Yubin ZHENG, Cheng CHENG, Yong ZHANG, Lingyi WANG, Qixuan LI, Hailin ZHANG

Vehicle turn-in rate is a critical and widely adopted input for expressway rest area design and operation. With the implementation of expressway ETC gantries, the ERA turn-in rate can be further estimated by measuring the travel speed distribution via ETC gantry data. This paper proposed an adaptive density peak clustering Gaussian mixture model (ADPC-GMM) for ERA turn-in rate estimation. The ADPC algorithm is applied to generate the GMM’s inputs accommodating to the traffic characteristic of ERA expressway segments and GMM would further provide the turn-in rate estimation results. To validate the model precision, the turn-in rate data of four selected ERAs in Sichuan, China, as well as the ETC gantry data of their corresponding expressway sections are obtained. According to the estimation results, the MAE and RMSE are 0.0228 and 0.0267 for the passenger car scenario and 0.0264 and 0.0356 for the commercial truck scenario, respectively. These results are also at the lowest level compared with the results acquired from ordinary GMM, K-Means and DBSCAN algorithms. The proposed method has good applicability for vehicle turn-in rate estimation and can be deployed at different ERAs, especially those ERAs without traffic monitoring.

2024 (Vol 36), Issue 5

Mesut ULU, Yusuf Sait TÜRKAN

Traffic accidents are one of the main causes of fatalities and serious injuries among both adults and children worldwide. Due to the ongoing significant socio-economic losses brought on by traffic accidents, precise estimation of the risk of accidents is crucial to reducing subsequent incidents. For this reason, a significant proportion of the studies in the literature include studies on estimating the risk, severity, frequency, location and duration of accidents. The objective of this article is to identify patterns, gaps and future research trends in traffic accident prediction studies conducted between 2003 and 2023. A bibliometric study is carried out to investigate the links and trends in traffic accident and forecasting studies, with a focus on identifying dominant narratives and networks within the academic community. In the keyword search, 1,566 articles were analysed using the Web of Science main collection and bibliometric indicators such as annual publications and citations, top 10, authors, journals, institutions, most cited articles, and a citation analysis of the articles was presented. The results obtained suggest that the discernible patterns identified in this bibliometric analysis of traffic accidents and their predictions will find a much broader application in new paradigms that are ready to catalyse transformative advances in this field, such as artificial intelligence, machine learning and Industry 4.0 applications.

2024 (Vol 36), Issue 5

Meng ZHANG, Hua GUO, Jing-yang LI, Li LI, Feng ZHU

Low temperatures and icing in winter are significant factors that severely affect highway safety and traffic mobility. To enhance the precision and reliability of real-time winter road surface temperature (RST) prediction, a short-term prediction model is developed that harnesses both feature selection and deep learning. Leveraging meteorological data from a mountain highway in Yunnan, China, the key environmental variables affecting road surface temperature were first extracted using a random forest (RF) model for feature selection. These features were then combined with RST data to construct multiple groups of input variable combinations for the prediction model. A short-term prediction model with a 10-minute update frequency was built using a long short-term memory neural network (LSTM), namely RF-LSTM. The best input variable combination and preset parameters for the prediction model were determined through comparative testing with prevalent machine learning models, and the transferability of the prediction model was verified. The results showed that the best input variable combination for the RF-LSTM prediction model was road surface temperature and air temperature. The model recognised that the short-term RST was affected by long and short-term memory characteristics within a two-hour timeframe. When compared to the RF model, backpropagation (BP) neural network model and the standard LSTM model, the proposed model reduces prediction errors by 59.15%, 31.10% and 20.26%, respectively, while the prediction accuracy is 99.13% within an error margin of ±0.5℃. On the verification dataset, the proposed model maintains its time transferability with an average prediction absolute error of 0.0478. In all, the proposed model not only achieves a higher level of precision in real-time RST predictions but also ensures a more consistent and reliable performance under the challenging conditions of high altitude and mountainous terrain, offering enhanced support for traffic safety and road maintenance decision-making.

2024 (Vol 36), Issue 5

Luka DEDIĆ, Miroslav VUJIĆ

In urban networks, periodic peak traffic congestion often occurs during the day, namely in the morning and afternoon hours. Due to spatial constraints and the inability to increase capacity through physical road expansion, modern traffic management increasingly relies on Intelligent Transport Systems (ITS) solutions. One such solution is the integration of automatic licence plate recognition, an expert system and microsimulation tools aimed at optimising the network performance of signalised intersections within a network. Based on real-time and historical data on individual vehicle trajectories, the system predicts the route of each vehicle through the observed segment of the traffic network, determines the network load and proposes optimal signal plans. This paper provides an overview of conducted research related to the optimisation of signal plans utilising expert systems. Mathematical models for capacity and load determination, as well as computational intelligence-based systems used for signalised intersection management strategies, are described. Finally, the paper proposes a basic framework and guidelines related to the suggested system, highlighting open questions and potential challenges in its development.

2024 (Vol 36), Issue 5

Qing LIU, Qiwei QIAN

Airport clusters are of great significance to the sustainable development of the civil aviation transportation industry. The study utilises common frontier and super-efficiency DEA methods to assess the efficiency of China’s six major airport groups. It then employs the Malmquist index method to analyse changes in airport productivity. The results highlight regional disparities in airport efficiency. The East China Airport Group and the Southwest Airport Group consistently demonstrate excellent efficiency values, while the North China Airport Group and the Northeast Airport Group have significant room for improvement. Most airports within the groups operate at low and ineffective levels, with efficiency initially increasing and then decreasing. Moreover, the technology gap ratio (TGR) for each airport group somewhat shows a downward trend. The Malmquist index indicates that the overall factor productivity of each airport has generally remained stable, with efficiency growth primarily dependent on scale efficiency. On average, technical efficiency has increased by 1.5%. However, in terms of technological changes, most airports have experienced technological regression, indicating insufficient focus on technological improvement. Therefore, it is crucial to prioritise technological innovation and enhance management efficiency to achieve efficiency improvements in airport clusters. It is necessary to formulate strategies accurately based on the specific conditions of different regions, promote coordinated development, foster regional exchanges and cooperation, address regional disparities, ensure sustainable development of China’s airport clusters, and establish a world-class airport cluster.

2024 (Vol 36), Issue 5

Yi ZHANG, Wenfei HU, JunWu ZHANG, Jing ZHANG

Intelligent shipping is a crucial part of the transportation system, while inland river intelligent shipping is a major safeguard of intelligent transportation. Compared with the studies of mobile fading channels in land-based environments, less current research has focused on channel measurements and modeling for inland waterway bridge environments. In this paper, a segmenting radio channel model is proposed for inland highway and railway combined bridges. The ship's path under the bridge was divided into three phases, and the attenuation of signal strength was modelled separately for each. Hence, it shows ship-to-ship wireless channels in different areas and path loss on inland navigation bridges. A segmented model, instead of a basic path loss model, can accurately forecast path loss and provide a practical approach in ship-to-ship wireless channel transmission scenarios over bridges. Consequently, the channel measurements and modeling in the typical inland waterway are of great significance for establishing a reliable inland navigation broadband radio communication system.

2024 (Vol 36), Issue 5