Chao Zhang
Chao Zhang is Research Scientist at Google Research. His research interests concentrate on transportation science, systems modeling, and large-scale simulations. He currently focuses on solving large-scale real-world problems by leveraging techniques from simulation, operations research, transportation & logistics, and machine learning.
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Improving simulation-based origin-destination demand calibration using sample segment counts data
Arwa Alanqary
Yechen Li
The 12th Triennial Symposium on Transportation Analysis conference (TRISTAN XII), Okinawa, Japan (2025)
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This paper introduces a novel approach to demand estimation that utilizes partial observations of segment-level track counts. Building on established simulation-based demand estimation methods, we present a modified formulation that integrates sample track counts as a regularization term. This approach effectively addresses the underdetermination challenge in demand estimation, moving beyond the conventional reliance on a prior OD matrix. The proposed formulation aims to preserve the distribution of the observed track counts while optimizing the demand to align with observed path-level travel times. We tested this approach on Seattle's highway network with various congestion levels. Our findings reveal significant enhancements in the solution quality, particularly in accurately recovering ground truth demand patterns at both the OD and segment levels.
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Origin-destination travel demand estimation: an approach that scales worldwide, and its application to five metropolitan highway networks
Christopher Bian
Yechen Li
Willa Ng
Bin Yan
Janny Zhang
Transportation Research Part B: Methodological (2025) (to appear)
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Estimating Origin-Destination (OD) travel demand is vital for effective urban planning
and traffic management. Developing universally applicable OD estimation
methodologies is significantly challenged by the pervasive scarcity of high-fidelity traffic
data and the difficulty in obtaining city-specific prior OD estimates (or seed ODs), which
are often prerequisite for traditional approaches. Our proposed method directly
estimates OD travel demand by systematically leveraging aggregated, anonymized
statistics from Google Maps Traffic Trends, obviating the need for conventional census
or city-provided OD data. The OD demand is estimated by formulating a single-level,
one-dimensional, continuous nonlinear optimization problem with nonlinear equality
and bound constraints to replicate highway path travel times. The method achieves
efficiency and scalability by employing a differentiable analytical macroscopic network
model. This model by design is computationally lightweight, distinguished by its
parsimonious parameterization that requires minimal calibration effort and its capacity
for instantaneous evaluation. These attributes ensure the method's broad applicability
and practical utility across diverse cities globally. Using segment sensor counts from
Los Angeles and San Diego highway networks, we validate our proposed approach,
demonstrating a two-thirds to three-quarters improvement in the fit to segment count
data over a baseline. Beyond validation, we establish the method's scalability and
robust performance in replicating path travel times across diverse highway networks,
including Seattle, Orlando, Denver, Philadelphia, and Boston. In these expanded
evaluations, our method not only aligns with simulation-based benchmarks but also
achieves an average 13% improvement in it's ability to fit travel time data compared to
the baseline during afternoon peak hours.
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Traffic simulations: multi-city calibration of metropolitan highway networks
Yechen Li
Damien Pierce
The 27th IEEE International Conference on Intelligent Transportation Systems (ITSC), Edmonton, Canada (2024)
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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, departing from the standard practice of resorting to scarce segment-level sensor counts. The proposed approach is shown to tackle high-dimensional instances in a sample-efficient way. For the first time, case studies on 6 metropolitan highway networks are carried out, considering a total of 54 calibration scenarios. This is the first work to show the ability of a calibration algorithm to systematically scale across networks. Compared to the state-of-the-art simultaneous perturbation stochastic approximation (SPSA) algorithm, the proposed approach enhances fit to field data by an average 43.5% with a maximum improvement of 80.0%, and does so within fewer simulation calls.
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On how traffic signals impact the fundamental diagrams of urban roads
Yechen Li
The 4th Symposium on Management of Future Motorway and Urban Traffic Systems 2022 (MFTS2022), Dresden, Germany
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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 speed, flow, and density. We empirically analyze the relation between vehicular space-mean speeds and flows given different signal settings and postulate a parsimonious parametric function form of the traditional FD where its function parameters are explicitly modeled as a function of the signal plan factors. We validate the proposed formulation using data from signalized urban road segments in Salt Lake City, Utah, USA. The proposed formulation builds our understanding of how changes to signal settings impact the FDs, and more generally the congestion patterns, of signalized urban segments.
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