Nicholas Fournier, PhD

Consolidated one-off scripts into a modular, automated process with QA/QC and CI/CD, reducing bugs and labor, enabling junior staff, and supporting parallel processing for large-scale surveys (e.g., MassDOT, Oregon, Met-Council, NYC DOT, PSRC).

Introduced day-of-week weighting and transit ridership targets to improve temporal fidelity and mode-share alignment.

Lead developer of algorithms to prepare household travel survey data for tour-based ABMs, eliminating manual reprocessing.

Designed Mahalanobis distance and graph-clique method to identify joint trips, improving linked-trip modeling.

Built heuristic algorithm to identify underreported child trips in household surveys.

Managed tool to convert client-facing Word specifications into YAML/JSON, reducing manual effort and time.

Developed Python API for automated GPS trace map-matching and routing on a self-hosted OSRM server.

Created concave-hull-based method to delineate parking zones for city policy development.

Developed disaggregated accessibility model in the open-source activity-based travel model ActivitySim, linking higher-level models to lower-level choice models.

Developed toll pricing “futures” market model using bi-parabolic macroscopic traffic flow model and price elasticities to optimize traffic flow. Funded by California State SB1.

Developed graph theory-based bicycle network connectivity performance measure. Funded by Caltrans.

Python program to detect operational but mislabeled traffic sensors using variety of machine learning techniques (e.g., k-Nearest Neighbor, Logistic Regression, Random Forest, Support Vector Machines, Local Outlier Factor, Isolation Forest, and Robust Covariance Anomaly Detection). Funded by Caltrans.

UX research/human factors study to determine bicycle infrastructure preferences using virtual reality bicycle simulator. Estimated using a Latent Class Choice Model capable of accounting for user heterogeneity. Funded by Caltrans.

Advised team of 19 doctoral students conducting industry partnered research in public transportation engineering, planning, policy analysis, and economics.

Generated synthetic population of Boston for activity-based travel simulation model as part of joint MIT/UMass ARPA-e competitive research project. Developed a novel data fusion method to synthesize fixed work location data in population synthesis.

Conducted human factors research using full-scale immersive driving simulator with eye-tracking and control monitoring (steering, gas, and brake) to study responses to novel roadway infrastructure. Sponsored by the U.S. DOT SaferSim UTC.

Developed a sinusoidal model for seasonal bicycle demand estimation for calculating bicycle-vehicle crash risk where bicycle traffic data are limited.

Evaluated and prepared a report on the approval process of roadside safety hardware for the Federal Highway Administration.

Collected and analyzed LIDAR and video data from instrumented vehicle to estimate microscopic car-following model parameters.