This web page was created programmatically, to learn the article in its authentic location you’ll be able to go to the hyperlink bellow:
https://link.springer.com/article/10.1007/s11116-025-10713-7
and if you wish to take away this text from our web site please contact us
Asgari, H., Jin, X.: An analysis of part-day telecommute impacts on work journey departure occasions. Travel Behav. Soc. 12, 84–92 (2018)
Bao, J., Xu, C., Liu, P., Wang, W.: Exploring bikesharing journey patterns and journey functions utilizing sensible card information and on-line level of pursuits. Netw. Spat. Econ. 17, 1231–1253 (2017)
Bi, H., Ye, Z.: Exploring ridesourcing journey patterns by fusing multi-source information: an enormous information method. Sustain. Cities Soc. 64, 102499 (2021)
Bonnetain, L., Furno, A., El Faouzi, N.E., Fiore, M., Stanica, R., Smoreda, Z., Ziemlicki, C.: TRANSIT: fine-grained human mobility trajectory inference at scale with cellular community signaling information. Transp. Res. Part C Emerg. Technol. 130, 103257 (2021)
Cai, H., Kulkarni, S.R., Verdú, S.: Universal entropy estimation by way of block sorting. IEEE Trans. Inf. Theory 50(7), 1551–1561 (2004)
Chang, Y., Duan, Z., Yang, D.: Using ALPR information to know the car use behaviour underneath TDM measures. IET Intell. Transp. Syst. 12(10), 1264–1270 (2018)
Chen, H., Yang, C., Xu, X.: Clustering car temporal and spatial journey conduct utilizing license plate recognition information. J. Adv. Transp. 2017(1), 1738085 (2017)
Chen, Y., Yin, C., Sun, B.: Nonlinear associations of constructed environments round residences and workplaces with commuting satisfaction. Transp. Res. Part D Transp. Environ. 133, 104315 (2024a)
Chen, Y., Zhao, P., Chen, Q.: Forecasting the commuting technology utilizing metropolis-informed GCN and the topological commuter portrait. Transportation, 1–28 (2024b)
Crawford, F., Watling, D.P., Connors, R.D.: Identifying street consumer lessons based mostly on repeated journey behaviour utilizing Bluetooth information. Transp. Res. Part A Policy Pract. 113, 55–74 (2018)
Deng, J., Gao, L., Chen, X., Yuan, Q.: Taking the identical route daily? An empirical investigation of commuting route stability utilizing private electrical car trajectory information. Transportation 51(4), 1547–1573 (2024)
Ezugwu, A., Ikotun, A., Oyelade, O., Abualigah, L., Agushaka, J., Eke, C., Akinyelu, A.: A complete survey of clustering algorithms: state-of-the-art machine studying purposes, taxonomy, challenges, and future analysis prospects. Eng. Appl. Artif. Intell. 110, 104743 (2022)
Gao, Y., Kontoyiannis, I., Bienenstock, E.: Estimating the entropy of binary time sequence: methodology, some idea and a simulation research. Entropy 10(2), 71–99 (2008)
Goulet-Langlois, G., Koutsopoulos, H.N., Zhao, Z., Zhao, J.: Measuring regularity of particular person journey patterns. IEEE Trans. Intell. Transp. Syst. 19(5), 1583–1592 (2017)
Hanson, S., Huff, J.: Classification points within the evaluation of advanced journey conduct. Transportation 13(3), 271–293 (1986)
Huang, Y., Xiao, Z., Wang, D., Jiang, H., Wu, D.: Exploring particular person journey patterns throughout non-public automobile trajectory information. IEEE Trans. Intell. Transp. Syst. 21(99), 1–15 (2019)
Huff, J.O., Hanson, S.: Repetition and variability in city journey. Geogr. Anal. 18(2), 97–114 (1986)
Ingvardson, J.B., Thorhauge, M., Kaplan, S., Nielsen, O.A., Raveau, S.: Incorporating psychological wants in commute mode alternative modelling: a hybrid alternative framework. Transportation 49(6), 1861–1889 (2022)
Jiang, J., Pan, D., Ren, H., Jiang, X., Li, C., Wang, J.: Self-supervised trajectory illustration studying with temporal regularities and journey semantics. In: 2023 IEEE thirty ninth International Conference on Data Engineering (ICDE), pp. 843–855 (2023). IEEE
Kapitza, J.: Commuting at evening: how time of day impacts commuter perceptions. Travel Behav. Soc. 35, 100750 (2024)
Lei, D., Chen, X., Cheng, L., Zhang, L., Ukkusuri, S.V., Witlox, F.: Inferring temporal motifs for journey sample evaluation utilizing giant scale sensible card information. Transp. Res. Part C Emerg. Technol. 120, 102810 (2020)
Li, Y., Dai, Z., Zhu, L., Liu, X.: Analysis of spatial and temporal traits of residents’ mobility based mostly on E-bike GPS trajectory information in Tengzhou metropolis, China. Sustainability 11(18), 5003 (2019)
Li, Z., Yan, H., Zhang, C., Tsung, F.: Individualized passenger journey sample multi-clustering based mostly on graph regularized tensor latent dirichlet allocation. Data Min. Knowl. Discov. 36(4), 1247–1278 (2022)
Li, W., Zhang, Y., Chen, Y., Ding, L., Zhu, Y., Chen, X.M.: Multi-day exercise sample recognition based mostly on semantic embeddings of exercise chains. Travel Behav. Soc. 34, 100682 (2024)
Lin, Y., Wan, H., Guo, S., Lin, Y.: Contrastive pre-training of spatial-temporal trajectory embeddings. arXiv preprint arXiv:2207.14539 (2022)
Lin, Y., Zhou, Z., Liu, Y., Lv, H., Wen, H., Li, T., Wan, H.: UniTE: A survey and unified pipeline for pre-training spatiotemporal trajectory embeddings. IEEE Trans. Knowl. Data Eng. (2024)
Ling, C., Niu, X., Yang, J., Zhou, J., Yang, T.: Unravelling heterogeneity and dynamics of commuting effectivity: industry-level insights into evolving effectivity gaps based mostly on a disaggregated excess-commuting framework. J. Transp. Geogr. 115, 103820 (2024)
Liu, Y., Fang, F., Jing, Y.: How city land use influences commuting flows in Wuhan, Central China: a cell phone signaling information perspective. Sustain. Cities Soc. 53, 101914 (2020)
Liu, X., Tan, X., Guo, Y., Chen, Y., Zhang, Z.: Cstrm: contrastive self-supervised trajectory illustration mannequin for trajectory similarity computation. Comput. Commun. 185, 159–167 (2022)
Liu, Z., Dai, J., Lin, S., Wang, X.C., Li, X., Lian, Y., Li, R.: Urban mobility within the postpandemic stage: a complete investigation of a wide range of cities in China. J. Transp. Eng. Part A Syst. 149(8), 05023005 (2023)
Lizana, M., Tudela, A., Tapia, A.: Analysing the affect of angle and behavior on bicycle commuting. Transp. Res. Part F Traffic Psychol. Behav. 82, 70–83 (2021)
Ma, X., Wu, Y.J., Wang, Y., Chen, F., Liu, J.: Mining sensible card information for transit riders’ journey patterns. Transp. Res. Part C Emerg. Technol. 36, 1–12 (2013)
Ma, X., Liu, C., Wen, H., Wang, Y., Wu, Y.J.: Understanding commuting patterns utilizing transit sensible card information. J. Transp. Geogr. 58, 135–145 (2017)
Ma, X., Tian, X., Jin, Z., Cui, H., Ji, Y., Cheng, L.: Evaluation and determinants of metro customers’ regularity: insights from transit one-card information. J. Transp. Geogr. 118, 103933 (2024)
Marcińczak, S., Bartosiewicz, B.: Commuting patterns and concrete kind: proof from Poland. J. Transp. Geogr. 70, 31–39 (2018)
Minnen, J., Glorieux, I., van Tienoven, T.P.: Transportation habits: proof from time diary information. Transp. Res. Part A Policy Pract. 76, 25–37 (2015)
Park, S.Y., Ham, S.W., Kim, D.Okay.: User segmentation based mostly on journey regularity in e-scooter sharing service. Transp. Res. Rec. 2677(7), 290–306 (2023)
Pedregosa, F., Varoquaux, G., Gramfort, A., et al.: Scikit-learn: machine studying in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Qiu, G., Song, R., He, S., Xu, W., Jiang, M.: Clustering passenger journey information for the potential passenger investigation and line design of personalized commuter bus. IEEE Trans. Intell. Transp. Syst. 20(9), 3351–3360 (2018)
Rahman, M., Murthy Gurumurthy, Okay., Kockelman, Okay.M.: Impact of flextime on departure time alternative for home-based commuting journeys in Austin, Texas. Transp. Res. Rec. 2676(1), 446–459 (2022)
Raux, C., Ma, T.Y., Cornelis, E.: Variability in every day activity-travel patterns: the case of a one-week journey diary. Eur. Transp. Res. Rev. 8(4), 1–14 (2016)
Shen, X., Zhou, Y., Jin, S., Wang, D.: Spatiotemporal affect of land use and family properties on vehicle journey demand. Transp. Res. Part D Transp. Environ. 84, 102359 (2020)
Song, C., Qu, Z., Blumm, N., Barabási, A.L.: Limits of predictability in human mobility. Science 327(5968), 1018–1021 (2010)
Sun, L., Zhao, J., Zhang, J., Zhang, F., Ye, Okay., Xu, C.: Activity-based particular person journey regularity exploring with entropy-space Okay-means clustering utilizing sensible card information. Phys. A Stat. Mech. Appl. 636, 129522 (2024)
Tao, Y., van Ham, M., Petrović, A.: Changes in commuting mode and the connection with psychological stress: a quasi-longitudinal evaluation in urbanizing China. Travel Behav. Soc. 34, 100667 (2024)
Thorhauge, M., Cherchi, E., Rich, J.: How versatile is versatile? Accounting for the impact of rescheduling prospects in alternative of departure time for work journeys. Transp. Res. Part A Policy Pract. 86, 177–193 (2016)
Verhetsel, A., Beckers, J., De Meyere, M.: Assessing every day city programs: a heterogeneous commuting community method. Netw. Spat. Econ. 18, 633–656 (2018)
Wang, H., Wang, Q., Qu, Y., Wu, X.: Household duty and commuting: the spatial constraints of workers and self-employed rural-to-urban migrant ladies in China—the case of Nanjing. Transportation, 1–18 (2023)
Williams, M.J., Whitaker, R.M., Allen, S.M.: Measuring particular person regularity in human visiting patterns. In 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing, pp. 117–122 (2012). IEEE
Xiao, Z., Xu, S., Li, T., Jiang, H., Zhang, R., Regan, A.C., Chen, H.: On extracting common journey conduct of personal vehicles based mostly on trajectory information evaluation. IEEE Trans. Veh. Technol. 69(12), 14537–14549 (2020)
Xu, C., Zhang, Z., Fu, F., Yao, W., Su, H., Hu, Y., Jin, S.: Analysis of spatiotemporal elements affecting visitors security based mostly on multisource information fusion. J. Transp. Eng. Part A Syst. 149(10), 04023098 (2023)
Yang, C., Yan, F., Ukkusuri, S.V.: Unraveling traveler mobility patterns and predicting consumer conduct within the Shenzhen metro system. Transportmetrica A Transp. Sci. 14(7), 576–597 (2018)
Yao, W., Zhang, M., Jin, S., Ma, D.: Understanding autos commuting sample based mostly on license plate recognition information. Transp. Res. Part C Emerg. Technol. 128, 103142 (2021)
Yao, W., Chen, C., Su, H., Chen, N., Jin, S., Bai, C.: Analysis of key commuting routes based mostly on spatiotemporal journey chain. J. Adv. Transp. 2022(1), 6044540 (2022a)
Yao, W., Yu, J., Yang, Y., Chen, N., Jin, S., Hu, Y., Bai, C.: Understanding journey conduct adjustment underneath COVID-19. Commun. Transp. Res. 2, 100068 (2022b)
Yao, W., Chen, N., Su, H., Hu, Y., Jin, S., Rong, D.: A novel self-adaption macroscopic elementary diagram contemplating community heterogeneity. Phys. A Stat. Mech. Appl. 613, 128531 (2023)
Yao, W., Hu, Y., Bai, C., Jin, S., Yang, C.: Exploring impression of COVID-19 on journey conduct. Netw. Spat. Econ. 24(1), 165–197 (2024)
Yao, W., Shen, X., He, Z., Liu, Y., Yang, X., Zeng, J., Jin, S.: Unlocking the potential of cooperative staggered shifts in city networks. Transp. Res. Part C Emerg. Technol. 180, 105354 (2025)
Yin, C., Shao, C.: Revisiting commuting, constructed atmosphere and happiness: new proof on a nonlinear relationship. Transp. Res. Part D Transp. Environ. 100, 103043 (2021)
Yong, N., Ni, S., Shen, S., Chen, P., Ji, X.: Uncovering steady and occasional human mobility patterns: a case research of the Beijing subway. Phys. A Stat. Mech. Appl. 492, 28–38 (2018)
Yu, Y., Cui, Y., Zeng, J., He, C., Wang, D.: Identifying visitors clusters in city networks based mostly on graph idea utilizing license plate recognition information. Phys. A Stat. Mech. Appl. 591, 126750 (2022)
Yu, C., Lin, H., Chen, Y., Yang, C., Yin, A., Yuan, Q.: Creating most wanted personalized bus companies: a collaborative evaluation of user-route dynamics. Transp. Res. Part D Transp. Environ. 133, 104312 (2024)
Zahnow, R., Abewickrema, W.: Examining regularity in vehicular visitors by means of Bluetooth scanner information: is the every day commuter the common street consumer? J. Transp. Geogr. 109, 103578 (2023)
Zeng, J., Yu, Y., Chen, Y., Yang, D., Zhang, L., Wang, D.: Trajectory-as-a-sequence: a novel journey mode identification framework. Transp. Res. Part C Emerg. Technol. 146, 103957 (2023)
Zhang, Z., Su, H., Yao, W., Wang, F., Hu, S., Jin, S.: Uncovering the CO2 emissions of autos: a well-to-wheel method. Fundam. Res. 4(5), 1025–1035 (2024)
Zhang, C., Huang, Y., Ji, A., Liu, H., Li, J., Ni, A., Lu, W.: Policy implications of the transit metropolis undertaking: a quasi-natural experiment from China. Transp. Policy 162, 155–170 (2025a)
Zhang, C., Liu, H., Pan, D., Zheng, L., Skitmore, M., Xia, P., Xiao, G.: Modeling and utility of the competitors and cooperation relationship between on-line ride-hailing and subways. Cities 166, 106230 (2025b)
Zhang, C., Ma, H., Xing, X., Huang, M., Lin, N., Yao, D.: The affect mechanism of the willingness to make use of autonomous taxis: A mixed evaluation of social listening and questionnaire survey. Transportation 52, 2475–2509 (2025c). https://doi.org/10.1007/s11116-025-10663-0
Zhao, X., Papaix, C., Cao, M., Lyu, N.: Boat commuting, journey satisfaction and well-being: empirical proof from Greater London. Transp. Res. Part D Transp. Environ. 129, 104122 (2024)
Ziakopoulos, A., Kontaxi, A., Yannis, G.: Analysis of cell phone use engagement throughout naturalistic driving by means of explainable imbalanced machine studying. Accid. Anal. Prev. 181, 106936 (2023)
Zubair, H., Susilawati, S., Talei, A., Pu, Z.: Investigating the position of flex-time working preparations in optimising morning peak-hour journey demand: a survival evaluation method. Transp. Res. Part A Policy Pract. 190, 104229 (2024)
This web page was created programmatically, to learn the article in its authentic location you’ll be able to go to the hyperlink bellow:
https://link.springer.com/article/10.1007/s11116-025-10713-7
and if you wish to take away this text from our web site please contact us
This web page was created programmatically, to learn the article in its unique location you…
This web page was created programmatically, to learn the article in its authentic location you'll…
This web page was created programmatically, to learn the article in its unique location you…
This web page was created programmatically, to learn the article in its unique location you…
This web page was created programmatically, to learn the article in its authentic location you…
This web page was created programmatically, to learn the article in its authentic location you'll…