The self-adaptive traffic signal control system serves as an effective measure for relieving urban traffic congestion. The system is capable of adjusting the signal timing parameters in real time according to the seasonal changes and short-term fluctuation of traffic demand, resulting in improvement of the efficiency of traffic operation on urban road networks. The development of information technologies on computing science, autonomous driving, vehicle-to-vehicle, and mobile Internet has created a sufficient abundance of acquisition means for traffic data. Great improvements for data acquisition include the increase of available amount of holographic data, available data types, and accuracy. The article investigates the development of commonly used self-adaptive signal control systems in the world, their technical characteristics, the current research status of self-adaptive control methods, and the signal control methods for heterogeneous traffic flow composed of connected vehicles and autonomous vehicles. Finally, the article concluded that signal control based on multiagent reinforcement learning is a kind of closed-loop feedback adaptive control method, which outperforms many counterparts in terms of real-time characteristic, accuracy, and self-learning and therefore will be an important research focus of control method in future due to the property of “model-free” and “self-learning” that well accommodates the abundance of traffic information data. Besides, it will also provide an entry point and technical support for the development of Vehicle-to-X systems, Internet of vehicles, and autonomous driving industries. Therefore, the related achievements of the adaptive control system for the future traffic environment have extremely broad application prospects.

This paper presents an extensive investigation regarding the impacts of green signal countdown devices (GSCD) on the intersection safety and efficiency, based on field observation of critical driver and vehicle related parameters at two similar intersections (one with GSCD and the other without GSCD) in Shanghai. Statistical analysis results have revealed that the installation of GSCD can (1) encourage drivers to pass the stop-line during the amber time with higher speeds and thus result in better utilization of the amber time and increased capacity of the intersection approach; (2) smooth the driver’s response to the phase transition and effectively prevent the sudden change of speeds; (3) effectively eliminate the intersection dilemma zones by allowing the drivers to envision the phase transition and make decisions in advance; and (4) significantly reduce the number of red-light violations. Despite its effectiveness in facilitating the driver decision-making process, the installation of GSCD may adverse...

Bus-arrival-time information service is a key component of an advanced transit information system. Instantaneous and accurate prediction of bus arrival time can help improve the quality of service, and attracts additional ridership. On the bases of analyses of bus running processes, bus arrival time was divided into interzone link travel time and section travel time. A self-adaptive exponential smoothing-based algorithm was proposed for interzone link travel time prediction, whereas link-based and section-based algorithms were proposed for section travel time prediction. With the automatic vehicle location data collected from an actual bus route, an experiment was conducted to measure the performance (accuracy and precision in prediction) of link-based and section-based models with respect to three dominant factors: day of week, time of day, and length of segment. The research results show that: (1) the overall performance of section-based models is superior to that of link-based models on all weekdays bu...

UAV delivery and ground transfer scheduling in emergency scenarios represent critical technological systems for enhancing disaster response capabilities and safeguarding lives and property. This study systematically reviews recent advances across eight core research domains: UAV emergency delivery systems, ground–air integrated transportation coordination, emergency logistics optimization, UAV path planning and scheduling algorithms, collaborative optimization between ground vehicles and UAVs, emergency response decision support systems, low-altitude economy and urban air traffic management, and intelligent transportation system integration. Research findings indicate that UAV delivery technologies in emergency contexts have evolved from single-aircraft applications to intelligent multi-modal collaborative systems, demonstrating significant advantages in medical supply distribution, disaster relief, and search-and-rescue operations. Current technological development exhibits four major trends: hybrid optimization algorithms, multi-UAV cooperation, artificial intelligence enhancement, and real-time adaptation capabilities. However, critical challenges persist, including regulatory framework integration, adverse weather adaptability, cybersecurity protection, human–machine interface design, cost–benefit assessment, and standardization deficiencies. Future research should prioritize distributed decision architectures, robustness optimization, cross-domain collaboration mechanisms, emerging technology integration, and practical application validation. This comprehensive review provides systematic theoretical foundations and practical guidance for emergency management agencies in formulating technology development strategies, enterprises in investment planning, and research institutions in determining research priorities.

With rapid urbanization and increasing mobility demand, urban traffic systems face intensifying congestion, resulting in elevated CO2 emissions. This paper provides a systematic review of the current status of models estimating CO2 emissions from urban road traffic, considering their applicability across various traffic management scenarios. Urban road traffic CO2 emission models can generally be categorized into two main types. Traditional models typically estimate emissions based on average speed, traffic conditions, or vehicle operation modes, whereas data-driven models leverage techniques such as machine learning and deep learning to capture complex emission patterns. The review proposes a set of model selection criteria, namely data availability, computational complexity, interpretability, and transferability. Based on a comparative evaluation of these criteria, the study finds that there is no one-size-fits-all model so far. Instead, model suitability depends heavily on local data infrastructure and specific application needs. Therefore, future work needs to enhance model localization and personalization to improve estimation accuracy, while the integration of spatiotemporal data-driven modeling approaches is likely to become a research hotspot in upcoming studies.