5 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…
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…
On How Traffic Signals Impact the Fundamental Diagrams of Urban Roads
Chao Zhang, Yechen Li, Neha Arora +1
Being widely adopted by the transportation and planning practitioners, the fundamental diagram (FD) is the primary tool used to relate the key macroscopic traffic variables of spee…
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-…