6 papers
From Lagging to Leading: Validating Hard Braking Events as High-Density Indicators of Segment Crash Risk
Yechen Li, Shantanu Shahane, Shoshana Vasserman +7
Identifying high crash risk road segments and accurately predicting crash incidence is fundamental to implementing effective safety countermeasures. While collision data inherently…
Origin-Destination Travel Demand Estimation: An Approach That Scales Worldwide, and Its Application to Five Metropolitan Highway Networks
Chao Zhang, Neha Arora, Christopher Bian +6
Estimating Origin-Destination (OD) travel demand is vital for effective urban planning and traffic management. Developing universally applicable OD estimation methodologies is sign…
Improving Simulation-Based Origin-Destination Demand Calibration Using Sample Segment Counts Data
Arwa Alanqary, Chao Zhang, Yechen Li +2
This paper introduces a novel approach to demand estimation that utilizes partial observations of segment-level track counts. Building on established simulation-based demand estima…
Traffic Simulations: Multi-City Calibration of Metropolitan Highway Networks
Chao Zhang, Yechen Li, Neha Arora +2
This paper proposes an approach to perform travel demand calibration for high-resolution stochastic traffic simulators. It employs abundant travel times at the path-level, departin…
On the Use of Abundant Road Speed Data for Travel Demand Calibration of Urban Traffic Simulators
Suyash Vishnoi, Akhil Shetty, Iveel Tsogsuren +2
This work develops a compute-efficient algorithm to tackle a fundamental problem in transportation: that of urban travel demand estimation. It focuses on the calibration of origin-…
Scalable Learning of Segment-Level Traffic Congestion Functions
Shushman Choudhury, Abdul Rahman Kreidieh, Iveel Tsogsuren +3
We propose and study a data-driven framework for identifying traffic congestion functions (numerical relationships between observations of traffic variables) at global scale and se…