{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T06:30:46Z","timestamp":1791959446191,"version":"4.3.4"},"reference-count":64,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,3,3]],"date-time":"2023-03-03T00:00:00Z","timestamp":1677801600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52072025"],"award-info":[{"award-number":["52072025"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJERPH"],"abstract":"<jats:p>With urban expansion and traffic environment improvement, travel chains continue to grow, and the combination of travel purposes and modes becomes more complex. The promotion of mobility as a service (MaaS) has positive effects on facilitating the public transport traffic environment. However, public transport service optimization requires an accurate understanding of the travel environment, selection preferences, demand prediction, and systematic dispatch. Our study focused on the relationship between the trip-chain complexity environment and travel intention, combining the Theory of Planned Behavior (TPB) with travelers\u2019 preferences to construct a bounded rationality theory. First, this study used K-means clustering to transform the characteristics of the travel trip chain into the complexity of the trip chain. Then, based on the partial least squares structural equation model (PLS-SEM) and the generalized ordered Logit model, a mixed-selection model was established. Finally, the travel intention of PLS-SEM was compared with the travel sharing rate of the generalized ordered Logit model to determine the trip-chain complexity effects for different public transport modes. The results showed that (1) the proposed model, which transformed travel-chain characteristics into travel-chain complexity using K-means clustering and adopted a bounded rationality perspective, had the best fit and was the most effective with comparison to the previous prediction approaches. (2) Compared with service quality, trip-chain complexity negatively affected the intention of using public transport in a wider range of indirect paths. Gender, vehicle ownership, and with children\/without children had significant moderating effects on certain paths of the SEM. (3) The research results obtained by PLS-SEM indicated that when travelers were more willing to travel by subway, the subway travel sharing rate corresponding to the generalized ordered Logit model was only 21.25\u201343.49%. Similarly, the sharing rate of travel by bus was only 32\u201344% as travelers were more willing to travel by bus obtained from PLS-SEM. Therefore, it is necessary to combine the qualitative results of PLS-SEM with the quantitative results of generalized ordered Logit. Moreover, when service quality, preferences, and subjective norms were based on the mean value, with each increase in trip-chain complexity, the subway travel sharing rate was reduced by 3.89\u20138.30%, while the bus travel sharing rate was reduced by 4.63\u20136.03%.<\/jats:p>","DOI":"10.3390\/ijerph20054547","type":"journal-article","created":{"date-parts":[[2023,3,6]],"date-time":"2023-03-06T03:22:05Z","timestamp":1678072925000},"page":"4547","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["The Effect of Travel-Chain Complexity on Public Transport Travel Intention: A Mixed-Selection Model"],"prefix":"10.3390","volume":"20","author":[{"given":"Yuan","family":"Yuan","sequence":"first","affiliation":[{"name":"Key Laboratory of Integrated Transportation Big Data Application Technology in Transportation Industry, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunfu","family":"Shao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Integrated Transportation Big Data Application Technology in Transportation Industry, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7815-3889","authenticated-orcid":false,"given":"Zhichao","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Transportation and Civil Engineering, Nantong University, Nantong 226000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chaoying","family":"Yin","sequence":"additional","affiliation":[{"name":"Key Laboratory of Integrated Transportation Big Data Application Technology in Transportation Industry, Beijing Jiaotong University, Beijing 100044, China"},{"name":"College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1007\/s11116-007-9134-8","article-title":"Defining and understanding trip chaining behaviour","volume":"35","author":"Primerano","year":"2007","journal-title":"Transportation"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"953","DOI":"10.1007\/s11116-020-10084-1","article-title":"Trip chain complexity: A comparison among latent classes of daily mobility patterns","volume":"48","author":"Schneider","year":"2020","journal-title":"Transportation"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1016\/j.tranpol.2010.08.003","article-title":"Exploring the trip chaining behaviour of public transport users in Melbourne","volume":"18","author":"Currie","year":"2010","journal-title":"Transp. 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