Future Transportation

Emergency traffic incidents during motorway reconstruction and expansion projects exhibit characteristics distinct from those under normal operating conditions. To identify incident types and examine their temporal, seasonal, and vehicle-related patterns during the construction period, this study analyzed 372 emergency traffic incident reports from the Guilin–Liuzhou Expressway reconstruction and expansion project in 2023. Text mining was applied to extract incident-related keywords, followed by term frequency–inverse document frequency (TF-IDF) vectorization. Unsupervised learning methods were then used to identify strong word–word association rules. A text network was constructed based on these associations, and the Leiden algorithm was employed for community detection to derive thematic clusters and further examine incident characteristics and response differences. The results indicate that emergency traffic incidents during motorway reconstruction and expansion were grouped into six categories: night-time expressway incidents, summer weekday afternoon tyre blowouts, morning incidents during spring holidays, congestion caused by immobile vehicles, winter lorry incidents, and incidents during the National Day holiday. These categories display pronounced variation across seasons, time periods, and vehicle types, suggesting that traffic safety management should pay particular attention to holidays and peak travel periods. Incident duration analysis further shows that congestion-related incidents caused by immobile vehicles exhibit the widest incident-duration range and the longest duration, indicating substantial operational challenges for emergency handling. Overall, the findings demonstrate that unstructured traffic incident reports contain rich operational information that can be effectively extracted through text-mining techniques. The proposed analytical framework offers a practical approach for identifying risk patterns during motorway reconstruction and expansion projects and for supporting targeted traffic safety management, emergency resource allocation, and incident response optimization.

Adaptive traffic signal control can reduce congestion, but network-wide averages may conceal access-level penalties and spillback. This study evaluates an interpretable Coordinated Adaptive Control Model (MCAC) for two closely spaced signalized junctions in Loja, Ecuador. MCAC uses eight lane-area detectors to trigger temporary coordinated decongestion phases, inserting additional signal-control time before resuming an observed 137 s fixed-time base plan. A matched stochastic SUMO 1.27.0 experiment compared fixed-time, MCAC, and SUMO native actuated control at 80%, 100%, 120%, and 140% of nominal demand, with 10 common-random-number replications per condition (120 runs). The study is a controlled comparative simulation rather than a field-calibrated prediction of absolute corridor performance. Relative to fixed-time control, MCAC reduced mean waiting time by 51.1%, 38.7%, 36.2%, and 37.0%, respectively, with Holm-adjusted Wilcoxon $p=0.0234$ at each demand level. MCAC also reduced extreme waiting, departure delay, and a supplementary combined measure of pre-entry and in-network stopped waiting. Benefits were unevenly distributed: Orillas del Zamora improved strongly, both Guayaquil directions incurred systematic waiting penalties, and Nueva Loja shifted from a low-demand penalty to a high-demand benefit. The generic independently actuated benchmark exhibited prolonged blocking and reached 164 teleports at 140% demand; the data do not isolate the specific downstream-blocking mechanism or establish a consistent teleport advantage over fixed-time control. These comparative simulation findings support joint reporting of aggregate efficiency, access-level redistribution, and operational robustness, without implying field-validated effectiveness.

This study examines factors associated with the intention to use intercity High-Speed Rail (HSR) in Thailand before its operation, using an extended Theory of Planned Behavior (TPB) that adds External Influence, Trust, and Novelty Seeking. Survey data from 1546 intercity travelers were analyzed with Multi-Group Structural Equation Modeling across Generations X, Y, and Z using robust maximum likelihood. Configural, metric, and scalar measurement invariance and, under the same ΔCFI criterion, structural invariance were supported. In the hypothesized model, Subjective Norm and Perceived Behavioral Control were positively associated with intention in all cohorts, and External Influence was related to intention indirectly through Trust, Subjective Norm, and Perceived Behavioral Control. When direct paths from the added constructs to intention were estimated, Trust was directly associated with intention in every cohort, whereas Perceived Behavioral Control was not. A stronger Perceived Behavioral Control–intention association in Generation Z did not persist under partial measurement invariance, among working adults, without items with overlapping content, or with direct Trust–intention paths. The pathways to intention were therefore largely similar across generations, and apparent generational differences reflected model specification and life stage. Building trust before operation may help strengthen intentions, although such strategies were not tested directly.