Key Laboratory of Integrated Transportation Big Data Application Technology in Transportation Industry, Beijing Jiaotong University, Beijing 100044, China.
School of Transportation and Civil Engineering, Nantong University, Nantong 226000, China.
Int J Environ Res Public Health. 2023 Mar 3;20(5):4547. doi: 10.3390/ijerph20054547.
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' 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 21.25-43.49%. Similarly, the sharing rate of travel by bus was only 32-44% 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 3.89-8.30%, while the bus travel sharing rate was reduced by 4.63-6.03%.
随着城市的扩张和交通环境的改善，出行链不断增长，出行目的和方式的组合变得更加复杂。出行即服务（MaaS）的推广对改善公共交通出行环境具有积极影响。然而，公共交通服务的优化需要准确了解出行环境、选择偏好、需求预测和系统调度。我们的研究关注出行链复杂性环境与出行意愿之间的关系，将计划行为理论（TPB）与出行者偏好相结合，构建了一种有限理性理论。首先，本研究使用 K 均值聚类将出行链特征转化为出行链复杂性。然后，基于偏最小二乘结构方程模型（PLS-SEM）和广义有序逻辑模型，建立了混合选择模型。最后，通过比较 PLS-SEM 的出行意愿和广义有序逻辑模型的出行共享率，确定了不同公共交通模式下出行链复杂性的影响。结果表明：（1）采用 K 均值聚类将出行链特征转化为出行链复杂性的所提出模型，与之前的预测方法相比，具有最佳的拟合效果和有效性。（2）与服务质量相比，出行链复杂性通过更广泛的间接路径对使用公共交通的意愿产生负面影响。性别、车辆拥有情况和是否有孩子对 SEM 的某些路径具有显著的调节作用。（3）PLS-SEM 得到的研究结果表明，当出行者更愿意乘坐地铁出行时，广义有序逻辑模型对应的地铁出行共享率仅为 21.25%至 43.49%。同样，当出行者更愿意乘坐公共汽车出行时，从 PLS-SEM 得到的公共汽车出行共享率仅为 32%至 44%。因此，需要将 PLS-SEM 的定性结果与广义有序逻辑的定量结果相结合。此外，当服务质量、偏好和主观规范基于平均值时，出行链复杂性每增加一次，地铁出行共享率降低 3.89%至 8.30%，而公共汽车出行共享率降低 4.63%至 6.03%。
