The focus of the LSTS workshop at KDD 2016 is on machine learning applications to transportation systems where

1. a large number of transportation vehicles are in the system
2. remote sensors provide real-time, noisy data from each vehicle in the system
3. some feedback to the vehicles may be possible to influence the system

These types of systems are becoming more common with applications including but not limited to on-demand transportation, on-demand delivery of goods, ride sharing, usage based insurance, and safe-driving gamification.

This workshop is aimed at both researchers and data science practitioners working at the intersection of machine learning and transportation systems.

We invite papers from research areas such as signal processing of transportation sensor data, demand forecasting and optimal pricing, predictive modeling of risk through telematics, and big data processing and machine learning platform design for transportation systems.