Short-Term Speed Forecasting of Large-Scale Urban Road Network Based on Transformer

2021 ◽  
Author(s):  
Jiawei Zhang ◽  
Maosi Geng ◽  
Jiangsa Gu ◽  
Xiqun (Michael) Chen
Keyword(s):  
Road Network ◽  
Large Scale ◽  
Short Term ◽  
Urban Road Network ◽  
Urban Road

scholarly journals Research on Large-Scale Road Network Partition and Route Search Method Combined with Traveler Preferences

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
De-Xin Yu ◽  
Zhao-Sheng Yang ◽  
Yao Yu ◽  
Xiu-Rong Jiang
Keyword(s):  
Data Storage ◽  
Road Network ◽  
Large Scale ◽  
Search Algorithm ◽  
Optimal Path ◽  
Search Method ◽  
Urban Road Network ◽  
Urban Road ◽  
Path Search ◽  
Route Search

Combined with improved Pallottino parallel algorithm, this paper proposes a large-scale route search method, which considers travelers’ route choice preferences. And urban road network is decomposed into multilayers effectively. Utilizing generalized travel time as road impedance function, the method builds a new multilayer and multitasking road network data storage structure with object-oriented class definition. Then, the proposed path search algorithm is verified by using the real road network of Guangzhou city as an example. By the sensitive experiments, we make a comparative analysis of the proposed path search method with the current advanced optimal path algorithms. The results demonstrate that the proposed method can increase the road network search efficiency by more than 16% under different search proportion requests, node numbers, and computing process numbers, respectively. Therefore, this method is a great breakthrough in the guidance field of urban road network.

scholarly journals Measuring and visualizing space–time congestion patterns in an urban road network using large-scale smartphone-collected GPS data

2017 ◽  
Vol 11 (7) ◽  
pp. 391-401 ◽  
Author(s):  
Joshua Stipancic ◽  
Luis Miranda-Moreno ◽  
Aurélie Labbe ◽  
Nicolas Saunier
Keyword(s):  
Road Network ◽  
Large Scale ◽  
Space Time ◽  
Gps Data ◽  
Urban Road Network ◽  
Urban Road

scholarly journals Bottleneck Based Gridlock Prediction in an Urban Road Network Using Long Short-Term Memory

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1412
Author(s):  
Ei Ei Mon ◽  
Hideya Ochiai ◽  
Chaiyachet Saivichit ◽  
Chaodit Aswakul
Keyword(s):  
Road Network ◽  
Short Term Memory ◽  
Road Networks ◽  
Sample Sizes ◽  
Short Term ◽  
Urban Road Network ◽  
Term Memory ◽  
Urban Road ◽  
Wide Range ◽  
Long Short Term Memory

The traffic bottlenecks in urban road networks are more challenging to investigate and discover than in freeways or simple arterial networks. A bottleneck indicates the congestion evolution and queue formation, which consequently disturb travel delay and degrade the urban traffic environment and safety. For urban road networks, sensors are needed to cover a wide range of areas, especially for bottleneck and gridlock analysis, requiring high installation and maintenance costs. The emerging widespread availability of GPS vehicles significantly helps to overcome the geographic coverage and spacing limitations of traditional fixed-location detector data. Therefore, this study investigated GPS vehicles that have passed through the links in the simulated gridlock-looped intersection area. The sample size estimation is fundamental to any traffic engineering analysis. Therefore, this study tried a different number of sample sizes to analyze the severe congestion state of gridlock. Traffic condition prediction is one of the primary components of intelligent transportation systems. In this study, the Long Short-Term Memory (LSTM) neural network was applied to predict gridlock based on bottleneck states of intersections in the simulated urban road network. This study chose to work on the Chula-Sathorn SUMO Simulator (Chula-SSS) dataset. It was calibrated with the past actual traffic data collection by using the Simulation of Urban MObility (SUMO) software. The experiments show that LSTM provides satisfactory results for gridlock prediction with temporal dependencies. The reported prediction error is based on long-range time dependencies on the respective sample sizes using the calibrated Chula-SSS dataset. On the other hand, the low sampling rate of GPS trajectories gives high RMSE and MAE error, but with reduced computation time. Analyzing the percentage of simulated GPS data with different random seed numbers suggests the possibility of gridlock identification and reports satisfying prediction errors.

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