses (BEBs). One challenge experienced by BEB users is extended charge times, which create logistical challenges and may force BEBs to charge when energy is more expensive. Furthermore, BEB charging leads to high power demands, which can significantly increase monthly power costs and may push the electrical infrastructure beyond its present capacity, requiring expensive upgrades. This work presents a novel method for minimizing the monthly cost of BEB charging while meeting bus route constraints. This method extends previous work by incorporating a more novel cost model, effects from uncontrolled loads, differences between daytime and overnight charging, and variable rate charging. A graph-based network-flow framework, represented by a mixed-integer linear program, encodes the charging action space, physical bus constraints, and battery state of the charge dynamics. The results for three scenarios are considered: uncontested charging, which uses equal numbers of buses and chargers; contested charging, which has more buses than chargers; and variable charge rates. Among other findings, we show that BEBs can be added to the fleet without raising the peak power demand for only the cost of the energy, suggesting that conversion to electrified transit is possible without upgrading power delivery infrastructure.

Measuring the impacts of disruptions on public transit accessibility and reliability

Public transit systems are facing higher risk of system degradation from external disruptions, affecting their ability to deliver reliable accessibility to transit users. Therefore, resilience, the ability to maintain functions during a disruption, becomes a crucial assessment of public transit systems. In this paper, we calculate two space-time prism-based measures with General Transit Feed Specification real-time (GTFS-RT) data: realizable real-time accessibility, a conservative real-time accessibility measure that can be achieved by users subject to delays, and scheduled accessibility, accessibility based on schedule. We also define accessibility unreliability, the deviation between realizable accessibility and scheduled accessibility, to measure the reliability of delivered accessibility. We use the two measures to gauge the resilience of public transit systems and conduct two case studies of short- and long-term disruptions, namely Ohio State football games and the COVID-19 pandemic, on the Central Ohio Transit Authority (COTA) bus system in Columbus, Ohio. We find there are two peaks of high unreliability before and after each football games, with the stadium as the geographic center of the disruption. The after-game peaks are shorter and more intense than the before-game. We also find COVID-19 had persistent negative impacts on accessibility and reliability: Realizable accessibility universally declined during the pandemic, but only part of cities experienced unreliability increase, primarily in urban perimeters and suburbs. Improved traffic conditions during the pandemic may help to reduce unreliability, but the later service cuts increased unreliability. The two case studies prove the effectiveness of the method to detect system disturbances and provide important guidance for public transit system operation and planning.