conference paper
Freeway incident detection using artificial neural networks
Proceedings, International Conference on Artificial Intelligence Applications in Transportation Engineering
Publication Date
January 1, 1998
Area of Expertise: Unspecified
conference paper
On the existence of stationary states in general road networks
21st international symposium on transportation and traffic theory
Publication Date
January 1, 2015
Author(s)
Abstract
Our daily driving experience and empirical observations suggest that traffic patterns in a road network are relatively stationary during peak periods. In numerous transportation network studies, there has been an implicit conjecture that stationary states exist in a network when origin demands, route choice proportions, and destination supplies are constant. In this study, we first rigorously formulate the conjecture within the framework of a network kinematic wave theory with an invariant junction model. After defining stationary states, we derive a system of algebraic equations in 3-tuples of stationary link flow-rates, demands, and supplies. We then introduce a new definition of junction critical demand levels based on effective demands and supplies. With a map in critical demand levels, we show that its fixed points and, therefore, stationary states exist with the help of Brouwer’s fixed point theorem. For two simple road networks, we show that the map is well-defined and can be used to solve stationary states with a brute-force method. Finally we summarize the study and present some future extensions and applications. (C) 2015 The Authors. Published by Elsevier B.V.
Suggested Citation
Wen-Long Jin (2015) “On the existence of stationary states in general road networks”, in . Kuwahara, M and Kita, H and Asakura, Y (ed.) 21st international symposium on transportation and traffic theory. ELSEVIER SCIENCE BV (Transportation research procedia), pp. 689–703. Available at: 10.1016/j.trpro.2015.06.036.
published journal article
Microscopic-macroscopic models systems integration: A simulation case study for ATMIS
SIMULATION
Publication Date
May 1, 2005
Author(s)
Abd-El-Kader Sahraoui, R. (Jay) Jayakrishnan
Suggested Citation
Abd-El-Kader Sahraoui and R. Jayakrishnan (2005) “Microscopic-macroscopic models systems integration: A simulation case study for ATMIS”, SIMULATION, 81(5), pp. 353–363. Available at: 10.1177/0037549705052771. published journal article
Another view of freight forecasting modeling trends
Ksce Journal of Civil Engineering
Publication Date
March 1, 2010
Author(s)
Choong Heon Yang, Amelia Regan, Young Tae Son
Suggested Citation
ChoongHeon Yang, Amelia C. Regan and Young Tae Son (2010) “Another view of freight forecasting modeling trends”, Ksce Journal of Civil Engineering, 14(2), pp. 237–242. Available at: 10.1007/s12205-010-0237-y. published journal article
Estimation of vehicular emissions by capturing traffic variations
Atmospheric Environment
Publication Date
May 1, 2007
Suggested Citation
K.S. Nesamani, Lianyu Chu, Michael G. McNally and R. Jayakrishnan (2007) “Estimation of vehicular emissions by capturing traffic variations”, Atmospheric Environment, 41(14), pp. 2996–3008. Available at: 10.1016/j.atmosenv.2006.12.027. conference paper
Towards robust {LiDAR-based} perception in autonomous driving: General black-box adversarial sensor attack and countermeasures
29th USENIX Security Symposium (USENIX Security 20)
Publication Date
January 1, 2020
Author(s)
Zhe (Jared) Sun, Yulong Cao, Qi Alfred Chen, Z. Morley Mao
Suggested Citation
Jiachen Sun, Yulong Cao, Qi Alfred Chen and Z. Morley Mao (2020) “Towards robust {LiDAR-based} perception in autonomous driving: General black-box adversarial sensor attack and countermeasures”, in 29th USENIX Security Symposium (USENIX Security 20), pp. 877–894. Available at: https://www.usenix.org/conference/usenixsecurity20/presentation/sun (Accessed: October 11, 2023). conference paper
Intriguing Properties of Diffusion Models: An Empirical Study of the Natural Attack Capability in Text-to-Image Generative Models
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
Publication Date
January 1, 2024
Author(s)
Suggested Citation
Takami Sato, Justin Yue, Nanze Chen, Ningfei Wang and Qi Alfred Chen (2024) “Intriguing Properties of Diffusion Models: An Empirical Study of the Natural Attack Capability in Text-to-Image Generative Models”, pp. 24635–24644. Available at: https://openaccess.thecvf.com/content/CVPR2024/html/Sato_Intriguing_Properties_of_Diffusion_Models_An_Empirical_Study_of_the_CVPR_2024_paper.html (Accessed: October 23, 2024). conference paper
Towards Secure and Robust Autonomy Software in Autonomous Driving and Smart Transportation
Proceedings of the 7th ACM on Cyber-Physical System Security Workshop
Publication Date
May 25, 2021
Author(s)
Abstract
Autonomous Driving (AD) technology has always been an international pursuit due to its significant benefit in driving safety, efficiency, and mobility. Over 15 years after the first DARPA Grand Challenge, its development and deployment are becoming increasingly mature and practical, with some AD vehicles already providing services on public roads (e.g., Google Waymo One in Phoenix and Baidu Apollo Go in China). In AD technology, the autonomy software stack, or the AD software, is highly security critical: it is in charge of safety-critical driving decisions such as collision avoidance and lane keeping, and thus any security problems in it can directly impact road safety. In this talk, I will describe my recent research that initiates the first systematic effort towards understanding and addressing the security problems in production AD software. I will be focusing on two critical modules: perception and localization, and talk about how we are able to discover novel and practical sensor/physical-world attacks that can cause end-to-end safety impacts such as crashing into obstacles or driving off road. Besides AD software, I will also briefly talk about my recent research on autonomy software security in smart transportation in general, especially those enabled by Connected Vehicle (CV) technology. I will conclude with a discussion on defense and future research directions.
Suggested Citation
Alfred Qi Chen (2021) “Towards Secure and Robust Autonomy Software in Autonomous Driving and Smart Transportation”, in Proceedings of the 7th ACM on Cyber-Physical System Security Workshop. New York, NY, USA: Association for Computing Machinery (CPSS '21), p. 1. Available at: 10.1145/3457339.3457978. published journal article
Innovative single-loop speed estimation model with advanced loop data
IET Intelligent Transport Systems
Publication Date
December 3, 2010
Author(s)
Suggested Citation
S. Park and S.G. Ritchie (2010) “Innovative single-loop speed estimation model with advanced loop data”, IET Intelligent Transport Systems, 4(4), pp. 232–243. Available at: 10.1049/iet-its.2009.0126. published journal article
Electric Vehicle Charging as a Monthly Budget: Portfolio Preferences Across Working Americans
Publication Date
May 17, 2026
Author(s)
Amin Akbari, Matthew Dean, Katherine E. Asmussen
Abstract
The researchers examined electric vehicle (EV) charging location preferences by investigating how drivers allocate monthly charging sessions across home, workplace, and public locations. In contrast to earlier studies that treat charging location as a one-time, discrete choice, they examined preferences for combinations of all three locations over a monthly period. They employed a multiple discrete–continuous extreme value (MDCEV) model using stated preference data from 881 employed EV drivers across the United States. Results show that 88.9% of charging decisions involve multiple locations. Home charging is preferred overall, followed by workplace and public. Drivers with private driveways, single-family homes, or solar panels, as well as older adults have higher home charging preferences, though battery storage moderates the pull of solar toward home. Urban residents rely more on public charging, while workplace charging is highly price-elastic, offering employers a demand management lever. The study underscores the importance of examining charging behavior as a portfolio allocation decision.