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The phenomenon of high-speed-car-following on Chinese highways

This study investigates the phenomenon of high-speed car following on Chinese highways for understanding traffic flow dynamics. Data from traffic videos reveal the characteristics and impact of High-Speed Car Following (HSCF) on different lanes and vehicle types. Results show how HSCF affects traffic flow, density, and driver behavior, with implications for road safety and traffic management. The analysis classifies HSCF into active and passive types, highlighting the risks and consequences associated with this driving behavior.

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The phenomenon of high-speed-car-following on Chinese highways

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  1. The phenomenon of high-speed-car-followingon Chinese highways MingminGuo, Zheng Wu Department of Mechanics and Engineering Science Fudan University

  2. Outline • Background and motivation • Data Sets • Results and analysis • Conclusions

  3. Background and motivation • Traffic flow is of complicated nonlinearity: stop-and-go phenomenon under high density. • We found the nonlinearity under low density on urban expressways: high-speed-car-following (HSCF).

  4. Background and motivation • Similar results in literatures U.S. highway 101, NGSIM data Chen et al., IEEE Trans. Intell. Transp. 2010: 11(4) : 773-785 German highway A1, loop detector data Neubert et al., PRE 1999, 60(6) : 6480-6490

  5. Background and motivation • Empirical observations play crucial roles. • Numerous important achievements • Next Generation Simulation (NGSIM) project • The traffic videos taken from the intercity highways are lacking. • So we studied the HSCF phenomenon on intercity highways based on traffic videos.

  6. Outline • Background and motivation • Data Sets • Results and analysis • Conclusions

  7. Data Sets • 100 hours of traffic video from fours locations

  8. Data Sets • Seven samples from 17.5 hours out of total videos • About 40,000 vehicles • 94392 speed data、28201 space headway data

  9. Data Sets • The vehicle class was collected. • The proportions of heavy vehicles of two frequently-used NGSIM data • I-80 (Berkeley Highway): 2.2% • US-101 (Hollywood Freeway): 3.8%

  10. Data Sets • Overview of the traffic situation

  11. Outline • Background and motivation • Data Sets • Results and analysis • Conclusions

  12. Results and analysis • Time headway (ht): the time difference between two successive vehicles passing the same location. • In this paper, it was calculated by where hsis space headway, v is speed.

  13. Results and analysis • The percentages of ht ≤ 2 s and ht ≤ 1 s are remarkable.

  14. Frequency distribution of the data with ht≤1 s in sample 3 for different speed ranges Results and analysis • The small ht are related to the driving behavior with high speed and small spacing, so-called “high-speed-car-following”.

  15. Proportion of the vehicles with ht≤1 s in sample 3 for each lane Results and analysis • The distributions of HSCF vehicles on each lane are different. Most of the HSCF vehicles are cars.

  16. Results and analysis • Dependence of the average speed at different lanes on the number of HSCF vehicles----in 1 min interval Sample 3, G2 Sample 5, G15w

  17. Results and analysis • Dependence of the flow at different lanes on the number of HSCF vehicles----in 1 min interval Sample 3, G2 Sample 5, G15w

  18. Results and analysis • Dependence of the density at different lanes on the number of HSCF vehicles----in 1 min interval Sample 3, G2 Sample 5, G15w

  19. Results and analysis • HSCF can roughly be classified into two types: Active HSCF Passive HSCF

  20. Results and analysis • HSCF often makes the rear vehicle’s driver take a lane-changing maneuver as well.

  21. Results and analysis • Dependence of the average frequency of lane-changing at different lanes on the number of HSCF vehicles----in 1 min interval

  22. Outline • Background and motivation • Data Sets • Results and analysis • Conclusions

  23. Conclusions • HSCF, such a dangerous driving behavior, has a relatively high frequency of occurrence on Chinese intercity highways. • Cars, fast lanes and locations near the urban area have higher proportion of HSCF than trucks, slow lanes and locations far from the urban area, respectively.

  24. Conclusions • In general, the flow and the density will increase as the frequency of HSCF increases. HSCF may affect the capacity and level of service. • As the frequency of HSCF increases, the average velocity of the left lane decreases, while the average velocity of the other lanes increases.

  25. Conclusions • HSCF can be classified into active type and passive type. The latter type is caused by lane-changing and overtaking. • HSCF may cause lane-changing as well, and the frequencies of them vary synchronously. • HSCF enhances the nonlinearity of traffic flowunder low density.

  26. Thank you!

  27. In sample 3 (09:37-11:24) • 258 cars with ht ≤ 1s were observed. • 13 trucks with ht ≤ 1s were observed. • Among the 258 HSCF cars, only 2 of them followed trucks. • Among the 13 HSCF trucks, more than a half, i.e. 7 of them, travelled after cars.

  28. The time series of the flow, the density and the velocity of sample 3

  29. The fundamental diagram of sample 3----in 1 min interval

  30. The fundamental diagram of sample 3---- based on the moving average of 15-min period

  31. The trends of the flow and the HSCF----based on the moving average of 15-min period

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