# An Arch-Bridge Topology-Based Expressway Network Structure and Automatic Generation
## 1 Introduction  
As of the end of 2021, China's expressway mileage reached 169,100 km, ranking first in the world [1]. Currently, China's expressway electronic toll collection (ETC) system has achieved interconnectivity across 29 provinces [2]. Over 27,000 sets of ETC gantries have been installed, 48,000 ETC lanes have been renovated, and the total number of ETC users in the country has surpassed 250 million. As one of the world's largest Internet of Vehicles (IoV) systems, the ETC system undertakes daily non-stop toll collection for tens of thousands of vehicles and the clearing of expressway tolls in various provinces, accumulating a significant amount of ETC transaction data. Both domestic and foreign researchers are gradually conducting intelligent expressway research based on these data, including travel time estimation [3; 4; 5], traffic situation estimation [6; 7; 8; 9], visualization of traffic demand [10], identification of maximum speed limit information [11], automatic generation of ETC gantry position [12], abnormal data detection [13], etc. It is evident that research on intelligent expressways based on big ETC data mining is gaining momentum as a new research hotspot. The ETC gantry topology, as the most important network topology information of the ETC system, is not only the underlying foundation of the entire intelligent transportation system but also the key to the expansion of application services based on ETC data mining. However, the ETC gantry topology of expressways is not immutable; for example, the permanent addition or removal of roads or road maintenance causes permanent changes in the network topology and sudden events cause temporary changes in the network topology. Therefore, it is of great practical significance and application value to quicklygenerate and automatically update road network topology information for the service of intelligent expressways. The traditional method of extracting road network information mainly relies on professional surveyors driving road-measuring vehicles equipped with GPS devices for on-site measurement [14]. This method suffers from serious problems [15], such as long information acquisition cycles, high workload, and high cost, making it difficult to meet the travel needs of hundreds of millions of people in China. With the rapid development of advanced technologies such as big data mining and image recognition in recent decades, numerous domestic and foreign research scholars have conducted extensive research on the generation of road network information and obtained a batch of phased achievements, making the automatic generation of road network topology information possible. Currently, road network topology generation methods can be divided into two categories based on different data sources: remote sensing image recognition and spatiotemporal trajectory mining. Remote sensing image recognition mainly uses remote sensing images as data sources and, through a series of image recognition methods, such as restricted Boltzmann machine and convolutional neural network, extracts road areas, centerlines, and edges and other information to achieve the automatic recognition of road network topology information [16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35]. However, remote sensing image recognition has drawbacks, such as large computational complexity, complex remote sensing image backgrounds, and easy occlusion, and ETC gantries are very similar to traffic sign gantries, which can cause confusion. On the other hand, spatiotemporal trajectory mining mainly uses GPS trajectory data as data sources and employs big data mining methods such as clustering and map matching to extract and update road network information [36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58]. This is also the main method used in this study.