  Truck platooning refers to a series of trucks driving in close proximity via
communication technologies, and it is considered one of the most implementable
systems of connected and automated vehicles, bringing huge energy savings and
safety improvements. Properly planning platoons and evaluating the potential of
truck platooning are crucial to trucking companies and transportation
authorities. This study proposes a series of data mining approaches to learn
spontaneous truck platooning patterns from massive trajectories. An enhanced
map matching algorithm is developed to identify truck headings by using digital
map data, followed by an adaptive spatial clustering algorithm to detect
instantaneous co-moving truck sets. These sets are then aggregated to find the
network-wide maximum platoon duration and size through frequent itemset mining
for computational efficiency. We leverage real GPS data collected from truck
fleeting systems in Liaoning Province, China, to evaluate platooning
performance and successfully extract spatiotemporal platooning patterns.
Results show that approximately 36% spontaneous truck platoons can be
coordinated by speed adjustment without changing routes and schedules. The
average platooning distance and duration ratios for these platooned trucks are
9.6% and 9.9%, respectively, leading to a 2.8% reduction in total fuel
consumption. We also distinguish the optimal platooning periods and space
headways for national freeways and trunk roads, and prioritize the road
segments with high possibilities of truck platooning. The derived results are
reproducible, providing useful policy implications and operational strategies
for large-scale truck platoon planning and roadside infrastructure
construction.
