  Dynamic origin-destination (OD) demand is central to transportation system
modeling and analysis. The dynamic OD demand estimation problem (DODE) has been
studied for decades, most of which solve the DODE problem on a typical day or
several typical hours. There is a lack of methods that estimate high-resolution
dynamic OD demand for a sequence of many consecutive days over several years
(referred to as 24/7 OD in this research). Having multi-year 24/7 OD demand
would allow a better understanding of characteristics of dynamic OD demands and
their evolution/trends over the past few years, a critical input for modeling
transportation system evolution and reliability. This paper presents a
data-driven framework that estimates day-to-day dynamic OD using high-granular
traffic counts and speed data collected over many years. The proposed framework
statistically clusters daily traffic data into typical traffic patterns using
t-Distributed Stochastic Neighbor Embedding (t-SNE) and k-means methods. A
GPU-based stochastic projected gradient descent method is proposed to
efficiently solve the multi-year 24/7 DODE problem. It is demonstrated that the
new method efficiently estimates the 5-minute dynamic OD demand for every
single day from 2014 to 2016 on I-5 and SR-99 in the Sacramento region. The
resultant multi-year 24/7 dynamic OD demand reveals the daily, weekly, monthly,
seasonal and yearly change in travel demand in a region, implying intriguing
demand characteristics over the years.
