Management system for optimizing public transport networks: GPS record

As cities continue to grow in size and population, the design of public transport networks becomes complicated, given the wide diversity in the origins and destinations of users [1], as well as the saturation of vehicle infrastructure in large cities despite their attempts to adapt it according to p...

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Autores:
Silva, Jesús
GUERRA ALEMAN, ERICK
Varela Izquierdo, Noel
Pineda, Omar
Tipo de recurso:
http://purl.org/coar/resource_type/c_816b
Fecha de publicación:
2020
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/8038
Acceso en línea:
https://hdl.handle.net/11323/8038
https://doi.org/10.1007/978-981-15-6648-6_18
https://repositorio.cuc.edu.co/
Palabra clave:
Machine learning
Proactive control
Traffic
Smart cities
Public transport networks
Rights
openAccess
License
CC0 1.0 Universal
id RCUC2_dfaf9b246f341c3a87cb4d6876805c6c
oai_identifier_str oai:repositorio.cuc.edu.co:11323/8038
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Management system for optimizing public transport networks: GPS record
title Management system for optimizing public transport networks: GPS record
spellingShingle Management system for optimizing public transport networks: GPS record
Machine learning
Proactive control
Traffic
Smart cities
Public transport networks
title_short Management system for optimizing public transport networks: GPS record
title_full Management system for optimizing public transport networks: GPS record
title_fullStr Management system for optimizing public transport networks: GPS record
title_full_unstemmed Management system for optimizing public transport networks: GPS record
title_sort Management system for optimizing public transport networks: GPS record
dc.creator.fl_str_mv Silva, Jesús
GUERRA ALEMAN, ERICK
Varela Izquierdo, Noel
Pineda, Omar
dc.contributor.author.spa.fl_str_mv Silva, Jesús
GUERRA ALEMAN, ERICK
Varela Izquierdo, Noel
Pineda, Omar
dc.subject.spa.fl_str_mv Machine learning
Proactive control
Traffic
Smart cities
Public transport networks
topic Machine learning
Proactive control
Traffic
Smart cities
Public transport networks
description As cities continue to grow in size and population, the design of public transport networks becomes complicated, given the wide diversity in the origins and destinations of users [1], as well as the saturation of vehicle infrastructure in large cities despite their attempts to adapt it according to population distribution. This indicates that, in order to reduce users’ travel time, it is necessary to implement alternative road solutions to the use of cars, increasing investment in public transportation [2, 3] by conducting a comprehensive analysis of the state of transportation. This situation has made appear the solutions and development oriented to transportation based on Internet of Things (IoT) which allows, in a first stage, monitoring of public transport systems, in order to optimize the deployment of transport units and thus reduce the time of transfer of users through the cities [4]. These solution proposals are focused on information collected from user resources (data collected through smart phones) to create a common database [5]. The present study proposes the development of an intelligent monitoring and management system for public transportation networks using a hybrid communication architecture based on wireless node networks using IPv6 and cellular networks (LTE, LTE-M).
publishDate 2020
dc.date.issued.none.fl_str_mv 2020-07-19
dc.date.accessioned.none.fl_str_mv 2021-03-17T19:46:16Z
dc.date.available.none.fl_str_mv 2021-03-17T19:46:16Z
dc.type.spa.fl_str_mv Pre-Publicación
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_816b
dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/preprint
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/ARTOTR
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
format http://purl.org/coar/resource_type/c_816b
status_str acceptedVersion
dc.identifier.issn.spa.fl_str_mv 18650929
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/8038
dc.identifier.doi.spa.fl_str_mv https://doi.org/10.1007/978-981-15-6648-6_18
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv 18650929
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/8038
https://doi.org/10.1007/978-981-15-6648-6_18
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv 1. Handte, M., Foell, S., Wagner, S., Kortuem, G., Marron, P.J.: An Internet-of-Things enabled connected navigation system for urban bus riders. IEEE Internet Things J. 3, 735–744 (2016). https://doi.org/10.1109/JIOT.2016.2554146
2. Cats, O., Vermeulen, A., Warnier, M., van Lint, H.: Modelling growth principles of metropolitan public transport networks. J. Transp. Geogr. 82, 102567 (2020)
3. Tomej, K., Liburd, J.J.: Sustainable accessibility in rural destinations: a public transport network approach. J. Sustain. Tour. 28(2), 222–239 (2020)
4. Lohokare, J., Dani, R., Sontakke, S., Adhao, R.: Scalable tracking system for public buses using IoT technologies. In: 2017 International Conference on Emerging Trends & Innovation, ICT, ICEI 2017, pp. 104–109 (2017)
5. Raj, J.T., Sankar, J.: IoT based smart school bus monitoring and notification system. In: 5th IEEE Region 10 Humanitarian Technology Conference 2017, R10-HTC 2017, pp. 89–92 (2018).
6. Spyropoulou, I.: Impact of public transport strikes on the road network: the case of Athens. Transp. Res. Part A: Policy Pract. 132, 651–665 (2020)
7. de Regt, R., von Ferber, C., Holovatch, Y., Lebovka, M.: Public transportation in Great Britain viewed as a complex network. Transportmetrica A: Transp. Sci. 15(2), 722–748 (2019)
8. Lusikka, T., Kinnunen, T.K., Kostiainen, J.: Public transport innovation platform boosting intelligent transport system value chains. Util. Policy 62, 100998 (2020)
9. Munizaga, M.A., Palma, C.: Estimation of a disaggregate multimodal public transport origin-destination matrix from passive smartcard data from Santiago, Chile. Transp. Res. Part C: Emerg. Technol. 24, 9–18 (2012)
10. Perez, R., Vásquez, C., Viloria, A.: An intelligent strategy for faults location in distribution networks with distributed generation. J. Intell. Fuzzy Syst. 36(2), 1627–1637 (2019)
11. Liu, Y., Cheng, T.: Understanding public transit patterns with open geodemographics to facilitate public transport planning. Transportmetrica A: Transp. Sci. 16(1), 76–103 (2020)
12. Petersen, N.C., Rodrigues, F., Pereira, F.C.: Multi-output bus travel time prediction with convolutional LSTM neural network. Expert Syst. Appl. 120, 426–435 (2019)
13. Viloria, A., Robayo, P.V.: Virtual network level of application composed IP networks connected with systems-(NETS peer-to-peer). Indian J. Sci. Technol. 9, 46 (2016)
14. Viloria, A., Acuña, G.C., Franco, D.J.A., Hernández-Palma, H., Fuentes, J.P., Rambal, E.P.: Integration of data mining techniques to PostgreSQL database manager system. Procedia Comput. Sci. 155, 575–580 (2019)
15. Tomasiello, D.B., Giannotti, M., Arbex, R., Davis, C.: Multi-temporal transport network models for accessibility studies. Trans. GIS 23(2), 203–223 (2019)
16. Muro, F.J.M.: Planificación y optimización de redes ópticas en el Internet del futuro (Doctoral dissertation, Universidad Politécnica de Cartagena) (2019)
17. Saif, M.A., Zefreh, M.M., Torok, A.: Public transport accessibility: a literature review. Period. Polytech. Transp. Eng. 47(1), 36–43 (2019)
18. Gatta, V., Marcucci, E., Nigro, M., Serafini, S.: Sustainable urban freight transport adopting public transport-based crowdshipping for B2C deliveries. Eur. Transp. Res. Rev. 11(1), 13 (2019)
19. Cervantes, M.E.S., García, L.D.J.M.: El uso de modelos de redes y modelos de transporte para la optimización y reducción de tiempos y costos de transporte en la Comercializadora Gonac S. A de CV/The use of network models and transport models for the optimization and reduction of transport times and costs in the Comercializadora Gonac S. A de CV. RICEA Revista Iberoamericana de Contaduría, Economía y Administración 8(15), 29–53 (2019)
20. Allulli, L., Italiano, G.F., Santaroni, F.: Exploiting GPS data in public transport journey planners. In: Gudmundsson, J., Katajainen, J. (eds.) SEA 2014. LNCS, vol. 8504, pp. 295–306. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07959-2_25
21. Shen, L., Stopher, P.R.: Review of GPS travel survey and GPS data-processing methods. Transp. Rev. 34(3), 316–334 (2014)
22. Schüssler, N., Axhausen, K.W.: Identifying trips and activities and their characteristics from GPS raw data without further information. Arbeitsberichte Verkehrs-und Raumplanung 502, 1–29 (2008)
23. Edwards, D., Griffin, T.: Understanding tourists’ spatial behaviour: GPS tracking as an aid to sustainable destination management. J. Sustain. Tour. 21(4), 580–595 (2013)
24. Schuessler, N., Axhausen, K.W.: Map-matching of GPS traces on high-resolution navigation networks using the multiple hypothesis technique (MHT). Arbeitsberichte Verkehrsund Raumplanung 568, 1–22 (2009)
25. Wang, Y., Ram, S., Currim, F., Dantas, E., Sabóia, L.A.: A big data approach for smart transportation management on bus network. In: 2016 IEEE International Smart Cities Conference (ISC2), pp. 1–6. IEEE (2016)
26. Gonzalez, P., et al.: Automating mode detection using neural networks and assisted GPS data collected using GPS-enabled mobile phones. In: 15th World Congress on Intelligent Transportation Systems, pp. 16–20 (2008)
27. Ma, X., Yu, H., Wang, Y., Wang, Y.: Large-scale transportation network congestion evolution prediction using deep learning theory. PLoS ONE 10(3), e0119044 (2015)
28. Chaix, B., et al.: Active transportation and public transportation use to achieve physical activity recommendations? A combined GPS, accelerometer, and mobility survey study. Int. J. Behav. Nutr. Phys. Act. 11(1), 124 (2014)
29. Strutu, M., Stamatescu, G., Popescu, D.: A mobile sensor network based road surface monitoring system. In: 2013 17th International Conference on System Theory, Control and Computing (ICSTCC), pp. 630–634. IEEE (2013)
30. Dabiri, S., Heaslip, K.: Inferring transportation modes from GPS trajectories using a convolutional neural network. Transp. Res. Part C: Emerg. Technol. 86, 360–371 (2018)
31. Harrison, F., Burgoine, T., Corder, K., van Sluijs, E.M., Jones, A.: How well do modelled routes to school record the environments children are exposed to?: a cross-sectional comparison of GIS-modelled and GPS-measured routes to school. Int. J. Health Geogr. 13(1), 5 (2014)
32. Anderson, M.K., Rasmussen, T.K.: Matching observed public route choice data to a GIS network. In: Selected Proceedings from the Annual Transport Conference at Aalborg University, vol. 5, no. 1 (2010)
33. Badland, H.M., Duncan, M.J., Oliver, M., Duncan, J.S., Mavoa, S.: Examining commute routes: applications of GIS and GPS technology. Environ. Health Prev. Med. 15(5), 327 (2010)
33. Badland, H.M., Duncan, M.J., Oliver, M., Duncan, J.S., Mavoa, S.: Examining commute routes: applications of GIS and GPS technology. Environ. Health Prev. Med. 15(5), 327 (2010)
34. Stopher, P., FitzGerald, C., Xu, M.: Assessing the accuracy of the Sydney household travel survey with GPS. Transportation 34(6), 723–741 (2007)
35. Gallet, M., Massier, T., Hamacher, T.: Estimation of the energy demand of electric buses based on real-world data for large-scale public transport networks. Appl. Energy 230, 344–356 (2018)
36. Holleczek, T., Yu, L., Lee, J.K., Senn, O., Ratti, C., Jaillet, P.: Detecting weak public transport connections from cellphone and public transport data. In: Proceedings of the 2014 International Conference on Big Data Science and Computing, pp. 1–8 (2014)
37. Wang, H., Calabrese, F., Di Lorenzo, G., Ratti, C.: Transportation mode inference from anonymized and aggregated mobile phone call detail records. In: 13th International IEEE Conference on Intelligent Transportation Systems, pp. 318–323. IEEE (2010)
38. Chaix, B., et al.: GPS tracking in neighborhood and health studies: a step forward for environmental exposure assessment, a step backward for causal inference? Health Place 21, 46–51 (2013)
39. Buys, L., Snow, S., van Megen, K., Miller, E.: Transportation behaviours of older adults: an investigation into car dependency in urban Australia. Australas. J. Ageing 31(3), 181–186 (2012)
40. Gonzalez, P.A., et al.: Automating mode detection for travel behaviour analysis by using global positioning systems-enabled mobile phones and neural networks. IET Intell. Transp. Syst. 4(1), 37–49 (2010)
41. Arellana, J., de Dios Ortúzar, J., Rizzi, L.I., Zuñiga, F.: Obtaining public transport level-of-service measures using in-vehicle GPS data and freely available GIS web-based tools. In: Mobile Technologies for Activity-Travel Data Collection and Analysis, pp. 258–275. IGI Global (2014)
42. Ladha, A., Bhattacharya, P., Chaubey, N., Bodkhe, U.: IIGPTS: IoT-based framework for intelligent green public transportation system. In: Singh, P.K., Pawłowski, W., Tanwar, S., Kumar, N., Rodrigues, J.J.P.C., Obaidat, M.S. (eds.) Proceedings of First International Conference on Computing, Communications, and Cyber-Security (IC4S 2019). LNNS, vol. 121, pp. 183–195. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-3369-3_14
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spelling Silva, JesúsGUERRA ALEMAN, ERICKVarela Izquierdo, NoelPineda, Omar2021-03-17T19:46:16Z2021-03-17T19:46:16Z2020-07-1918650929https://hdl.handle.net/11323/8038https://doi.org/10.1007/978-981-15-6648-6_18Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/As cities continue to grow in size and population, the design of public transport networks becomes complicated, given the wide diversity in the origins and destinations of users [1], as well as the saturation of vehicle infrastructure in large cities despite their attempts to adapt it according to population distribution. This indicates that, in order to reduce users’ travel time, it is necessary to implement alternative road solutions to the use of cars, increasing investment in public transportation [2, 3] by conducting a comprehensive analysis of the state of transportation. This situation has made appear the solutions and development oriented to transportation based on Internet of Things (IoT) which allows, in a first stage, monitoring of public transport systems, in order to optimize the deployment of transport units and thus reduce the time of transfer of users through the cities [4]. These solution proposals are focused on information collected from user resources (data collected through smart phones) to create a common database [5]. The present study proposes the development of an intelligent monitoring and management system for public transportation networks using a hybrid communication architecture based on wireless node networks using IPv6 and cellular networks (LTE, LTE-M).Silva, JesúsGUERRA ALEMAN, ERICK-will be generated-orcid-0000-0002-3143-2581-600Varela Izquierdo, Noel-will be generated-orcid-0000-0001-7036-4414-600Pineda, Omar-will be generated-orcid-0000-0002-8239-3906-600application/pdfengCorporación Universidad de la CostaRetractedCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Communications in Computer and Information Sciencehttps://link.springer.com/chapter/10.1007/978-981-15-6648-6_18Machine learningProactive controlTrafficSmart citiesPublic transport networksManagement system for optimizing public transport networks: GPS recordPre-Publicaciónhttp://purl.org/coar/resource_type/c_816bTextinfo:eu-repo/semantics/preprinthttp://purl.org/redcol/resource_type/ARTOTRinfo:eu-repo/semantics/acceptedVersion1. Handte, M., Foell, S., Wagner, S., Kortuem, G., Marron, P.J.: An Internet-of-Things enabled connected navigation system for urban bus riders. IEEE Internet Things J. 3, 735–744 (2016). https://doi.org/10.1109/JIOT.2016.25541462. Cats, O., Vermeulen, A., Warnier, M., van Lint, H.: Modelling growth principles of metropolitan public transport networks. J. Transp. Geogr. 82, 102567 (2020)3. Tomej, K., Liburd, J.J.: Sustainable accessibility in rural destinations: a public transport network approach. J. Sustain. Tour. 28(2), 222–239 (2020)4. Lohokare, J., Dani, R., Sontakke, S., Adhao, R.: Scalable tracking system for public buses using IoT technologies. In: 2017 International Conference on Emerging Trends & Innovation, ICT, ICEI 2017, pp. 104–109 (2017)5. Raj, J.T., Sankar, J.: IoT based smart school bus monitoring and notification system. In: 5th IEEE Region 10 Humanitarian Technology Conference 2017, R10-HTC 2017, pp. 89–92 (2018).6. Spyropoulou, I.: Impact of public transport strikes on the road network: the case of Athens. Transp. Res. Part A: Policy Pract. 132, 651–665 (2020)7. de Regt, R., von Ferber, C., Holovatch, Y., Lebovka, M.: Public transportation in Great Britain viewed as a complex network. Transportmetrica A: Transp. Sci. 15(2), 722–748 (2019)8. Lusikka, T., Kinnunen, T.K., Kostiainen, J.: Public transport innovation platform boosting intelligent transport system value chains. Util. Policy 62, 100998 (2020)9. Munizaga, M.A., Palma, C.: Estimation of a disaggregate multimodal public transport origin-destination matrix from passive smartcard data from Santiago, Chile. Transp. Res. Part C: Emerg. Technol. 24, 9–18 (2012)10. Perez, R., Vásquez, C., Viloria, A.: An intelligent strategy for faults location in distribution networks with distributed generation. J. Intell. Fuzzy Syst. 36(2), 1627–1637 (2019)11. Liu, Y., Cheng, T.: Understanding public transit patterns with open geodemographics to facilitate public transport planning. Transportmetrica A: Transp. Sci. 16(1), 76–103 (2020)12. Petersen, N.C., Rodrigues, F., Pereira, F.C.: Multi-output bus travel time prediction with convolutional LSTM neural network. Expert Syst. Appl. 120, 426–435 (2019)13. Viloria, A., Robayo, P.V.: Virtual network level of application composed IP networks connected with systems-(NETS peer-to-peer). Indian J. Sci. Technol. 9, 46 (2016)14. Viloria, A., Acuña, G.C., Franco, D.J.A., Hernández-Palma, H., Fuentes, J.P., Rambal, E.P.: Integration of data mining techniques to PostgreSQL database manager system. Procedia Comput. Sci. 155, 575–580 (2019)15. Tomasiello, D.B., Giannotti, M., Arbex, R., Davis, C.: Multi-temporal transport network models for accessibility studies. Trans. GIS 23(2), 203–223 (2019)16. Muro, F.J.M.: Planificación y optimización de redes ópticas en el Internet del futuro (Doctoral dissertation, Universidad Politécnica de Cartagena) (2019)17. Saif, M.A., Zefreh, M.M., Torok, A.: Public transport accessibility: a literature review. Period. Polytech. Transp. Eng. 47(1), 36–43 (2019)18. Gatta, V., Marcucci, E., Nigro, M., Serafini, S.: Sustainable urban freight transport adopting public transport-based crowdshipping for B2C deliveries. Eur. Transp. Res. Rev. 11(1), 13 (2019)19. Cervantes, M.E.S., García, L.D.J.M.: El uso de modelos de redes y modelos de transporte para la optimización y reducción de tiempos y costos de transporte en la Comercializadora Gonac S. A de CV/The use of network models and transport models for the optimization and reduction of transport times and costs in the Comercializadora Gonac S. A de CV. RICEA Revista Iberoamericana de Contaduría, Economía y Administración 8(15), 29–53 (2019)20. Allulli, L., Italiano, G.F., Santaroni, F.: Exploiting GPS data in public transport journey planners. In: Gudmundsson, J., Katajainen, J. (eds.) SEA 2014. LNCS, vol. 8504, pp. 295–306. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07959-2_2521. Shen, L., Stopher, P.R.: Review of GPS travel survey and GPS data-processing methods. Transp. Rev. 34(3), 316–334 (2014)22. Schüssler, N., Axhausen, K.W.: Identifying trips and activities and their characteristics from GPS raw data without further information. Arbeitsberichte Verkehrs-und Raumplanung 502, 1–29 (2008)23. Edwards, D., Griffin, T.: Understanding tourists’ spatial behaviour: GPS tracking as an aid to sustainable destination management. J. Sustain. Tour. 21(4), 580–595 (2013)24. Schuessler, N., Axhausen, K.W.: Map-matching of GPS traces on high-resolution navigation networks using the multiple hypothesis technique (MHT). Arbeitsberichte Verkehrsund Raumplanung 568, 1–22 (2009)25. Wang, Y., Ram, S., Currim, F., Dantas, E., Sabóia, L.A.: A big data approach for smart transportation management on bus network. In: 2016 IEEE International Smart Cities Conference (ISC2), pp. 1–6. IEEE (2016)26. Gonzalez, P., et al.: Automating mode detection using neural networks and assisted GPS data collected using GPS-enabled mobile phones. In: 15th World Congress on Intelligent Transportation Systems, pp. 16–20 (2008)27. Ma, X., Yu, H., Wang, Y., Wang, Y.: Large-scale transportation network congestion evolution prediction using deep learning theory. PLoS ONE 10(3), e0119044 (2015)28. Chaix, B., et al.: Active transportation and public transportation use to achieve physical activity recommendations? A combined GPS, accelerometer, and mobility survey study. Int. J. Behav. Nutr. Phys. Act. 11(1), 124 (2014)29. Strutu, M., Stamatescu, G., Popescu, D.: A mobile sensor network based road surface monitoring system. In: 2013 17th International Conference on System Theory, Control and Computing (ICSTCC), pp. 630–634. IEEE (2013)30. Dabiri, S., Heaslip, K.: Inferring transportation modes from GPS trajectories using a convolutional neural network. Transp. Res. Part C: Emerg. Technol. 86, 360–371 (2018)31. Harrison, F., Burgoine, T., Corder, K., van Sluijs, E.M., Jones, A.: How well do modelled routes to school record the environments children are exposed to?: a cross-sectional comparison of GIS-modelled and GPS-measured routes to school. Int. J. Health Geogr. 13(1), 5 (2014)32. Anderson, M.K., Rasmussen, T.K.: Matching observed public route choice data to a GIS network. In: Selected Proceedings from the Annual Transport Conference at Aalborg University, vol. 5, no. 1 (2010)33. Badland, H.M., Duncan, M.J., Oliver, M., Duncan, J.S., Mavoa, S.: Examining commute routes: applications of GIS and GPS technology. Environ. Health Prev. Med. 15(5), 327 (2010)33. Badland, H.M., Duncan, M.J., Oliver, M., Duncan, J.S., Mavoa, S.: Examining commute routes: applications of GIS and GPS technology. Environ. Health Prev. Med. 15(5), 327 (2010)34. Stopher, P., FitzGerald, C., Xu, M.: Assessing the accuracy of the Sydney household travel survey with GPS. Transportation 34(6), 723–741 (2007)35. Gallet, M., Massier, T., Hamacher, T.: Estimation of the energy demand of electric buses based on real-world data for large-scale public transport networks. Appl. Energy 230, 344–356 (2018)36. Holleczek, T., Yu, L., Lee, J.K., Senn, O., Ratti, C., Jaillet, P.: Detecting weak public transport connections from cellphone and public transport data. In: Proceedings of the 2014 International Conference on Big Data Science and Computing, pp. 1–8 (2014)37. Wang, H., Calabrese, F., Di Lorenzo, G., Ratti, C.: Transportation mode inference from anonymized and aggregated mobile phone call detail records. In: 13th International IEEE Conference on Intelligent Transportation Systems, pp. 318–323. IEEE (2010)38. Chaix, B., et al.: GPS tracking in neighborhood and health studies: a step forward for environmental exposure assessment, a step backward for causal inference? Health Place 21, 46–51 (2013)39. Buys, L., Snow, S., van Megen, K., Miller, E.: Transportation behaviours of older adults: an investigation into car dependency in urban Australia. Australas. J. Ageing 31(3), 181–186 (2012)40. Gonzalez, P.A., et al.: Automating mode detection for travel behaviour analysis by using global positioning systems-enabled mobile phones and neural networks. IET Intell. Transp. Syst. 4(1), 37–49 (2010)41. Arellana, J., de Dios Ortúzar, J., Rizzi, L.I., Zuñiga, F.: Obtaining public transport level-of-service measures using in-vehicle GPS data and freely available GIS web-based tools. In: Mobile Technologies for Activity-Travel Data Collection and Analysis, pp. 258–275. IGI Global (2014)42. 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