Abstract
Mobile application based ride-hailing systems, e.g., DiDi, Uber have become part of day to day life and natural choices of transport for urban commuters. However, the pick-up demand in any area is not always matching with the supply or drop-off request in the same area. Urban planners and researchers are working hard to balance this demand and supply situation for taxi requests. The existing approaches have mainly focused on clustering of the spatial regions to identify hotspots, which refer to the locations with a high demand for pick-up requests. In our study, we determined that if the hotspots focus on the clustering of high demand for pick-up requests, most of the hotspots pivot near the city center or two-three spatial regions, ignoring the other parts of the city. In this work, we proposed a method, which can help in finding a local hotspot to cover the whole city area. We proposed a dominating set problem based solution, which covers every part of the city. This will help the drivers looking for near-by next customer in the region wherever they drop their last customer. It will also reduce the waiting time for customers as well as for a driver looking for next pick-up request. This would maximize their profit as well as help in improving their services.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Yu, H., Li, Z., Zhang, G., Liu, P., Wang, J.: Extracting and predicting taxi hotspots in spatiotemporal dimensions using conditional generative adversarial neural networks. IEEE Trans. Veh. Technol. 69(4), 3680–3692 (2020)
Li, M., He, D., Zhou, X.: Efficient kNN search with occupation in large-scale on-demand ride-hailing. In: Borovica-Gajic, R., Qi, J., Wang, W. (eds.) ADC 2020. LNCS, vol. 12008, pp. 29–41. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-39469-1_3
Zhou, D., Hong, R., Xia, J.: Identification of taxi pick-up and drop-off hotspots using the density-based spatial clustering method. In: CICTP 2017: Transportation Reform and Change-Equity, Inclusiveness, Sharing, and Innovation, pp. 196–204. American Society of Civil Engineers, Reston (2017)
Chang, H.W., Tai, Y.C., Hsu, J.Y.J.: Context-aware taxi demand hotspots prediction. Int. J. Bus. Intell. Data Min. 5(1), 3–18 (2010)
Fagin, R., Kumar, R., Sivakumar, D.: Efficient similarity search and classification via rank aggregation. In: SIGMOD, pp. 301–312 (2003)
Dwork, C., Kumar, R., Naor, M., Sivakumar, D.: Rank aggregation methods for the web. In: WWW, pp. 613–622 (2001)
Mamoulis, N., Cheng, K.H., Yiu, M.L., Cheung, D.W.: Efficient aggregation of ranked inputs. In: ICDE, pp. 72–84 (2006)
Ailon, N., Charikar, M., Newman, A.: Aggregating inconsistent information: ranking and clustering. J. ACM 55(5), 23 (2008)
Shekhar, S., Feiner, S.K., Aref, W.G.: Spatial computing. Commun. ACM 59(1), 72–81 (2016)
Tao, Y., Hristidis, V., Papadias, D., Papakonstantinou, Y.: Branch-and-bound processing of ranked queries. Inf. Syst. 32(3), 424–445 (2007)
Li, M., Bao, Z., Sellis, T., Yan, S.: Visualization-aided exploration of the real estate data. In: Cheema, M.A., Zhang, W., Chang, L. (eds.) ADC 2016. LNCS, vol. 9877, pp. 435–439. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46922-5_34
Mouratidis, K., Bakiras, S., Papadias, D.: Continuous monitoring of top-k queries over sliding windows. In: SIGMOD, pp. 635–646 (2006)
Cormode, G., Hadjieleftheriou, M.: Finding frequent items in data streams. PVLDB 1(2), 1530–1541 (2008)
Papapetrou, O., Garofalakis, M., Deligiannakis, A.: Sketch-based querying of distributed sliding-window data streams. PVLDB 5(10), 992–1003 (2012)
Bohm, C., Ooi, B.C., Plant, C., Yan, Y.: Efficiently processing continuous k-NN queries on data streams. In: ICDE, pp. 156–165 (2007)
Korn, F., Muthukrishnan, S., Srivastava, D.: Reverse nearest neighbor aggregates over data streams. In: PVLDB, pp. 814–825 (2002)
Li, C., Gu, Y., Qi, J., Yu, G., Zhang, R., Yi, W.: Processing moving KNN queries using influential neighbor sets. PVLDB 8(2), 113–124 (2014)
Cheema, M., Zhang, W., Lin, X., Zhang, Y., Li, X.: Continuous reverse k nearest neighbors queries in Euclidean space and in spatial networks. VLDB J. 21(1), 69–95 (2012). https://doi.org/10.1007/s00778-011-0235-9
Khetarpaul, S., Gupta, S.K., Malhotra, S., Subramaniam, L.V.: Bus arrival time prediction using a modified amalgamation of fuzzy clustering and neural network on spatio-temporal data. In: Sharaf, M.A., Cheema, M.A., Qi, J. (eds.) ADC 2015. LNCS, vol. 9093, pp. 142–154. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19548-3_12
Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: PVLDB, pp. 946–957 (2005)
Li, C.-L., Wang, E.T., Huang, G.-J., Chen, A.L.P.: Top-n query processing in spatial databases considering bi-chromatic reverse k-nearest neighbors. Inf. Syst. 42, 123–138 (2014)
Koh, J.-L., Lin, C.-Y., Chen, A.P.: Finding k most favorite products based on reverse top-t queries. PVLDB 23(4), 541–564 (2014). https://doi.org/10.1007/s00778-013-0336-8
Vlachou, A., Doulkeridis, C., Nørvåg, K., Kotidis, Y.: Identifying the most influential data objects with reverse top-k queries. PVLDB 3(1–2), 364–372 (2010)
Wong, R.C.-W., Özsu, M.T., Yu, P.S., Fu, A.W.-C., Liu, L.: Efficient method for maximizing bichromatic reverse nearest neighbor. PVLDB 2(1), 1126–1137 (2009)
Gkorgkas, O., Vlachou, A., Doulkeridis, C., Nørvåg, K.: Discovering influential data objects over time. In: Nascimento, M.A., et al. (eds.) SSTD 2013. LNCS, vol. 8098, pp. 110–127. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40235-7_7
Choudhury, F.M., Bao, Z., Culpepper, J.S., Sellis, T.: Monitoring the top-m aggregation in a sliding window of spatial queries (2016)
Sampathkumar, E., Walikar, H.B.: Connected domination number of a graph. J. Math. Phys. 13, 1–7 (1979)
Pang, C., Zhang, R., Zhang, Q., Wang, J.: Dominating sets in directed graphs. Inf. Sci. 180(19), 3647–3652 (2010)
He, H., Zhu, Z., Makinen, E.: A neural network model to minimize the connected dominating set for self-configuration of wireless sensor networks. IEEE Trans. Neural Netw. 20(6), 973–982 (2009)
https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Mishra, S., Khetarpaul, S. (2021). Optimal Placement of Taxis in a City Using Dominating Set Problem. In: Qiao, M., Vossen, G., Wang, S., Li, L. (eds) Databases Theory and Applications. ADC 2021. Lecture Notes in Computer Science(), vol 12610. Springer, Cham. https://doi.org/10.1007/978-3-030-69377-0_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-69377-0_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-69376-3
Online ISBN: 978-3-030-69377-0
eBook Packages: Computer ScienceComputer Science (R0)