Jing Shi

The China–Pakistan Economic Corridor (CPEC) represents a transformative transport infrastructure initiative with the potential to reshape tourism in South Asia. However, the behavioral mechanisms through which corridor development translate into travel willingness remain insufficiently understood, particularly between domestic and cross-border tourism. This study investigated the determinants of domestic tourism willingness within Pakistan and cross-border tourism willingness toward China using a stated preference survey of 441 Pakistani respondents collected through an online questionnaire. To balance behavioral interpretation and predictive performance, this study integrated ordinal logistic regression (OLR) with multiple machine learning classifiers. The results revealed clear behavioral asymmetries between domestic and cross-border tourism decisions. Domestic tourism willingness was primarily driven by attitudinal evaluations, particularly perceived desirability, pleasantness, and comfort of travel along the CPEC. In contrast, cross-border tourism willingness was more strongly constrained by knowledge-related and institutional factors, including awareness of visa procedures, accommodation arrangements, and destination information. Comparative performance analysis indicated that machine learning models outperformed ordinal logistic regression, improving predictive accuracy by approximately 12.6 percentage points for domestic tourism (93.6% vs. 81.0%) and 1.7 percentage points for cross-border tourism (81.1% vs. 79.4%). These findings demonstrate that corridor-induced tourism demand is governed by distinct behavioral mechanisms across domestic and international contexts, highlighting the need for differentiated tourism development strategies. From a policy perspective, the results suggest that domestic tourism development along the CPEC should prioritize experiential quality and travel comfort, whereas cross-border tourism promotion should focus on reducing informational and procedural barriers such as visa knowledge, accommodation awareness, and travel facilitation.

The driving simulator’s ability to provide practical, safe, and controlled environments has made it a widely used tool for evaluating driving behaviours in the realm of road safety. To consolidate the fragmented research in this area, this study is divided into two parts: a bibliometric analysis and a narrative review: (a) the bibliometric analysis identified 4992 studies, expanding from 2000 to June 2025, sourced from four databases—Web of Science, Scopus, TRID, and Google Scholar (supplementary)—and examined trends over the years, the general topics covered, the countries where studies were conducted, and the main research fields associated with driving simulators; and (b) the narrative review further analysed 48 selected studies from eight domains (distraction, fatigue and drowsiness, traffic-calming measures, impairment from psychoactive drugs, road curves, intersections, tunnels, and adverse weather conditions) to provide insights into how driving simulators have contributed to these fields, the methodologies employed by researchers, and the practical applications of the findings. The study aims to provide clear and essential insights for new and emerging researchers, offering an accessible overview of how driving simulators have evolved, why they are important, how they measure different driving metrics, and how they ultimately improve road safety. The findings indicate that driving simulator studies are increasingly prominent in research on driver behaviour (e.g., driving speed, lateral movement, and acceleration/deceleration).

The purpose of this study was to investigate how environmental judgments and psychological factors jointly influence self-reported violation riding behaviors among e-bike riders in China, with attention to sociodemographic heterogeneity. To achieve this, the e-bike violation riding behavior questionnaire was designed. Additionally, a hybrid approach integrating the Structural Equation Model (SEM) with the Random Parameters Logit (RP-Logit) model was constructed to reveal the impact factors of e-bike riders’ violation riding behaviors, in which demographic information and latent variables were comprehensively considered. This methodology simultaneously analyzed the complex relationships among latent variables (measured by SEM) and captured the heterogeneous effects of demographic factors on discrete violation tendencies (modeled by RP-Logit). The following two main findings emerged: (1) Experienced riders and those who use e-bikes as operating tools tend to exhibit a higher tendency to engage in violation riding. (2) Perceived Risk has the greatest impact on the performance of high-violation tendencies. Specifically, the probability of choosing high-violation riding behaviors decreases by 0.18 for each unit increase in the rider’s Perceived Risk. (3) Similarly, for each unit increase in riders’ Perceived Law Enforcement, the probability of choosing high-violation riding behaviors decreases by 0.15. The findings suggest that relevant authorities should address e-bike violation behaviors through enhanced safety education and strengthened enforcement measures, particularly targeting high-risk rider groups.

Intelligent transportation systems require connected and automated vehicles (CAVs) to conduct safe and efficient cooperation with human-driven vehicles (HVs) in complex real-world traffic environments. However, the inherent unpredictability of human behaviour, especially at bottlenecks such as highway on-ramp merging areas, often disrupts traffic flow and compromises system performance. To address the challenge of cooperative on-ramp merging in heterogeneous traffic environments, this study proposes a trust-based multi-agent reinforcement learning (Trust-MARL) framework. At the macro level, Trust-MARL enhances global traffic efficiency by leveraging inter-agent trust to improve bottleneck throughput and mitigate traffic shockwave through emergent group-level coordination. At the micro level, a dynamic trust mechanism is designed to enable CAVs to adjust their cooperative strategies in response to real-time behaviors and historical interactions with both HVs and other CAVs. Furthermore, a trust-triggered game-theoretic decision-making module is integrated to guide each CAV in adapting its cooperation factor and executing context-aware lane-changing decisions under safety, comfort, and efficiency constraints. An extensive set of ablation studies and comparative experiments validates the effectiveness of the proposed Trust-MARL approach, demonstrating significant improvements in safety, efficiency, comfort, and adaptability across varying CAV penetration rates and traffic densities.

This study aims at analyzing the factors of aberrant driving behaviors and road accidents among Chinese ride-hailing drivers. Four hundred and twenty ride-hailing drivers (Male = 65%) completed a web-based questionnaire survey that assessed personal attributes, work-condition factors, aberrant driving behaviors, and self-reported road accidents in the last three years. A 10-item violations Driver Behavior Questionnaire (DBQ) scale was adopted to explore the aberrant driving behaviors of ride-hailing drivers. The ordinal regression model was used to examine the effects of personal attributes and work-condition factors on aberrant driving behaviors. A binary logistic regression model was employed to investigate the predictors of road accidents. The descriptive statistics indicate that among ride-hailing drivers, the traditional taxi drivers were found to be more involved in aberrant driving behaviors than private car drivers. The results from the Principal Component Analysis (PCA) reveal that ride-hailing drivers were involved in "risky violations." Male and young ride-hailing drivers were found to be more involved in risky violations than their counterparts. Furthermore, it is revealed that a one-unit increase in risky violations increased the probability of being involved in road accidents by 60%. Furthermore, a one-unit increase in work-condition factors increased the likelihood of being involved in road accidents by 41%. The findings in this study can help better understand the aberrant driving behaviors of ride-hailing drivers and contribute to a more effective policy for reducing the road accidents caused by ride-hailing drivers.