research report
published journal article |
Jan 2011
published journal article
Providing quality transit service to travelers in low-density areas, particularly travelers without personal vehicles, is a constant challenge for transit agencies. The advent of fully-autonomous vehicles (AVs) and their inclusion in mobility service fleets may allow transit agencies to offer better service and/or reduce their own capital and operational costs. This study focuses on the problem of allocating resources between transit patterns and operating (or subsidizing) shared-use AV mobility services (SAMSs) in a large metropolitan area. To address this question, a joint transit network redesign and SAMS fleet size determination problem (JTNR-SFSDP) is introduced, and a bi-level mathematical programming formulation and solution approach are presented. The upper-level problem modifies a transit network frequency setting problem (TNFSP) formulation via incorporating SAMS fleet size as a decision variable and allowing the removal of bus routes. The lower-level problem consists of a dynamic combined mode choice-traveler assignment problem (DCMC-TAP) formulation. The heuristic solution procedure involves solving the upper-level problem using a nonlinear programming solver and solving the lower-level problem using an iterative agent-based assignment-simulation approach. To illustrate the effectiveness of the modeling framework, this study uses traveler demand from Chicago along with the region’s existing multimodal transit network. The computational results indicate significant traveler benefits, in terms of improved average traveler wait times, associated with optimizing the joint design of multimodal transit networks and SAMS fleets compared with the initial transit network design.
Phd Dissertation
As sensors that measure daily human activity become increasingly affordable and ubiquitous, there is a corresponding need for algorithms that unearth useful information from the resulting sensor observations. Many of these sensors record a time series of counts reflecting two behaviors: (1) the underlying hourly, daily, and weekly rhythms of natural human activity, and (2) bursty periods of unusual behavior. This dissertation explores a probabilistic framework for human-generated count data that (a) models the underlying recurrent patterns and (b) simultaneously separates and characterizes unusual activity via a Poisson-Markov model. The problems of event detection and characterization using real world, noisy sensor data with significant portions of data missing and corrupted measurements due to sensor failure are investigated. The framework is extended in order to perform higher level inferences, such as linking event models in a multi-sensor building occupancy model, and incorporating the occupancy measurement from loop detectors (in addition to the count measurement) to apply the model to problems in transportation research.
Phd Dissertation
Global Navigation Satellite Systems (GNSSs) have long been the cornerstone for positioning, navigation, and timing. Despite their widespread use, GNSS signals face vulnerabilities such as jamming, spoofing, and unreliable coverage in various environments like urban canyons, indoors, tunnels, and parking structures. These limitations make exclusive reliance on GNSS inadequate for the rigorous demands of future applications, including autonomous vehicles (AVs), intelligent transportation systems, and location-based services. To enhance GNSS performance in challenging settings, traditional methods have typically incorporated dead-reckoning sensors like inertial measurement units, lidars, or cameras. These sensors, however, accumulate errors over time and only offer navigation solutions within a local frame, relative to the user equipment’s (UE) initial position. In contrast, alternative signal-based approaches, known as signals of opportunity (SOPs) – encompassing AM/FM radio, satellite communication signals, digital television signals, Wi-Fi, and cellular – hold considerable promise as global navigation sources in GNSS-challenged environments. Among SOPs, cellular signals, particularly from third-generation (3G, code-division multiple access (CDMA)), fourth-generation (4G, long-term evolution (LTE)), and fifth-generation (5G, new radio (NR)) networks, stand out as potential navigation aids. Their navigation-friendly characteristics include ubiquity, geometric diversity, high carrier frequencies, spectral diversity, spatial diversity, broad bandwidth, strong signal strength, and free accessibility. Nevertheless, as SOPs are primarily designed for communication rather than navigation, utilizing cellular signals for navigational purposes presents several challenges. These include (1) the lack of specific low-level signal and error models for optimal state and parameter extraction for positioning and timing, (2) the absence of published robust, efficient, and reliable receiver architectures to generate navigation observables, (3) continual updates and changes in cellular protocols, and (4) the scarcity of frameworks for high-accuracy navigation using such signals. This dissertation addresses these challenges, focusing on cellular signals from 4G and 5G networks, with potential extensions to future cellular systems. The foundational contributions of this work are empirically validated on various platforms including ground vehicles (GVs), unmanned aerial vehicles (UAVs), and high-altitude aircraft, demonstrating GNSS-level navigation accuracy.
research report |
Nov 2023
Phd Dissertation |
Jan 2024
conference paper
Providing quality transit service to travelers in low-density areas, particularly travelers without personal vehicles, is a constant challenge for transit agencies. The advent of fully-autonomous vehicles (AVs) and their inclusion in mobility service fleets may allow transit agencies to offer travelers better service and/or reduce their own capital and operational costs. This study focuses on the problem of allocating resources between transit patterns and operating (or subsidizing) shared-use AV mobility services (SAMSs) in a large metropolitan area. To address this problem, a bi-level mathematical programming formulation and solution algorithm are presented for the joint transit network redesign and SAMS fleet size determination problem (JTNR-SFSDP). The upper-level problem modifies a transit network frequency setting problem (TNFSP) formulation via incorporating SAMS fleet size as a decision variable. The lower-level problem consists of a dynamic combined mode choiceâ??traveler assignment problem (DCMC-TAP) formulation. The solution procedure involves solving the upper-level problem using a nonlinear programming solver and solving the lower-level problem using an iterative agent-based simulation-assignment approach. To illustrate the effectiveness of the modeling framework, this study uses traveler demand from Chicago along with the regionâ??s existing multimodal transit network. The results indicate the ability of the solution procedure to solve the bi-level JTNR-SFSDP. Moreover, computational results indicate significant traveler benefits associated with optimizing the joint design of multimodal transit networks and SAMS fleets.
conference paper
working paper
In 1995-96, the authors participated in an eight-month long trial of prototype EVs, with the proviso that we could use some of the results for academic research. We were particularly interested in comparing data collected from trials with matched data collected from a panel survey. Our objective was to better understand vehicle trials as a source of information for transportation planning and market research, beyond the usual consumer preference information gathered for vehicle design purposes. The methodological issues were of particular concern, for as we discuss in the next section, trials provide useful data at one level, but they can also introduce new sources of bias and uncertainty to data collection and interpretation. We also investigated how perceptions towards EVs would change with the “hands-on” experience of a trial. In this paper we report findings from this trial, with a particular emphasis upon the methodological issues. We intentionally do not discuss purchase intentions, and focus, instead, upon a broader set of results. An objective is to provide transportation planners with useful data about characteristics like vehicle miles travelled, intra-household vehicle switching, and long trip taking when there are multiple data sources from the same respondents, including travel diaries and pre- and post trial panel survey data. This provides insight into how households might choose to use future electric vehicles, and it also addresses the issue of whether trials are an effective and efficient data collection method. The research is expected to provide useful information for those who wish to organize and interpret data from future consumer vehicle trials and it also provides more limited evidence about how households would use future electric vehicles that had a limited range.
conference paper
published journal article
High-quality strategic planning of autonomous mobility-on-demand (AMOD) systems is critical for the success of the subsequent phases of AMOD system implementation. To assist in strategic AMOD planning, we propose a dynamic and flexible flow-based model of an AMOD system. The proposed model is computationally fast while capturing the state transitions of two coordinated flows (i.e. co-flows): the AMOD service fleet vehicles and AMOD customers. Capturing important quantity dynamics and conservations through a system of ordinary differential equations, the model can economically respond to a large number and a wide range of scenario-testing requests. The paper illustrates the model efficacy through a basic example and a more realistic case study. The case study envisions replacing Manhattan’s existing taxi service with a hypothetical AMOD system. The results show that even a simple co-flow model can robustly predict the systemwide AMOD dynamics and support the strategic planning of AMOD systems.
published journal article |
Jul 2022
published journal article |
Jan 2025
conference paper |
Jun 2023
conference paper