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Research
Featured Papers
Urban Traffic Signal Control with Connected and Automated Vehicles: A Survey
Qiangqiang Guo, Li Li, and Xuegang (Jeff) Ban
Transportation Research Part C: Emerging Technologies Volume 101, April 2019
Inefficient traffic control is pervasive in modern urban areas, which would exaggerate traffic congestion as well as deterio- rate mobility, fuel economy and safety. In this paper, we systematically review the potential solutions that take advantage of connected and automated vehicles (CAVs) to improve the control performances of urban signalized intersections. The benefits and drawbacks of various CAV-based control methods are explained, and future research directions are discussed.
Smart Mobility for Seniors: Challenges and Solutions in El Paso, TX, and New York, NY
Matthew Vechione, Corina Marrufo, Raul Alejandro Vargas-Acosta, Maria Guadalupe Jimenez-Velasco, Okan Gurbuz, Assel Dmitriyeva, Ruey Long Cheu, Natalia Villanueva-Rosales, Guillermina Gina Nunez-Mchiri, and Joseph Y. J. Chow
The 4th IEEE International Smart Cities Conference (ISC2), September 2018
This paper focuses on investigating seniorn citizens’ mobility needs in El Paso, Texas and New York City, New York in order to define the requirements and recommendations for an ad-hoc solution on smart mobility for seniors, using state-of-the- art mobile technologies. In order to identify the main concerns and requirements to assist in mobility of seniors, a survey was conducted at various senior recreation centers across El Paso and New York City, with results indicating that: (i) the most required assistance for seniors is the avoidance of traffic congestion; (ii) the majority of seniors who own mobile devices are not using the available applications or functions to meet their mobility needs; and (iii) seniors prefer mobile applications that are easy and intuitive to use.
Mapping of Truck Traffic in New Jersey using Weigh-in-Motion Data
Sami Demiroluk, Kaan Ozbay, and Hani Nassif
IET Intelligent Transport Systems Volume 12, Issue 9, November 2018
This study presents an innovative hierarchical Bayesian model for mapping of county level truck traffic in New Jersey. The goal in using the overweight trucks in the spatial model is to demonstrate the importance of representing their spatial vari- ation due to their impact on the life of roadway network elements. The results of the study indicate that the most influential covariate for the truck traffic is the length of interstate roadways, followed by employment and population. The developed truck count maps can help transportation professionals on identifying and ranking the locations at an aggregate level, which requires closer attention.
Mining Automatically Extracted Vehicle Trajectory Data for Proactive Safety Analytics
Kun Xie, Kaan Ozbay, Hong Yang, and Cheng Li
Transportation Research Part C: Emerging Technologies, Volume 106, September 2019
This study aims to leverage massive vehicle trajectory data for proactive safety analytics. State-of-the-art computer vision techniques are employed to automatically extract massive vehicle trajectories from 70-hour traffic video data at two inter- sections in Brooklyn, New York City. The novelty of our trajectory extraction algorithm includes the inclusion of high-level information from foreground/background separation to cluster feature points that belong to the same vehicle and the use of non-parametric clustering method, Dirichlet process Gaussian mixture model (DPGMM), that does not require specifi- cation of the cluster number. Surrogate safety measures in terms of time to collision are introduced to identify rear-end conflict risk for adjacent vehicles. Hidden Markov models (HMMs) are then proposed to model the rear-end conflicts, and their results imply that HMMs can help monitor the prevailing traffic conditions and facilitate proactive safety management.
15 C2SMART Center Annual Report