Data-Driven Prediction and Optimization of Energy Use for Transit Fleets of Electric and ICE Vehicles

Due to the high upfront cost of electric vehicles, many public transit agencies can afford only mixed fleets of internal combustion and electric vehicles. Optimizing the operation of such mixed fleets is challenging because it requires accurate trip-level predictions of electricity and fuel use as well as efficient algorithms for assigning vehicles to transit routes. We present a novel framework for the data-driven prediction of trip-level energy use for mixed-vehicle transit fleets and for the optimization of vehicle assignments, which we evaluate using data collected from the bus fleet of CARTA, the public transit agency of Chattanooga, TN. We first introduce a data collection, storage, and processing framework for system-level and high-frequency vehicle-level transit data, including domain-specific data cleansing methods. We train and evaluate machine learning models for energy prediction, demonstrating that deep neural networks attain the highest accuracy. Based on these predictions, we formulate the problem of minimizing energy use through assigning vehicles to fixed-route transit trips. We propose an optimal integer program as well as efficient heuristic and meta-heuristic algorithms, demonstrating the scalability and performance of these algorithms numerically using the transit network of CARTA.

Facing the future of transit ridership: which riders bought a car; who is planning on riding less?

Public transit agencies face a transformed landscape of rider demand and political support as the COVID-19 pandemic continues. We explore people’s motivations for returning to or avoiding public transit a year into the pandemic. We draw on a March 2021 follow up survey of over 1,900 people who rode transit regularly prior to the COVID-19 pandemic in Toronto and Vancouver, Canada, and who took part in a prior survey on the topic in May, 2020. We model how transit demand has changed due to the pandemic, and investigate how this relates to changes in automobile ownership and its desirability. We find that pre-COVID frequent transit users between the ages of 18-29, a part of the so-called “Gen Z,” and recent immigrants are more attracted to driving due to the pandemic, with the latter group more likely to have actually purchased a vehicle. Getting COVID-19 or living with someone who did is also a strong and positive predictor of buying a car and anticipating less transit use after the pandemic. Our results suggest that COVID-19 heightened the attractiveness of auto ownership among transit riders likely to eventually purchase cars anyways (immigrants, twentysomethings), at least in the North American context. We also conclude that getting COVID-19 or living with someone who did is a significant and positive predictor of having bought a car. Future research should consider how the experiencing of having COVID-19 has transformed some travelers’ views, values, and behaviour.

Metro Transport Corporations:

Urban mobility revolution is transforming and traditional transportation agencies may be ill-equipped to oversee the changes. Even before the COVID-19 pandemic, U.S. transit ridership was down as more people in metropolitan areas chose the convenience of options like Uber and Lyft. The apparent durability of working from home has exacerbated both fiscal and equity challenges for transit. Meanwhile, vehicle travel is already ahead of pre-pandemic levels in 15 states. The combination of reduced transit ridership and more cars threatens to worsen the challenges posed by climate change. Consumers have demonstrated their preference for the convenience new technologies provide. But the skills and capabilities of traditional urban transit agencies do not foster innovation. We propose that urban mobility be overseen by “Metro Transport Corporations,” public-private partnerships that combine the accountability of government with the entrepreneurial and technology-savvy influence of the private sector to address equity and sustainability challenges while driving superior customer service.

Assessing The Maturity Of The Asset Lifecycle Management In The Case Of The Massachusetts Bay Transportation Authority (MBTA)

Asset management is a central capability that organizations have to perform well. Asset management is concerned about the management of assets that are valuable or potentially valuable to an organization. This article focuses on asset management as it is performed in the railway transit industry. The past decade has seen a number of positive changes in the way that transit agencies manage their assets. While many transit agencies have introduced asset management approaches, work still needs to be done in the area of how we assess the maturity of asset management programs. This article proposes a framework for assessing the maturity of asset management programs, especially those that are used to manage individual assets according to their lifecycles. To illustrate the value of the proposed asset management maturity framework, we describe the asset management transformation at a transit agency and use the proposed framework to document the gains of the improvement effort.

Crew Assignment with Duty Time Limits for Transport Services: Tight Multicommodity Models

Modeling Crew Assignments for Urban Transport Services Using Differentiated Flows Public transit agencies need to judiciously deploy their limited crew members to operate numerous daily scheduled services, while meeting duty and working time regulations for each crew member. Since crew costs account for a large portion of the organizations’ operating expenses, minimizing the total crew and transfer costs is very important. But, with hundreds of daily trips and millions of possible crew itineraries, optimizing trip-to-crew assignment decisions is challenging. In “Crew Assignment with Duty Time Limits for Transport Services: Tight Multicommodity Models,” Balakrishnan, Mirchandani, and Lin propose a novel integer optimization model that represents itineraries as multicommodity flows, differentiated by first trip and depot, to capture the duty time limits and incorporate additional requirements such as selecting equitable schedules. The authors show that this compact model can be tighter than previous formulations, further strengthen the model, and propose a restricted optimization approach combined with an optimality test to generate near-optimal solutions quickly. Extensive computational tests using well-known and real-life problem instances show that the proposed model and solution approach can be very effective in practice.

Linking Incidents to Customers (LINC): An Algorithm for Linking Incidents to Rail Customer Delays Inspired by Traffic Flow Theory

Rail transit agencies have greatly advanced the ability to measure delays to rail system customers and have developed key performance indicators for rail systems based on customer travel time. The ability for operators to link these customer delay metrics to root causes would provide great benefit to agencies, from incident response improvement to capital program prioritization. This paper describes a method for linking late train arrivals to both late customers and incident tickets. Inspired by traffic flow theory, the method identifies impact zones in time and space that can then be linked to a potential root cause by way of incident tickets. This algorithm is currently under development by the Washington Metropolitan Area Transit Authority’s Office of Planning, and its outputs are being integrated into a variety of operations- and capital-related business processes.

Exploring the interaction effect of poverty concentration and transit service on highway traffic during the COVID-19 lockdown

During COVID-19 lockdowns, transit agencies need to respond to the decline in travel but also maintain the essential mobility of transit-dependent people. However, there are a few lessons that scholars and practitioners can learn from. Using highway traffic data in the Twin Cities, this study applies a generalized additive model to explore the relationships among the share of low-income population, transit service, and highway traffic during the week that occurred right after the 2020 stay-at-home order. Our results substantiate that transportation impacts are spread unevenly across different income groups and low-income people are less able to reduce travel, leading to equity concerns. Moreover, transit supply influences highway traffic differently in areas with different shares of low-income people. Our study suggests that transportation agencies should provide more affordable travel options for areas with concentrated poverty during lockdowns. In addition, transit agencies should manage transit supply strategically depending on the share of low-income people to better meet people’s mobility needs.

A literature review on park-and-rides

American cities have been implementing park-and-rides (PNRs) since the 1930s; however, there has been a recent resurgence of literature examining this type of transit station. This paper aims to provide a comprehensive review of the current literature on PNRs and directions for future research. PNR studies published in the last ten years were reviewed and text mining was applied to extract key themes. Six themes were identified. The two most common areas of research were network equilibrium and optimization (12 of 37 studies) and demand models (8 of 37 studies). This was followed by guidelines and best practices as well as comparative studies (6 of 37 studies each). Parking utilization had the fewest number of recent studies (3 of 37 studies). This review revealed that the majority of PNR studies were conducted in geographic areas with extensive transit services, most studies have focused on rail-based PNRs, and the most widely used method was multinomial logit. Some areas for future research include studying remote PNRs, examining bus-based PNRs, and assessing the impact of emerging modes on PNR utilization. This systematic review could assist planners and transit agencies in further improving sustainable PNR networks in their cities.

Deadhead Minimization with a Flexible Facility Locator Tool

As transit agencies expand, they may outgrow their existing bus storage and service facilities. When selecting a site for an additional facility, an important consideration is the change in bus deadhead time, which affects the agency’s operating costs. Minimizing bus deadhead time is the subject of many studies, though agencies may lack the necessary software or programming skill to implement those methods. This study presents a flexible tool for determination of bus facility location. Using the R dodgr package, it evaluates each candidate site based on a given bus network and existing depots and calculates the network minimum deadhead time for each potential set of facilities. Importantly, the tool could be used by any transit agency, no matter its resources. It runs on open-source software and uses only General Transit Feed Specification (GTFS) and data inputs readily available to transit agencies in the U.S.A., filling the accessibility gap identified in the literature. The tool is demonstrated through a case study with the Metropolitan Atlanta Rapid Transit Authority (MARTA), which is considering a new bus depot as it builds its bus rapid transit network. The case study used current MARTA bus GTFS data, existing depot locations, and vacant properties from Fulton County, Georgia. The tool evaluated 17 candidate sites and found that the winning site would save 29.7 deadhead hours on a typical weekday, which translates to more than $12,000 daily based on operating cost assumptions. The output provides important guidance to transit agencies evaluating sites for a new bus depot.

Equity Implications of Ride-Hail Travel during COVID-19 in California

COVID-19 has shocked every system in the U.S., including transportation. In the first months of the pandemic, driving and transit use fell far below normal levels. Yet people still need to travel for essential purposes like medical appointments, buying groceries, and—for those who cannot work from home—to work. For some, the pandemic may exacerbate extant travel challenges as transit agencies reduce service hours and frequency. As travelers reevaluate modal options, it remains unclear how one mode—ride-hailing—fits into the transportation landscape during COVID-19. In particular, how does the number of ride-hail trips vary across neighborhood characteristics before versus during the pandemic? And how do patterns of essential trips pre-pandemic compare with those during COVID-19? To answer these questions, we analyzed aggregated Uber trip data before and during the first two months of the COVID-19 pandemic across four regions in California. We find that during these first months, ride-hail trips fell at levels commensurate with transit (82%), while trips serving identified essential destinations fell by less (62%). Changes in ride-hail use were unevenly distributed across neighborhoods, with higher-income areas and those with more transit commuters and higher shares of zero-car households showing steeper declines in the number of trips made during the pandemic. Conversely, neighborhoods with more older (aged 45+) residents, and a greater proportion of Black, Hispanic/Latinx, and Asian residents still appear to rely more on ride-hail during the pandemic compared with other neighborhoods. These findings further underscore the need for cities to invest in robust and redundant transportation systems to create a resilient mobility network.