This study aims at investigating the effects of highway landscapes and alignments on drivers’ eye movement behavior and emergency reaction time, based on a driving simulator experiment. In this study, four simulation scenarios are evaluated including open space, semiopen space, semiclosed space, and enclosed space landscapes on highways in Yunnan Province, China. Twenty-four experienced drivers participated in a 6-kilometer driving experiment in each landscape scenario. Each subject was required to drive at 80 km/h in the scenarios and the driving behavior data were collected. Three different data analysis methods were employed: (1) descriptive analysis of the characteristics of drivers’ visual fixation area; (2) statistical tests of emergency reaction time with drivers’ demographic characteristics, highway landscapes, and alignments; and (3) multiple linear regression analysis of emergency reaction time, highway landscapes, and alignments. The results show that emergency reaction time is significantly influenced by highway landscapes and alignments, and the multiple linear regression model built in this experiment could accurately predict drivers’ emergency reaction time in different highway landscapes and alignments.

This study proposes a prediction model considering external weather and holiday factors to address the issue of accurately predicting urban taxi travel demand caused by complex data and numerous influencing factors. The model integrates the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Convolutional Long Short Term Memory Neural Network (ConvLSTM) to predict short-term taxi travel demand. The CEEMDAN decomposition method effectively decomposes time series data into a set of modal components, capturing sequence characteristics at different time scales and frequencies. Based on the sample entropy value of components, secondary processing of more complex sequence components after decomposition is employed to reduce the cumulative prediction error of component sequences and improve prediction efficiency. On this basis, considering the correlation between the spatiotemporal trends of short-term taxi traffic, a ConvLSTM neural network model with Long Short Term Memory (LSTM) time series processing ability and Convolutional Neural Networks (CNN) spatial feature processing ability is constructed to predict the travel demand for urban taxis. The combined prediction model is tested on a taxi travel demand dataset in a certain area of Beijing. The results show that the CEEMDAN-ConvLSTM prediction model outperforms the LSTM, Autoregressive Integrated Moving Average model (ARIMA), CNN, and ConvLSTM benchmark models in terms of Symmetric Mean Absolute Percentage Error (SMAPE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R2 metrics. Notably, the SMAPE metric exhibits a remarkable decline of 21.03% with the utilization of our proposed model. These results confirm that our study provides a highly accurate and valid model for taxi travel demand forecasting.

Based on operation kilometers, amounts and station numbers of urban rail transit (URT) lines in a city, this study utilizes simultaneous panel data equations (SPDE) to comprehensively analyze the bidirectional dynamics of urban rail transit (URT) on urban economies. Drawing on data from 2010 to 2019 across 40 Chinese cities, we rigorously examine the SPDE model’s robustness through elastic analysis and placebo tests. Economic variables within the SPDE framework are aggregated based on geographic population scales, yielding new endogenous variables that facilitate a deeper exploration of URT’s impact on economic agglomeration. Our findings highlight URT’s positive spillover effects, promoting both geographic and demographic economic agglomeration. Nevertheless, it is imperative to note that URT’s capacity to ameliorate economic conditions remains constrained in cities with sluggish or stagnant development.

To address the challenges posed by incomplete data in passenger flow prediction and organizational tasks, this paper proposes ProbSparse self‐attention conditional generative adversarial imputation net (ProbSA‐CGAIN), a novel imputation model framework built on the enhanced generative adversarial network (GAN). The model leverages conditional GANs for controlled data generation using external conditional information. It adopts a denoising autoencoder structure for reconstructing and estimating missing passenger flow data. The integration of an efficient ProbSparse self‐attention mechanism captures spatiotemporal evolution features, reducing computational complexity. Additionally, the model incorporates auxiliary conditional information to enhance data imputation accuracy by learning interdependencies among multiple data variables. Further, the model integrates local positional encoding and multi‐layer global temporal encoding, offering diverse perspectives on spatiotemporal information. Experimental evaluations with real passenger flow data demonstrate the model's superiority over advanced baseline models across various missing patterns and rates. Notably, it exhibits high stability in data restoration, particularly for datasets with higher missing rates, affirming its effectiveness in predicting and inferring missing passenger flow data based on auxiliary data and multi‐view positional information, ensuring reliable imputation. The experiments also assess the model's proficiency in attributing different spatiotemporal features, confirming its commendable training and restoration efficiency.

This study develops difference-in-differences (DID) models to examine the direct and indirect effects of high-speed railway (HSR) operation on the urban gross domestic product (GDP) and its agglomeration. The period from 2010 to 2019 is selected as the study period, and 30 HSR-operating cities across the Chinese mainland are chosen as the study sites to be investigated. Individual fixed effects and time fixed effects are introduced to panel data models to account for the heterogeneities between cities and the endogeneities of explanatory variables. Estimation results suggest that the operation of HSR can improve the development of the urban GDP by accelerating the migration of population to HSR-operating cities, promoting the upgrading of the urban industrial structure, and improving the level of urban scientific research. Moreover, the level of urban economic agglomeration can also be improved as a result of HSR. However, the ability of HSR-promoting economic development is more significant in cities with more developed economies. Therefore, when formulating a sensible plan for the development of HSR, policymakers should prioritize the construction of HSR in more developed cities.