Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/31042Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | KNAPEN, Luk | - |
| dc.contributor.author | Holmgren, Johan | - |
| dc.date.accessioned | 2020-04-20T13:17:51Z | - |
| dc.date.available | 2020-04-20T13:17:51Z | - |
| dc.date.issued | 2020 | - |
| dc.date.submitted | 2020-04-16T12:06:43Z | - |
| dc.identifier.citation | Elsevier, p. 195 -202 | - |
| dc.identifier.issn | 1877-0509 | - |
| dc.identifier.uri | http://hdl.handle.net/1942/31042 | - |
| dc.description.abstract | A set of GPS traces for bicyclists and a set of notifications by bicyclists of problematic situations (spots identified by GPS records) had been collected independently. The data collection periods did not coincide but overlapped and none was contained in the other one. The aim is to use both datasets to determine an optimal action plan for problem solving given a limited budget. First, problematic locations are clustered. Each cluster corresponds to an impediment. Impediments are then associated with trips using a distance function. The aim is to find out which impediments to solve under a given budget constraint in order to maximize the number of impediment free trips. Thereto the trip set is partitioned by matching each trip with the largest set of its affecting impediments. Solving all impediments in such set induces a cost and makes the associated part of trips impediment free. An optimizer is presented and evaluated. | - |
| dc.description.sponsorship | The research leading to this paper was partially supported by theSmarta Offentliga Milj ̈oer II (SOM II)project ofthe Lund (Sweden) municipality by supplying data | - |
| dc.language.iso | en | - |
| dc.publisher | Elsevier | - |
| dc.subject.other | bicyclist | - |
| dc.subject.other | GPS traces | - |
| dc.subject.other | clustering | - |
| dc.subject.other | data fusion | - |
| dc.title | Identifying bicycle trip impediments by data fusion | - |
| dc.type | Proceedings Paper | - |
| local.bibliographicCitation.conferencedate | April 6 - 9, 2020, | - |
| local.bibliographicCitation.conferencename | The 11th International Conference on Ambient Systems, Networks and Technologies (ANT) | - |
| local.bibliographicCitation.conferenceplace | Warsaw | - |
| dc.identifier.epage | 202 | - |
| dc.identifier.spage | 195 | - |
| dc.identifier.volume | 170 | - |
| local.bibliographicCitation.jcat | C1 | - |
| dc.description.other | - reviews.pdf contains the peer review - notesOnReviews contains my "response to reviewers" (which was not required in this case); it is added because i do not agree with particular technical details in the reviewers comments | - |
| local.publisher.place | SARA BURGERHARTSTRAAT 25, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS | - |
| dc.relation.references | [1] Andrienko, G., Andrienko, N., Hurter, C., Rinzivillo, S., Wrobel, S., 2011. From Movement Tracks through Events to Places: Extracting and Characterizing Significant Places from Mobility Data, in: IEEE Conference on Visual Analytics Science and Technology, IEEE, Providence, Rhode Island, USA. [2] Bierlaire, M., Chen, J., Newman, J., 2013. A probabilistic map matching method for smartphone {GPS} data. Transportation Research Part C: Emerging Technologies 26, 78 – 98. URL: http://www.sciencedirect.com/science/article/pii/S0968090X12001064, doi:10.1016/j.trc.2012.08.001. [3] Cruz, M.O., Macedo, H., Guimar£es, A., 2015. Grouping Similar Trajectories for Carpooling Purposes, in: 2015 Brazilian Conference on Intelligent Systems (BRACIS), pp. 234–239. doi:10.1109/BRACIS.2015.36. [4] Deka, L., Quddus, M., 2015. Trip-Based Weighted Trajectory Matching Algorithm for Sparse GPS Data, in: TRB 94th Annual Meeting Compendium of Papers, TRB (Transportation Research Board), Washington, D.C. [5] Deng, Z., Hu, Y., Zhu, M., Huang, X., Du, B., 2014. A scalable and fast OPTICS for clustering trajectory big data. Cluster Computing 18, 549–562. doi:10.1007/s10586-014-0413-9. [6] Furletti, B., Cintia, P., Renso, C., Spinsanti, L., 2013. Inferring human activities from GPS tracks, in: UrbComp 13 Proceedings of the second ACM SIGKDD International Workshop on Urban Computing, ACM, Chicago. [7] Giannotti, F., Nanni, M., Pinelli, F., Pedreschi, D., 2007. Trajectory pattern mining, in: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, New York, NY, USA. pp. 330–339. URL: http://doi.acm.org/10.1145/ 1281192.1281230, doi:10.1145/1281192.1281230. [8] Kellaris, G., Pelekis, N., Theodoridis, Y., 2013. Map-matched trajectory compression. Journal of Systems and Software 86, 1566 – 1579. URL: http://www.sciencedirect.com/science/article/pii/S0164121213000289, doi:10.1016/j.jss.2013.01.071. [9] Kim, J., Mahmassani, H.S., 2015. Spatial and Temporal Characterization of Travel Patterns in a Traffic Network Using Vehicle Trajectories. Transportation Research Procedia 9, 164 – 184. URL: http://www.sciencedirect.com/science/article/pii/S2352146515001702, doi:10.1016/j.trpro.2015.07.010. [10] Knapen, L., Bellemans, T., Janssens, D., Wets, G., 2018. Likelihood-based offline map matching of {GPS} recordings using global trace information. Transportation Research Part C: Emerging Technologies 93, 13 – 35. URL: https://www.sciencedirect.com/science/ article/pii/S0968090X18307022, doi:10.1016/j.trc.2018.05.014. [11] Lou, Y., Zhang, C., Zheng, Y., Xie, X., Wang, W., Huang, Y., 2009. Map-matching for Low-sampling-rate GPS Trajectories, in: Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM, New York, NY, USA. pp. 352–361. URL: http://doi.acm.org/10.1145/1653771.1653820, doi:10.1145/1653771.1653820. [12] Marchal, F., Hackney, J., Axhausen, K.W., 2005. Efficient Map Matching of Large Global Positioning System Data Sets: Tests on Speed- Monitoring Experiment in Zuerich. Transportation Research Record: Journal of the Transportation Research Board 1935, 93–100. doi:10. 3141/1935-11. [13] Ochieng, W.Y., Quddus, M., Noland, R., 2010. Map-Matching in Complex Urban Road Networks. Revista da Sociedade Brasileira de Cartografia, Geodsia, Fotogrametria e Sensoriamento Remoto 55, 14. [14] Persson, M.A., Olsson, V., 2019. Cyclists perceived insecurity in urban environment - An unsupervised machine learning study. Bachelor Data Science. Malmo University. Malmo, Sweden. [15] Pillat, J., Mandir, E., Friedrich, M., 2011. Dynamic Choice Set Generation Based on Global Positioning System Trajectories and Stated Preference Data. Transportation Research Record 2231, 18–26. URL: http://dx.doi.org/10.3141/2231-03, doi:10.3141/2231-03. [16] Quddus, M.A., Ochieng, W.Y., Noland, R.B., 2007. Current map-matching algorithms for transport applications: State-of-the art and future research directions. Transportation Research Part C: Emerging Technologies 15, 312 – 328. URL: http://www.sciencedirect.com/ science/article/B6VGJ-4P2JCX9-1/2/21d1ef73e42329087fc872e39ce78b1b, doi:10.1016/j.trc.2007.05.002. [17] Schssler, N., Axhausen, K.W., 2009. Map-matching of GPS traces on high-resolution navigation networks using the Multiple Hypothesis Technique (MHT). Working Paper 589. ETH Zrich. Zrich. [18] Tampakis, P., Doulkeridis, C., Pelekis, N., Theodoridis, Y., 2019a. Distributed Subtrajectory Join on Massive Datasets. URL: https:// arxiv.org/abs/1903.07748. [19] Tampakis, P., Pelekis, N., Doulkeridis, C., Theodoridis, Y., 2019b. Scalable Distributed Subtrajectory Clustering. URL: https://arxiv. org/abs/1906.06956. [20] Yan, Z., Spaccapietra, S., 2009. Towards Semantic Trajectory Data Analysis: A Conceptual and Computational Approach, in: VLDB2009 (PhD Workshop). [21] Zheng, Y., Chen, Y., Li, Q., Xie, X., Ma, W.Y., 2010. Understanding Transportation Modes Based on GPS Data for Web Applications. ACM Trans. Web 4, 1:1–1:36. URL: http://doi.acm.org/10.1145/1658373.1658374, doi:10.1145/1658373.1658374. | - |
| local.type.refereed | Refereed | - |
| local.type.specified | Proceedings Paper | - |
| dc.identifier.doi | 10.1016/j.procs.2020.03.025 | - |
| dc.identifier.isi | WOS:000582714500024 | - |
| dc.identifier.eissn | - | |
| local.provider.type | - | |
| local.uhasselt.uhpub | yes | - |
| item.validation | ecoom 2021 | - |
| item.contributor | KNAPEN, Luk | - |
| item.contributor | Holmgren, Johan | - |
| item.accessRights | Open Access | - |
| item.fullcitation | KNAPEN, Luk & Holmgren, Johan (2020) Identifying bicycle trip impediments by data fusion. In: Elsevier, p. 195 -202. | - |
| item.fulltext | With Fulltext | - |
| crisitem.journal.issn | 1877-0509 | - |
| Appears in Collections: | Research publications | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 1-s2.0-S1877050920304543-main.pdf | Published version | 1.32 MB | Adobe PDF | View/Open |
WEB OF SCIENCETM
Citations
1
checked on Apr 30, 2024
Page view(s)
54
checked on Sep 7, 2022
Download(s)
708
checked on Sep 7, 2022
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.