Skip to main content
SpringerLink
Log in

Spatial crowdsourcing: a survey

  • Special Issue Paper
  • Published:
The VLDB Journal Aims and scope Submit manuscript

Abstract

Crowdsourcing is a computing paradigm where humans are actively involved in a computing task, especially for tasks that are intrinsically easier for humans than for computers. Spatial crowdsourcing is an increasing popular category of crowdsourcing in the era of mobile Internet and sharing economy, where tasks are spatiotemporal and must be completed at a specific location and time. In fact, spatial crowdsourcing has stimulated a series of recent industrial successes including sharing economy for urban services (Uber and Gigwalk) and spatiotemporal data collection (OpenStreetMap and Waze). This survey dives deep into the challenges and techniques brought by the unique characteristics of spatial crowdsourcing. Particularly, we identify four core algorithmic issues in spatial crowdsourcing: (1) task assignment, (2) quality control, (3) incentive mechanism design, and (4) privacy protection. We conduct a comprehensive and systematic review of existing research on the aforementioned four issues. We also analyze representative spatial crowdsourcing applications and explain how they are enabled by these four technical issues. Finally, we discuss open questions that need to be addressed for future spatial crowdsourcing research and applications.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. The term was coined for the first tine in [132].

  2. Sometimes Waze is also viewed as a crowdsensing application, which leverages users’ sensor-equipped mobile devices to collect and share data. Spatial crowdsourcing is a general framework and can subsume crowdsensing or participatory sensing [132].

References

  1. Datatang Taxi Dataset (2016). http://www.datatang.com/data/45888. Accessed 23 June 2016

  2. Amazon Mechanical Turk (2018). https://www.mturk.com/. Accessed 26 Dec 2018

  3. Cainiao (2018). https://www.cainiao.com/. Accessed 26 Dec 2018

  4. Didi Chuxing (2018). https://www.didiglobal.com/. Accessed 26 Dec 2018

  5. Facebook Editor (2018). https://www.facebook.com/editor. Accessed 26 Dec 2018

  6. FedEx (2018). https://www.fedex.com/. Accessed 26 Dec 2018

  7. Geohash (2018). https://en.wikipedia.org/wiki/Geohash. Accessed 26 Dec 2018

  8. Gigwalk (2018). http://www.gigwalk.com. Accessed 26 Dec 2018

  9. gMission Dataset Generator (2018). https://github.com/gmission/SCDataGenerator. Accessed 26 Dec 2018

  10. GrubHub (2018). https://www.grubhub.com/. Accessed 26 Dec 2018

  11. InterestingSport (2018). http://www.quyundong.com/. Accessed 26 Dec 2018

  12. Nanguache (2018). http://www.nanguache.com/. Accessed 26 Dec 2018

  13. OpenStreetMap (2018). https://www.openstreetmap.org/. Accessed 26 Dec 2018

  14. Pokémon Go (2018). https://www.pokemongo.com/. Accessed 26 Dec 2018

  15. TaskRabbit (2018). http://www.taskrabbit.com. Accessed 26 Dec 2018

  16. TLC Trip Record Data (2018). http://www.nyc.gov/html/tlc/html/about/trip_record_data.shtml. Accessed 23 June 2016. Accessed 26 Dec 2018

  17. Uber (2018). https://www.uber.com/. Accessed 26 Dec 2018

  18. UPS (2018). https://www.ups.com/. Accessed 26 Dec 2018

  19. Waze (2018). http://www.waze.com/. Accessed 26 Dec 2018

  20. CPLEX (2019). https://www.ibm.com/analytics/cplex-optimizer. Accessed 26 May 2019

  21. Didi Chuxing Corporate Citizenship Report (2019). https://www.didiglobal.com/about-didi/responsibility. Accessed 26 May 2019

  22. GAIA Open Dataset (2019). https://outreach.didichuxing.com/research/opendata. Accessed 26 May 2019

  23. Humanitarian OpenStreetMap Team (2019). https://www.hotosm.org/. Accessed 26 May 2019

  24. keepright (2019). https://www.keepright.at/. Accessed 26 May 2019

  25. MediaQ (2019). http://mediaq.usc.edu/. Accessed 26 May 2019

  26. Meituan (2019). https://www.meituan.com/. Accessed 26 May 2019

  27. Seamless (2019). https://www.seamless.com. Accessed 26 May 2019

  28. Upwork (2019). https://www.upwork.com/. Accessed 26 May 2019

  29. Wikimapia (2019). https://www.wikimapia.org/. Accessed 26 May 2019

  30. Agapie, E., Teevan, J., Monroy-Hernández, A.: Crowdsourcing in the field: A case study using local crowds for event reporting. In: Proceedings of the 3rd AAAI Conference on Human Computation and Crowdsourcing, pp. 2–11 (2015)

  31. Aggarwal, G., Goel, G., Karande, C., Mehta, A.: Online vertex-weighted bipartite matching and single-bid budgeted allocations. In: Proceedings of the 22nd Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1253–1264 (2011)

  32. Ahuja, R.K., Magnanti, T.L., Orlin, J.B.: Network Flows: Theory, Algorithms and Applications. Prentice Hall, Upper Saddle River (1993)

    MATH  Google Scholar 

  33. Alfarrarjeh, A., Emrich, T., Shahabi, C.: Scalable spatial crowdsourcing: A study of distributed algorithms. In: 16th IEEE International Conference on Mobile Data Management, pp. 134–144 (2015)

  34. Amsterdamer, Y., Milo, T.: Foundations of crowd data sourcing. SIGMOD Record 43(4), 5–14 (2014)

    Google Scholar 

  35. Andrés, M.E., Bordenabe, N.E., Chatzikokolakis, K., Palamidessi, C.: Geo-indistinguishability: differential privacy for location-based systems. In: 2013 ACM SIGSAC Conference on Computer and Communications Security, pp. 901–914 (2013)

  36. Artikis, A., Weidlich, M., Schnitzler, F., Boutsis, I., Liebig, T., Piatkowski, N., Bockermann, C., Morik, K., Kalogeraki, V., Marecek, J., Gal, A., Mannor, S., Gunopulos, D., Kinane, D.: Heterogeneous stream processing and crowdsourcing for urban traffic management. In: Proceedings of the 17th International Conference on Extending Database Technology, pp. 712–723 (2014)

  37. Asghari, M., Shahabi, C.: An on-line truthful and individually rational pricing mechanism for ride-sharing. In: Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 7:1–7:10 (2017)

  38. Asghari, M., Shahabi, C.: On on-line task assignment in spatial crowdsourcing. In: 2017 IEEE International Conference on Big Data, pp. 395–404 (2017)

  39. Asghari, M., Shahabi, C.: Adapt-pricing: a dynamic and predictive technique for pricing to maximize revenue in ridesharing platforms. In: Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 189–198 (2018)

  40. Asghari, M., Deng, D., Shahabi, C., Demiryurek, U., Li, Y.: Price-aware real-time ride-sharing at scale: an auction-based approach. In: Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 3:1–3:10 (2016)

  41. Ashlagi, I., Azar, Y., Charikar, M., Chiplunkar, A., Geri, O., Kaplan, H., Makhijani, R.M., Wang, Y., Wattenhofer, R.: Min-cost bipartite perfect matching with delays. In: Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2017), pp. 1:1–1:20 (2017)

  42. Azar, Y., Fanani, A.J.: Deterministic min-cost matching with delays. In: 16th International Workshop on Approximation and Online Algorithms, pp. 21–35 (2018)

    Google Scholar 

  43. Azar, Y., Chiplunkar, A., Kaplan, H.: Polylogarithmic bounds on the competitiveness of min-cost perfect matching with delays. In: Proceedings of the 28th Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1051–1061 (2017)

  44. Banerjee, S., Johari, R., Riquelme, C.: Pricing in ride-sharing platforms: A queueing-theoretic approach. In: Proceedings of the 16th ACM Conference on Economics and Computation, p. 639 (2015)

  45. Banerjee, S., Freund, D., Lykouris, T.: Pricing and optimization in shared vehicle systems: An approximation framework. In: Proceedings of the 2017 ACM Conference on Economics and Computation, p. 517 (2017)

  46. Bansal, N., Buchbinder, N., Gupta, A., Naor, J.: A randomized o(log2 k)-competitive algorithm for metric bipartite matching. Algorithmica 68(2), 390–403 (2014)

    MathSciNet  MATH  Google Scholar 

  47. Bar-Yehuda, R., Bendel, K., Freund, A., Rawitz, D.: Local ratio: a unified framework for approxmation algrithms. ACM Comput. Surv. 36(4), 422–463 (2004)

    Google Scholar 

  48. Bei, X., Zhang, S.: Algorithms for trip-vehicle assignment in ride-sharing. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence, pp. 3–9 (2018)

  49. Bentley, J.L.: Multidimensional binary search trees used for associative searching. Commun. ACM 18(9), 509–517 (1975)

    MathSciNet  MATH  Google Scholar 

  50. Birnbaum, B.E., Mathieu, C.: On-line bipartite matching made simple. SIGACT News 39(1), 80–87 (2008)

    Google Scholar 

  51. Brubach, B., Sankararaman, K.A., Srinivasan, A., Xu, P.: New algorithms, better bounds, and a novel model for online stochastic matching. In: 24th Annual European Symposium on Algorithms, pp. 24:1–24:16 (2016)

  52. Burkard, R.E., Dell’Amico, M., Martello, S.: Assignment problems. Springer, Berlin (2009)

    MATH  Google Scholar 

  53. Cao, C.C., She, J., Tong, Y., Chen, L.: Whom to ask? Jury selection for decision making tasks on micro-blog services. PVLDB 5(11), 1495–1506 (2012)

    Google Scholar 

  54. Castillo, J., Knoepfle, D., Weyl, G.: Surge pricing solves the wild goose chase. In: Proceedings of the 2017 ACM Conference on Economics and Computation, pp. 241–242 (2017)

  55. Chen, C., Cheng, S., Misra, A., Lau, H.C.: Multi-agent task assignment for mobile crowdsourcing under trajectory uncertainties. In: Proceedings of the 14th International Conference on Autonomous Agents and MultiAgent Systems, pp. 1715–1716 (2015)

  56. Chen, J., Zipf, A.: Deepvgi: Deep learning with volunteered geographic information. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 771–772 (2017)

  57. Chen, L., Shahabi, C.: Spatial crowdsourcing: challenges and opportunities. IEEE Data Eng. Bull. 39(4), 14–25 (2016)

    Google Scholar 

  58. Chen, L., Lee, D., Zhang, M.: Crowdsourcing in information and knowledge management. In: Proceedings of the 23rd ACM International Conference on Information and Knowledge Management (2014)

  59. Chen, L., Lee, D., Milo, T.: Data-driven crowdsourcing: Management, mining, and applications. In: 31st IEEE International Conference on Data Engineering, pp. 1527–1529 (2015)

  60. Chen, M., Shen, W., Tang, P., Zuo, S.: Optimal vehicle dispatching for ride-sharing platforms via dynamic pricing. In: Companion of The Web Conference, pp. 51–52 (2018)

  61. Chen, M.K.: Dynamic pricing in a labor market: Surge pricing and flexible work on the uber platform. In: Proceedings of the 2016 ACM Conference on Economics and Computation, p. 455 (2016)

  62. Chen, X., Wu, X., Li, X., Ji, X., He, Y., Liu, Y.: Privacy-aware high-quality map generation with participatory sensing. IEEE Trans. Mob. Comput. 15(3), 719–732 (2016)

    Google Scholar 

  63. Chen, Z., Fu, R., Zhao, Z., Liu, Z., Xia, L., Chen, L., Cheng, P., Cao, C.C., Tong, Y., Zhang, C.J.: gMission: a general spatial crowdsourcing platform. PVLDB 7(13), 1629–1632 (2014)

    Google Scholar 

  64. Chen, Z., Cheng, P., Zeng, Y., Chen, L.: Minimizing maximum delay of task assignment in spatial crowdsourcing. In: 35th IEEE International Conference on Data Engineering, pp. 1454–1465 (2019)

  65. Cheng, P., Lian, X., Chen, Z., Fu, R., Chen, L., Han, J., Zhao, J.: Reliable diversity-based spatial crowdsourcing by moving workers. PVLDB 8(10), 1022–1033 (2015)

    Google Scholar 

  66. Cheng, P., Lian, X., Chen, L., Han, J., Zhao, J.: Task assignment on multi-skill oriented spatial crowdsourcing. IEEE Trans. Knowl. Data Eng. 28(8), 2201–2215 (2016)

    Google Scholar 

  67. Cheng, P., Jian, X., Chen, L.: An experimental evaluation of task assignment in spatial crowdsourcing. PVLDB 11(11), 1428–1440 (2018)

    Google Scholar 

  68. Cheng, S., Nguyen, D.T., Lau, H.C.: Mechanisms for arranging ride sharing and fare splitting for last-mile travel demands. In: Proceedings of the 13th International Conference on Autonomous Agents and MultiAgent Systems, pp. 1505–1506 (2014)

  69. Chittilappilly, A.I., Chen, L., Amer-Yahia, S.: A survey of general-purpose crowdsourcing techniques. IEEE Trans. Knowl. Data Eng. 28(9), 2246–2266 (2016)

    Google Scholar 

  70. Chuang, T., Deng, D., Hsu, C., Lemmens, R.: The one and many maps: participatory and temporal diversities in openstreetmap. In: Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information, pp. 79–86 (2013)

  71. Cormode, G., Procopiuc, C.M., Srivastava, D., Shen, E., Yu, T.: Differentially private spatial decompositions. In: 28th IEEE International Conference on Data Engineering, pp. 20–31 (2012)

  72. Corral, A., Manolopoulos, Y., Theodoridis, Y., Vassilakopoulos, M.: Closest pair queries in spatial databases. In: Proceedings of the 2000 ACM International Conference on Management of Data, pp. 189–200 (2000)

    Google Scholar 

  73. Costa, C.F., Nascimento, M.A.: In-route task selection in crowdsourcing. In: Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 524–527 (2018)

  74. Cranshaw, J., Toch, E., Hong, J.I., Kittur, A., Sadeh, N.M.: Bridging the gap between physical location and online social networks. In: Proceedings of the 12th ACM International Conference on Ubiquitous Computing, pp. 119–128 (2010)

  75. Das, A., Gollapudi, S., Kim, A., Panigrahi, D., Swamy, C.: Minimizing latency in online ride and delivery services. In: Proceedings of the 27th International Conference on World Wide Web, pp. 379–388 (2018)

  76. Datar, M., Immorlica, N., Indyk, P., Mirrokni, V.S.: Locality-sensitive hashing scheme based on p-stable distributions. In: Proceedings of the 20th ACM Symposium on Computational Geometry, pp. 253–262 (2004)

  77. Demartini, G., Difallah, D.E., Cudré-Mauroux, P.: Zencrowd: leveraging probabilistic reasoning and crowdsourcing techniques for large-scale entity linking. In: Proceedings of the 21st International Conference on World Wide Web, pp. 469–478 (2012)

  78. Deng, D., Shahabi, C., Demiryurek, U.: Maximizing the number of worker’s self-selected tasks in spatial crowdsourcing. In: Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 314–323 (2013)

  79. Deng, D., Shahabi, C., Zhu, L.: Task matching and scheduling for multiple workers in spatial crowdsourcing. In: Proceedings of the 23rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 21:1–21:10 (2015)

  80. Deng, D., Shahabi, C., Demiryurek, U., Zhu, L.: Task selection in spatial crowdsourcing from worker’s perspective. GeoInformatica 20(3), 529–568 (2016)

    Google Scholar 

  81. Derigs, U.: A shortest augmenting path method for solving minimal perfect matching problems. Networks 11(4), 379–390 (1981)

    MathSciNet  MATH  Google Scholar 

  82. Devanur, N.R., Jain, K., Kleinberg, R.D.: Randomized primal-dual analysis of RANKING for online bipartite matching. In: Proceedings of the 24th Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 101–107 (2013)

  83. Dickerson, J.P., Sankararaman, K.A., Srinivasan, A., Xu, P.: Allocation problems in ride-sharing platforms: Online matching with offline reusable resources. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence, pp. 1007–1014 (2018)

  84. Dickerson, J.P., Sankararaman, K.A., Srinivasan, A., Xu, P.: Assigning tasks to workers based on historical data: Online task assignment with two-sided arrivals. In: Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, pp. 318–326 (2018)

  85. Dittus, M., Quattrone, G., Capra, L.: Analysing volunteer engagement in humanitarian mapping: building contributor communities at large scale. In: Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing, pp. 108–118 (2016)

  86. Dwork, C.: Differential privacy. In: International Colloquium on Automata, Languages and Programming, pp. 1–12 (2006)

    Google Scholar 

  87. Dwork, C.: Differential privacy: a survey of results. In: 5th International Conference on Theory and Applications of Models of Computation, pp. 1–19 (2008)

  88. Emek, Y., Kutten, S., Wattenhofer, R.: Online matching: haste makes waste! In: Proceedings of the 48th Annual ACM SIGACT Symposium on Theory of Computing, pp. 333–344 (2016)

  89. Fan, X., Liu, J., Wang, Z., Jiang, Y., Liu, X.: Crowdsourced road navigation: concept, design, and implementation. IEEE Commun. Mag. 55(6), 126–128 (2017)

    Google Scholar 

  90. Fang, Z., Huang, L., Wierman, A.: Prices and subsidies in the sharing economy. In: Proceedings of the 26th International Conference on World Wide Web, pp. 53–62 (2017)

  91. Feldman, J., Mehta, A., Mirrokni, V.S., Muthukrishnan, S.: Online stochastic matching: Beating 1-1/e. In: 50th Annual IEEE Symposium on Foundations of Computer Science, pp. 117–126 (2009)

  92. Ferguson, T.S., et al.: Who solved the secretary problem? Stat. Sci. 4(3), 282–289 (1989)

    MathSciNet  MATH  Google Scholar 

  93. Ford, L.R., Fulkerson, D.R.: Maximal flow through a network. Can. J. Math. 8(3), 399–404 (1956)

    MathSciNet  MATH  Google Scholar 

  94. Gale, D., Shapley, L.S.: College admissions and the stability of marriage. Am. Math. Mon. 69(1), 9–15 (1962)

    MathSciNet  MATH  Google Scholar 

  95. Gamal, T.E.: A public key cryptosystem and a signature scheme based on discrete logarithms. IEEE Trans. Inf. Theory 31(4), 469–472 (1985)

    MathSciNet  Google Scholar 

  96. Gao, D., Tong, Y., She, J., Song, T., Chen, L., Xu, K.: Top-k team recommendation in spatial crowdsourcing. In: 17th International Conference on Web-Age Information Management, pp. 191–204 (2016)

    Google Scholar 

  97. Gao, D., Tong, Y., Ji, Y., Xu, K.: Team-oriented task planning in spatial crowdsourcing. In: Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint Conference on Web and Big Data, pp. 41–56 (2017)

    Google Scholar 

  98. Gao, D., Tong, Y., She, J., Song, T., Chen, L., Xu, K.: Top-k team recommendation and its variants in spatial crowdsourcing. Data Sci. Eng. 2(2), 136–150 (2017)

    Google Scholar 

  99. Garcia-Molina, H., Joglekar, M., Marcus, A., Parameswaran, A.G., Verroios, V.: Challenges in data crowdsourcing. IEEE Trans. Knowl. Data Eng. 28(4), 901–911 (2016)

    Google Scholar 

  100. Garcia-Ulloa, D.A., Xiong, L., Sunderam, V.S.: Truth discovery for spatiotemporal events from crowdsourced data. PVLDB 10(11), 1562–1573 (2017)

    Google Scholar 

  101. Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. W. H. Freeman, San Francisco (1979)

    MATH  Google Scholar 

  102. Goel, G., Mehta, A.: Online budgeted matching in random input models with applications to adwords. In: Proceedings of the 19th Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 982–991 (2008)

  103. Gu, L., Wang, K., Liu, X., Guo, S., Liu, B.: A reliable task assignment strategy for spatial crowdsourcing in big data environment. In: IEEE International Conference on Communications, pp. 1–6 (2017)

  104. Guo, B., Liu, Y., Wang, L., Li, V.O.K., Lam, J.C.K., Yu, Z.: Task allocation in spatial crowdsourcing: Current state and future directions. IEEE Internet Things J. 5(3), 1749–1764 (2018)

    Google Scholar 

  105. Guo, S., Parameswaran, A.G., Garcia-Molina, H.: So who won?: dynamic max discovery with the crowd. In: Proceedings of the 2012 ACM International Conference on Management of Data, pp. 385–396 (2012)

  106. Guttman, A.: R-trees: A dynamic index structure for spatial searching. In: Proceedings of the 1984 ACM International Conference on Management of Data, pp. 47–57 (1984)

  107. Haklay, M.M., Weber, P.: Openstreetmap: user-generated street maps. IEEE Pervasive Comput. 7(4), 12–18 (2008)

    Google Scholar 

  108. Han, S., Xu, Z., Zeng, Y., Chen, L.: Fluid: A blockchain based framework for crowdsourcing. In: Proceedings of the 2019 ACM International Conference on Management of Data, pp. 1921–1924 (2019)

  109. Hasenfratz, D., Saukh, O., Sturzenegger, S., Thiele, L.: Participatory air pollution monitoring using smartphones. Mob. Sens. 1, 1–5 (2012)

    Google Scholar 

  110. Hashemi, P., Abbaspour, R.A.: Assessment of logical consistency in openstreetmap based on the spatial similarity concept. In: OpenStreetMap in GIScience, Lecture Notes in Geoinformation and Cartography, pp. 19–36. Springer, Berlin (2015)

    Google Scholar 

  111. ul Hassan, U., Curry, E.: A multi-armed bandit approach to online spatial task assignment. In: 2014 IEEE 11th International Conference on Ubiquitous Intelligence and Computing and 2014 IEEE 11th International Conference on Autonomic and Trusted Computing and 2014 IEEE 14th International Conference on Scalable Computing and Communications and Its Associated Workshops, pp. 212–219 (2014)

  112. ul Hassan, U., Curry, E.: Efficient task assignment for spatial crowdsourcing: a combinatorial fractional optimization approach with semi-bandit learning. Expert Syst. Appl. 58, 36–56 (2016)

    Google Scholar 

  113. He, S., Shin, D., Zhang, J., Chen, J.: Toward optimal allocation of location dependent tasks in crowdsensing. In: 2014 IEEE Conference on Computer Communications, pp. 745–753 (2014)

  114. Heipke, C.: Crowdsourcing geospatial data. ISPRS J. Photogramm. Remote Sens. 65(6), 550–557 (2010)

    Google Scholar 

  115. Ho, C., Jabbari, S., Vaughan, J.W.: Adaptive task assignment for crowdsourced classification. In: Proceedings of the 30th International Conference on Machine Learning, pp. 534–542 (2013)

  116. Hu, H., Li, G., Bao, Z., Cui, Y., Feng, J.: Crowdsourcing-based real-time urban traffic speed estimation: from trends to speeds. In: 32nd IEEE International Conference on Data Engineering, pp. 883–894 (2016)

  117. Hu, H., Zheng, Y., Bao, Z., Li, G., Feng, J., Cheng, R.: Crowdsourced POI labelling: location-aware result inference and task assignment. In: 32nd IEEE International Conference on Data Engineering, pp. 61–72 (2016)

  118. Huang, P., Zhu, W., Liao, K., Sellis, T., Yu, Z., Guo, L.: Efficient algorithms for flexible sweep coverage in crowdsensing. IEEE Access 6, 50055–50065 (2018)

    Google Scholar 

  119. Huang, Y., Bastani, F., Jin, R., Wang, X.S.: Large scale real-time ridesharing with service guarantee on road networks. PVLDB 7(14), 2017–2028 (2014)

    Google Scholar 

  120. Huang, Z., Kang, N., Tang, Z.G., Wu, X., Zhang, Y., Zhu, X.: How to match when all vertices arrive online. In: Proceedings of the 50th Annual ACM SIGACT Symposium on Theory of Computing, pp. 17–29 (2018)

  121. Jaillet, P., Lu, X.: Online stochastic matching: new algorithms with better bounds. Math. Oper. Res. 39(3), 624–646 (2014)

    MathSciNet  MATH  Google Scholar 

  122. Jiang, S., Chen, L., Mislove, A., Wilson, C.: On ridesharing competition and accessibility: Evidence from uber, lyft, and taxi. In: Proceedings of the 2018 International Conference on World Wide Web, pp. 863–872 (2018)

  123. Jin, H., Su, L., Xiao, H., Nahrstedt, K.: Incentive mechanism for privacy-aware data aggregation in mobile crowd sensing systems. IEEE/ACM Trans. Netw. 26(5), 2019–2032 (2018)

    Google Scholar 

  124. Jin, X., Zhang, Y.: Privacy-preserving crowdsourced spectrum sensing. In: Proceedings of the IEEE International Conference on Computer Communications, pp. 1–9 (2016)

  125. Jin, X., Zhang, Y.: Privacy-preserving crowdsourced spectrum sensing. IEEE/ACM Trans. Netw. 26(3), 1236–1249 (2018)

    Google Scholar 

  126. Jonathan, C., Mokbel, M.F.: Stella: geotagging images via crowdsourcing. In: Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 169–178 (2018)

  127. Kalyanasundaram, B., Pruhs, K.: Online weighted matching. J. Algorithms 14(3), 478–488 (1993)

    MathSciNet  MATH  Google Scholar 

  128. Kalyanasundaram, B., Pruhs, K.: An optimal deterministic algorithm for online b-matching. Theor. Comput. Sci. 233(1–2), 319–325 (2000)

    MathSciNet  MATH  Google Scholar 

  129. Karp, R.M., Vazirani, U.V., Vazirani, V.V.: An optimal algorithm for on-line bipartite matching. In: Proceedings of the 22nd Annual ACM Symposium on Theory of Computing, pp. 352–358 (1990)

  130. Kazemi, L., Shahabi, C.: A privacy-aware framework for participatory sensing. SIGKDD Explor. 13(1), 43–51 (2011)

    Google Scholar 

  131. Kazemi, L., Shahabi, C.: Towards preserving privacy in participatory sensing. In: 9th Annual IEEE International Conference on Pervasive Computing and Communications, pp. 328–331 (2011)

  132. Kazemi, L., Shahabi, C.: Geocrowd: enabling query answering with spatial crowdsourcing. In: Proceedings of the 20th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 189–198 (2012)

  133. Kazemi, L., Shahabi, C., Chen, L.: Geotrucrowd: trustworthy query answering with spatial crowdsourcing. In: Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 304–313 (2013)

  134. Kesselheim, T., Radke, K., Tönnis, A., Vöcking, B.: An optimal online algorithm for weighted bipartite matching and extensions to combinatorial auctions. In: 21st Annual European Symposium on Algorithms, pp. 589–600 (2013)

    Google Scholar 

  135. Khuller, S., Mitchell, S.G., Vazirani, V.V.: On-line algorithms for weighted bipartite matching and stable marriages. Theor. Comput. Sci. 127(2), 255–267 (1994)

    MathSciNet  MATH  Google Scholar 

  136. Kim, S., Mankoff, J., Paulos, E.: Sensr: evaluating a flexible framework for authoring mobile data-collection tools for citizen science. In: Proceedings of the 2013 Conference on Computer Supported Cooperative Work, pp. 1453–1462 (2013)

  137. Kim, S., Mankoff, J., Paulos, E.: Exploring barriers to the adoption of mobile technologies for volunteer data collection campaigns. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, pp. 3117–3126 (2015)

  138. Kooti, F., Grbovic, M., Aiello, L.M., Djuric, N., Radosavljevic, V., Lerman, K.: Analyzing uber’s ride-sharing economy. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 574–582 (2017)

  139. Korula, N., Pál, M.: Algorithms for secretary problems on graphs and hypergraphs. In: 36th International Colloquium on Automata, Languages, and Programming, pp. 508–520 (2009)

    MATH  Google Scholar 

  140. Kuncheva, L.I., Whitaker, C.J., Shipp, C.A., Duin, R.P.W.: Limits on the majority vote accuracy in classifier fusion. Pattern Anal. Appl. 6(1), 22–31 (2003)

    MathSciNet  MATH  Google Scholar 

  141. Li, G., Wang, J., Zheng, Y., Franklin, M.J.: Crowdsourced data management: a survey. IEEE Trans. Knowl. Data Eng. 28(9), 2296–2319 (2016)

    Google Scholar 

  142. Li, G., Zheng, Y., Fan, J., Wang, J., Cheng, R.: Crowdsourced data management: Overview and challenges. In: Proceedings of the 2017 ACM International Conference on Management of Data, pp. 1711–1716 (2017)

  143. Li, L., Chu, W., Langford, J., Schapire, R.E.: A contextual-bandit approach to personalized news article recommendation. In: Proceedings of the 19th International Conference on World Wide Web, pp. 661–670 (2010)

  144. Li, Y., Yiu, M.L., Xu, W.: Oriented online route recommendation for spatial crowdsourcing task workers. In: International Symposium on Spatial and Temporal Databases, pp. 137–156 (2015)

    Google Scholar 

  145. Li, Y., Fang, J., Zeng, Y., Maag, B., Tong, Y., Zhang, L.: Two-sided online bipartite matching in spatial data: experiments and analysis. GeoInformatica (2019). https://doi.org/10.1007/s10707-019-00359-w

    Article  Google Scholar 

  146. Lindell, Y., Pinkas, B.: A proof of security of Yao’s protocol for two-party computation. J. Cryptol. 22(2), 161–188 (2009)

    MathSciNet  MATH  Google Scholar 

  147. Littlestone, N., Warmuth, M.K.: The weighted majority algorithm. Inf. Comput. 108(2), 212–261 (1994)

    MathSciNet  MATH  Google Scholar 

  148. Liu, A., Li, Z., Liu, G., Zheng, K., Zhang, M., Li, Q., Zhang, X.: Privacy-preserving task assignment in spatial crowdsourcing. J. Comput. Sci. Technol. 32(5), 905–918 (2017)

    MathSciNet  Google Scholar 

  149. Liu, A., Wang, W., Shang, S., Li, Q., Zhang, X.: Efficient task assignment in spatial crowdsourcing with worker and task privacy protection. GeoInformatica 22(2), 335–362 (2018)

    Google Scholar 

  150. Liu, B., Chen, L., Zhu, X., Zhang, Y., Zhang, C., Qiu, W.: Protecting location privacy in spatial crowdsourcing using encrypted data. In: Proceedings of the 20th International Conference on Extending Database Technology, pp. 478–481 (2017)

  151. Liu, J., Ji, Y., Lv, W., Xu, K.: Budget-aware dynamic incentive mechanism in spatial crowdsourcing. J. Comput. Sci. Technol. 32(5), 890–904 (2017)

    Google Scholar 

  152. Liu, S.B., Iacucci, A.A., Meier, P.: Ushahidi haiti and chile: next generation crisis mapping. ACSM Bulletin 246 (2010)

  153. Liu, X., He, Q., Tian, Y., Lee, W., McPherson, J., Han, J.: Event-based social networks: linking the online and offline social worlds. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1032–1040 (2012)

  154. Liu, Y., Guo, B., Du, H., Yu, Z., Zhang, D., Chen, C.: Foodnet: Optimized on demand take-out food delivery using spatial crowdsourcing. In: Proceedings of the 23rd Annual International Conference on Mobile Computing and Networking, pp. 564–566 (2017)

  155. Liu, Z., Chen, L., Tong, Y.: Realtime traffic speed estimation with sparse crowdsourced data. In: 34th IEEE International Conference on Data Engineering, pp. 329–340 (2018)

  156. Long, C., Wong, R.C., Yu, P.S., Jiang, M.: On optimal worst-case matching. In: Proceedings of the 2013 ACM International Conference on Management of Data, pp. 845–856 (2013)

  157. Lu, A., Frazier, P.I., Kislev, O.: Surge pricing moves uber’s driver-partners. In: Proceedings of the 2018 ACM Conference on Economics and Computation, p. 3 (2018)

  158. Ma, S., Zheng, Y., Wolfson, O.: T-share: A large-scale dynamic taxi ridesharing service. In: 29th IEEE International Conference on Data Engineering, pp. 410–421 (2013)

  159. Manshadi, V.H., Gharan, S.O., Saberi, A.: Online stochastic matching: online actions based on offline statistics. In: Proceedings of the 32nd Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1285–1294 (2011)

  160. McSherry, F., Talwar, K.: Mechanism design via differential privacy. In: 48th Annual IEEE Symposium on Foundations of Computer Science, pp. 94–103 (2007)

  161. Meyerson, A., Nanavati, A., Poplawski, L.J.: Randomized online algorithms for minimum metric bipartite matching. In: Proceedings of the 17th Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 954–959 (2006)

  162. Mineraud, J., Lancerin, F., Balasubramaniam, S., Conti, M., Tarkoma, S.: You are airing too much: assessing the privacy of users in crowdsourcing environmental data. In: 2015 IEEE TrustCom/BigDataSE/ISPA, pp. 523–530 (2015)

  163. Mitsopoulou, E., Boutsis, I., Kalogeraki, V., Yu, J.Y.: A cost-aware incentive mechanism in mobile crowdsourcing systems. In: 2018 19th IEEE International Conference on Mobile Data Management, pp. 239–244 (2018)

  164. Musthag, M., Ganesan, D.: Labor dynamics in a mobile micro-task market. In: 2013 ACM SIGCHI Conference on Human Factors in Computing Systems, pp. 641–650 (2013)

  165. Nascimento, M.A., Silva, J.R.O.: Towards historical r-trees. In: Proceedings of the 1998 ACM Symposium on Applied Computing, pp. 235–240 (1998)

  166. Nemhauser, G.L., Wolsey, L.A., Fisher, M.L.: An analysis of approximations for maximizing submodular set functions—i. Math. Program. 14(1), 265–294 (1978)

    MathSciNet  MATH  Google Scholar 

  167. Ouyang, W.R., Srivastava, M.B., Toniolo, A., Norman, T.J.: Truth discovery in crowdsourced detection of spatial events. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, pp. 461–470 (2014)

  168. Ouyang, W.R., Srivastava, M.B., Toniolo, A., Norman, T.J.: Truth discovery in crowdsourced detection of spatial events. IEEE Trans. Knowl. Data Eng. 28(4), 1047–1060 (2016)

    Google Scholar 

  169. Paillier, P.: Public-key cryptosystems based on composite degree residuosity classes. In: International Conference on the Theory and Applications of Cryptographic Techniques, pp. 223–238 (1999)

  170. Pournajaf, L., Xiong, L., Sunderam, V.S., Goryczka, S.: Spatial task assignment for crowd sensing with cloaked locations. In: IEEE 15th International Conference on Mobile Data Management, pp. 73–82 (2014)

  171. Pournajaf, L., Garcia-Ulloa, D.A., Xiong, L., Sunderam, V.S.: Participant privacy in mobile crowd sensing task management: A survey of methods and challenges. In: Proceedings of the 2015 ACM International Conference on Management of Data, vol. 44, no. 4, pp. 23–34 (2015)

    Google Scholar 

  172. Pournajaf, L., Xiong, L., Sunderam, V.S., Xu, X.: STAC: spatial task assignment for crowd sensing with cloaked participant locations. In: Proceedings of the 23rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 90:1–90:4 (2015)

  173. Quattrone, G., Mashhadi, A., Capra, L.: Mind the map: the impact of culture and economic affluence on crowd-mapping behaviours. In: Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work and Social Computing, pp. 934–944 (2014)

  174. Raykar, V.C., Yu, S., Zhao, L.H., Valadez, G.H., Florin, C., Bogoni, L., Moy, L.: Learning from crowds. J. Mach. Learn. Res. 11, 1297–1322 (2010)

    MathSciNet  Google Scholar 

  175. Ren, X., Yu, C., Yu, W., Yang, S., Yang, X., McCann, J.A., Yu, P.S.: Lopub: high-dimensional crowdsourced data publication with local differential privacy. IEEE Trans. Inf. Forensics Secur. 13(9), 2151–2166 (2018)

    Google Scholar 

  176. Robbins, H.: Some aspects of the sequential design of experiments. Bull. Am. Math. Soc. 58(5), 527–535 (1952)

    MathSciNet  MATH  Google Scholar 

  177. Saltenis, S., Jensen, C.S., Leutenegger, S.T., López, M.A.: Indexing the positions of continuously moving objects. In: Proceedings of the 2000 ACM International Conference on Management of Data, pp. 331–342 (2000)

  178. Samet, H.: The quadtree and related hierarchical data structures. ACM Comput. Surv. 16(2), 187–260 (1984)

    MathSciNet  Google Scholar 

  179. Senaratne, H., Mobasheri, A., Ali, A.L., Capineri, C., Haklay, M.: A review of volunteered geographic information quality assessment methods. Int. J. Geogr. Inf. Sci. 31(1), 139–167 (2017)

    Google Scholar 

  180. Shahabi, C.: Towards a generic framework for trustworthy spatial crowdsourcing. In: Proceedings of the 12th International ACM Workshop on Data Engineering for Wireless and Mobile Access, pp. 1–4 (2013)

  181. She, J., Tong, Y., Chen, L.: Utility-aware social event-participant planning. In: Proceedings of the 2015 ACM International Conference on Management of Data, pp. 1629–1643 (2015)

  182. She, J., Tong, Y., Chen, L., Cao, C.C.: Conflict-aware event-participant arrangement. In: IEEE 31st International Conference on Data Engineering, pp. 735–746 (2015)

  183. She, J., Tong, Y., Chen, L., Cao, C.C.: Conflict-aware event-participant arrangement and its variant for online setting. IEEE Trans. Knowl. Data Eng. 28(9), 2281–2295 (2016)

    Google Scholar 

  184. Shen, W., Lopes, C.V., Crandall, J.W.: An online mechanism for ridesharing in autonomous mobility-on-demand systems. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence, pp. 475–481 (2016)

  185. Sheng, X., Tang, J., Zhang, W.: Energy-efficient collaborative sensing with mobile phones. In: 2012 IEEE Conference on Computer Communications, pp. 1916–1924 (2012)

  186. Song, T., Tong, Y., Wang, L., She, J., Yao, B., Chen, L., Xu, K.: Trichromatic online matching in real-time spatial crowdsourcing. In: 33rd IEEE International Conference on Data Engineering, pp. 1009–1020 (2017)

  187. Song, T., Xu, K., Li, J., Li, Y., Tong, Y.: Multi-skill aware task assignment in real-time spatial crowdsourcing. GeoInformatica (2019). https://doi.org/10.1007/s10707-019-00351-4

    Article  Google Scholar 

  188. Stevens, M., D’Hondt, E.: Crowdsourcing of pollution data using smartphones. In: Workshop on Ubiquitous Crowdsourcing (2010)

  189. Su, H., Zheng, K., Huang, J., Jeung, H., Chen, L., Zhou, X.: Crowdplanner: a crowd-based route recommendation system. In: 30th IEEE International Conference on Data Engineering, pp. 1144–1155 (2014)

  190. Sun, D., Xu, K., Cheng, H., Zhang, Y., Song, T., Liu, R., Xu, Y.: Online delivery route recommendation in spatial crowdsourcing. World Wide Web 11, 1–22 (2018)

    Google Scholar 

  191. Sweeney, L.: k-anonymity: a model for protecting privacy. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 10(5), 557–570 (2002)

    MathSciNet  MATH  Google Scholar 

  192. Tao, Q., Zeng, Y., Zhou, Z., Tong, Y., Chen, L., Xu, K.: Multi-worker-aware task planning in real-time spatial crowdsourcing. In: International Conference on Database Systems for Advanced Applications, pp. 301–317 (2018)

    Google Scholar 

  193. Theodoridis, Y., Vazirgiannis, M., Sellis, T.K.: Spatio-temporal indexing for large multimedia applications. In: Proceedings of the IEEE International Conference on Multimedia Computing and Systems, pp. 441–448 (1996)

  194. Ting, H., Xiang, X.: Near optimal algorithms for online maximum edge-weighted b-matching and two-sided vertex-weighted b-matching. Theor. Comput. Sci. 607, 247–256 (2015)

    MathSciNet  MATH  Google Scholar 

  195. To, H., Shahabi, C.: Location privacy in spatial crowdsourcing. In: Handbook of Mobile Data Privacy, pp. 167–194 (2018)

    Google Scholar 

  196. To, H., Ghinita, G., Shahabi, C.: A framework for protecting worker location privacy in spatial crowdsourcing. PVLDB 7(10), 919–930 (2014)

    Google Scholar 

  197. To, H., Shahabi, C., Kazemi, L.: A server-assigned spatial crowdsourcing framework. ACM Trans. Spat. Algorithms Syst. 1(1), 2 (2015)

    Google Scholar 

  198. To, H., Asghari, M., Deng, D., Shahabi, C.: SCAWG: A toolbox for generating synthetic workload for spatial crowdsourcing. In: 2016 IEEE International Conference on Pervasive Computing and Communication Workshops, pp. 1–6 (2016)

  199. To, H., Fan, L., Tran, L., Shahabi, C.: Real-time task assignment in hyperlocal spatial crowdsourcing under budget constraints. In: IEEE International Conference on Pervasive Computing and Communications, pp. 1–8 (2016)

  200. To, H., Ghinita, G., Fan, L., Shahabi, C.: Differentially private location protection for worker datasets in spatial crowdsourcing. IEEE Trans. Mob. Comput. 16(4), 934–949 (2017)

    Google Scholar 

  201. To, H., Shahabi, C., Xiong, L.: Privacy-preserving online task assignment in spatial crowdsourcing with untrusted server. In: 34th IEEE International Conference on Data Engineering, pp. 833–844 (2018)

  202. Tong, X., Gupta, A., Lo, H., Choo, A., Gromala, D., Shaw, C.D.: Chasing lovely monsters in the wild, exploring players’ motivation and play patterns of pokémon go: go, gone or go away? In: Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, Companion Volume, pp. 327–330 (2017)

  203. Tong, Y., Zhou, Z.: Dynamic task assignment in spatial crowdsourcing. In: Proceedings of the 26rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, vol. 10, no. 2, pp. 18–25 (2018)

    Google Scholar 

  204. Tong, Y., She, J., Ding, B., Chen, L., Wo, T., Xu, K.: Online minimum matching in real-time spatial data: experiments and analysis. PVLDB 9(12), 1053–1064 (2016)

    Google Scholar 

  205. Tong, Y., She, J., Ding, B., Wang, L., Chen, L.: Online mobile micro-task allocation in spatial crowdsourcing. In: 32nd IEEE International Conference on Data Engineering, pp. 49–60 (2016)

  206. Tong, Y., Chen, L., Shahabi, C.: Spatial crowdsourcing: challenges, techniques, and applications. PVLDB 10(12), 1988–1991 (2017)

    Google Scholar 

  207. Tong, Y., Chen, Y., Zhou, Z., Chen, L., Wang, J., Yang, Q., Ye, J., Lv, W.: The simpler the better: a unified approach to predicting original taxi demands based on large-scale online platforms. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1653–1662 (2017)

  208. Tong, Y., Wang, L., Zhou, Z., Ding, B., Chen, L., Ye, J., Xu, K.: Flexible online task assignment in real-time spatial data. PVLDB 10(11), 1334–1345 (2017)

    Google Scholar 

  209. Tong, Y., Chen, L., Zhou, Z., Jagadish, H.V., Shou, L., Lv, W.: SLADE: a smart large-scale task decomposer in crowdsourcing. IEEE Trans. Knowl. Data Eng. 30(8), 1588–1601 (2018)

    Google Scholar 

  210. Tong, Y., Wang, L., Zhou, Z., Chen, L., Du, B., Ye, J.: Dynamic pricing in spatial crowdsourcing: a matching-based approach. In: Proceedings of the 2018 ACM International Conference on Management of Data, pp. 773–788 (2018)

  211. Tong, Y., Zeng, Y., Zhou, Z., Chen, L., Ye, J., Xu, K.: A unified approach to route planning for shared mobility. PVLDB 11(11), 1633–1646 (2018)

    Google Scholar 

  212. Tran, L., To, H., Fan, L., Shahabi, C.: A real-time framework for task assignment in hyperlocal spatial crowdsourcing. ACM Trans. Intell. Syst. Technol. 9(3), 37:1–37:26 (2018)

    Google Scholar 

  213. U, L.H., Yiu, M.L., Mouratidis, K., Mamoulis, N.: Capacity constrained assignment in spatial databases. In: Proceedings of the 2008 ACM International Conference on Management of Data, pp. 15–28 (2008)

  214. Vansteenwegen, P., Souffriau, W., Oudheusden, D.V.: The orienteering problem: a survey. Eur. J. Oper. Res. 209(1), 1–10 (2011)

    MathSciNet  MATH  Google Scholar 

  215. Venanzi, M., Guiver, J., Kazai, G., Kohli, P., Shokouhi, M.: Community-based Bayesian aggregation models for crowdsourcing. In: Proceedings of the 23rd International Conference on World Wide Web, pp. 155–164 (2014)

  216. Vu, K., Zheng, R., Gao, J.: Efficient algorithms for k-anonymous location privacy in participatory sensing. In: Proceedings of the IEEE International Conference on Computer Communications, pp. 2399–2407 (2012)

  217. Wang, L., Zhang, D., Yang, D., Lim, B.Y., Ma, X.: Differential location privacy for sparse mobile crowdsensing. In: IEEE 16th International Conference on Data Mining, pp. 1257–1262 (2016)

  218. Wang, Q., He, W., Wang, X., Cui, L.: Quality-assure and budget-aware task assignment for spatial crowdsourcing. In: International Conference on Collaborative Computing: Networking, Applications and Worksharing, pp. 60–70 (2016)

    Google Scholar 

  219. Wang, Q., He, W., Wang, X., Cui, L.: Quality-assure and budget-aware task assignment for spatial crowdsourcing. In: 12th International Conference on Collaborate Computing: Networking, Applications and Worksharing, pp. 60–70 (2016)

    Google Scholar 

  220. Wang, Y., Wong, S.C.: Two-sided online bipartite matching and vertex cover: beating the greedy algorithm. In: 42nd International Colloquium on Automata, Languages, and Programming, pp. 1070–1081 (2015)

    MATH  Google Scholar 

  221. Wang, Y., Tong, Y., Long, C., Xu, P., Xu, K., Lv, W.: Adaptive dynamic bipartite graph matching: a reinforcement learning approach. In: 35th IEEE International Conference on Data Engineering, pp. 1478–1489 (2019)

  222. Whitehill, J., Ruvolo, P., Wu, T., Bergsma, J., Movellan, J.R.: Whose vote should count more: optimal integration of labels from labelers of unknown expertise. In: Advances in Neural Information Processing Systems, pp. 2035–2043 (2009)

  223. Williamson, D.P., Shmoys, D.B.: The Design of Approximation Algorithms. Cambridge University Press, Cambridge (2011)

    MATH  Google Scholar 

  224. Wong, R.C., Tao, Y., Fu, A.W., Xiao, X.: On efficient spatial matching. In: Proceedings of the 33rd International Conference on Very Large Data Bases, pp. 579–590 (2007)

  225. Wu, P., Ngai, E.W., Wu, Y.: Toward a real-time and budget-aware task package allocation in spatial crowdsourcing. Decis. Support Syst. 110, 107–117 (2018)

    Google Scholar 

  226. Xia, H., Yang, H.: Is last-mile delivery a ‘killer app’ for self-driving vehicles? Commun. ACM 61(11), 70–75 (2018)

    Google Scholar 

  227. Xu, Y., Tong, Y., Shi, Y., Tao, Q., Xu, K., Li, W.: An efficient insertion operator in dynamic ridesharing services. In: 35th IEEE International Conference on Data Engineering, pp. 1022–1033 (2019)

  228. Yang, C., Lin, K.: An index structure for improving closest pairs and related join queries in spatial databases. In: International Database Engineering & Applications Symposium, pp. 140–149 (2002)

  229. Yao, A.C.: How to generate and exchange secrets (extended abstract). In: 27th Annual Symposium on Foundations of Computer Science, pp. 162–167 (1986)

  230. Yu, H., Miao, C., Shen, Z., Leung, C.: Quality and budget aware task allocation for spatial crowdsourcing. In: Proceedings of the 14th International Conference on Autonomous Agents and MultiAgent Systems, pp. 1689–1690 (2015)

  231. Yuan, J., Zheng, Y., Xie, X., Sun, G.: T-drive: enhancing driving directions with taxi drivers’ intelligence. IEEE Trans. Knowl. Data Eng. 25(1), 220–232 (2013)

    Google Scholar 

  232. Zeng, Y., Tong, Y., Chen, L., Zhou, Z.: Latency-oriented task completion via spatial crowdsourcing. In: 34th IEEE International Conference on Data Engineering, pp. 317–328 (2018)

  233. Zhai, D., Sun, Y., Liu, A., Li, Z., Liu, G., Zhao, L., Zheng, K.: Towards secure and truthful task assignment in spatial crowdsourcing. World Wide Web 22, 2017–2040 (2018)

    Google Scholar 

  234. Zhang, C.J., Tong, Y., Chen, L.: Where to: crowd-aided path selection. PVLDB 7(14), 2005–2016 (2014)

    Google Scholar 

  235. Zhang, G., Zhu, A., Huang, Z., Ren, G., Qin, C., Xiao, W.: Validity of historical volunteered geographic information: evaluating citizen data for mapping historical geographic phenomena. Trans. GIS 22(1), 149–164 (2018)

    Google Scholar 

  236. Zhang, J., Wen, D., Zeng, S.: A discounted trade reduction mechanism for dynamic ridesharing pricing. IEEE Trans. Intell. Transp. Syst. 17(6), 1586–1595 (2016)

    Google Scholar 

  237. Zhang, L., Hu, T., Min, Y., Wu, G., Zhang, J., Feng, P., Gong, P., Ye, J.: A taxi order dispatch model based on combinatorial optimization. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2151–2159 (2017)

  238. Zhang, X., Yang, Z., Zhou, Z., Cai, H., Chen, L., Li, X.: Free market of crowdsourcing: incentive mechanism design for mobile sensing. IEEE Trans. Parallel Distrib. Syst. 25(12), 3190–3200 (2014)

    Google Scholar 

  239. Zhang, X., Yang, Z., Sun, W., Liu, Y., Tang, S., Xing, K., Mao, X.: Incentives for mobile crowd sensing: a survey. IEEE Commun. Surv. Tutor. 18(1), 54–67 (2016)

    Google Scholar 

  240. Zhang, X., Yang, Z., Liu, Y., Tang, S.: On reliable task assignment for spatial crowdsourcing. IEEE Trans. Emerg. Top. Comput. 7(1), 174–186 (2019)

    Google Scholar 

  241. Zhang, Y., Chen, Q., Zhong, S.: Privacy-preserving data aggregation in mobile phone sensing. IEEE Trans. Inf. Forensics Secur. 11(5), 980–992 (2016)

    Google Scholar 

  242. Zhao, B., Xu, P., Shi, Y., Tong, Y., Zhou, Z., Zeng, Y.: Preference-aware task assignment in on-demand taxi dispatching: An online stable matching approach. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence, pp. 2245–2252 (2019)

    Google Scholar 

  243. Zhao, D., Li, X., Ma, H.: How to crowdsource tasks truthfully without sacrificing utility: Online incentive mechanisms with budget constraint. In: Proceedings of the IEEE Conference on Computer Communications, pp. 1213–1221 (2014)

  244. Zhao, Y., Han, Q.: Spatial crowdsourcing: current state and future directions. IEEE Commun. Mag. 54(7), 102–107 (2016)

    Google Scholar 

  245. Zhao, Y., Li, Y., Wang, Y., Su, H., Zheng, K.: Destination-aware task assignment in spatial crowdsourcing. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 297–306 (2017)

  246. Zhao, Z., Wei, F., Zhou, M., Chen, W., Ng, W.: Crowd-selection query processing in crowdsourcing databases: A task-driven approach. In: Proceedings of the 18th International Conference on Extending Database Technology, pp. 397–408 (2015)

  247. Zheng, L., Chen, L., Ye, J.: Order dispatch in price-aware ridesharing. PVLDB 11(8), 853–865 (2018)

    Google Scholar 

  248. Zheng, Y., Li, G., Li, Y., Shan, C., Cheng, R.: Truth inference in crowdsourcing: is the problem solved? PVLDB 10(5), 541–552 (2017)

    Google Scholar 

Download references

Acknowledgements

We are grateful to anonymous reviewers for their constructive comments. Yongxin Tong’s work is partially supported by the National Science Foundation of China (NSFC) under Grant Nos. 61822201, U1811463 and 71531001, Science and Technology Major Project of Beijing under Grant Nos. Z171100005117001 and Didi Gaia Collborative Research Funds for Young Scholars. Yuxiang Zeng and Lei Chen’s works are partially supported by the Hong Kong RGC CRF C6030-18G Project, the National Science Foundation of China (NSFC) under Grant No. 61729201, Science and Technology Planning Project of Guangdong Province, China, No. 2015B010110006, Hong Kong ITC ITF Grants ITS/212/16FP and ITS/470/18FX, Didi-HKUST joint research lab project, Microsoft Research Asia Collaborative Research Grant and Wechat Research Grant. Cyrus Shahabi’s work has been funded in part by NSF Grants IIS1320149 and CNS-1461963, the USC Integrated Media Systems Center (IMSC), and unrestricted cash gifts from Google and Oracle. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of any of the sponsors such as the National Science Foundation.

Author information

Authors and Affiliations

  1. State Key Laboratory of Software Development Environment, Beijing Advanced Innovation Center for Big Data and Brain Computing and International Research Institute for Multidisciplinary Science, Beihang University, Beijing, China

    Yongxin Tong

  2. Computer Engineering and Networks Laboratory, ETH Zurich, Zurich, Switzerland

    Zimu Zhou

  3. Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, China

    Yuxiang Zeng & Lei Chen

  4. Department of Computer Science, University of Southern California, Los Angeles, CA, USA

    Cyrus Shahabi

Authors
  1. Yongxin Tong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zimu Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuxiang Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lei Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Cyrus Shahabi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lei Chen.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tong, Y., Zhou, Z., Zeng, Y. et al. Spatial crowdsourcing: a survey. The VLDB Journal 29, 217–250 (2020). https://doi.org/10.1007/s00778-019-00568-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00778-019-00568-7

Keywords

Search

Navigation