Understanding Urban Traffic-Flow Characteristics: A Rethinking of Betweenness Centrality

2013 ◽  
Vol 40 (1) ◽  
pp. 135-153 ◽  
Author(s):  
Song Gao ◽  
Yaoli Wang ◽  
Yong Gao ◽  
Yu Liu
Keyword(s):  
Traffic Flow ◽  
Betweenness Centrality ◽  
Flow Characteristics ◽  
Urban Traffic ◽  
Traffic Flow Characteristics

scholarly journals Traffic Flow Parameters Collection under Variable Illumination Based on Data Fusion

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Shaojie Jin ◽  
Ying Gao ◽  
Shoucai Jing ◽  
Fei Hui ◽  
Xiangmo Zhao ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Millimeter Wave ◽  
Flow Characteristics ◽  
Vehicle Detection ◽  
Radar Data ◽  
Urban Traffic ◽  
Millimeter Wave Radar ◽  
Flow Parameters ◽  
Wave Radar ◽  
Traffic Flow Characteristics

Accurate traffic flow parameters are the supporting data for analyzing traffic flow characteristics. Vehicle detection using traffic surveillance pictures is a typical method for gathering traffic flow characteristics in urban traffic scenes. In complicated lighting conditions at night, however, neither classical nor deep-learning-based image processing algorithms can provide adequate detection results. This study proposes a fusion technique combining millimeter-wave radar data with image data to compensate for the lack of image-based vehicle detection under complicated lighting to complete all-day parameters collection. The proposed method is based on an object detector named CenterNet. Taking this network as the cornerstone, we fused millimeter-wave radar data into it to improve the robustness of vehicle detection and reduce the time-consuming postcalculation of traffic flow parameters collection. We collected a new dataset to train the proposed method, which consists of 1000 natural daytime images and 1000 simulated nighttime images with a total of 23094 vehicles counted, where the simulated nighttime images are generated by a style translator named CycleGAN to reduce labeling workload. Another four datasets of 2400 images containing 20161 vehicles were collected to test the proposed method. The experimental results show that the method proposed has good adaptability and robustness at natural daytime and nighttime scenes.

scholarly journals Semi-Continuous Spatial Statistical Analysis Using AIS Data for Vessel Traffic Flow Characteristics in Fairway

2021 ◽  
Vol 9 (4) ◽  
pp. 378
Author(s):  
Jong Kwan Kim
Keyword(s):  
Statistical Analysis ◽  
Traffic Flow ◽  
Flow Characteristics ◽  
Automatic Identification ◽  
Identification System ◽  
Spatial Statistical Analysis ◽  
Traffic Characteristics ◽  
Traffic Distribution ◽  
Traffic Flow Characteristics ◽  
Marine Accidents

As high vessel traffic in fairways is likely to cause frequent marine accidents, understanding vessel traffic flow characteristics is necessary to prevent marine accidents in fairways. Therefore, this study conducted semi-continuous spatial statistical analysis tests (the normal distribution test, kurtosis test and skewness test) to understand vessel traffic flow characteristics. First, a vessel traffic survey was conducted in a designated area (Busan North Port) for seven days. The data were collected using an automatic identification system and subsequently converted using semi-continuous processing methods. Thereafter, the converted data were used to conduct three methods of spatial statistical analysis. The analysis results revealed the vessel traffic distribution and its characteristics, such as the degree of use and lateral positioning on the fairway based on the size of the vessel. In addition, the generalization of the results of this study along with that of further studies will aid in deriving the traffic characteristics of vessels on the fairway. Moreover, these characteristics will reduce maritime accidents on the fairway, in addition to establishing the foundation for research on autonomous ships.

Traffic Flow Characteristics on a Three-Lane Rural Highway in Finland: Results from Empirical Studies and Simulations

1997 ◽  
Vol 1572 (1) ◽  
pp. 33-42 ◽  
Author(s):  
Åsa Enberg ◽  
Matti Pursula
Keyword(s):  
Traffic Flow ◽  
Flow Rate ◽  
Empirical Studies ◽  
Flow Characteristics ◽  
Field Studies ◽  
Optimum Length ◽  
Before And After ◽  
Traffic Flow Characteristics ◽  
Simulation Results ◽  
Rural Highway

The traffic-flow characteristics on an experimental, 20-km-long three-lane highway section in Finland were studied. The sections of highway that have a separate passing lane consist of three lanes. The central lane is assigned alternately to each direction as a passing lane with a length of 1.05 to 1.70 km. The lengths of the no-overtaking zones between successive passing lanes are 1.5 to 4.0 km. The traffic-flow characteristics on the three-lane highway have been observed by comprehensive before-and-after field studies and complementary simulations. Because it was possible to use passing lanes, the number of overtakings on the three-lane highway was remarkably higher than on the former two-lane highway. The overall average travel speeds were slightly higher, and the speed decreased a little more slowly with increasing flow on the three-lane compared with the two-lane highway. Overall platooning and mean platoon lengths decreased as a result of platoon dispersal on the passing lanes. The speeds used in the passing lanes were clearly higher than in the basic lanes. According to the simulation results, the optimum length for a single passing lane was between 0.5 and 2.5 km depending on flow rate and measure of effectiveness. For the actual three-lane highway conditions, passing lanes 1.0 to 1.5 km long seemed to bring the most benefits.

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