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With the increasing density of the freeway network, frequent traffic incidents on road segments have a significant impact on the operational efficiency of the road network. Therefore, it has become urgent and important to study traffic route guidance strategy on the road network level. The previous traffic route guidance method primarily focused on the congestion on the road segments where incidents occurred, with insufficient attention given to the impact of congestion on the road network level. In this study, a route guidance model with limited overlap is proposed to improve freeway network reliability under traffic incidents. Specifically, in order to explore alternative paths, we conducted a study on the problem of finding k-short paths with limited overlap. The objective is to identify a set of k-paths that are both sufficiently dissimilar and as short as possible. Then, we promptly update the route guidance information using a stochastic dynamic traffic assignment model that aligns with travelers' path choice psychology. Moreover, we use the reliability of the road network to evaluate the network performance. To illustrate the model, the Jinan freeway network is selected as an experimental study. The effectiveness of this method was validated through SUMO simulations, comparing it with alternative route guidance methods, including Yen's algorithm, [sup.A∗] algorithm, and ant colony algorithm. These results show that the proposed method has proven effective in mitigating traffic congestion arising from incidents and performs well in regard to the reliability of the road network under the impact of incidents., Author(s): Xuan Zhang [1]; Jinjun Tang (corresponding author) [1]; Chengcheng Wang [2]; Chao Wang [2] 1. Introduction Freeway network plays an important role in establishing connectivity among cities. They are [...]

Driving style identification is of vital importance for intelligent driving system design and urban traffic management. This study aims to identify and analyze driving styles using large-scale ride-hailing GPS data taking different time periods, traffic, and weather conditions into account. The large-scale GPS data are collected and preprocessed, and then, the k-means clustering is implemented to acquire driving behavior. The modified latent Dirichlet allocation topic approach is applied to extract the driving states as the latent variables behind driving behaviors and finally recognize driving styles. The results show that driving styles are composed of five driving states with different probability combinations. Different driving styles in different situations are further analyzed and compared. When considering the impact of peak periods on the driving style, it indicates that styles tend to be conservative in the morning peak, free and dispersed in the evening peak, and diverse in the off-peak hours. While comparing styles regarding the influence of workdays, drivers act more cautiously and conservatively on weekdays but freer on weekends without the pressure of peak hours. The weather factor is also explored and rainy days are verified to be the resistance of driving so that most drivers become cautious and conservative. Finally, two aberrant driving styles are discovered and countermeasures are suggested to improve traffic safety., Author(s): Ye Li (corresponding author) [1]; Yiqi Chen [1]; Jie Bao [2]; Lu Xing [3]; Jinjun Tang [1]; Changyin Dong [4]; Ruifeng Gu [1] 1. Introduction Intelligent driving systems are [...]

Mapping relationships of multidimensional architectures play an essential role in the autonomous transportation system (ATS), which can help understand the complex relationships between multidimensional architectures. The current mapping relationship discovery for multidimensional architectures in ATS requires significant manual involvement, leading to the underutilization of textual data and the intense subjectivity of results. In order to address the above issues, it is necessary to mine and further utilize the semantic information in the textual data. This study applies the text-matching model to the mapping relationship discovery of multi-dimensional architectures, which can calculate the semantic similarity between texts. On this basis, a method based on the Siamese-BERT-wwm-Bi-LSTM model is proposed, which incorporates Chinese BERT with whole word masking (BERT-wwm), bidirectional long-short term memory (Bi-LSTM), and the Siamese Network. A series of experiments are conducted with different text-matching models. The results show that the precision rate, recall rate, and F1-score exceed 80% for most applied methods, which verifies the feasibility of using the text-matching model for mapping relationship discovery. These results expect to provide a method with good performance that can automatically perform mapping relationship discovery., Author(s): You Liu [1]; Haonan Tuo [1]; Mingfan He [1]; Qiang Fu [1]; Tianjian Yu (corresponding author) [1]; Jinjun Tang (corresponding author) [1] 1. Introduction With the development of emerging [...]

Travel patterns reflect the regularity of residents' mobility, and it is a crucial factor to evaluate the reasonability of urban spatial structure and connectivity of road networks. Therefore, exploring travel patterns is of practical significance for urban planning, traffic management, and improvement of the operational efficiency of the transportation system. In this study, we apply the tensor model to explore travel patterns under temporal and spatial dimensions based on the license plate recognition (LPR) data collected from the Changsha city, China. As travel patterns are influenced by many variables, a method framework based on the tensor model is proposed to explore the influence of variables on travel characteristics. Firstly, we apply clustering algorithms and the principal component analysis method to extract main feature variables, which can achieve the purpose of dimensionality reduction and eliminate the complex collinearity among variables. Then, the tensor decomposition and reconstruction algorithms are performed based on extracted feature variables to analyze their influence on travel patterns. The experiments demonstrate the advantages of the proposed method framework., Author(s): Zhaoxin Liu [1]; Xiaolu Wang [2]; Yufeng Bi [3]; Jun Kong [1,4]; Run Xu [3]; Yuanpei Chen [1,4]; Jinjun Tang (corresponding author) [2] 1. Introduction The transportation system is [...]