Driving Characteristics and Speed Behaviour Parameters of Direct Traffic at Intersections Based on Field Driving Tests

To reveal the speed control behaviour and manoeuvring characteristics of direct vehicles that stop-go through signalised intersections, a large-scale field driving test was carried out in Chongqing to collect vehicle data under natural driving conditions. The characteristics of speed, longitudinal acceleration rate and their two-dimensional correlation were analysed for deceleration and acceleration behaviour at signalised intersections. Further, a sensitivity analysis of the simulation model on measured data was done with the micro-traffic simulation experiment of a signalised intersection. The following were observed: (1) Drivers’ speed-selection behaviours become more concentrated with closer distance from the stop point. The transects ±25 m from the stop point are abrupt change points in the discrete nature of driver speed-selective behaviours. (2) Drivers’ desire to decelerate during the stop-go through signalised intersections is more robust, with the magnitude of pedal manoeuvres for deceleration behaviours being more intense than that for acceleration behaviours. (3) There is a nonlinear correlation between longitudinal acceleration rate and speed. The longitudinal acceleration rate increased with increase in speed and then decreased with the inflection point at 15 km/h. (4) The micro-traffic simulation’s acceleration rate model is sensitive to measured acceleration rate parameters. This study guides the parameter setting of speed, deceleration rate and acceleration rate models for microscopic traffic simulation and for parameter calibration of the car-following model.

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Publication Details

Journal
PROMET - Traffic&Transportation
Published
2024-12-20
DOI
https://doi.org/10.7307/ptt.v36i6.587
Citations
1
Primary Topic
Traffic Prediction and Management Techniques
Type
article
Field-Weighted Citation Impact
0.27
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article

Driving Characteristics and Speed Behaviour Parameters of Direct Traffic at Intersections Based on Field Driving Tests

Fanxi Kong, Jin Xu, Shanze Li, Y.S. Long et al.
1 citations
PROMET - Traffic&Transportation
Traffic Prediction and Management Techniques
0.27
article

Driving Characteristics and Speed Behaviour Parameters of Direct Traffic at Intersections Based on Field Driving Tests

Fanxi Kong, Jin Xu, Shanze Li, Y.S. Long, Yahui Guo Yahui Guo
article en
1 citations

Abstract

To reveal the speed control behaviour and manoeuvring characteristics of direct vehicles that stop-go through signalised intersections, a large-scale field driving test was carried out in Chongqing to collect vehicle data under natural driving conditions. The characteristics of speed, longitudinal acceleration rate and their two-dimensional correlation were analysed for deceleration and acceleration behaviour at signalised intersections. Further, a sensitivity analysis of the simulation model on measured data was done with the micro-traffic simulation experiment of a signalised intersection. The following were observed: (1) Drivers’ speed-selection behaviours become more concentrated with closer distance from the stop point. The transects ±25 m from the stop point are abrupt change points in the discrete nature of driver speed-selective behaviours. (2) Drivers’ desire to decelerate during the stop-go through signalised intersections is more robust, with the magnitude of pedal manoeuvres for deceleration behaviours being more intense than that for acceleration behaviours. (3) There is a nonlinear correlation between longitudinal acceleration rate and speed. The longitudinal acceleration rate increased with increase in speed and then decreased with the inflection point at 15 km/h. (4) The micro-traffic simulation’s acceleration rate model is sensitive to measured acceleration rate parameters. This study guides the parameter setting of speed, deceleration rate and acceleration rate models for microscopic traffic simulation and for parameter calibration of the car-following model.

PROMET - Traffic&TransportationVol. 36(6)
United States Department of Transportation (US), Southeast University (BD), China Railway Group (China) (CN), China Railway Eryuan Engineering Group Co., Chongqing Jiaotong University (CN)
Openalex Percentile: Top 40%
Traffic Prediction and Management Techniques
0.27
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