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The Cellular Traffic Capacity Model of Toll Station Square

Bai Yuning, Wang Kaixuan, Yi Lei, Peng Bo

Archives of Transport

2023

Vol. 67, iss. 3

35--47

Traffic capacity is an important index to measure the operation efficiency of expressway toll stations. In order to provide relevant theoretical support for accurately evaluating the congestion degree and service level of toll stations, this paper establishes a traffic capacity calculation model for the square in front of expressway toll stations based on the traditional cellular transmission method. Firstly, by dividing the square in front of the station into regular cells, the process of calculating the traffic capacity is simplified; Secondly, the capacity model of the square in front of the station is established based on cellular transmission, and a large amount of data is collected through the monitoring videos of several toll stations. The theoretical capacity of the square in front of the station is calculated by using the important parameters of the model calibrated by the measured data under different lengths of the square in front of the station and the configuration of toll lanes. Then, the simulation platform of VISSIM software is used, and many experiments are carried out with relevant measured data to verify the accuracy of the model in multiple scenarios. Finally, the simulation value of the capacity of the square in front of the station is obtained, and the error is calculated by using the simulation value and the calculation value. The results show that the error of the verification result is 5.19%, and the error is within the allowable range, which shows that the model is accurate and feasible. The theoretical capacity calculated by the capacity model of the square in front of the toll station established in this paper is compared with the actual capacity, which can be used as a standard to judge the congestion degree of the square in front of the toll station and further provide a theoretical reference for evacuation of congestion.

Spatiotemporal attention mechanism-based multistep traffic volume prediction model for highway toll stations

Huang Zijing, Lin Peiqun, Lin Xukun, Zhou Chuhao, Huang Tongge

Archives of Transport

2022

Vol. 61, iss. 1

21--38

As the fundamental part of other Intelligent Transportation Systems (ITS) applications, short-term traffic volume prediction plays an important role in various intelligent transportation tasks, such as traffic management, traffic signal control and route planning. Although Neural-network-based traffic prediction methods can produce good results, most of the models can’t be explained in an intuitive way. In this paper, we not only proposed a model that increase the short-term prediction accuracy of the traffic volume, but also improved the interpretability of the model by analyzing the internal attention score learnt by the model. we propose a spatiotemporal attention mechanism-based multistep traffic volume prediction model (SAMM). Inside the model, an LSTM-based Encoder-Decoder network with a hybrid attention mechanism is introduced, which consists of spatial attention and temporal attention. In the first level, the local and global spatial attention mechanisms considering the micro traffic evolution and macro pattern similarity, respectively, are applied to capture and amplify the features from the highly correlated entrance stations. In the second level, a temporal attention mechanism is employed to amplify the features from the time steps captured as contributing more to the future exit volume. Considering the time-dependent characteristics and the continuity of the recent evolutionary traffic volume trend, the timestamp features and historical exit volume series of target stations are included as the external inputs. An experiment is conducted using data from the highway toll collection system of Guangdong Province, China. By extracting and analyzing the weights of the spatial and temporal attention layers, the contributions of the intermediate parameters are revealed and explained with knowledge acquired by historical statistics. The results show that the proposed model outperforms the state-of-the-art model by 29.51% in terms of MSE, 13.93% in terms of MAE, and 5.69% in terms of MAPE. The effectiveness of the Encoder-Decoder framework and the attention mechanism are also verified.