Public transportation systems often suffer from unexpected fluctuations in demand and disruptions, such as mechanical failures and medical emergencies. These fluctuations and disruptions lead to delays and overcrowding, which are detrimental to the passengers' experience and to the overall performance of the transit service. To proactively mitigate such events, many transit agencies station substitute (reserve) vehicles throughout their service areas, which they can dispatch to augment or replace vehicles on routes that suffer overcrowding or disruption. However, determining the optimal locations where substitute vehicles should be stationed is a challenging problem due to the inherent randomness of disruptions and due to the combinatorial nature of selecting locations across a city. In collaboration with the transit agency of Nashville, TN, we address this problem by introducing data-driven statistical and machine-learning models for forecasting disruptions and an effective randomized local-search algorithm for selecting locations where substitute vehicles are to be stationed. Our research demonstrates promising results in proactive disruption management, offering a practical and easily implementable solution for transit agencies to enhance the reliability of their services. Our results resonate beyond mere operational efficiency: by advancing proactive strategies, our approach fosters more resilient and accessible public transportation, contributing to equitable urban mobility and ultimately benefiting the communities that rely on public transportation the most.

Additionally, new on-demand modalities including ride-share, bike-share, and e-scooters have been introduced in recent years and transformed the transportation landscape in urban environments. A wellfunctioning transit system fosters the growth and expansion of businesses, distributes social and economic benefits, and links the capabilities of community members, thereby enhancing what they can accomplish as a society [6, 11, 15]. However, the explosion in transportation options and the complicated relationship between public and private offerings present myriad new challenges in the design and operation of these systems. There are also complex, and often competing, operational objectives that complicate the implementation of efficient services. Since affordable public transit services are the backbones of many communities, solving these problems and understanding state-of-the-art methods for AI-driven smart transportation has the potential to strengthen urban communities, address the climate challenge, and foster equitable growth. Fundamentally, the design of a well-functioning transit system requires solving complex combinatorial optimization problems related to planning and real-time operations. These problems span many well studied fields, from classical line planning to offline and online vehicle routing problems (VRPs). While there are many ways to assess the performance of smart transportation systems, we largely focus on evaluating these systems in the context of optimizing utilization (i.e.

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 due to 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 (WMATA). We then use this dataset to train and thoroughly evaluate a language model based on the RoBERTa architecture. We compare our LLM, MetRoBERTa, to 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 like 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.

The ability to accurately predict public transit ridership demand benefits passengers and transit agencies. Agencies will be able to reallocate buses to handle under or over-utilized bus routes, improving resource utilization, and passengers will be able to adjust and plan their schedules to avoid overcrowded buses and maintain a certain level of comfort. However, accurately predicting occupancy is a non-trivial task. Various reasons such as heterogeneity, evolving ridership patterns, exogenous events like weather, and other stochastic variables, make the task much more challenging. With the progress of big data, transit authorities now have access to real-time passenger occupancy information for their vehicles. The amount of data generated is staggering. While there is no shortage in data, it must still be cleaned, processed, augmented, and merged before any useful information can be generated. In this paper, we propose the use and fusion of data from multiple sources, cleaned, processed, and merged together, for use in training machine learning models to predict transit ridership. We use data that spans a 2-year period (2020-2022) incorporating transit, weather, traffic, and calendar data. The resulting data, which equates to 17 million observations, is used to train separate models for the trip and stop level prediction. We evaluate our approach on real-world transit data provided by the public transit agency of Nashville, TN. We demonstrate that the trip level model based on Xgboost and the stop level model based on LSTM outperform the baseline statistical model across the entire transit service day.

Public transit can have significantly lower environmental impact than personal vehicles; however, it still uses a substantial amount of energy, causing air pollution and greenhouse gas emission. While electric vehicles (EVs) can reduce energy use, most public transit agencies have to employ them in combination with conventional, internal-combustion engine vehicles due to the high upfront costs of EVs. To make the best use of such a mixed fleet of vehicles, transit agencies need to optimize route assignments and charging schedules, which presents a challenging problem for large public transit networks. We introduce a novel problem formulation to minimize fuel and electricity use by assigning vehicles to transit trips and scheduling them for charging while serving an existing fixed-route transit schedule. We present an integer program for optimal discrete-time scheduling, and we propose polynomial-time heuristic algorithms and a genetic algorithm for finding solutions for larger networks. We evaluate our algorithms on the transit service of a mid-size U.S. city using operational data collected from public transit vehicles. Our results show that the proposed algorithms are scalable and achieve near-minimum energy use.

Public transport forms the backbone of any urban mobility system, enabling cities to be more dynamic and competitive while creating more jobs. However, most cities' public bus systems are hemorrhaging riders in recent years due to slow speeds compared to options such as ridesharing services. Not to mention the continuously rising threats of transit agency budget cuts, traffic congestion and public safety. Hayden AI – a company creating smart city solutions purposely built for modern traffic conditions and increased urbanization – asking the question, What can we do to reverse this? The answer isn't bulking up on traffic enforcement, but rather, to enable smarter, more scalable enforcement.

Reno, Nevada, may not seem like the place to develop the country's first self-driving public bus, but Richard Kelley thinks it presents all the right challenges. The buildings are taller than those in the office parks of the Silicon Valley, providing a good visual test for the complex algorithms. The weather is more taxing, arid with occasional snowfall. "You have people who are just walking out of the casino," says Kelley, the chief engineer at the University of Nevada at Reno's Advanced Autonomous Systems Innovation Center. If you're teaching a 14-ton machine to navigate urban chaos by itself, Reno's not a bad schoolyard.

US Transportation Secretary Anthony Foxx delivers an announcement in Washington, DC, in 2014. Automakers and ride-hail companies are racing to put self-driving cars on the road. In a few weeks, Uber passengers in Pittsburgh will be able to hail self-driving Volvos. Last month, Tesla announced its hopes to build an autonomous ride-hailing fleet. And this month, Ford said it plans to mass-produce autonomous vehicles by 2021.