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MEILI: Multiple Day Travel Behaviour Data Collection, Automation and Analysis
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, System Analysis and Economics.ORCID iD: 0000-0002-0916-0188
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Researchers' pursuit for the better understanding of the dynamics of travel and travel behaviour led to a constant advance in data collection methods. One such data collection method, the travel diary, is a common proxy for travel behaviour and its use has a long history in the transportation research community. These diaries summarize information about when, where, why and how people travel by collecting information about trips, and their destination and purpose, and triplegs, and their travel mode. Whereas collecting travel diaries for short periods of time of one day was commonplace due to the high cost of conducting travel surveys, visionary researchers have tried to better understand whether travel and travel behaviour is stable or if, and how, it changes over time by collecting multiple day travel diaries from the same users. While the initial results of these researchers were promising, the high cost of travel surveys and the fill in burden of the survey participants limited the research contribution to the scientific community. Before identifying travel diary collection methods that can be used for long periods of time, an interesting phenomenon started to occur: a steady decrease in the response rate to travel diaries. This meant that the pursuit of understanding the evolution of travel behaviour over time stayed in the scientific community and did not evolve to be used by policy makers and industrial partners.

However, with the development of technologies that can collect trajectory data that describe how people travel, researchers have investigated ways to complement and replace the traditional travel diary collection methods. While the initial efforts were only partially successful because scientists had to convince people to carry devices that they were not used to, the wide adoption of smartphones opened up the possibility of wide-scale trajectory-based travel diary collection and, potentially, for long periods of time. This thesis contributes among the same direction by proposing MEILI, a travel diary collection system, and describes the trajectory collection outlet (Paper I) and the system architecture (Paper II). Furthermore, the process of transforming a trajectory into travel diaries by using machine learning is thoroughly documented (Papers III and IV), together with a robust and objective methodology for comparing different travel diary collection system (Papers V and VI). MEILI is presented in the context of current state of the art (Paper VIII) and the researchers' common interest (Paper IX), and has been used in various case studies for collecting travel diaries (Papers I, V, VI, VII). Finally, since MEILI has been successfully used for collecting travel diaries for a period of one week, a new method for understanding the stability and variability of travel patterns over time has been proposed (Paper X).

Place, publisher, year, edition, pages
KTH Royal Institute of Technology, 2018. , p. 48
Series
TRITA-ABE-DLT ; 2018:13
Keywords [en]
multiple day travel diary collection, trajectory segmentation, travel mode destination and purpose inference, travel diary collection system comparison, travel pattern stability and variability over time
National Category
Transport Systems and Logistics Computer Sciences
Research subject
Transport Science; Computer Science; Geodesy and Geoinformatics
Identifiers
URN: urn:nbn:se:kth:diva-227294ISBN: 978-91-7729-793-2 (print)OAI: oai:DiVA.org:kth-227294DiVA, id: diva2:1204245
Public defence
2018-06-05, L1, Drottning Kristinas väg 30, Stockholm, 13:00 (English)
Opponent
Supervisors
Note

QC 20180507

Available from: 2018-05-07 Created: 2018-05-07 Last updated: 2018-05-07Bibliographically approved
List of papers
1. Mobility Collector
Open this publication in new window or tab >>Mobility Collector
2014 (English)In: Journal of Location Based Services, ISSN 1748-9725, Vol. 8, no 4, p. 229-255Article in journal (Refereed) Published
Abstract [en]

Despite the availability of mobile positioning technologies and scientists' interests in tracking, modelling and predicting the movements of individuals and populations, these technologies are seldom efficiently used. The continuous changes in mobile positioning and other sensor technologies overburden scientists who are interested in data collection with the task of developing, implementing and testing tracking algorithms and their efficiency in terms of battery consumption. To this extent, this article proposes an adaptive, battery conscious tracking algorithm that collects trajectory data fused with accelerometer data and presents Mobility Collector, which is a prototype platform that, using the tracking algorithm, can produce highly configurable, off-the-shelf, multi-user tracking systems suitable for research purposes. The applicability of the tracking system is tested within the transport science domain by collecting labelled movement traces and related motion data, i.e. accelerometer data and derived information (number of steps and other useful movement features based on temporal aggregates of the raw readings) to develop and evaluate a method that automatically classifies the transportation mode of users with a 90.8% prediction accuracy.

Keywords
battery conscious, data collection, location and accelerometer data fusion, location awareness, location tracking, movement, smartphones
National Category
Other Engineering and Technologies Computer Sciences
Research subject
Geodesy and Geoinformatics; Transport Science
Identifiers
urn:nbn:se:kth:diva-161529 (URN)10.1080/17489725.2014.973917 (DOI)2-s2.0-84914664420 (Scopus ID)
Conference
Mobile Ghent 2013, Ghent, Belgium, 23-25 October, 2013
Projects
SPOT
Funder
TrenOp, Transport Research Environment with Novel Perspectives
Note

QC 20150312

Available from: 2015-03-12 Created: 2015-03-12 Last updated: 2018-05-07Bibliographically approved
2. MEILI: A travel diary collection, annotation and automation system
Open this publication in new window or tab >>MEILI: A travel diary collection, annotation and automation system
2018 (English)In: Computers, Environment and Urban Systems, ISSN 0198-9715, E-ISSN 1873-7587, Vol. 70, no July 2018, p. 24-34Article in journal (Refereed) Published
Abstract [en]

The increased interest in the automation of travel diary collection, together with the ease of access to new artificial intelligence methods led scientists to explore the prerequisites to the automatic generation of travel diaries. One of the most promising methods for this automation relies on collecting GPS traces of multiple users over a period of time, followed by asking the users to annotate their collected data by specifying the base entities for a travel diary, i.e., trips and triplegs. This led scientist on one of two paths: either develop an in-house solution for data collection and annotation, which is usually an undocumented prototype implementation limited to few users, or contract an external provider for the development, which results in additional costs. This paper provides a third path: an open-source highly modular system for the collection and annotation of travel diaries of multiple users, named MEILI. The paper discusses the architecture of MEILI with an emphasis on the data model, which allows scientists to implement and evaluate their methods of choice for the detection of the following entities: trip start/end, trip destination, trip purpose, tripleg start/end, and tripleg mode. Furthermore, the open source nature of MEILI allows scientists to modify the MEILI solution in compliance with their legal and ethical specifications. MEILI was successfully trialed in multiple case studies in Stockholm and Gothenburg, Sweden between 2014 and 2017.

Keywords
Travel diaries, Destinations purpose and travel mode inferences, Travel diary collection system, Open source, System design and architecture
National Category
Transport Systems and Logistics
Research subject
Transport Science; Geodesy and Geoinformatics; Computer Science
Identifiers
urn:nbn:se:kth:diva-227250 (URN)10.1016/j.compenvurbsys.2018.01.011 (DOI)000436887900003 ()2-s2.0-85041358233 (Scopus ID)
Funder
Swedish Transport Administration, TRV 2014/10422
Note

QC 20180605

Available from: 2018-05-04 Created: 2018-05-04 Last updated: 2018-07-18Bibliographically approved
3. Measures of transport mode segmentation of trajectories
Open this publication in new window or tab >>Measures of transport mode segmentation of trajectories
2016 (English)In: International Journal of Geographical Information Science, ISSN 1365-8816, E-ISSN 1365-8824, Vol. 30, no 9, p. 1763-1784Article in journal (Refereed) Published
Abstract [en]

Rooted in the philosophy of point- and segment-based approaches for transportation mode segmentation of trajectories, the measures that researchers have adopted to evaluate the quality of the results (1) are incomparable across approaches, hence slowing the progress in the field and (2) do not provide insight about the quality of the continuous transportation mode segmentation. To address these problems, this paper proposes new error measures that can be applied to measure how well a continuous transportation mode segmentation model performs. The error measures introduced are based on aligning multiple inferred continuous intervals to ground truth intervals, and measure the cardinality of the alignment and the spatial and temporal discrepancy between the corresponding aligned segments. The utility of this new way of computing errors is shown by evaluating the segmentation of three generic transportation mode segmentation approaches (implicit, explicit–holistic, and explicit–consensus-based transport mode segmentation), which can be implemented in a thick client architecture. Empirical evaluations on a large real-word data set reveal the superiority of explicit–consensus-based transport mode segmentation, which can be attributed to the explicit modeling of segments and transitions, which allows for a meaningful decomposition of the complex learning task.

Place, publisher, year, edition, pages
Taylor & Francis Group, 2016
Keywords
Continuous model evaluation, transportation mode segmentation and detection, trajectory data mining, error analysis, interval algebra
National Category
Transport Systems and Logistics Computer Sciences Other Mathematics
Research subject
Geodesy and Geoinformatics; Transport Science
Identifiers
urn:nbn:se:kth:diva-184485 (URN)10.1080/13658816.2015.1137297 (DOI)000378064300005 ()2-s2.0-84958541974 (Scopus ID)
Note

QC 20160509

Available from: 2016-03-31 Created: 2016-03-31 Last updated: 2018-05-07Bibliographically approved
4. Transportation mode detection – an in-depth review of applicability and reliability
Open this publication in new window or tab >>Transportation mode detection – an in-depth review of applicability and reliability
(English)In: Transport reviews, ISSN 0144-1647, E-ISSN 1464-5327Article in journal (Refereed) In press
Abstract [en]

The wide adoption of location-enabled devices, together with the acceptance of services that leverage (personal) data as payment, allows scientists to push through some of the previous barriers imposed by data insufficiency, ethics and privacy skepticism. The research problems whose study require hard-to-obtain data (e.g. transportation mode detection, service contextualisation, etc.) have now become more accessible to scientists because of the availability of data collecting outlets. One such problem is the detection of a user's transportation mode. Different fields have approached the problem of transportation mode detection with different aims: Location-Based Services (LBS) is a field that focuses on understanding the transportation mode in real-time, Transportation Science is a field that focuses on measuring the daily travel patterns of individuals or groups of individuals, and Human Geography is a field that focuses on enriching a trajectory by adding domain-specific semantics. While different fields providing solutions to the same problem could be viewed as a positive outcome, it is difficult to compare these solutions because the reported performance indicators depend on the type of approach and its aim (e.g. the real-time availability of LBS requires the performance to be computed on each classified location). The contributions of this paper are three fold. First, the paper reviews the critical aspects desired by each research field when providing solutions to the transportation mode detection problem. Second, it proposes three dimensions that separate three branches of science based on their main interest. Finally, it identifies important gaps in research and future directions, that is, proposing: widely accepted error measures meaningful for all disciplines, methods robust to new data sets and a benchmark data set for performance validation.

Place, publisher, year, edition, pages
Taylor & Francis Group
Keywords
Transportation mode detection, transportation segmentation, location-based services, transportation science, human geography
National Category
Transport Systems and Logistics
Research subject
Transport Science
Identifiers
urn:nbn:se:kth:diva-196665 (URN)10.1080/01441647.2016.1246489 (DOI)000396893800003 ()
Note

QC 20161121

Available from: 2016-11-17 Created: 2016-11-17 Last updated: 2018-05-07Bibliographically approved
5. Comparative framework for activity-travel diary collection systems
Open this publication in new window or tab >>Comparative framework for activity-travel diary collection systems
2015 (English)In: 2015 International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2015, IEEE conference proceedings, 2015, p. 251-258Conference paper, Published paper (Refereed)
Abstract [en]

The needs for cheaper and less intrusive ways to collect activity-travel diaries led scientist to pursue new technologies, e.g., positioning technologies like GPS. While a fully, reliable and widely accepted automatic activity-travel diary collection system is yet to be developed, scientists have presented systems that automate parts of an activity-travel diary collection. In the advent of automated systems, it is important to discuss how to analyse the potential of such systems and how to compare different activity-travel diary collection systems. To achieve this objective, this paper introduces a parallel survey design and a comparison framework for collection systems. The framework can be used as a development tool to optimise system design, to report and monitor progress of different system designs, to objectively weigh benefits in decision making, and to automate systematic analysis. In particular, the framework can be used as a comparison tool to reveal the qualitative difference in the data gathered using different collection systems. To achieve this, the framework defines: 1) a number of activity-travel diary measurement entities (trips and triplegs), entity attributes (e.g., trip purpose, origin / destination, etc.), 2) similarity functions between instances of the same entities, and 3) spatial and temporal quality indices to establish a notion of ground truth. The utility of the proposed framework is demonstrated by analysing the results of a trial survey where data is collected via two activity-travel collection systems: a web-based system (PP) and a smartphone-app-based system (MEILI). PP was collected for one day period and MEILI was used for one week period (with one day overlapping). The results show that half of the trips are captured by both systems, while each system roughly captures the same number of trips as the other. The strengths and weaknesses of MEILI are analysed using the framework on the entire week dataset.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2015
Keywords
activity-travel diary collection, comparison of collection methods, ground truth, spatial and temporal quality indices, Automation, Decision making, Intelligent systems, Intelligent vehicle highway systems, Surveys, Systems analysis, Collection methods, Origin destination, Positioning technologies, Qualitative differences, Similarity functions, Systematic analysis, Temporal quality, Transportation
National Category
Embedded Systems
Identifiers
urn:nbn:se:kth:diva-181605 (URN)10.1109/MTITS.2015.7223264 (DOI)000380478600032 ()2-s2.0-84951044698 (Scopus ID)9789633131428 (ISBN)
Conference
International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2015, 3 June 2015 through 5 June 2015
Note

QC 20160316

Available from: 2016-03-16 Created: 2016-02-02 Last updated: 2018-05-07Bibliographically approved
6. A series of three case studies on the semi-automation of activity travel diary generation using smarpthones
Open this publication in new window or tab >>A series of three case studies on the semi-automation of activity travel diary generation using smarpthones
2017 (English)In: Proceedings of TRB 2017 Annual Meeting, 2017Conference paper, Published paper (Refereed)
Abstract [en]

The growing need of acquiring data that is useful for travel behaviour analysis led scientists topursue new ways of obtaining travel diaries from large groups of people. The most promising al-ternative to traditional (declarative) travel diary collection methods are those that rely on collectingtrajectories from individuals and then extract travel diary semantics from the trajectories. However,most studies report on routines specific to the post-processing of data, and seldom focus on datacollection. Even the few studies that deal explicitly with data collection describe the final state ofthe collection system, but do not go at the lengths that are required to describe the decision thatwere taken to bring the system to its current state. This leads to a considerable amount of work thatis needed for designing collection systems that are often undocumented, which impedes the reuseof the aforementioned systems. In light of the aforementioned problems, this paper presents a series of three case studies behind the continuous development of MEILI, a travel diary collection,annotation and automation system, in an effort to: 1) illustrate the utility of the developed systemto collect travel diaries, 2) identify how MEILI and other semi-automatic travel diaries collectionsystems can be improved, and 3) propose MEILI as an open source system that has the potentialof being improved into a widely available semi-automated travel diary collection system.

Keywords
Travel Diary Collection, Case Studies, MEILI
National Category
Transport Systems and Logistics
Research subject
Transport Science; Transport Science
Identifiers
urn:nbn:se:kth:diva-227251 (URN)
Conference
Transportation Research Board 2017
Note

QC 201880508

Available from: 2018-05-04 Created: 2018-05-04 Last updated: 2018-05-08Bibliographically approved
7. Lessons from a trial of MEILI, a smartphone based semi-automatic activity-travel diary collector, in Stockholm city, Sweden
Open this publication in new window or tab >>Lessons from a trial of MEILI, a smartphone based semi-automatic activity-travel diary collector, in Stockholm city, Sweden
Show others...
(English)Manuscript (preprint) (Other academic)
Abstract [en]

This paper describes the lessons learned from the trial of MEILI, a smartphone based semi-automatic activity-travel diarycollection system, in Stockholm city, Sweden. The design of the system, together with state-of-the-art improvements of different elements of the tool, are presented before and after the trial to better illustrate the improvements based on the lessons learned. During the trial, both MEILI and a paper-based diary captured about 65% of the total number of detected trips, but only about half of the trips were captured by both systems. The unmatchable trips are partly due to different activity declaration and system specific destination specification, i.e., a verbose specification of address in the paper-and-pencil survey and a point of interest selection / declaration in MEILI. In terms of subjective appreciation, the user experiences vary greatly between the different participants in the pilot. Presumably, this is mainly due to different level of IT-knowledge of the respondents, but also due to the occasionally non-uniform behaviour of the location collection service caused by hardware and / or software difficulties. Based on these inputs, further web and support system improvements have been implemented for future trials.

Keywords
smartphone based survey; semi-automatic travel diary collection system; activity-travel diary; destination and purpose inference; travel mode detection
National Category
Transport Systems and Logistics
Research subject
Transport Science; Geodesy and Geoinformatics
Identifiers
urn:nbn:se:kth:diva-187489 (URN)
Projects
TRV 2014/10422
Note

QC 20160525

Available from: 2016-05-24 Created: 2016-05-24 Last updated: 2018-05-07Bibliographically approved
8. Future directions of research for automatic travel diary collection
Open this publication in new window or tab >>Future directions of research for automatic travel diary collection
2018 (English)In: Proceedings of the 11th International conference on Transport Survey Methods, 2018Conference paper, Published paper (Refereed)
Abstract [en]

The amount of useful information that can be extracted from travel diaries is matched by the difficulty of obtaining travel diariesin a modern era where the response rate to traditional travel diary collection methods has seen a decrease in most countries.Prompted by this, a body of research has been dedicated to study how travel diaries can be collected via new methods, namelylocation enabled devices such as smartphones, that have a higher penetration rate (in terms of device ownerships and userattachment) and are both easier and cheaper to manage compared to traditional data collection method, e.g. paper-and-pencil,phone, or web-based questionnaires. This paper offers an overview of the current state of travel diary collection, a potentialfuture state and a practical checklist for travel diary collection case studies. A thorough discussion on different pros and cons oftravel diary collection methods and efforts needed for the convergence of methods to collect travel diaries for all demographicsare provided. The practical checklist to aid researchers to organise case studies is based on the authors’ experience and it is meantto raise awareness of difficulties that can be encountered while collecting travel surveys with automated and semi-automatedsystems, and how to overcome them.

Keywords
travel diary collection systems, travel mode destination and purpose inferences, best practices
National Category
Transport Systems and Logistics
Research subject
Transport Science
Identifiers
urn:nbn:se:kth:diva-227256 (URN)
Conference
11th International conference on Transport Survey Methods
Note

QC 20180508

Available from: 2018-05-04 Created: 2018-05-04 Last updated: 2018-05-08Bibliographically approved
9. Workshop synthesis: New developments in travel diary collection systems based on smartphones and GPS receivers
Open this publication in new window or tab >>Workshop synthesis: New developments in travel diary collection systems based on smartphones and GPS receivers
2018 (English)In: Proceedings of the 11th International conference on Transport Survey Methods, 2018Conference paper, Published paper (Refereed)
Abstract [en]

This workshop examined the state of the art of existing travel diary collection systems that make use of GPS data in relationshipto the needs of the practitioners that collect and analyze travel diaries. While the new data collection methods are a promisingalternative that can collect both data on previously ignored demographic segments as well as short trips that are usually forgottenby respondents, they do not solve all the issues the traditional methods are prone to, and also introduce new issues on their own.The workshop participants have identified, discussed and summarized the most pressing concerns regarding the use of new traveldiary collection systems based on smartphones and GPS receivers.

Keywords
travel surveys, current status, semantics, performance, usability, applications
National Category
Transport Systems and Logistics
Research subject
Transport Science
Identifiers
urn:nbn:se:kth:diva-227257 (URN)2-s2.0-85058853208 (Scopus ID)
Conference
11th International conference on Transport Survey Methods
Note

QC 20190418

Available from: 2018-05-04 Created: 2018-05-04 Last updated: 2019-04-18Bibliographically approved
10. Longest common subsequences: Identifying the stability of individuals’ travel patterns
Open this publication in new window or tab >>Longest common subsequences: Identifying the stability of individuals’ travel patterns
(English)Manuscript (preprint) (Other academic)
Abstract [en]

There is a strong consensus in the travel behaviour research community that the one day travel diary collection is insufficient to understand the finer aspects of behaviour that transcend attributes such as average trip length, duration, travel modes, etc. While a large body research was done on exploring the spatial, temporal and spatio-temporal travel behavioural patterns, the sequential aspect of behaviour is seldom studied. The consensus of the few papers that have studied travel behaviour variability from a sequential perspective has been to use edit distance and compute the costs of transforming one day of travel activities into another. While useful, this approach generates difficult to understand metrics since it does not directly extract (sub)sequences but computes penalties. This paper provides an alternative for investigating the sequential aspect of travel behaviour that makes use of longest common subsequences to extract the activities that are common to multiple days and / or users. The proposed methodology provides indexes for measuring the inter- and intra-personal stability of a given user base and its usefulness is proved in a case study on travel diaries collected from 51 users for a period of 7 days.

Keywords
longest common subsequence, multiple day travel patterns, travel behaviour
National Category
Transport Systems and Logistics Computer Sciences
Research subject
Transport Science; Computer Science; Geodesy and Geoinformatics
Identifiers
urn:nbn:se:kth:diva-227260 (URN)
Note

QC 20180508

Available from: 2018-05-04 Created: 2018-05-04 Last updated: 2018-05-08Bibliographically approved

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