Afiya Ayman |

Ensemble Prediction of Task Affinity for Efficient Multi-Task Learning

Wed, 01 Apr 2026 00:00:00 +0000

AI for Smart Transportation

Thu, 01 Jan 2026 00:00:00 +0000

Rural Transit Solutions for Enhancing Access & Connectivity

Tue, 01 Oct 2024 00:00:00 +0000

This Civic Innovation Challenge (CIVIC) Stage 1 project will perform research to address transportation challenges faced by residents and workers in rural areas around industrial hubs. Rapid economic growth in these areas demands efficient public transit systems that can serve a geographically dispersed workforce with strict arrival times. However, existing solutions often struggle with these unique requirements, leading to traffic congestion, pollution, and limited access to essential services for residents. This research project will develop and deploy a novel multi-modal transit system for Blue Oval City (BOC), a new rural industrial hub in Stanton, Tennessee. The system will combine fixed-line buses with on-demand micro-transit services, addressing the challenges posed by the geographically dispersed workforce and strict arrival times. Further, the project will design equitable fare structures to ensure that the system is sustainable for all stakeholders. If successful, the research will benefit communities around BOC by enhancing their access to employment opportunities, essential resources, and services, ultimately improving quality of life and community resilience. Furthermore, developing efficient and reliable transit can promote the growth of businesses in the surrounding areas and offer more affordable living options. The research findings and deliverables will be transferable to other rural industrial hubs across the nation, fostering economic development and improving public transportation infrastructure.

This project will be led by a multi-disciplinary, multi-stakeholder team of researchers and civic partners to co-create a scalable pilot project for BOC. The research project has two main goals. (i) Develop a system to optimize multi-modal transit networks, including micro-transit services that act as feeders for large, fixed-line buses. Models based on artificial intelligence will be designed to handle high volumes of employee trips and create service zones with optimal pick-up and drop-off locations, guaranteeing on-time arrivals for workers regardless of their shift schedules. (ii) Explore sustainable funding plans through stakeholder engagement, designing a fare structure that incentivizes ridership and ensures social equity. This includes examining how the system can improve access to healthcare, education, and other essential services.

The Benefits of Vulnerability Discovery and Bug Bounty Programs: Case Studies of Chromium and Firefox

Mon, 01 May 2023 00:00:00 +0000

Acceptance Rate: 19%

Impact of COVID-19 on Public Transit Accessibility and Ridership

Sat, 01 Apr 2023 00:00:00 +0000

Neural Architecture and Feature Search for Predicting the Ridership of Public Transportation Routes

Wed, 01 Jun 2022 00:00:00 +0000

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

Tue, 01 Feb 2022 00:00:00 +0000

Minimizing Energy Use of Mixed-Fleet Public Transit for Fixed-Route Service

Mon, 01 Feb 2021 00:00:00 +0000

Acceptance Rate: 21%

AI-Engine for Optimizing Mixed-Fleet Transit Operations

Thu, 01 Oct 2020 00:00:00 +0000

In every public transit system, a trade-off has to be made between concentrating service into very useful routes that serve large numbers of people and spreading service out to ensure that people everywhere have access to at least some service. Improving the efficiency of an existing system while enhancing service in terms of both usefulness and coverage presents considerable challenges. These challenges to operational efficiency are exacerbated by the requirement to provide complementary paratransit services, which are typically characterized by very low efficiency (energy per passenger per mile) and attendant high cost of operation. Our vision is to address these challenges by combining the complementary advantages of fixed- and dynamic-route transit services and seamlessly integrating them. We focus on the following objectives: minimizing energy used per passenger per mile, minimizing passenger wait and trip times, maximizing service coverage, and maximizing the percentage of daily trips serviced by transit. To explore this complex decision space, we will design, implement, and evaluate an artificial intelligence engine, which will enable agencies with mixed-vehicle fleets (EVs, ICEVs, etc.) to operate integrated fixed-dynamic transit services that maximize energy efficiency and make transit more accessible.

This project will bring together a team consisting of people with complementary background in transit operations, transit optimization, simulations, and artificial intelligence. This team will address the above challenges using state-of-the-art artificial intelligence, machine learning, and data-driven optimization techniques. Deep reinforcement learning and Monte-Carlo tree search will form the core of our real-time operational optimization, which will be supported by data-driven optimization for offline planning and by machine learning techniques for predicting transit demand, maintenance requirements, and traffic conditions.

The success of transit optimization and increased ridership depend on the acceptance of the public. Therefore, we are going to develop a novel methodology based on social relational networks to maximize our community outreach. We will augment these methods by providing affordable and complete transit subscription packages and a guidance app leveraged by CARTA’s unique role in managing transit, parking, bike share, and EV car share to support integrated subscription-based access to multiple modes of transportation. To mitigate the risk of operationalization, we will leverage Siemens’ Railigent platform, which provides the capability to manage large-scale geo-spatial data in the cloud and to generate comparative analytics. This partnership also ensures that the software developed here will be of a high technical readiness level and will be provided with required maintenance support to other agencies that face similar problems.

The impact of this project will be immense. The project provides mechanisms to improve transit accessibility with a projected increase from 41% to 73% of residents within ¼ mile of frequent transit service which will reduce personal-vehicle trips taken by those residents, significantly decreasing the energy used for transportation at the city level. The project will also reduce the per passenger per mile energy use and operating costs of the public transit service through operational optimization.

Data-Driven Prediction of Route-Level Energy Use for Mixed-Vehicle Transit Fleets

Tue, 01 Sep 2020 00:00:00 +0000

Microtransit Solutions for Underserved Communities

Sat, 01 Aug 2020 00:00:00 +0000

Public transportation infrastructure is an essential component in cultivating equitable communities. However, public transit agencies have historically struggled to achieve this since they are often severely stressed in terms of resources as they have to make the trade-off between concentrating service into routes that serve large numbers of people and spreading service out to ensure that people everywhere have access to at least some service. A solution that holds great promise for improving public transit systems is the integration of fixed-route services with microtransit systems: multi-passenger transportation services that serve passengers using dynamically generated routes and may expect passengers to make their way to and from common pick-up or drop-off points. However, most microtransit systems have failed in the past due to the lack of community engagement, inability to handle the uncertainty of operations when integrating the fixed transit, and inability to handle the system-level optimization challenges.

This project takes a socio-relational approach to community engagement in collaboration with the Chattanooga Area Regional Transportation Authority (CARTA), designs a community-centric micro-transit service that augments fixed-line public transit networks (improving transit accessibility), and demonstrate its effectiveness in the representative city of Chattanooga. The outcome of the project will be a deployment-ready software system that can be used by an agency to design and operate a micro-transit service effectively. The algorithmic toolchain will be complemented by mechanisms to optimally select the parameters and sustainably manage the data required by the algorithms. In addition, this project will provide a set of exemplar case studies and a validated social methodology to engage the community and learn their requirements, which will be fed into the algorithms. This will potentially impact a wide range of cities in the U.S. that do not have well-developed transit systems as the project will not only provide a reusable operations system but also demonstrate how integrated socio-technical research and strong community engagement can provide a pattern for sustainability and expansion.

The intellectual merit of this project lies in the novel community engagement approach and combined operations research and data-driven, learning-based integrated system optimization. Towards this goal, the project will make four key contributions.

First, the project will develop a novel targeted outreach approach that uses the relational networks of social capital (e.g., outreach to community centers, congregations and faith communities, schools, and similar organizational structures) and builds a categorical demand model to design an innovative micro-transit system. The project’s hypothesis is that the behavioral impact on public-transit ridership with the proposed method will be significantly higher than with an approach that focuses only on the economic or time-saving benefits of the improved transit system.
Second, the project will introduce a sustainable and resilient data-integration platform that dynamically adjusts the location of the sensor data used to affect the design parameters and assess performance of the transit system. This is crucial because cloud computing is still very expensive for community partners, especially for real-time high-velocity and high-volume data analysis. Further, this data store provides us an opportunity to take a privacy-by-design approach for the datasets collected during the project. Specifically, the project will develop novel integrated anonymization mixers for multi-modal datasets (e.g., mixing information of different modalities, such as location traces and transactions, together in a spatiotemporal-transactional mixer) that achieve a given level of privacy (quantified using the notation of differential privacy) while maximizing the accuracy of transit queries, relying on not just the privacy-properties of the data but also on the needs of the queries.
Third, the project will develop uncertainty-aware fleet management and dispatch algorithms that incorporate demand aggregation and environmental uncertainty caused by congestion, incidents, and their impact on the system (both for the users and fleet operations).
Fourth, the project will leverage recent advances in active learning for non-stationary environments with contextual side information to design algorithms that will aid in the exploration and optimal selection of hyperparameters for microtransit algorithms.

Addressing Transit Accessibility Challenges due to COVID-19

Mon, 01 Jun 2020 00:00:00 +0000

The COVID-19 pandemic has not only disrupted the lives of millions but also created exigent operational and scheduling challenges for public transit agencies. Agencies are struggling to maintain transit accessibility with reduced resources, changing ridership patterns, vehicle capacity constraints due to social distancing, and reduced services due to driver unavailability. A number of transit agencies have also begun to help the local food banks deliver food to shelters, which further strains the available resources if not planned optimally. At the same time, the lack of situational information is creating a challenge for riders who need to understand what seating is available on the vehicles to ensure sufficient distancing. In partnership with the transit agencies of Chattanooga, TN, and Nashville, TN, the proposed research will rapidly develop integrated transit operational optimization algorithms, which will provide proactive scheduling and allocation of vehicles to transit and cargo trips, considering exigent vehicle maintenance requirements (i.e., disinfection). A key component of the research is the design of privacy-preserving camera-based ridership detection methods that can help provide commuters with real-time information on available seats considering social-distancing constraints. The datasets and algorithms developed through this program will be swiftly released to the research community to encourage a wider collaborative effort that will help other transit agencies that face similar challenges.

The intellectual merit of the proposed research lies in the design and evaluation of integrated operational optimization for both fixed-line and on-demand transit (including paratransit) under atypical capacity constraints, which requires maximizing transit access but minimizing contact. The challenge for optimization is the uncertainties that arise due to the atypical travel time and travel demand distribution, both of which need to be learned online again due to the changed scenarios. While it is possible to optimize these transit modes separately as prior work has done, integrated optimization can lead to significantly better results. However, this is difficult as the solution space of these problems is very large. The approach is based on rapidly composing and comparing the effectiveness of principled decision-theoretic approaches such as Monte Carlo tree search, optimal trip assignments using integer programming and problem-specific heuristics, and demand aggregation for on-demand transit. To develop a model for varying travel demand, the research uses novel neural network architectures to estimate usage and seating patterns in real-time from cameras that are already installed within transit vehicles. This will enable transit agencies to obtain travel demand even when they are running fare-free operations to minimize contact with drivers. Working with partner transit agencies, the researchers will be able to make the services more accessible for the community during these challenging times. This project directly relates to Smart and Connected Communities program as it demonstrates the importance of the integration of technical and social research with strong community engagement in improving resilience of transit systems due to pandemics and other crises.

Smart Contract Development from the Perspective of Developers: Topics and Issues Discussed on Social Media

Sat, 01 Feb 2020 00:00:00 +0000

Data-Driven Detection of Anomalies and Cascading Failures in Traffic Networks

Sun, 01 Sep 2019 00:00:00 +0000

Efficient and Scalable Bug-Bounty Programs

Fri, 01 Mar 2019 00:00:00 +0000

Many organizations and companies have recently chosen to use so-called bug-bounty programs (also known as vulnerability reward programs), which allow outside security experts to evaluate the security of an organization’s products and services and to report security vulnerabilities in exchange for rewards. Bug-bounty programs provide unique benefits by allowing organizations to publicly signal their commitment to security and to harness the diverse expertise of thousands of security experts in an affordable way. Despite their rapidly growing popularity, bug-bounty programs are not well understood and can be mismanaged. As a result, bug bounty programs can waste substantial resources and they rarely live up to their potential to improve cybersecurity. This project will significantly improve the efficiency of bug-bounty programs by collecting and publishing comprehensive datasets on the bug-bounty ecosystem, by establishing a sound theory of bug-bounty programs, and by providing practical recommendations for organizations and regulators. The project will directly benefit organizations and companies by enabling them to manage bug-bounty programs more efficiently, which will allow them to eliminate security vulnerabilities at a lower cost; and it will also benefit users by improving the security of software products and services.

This project comprises four research thrusts. The first thrust will build a dataset that captures the entire bug-bounty ecosystem by collecting activity data and rule descriptions from public bug-bounty programs, conducting interviews and focus-group studies with experts who regularly participate in programs, collecting social-media posts, and incorporating existing datasets, such as vulnerability databases. The second thrust will analyze this dataset to understand the ecosystem and its actors’ incentives and actions, and to characterize processes, such as discovering and reporting vulnerabilities and rewarding reports. To code textual data, the project will first develop a terminology and taxonomy of bug-bounty related concepts. Driven by the results of the data analysis, the third thrust will develop a formal model of the bug-bounty ecosystem, which will incorporate technological processes as well as behavioral and economic incentives. To capture all aspects of such a complex ecosystem, the project will employ models and techniques from multiple disciplines and areas, including economics and cybersecurity. Building on this model, the fourth thrust will propose and evaluate novel approaches for improving the efficiency and scalability of bug-bounty programs. The project will consider policies for individual bug-bounty programs, such as rules for rewarding vulnerability reports, as well as mechanisms for regulating bug-bounty programs and for instigating coordination between them.

Data-Driven Energy Optimization for Multi-Modal Transit

Tue, 01 Jan 2019 00:00:00 +0000

The goal of this project is to develop a high-resolution system-level data capture and analysis framework to revolutionize the operational planning of a regional transportation authority, specifically the Chattanooga Area Regional Transportation Authority (CARTA). There is existing research on improving energy efficiency in transportation networks through analyzing energy consumption data per vehicle type and driving context. However, these studies are based on trip-specific estimation and thus cannot be applied to a regional transportation network. Further, a number of these studies are based on simplified model estimation that is used within a simulation framework for analysis and are therefore difficult to validate during actual driving/road conditions that are not captured in the training dataset (which is typically limited in size and features).

The availability of ubiquitous high-speed networking in Chattanooga provides us with a unique opportunity to change this status quo by providing mechanisms to significantly improve the operational efficiency of fleet operations. Specifically, we collect high-resolution datasets containing all information about engine status, vehicle location, fuel usage, etc. in real-time from CARTA’s fleet of buses, car sharing, and e-bike sharing vehicles and send them to a central station for analysis. Additionally, we get state of charge data from the electric vehicles, which can then be used to estimate vehicle health using data-driven prognostic algorithms developed by the team. Combined with the traffic congestion information obtained from external sources, such as HERE, this data can help create high-resolution energy consumption predictors, contextualized with features such as vehicle types and events in the city. These predictors can then be used by agencies like CARTA for operational optimization.

Overall, this project will enable the development and evaluation of tools to promote energy efficiency within a mobility-as-a-service transportation model in a mid-sized city. In addition to energy efficiency within each specific mode of operation, such as electric bus and electric car, this project will identify network mobility and energy efficiency associated with movement throughout the continuum of transportation choice present within Chattanooga. Further, the proposed project can complement the DoE national labs effort on vehicle energy consumption modeling by exploiting new data to investigate the impacts of road/driver factors on vehicle energy consumption. In addition, the project can supplement DoE national labs efforts by providing more data on electric bus operations under various driving conditions for model validation.