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Growth in leisure travel has become increasingly significant economically,socially, and environmentally. However, flexible but uncoordinated travelbehaviors exacerbate traffic congestion. Mobile phone records not only revealhuman mobility patterns, but also enable us to manage travel demand for systemefficiency. In this paper, we propose a location recommendation system thatinfers personal preferences while accounting for constraints imposed by roadcapacity in order to manage travel demand. We first infer unobservedpreferences using a machine learning technique from phone records. We thenformulate an optimization method to improve system efficiency. Coupling mobilephone data with traffic counts and road network infrastructures collected inAndorra, this study shows that uncoordinated travel behaviors lead to longeraverage travel delay, implying the opportunities in managing travel demand bycollective decisions. The interplay between congestion relief and overallsatisfied location preferences observed in extensive simulations indicate thatmoderate sacrifices of individual utility lead to significant travel timesavings. Specifically, the results show that under full compliance rate, traveldelay fell by 52% at a cost of 31% less satisfaction. Under 60% compliancerate, 41% travel delay is saved with a 17% reduction in satisfaction. Thispaper highlights the effectiveness of the synergy among collective behaviors inincreasing system efficiency.

Managing travel demand: Location recommendation for system efficiency based on mobile phone data