[1]

Chen CL, Pan H, Shen Q, Wang J. (Eds.) 2020. Handbook on Transport and Urban Transformation in China. London: Edward Elgar. doi: 10.4337/9781786439246.00006

[2]

The Rising Lab. 2020. Ranking of Cities' Business Attractiveness 2020. (Chinese version)

[3]

The Rising Lab. 2021. Ranking of Cities' Business Attractiveness 2021. (Chinese version)

[4]

The Rising Lab. 2022. Ranking of Cities' Business Attractiveness 2022. (Chinese version)

[5]

The Rising Lab. 2023. Ranking of Chinese Cities' Business Attractiveness 2023. (Chinese version)

[6]

The Rising Lab. 2024. Ranking of Cities' Business Attractiveness in China 2024. (Chinese version)

[7]

The State Council of the People's Republic of China. 2022. Tabulation on 2020 China Population Census by County (Chinese version). www.stats.gov.cn/zs/tjwh/tjkw/tjzl/202302/t20230220_1913738.html (Accessed: 10 June 2024)

[8]

Yao D, Xu L, Zhang C, Li J. 2021. Revisiting the interactions between bus service quality, car ownership and mode use: a case study in Changzhou, China. Transportation Research Part A: Policy and Practice 154:329−344

doi: 10.1016/j.tra.2021.10.017
[9]

Liu Q, Liu Y, Chen CL, Papa E, Ling Y, et al. 2023. Is it possible to compete with car use? How buses can facilitate sustainable transport. Urban Planning 8(3):69−83

doi: 10.17645/up.v8i3.6368
[10]

Li J, Xu L, Tang F, Yao D, Zhang C. 2024. The impact of public transport priority policy on private car own and use: a study on the moderating effects of bus satisfaction. Transportation Research Part F: Traffic Psychology and Behaviour 106:112−27

doi: 10.1016/j.trf.2024.08.010
[11]

Li J, Tang F, Zhang S, Zhang C. 2023. The effects of low-carbon city construction on bus trips. Journal of Public Transportation 25:100057

doi: 10.1016/j.jpubtr.2023.100057
[12]

Rasca S, Saeed N. 2022. Exploring the factors influencing the use of public transport by commuters living in networks of small cities and towns. Travel Behaviour and Society 28:249−63

doi: 10.1016/j.tbs.2022.03.007
[13]

Yao D, Xu L, Li J. 2020. Does technical efficiency play a mediating role between bus facility scale and ridership attraction? Evidence from bus practices in China. Transportation Research Part A: Policy and Practice 132:77−96

doi: 10.1016/j.tra.2019.11.002
[14]

The Ministry of Public Security of the People's Republic of China. 2013. Private Cars in China Have Increased 13 Times in the Past 10 Years (Chinese version). www.gov.cn/jrzg/2013-12/01/content_2539481.htm (Accessed: 2 December 2024)

[15]

The Ministry of Public Security of the People's Republic of China. 2019. In 2018, Chinese Car Ownership Exceeded 200 Million for the First Time (Chinese version). www.gov.cn/xinwen/2019-01/13/content_5357441.htm (Accessed: 2 December 2024)

[16]

Wang G. 2014. Research on traffic problems in second and third-tier cities. Management & Technology of SME 4. (Chinese version)

[17]

Baidu Maps. 2022. 2021 China Urban Transportation Report (Chinese version). https://jiaotong.baidu.com/cms/reports/traffic/2021/index.html (Accessed: 1 June 2024)

[18]

Brechan I. 2017. Effect of price reduction and increased service frequency on public transport travel. Journal of Public Transportation 20(1):139−56

doi: 10.5038/2375-0901.20.1.8
[19]

Buehler R. 2011. Determinants of transport mode choice: a comparison of Germany and the USA. Journal of Transport Geography 19(4):644−57

doi: 10.1016/j.jtrangeo.2010.07.005
[20]

Chakrabarti S, Joh K. 2019. The effect of parenthood on travel behavior: Evidence from the California household travel survey. Transportation Research Part A: Policy and Practice 120:101−15

doi: 10.1016/j.tra.2018.12.022
[21]

Chng S, White M, Abraham C, Skippon S. 2016. Commuting and wellbeing in London: The roles of commute mode and local public transport connectivity. Preventive Medicine 88:182−88

doi: 10.1016/j.ypmed.2016.04.014
[22]

Ding C, Wang D, Liu C, Zhang Y, Yang J. 2017. Exploring the influence of built environment on travel mode choice considering the mediating effects of car ownership and travel distance. Transportation Research Part A: Policy and Practice 100:65−80

doi: 10.1016/j.tra.2017.04.008
[23]

Ha J, Lee S, Ko J. 2020. Unraveling the impact of travel time, cost, and transit burdens on commute mode choice for different income and age groups. Transportation Research Part A: Policy and Practice 141:147−66

doi: 10.1016/j.tra.2020.07.020
[24]

Ng WS, Acker A. 2018. Understanding urban travel behaviour by gender for efficient and equitable transport policies. International Transport Forum Discussion Papers (No. 2018/01). Paris: OECD. www.oecd.org/en/publications/understanding-urban-travel-behaviour-by-gender-for-efficient-and-equitable-transport-policies_eaf64f94-en.html

[25]

O'Fallon C, Sullivan C, Hensher DA. 2004. Constraints affecting mode choices by morning car commuters. Transport Policy 11(1):17−29

doi: 10.1016/S0967-070X(03)00015-5
[26]

Paulley N, Balcombe R, Mackett R, Titheridge H, Preston J, et al. 2006. The demand for public transport: the effects of fares, quality of service, income and car ownership. Transport Policy 13(4):295−306

doi: 10.1016/j.tranpol.2005.12.004
[27]

Rachele JN, Kavanagh AM, Badland H, Giles-Corti B, Washington S, et al. 2015. Associations between individual socioeconomic position, neighbourhood disadvantage and transport mode: Baseline results from the HABITAT multilevel study. Journal of Epidemiology and Community Health 69(12):1217−23

doi: 10.1136/jech-2015-205620
[28]

Balcombe R, Mackett R, Paulley N, Preston J, Shires J, et al. 2004. The demand for public transport: a practical guide. TRL Report No. 593. Transport Research Laboratory. www.trl.co.uk/uploads/trl/documents/TRL593%20-%20The%20Demand%20for%20Public%20Transport.pdf

[29]

Chakrabarti S. 2017. How can public transit get people out of their cars? An analysis of transit mode choice for commute trips in Los Angeles. Transport Policy 54:80−89

doi: 10.1016/j.tranpol.2016.11.005
[30]

Eriksson L, Friman M, Gärling T. 2008. Stated reasons for reducing work-commute by car. Transportation Research Part F: Traffic Psychology and Behaviour 11(6):427−33

doi: 10.1016/j.trf.2008.04.001
[31]

Lee S, Lee YH, Park JH. 2003. Estimating price and service elasticity of urban transportation demand with stated preference technique: case in Korea. Transportation Research Record: Journal of the Transportation Research Board 1839(1):167−72

doi: 10.3141/1839-19
[32]

Liu Y, Cirillo C. 2015. Measuring transit service impacts on vehicle ownership and use. Public Transport 7(2):203−22

doi: 10.1007/s12469-014-0098-8
[33]

The State Council of the People's Republic of China. 2014. Notice of the State Council on Adjusting the Standards for City Classifction (Chinese version). www.gov.cn/zhengce/content/2014-11/20/content_9225.htm (Accessed: 04 June 2021)

[34]

Globalization & World Cities (GaWC). 2024. The World According to GaWC 2024. https://gawc.lboro.ac.uk/gawc-worlds/the-world-according-to-gawc (Accessed: 15 March 2025)

[35]

China Central Television (CCTV). 2023. Ranking of Chinese New First-tier Cities in 2023 has been Established (Chinese version). https://news.cctv.com/2023/05/30/ARTI5kyAHUzgByGH08lUpEQ4230530.shtml (Accessed: 20 March 2025)

[36]

China Daily. 2017. Ranking of China Cities' Business Attractiveness 2017: Chengdu Ranks First among the New First-tier Chinese Cities (Chinese version). https://sc.chinadaily.com.cn/2017-05/26/content_29507094.htm (Accessed: 20 March 2025)

[37]

Urban Planning Society of China. 2017. Ranking of Chinese New First-tier Cities has been Established (Chinese version). www.planning.org.cn/law/news_view?id=6784 (Accessed: 20 March 2025)

[38]

The Ministry of Transport of the People's Republic of China. 2024. 2023 Urban Rail Transit Operation Data Quick Report (Chinese version). https://mp.weixin.qq.com/s?__biz=MzI3MDQwMDQ5NQ==&mid=2247593667&idx=1&sn=5a5fd0f406b50ad9307620d7e3964d35&scene=0 (Accessed: 04 June 2024)

[39]

The State Council of the People's Republic of China. 2018. Opinions on Further Strengthening the Planning, Construction and Management of Urban Rail Transit (Chinese version). www.gov.cn/zhengce/content/2018-07/13/content_5306202.htm (Accessed: 02 June 2024)

[40]

Lin X, Chen Y. 2022. The National Development and Reform Commission still does not accept the first round of rail transit construction plans for general prefecture-level cities. Why has the subway dream of Chinese third-tier cities been shelved? (Chinese version). https://m.yicai.com/news/101517821.html (Accessed: 02 June 2024)

[41]

Reeder LG. 1956. Social differentials in mode of travel, time and cost in the journey to work. American Sociological Review 21(1):56−63

doi: 10.2307/2089341
[42]

Coogan M, Spitz G, Adler T, McGuckin N, Kuzmyak, R, et al. 2018. Understanding changes in demographics, preferences, and markets for public transportation. TCRP, Research Report 201. Washington, DC: The National Academies Press. doi: 10.17226/25160

[43]

Litman T. 2004. Transit price elasticities and cross-elasticities. Journal of Public Transportation 7(2):37−58

doi: 10.5038/2375-0901.7.2.3
[44]

Litman T. 2008. Evaluating accessibility for transport planning. Transportation Research Board 87th Annual Meeting. Washington, DC. https://trid.trb.org/view.aspx?id=859513

[45]

Kawabata M. 2009. Spatiotemporal dimensions of modal accessibility disparity in Boston and San Francisco. Environment and Planning A: Economy and Space 41(1):183−98

doi: 10.1068/a4068
[46]

Sun L, Tang C, Yang L. 2014. Research on traffic improvement strategies for second and third-tier cities in the development process: take Qujing as an example. New urbanization and traffic development - Proceedings of the 2013 China Urban Traffic Planning Annual Conference and the 27th Academic Seminar (Chinese version). www.chinautc.com/templates/H_dongtai/article.aspx?nodeid=4117&page=ContentPage&contentid=80968

[47]

Ding C, Wang Y, Tang T, Mishra S, Liu C. 2018. Joint analysis of the spatial impacts of built environment on car ownership and travel mode choice. Transportation Research Part D: Transport and Environment 60:28−40

doi: 10.1016/j.trd.2016.08.004
[48]

Fairhurst MH. 1975. The influence of public transport on car ownership in London. Journal of Transport Economics and Policy 9(3):193−208

[49]

Goodwin PB. 1993. Car ownership and public transport use: Revisiting the interaction. Transportation 20(1):21−33

doi: 10.1007/BF01099974
[50]

Wang L, Li L, Wu B, Bai Y. 2013. Private car switched to public transit by commuters in Shanghai, China. Procedia—Social and Behavioral Sciences 96:1293−303

doi: 10.1016/j.sbspro.2013.08.147
[51]

Cullinane S. 2002. The relationship between car ownership and public transport provision: a case study of Hong Kong. Transport Policy 9(1):29−39

doi: 10.1016/S0967-070X(01)00028-2
[52]

Eriksson L, Nordlund AM, Garvill J. 2010. Expected car use reduction in response to structural travel demand management measures. Transportation Research Part F: Traffic Psychology and Behaviour 13(5):329−42

doi: 10.1016/j.trf.2010.06.001
[53]

Kingham S, Dickinson J, Copsey S. 2001. Travelling to work: will people move out of their cars. Transport Policy 8(2):151−60

doi: 10.1016/S0967-070X(01)00005-1
[54]

Mackett RL. 2001. Policies to attract drivers out of their cars for short trips. Transport Policy 8(4):295−306

doi: 10.1016/S0967-070X(01)00025-7
[55]

Fiorio CV, Percoco M. 2007. Would you stick to using your car even if charged? Evidence from Trento, Italy. Transport Reviews 27(5):605−20

doi: 10.1080/01441640701322727
[56]

Kim HS, Kim E. 2004. Effects of public transit on automobile ownership and use in households of the USA. Review of Urban and Regional Development Studies 16(3):245−262

doi: 10.1111/j.1467-940X.2005.00090.x
[57]

Li M, Lau D, Seah D. 2011. Car ownership and urban transport demand in Singapore. International Journal of Transport Economics 38:47−70

[58]

Guan J, Zhang K, Shen Q, He Y. 2020. Dynamic modal accessibility gap: measurement and application using travel routes data. Transportation Research Part D: Transport and Environment 81:102272

doi: 10.1016/j.trd.2020.102272
[59]

Collins CM, Chambers SM. 2005. Psychological and situational influences on commuter-transport-mode choice. Environment and Behavior 37(5):640−61

doi: 10.1177/0013916504265440
[60]

Yangzhou Natural Resources and Planning Bureau. 2020. 2020 Annual Report on Urban Transport Development in Yangzhou. (Chinese version). Report. Yangzhou Natural Resources and Planning Bureau, China.

[61]

Zhao P, Yu Z. 2020. Investigating mobility in rural areas of China: features, equity, and factors. Transport Policy 94:66−77

doi: 10.1016/j.tranpol.2020.05.008
[62]

Galatoulas NF, Genikomsakis KN, Ioakimidis CS. 2020. Spatio-Temporal trends of e-bike sharing system deployment: a review in Europe, North America and Asia. Sustainability 12:4611

doi: 10.3390/su12114611
[63]

Jiangsu Traffic Police. 2020. Rules for the safe riding of electric bicycles are established in Yangzhou. https://new.qq.com/rain/a/20200327A0UNSU00 (Accessed: 4 October 2023)

[64]

Zhao X, Yan X, Yu A, Van Hentenryck P. 2020. Prediction and behavioral analysis of travel mode choice: a comparison of machine learning and logit models. Travel Behaviour and Society 20:22−35

doi: 10.1016/j.tbs.2020.02.003
[65]

Mahdi AJ, Esztergár-Kiss D. 2024. Understanding tourists' behavior toward transport mode choice by using machine learning methods. EURO Journal on Decision Processes 12:100054

doi: 10.1016/j.ejdp.2024.100054
[66]

Afghari AP, Haque MM, Washington S, Smyth T. 2019. Effects of globally obtained informative priors on Bayesian safety performance functions developed for Australian crash data. Accident Analysis & Prevention 129:55−65

doi: 10.1016/j.aap.2019.04.023
[67]

Guo Y, Li Z, Wu Y, Xu C. 2018. Exploring unobserved heterogeneity in bicyclists' red-light running behaviors at different crossing facilities. Accident Analysis & Prevention 115:118−27

doi: 10.1016/j.aap.2018.03.006
[68]

Li X, Wang Y, Wu Y, Chen J, Zhou J. 2021. Modeling intercity travel mode choice with data balance changes: a comparative analysis of Bayesian logit model and artificial neural networks. Journal of Advanced Transportation 2021:9219176

doi: 10.1155/2021/9219176
[69]

Zhang CX, Xu S, Zhang JS. 2019. A novel variational Bayesian method for variable selection in logistic regression models. Computational Statistics & Data Analysis 133:1−19

doi: 10.1016/j.csda.2018.08.025
[70]

Zhou X, Wang M, Li D. 2019. Bike-sharing or taxi? Modeling the choices of travel mode in Chicago using machine learning. Journal of Transport Geography 79:102479

doi: 10.1016/j.jtrangeo.2019.102479
[71]

Li X, Ma R, Guo Y, Wang W, Yan B, et al. 2021. Investigation of factors and their dynamic effects on intercity travel modes competition. Travel Behaviour and Society 23:166−76

doi: 10.1016/j.tbs.2021.01.003
[72]

Xiao G, Juan Z, Zhang C. 2015. Travel mode detection based on GPS track data and Bayesian networks. Computers, Environment and Urban Systems 54:14−22

doi: 10.1016/j.compenvurbsys.2015.05.005
[73]

Hagenauer J, Helbich M. 2017. A comparative study of machine learning classifiers for modeling travel mode choice. Expert Systems with Applications 78:273−82

doi: 10.1016/j.eswa.2017.01.057
[74]

Saini D, Chand T, Chouhan DK, Prakash M. 2021. A comparative analysis of automatic classification and grading methods for knee osteoarthritis focussing on X-ray images. Biocybernetics and Biomedical Engineering 41(2):419−44

doi: 10.1016/j.bbe.2021.03.002
[75]

Shobha G, Rangaswamy S. 2018. Machine Learning. In Handbook of Statistics, eds. Gudivada VN, Rao CR. vol. 38. Amsterdam, Netherlands: Elsevier. pp. 197−228 doi: 10.1016/bs.host.2018.07.004

[76]

Huang J, Ling CX. 2005. Using AUC and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17(3):299−310

doi: 10.1109/TKDE.2005.50
[77]

Xiao D, Xu X, Ma C, Lyu N. 2021. Addressing driving actions of at-fault older drivers: Bayesian bivariate ordered probit analysis. IEEE Access 9:45803−11

doi: 10.1109/ACCESS.2021.3067011
[78]

Yuan Q, Xu X, Xu M, Zhao J, Li Y. 2020. The role of striking and struck vehicles in side crashes between vehicles: Bayesian bivariate probit analysis in China. Accident Analysis & Prevention 134:105324

doi: 10.1016/j.aap.2019.105324
[79]

Xu X, Xie S, Wong SC, Xu P, Huang H, et al. 2016. Severity of pedestrian injuries due to traffic crashes at signalized intersections in Hong Kong: a Bayesian spatial logit model. Journal of Advanced Transportation 50:2015−28

doi: 10.1002/atr.1442
[80]

Zeng Q, Gu W, Zhang X, Wen H, Lee J, et al. 2019. Analyzing freeway crash severity using a Bayesian spatial generalized ordered logit model with conditional autoregressive priors. Accident Analysis & Prevention 127:87−95

doi: 10.1016/j.aap.2019.02.029
[81]

Zeng Q, Wen H, Huang H, Abdel-Aty M. 2017. A Bayesian spatial random parameters Tobit model for analyzing crash rates on roadway segments. Accident Analysis & Prevention 100:37−43

doi: 10.1016/j.aap.2016.12.023
[82]

Cheng L, De Vos J, Zhao P, Yang M, Witlox F. 2020. Examining non-linear built environment effects on elderly's walking: A random forest approach. Transportation Research Part D: Transport and Environment 88:102552

doi: 10.1016/j.trd.2020.102552
[83]

Nielsen G, Lange T. 2007. Network design for public transport success – theory and examples. International Conference on Competition and Ownership in Land Passenger Transport, 10th. Hamilton Island, Queensland, Australia. https://thredbo-conference-series.org/downloads/thredbo10_papers/thredbo10-themeE-Nielsen-Lange.pdf

[84]

Wang F, Ross CL. 2018. Machine learning travel mode choices: Comparing the performance of an extreme gradient boosting model with a multinomial logit model. Transportation Research Record Journal of the Transportation Research Board 2672(47):35−45

doi: 10.1177/0361198118773556
[85]

Bigazzi A, Wong K. 2020. Electric bicycle mode substitution for driving, public transit, conventional cycling, and walking. Transportation Research Part D: Transport and Environment 85:102412

doi: 10.1016/j.trd.2020.102412
[86]

Bourne JE, Cooper AR, Kelly P, Kinnear FJ, England C, et al. 2020. The impact of e-cycling on travel behaviour: a scoping review. Journal of Transport & Health 19:100910

doi: 10.1016/j.jth.2020.100910
[87]

Cherry CR, Yang H, Jones LR, He M. 2016. Dynamics of electric bike ownership and use in Kunming, China. Transport Policy 45:127−35

doi: 10.1016/j.tranpol.2015.09.007
[88]

Fishman E, Cherry C. 2016. E-bikes in the mainstream: reviewing a decade of research. Transport Reviews 36(1):72−91

doi: 10.1080/01441647.2015.1069907
[89]

Johnson M, Rose G. 2013. Electric bikes - cycling in the New World City: An investigation of Australian electric bicycle owners and the decision making process for purchase. Australasian Transport Research Forum 2013 Proceedings, Brisbane, Australia, 2−4 October 2013. https://australasiantransportresearchforum.org.au/wp-content/uploads/2022/03/2013_johnson_rose.pdf (Accessed: 14 September 2023)

[90]

MacArthur J, Dill J, Person M. 2014. Electric Bikes in North America: Results from an online survey. Transportation Research Record: Journal of the Transportation Research Board 2468:123−30

doi: 10.3141/2468-14
[91]

Shao Q, Zhang W, Cao X, Yang J. 2022. Nonlinear and interaction effects of land use and motorcycles/e-bikes on car ownership. Transportation Research Part D: Transport and Environment 102:103115

doi: 10.1016/j.trd.2021.103115
[92]

Redman L, Friman M, Gärling T, Hartig T. 2013. Quality attributes of public transport that attract car users: a research review. Transport Policy 25:119−27

doi: 10.1016/j.tranpol.2012.11.005