References
[1] Liu YL, Fang FG, Jing Y. How urban land use influences commuting flows in Wuhan, Central China: A mobile phone signaling data perspective. Sustainable Cities and Society. 2020;53. DOI: 10.1016/j.scs.2019.101914.
[2] Ferreira JP, et al. Economic, social, energy and environmental assessment of inter-municipality commuting: The case of Portugal. Energy Policy. 2014;66:411-418. DOI: 10.1016/j.enpol.2013.11.010.
[3] Liu Y, et al. The spatial distribution of commuting CO_2 emissions and the influential factors: A case study in Xi'an, China. Advances in Climate Change Research. 2015;6(1):46-55. DOI: 10.1016/j.accre.2015.09.001.
[4] Ministry of Ecology and Environment of the People's Republic of China. China Mobile Source Environment Management Annual Report. 2020. https://www.mee.gov.cn/hjzl/sthjzk/ydyhjgl/202109/t20210910_920787.shtml [Accessed March 22nd 2023].
[5] Clark B, Chatterjee K, Melia S. Changes to commute mode: The role of life events, spatial context and environmental attitude. Transportation Research Part A - Policy and Practice. 2016;89:89-105. DOI: 10.1016/j.tra.2016.05.005.
[6] Guo YY, et al. Dockless bike-sharing as a feeder mode of metro commute? The role of the feeder-related built environment: Analytical framework and empirical evidence. Sustainable Cities and Society. 2021;65. DOI: 10.1016/j.scs.2020.102594.
[7] Sun BD, Ermagun A, Dan B. Built environmental impacts on commuting mode choice and distance: Evidence from Shanghai. Transportation Research Part D - Transport and Environment. 2017;52:441-453. DOI: 10.1016/j.trd.2016.06.001.
[8] Yang YY, et al. Towards a cycling-friendly city: An updated review of the associations between built environment and cycling behaviors (2007-2017). Journal of Transport & Health. 2019;14. DOI: 10.1016/j.jth.2019.100613.
[9] Zhu ZJ, et al. The impact of urban characteristics and residents' income on commuting in China. Transportation Research Part D - Transport and Environment. 2017;57:474-483. DOI: 10.1016/j.trd.2017.09.015.
[10] Nagy S, Csiszar C. The quality of smart mobility: A systematic review. Scientific Journal of Silesian University of Technology-Series Transport. 2020;109:117-127. DOI: 10.20858/sjsutst.2020.109.11.
[11] Rahman FI. Analysing the factor influencing travel pattern and mode choice based on household interview survey data: A case study of Dhaka city, Bangladesh. Scientific Journal of Silesian University of Technology. Series Transport. 2020;109:153-162. DOI: 10.20858/sjsutst.2020.109.14.
[12] Yang JW, et al. Transport impacts of clustered development in Beijing: Compact development versus overconcentration. Urban Studies. 2012;49(6):1315-1331. DOI: 10.1177/0042098011410336.
[13] Cole-Hunter T, et al. Objective correlates and determinants of bicycle commuting propensity in an urban environment. Transportation Research Part D - Transport and Environment. 2015;40:132-143. DOI: 10.1016/j.trd.2015.07.004.
[14] Fan JX, Wen M, Wan N. Built environment and active commuting: Rural-urban differences in the U.S. SSM - Population Health. 2017;3:435-441. DOI: 10.1016/j.ssmph.2017.05.007.
[15] Piatkowski DP, Marshall WE. Not all prospective bicyclists are created equal: The role of attitudes, socio-demographics, and the built environment in bicycle commuting. Travel Behaviour and Society. 2015;2(3):166-173. DOI: 10.1016/j.tbs.2015.02.001.
[16] Yang L, et al. Longitudinal associations between built environment characteristics and changes in active commuting. BMC Public Health. 2017;17. DOI: 10.1186/s12889-017-4396-3.
[17] Esztergar-Kiss D, et al. Promoting sustainable mode choice for commuting supported by persuasive strategies. Sustainable Cities and Society. 2021;74. DOI: 10.1016/j.scs.2021.103264.
[18] Liu YQ, et al. Spatial pattern of leisure activities among residents in Beijing, China: Exploring the impacts of urban environment. Sustainable Cities and Society. 2020;52. DOI: 10.1016/j.scs.2019.101806.
[19] Chatterjee K, et al. Commuting and wellbeing: A critical overview of the literature with implications for policy and future research. Transport Reviews. 2020;40(1):5-34. DOI: 10.1080/01441647.2019.1649317.
[20] Dinu M, et al. Active commuting and multiple health outcomes: A systematic review and meta-analysis. Sports Medicine. 2019;49(3):437-452. DOI: 10.1007/s40279-018-1023-0.
[21] Zhu J, Fan YL. Commute happiness in Xi'an, China: Effects of commute mode, duration, and frequency. Travel Behaviour and Society. 2018;11:43-51. DOI: 10.1016/j.tbs.2018.01.001.
[22] Cloutier S, Jambeck J, Scott N. The Sustainable Neighborhoods for Happiness Index (SNHI): A metric for assessing a community's sustainability and potential influence on happiness. Ecological Indicators. 2014;40:147-152. DOI: 10.1016/j.ecolind.2014.01.012.
[23] Mao ZD, Ettema D, Dijst M. Commuting trip satisfaction in Beijing: Exploring the influence of multimodal behavior and modal flexibility. Transportation Research Part A - Policy and Practice. 2016;94:592-603. DOI: 10.1016/j.tra.2016.10.017.
[24] Clark B, et al. How commuting affects subjective wellbeing. Transportation. 2020;47(6):2777-2805. DOI: 10.1007/s11116-019-09983-9.
[25] Smith O. Commute well-being differences by mode: Evidence from Portland, Oregon, USA. Journal of Transport & Health. 2017;4:246-254. DOI: 10.1016/j.jth.2016.08.005.
[26] Humphreys DK, Goodman A, Ogilvie D. Associations between active commuting and physical and mental wellbeing. Preventive Medicine. 2013;57(2):135-139. DOI: 10.1016/j.ypmed.2013.04.008.
[27] Tran PTM, Nguyen T, Balasubramanian R. Personal exposure to airborne particles in transport micro-environments and potential health impacts: A tale of two cities. Sustainable Cities and Society. 2020;63. DOI: 10.1016/j.scs.2020.102470.
[28] Ding C, et al. Influences of built environment characteristics and individual factors on commuting distance: A multilevel mixture hazard modeling approach. Transportation Research Part D - Transport and Environment. 2017;51:314-325. DOI: 10.1016/j.trd.2017.02.002.
[29] Yang LY, et al. Driving as a commuting travel mode choice of car owners in urban China: Roles of the built environment. Cities. 2021;112. DOI: 10.1016/j.cities.2021.103114.
[30] Xu HW. Comparing spatial and multilevel regression models for binary outcomes in neighborhood studies. Sociological Methodology. 2014;44:229-272. DOI: 10.1177/0081175013490188.
[31] Cao XY. Heterogeneous effects of neighborhood type on commute mode choice: An exploration of residential dissonance in the Twin Cities. Journal of Transport Geography. 2015;48:188-196. DOI: 10.1016/j.jtrangeo.2015.09.010.
[32] Guo J, Feng T, Timmermans HJP. Co-dependent workplace, residence and commuting mode choice: Results of a multi-dimensional mixed logit model with panel effects. Cities. 2020;96. DOI: 10.1016/j.cities.2019.102448.
[33] Shier ML, Graham JR. Work-related factors that impact social work practitioners' subjective well-being: Well-being in the workplace. Journal of Social Work. 2011;11(4):402-421. DOI: 10.1177/1468017310380486.
[34] Choi B, et al. Sedentary work, low physical job demand, and obesity in US workers. American Journal of Industrial Medicine. 2010;53(11):1088-1101. DOI: 10.1002/ajim.20886.
[35] Li W, Pu Z, Li Y, Tu M. How does ridesplitting reduce emissions from ridesourcing? A spatiotemporal analysis in Chengdu, China. Transportation Research Part D: Transport and Environment. 2021;95. DOI: 10.1016/j.trd.2021.102885.
[36] Pu ZY, et al. Multimodal traffic speed monitoring: A real-time system based on passive Wi-Fi and bluetooth sensing technology. IEEE Internet of Things Journal. 2021;9(14):12413-12424. DOI: 10.1109/Jiot.2021.3136031.
[37] Zhuang YF, et al. Edge-artificial intelligence-powered parking surveillance with quantized neural networks. IEEE Intelligent Transportation Systems Magazine. 2022;14(6):107-121. DOI: 10.1109/Mits.2022.3182358.
[38] Wang S, et al. Estimating crowd density with edge intelligence based on lightweight convolutional neural networks. Expert Systems with Applications. 2022;206. DOI: 10.1016/j.eswa.2022.117823.