Development of Speed Prediction Model for Mixed Traffic Conditions: Case Study of Urban Streets

Mondal, Satyajit; Arya, Vijai Kumar; Gupta, Ankit

doi:10.1007/978-981-13-7162-2_9

Cited by 1 publication

(1 citation statement)

References 5 publications

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“…However, space mean speed cannot be measured directly in the field and it is difficult to be measured based on traffic surveillance systems [4]. Various methods have been proposed to solve this problem [6], [7]. Lee et al used a convolutional neural network (CNN) to measure space mean speed on straight road sections based on generated images from cameras [4].…”

Section: Introductionmentioning

confidence: 99%

Intersection Congestion Analysis Based on Cellular Activity Data

Shen

Cheng

et al. 2020

IEEE Access

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Intersections are the key joint points on arterials in urban transportation networks. Congestions at intersection areas significantly degrade human travel efficiency. Therefore, analyzing congestion and exploring congestion detection technologies to mitigate intersection congestions is important for intelligent transportation systems. This paper proposed the space mean speed calculation method at intersection related areas and explored the relationship between space mean speed and the number of cellular activities around the examined intersections. The number of vehicles in queue in each traffic signal phase-cycle at signalized intersections in Taicang city of China was counted from videos to verify the effectiveness of our proposed space mean speed calculation method. The results showed that our proposed calculation method of space mean speed at intersection areas were effective in describing traffic status. The investigated relationship between space mean speed and the number of cellular activities indicated that the number of cellular activities near intersections was a promising indicator for congestion detection. INDEX TERMSIntersection, congestion analysis, cellular activity data.

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