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Workshop Synthesis: Passive and sensor data - potential and application
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Transportation Research Procedia 32 (2018) 54–61
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International Steering Committee for Transport Survey Conferences International Steering Committee for Transport Survey Conferences
Workshop Synthesis: Passive and sensor data - potential and Workshop Synthesis: Passive and sensor data - potential and application application a
Doina Olarua*, Alejandro Tudelab a b Perth WA 6009, Australia The University of Western Doina Australia, Business M261, 35 Stirling Highway, OlaruSchool *, Alejandro Tudela
b a The b
Departamento deSchool Ingeniería Civil, Edmundo LarenasPerth 219, Barrio Universitario, TheUniversity UniversityofofConcepción, Western Australia, Business M261, 35 Stirling Highway, WA 6009, Australia The University of Concepción, Departamento Concepción, de IngenieríaChile Civil, Edmundo Larenas 219, Barrio Universitario, Concepción, Chile
Abstract Abstract
The workshop on technology, tools and applications around passive and sensor travel data is summarized in this paper. Such data requires protocols for collection, sharing and processing; as well as the need for validation and multi-disciplinary work. surveys The workshopstoring/retrieving, on technology, tools and applications around passive and sensor travel datamethods is summarized in this paper. Such dataTraditional requires protocols can complement passive and sensor data and to aid a deeper understanding of travel behaviour.methods Passive and datamulti-disciplinary is particularly beneficial for planning longfor collection, storing/retrieving, sharing processing; as well as the need for validation work. Traditional surveys distance travel and freightand transport. While access to massive data collected by behaviour. licensed operators should be guaranteed, maintaining data privacy can complement passive sensor data to aid a deeper understanding of travel Passive data is particularly beneficial for planning longand cultural sensitivity is atransport. priority. While access to massive data collected by licensed operators should be guaranteed, maintaining data privacy distance travel and freight and cultural sensitivity is a priority.
© 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license © 2018 The Authors. Published by Elsevier Ltd. (http://creativecommons.org/licenses/by-nc-nd/3.0/) © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/) Peer-review responsibility of International Steering Committee for for Transport Transport Survey Survey Conferences Conferences (ISCTSC). (ISCTSC) (http://creativecommons.org/licenses/by-nc-nd/3.0/) Peer-review under under responsibility of the the International Steering Committee Peer-review under responsibility of the International Steering Committee for Transport Survey Conferences (ISCTSC) Keywords: passive data; big data, sensor; GPS; Wi-Fi; smartcard; transport; travel surveys. Keywords: passive data; big data, sensor; GPS; Wi-Fi; smartcard; transport; travel surveys.
1. Introduction 1. Introduction The nature of the research questions the modellers attempt to answer dictates the type of data collected for transport The nature thethe research questions the modellers attempt to answer thecredibility type of data(Ortúzar collected transport modelling. In of turn, quality of the data will influence model resultsdictates and their &for Willumsen, modelling. In turn, the quality of the data will influence model results and their credibility (Ortúzar & Willumsen, 2011). For decades, reliance has been primary on data collected via surveys and other more aggregated data methods 2011). Fordedicated decades, sensors, reliance floating has beencars, primary on data counts collected via surveys and other aggregated methods including and overall (Leduc, 2008; Chen et al.,more 2010; Ortúzar &data Willumsen, including dedicated sensors, floating cars, and overall counts (Leduc, 2008; Chen et al., 2010; Ortúzar & Willumsen, 2011; El Faouzi et al., 2011; Bhat, 2015; Wang et al., 2016). These are costly and often insufficient to capture new 2011; El patterns Faouzi etand al.,aspects, 2011; Bhat, Wang et al.,participant 2016). These are costly(Buliung and often&insufficient capture new mobility with 2015; surveys requiring engagement Kanaroglu,to2007; Stopher mobility patterns and aspects, with surveys requiring participant engagement (Buliung & Kanaroglu, 2007; Stopher & Greaves, 2007; Van Acker et al., 2010; Harms et al., 2017). & Greaves, 2007; Van Acker et al., 2010; Harms et al., 2017).
* Corresponding author. address: [email protected] *E-mail Corresponding author. E-mail address: [email protected]
2352-1465 © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/) 2352-1465 © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license Peer-review under responsibility of the International Steering Committee for Transport Survey Conferences (ISCTSC) (http://creativecommons.org/licenses/by-nc-nd/3.0/) Peer-review under responsibility of the International Steering Committee for Transport Survey Conferences (ISCTSC) 2352-1465 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/) Peer-review under responsibility of the International Steering Committee for Transport Survey Conferences (ISCTSC). 10.1016/j.trpro.2018.10.010
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The use of new technology and methods has increased the availability of passively collected data. Mobile phones, Wi-Fi scanners, cameras, Bluetooth devices, GPS trackers or smartcards are sources of valuable information and knowledge on travel patterns and the use of transport networks (Bricka & Bhat, 2006; Bayart et al., 2009; El Faouzi et al., 2011; Liew et al., 2015; Morency et al., 2017). To what extent they would be considered as replacement or complement of the existing methods, depends on the ability to integrate and process multiple sources of data, which is often sparse or incomplete. Passive data is a key ingredient of “smart cities”, propelled as the new thinking paradigm, where infrastructure, traffic participants, goods and various information sources are interconnected (Batty, 2013; Kitchin, 2014; Nitsche et al., 2014). These interactions could be captured to better understand mobility, manage incidents, reduce congestion, pollution and other externalities, increase and improve the management, performance and utilisation of transport resources, and improve traveller experiences and welfare (Chen et al., 2010; Van Acker et al., 2010; Batty, 2013; Nitsche et al., 2014). This document does not represent a literature review of the passive and sensor data; rather it presents findings from the 2017 ISCTSC workshop Passive and sensor data: potential and application, which included 23 participants. The workshop addressed architectural design and methodological issues related to data analytics revolving around three research questions, addressed in nine papers; three as oral presentations and six as posters: • How can we better use mobile network data and other data sources for OD matrix estimation, route choices, and traffic conditions? • What are the benefits and challenges of integrating multiple sources of data (GPS, smartcards) to augment our understanding of travel (public transport use, car and bike sharing)? • Are combined kits (including automated cameras, activity tracking/fitness monitors, GPS, etc.) better for collecting data on daily patterns of activity? • After the presentation of the nine studies, a full session was dedicated to the discussion of passive and sensor data issues. Section 2 provides a summary of the nine papers, whereas Section 3 presents what surfaced from the workshop activities and discussions. 2. Contents of the workshop The nine paper presentations (details in the Appendix) and workshop debate were structured around the three questions presented above. With respect to the first research question, five papers dealt with this issue, which are described as follows. Bonnel et al. (2017) highlighted that the increasing attention received by mobile phone data in recent years, makes these devices a critical source of information for mobility studies. The authors examined mobile phone traces to determine the reliability of such data in constructing origin-destination (OD) matrices. They used a dataset issued from Orange mobile network probes (2G/3G), with a telephone travel survey collected between 2013 and 2015 on the Rhône-Alpes region (France). The data contains over 1.2 million mobile phone users per day. Tudela (2017) presented a research in progress aimed to develop a method to generate public transport OD matrices based on information collected through sensors available on buses. His paper focused on the method, rather than the possible technology, which could be used to gather passive or active data: cameras, floor sensors, weight sensors and MAC addresses from mobile devices. The method relies on the entropy maximization principle, searching for the OD matrix that can best replicate the information collected via sensors. Various data sources can be considered: boarding/alighting, utilisation/crowding on various sections or routes, or previous information such as load diagrams. The feasible set of solutions is formed by linear restrictions, where the objective function is non-linear. The OD matrix obtained from these various data sources is unique and it can be generated for different spatial and temporal aggregation criteria. In the same vein, Ban et al. (2017) argued for the use of new alternative sources to generate OD matrices, overcoming some of the issues with outdated travel survey data. These can also address the dynamics of the demand as a result of changing land use, demographics, traffic conditions and level of service. Moreover, efficient traffic control measures require the traditional average OD matrix, but also time-dependent ODs and trip purpose-specific ODs, as well as an understanding of the day-to-day variability. The objective of the work presented by Ban et al. (2017) was to update the time-dependent OD matrices by trip purpose, using aggregated mobile phone GPS location
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data collected in Nagoya in 2011. The maximum entropy principle was also used to update the OD matrices. Seven categories of trips, home-based or non-home-based, were considered. Differences in the number of people in each zone observed from mobile phone data between successive times of day were used as constraints when maximizing the entropy of the OD trip patterns by trip category. Their results confirmed a higher variability of discretionary trips at night, after work, and the lowest variability for AM peak commuting. Mai et al. (2017) drew their attention to another aspect, particularly relevant for the mixed traffic in cities from developing countries: the high proportion of motorbikes, rickshaws, etc. (> 50%), which make typical methodologies (such as traffic detectors, GPS, or probe cars) ineffective. They propose the use of Wi-Fi or Bluetooth signals of mobile phones to estimate travel time in mixed traffic, dominated by motorcycles. The authors suggested that storing the MAC addresses of the devices is more reliable compared to other approaches, as it is more immune to signal loss in tunnel sections or in bad weather conditions. Surveys were conducted in Hanoi, Vietnam, with Wi-Fi scanners set up at intersections and along arterial roads, during AM and PM peak periods. Cameras and Bluetooth scanners were also used as supplementary devices for verification. Travel times for different modes were estimated from 2,000 trips per hour. Finally, King and Mahmoud (2017) presented a case of using INRIX and Google Maps systems for collecting travel time information. They highlighted many limitations of the traditional approaches, including costly/labourintensive and time-consuming efforts for individual surveys, yet limited in space and duration, or limited coverage of travel time surveys using GPS-equipped probe vehicles. Crowd-sourced data from both personal and commercial fleet vehicles enables service providers to offer better-archived and real-time travel time data. Many organisations have exploited these new data collection methods to develop personal and in-vehicle navigation systems (e.g. Google Maps, INRIX, TomTom, HERE, Cellint, etc.). From the end user’s perspective, the systems offer value through provision of current travel time estimates and suggestions of shortest path routes. The raw data is useful for traffic planners to analyze travel times along specific corridors or to report on congestion levels. A regional network was examined and segmented into approximately 1,500 one-way segments of 1 kilometre in length, including highways, major arterials, and water crossings. To analyze regional and local congestion, the authors explored travel times across three spatial layers: (i) 14 activity centres across the region (macro-level); (ii) 14 sub-regions largely defined by local municipal boundaries (meso-level); and (iii) road network (micro-level). The average travel times obtained with Google Maps were compared to another study conducted in 2003 (which used the same 14 regional activity centres). In relation with the second research question, on the benefits and challenges of integrating multiple sources of data, two papers were presented. Kolarova et al. (2017) introduced the benefits, as well as challenges, related to assessing and analyzing electric vehicle (EV) data, using as an example a test fleet of 130 EVs set up in Berlin and Brandenburg, Germany. The sample included commercial and privately used battery (B) EVs, as well as plug-in hybrid (PH) EVs, monitored over one year. An online questionnaire survey and workshops with the EV drivers provided additional information on the fleet and the use of the vehicles. Their EV data set includes information on each charging, trip or parking event (e.g., start and end time of each segment, odometer value, state of charge, outside temperature, GPS positions, as well as travelled distance and energy consumed or charged). Two assessment methods were used: a) data logging devices which communicate with the vehicle via the Controller Area Network (CAN) and with IT back-end via 3G mobile networks; b) direct vehicle data supply from the car manufacturer. The authors reported that data quality by direct data supply was better than indirect measurement via data logging devices, yet such a measurement option was not available for all vehicle models. In the second paper, Morency et al. (2017) presented a research project aiming to combine data from five data sources on five transport modes, to understand the role each mode plays in the daily travel in Montreal. Their analysis used: GPS data from a fleet of more than 1,000 taxis, transaction data from the Montreal bike-sharing system, smartcard validation data from the Montreal Transit authority, and car availability from the two free-floating systems operating in Montreal. These sets were converted into vectors summarising daily, weekly, and yearly patterns, processed independently and then pooled to generate typical patterns of use, illustrating the value of combining data as well as the difficulties arising in the process. The authors also highlighted that given the scarce data, few travel surveys are able to provide insight into the variability of behaviour over time, as well as on the interaction between various transport modes, implying that fusion with other secondary sources is both necessary and timely.
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With respect respect to to the the third third research research question, question, about about combined combined kits, kits, two two papers papers were were considered. considered. With Harms et al. (2017) offered insight into the possibility of merging conventional travel diary and and time-use time-use diary diary Harms et al. (2017) offered insight into the possibility of merging conventional travel diary methodologies with passive recording devices (‘wearables’), to offer a substantially greater understanding and better methodologies with passive recording devices (‘wearables’), to offer a substantially greater understanding and better prediction of of travel travel behaviour. behaviour. They They have have drawn drawn on on scholarly scholarly literature literature and and relied relied on on aa pilot pilot study study to to suggest suggest two two clear clear prediction advantages of this multi-method approach: a) the continuous nature of the time diary record offers cues for trips that advantages of this multi-method approach: a) the continuous nature of the time diary record offers cues for trips that might otherwise be forgotten or overlooked, as well as additional information about the context of other activities might otherwise be forgotten or overlooked, as well as additional information about the context of other activities within which which the the trip trip is is situated situated and; and; b) b) real-time real-time passive passive monitoring monitoring gives gives accurate accurate and and objectively objectively verifiable verifiable timings, timings, within locations, and activity intensity, more reliable than the estimates contained in self-report travel and time diaries. The locations, and activity intensity, more reliable than the estimates contained in self-report travel and time diaries. The pilot data from Perth, Western Australia, allowed the researchers to: (1) improve estimates of travel demand by pilot data from Perth, Western Australia, allowed the researchers to: (1) improve estimates of travel demand by benchmarking the reliability and validity of various travel behaviour metrics; (2) calculate the levels of physical benchmarking the reliability and validity of various travel behaviour metrics; (2) calculate the levels of physical activity (PA) (PA) associated associated with with different different modes modes of of travel; travel; and and (3) (3) explore explore traveller traveller experiences experiences and and attitudes. attitudes. This This was was activity considered a proof-of-concept study that could be further tested and applied at the population level to answer policyconsidered a proof-of-concept study that could be further tested and applied at the population level to answer policyrelevant questions questions in in transport, transport, health health and and wellbeing, wellbeing, as as well well as as land-use land-use planning. planning. relevant On a related research project on time spent waiting for transport connections, Kusakabe et et al. al. (2017) (2017) monitored monitored On a related research project on time spent waiting for transport connections, Kusakabe and estimated passengers’ behaviour in the waiting rooms of transit facilities in the largest station in Tokyo before and estimated passengers’ behaviour in the waiting rooms of transit facilities in the largest station in Tokyo before boarding buses and trains. Waiting time is an important parameter for planning and designing facilities and for boarding buses and trains. Waiting time is an important parameter for planning and designing facilities and for managing passenger crowds. Kusakabe et al. (2017) proposed a method to estimate the duration of passenger waiting managing passenger crowds. Kusakabe et al. (2017) proposed a method to estimate the duration of passenger waiting within aa coach coach terminal terminal by by detecting detecting Wi-Fi Wi-Fi probe probe requests requests from from passengers’ passengers’ Wi-Fi Wi-Fi devices. devices. Their Their study study employed employed aa within device that scans MAC addresses in Wi-Fi probe requests, referred to as AMP sensor (Anonymous MAC address device that scans MAC addresses in Wi-Fi probe requests, referred to as AMP sensor (Anonymous MAC address Probe Sensor). On average, 32,515 MAC addresses per day were observed during the two weeks of monitoring in Probe Sensor). On average, 32,515 MAC addresses per day were observed during the two weeks of monitoring in 2016. The number of MAC addresses was much larger than the number of boarding passengers, suggesting that AMP 2016. The number of MAC addresses was much larger than the number of boarding passengers, suggesting that AMP sensor observed observed some some through through traffic, traffic, as as well well as as waiting waiting passengers. passengers. After After applying applying data data cleaning, cleaning, they they used used more more sensor than 150,000 records for the analysis of waiting times. than 150,000 records for the analysis of waiting times. 3. Workshop Workshop discussion discussion 3. The workshop workshop continued continued with with four four round round tables tables and and aa joint joint final final discussion discussion which which led led to to the the following following findings, findings, The summarised in the word cloud shown in Figure 1. summarised in the word cloud shown in Figure 1.
Figure 1. 1. Word Word cloud cloud using using the the facilitator facilitator notes notes during during the the workshop. workshop. Figure
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As the cloud reveals, except for ‘data’, the prevailing aspects were: definitional clarity, integration, and use, benefits and issues. Not surprisingly, the most frequently used words in the commentary were: ‘passive’, ‘collection/gathering’, ‘processing’, ‘storing’, ‘analysis/processing’, ‘integration/fusion’, ‘quality’ and ‘standards’. The discussion of the results is provided in the following sections. 3.1. Strengths of passive data Passive data has several strengths: The data fusion or integration is a promising alternative to traditional surveys for understanding travel behaviour, for planning purposes, and for operational management. This approach has a strong financial impact due to its lower marginal long-run data collection cost, when compared with traditional approaches (Chen et al., 2010; George et al., 2014; Batty, 2013; Shen & Stopher, 2014; Liew et al., 2015; Wang et al., 2016). Self-report measures used in transport research include household travel surveys and travel diary logs with or without GPS tracking (Bricka & Bhat, 2006; Nitsche et al., 2014; Moutou et al., 2015). Self-report methods without GPS often fail to provide detailed valid location information and respondents often have incomplete spatial knowledge, so tend to approximate durations and succumb to recall bias. Therefore, passive GPS devices overcome this shortcoming (Shen & Stopher, 2014). Moreover, these data collection procedures reduce respondent burden, since data gathering is carried out almost anonymously, without direct intervention of the respondent. However, face-to-face or similar surveys resting on samples are still needed, since passive data cannot deliver (as yet) individual information, such as income, gender, constraints, or their attitudes (Van Acker et al., 2010; Moutou et al., 2015). Multi-source data fusion and mixed modes of data collection (De Leeuw, 2005; Chen et al., 2010; Batty, 2013; Varshney, 2014) can produce a better understanding of the observed phenomena (e.g., active travel, traffic congestion, or fleet utilisation) by decreasing the uncertainty related to the individual sources (El Faouzi et al., 2011). The ubiquity and potential of passive and sensor data enables multi-, inter- and trans-disciplinarity† (Choi & Pak, 2006), as many other non-transport or planning fields may benefit from the data collection and integration (health, air quality, insurance, tourism) (Doherty et al., 2013; Varshney, 2014; Liew et al., 2015; Handy & Davis, 2016). 3.2. Challenges or limitations for transport research On the other hand, the following issues must be kept in mind when dealing with massive data: There are differences in the spatial and temporal resolution, availability and quality of alternative data and compatibility issues with similar data coming from different sources. Protocols for data collection, transmission, storing, accessing, retrieving, processing and usage should become a research and practice priority (George et al., 2014; Mohammadian & Bricka, 2015). Data management requires ‘skilling up’ to work with multiple data sources (learning powerful computational techniques) and ad-hoc methodologies oriented to their processing. Data storing, accessing, and processing also require standards, as well as participation of experts from various disciplines (e.g., computer science, transport, urban planning, sociology, psychology, marketing, population health), to better understand the complexities of merging and calibrating data from various sources and their appropriate use (Batty, 2013; George et al., 2014; Wang et al., 2016). Government institutions and corporations should work together towards making data freely available, implying that licensed operators should not withhold the information they passively collect. Ethical issues arise from data gathering and management. Problems with individual privacy and analytical issues around data captured with sensors and other devices such as cameras are envisaged (Mok et al., 2015). Besides, cultural issues may affect data collection. Fear of invasion of privacy, data transfer to third parties, lack of trust in the
† “Multidisciplinarity draws on knowledge from different disciplines but stays within their boundaries. Interdisciplinarity analyzes, synthesizes and harmonizes links between disciplines into a coordinated and coherent whole. Transdisciplinarity integrates the natural, social and health sciences in a humanities context, and transcends their traditional boundaries.” (Choi & Pak, 2006: 351).
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capabilities of the data storage and management systems, including hacking of the systems, were put forward by most of the workshop participants. This topic should be given considerable attention by researchers and practitioners alike (George et al., 2014). These aspects emphasize the need for clearer communication of the data collection protocols and the potential reluctance of communities to participate in these big data exercises. 4. Policy recommendations The following recommendations were drawn from the workshop discussion: • It is necessary to engage scholars and professionals from various disciplines, moving from an off-line research and development space to an in-line state-of-the-art practice of mixed approaches of data collection. • It is necessary to create partnerships with the data providers/warehousing organisations, since their purpose is quite different from researchers’ interests. • It is critical to define protocols and standards for data gathering, transmission, storing, accessing, retrieving, processing, sharing, and analyzing, to guarantee fully data interoperability. • Data collected using public funds or through licensing should be made available, ensuring ‘data liberation’ from collectors. • It is possible and highly recommended to have methods, repositories, testing data banks made freely available for calibration and validation purposes (Kelly et al., 2015). 5. Directions for future research The discussion in relation to expected research developments revealed the following priorities: • It is necessary to advance methodologies oriented towards data fusion and integration. There is a scarcity of experience on this (at least in the planning and transport domains), with poor data interoperability, an unevenness of data availability and ‘islands of no data’, and missing records. • To complement big passive data, clear and precise information on which additional data should be collected is needed, to optimise the design of a full database that can be used for planning and management purposes. • The definition of quality standards should be revised and protocols to ensure the full sharing of information be generated. • Validation frameworks and methodologies must be developed as well, such that the data collected through passive devices and sensors fully complement other active sources. • The ‘white spots’ representing missing data should be investigated. Focused mainly in the urban setting, data on regional and long-distance transport, as well as freight movements, are lacking. This is relevant for transport infrastructure planning and management and has the potential to substantially improve the data platform for freight modelling and intercity transport. 6. Discussion and conclusion A dominant feature of this workshop was the methodological change from hypothesis testing and verification, to discovery knowledge through data mining and visualisation (Morency et al., 2007; Bonnel et al., 2017; King & Mahmoud, 2017). In other words, a move towards more data-driven approaches. Another salient aspect of the discussions referred to the conditions in which the passive and sensor data should be used, particularly individual data (Kolarova et al., 2017; Harms et al., 2017), and the aspiration to have open access to big data not only for research purposes, but also for practice across a number of organisations (Morency et al., 2017). The general conclusion of the workshop is that despite many challenges associated with data enrichment (compatibility issues, spatial and temporal resolution, availability and quality of alternative data, data processing and storing issues, etc.), fused/integrated data represent a promising alternative to traditional surveys for understanding travel behaviour, planning purposes, and operation, and, as expressed by Bhat (2015) “we are at exciting point” in the history of data collection, “with unprecedented opportunities to collect rich travel data at fine resolutions of space and time” (Bhat, 2015: 109). As already indicated, the passive manner of collecting new and richer data enables larger
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samples or more accurate measurements, with lower marginal costs in the long run. Thus, passive data is cheaper, larger, and could be more frequently collected than other traditional survey approaches. Acknowledgements The authors are grateful to all the workshop participants for their valuable contributions in the workshop discussion. We thank: Adham Badran (CA), Takumi Ban (JP), Patrick Bonnel (FR), Norbert Braendle (AU), Maguelone Chandesris (FR), Adrian Ellison (AU), Mark Fowler (CA), Lei Gong (JP), Ali Haloui (CA), Chris Harding (CA), Tobias Kuhnimhof (DE), Takahiko Kusakabe (JP), Mohamed Salah Mahmoud (CA), Catherine Morency (CA), Adrian Prelipcean (SE), Frederic Roulland (FR), Luc Samson (CA), Siva Srikukenthiran (CA), Sander van Cranenburgh (NL), Toshiyuki Yamamoto (JP), and Wataru Yamamoto (JP). References Ban, T., Yamamoto, T., Usui, T., 2017. Analysis on day-to-day variability of time-dependent origin-destination matrices by trip purpose with aggregated mobile phone location data. 11th International Conference on Transport Survey Methods, Esterel, Canada. Batty, M., 2013. Big data, smart cities and city planning. Dialogues in Human Geography, 33, 274-279. Bayart, C., Bonnel, P., Morency, C., 2009. Survey Mode Integration and Data Fusion: Methods and challenges. In Bonnel, P., Madre J.-L., LeeGosselin, M., & Zmud, J. Eds. Transport survey methods. Keeping up with a changing world, Emerald. Bhat, C., 2015. Workshop synthesis: Conducting travel surveys using portable devices - challenges and research needs. Transportation Research Procedia, 11, 199-205. Bonnel, P., Fekih, M., Smoreda, Z., 2017. Origin-Destination estimation using mobile network probe data. 11th International Conference on Transport Survey Methods, Esterel, Canada. Bricka, S., Bhat, C.R., 2006. Comparative analysis of global positioning system-based and travel survey-based data. Transportation Research Record, 1972, 9–20. Buliung, R.N., Kanaroglu, P.S., 2007 Activity–Travel Behaviour Research: Conceptual Issues, State of the Art, and Emerging Perspectives on Behavioural Analysis and Simulation Modelling. Transport Reviews, 272, 151-187. Chen, C., Gong, H., Lawson, C., Bialostozky, E., 2010. Evaluating the feasibility of a passive travel survey collection in a complex urban environment: Lessons learned from the New York City case study. Transportation Research A, 4410, 830-840. Choi, B.C., Pak, A.W., 2006. Multidisciplinarity, interdisciplinarity and transdisciplinarity in health research, services, education and policy: 1. Definitions, objectives, and evidence of effectiveness. Clinical and Investigative Medicine, 296, 351-364. De Leeuw, E.D., 2005. To Mix or Not to Mix Data Collection Modes in Surveys. Journal of Official Statistics, 212, 233– 255. Doherty, A., Hodges, S., King, A., Smeaton, A., Berry, E., Moulin, C., Lindley, S., Kelly, P., Foster, C., 2013. Wearable Cameras in Health: The State of the Art and Future Possibilities. American Journal of Preventive Medicine, 44, 320–323. El Faouzi, N.-E., Leung, H., Kurian, A., 2011. Data fusion in intelligent transportation systems: Progress and challenges – A survey. Information Fusion, 121, 4-10. George, G., Hass, M.R., Pentland, A., 2014 Big Data and Management. Academy of Management Journal, 572, 321-326. Handy, S.L., Davis, A., 2016. The science and art of intersectoral collaboration on transport and health. Journal of Transport & Health, 33, 230– 231. Harms, T., Muroni, G., Cangiano, F., Smith, B., Sun, Y., Olaru, D., 2017. New Data Collection Methods for Researching Travel Behaviour. 11th International Conference on Transport Survey Methods, Esterel, Canada. Jara-Diaz, S., Rosales-Salas, J., 2015. Understanding time use: Daily or weekly data? Transportation Research A, 76, 38-57. Kelly, P., Thomas, E., Doherty, A., Harms, T., Burke, Ó., Gershuny, J., Foster, C., 2015. Developing a Method to Test the Validity of 24 Hour Time Use Diaries Using Wearable Cameras: A Feasibility Pilot. PLoS ONE 10: e0142198. King, F., Mahmoud, S.M., 2017. An exploration of ‘passive big data’ sources to inform best practice travel time studies – Lessons learned from Metro Vancouver. 11th International Conference on Transport Survey Methods, Esterel, Canada. Kitchin, R., 2014. The real-time city? Big data and smart urbanism. GeoJournal, 79, 1-14. Kolarova, V., Kuhnimhof, T., Trommer, S., 2017. Assessment of real-world vehicle data from electric vehicles – potentials and challenges. 11th International Conference on Transport Survey Methods, Esterel, Canada. Kusakabe, T., Yaginuma, H., Fukuda, D., 2017. Estimation of bus passengers’ waiting time at a coach terminal with Wi-Fi MAC addresses. 11th International Conference on Transport Survey Methods, Esterel, Canada. Leduc, G., 2008. Road Traffic Data: Collection Methods and Applications, Working Papers on Energy, Transport and Climate Change N.1, Joint Research Group, European Commission, available at
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https://www.researchgate.net/profile/Guillaume_Leduc2/publication/254424803_Road_Traffic_Data_Collection_Methods_and_Applications /links/55645bf008ae6f4dcc99951f.pdf. Liew, C.S., Wah, T.Y., Shuja, J., Daghighi, B., 2015. Mining personal data using smartphones and wearable devices: A survey. Sensors, 152, 4430– 4469. Mai, V.H., Kusakabe, T., Suga, Y., Oguchi, T., 2017. Travel time estimation in mixed traffic using Wi-Fi detector based data. 11th International Conference on Transport Survey Methods, Esterel, Canada. Mohammadian, K., Bricka, S., 2015. Workshop Synthesis: Conducting travel surveys using portable devices - role of technology in travel surveys. Transportation Research Procedia, 11, 242-246. Mok, T.M., Cornish, F., Tarr, J., 2015. Too much information: visual research ethics in the age of wearable cameras. Integrative Psychological and Behavioral Science, 492, 309-322. Morency, C., Trépanier, M., Saunier, N., Verreault, H. & Bourdeau, J.-S. 2017. The challenges of using 5 parallel passive data streams to report on a wide range of mobility options. 11th International Conference on Transport Survey Methods, Esterel, Canada. Moutou, C., Longden, T., Stopher, P., Liu, W., 2015. The challenges and opportunities of in-depth analysis of multi-day and multi-year data. Journal of Transport Economics and Policy, 494, 579–602. Nitsche, P., Widhalm, P., Breuss, S., Brändle, N., Maurer, P., 2014. Supporting large-scale travel surveys with smartphones – A practical approach. Transportation Research C, 43, 212-221. Ortúzar, J. de D., Willumsen, L.G., 2011. Transport Modelling 4th ed., John Wiley & Sons: Chichester, UK. Shen, L., Stopher, P.R., 2014. Review of GPS travel survey and GPS data-processing methods. Transport Reviews, 343, 316–334. Stopher, P.R., Greaves, S.P., 2007. Household travel surveys: Where are we going? Transportation Research A, 415, 367-381. Tudela, A., 2017. Obtaining public transport OD matrices from data collected using sensors installed in buses. 11th International Conference on Transport Survey Methods, Esterel, Canada. Van Acker, V., van Wee, B, Witlox, F., 2010. When transport geography meets social psychology: toward a conceptual model of travel behaviour. Transport Reviews, 302, 219–240. Varshney, U., 2014. Mobile health: Four emerging themes of research. Decision Support Systems, 66, 20–35. Wang, C., Li, X., Zhou, X., Wang, A., Nedja, N., 2016. Soft computing in big data intelligent transportation systems. Applied Soft Computing, 38, 1099-1108.
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International Steering Committee for Transport Survey Conferences International Steering Committee for Transport Survey Conferences
Workshop Synthesis: Passive and sensor data - potential and Workshop Synthesis: Passive and sensor data - potential and application application a
Doina Olarua*, Alejandro Tudelab a b Perth WA 6009, Australia The University of Western Doina Australia, Business M261, 35 Stirling Highway, OlaruSchool *, Alejandro Tudela
b a The b
Departamento deSchool Ingeniería Civil, Edmundo LarenasPerth 219, Barrio Universitario, TheUniversity UniversityofofConcepción, Western Australia, Business M261, 35 Stirling Highway, WA 6009, Australia The University of Concepción, Departamento Concepción, de IngenieríaChile Civil, Edmundo Larenas 219, Barrio Universitario, Concepción, Chile
Abstract Abstract
The workshop on technology, tools and applications around passive and sensor travel data is summarized in this paper. Such data requires protocols for collection, sharing and processing; as well as the need for validation and multi-disciplinary work. surveys The workshopstoring/retrieving, on technology, tools and applications around passive and sensor travel datamethods is summarized in this paper. Such dataTraditional requires protocols can complement passive and sensor data and to aid a deeper understanding of travel behaviour.methods Passive and datamulti-disciplinary is particularly beneficial for planning longfor collection, storing/retrieving, sharing processing; as well as the need for validation work. Traditional surveys distance travel and freightand transport. While access to massive data collected by behaviour. licensed operators should be guaranteed, maintaining data privacy can complement passive sensor data to aid a deeper understanding of travel Passive data is particularly beneficial for planning longand cultural sensitivity is atransport. priority. While access to massive data collected by licensed operators should be guaranteed, maintaining data privacy distance travel and freight and cultural sensitivity is a priority.
© 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license © 2018 The Authors. Published by Elsevier Ltd. (http://creativecommons.org/licenses/by-nc-nd/3.0/) © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/) Peer-review responsibility of International Steering Committee for for Transport Transport Survey Survey Conferences Conferences (ISCTSC). (ISCTSC) (http://creativecommons.org/licenses/by-nc-nd/3.0/) Peer-review under under responsibility of the the International Steering Committee Peer-review under responsibility of the International Steering Committee for Transport Survey Conferences (ISCTSC) Keywords: passive data; big data, sensor; GPS; Wi-Fi; smartcard; transport; travel surveys. Keywords: passive data; big data, sensor; GPS; Wi-Fi; smartcard; transport; travel surveys.
1. Introduction 1. Introduction The nature of the research questions the modellers attempt to answer dictates the type of data collected for transport The nature thethe research questions the modellers attempt to answer thecredibility type of data(Ortúzar collected transport modelling. In of turn, quality of the data will influence model resultsdictates and their &for Willumsen, modelling. In turn, the quality of the data will influence model results and their credibility (Ortúzar & Willumsen, 2011). For decades, reliance has been primary on data collected via surveys and other more aggregated data methods 2011). Fordedicated decades, sensors, reliance floating has beencars, primary on data counts collected via surveys and other aggregated methods including and overall (Leduc, 2008; Chen et al.,more 2010; Ortúzar &data Willumsen, including dedicated sensors, floating cars, and overall counts (Leduc, 2008; Chen et al., 2010; Ortúzar & Willumsen, 2011; El Faouzi et al., 2011; Bhat, 2015; Wang et al., 2016). These are costly and often insufficient to capture new 2011; El patterns Faouzi etand al.,aspects, 2011; Bhat, Wang et al.,participant 2016). These are costly(Buliung and often&insufficient capture new mobility with 2015; surveys requiring engagement Kanaroglu,to2007; Stopher mobility patterns and aspects, with surveys requiring participant engagement (Buliung & Kanaroglu, 2007; Stopher & Greaves, 2007; Van Acker et al., 2010; Harms et al., 2017). & Greaves, 2007; Van Acker et al., 2010; Harms et al., 2017).
* Corresponding author. address: [email protected] *E-mail Corresponding author. E-mail address: [email protected]
2352-1465 © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/) 2352-1465 © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license Peer-review under responsibility of the International Steering Committee for Transport Survey Conferences (ISCTSC) (http://creativecommons.org/licenses/by-nc-nd/3.0/) Peer-review under responsibility of the International Steering Committee for Transport Survey Conferences (ISCTSC) 2352-1465 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/) Peer-review under responsibility of the International Steering Committee for Transport Survey Conferences (ISCTSC). 10.1016/j.trpro.2018.10.010
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The use of new technology and methods has increased the availability of passively collected data. Mobile phones, Wi-Fi scanners, cameras, Bluetooth devices, GPS trackers or smartcards are sources of valuable information and knowledge on travel patterns and the use of transport networks (Bricka & Bhat, 2006; Bayart et al., 2009; El Faouzi et al., 2011; Liew et al., 2015; Morency et al., 2017). To what extent they would be considered as replacement or complement of the existing methods, depends on the ability to integrate and process multiple sources of data, which is often sparse or incomplete. Passive data is a key ingredient of “smart cities”, propelled as the new thinking paradigm, where infrastructure, traffic participants, goods and various information sources are interconnected (Batty, 2013; Kitchin, 2014; Nitsche et al., 2014). These interactions could be captured to better understand mobility, manage incidents, reduce congestion, pollution and other externalities, increase and improve the management, performance and utilisation of transport resources, and improve traveller experiences and welfare (Chen et al., 2010; Van Acker et al., 2010; Batty, 2013; Nitsche et al., 2014). This document does not represent a literature review of the passive and sensor data; rather it presents findings from the 2017 ISCTSC workshop Passive and sensor data: potential and application, which included 23 participants. The workshop addressed architectural design and methodological issues related to data analytics revolving around three research questions, addressed in nine papers; three as oral presentations and six as posters: • How can we better use mobile network data and other data sources for OD matrix estimation, route choices, and traffic conditions? • What are the benefits and challenges of integrating multiple sources of data (GPS, smartcards) to augment our understanding of travel (public transport use, car and bike sharing)? • Are combined kits (including automated cameras, activity tracking/fitness monitors, GPS, etc.) better for collecting data on daily patterns of activity? • After the presentation of the nine studies, a full session was dedicated to the discussion of passive and sensor data issues. Section 2 provides a summary of the nine papers, whereas Section 3 presents what surfaced from the workshop activities and discussions. 2. Contents of the workshop The nine paper presentations (details in the Appendix) and workshop debate were structured around the three questions presented above. With respect to the first research question, five papers dealt with this issue, which are described as follows. Bonnel et al. (2017) highlighted that the increasing attention received by mobile phone data in recent years, makes these devices a critical source of information for mobility studies. The authors examined mobile phone traces to determine the reliability of such data in constructing origin-destination (OD) matrices. They used a dataset issued from Orange mobile network probes (2G/3G), with a telephone travel survey collected between 2013 and 2015 on the Rhône-Alpes region (France). The data contains over 1.2 million mobile phone users per day. Tudela (2017) presented a research in progress aimed to develop a method to generate public transport OD matrices based on information collected through sensors available on buses. His paper focused on the method, rather than the possible technology, which could be used to gather passive or active data: cameras, floor sensors, weight sensors and MAC addresses from mobile devices. The method relies on the entropy maximization principle, searching for the OD matrix that can best replicate the information collected via sensors. Various data sources can be considered: boarding/alighting, utilisation/crowding on various sections or routes, or previous information such as load diagrams. The feasible set of solutions is formed by linear restrictions, where the objective function is non-linear. The OD matrix obtained from these various data sources is unique and it can be generated for different spatial and temporal aggregation criteria. In the same vein, Ban et al. (2017) argued for the use of new alternative sources to generate OD matrices, overcoming some of the issues with outdated travel survey data. These can also address the dynamics of the demand as a result of changing land use, demographics, traffic conditions and level of service. Moreover, efficient traffic control measures require the traditional average OD matrix, but also time-dependent ODs and trip purpose-specific ODs, as well as an understanding of the day-to-day variability. The objective of the work presented by Ban et al. (2017) was to update the time-dependent OD matrices by trip purpose, using aggregated mobile phone GPS location
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data collected in Nagoya in 2011. The maximum entropy principle was also used to update the OD matrices. Seven categories of trips, home-based or non-home-based, were considered. Differences in the number of people in each zone observed from mobile phone data between successive times of day were used as constraints when maximizing the entropy of the OD trip patterns by trip category. Their results confirmed a higher variability of discretionary trips at night, after work, and the lowest variability for AM peak commuting. Mai et al. (2017) drew their attention to another aspect, particularly relevant for the mixed traffic in cities from developing countries: the high proportion of motorbikes, rickshaws, etc. (> 50%), which make typical methodologies (such as traffic detectors, GPS, or probe cars) ineffective. They propose the use of Wi-Fi or Bluetooth signals of mobile phones to estimate travel time in mixed traffic, dominated by motorcycles. The authors suggested that storing the MAC addresses of the devices is more reliable compared to other approaches, as it is more immune to signal loss in tunnel sections or in bad weather conditions. Surveys were conducted in Hanoi, Vietnam, with Wi-Fi scanners set up at intersections and along arterial roads, during AM and PM peak periods. Cameras and Bluetooth scanners were also used as supplementary devices for verification. Travel times for different modes were estimated from 2,000 trips per hour. Finally, King and Mahmoud (2017) presented a case of using INRIX and Google Maps systems for collecting travel time information. They highlighted many limitations of the traditional approaches, including costly/labourintensive and time-consuming efforts for individual surveys, yet limited in space and duration, or limited coverage of travel time surveys using GPS-equipped probe vehicles. Crowd-sourced data from both personal and commercial fleet vehicles enables service providers to offer better-archived and real-time travel time data. Many organisations have exploited these new data collection methods to develop personal and in-vehicle navigation systems (e.g. Google Maps, INRIX, TomTom, HERE, Cellint, etc.). From the end user’s perspective, the systems offer value through provision of current travel time estimates and suggestions of shortest path routes. The raw data is useful for traffic planners to analyze travel times along specific corridors or to report on congestion levels. A regional network was examined and segmented into approximately 1,500 one-way segments of 1 kilometre in length, including highways, major arterials, and water crossings. To analyze regional and local congestion, the authors explored travel times across three spatial layers: (i) 14 activity centres across the region (macro-level); (ii) 14 sub-regions largely defined by local municipal boundaries (meso-level); and (iii) road network (micro-level). The average travel times obtained with Google Maps were compared to another study conducted in 2003 (which used the same 14 regional activity centres). In relation with the second research question, on the benefits and challenges of integrating multiple sources of data, two papers were presented. Kolarova et al. (2017) introduced the benefits, as well as challenges, related to assessing and analyzing electric vehicle (EV) data, using as an example a test fleet of 130 EVs set up in Berlin and Brandenburg, Germany. The sample included commercial and privately used battery (B) EVs, as well as plug-in hybrid (PH) EVs, monitored over one year. An online questionnaire survey and workshops with the EV drivers provided additional information on the fleet and the use of the vehicles. Their EV data set includes information on each charging, trip or parking event (e.g., start and end time of each segment, odometer value, state of charge, outside temperature, GPS positions, as well as travelled distance and energy consumed or charged). Two assessment methods were used: a) data logging devices which communicate with the vehicle via the Controller Area Network (CAN) and with IT back-end via 3G mobile networks; b) direct vehicle data supply from the car manufacturer. The authors reported that data quality by direct data supply was better than indirect measurement via data logging devices, yet such a measurement option was not available for all vehicle models. In the second paper, Morency et al. (2017) presented a research project aiming to combine data from five data sources on five transport modes, to understand the role each mode plays in the daily travel in Montreal. Their analysis used: GPS data from a fleet of more than 1,000 taxis, transaction data from the Montreal bike-sharing system, smartcard validation data from the Montreal Transit authority, and car availability from the two free-floating systems operating in Montreal. These sets were converted into vectors summarising daily, weekly, and yearly patterns, processed independently and then pooled to generate typical patterns of use, illustrating the value of combining data as well as the difficulties arising in the process. The authors also highlighted that given the scarce data, few travel surveys are able to provide insight into the variability of behaviour over time, as well as on the interaction between various transport modes, implying that fusion with other secondary sources is both necessary and timely.
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With respect respect to to the the third third research research question, question, about about combined combined kits, kits, two two papers papers were were considered. considered. With Harms et al. (2017) offered insight into the possibility of merging conventional travel diary and and time-use time-use diary diary Harms et al. (2017) offered insight into the possibility of merging conventional travel diary methodologies with passive recording devices (‘wearables’), to offer a substantially greater understanding and better methodologies with passive recording devices (‘wearables’), to offer a substantially greater understanding and better prediction of of travel travel behaviour. behaviour. They They have have drawn drawn on on scholarly scholarly literature literature and and relied relied on on aa pilot pilot study study to to suggest suggest two two clear clear prediction advantages of this multi-method approach: a) the continuous nature of the time diary record offers cues for trips that advantages of this multi-method approach: a) the continuous nature of the time diary record offers cues for trips that might otherwise be forgotten or overlooked, as well as additional information about the context of other activities might otherwise be forgotten or overlooked, as well as additional information about the context of other activities within which which the the trip trip is is situated situated and; and; b) b) real-time real-time passive passive monitoring monitoring gives gives accurate accurate and and objectively objectively verifiable verifiable timings, timings, within locations, and activity intensity, more reliable than the estimates contained in self-report travel and time diaries. The locations, and activity intensity, more reliable than the estimates contained in self-report travel and time diaries. The pilot data from Perth, Western Australia, allowed the researchers to: (1) improve estimates of travel demand by pilot data from Perth, Western Australia, allowed the researchers to: (1) improve estimates of travel demand by benchmarking the reliability and validity of various travel behaviour metrics; (2) calculate the levels of physical benchmarking the reliability and validity of various travel behaviour metrics; (2) calculate the levels of physical activity (PA) (PA) associated associated with with different different modes modes of of travel; travel; and and (3) (3) explore explore traveller traveller experiences experiences and and attitudes. attitudes. This This was was activity considered a proof-of-concept study that could be further tested and applied at the population level to answer policyconsidered a proof-of-concept study that could be further tested and applied at the population level to answer policyrelevant questions questions in in transport, transport, health health and and wellbeing, wellbeing, as as well well as as land-use land-use planning. planning. relevant On a related research project on time spent waiting for transport connections, Kusakabe et et al. al. (2017) (2017) monitored monitored On a related research project on time spent waiting for transport connections, Kusakabe and estimated passengers’ behaviour in the waiting rooms of transit facilities in the largest station in Tokyo before and estimated passengers’ behaviour in the waiting rooms of transit facilities in the largest station in Tokyo before boarding buses and trains. Waiting time is an important parameter for planning and designing facilities and for boarding buses and trains. Waiting time is an important parameter for planning and designing facilities and for managing passenger crowds. Kusakabe et al. (2017) proposed a method to estimate the duration of passenger waiting managing passenger crowds. Kusakabe et al. (2017) proposed a method to estimate the duration of passenger waiting within aa coach coach terminal terminal by by detecting detecting Wi-Fi Wi-Fi probe probe requests requests from from passengers’ passengers’ Wi-Fi Wi-Fi devices. devices. Their Their study study employed employed aa within device that scans MAC addresses in Wi-Fi probe requests, referred to as AMP sensor (Anonymous MAC address device that scans MAC addresses in Wi-Fi probe requests, referred to as AMP sensor (Anonymous MAC address Probe Sensor). On average, 32,515 MAC addresses per day were observed during the two weeks of monitoring in Probe Sensor). On average, 32,515 MAC addresses per day were observed during the two weeks of monitoring in 2016. The number of MAC addresses was much larger than the number of boarding passengers, suggesting that AMP 2016. The number of MAC addresses was much larger than the number of boarding passengers, suggesting that AMP sensor observed observed some some through through traffic, traffic, as as well well as as waiting waiting passengers. passengers. After After applying applying data data cleaning, cleaning, they they used used more more sensor than 150,000 records for the analysis of waiting times. than 150,000 records for the analysis of waiting times. 3. Workshop Workshop discussion discussion 3. The workshop workshop continued continued with with four four round round tables tables and and aa joint joint final final discussion discussion which which led led to to the the following following findings, findings, The summarised in the word cloud shown in Figure 1. summarised in the word cloud shown in Figure 1.
Figure 1. 1. Word Word cloud cloud using using the the facilitator facilitator notes notes during during the the workshop. workshop. Figure
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As the cloud reveals, except for ‘data’, the prevailing aspects were: definitional clarity, integration, and use, benefits and issues. Not surprisingly, the most frequently used words in the commentary were: ‘passive’, ‘collection/gathering’, ‘processing’, ‘storing’, ‘analysis/processing’, ‘integration/fusion’, ‘quality’ and ‘standards’. The discussion of the results is provided in the following sections. 3.1. Strengths of passive data Passive data has several strengths: The data fusion or integration is a promising alternative to traditional surveys for understanding travel behaviour, for planning purposes, and for operational management. This approach has a strong financial impact due to its lower marginal long-run data collection cost, when compared with traditional approaches (Chen et al., 2010; George et al., 2014; Batty, 2013; Shen & Stopher, 2014; Liew et al., 2015; Wang et al., 2016). Self-report measures used in transport research include household travel surveys and travel diary logs with or without GPS tracking (Bricka & Bhat, 2006; Nitsche et al., 2014; Moutou et al., 2015). Self-report methods without GPS often fail to provide detailed valid location information and respondents often have incomplete spatial knowledge, so tend to approximate durations and succumb to recall bias. Therefore, passive GPS devices overcome this shortcoming (Shen & Stopher, 2014). Moreover, these data collection procedures reduce respondent burden, since data gathering is carried out almost anonymously, without direct intervention of the respondent. However, face-to-face or similar surveys resting on samples are still needed, since passive data cannot deliver (as yet) individual information, such as income, gender, constraints, or their attitudes (Van Acker et al., 2010; Moutou et al., 2015). Multi-source data fusion and mixed modes of data collection (De Leeuw, 2005; Chen et al., 2010; Batty, 2013; Varshney, 2014) can produce a better understanding of the observed phenomena (e.g., active travel, traffic congestion, or fleet utilisation) by decreasing the uncertainty related to the individual sources (El Faouzi et al., 2011). The ubiquity and potential of passive and sensor data enables multi-, inter- and trans-disciplinarity† (Choi & Pak, 2006), as many other non-transport or planning fields may benefit from the data collection and integration (health, air quality, insurance, tourism) (Doherty et al., 2013; Varshney, 2014; Liew et al., 2015; Handy & Davis, 2016). 3.2. Challenges or limitations for transport research On the other hand, the following issues must be kept in mind when dealing with massive data: There are differences in the spatial and temporal resolution, availability and quality of alternative data and compatibility issues with similar data coming from different sources. Protocols for data collection, transmission, storing, accessing, retrieving, processing and usage should become a research and practice priority (George et al., 2014; Mohammadian & Bricka, 2015). Data management requires ‘skilling up’ to work with multiple data sources (learning powerful computational techniques) and ad-hoc methodologies oriented to their processing. Data storing, accessing, and processing also require standards, as well as participation of experts from various disciplines (e.g., computer science, transport, urban planning, sociology, psychology, marketing, population health), to better understand the complexities of merging and calibrating data from various sources and their appropriate use (Batty, 2013; George et al., 2014; Wang et al., 2016). Government institutions and corporations should work together towards making data freely available, implying that licensed operators should not withhold the information they passively collect. Ethical issues arise from data gathering and management. Problems with individual privacy and analytical issues around data captured with sensors and other devices such as cameras are envisaged (Mok et al., 2015). Besides, cultural issues may affect data collection. Fear of invasion of privacy, data transfer to third parties, lack of trust in the
† “Multidisciplinarity draws on knowledge from different disciplines but stays within their boundaries. Interdisciplinarity analyzes, synthesizes and harmonizes links between disciplines into a coordinated and coherent whole. Transdisciplinarity integrates the natural, social and health sciences in a humanities context, and transcends their traditional boundaries.” (Choi & Pak, 2006: 351).
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capabilities of the data storage and management systems, including hacking of the systems, were put forward by most of the workshop participants. This topic should be given considerable attention by researchers and practitioners alike (George et al., 2014). These aspects emphasize the need for clearer communication of the data collection protocols and the potential reluctance of communities to participate in these big data exercises. 4. Policy recommendations The following recommendations were drawn from the workshop discussion: • It is necessary to engage scholars and professionals from various disciplines, moving from an off-line research and development space to an in-line state-of-the-art practice of mixed approaches of data collection. • It is necessary to create partnerships with the data providers/warehousing organisations, since their purpose is quite different from researchers’ interests. • It is critical to define protocols and standards for data gathering, transmission, storing, accessing, retrieving, processing, sharing, and analyzing, to guarantee fully data interoperability. • Data collected using public funds or through licensing should be made available, ensuring ‘data liberation’ from collectors. • It is possible and highly recommended to have methods, repositories, testing data banks made freely available for calibration and validation purposes (Kelly et al., 2015). 5. Directions for future research The discussion in relation to expected research developments revealed the following priorities: • It is necessary to advance methodologies oriented towards data fusion and integration. There is a scarcity of experience on this (at least in the planning and transport domains), with poor data interoperability, an unevenness of data availability and ‘islands of no data’, and missing records. • To complement big passive data, clear and precise information on which additional data should be collected is needed, to optimise the design of a full database that can be used for planning and management purposes. • The definition of quality standards should be revised and protocols to ensure the full sharing of information be generated. • Validation frameworks and methodologies must be developed as well, such that the data collected through passive devices and sensors fully complement other active sources. • The ‘white spots’ representing missing data should be investigated. Focused mainly in the urban setting, data on regional and long-distance transport, as well as freight movements, are lacking. This is relevant for transport infrastructure planning and management and has the potential to substantially improve the data platform for freight modelling and intercity transport. 6. Discussion and conclusion A dominant feature of this workshop was the methodological change from hypothesis testing and verification, to discovery knowledge through data mining and visualisation (Morency et al., 2007; Bonnel et al., 2017; King & Mahmoud, 2017). In other words, a move towards more data-driven approaches. Another salient aspect of the discussions referred to the conditions in which the passive and sensor data should be used, particularly individual data (Kolarova et al., 2017; Harms et al., 2017), and the aspiration to have open access to big data not only for research purposes, but also for practice across a number of organisations (Morency et al., 2017). The general conclusion of the workshop is that despite many challenges associated with data enrichment (compatibility issues, spatial and temporal resolution, availability and quality of alternative data, data processing and storing issues, etc.), fused/integrated data represent a promising alternative to traditional surveys for understanding travel behaviour, planning purposes, and operation, and, as expressed by Bhat (2015) “we are at exciting point” in the history of data collection, “with unprecedented opportunities to collect rich travel data at fine resolutions of space and time” (Bhat, 2015: 109). As already indicated, the passive manner of collecting new and richer data enables larger
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