Pinned Repositories
Autoencoder
Autoencoder to impute missing data
ChicagoOpenData
Import and cleanup of transportation-relevant datasets from Chicago's Open Data Portal and related repositories
ChicagoTNPFleetMakeup
String matching exercise to merge Chicago TNP vehicles with fueleconomy.gov data and estimate the fleet's age and MPG distributions.
FleetElectrification
Optimizing fleet vehicle purchases, routing, and battery charging to minimize costs + emissions
hps
High Performance C++11 Serialization Library
lintools
Tools for manipulating systems of linear (in)equalities
Multiway_Partitioning
Multi-way number partitioning with Gurobi to make same-sum subsets from a set of numbers
PHORUM_EV_2022
PJM Hourly Open-source Reduced-form Unit commitment Model (PHORUM)
RidePooling
Optimizing pooled ride fleets considering costs of operations, emissions, and traffic
TravelDemandSubsetting
Subsetting Chicago TNP trip data down to one representative week using unsupervised learning (partitioning around medoids with dynamic time warping distance).
mbbruch's Repositories
mbbruch/FleetElectrification
Optimizing fleet vehicle purchases, routing, and battery charging to minimize costs + emissions
mbbruch/PHORUM_EV_2022
PJM Hourly Open-source Reduced-form Unit commitment Model (PHORUM)
mbbruch/Autoencoder
Autoencoder to impute missing data
mbbruch/ChicagoOpenData
Import and cleanup of transportation-relevant datasets from Chicago's Open Data Portal and related repositories
mbbruch/ChicagoTNPFleetMakeup
String matching exercise to merge Chicago TNP vehicles with fueleconomy.gov data and estimate the fleet's age and MPG distributions.
mbbruch/hps
High Performance C++11 Serialization Library
mbbruch/lintools
Tools for manipulating systems of linear (in)equalities
mbbruch/Multiway_Partitioning
Multi-way number partitioning with Gurobi to make same-sum subsets from a set of numbers
mbbruch/RidePooling
Optimizing pooled ride fleets considering costs of operations, emissions, and traffic
mbbruch/TravelDemandSubsetting
Subsetting Chicago TNP trip data down to one representative week using unsupervised learning (partitioning around medoids with dynamic time warping distance).