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We analyze the effect of a bicycle lane on traffic speeds. Computer vision techniques are used to detect and classify the speed and trajectory of over 9,000 motor-vehicles at an intersection that was part of a pilot demonstration in which a bicycle lane was temporarily implemented. After controlling for direction, hourly traffic flow, and the behavior of the vehicle (i.e., free-flowing or stopped at a red light), we found that the effect of the delineator-protected bicycle lane (marked with traffic cones and plastic delineators) was associated with a 28 % reduction in average maximum speeds and a 21 % decrease in average speeds for vehicles turning right. For those going straight, a smaller reduction of up to 8 % was observed. Traffic moving perpendicular to the bicycle lane experienced no decrease in speeds. Painted-only bike lanes were also associated with a small speed reduction of 11–15 %, but solely for vehicles turning right. These findings suggest an important secondary benefit of bicycle lanes: by having a traffic calming effect, delineated bicycle lanes may decrease the risk and severity of crashes for pedestrians and other road users.
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This content will become publicly available on November 1, 2025
A data-driven traffic shockwave speed detection approach based on vehicle trajectory data
Traffic shockwaves demonstrate the formation and spreading of traffic fluctuation on roads. Existing methods mainly detect the shockwaves and their propagation by estimating traffic density and flow, which presents weaknesses in applications when traffic data is only partially or locally collected. This paper proposed a four-step data-driven approach that integrates machine learning with the traffic features to detect shockwaves and estimate their propagation speeds only using partial vehicle trajectory data. Specifically, we first denoise the speed data derived from trajectory data by the Fast Fourier Transform (FFT) to mitigate the effect of spontaneous random speed fluctuation. Next, we identify trajectory curves’ turning points where a vehicle runs into a shockwave and its speed presents a high standard deviation within a short interval. Furthermore, the Density-based Spatial Clustering of Applications with Noise algorithm (DBSCAN) combined with traffic flow features is adopted to split the turning points into different clusters, each corresponding to a shockwave with constant speed. Last, the one-norm distance regression method is used to estimate the propagation speed of detected shockwaves. The proposed framework was applied to the field data collected from the I-80 and US-101 freeway by the Next Generation Simulation (NGSIM) program. The results show that this four-step data-driven method could efficiently detect the shockwaves and their propagation speeds without estimating the traffic densities and flows nearby. It performs well for both homogenous and nonhomogeneous road segments with trajectory data collected from total or partial traffic flow.
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- PAR ID:
- 10472325
- Publisher / Repository:
- Talyor and Francis
- Date Published:
- Journal Name:
- Journal of Intelligent Transportation Systems
- ISSN:
- 1547-2450
- Page Range / eLocation ID:
- 1 to 17
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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