- Sponsor
- Department of Civil and Environmental Engineering
Uncertainty-Aware Characterization and Modeling of Mixed-Autonomy Traffic: From Driver Behavior to Stochastic Traffic Flow
Advisor: Associate Professor Alireza Talebpour
Abstract
Mixed-autonomy traffic is shaped by uncertainty at several levels: human drivers respond
heterogeneously to automated vehicles (AVs), naturalistic data omit counterfactual behavior,
occlusions hide portions of motion, and microscopic fluctuations aggregate into stochastic traffic
waves. Rather than treating these sources as residual error, this dissertation develops methods to
characterize, infer, and propagate them. Five studies span four information regimes: fully observed
behavior, examined through two microscopic studies; missing counterfactual behavior; partially
observed motion; and stochastic macroscopic flow.
The first study examines controlled human car-following behind human-driven and automated
leaders under matched conditions. Dynamic Time Warping (DTW) compares full trajectories of
speed, acceleration, relative speed, spacing, and time headway. Spacing most clearly separates the
leader regimes, whereas calibrated parameters of the deterministic Intelligent Driver Model (IDM)
do not. A stochastic IDM extension identifies a substantially lower calibrated acceleration-noise
parameter when the leader is automated. In this setting, the distinction between leader conditions
is therefore expressed more clearly through response variability than through deterministic meanresponse
parameters.
The second study moves from controlled longitudinal response to naturalistic two-dimensional
maneuvering. Discretionary lane changes extracted from the Third Generation Simulation (TGSIM)
dataset are represented by time series of lead and lag gaps and relative speeds. Pairwise DTW
distances and Affinity Propagation clustering reveal recurring interaction patterns without
predefined driver classes. Gap-based clusters describe asymmetric aggressive, neutral, and
cautious strategies toward target-lane leaders and followers, while relative-speed clusters
distinguish overtaking-like maneuvers from transitions into speed-compatible traffic. These
groups describe the observed sample rather than universal driver types, but they translate trajectory
heterogeneity into interpretable maneuver categories.
The first two studies characterize uncertainty when the relevant trajectories are observed. The third
addresses what observational data omit: the trajectory that would have occurred under an
alternative condition. Classical synthetic control estimates this counterfactual from weighted
donor trajectories, but large, correlated donor pools can produce many nearly equivalent pre2
intervention fits with unstable weights. Graph-Regularized Synthetic Control builds a donorsimilarity
graph from pre-intervention behavior and combines sparse selection with graph-
Laplacian smoothing to favor coherent solutions among similar donors. Theory characterizes the
resulting stability--bias tradeoff, while semi-synthetic experiments, a standard policy benchmark
based on California Proposition 99, and an AV-exposure application using the Waymo Open
Motion Dataset evaluate fit and donor-pool sensitivity. The method stabilizes an observational
comparison; without an appropriate identification design, it does not itself establish causal AV
effects.
A different form of missingness arises when the trajectory itself, rather than its counterfactual, is
unobserved. The fourth study represents short empirical trajectory clips as nodes in a
compositional motion graph and feasible transitions as directed edges. Hidden motion is inferred
as a posterior over graph walks, preserving multiple dynamically plausible explanations instead of
reducing an occluded interval to one interpolated curve. Given only a pre-occlusion prefix, the
framework generates controllable alternative futures; when post-occlusion tracks are available, it
jointly selects the matching identity and reconstructs the missing path. Experiments on the Waymo
Open Motion Dataset and TGSIM evaluate generation, association, imputation, and uncertainty.
They demonstrate the interpretability of clip-based hypotheses while identifying endpointconditioned
regimes in which simpler interpolation remains more accurate than the current graphwalk
implementation.
The final study changes scale from individual trajectories to stochastic first-order traffic flow. For
a concave fundamental diagram, it establishes that if the initial spatial density is a strong Markov
process with upward jumps, the entropy solution remains Markov at later times. Compatible spatial
and temporal generators yield kinetic equations for rarefaction drift, shock intensity, and shock
merging, providing a closed description of stochastic traffic-wave interactions. A finite-state
approximation admits an isospectral Lax evolution, while signalized-road and periodic-highway
examples illustrate queue and spillback probabilities, shock-front intensity, total variation, and
merging-time distributions. These Monte Carlo studies illustrate the framework's probabilistic
outputs rather than calibrate the kinetic closure to field data.
Taken together, the five studies form a progression from empirical characterization to structured
inference and stochastic propagation across scales. Under complete observation, uncertainty is
characterized through response variability and maneuver heterogeneity that deterministic averages
can conceal. When a counterfactual or trajectory segment is missing, it is represented through
regularized donor weights or posterior motion hypotheses. After aggregation, it evolves through
probability laws for interacting traffic waves. The dissertation therefore contributes a coherent
strategy for matching the representation of uncertainty to the available information and analytical
scale, while identifying causal design, posterior calibration, and empirical vehicle-to-flow
mapping as essential next steps.