  Growth in leisure travel has become increasingly significant economically,
socially, and environmentally. However, flexible but uncoordinated travel
behaviors exacerbate traffic congestion. Mobile phone records not only reveal
human mobility patterns, but also enable us to manage travel demand for system
efficiency. In this paper, we propose a location recommendation system that
infers personal preferences while accounting for constraints imposed by road
capacity in order to manage travel demand. We first infer unobserved
preferences using a machine learning technique from phone records. We then
formulate an optimization method to improve system efficiency. Coupling mobile
phone data with traffic counts and road network infrastructures collected in
Andorra, this study shows that uncoordinated travel behaviors lead to longer
average travel delay, implying the opportunities in managing travel demand by
collective decisions. The interplay between congestion relief and overall
satisfied location preferences observed in extensive simulations indicate that
moderate sacrifices of individual utility lead to significant travel time
savings. Specifically, the results show that under full compliance rate, travel
delay fell by 52% at a cost of 31% less satisfaction. Under 60% compliance
rate, 41% travel delay is saved with a 17% reduction in satisfaction. This
paper highlights the effectiveness of the synergy among collective behaviors in
increasing system efficiency.
