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Mining and visual exploration of closed contiguous sequential patterns in trajectories
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.ORCID iD: 0000-0001-5361-6034
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.ORCID iD: 0000-0003-1164-8403
2018 (English)In: International Journal of Geographical Information Science, ISSN 1365-8816, E-ISSN 1365-8824, Vol. 32, no 7, p. 1282-1303Article in journal (Refereed) Published
Abstract [en]

Large collections of trajectories provide rich insight into movement patterns of the tracked objects. By map matching trajectories to a road network as sequences of road edge IDs, contiguous sequential patterns can be extracted as a certain number of objects traversing a specific path, which provides valuable information in travel demand modeling and transportation planning. Mining and visualization of such patterns still face challenges in efficiency, scalability, and visual cluttering of patterns. To address these challenges, this article firstly proposes a Bidirectional Pruning based Closed Contiguous Sequential pattern Mining (BP-CCSM) algorithm. By employing tree structures to create partitions of input sequences and candidate patterns, closeness can be checked efficiently by comparing nodes in a tree. Secondly, a system called Sequential Pattern Explorer for Trajectories (SPET) is built for spatial and temporal exploration of the mined patterns. Two types of maps are designed where a conventional traffic map gives an overview of the movement patterns and a dynamic offset map presents detailed information according to user-specified filters. Extensive experiments are performed in this article. BP-CCSM is compared with three other state-of-the-art algorithms on two datasets: a small public dataset containing clickstreams from an e-commerce and a large global positioning system dataset with more than 600,000 taxi trip trajectories. The results show that BP-CCSM considerably outperforms three other algorithms in terms of running time and memory consumption. Besides, SPET provides an efficient and convenient way to inspect spatial and temporal variations in closed contiguous sequential patterns from a large number of trajectories.

Place, publisher, year, edition, pages
Taylor & Francis, 2018. Vol. 32, no 7, p. 1282-1303
Keywords [en]
Closed contiguous sequential pattern, trajectory pattern mining, trajectory pattern visualization
National Category
Transport Systems and Logistics
Identifiers
URN: urn:nbn:se:kth:diva-217997DOI: 10.1080/13658816.2017.1393542ISI: 000432634200002Scopus ID: 2-s2.0-85032699315OAI: oai:DiVA.org:kth-217997DiVA, id: diva2:1158527
Note

QC 20171121

Available from: 2017-11-20 Created: 2017-11-20 Last updated: 2024-03-15Bibliographically approved
In thesis
1. Discovering Contiguous Sequential Patterns in Network-Constrained Movement
Open this publication in new window or tab >>Discovering Contiguous Sequential Patterns in Network-Constrained Movement
2017 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

A large proportion of movement in urban area is constrained to a road network such as pedestrian, bicycle and vehicle. That movement information is commonly collected by Global Positioning System (GPS) sensor, which has generated large collections of trajectories. A contiguous sequential pattern (CSP) in these trajectories represents a certain number of objects traversing a sequence of spatially contiguous edges in the network, which is an intuitive way to study regularities in network-constrained movement. CSPs are closely related to route choices and traffic flows and can be useful in travel demand modeling and transportation planning. However, the efficient and scalable extraction of CSPs and effective visualization of the heavily overlapping CSPs are remaining challenges.

To address these challenges, the thesis develops two algorithms and a visual analytics system. Firstly, a fast map matching (FMM) algorithm is designed for matching a noisy trajectory to a sequence of edges traversed by the object with a high performance. Secondly, an algorithm called bidirectional pruning based closed contiguous sequential pattern mining (BP-CCSM) is developed to extract sequential patterns with closeness and contiguity constraint from the map matched trajectories. Finally, a visual analytics system called sequential pattern explorer for trajectories (SPET) is designed for interactive mining and visualization of CSPs in a large collection of trajectories.

Extensive experiments are performed on a real-world taxi trip GPS dataset to evaluate the algorithms and visual analytics system. The results demonstrate that FMM achieves a superior performance by replacing repeated routing queries with hash table lookups. BP-CCSM considerably outperforms three state-of-the-art algorithms in terms of running time and memory consumption. SPET enables the user to efficiently and conveniently explore spatial and temporal variations of CSPs in network-constrained movement.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2017. p. 63
Series
TRITA-SOM, ISSN 1653-6126 ; 2017-13
Keywords
map matching, trajectory pattern mining, closed contiguous sequential pattern mining, trajectory pattern visualization
National Category
Other Computer and Information Science
Research subject
Geodesy and Geoinformatics
Identifiers
urn:nbn:se:kth:diva-217998 (URN)978-91-7729-589-1 (ISBN)
Presentation
2017-12-13, Sal L51, Drottning Kristinas Väg 30, KTH, Stockholm, 14:00 (English)
Opponent
Supervisors
Note

QC 20171122

Available from: 2017-11-22 Created: 2017-11-21 Last updated: 2022-09-06Bibliographically approved
2. Efficient Map Matching and Discovery of Frequent and Dominant Movement Patterns in GPS Trajectory Data
Open this publication in new window or tab >>Efficient Map Matching and Discovery of Frequent and Dominant Movement Patterns in GPS Trajectory Data
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The wide deployment of Global Positioning System (GPS) sensors for movement data collection has enabled a wide range of applications in transportation and urban planning. Frequent and dominant movement patterns embedded in GPS trajectory data provide valuable knowledge including the spatial and temporal distribution of frequent routes selected by the tracked objects and the regular movement behavior in certain regions. Discovering frequent and dominant movement patterns embedded in GPS trajectory data needs to address several tasks including (1) matching noisy trajectories to the road network (referred as map matching), (2) extracting frequent and dominant movement patterns, and (3) retrieving the distribution of these patterns over user-specified attribute (e.g., timestamp, travel mode, etc.). These tasks confront several challenges in observation error, efficiency and large pattern search space.

To address those challenges, this thesis develops a set of algorithms and tools for efficient map matching and discovery of frequent and dominant movement patterns in GPS trajectory data. More specifically, two map matching algorithms are first developed, which improve the performance by precomputation and A-star search. Subsequently, a frequent route is extracted from map matched trajectories as a Contiguous Sequential Pattern (CSP). A novel CSP mining algorithm is developed by performing bidirectional pruning to efficiently search CSP and reduce redundancy in the result. After that, an efficient CSP comparison algorithm is developed to extend the bidirectional pruning to compare multiple sets of CSP. By comparing CSP mined from trajectories partitioned by a user-specified attribute, the distribution of frequent routes in the attribute space can be obtained. Finally, Regional Dominant Movement Pattern (RDMP) in trajectory data is discovered as regions where most of the objects follow a specific pattern. A novel movement feature descriptor called Directional Flow Image (DFI) is proposed to capture local directional movement information of trajectories in a multiple channel image and a convolutional neural network model is designed for DFI classification and RDMP detection.

Comprehensive experiments on both real-world and synthetic GPS datasets demonstrate the efficiency of the proposed algorithms as well as their superiority over state-of-the-art methods. The two map matching algorithms achieve considerable performance in matching densely sampled GPS data to small scale network and sparsely sampled GPS data to large scale network respectively. The CSP mining and comparison algorithms significantly outperform their competitors and effectively retrieve both spatial and temporal distribution of frequent routes. The RDMP detection method can robustly discover ten classes of commonly encountered RDMP in real-world trajectory data. The proposed methods in this thesis collectively provide an effective solution for answering sophisticated queries concerning frequent and dominant movement patterns in GPS trajectory data.

Abstract [sv]

Den utbredda användningen av GPS-sensorer (Global Positioning System) för insamling av rörelsedata har möjliggjort ett brett spektrum av tillämpningar inom transport- och stadsplanering. Frekventa och dominerande rörelsemönster som döljs i GPS-trajektorier ger värdefull kunskap, vilken omfattar den rumsliga och tidsmässiga fördelningen av rutter som frekvent väljs av de spårade föremålen samt det regelbundna rörelsebeteendet i vissa regioner. För att upptäcka frekventa och dominerande rörelsemönster gömda i GPS-trajektorier måste man ta itu med flera uppgifter, däribland (1) att matcha brusiga trajektorier till vägnätet (så kallad kartmatchning), (2) utvinna frekventa och dominerande rörelsemönster och (3) att erhålla fördelningen av dessa mönster över användardefinierat attribut (t.ex. tidsstämpel, reseläge, etc.). Dessa uppgifter står inför flera utmaningar i fråga om observationsfel, effektivitet och stora sökrum av mönster.

För att hantera dessa utmaningar utvecklar denna avhandling en antal algoritmer och verktyg för effektiv kartmatchning och utvinning av frekventa och dominerande rörelsemönster i GPS-trajektorier. Mer specifikt utvecklas först två kartmatchningsalgoritmer som förbättrar prestandan genom förberäkning och A-star sökning. Därefter utvinns en frekvent rutt från kartmatchade trajektorier som ett sammanhängande sekventiellt mönster (CSP). En ny CSP-utvinningsalgoritm har utvecklats genom att utföra dubbelriktad beskärning för att effektivt söka efter CSP och minska redundansen i resultatet. Därefter utvecklas en effektiv algoritm för CSP-jämförelse för att utvidga dubbelriktad beskärning för att jämföra flera uppsättningar CSP. Genom att jämföra CSP som extraheras från trajektorier partitionerade av ett användardefinierat attribut kan fördelningen av frekventa rutter i attributrummet erhållas. Slutligen upptäcks regionalt dominerande rörelsemönster (RDMP) i trajektoriedata i form av regioner där de flesta föremål följer ett specifikt mönster. En ny beskrivning av rörelseattribut som kallas Directional Flow Image (DFI) föreslås för att fånga lokal riktad rörelseinformation för trajektorier i en bild med flera kanaler och en konvolutionell neuralt nätverksmodell utformas för DFI-klassificering och RDMP-detektion.

Omfattande experiment på både verkliga och syntetiska GPS-datamängder visar effektiviteten hos de föreslagna algoritmerna samt deras överlägsenhet över toppmoderna metoder. De två kartmatchningsalgoritmerna uppnår avsevärd prestanda när de matchar tätt samplade GPS-data till småskaliga nätverk och glest samplade GPS-data till storskaliga nätverk. CSP-utvinning- och jämförelsesalgoritmerna överträffar klart sina konkurrenter och hittar effektivt både rumslig och tidsmässig fördelning av frekventa rutter. RDMP-detekteringsmetoden kan på ett robust sätt upptäcka tio klasser av vanligt förekommande RDMP i verkliga trajektoriedata. De föreslagna metoderna i denna avhandling ger tillsammans en effektiv lösning för att svara på sofistikerade frågor om frekventa och dominerande rörelsemönster i GPS-trajektoriedata.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2020. p. 62
Series
TRITA-ABE-DLT ; 2041
Keywords
map matching, contiguous sequential pattern mining, contiguous sequential pattern comparison, movement pattern detection, Kartmatchning, sammanhängande sekventiellt mönster utvinning, sammanhängande sekventiellt mönster jämförelse, rörelsemönster detektering
National Category
Transport Systems and Logistics Geosciences, Multidisciplinary
Research subject
Geodesy and Geoinformatics, Geoinformatics
Identifiers
urn:nbn:se:kth:diva-286318 (URN)978-91-7873-734-5 (ISBN)
Public defence
2020-12-16, Videolänk via Zoom - https://kth-se.zoom.us/j/61742782011, Du som saknar datorvana kan kontakta Gyözö Gidofalvi gyozo.gidofalvi@abe.kth.se / Use this e-mail address if you need technical assistance, Stockholm, 13:00 (English)
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Supervisors
Note

QC 20201125

Available from: 2020-11-25 Created: 2020-11-24 Last updated: 2022-06-25Bibliographically approved

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Yang, CanGidofalvi, Gyözö

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