publication . Preprint . 2016

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

Leng, Yan; Rudolph, Larry; Pentland, Alex 'Sandy'; Zhao, Jinhua; Koutsopolous, Haris N.;
Open Access English
  • Published: 21 Oct 2016
Abstract
Comment: Presented at the Data For Good Exchange 2016
Subjects
free text keywords: Computer Science - Computers and Society, Computer Science - Social and Information Networks
Download from
43 references, page 1 of 3

[1] W. T. O. (2016). Unwto annual report 2015. Technical report.

[2] D. Acemoglu, A. Makhdoumi, A. Malekian, and A. Ozdaglar. Informational braess' paradox: The e ect of information on tra c congestion. arXiv preprint arXiv:1601.02039, 2016. [OpenAIRE]

[3] R. Akcelik. Travel time functions for transport planning purposes: Davidson's function, its time dependent form and alternative travel time function. Australian Road Research, 21(3), 1991.

[4] L. Alexander, S. Jiang, M. Murga, and M. C. Gonzalez. Origin{destination trips by purpose and time of day inferred from mobile phone data. Transportation Research Part C: Emerging Technologies, 2015.

[5] F. Alhasoun, M. Alhazzani, and M. C. Gonzalez. City scale next place prediction from sparse data through similar strangers. 2016.

[6] A. Asahara, K. Maruyama, A. Sato, and K. Seto. Pedestrian-movement prediction based on mixed markov-chain model. In Proceedings of the 19th ACM SIGSPATIAL international conference on advances in geographic information systems, pages 25{33. ACM, 2011.

[7] D. Ashbrook and T. Starner. Using gps to learn signi cant locations and predict movement across multiple users. Personal and Ubiquitous Computing, 7(5):275{286, 2003. [OpenAIRE]

[8] J. Bao, Y. Zheng, D. Wilkie, and M. Mokbel. Recommendations in location-based social networks: a survey. GeoInformatica, 19(3):525{565, 2015.

[9] M. Ben-Akiva and M. Bierlaire. Discrete choice methods and their applications to short term travel decisions. In Handbook of transportation science, pages 5{33. Springer, 1999.

[10] B. Berjani and T. Strufe. A recommendation system for spots in location-based online social networks. In Proceedings of the 4th Workshop on Social Network Systems, page 4. ACM, 2011. [OpenAIRE]

[11] D. Brockmann, L. Hufnagel, and T. Geisel. The scaling laws of human travel. Nature, 439(7075):462{465, 2006. [OpenAIRE]

[12] S. Colak, A. Lima, and M. C. Gonzalez. Understanding congested travel in urban areas. Nature communications, 7, 2016.

[13] M. De Domenico, A. Lima, M. C. Gonzalez, and A. Arenas. Personalized routing for multitudes in smart cities. EPJ Data Science, 4(1):1{11, 2015. [OpenAIRE]

[14] M. De Domenico, A. Lima, and M. Musolesi. Interdependence and predictability of human mobility and social interactions. Pervasive and Mobile Computing, 9(6):798{807, 2013.

[15] W. De Mulder, S. Bethard, and M.-F. Moens. A survey on the application of recurrent neural networks to statistical language modeling. Computer Speech & Language, 30(1):61{98, 2015.

43 references, page 1 of 3
Abstract
Comment: Presented at the Data For Good Exchange 2016
Subjects
free text keywords: Computer Science - Computers and Society, Computer Science - Social and Information Networks
Download from
43 references, page 1 of 3

[1] W. T. O. (2016). Unwto annual report 2015. Technical report.

[2] D. Acemoglu, A. Makhdoumi, A. Malekian, and A. Ozdaglar. Informational braess' paradox: The e ect of information on tra c congestion. arXiv preprint arXiv:1601.02039, 2016. [OpenAIRE]

[3] R. Akcelik. Travel time functions for transport planning purposes: Davidson's function, its time dependent form and alternative travel time function. Australian Road Research, 21(3), 1991.

[4] L. Alexander, S. Jiang, M. Murga, and M. C. Gonzalez. Origin{destination trips by purpose and time of day inferred from mobile phone data. Transportation Research Part C: Emerging Technologies, 2015.

[5] F. Alhasoun, M. Alhazzani, and M. C. Gonzalez. City scale next place prediction from sparse data through similar strangers. 2016.

[6] A. Asahara, K. Maruyama, A. Sato, and K. Seto. Pedestrian-movement prediction based on mixed markov-chain model. In Proceedings of the 19th ACM SIGSPATIAL international conference on advances in geographic information systems, pages 25{33. ACM, 2011.

[7] D. Ashbrook and T. Starner. Using gps to learn signi cant locations and predict movement across multiple users. Personal and Ubiquitous Computing, 7(5):275{286, 2003. [OpenAIRE]

[8] J. Bao, Y. Zheng, D. Wilkie, and M. Mokbel. Recommendations in location-based social networks: a survey. GeoInformatica, 19(3):525{565, 2015.

[9] M. Ben-Akiva and M. Bierlaire. Discrete choice methods and their applications to short term travel decisions. In Handbook of transportation science, pages 5{33. Springer, 1999.

[10] B. Berjani and T. Strufe. A recommendation system for spots in location-based online social networks. In Proceedings of the 4th Workshop on Social Network Systems, page 4. ACM, 2011. [OpenAIRE]

[11] D. Brockmann, L. Hufnagel, and T. Geisel. The scaling laws of human travel. Nature, 439(7075):462{465, 2006. [OpenAIRE]

[12] S. Colak, A. Lima, and M. C. Gonzalez. Understanding congested travel in urban areas. Nature communications, 7, 2016.

[13] M. De Domenico, A. Lima, M. C. Gonzalez, and A. Arenas. Personalized routing for multitudes in smart cities. EPJ Data Science, 4(1):1{11, 2015. [OpenAIRE]

[14] M. De Domenico, A. Lima, and M. Musolesi. Interdependence and predictability of human mobility and social interactions. Pervasive and Mobile Computing, 9(6):798{807, 2013.

[15] W. De Mulder, S. Bethard, and M.-F. Moens. A survey on the application of recurrent neural networks to statistical language modeling. Computer Speech & Language, 30(1):61{98, 2015.

43 references, page 1 of 3
Powered by OpenAIRE Research Graph
Any information missing or wrong?Report an Issue