published journal article

Joint design of multimodal transit networks and shared autonomous mobility fleets

Transportation Research Part C: Emerging Technologies

Publication Date

April 1, 2020

Author(s)

Helen K. R. F. Pinto, Michael Hyland, Hani Mahmassani, I. Ömer Verbas

View published journal article

Abstract

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.

Suggested Citation

Helen K. R. F. Pinto, Michael F. Hyland, Hani S. Mahmassani and I. Ömer Verbas (2020) “Joint design of multimodal transit networks and shared autonomous mobility fleets”, Transportation Research Part C: Emerging Technologies, 113, pp. 2–20. Available at: 10.1016/j.trc.2019.06.010.

Phd Dissertation

Probabilistic learning for analysis of sensor-based human activity data

Publication Date

January 1, 2010

Author(s)

Jon Hutchins

Areas of Expertise

Intelligent Transportation Systems, Emerging Technologies, & Big Data Travel Behavior, Land Use, & the Built Environment

View Phd Dissertation

Abstract

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.

Suggested Citation

Jonathan Hutchins (2010) Probabilistic learning for analysis of sensor-based human activity data. Ph.D.. University of California, Irvine. Available at: https://uci.primo.exlibrisgroup.com/permalink/01CDL_IRV_INST/17uq3m8/alma991007581279704701 (Accessed: October 13, 2023).

Phd Dissertation

Cellular signals for navigation 4g, 5g, and beyond

Publication Date

December 1, 2023

Author(s)

Ali Abdallah

Areas of Expertise

Intelligent Transportation Systems, Emerging Technologies, & Big Data

View Phd Dissertation

Abstract

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.

Suggested Citation

Ali Abdallah (2023) Cellular signals for navigation 4g, 5g, and beyond. PhD Dissertation. UC Irvine. Available at: https://uci.primo.exlibrisgroup.com/permalink/01CDL_IRV_INST/17uq3m8/alma991035582060804701.

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Phd Dissertation | Sep 2024

Exploring novel security vulnerabilities and their safety implications in sensors and perception for autonomous systems

conference paper

Joint design of multimodal transit networks and shared autonomous mobility fleets

Proceedings of the 98th annual meeting of the transportation research board

Publication Date

January 1, 2019

Author(s)

Helen Pinto, Michael Hyland, Hani Mahmassani, öMer Verbas

Abstract

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.

Suggested Citation

Helen Pinto, Michael Hyland, Hani S. Mahmassani and Ömer Verbas (2019) “Joint design of multimodal transit networks and shared autonomous mobility fleets”, in Proceedings of the 98th annual meeting of the transportation research board, p. 7p.

conference paper

Causality between built environment and travel behavior: Structural equations model applied to Southern California

Proceedings of the 92nd annual meeting of transportation research board, washington, DC

Publication Date

January 1, 2013

Author(s)

Ke Wang

Suggested Citation

K. Wang (2013) “Causality between built environment and travel behavior: Structural equations model applied to Southern California”, in Proceedings of the 92nd annual meeting of transportation research board, washington, DC.

working paper

Projecting Use of Electric Vehicles from Household Vehicle Trials: Trial and Error?

Publication Date

September 5, 1998

Author(s)

Thomas Golob, Jane Gould

Working Paper

UCI-ITS-WP-98-2

Areas of Expertise

Travel Behavior, Land Use, & the Built Environment Zero-Emission Vehicles & Low-Carbon Fuels

View working paper

Abstract

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.

Suggested Citation

Thomas F. Golob and Jane Gould (1998) Projecting Use of Electric Vehicles from Household Vehicle Trials: Trial and Error?. Working Paper UCI-ITS-WP-98-2. Institute of Transportation Studies, Irvine. Available at: https://escholarship.org/uc/item/3d82n6k5.

working paper

Highways and Economic Productivity: Interpreting Recent Evidence

Publication Date

October 1, 1995

Associated Project

UCTC: (1991) ISTEA version

Author(s)

Marlon Boarnet

Areas of Expertise

Transportation Economics, Funding, & Finance

View working paper

Abstract

This paper reviews the recent literature on public infrastructure and economic productivity, with special attention to the particular case of highway infrastructure. Recent evidence suggests that, at the margin, highway infrastructure contributes little to state or national productivity. This is consistent with studies that show relatively small land use impacts from modern highways. Yet the idea that highways enhance economic health is common in the policy and planning communities. Two explanations can help reconcile this divergence between academic research and popular perception. First, some of the economic development observed near highways might not actually be caused by the highway. Second, some of the economic development near highways might be a shift of economic activity away from other areas. Either explanation suggests the need for reforms in highway project analysis and funding. Appropriate policy reforms and directions for future research are suggested.

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Population and Employment Densities: Structure and Change

working paper | Apr 1993

Endogenous Trip Scheduling: The Henderson Approach Reformulated and Compared with the Vickrey Approach

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Clean or a Day: Troubles with California's Smog Check

working paper | Oct 1995

Clean on Paper, Dirty on the Road: Troubles with California's Smog Check

working paper | Mar 1997

Urban Spatial Structure

published journal article

Individual truck speed estimation from advanced single inductive loops

Transportation Research Record

Publication Date

April 1, 2019

Author(s)

Yiqiao Li, Andre (Yeow Chern) Tok, Stephen Ritchie

View published journal article

Suggested Citation

Yiqiao Li, Andre Y.C. Tok and Stephen G. Ritchie (2019) “Individual truck speed estimation from advanced single inductive loops”, Transportation Research Record, 2673(5), pp. 272–284. Available at: 10.1177/0361198119841289.

conference paper

WIP: Deployability improvement, stealthiness user study, and safety impact assessment on real vehicle for dirty road patch attack

Workshop on Automotive and Autonomous Vehicle Security (AutoSec)

Publication Date

January 1, 2021

Author(s)

Takami Sato, Junjie Shen, Ningfei Wang, Yunhan Jack Jia, Xue Lin, Qi Alfred Chen

View conference paper

Suggested Citation

Takami Sato, Junjie Shen, Ningfei Wang, Yunhan Jack Jia, Xue Lin and Qi Alfred Chen (2021) “WIP: Deployability improvement, stealthiness user study, and safety impact assessment on real vehicle for dirty road patch attack”, in Workshop on Automotive and Autonomous Vehicle Security (AutoSec), p. 25. Available at: https://www.ndss-symposium.org/wp-content/uploads/autosec2021_23027_paper.pdf (Accessed: October 11, 2023).

published journal article

An instantaneous kinematic wave theory of diverging traffic

Transportation Research Part B: Methodological

Publication Date

February 1, 2013

Author(s)

Wenlong Jin, Michael Zhang

View published journal article

Suggested Citation

Wen-Long Jin and H. Michael Zhang (2013) “An instantaneous kinematic wave theory of diverging traffic”, Transportation Research Part B: Methodological, 48(1), pp. 1–16. Available at: 10.1016/j.trb.2012.12.001.