Abstract
The performance of a traffic system tends to improve as the percentage of connected vehicles (CV) in total flow increases. However, due to low CV penetration in the current vehicle market, improving the traffic signal operation remains a challenging task. In an effort to improve the performance of CV applications at low penetration rates, the authors develop a new method to estimate the speeds and positions of non-connected vehicles (NCV) along a signalized intersection. The algorithm uses CV information and initial speeds and positions of the NCVs from loop detectors and estimates the forward movements of the NCVs using the Gipps’ car-following model. Calibration parameters of the Gipps’ model were determined using a solver optimization tool. The estimation algorithm was applied to a previously developed connected vehicle signal control (CVSC) strategy on two different isolated intersections. Simulations in VISSIM showed the estimation accuracy higher for the intersection with less lanes. Estimation error increased with the decrease in CV penetration and decreased with the decrease in traffic demand. The CVSC strategy with 40% and higher CV penetration (for Intersection 1) and with 20% and higher CV penetration (for Intersection 2) showed better performance in reducing travel time delay and number of stops than the EPICS adaptive control.
Highlights
In recent years, the connected vehicle (CV) technology has been getting attention as a step towards the generation transportation system
The following can be said from the estimation accuracy results presented in Figure 5: 1) For a given intersection capacity utilization (ICU), with the decrease in the CV penetration (CVP), as the proportion of the non-connected vehicles (NCV) increases, the average speed and position root mean square error (RMSE) and the percent lane change of the NCVs increase in both Regions 1 and 2
4) For all the ICUs and all the CVPs, the average position RMSE in Region 1 was lower than in Region 2
Summary
The connected vehicle (CV) technology has been getting attention as a step towards the generation transportation system. Traffic signal systems under the CV environment can use real-time communication between vehicles (V2V), vehicles and infrastructure (V2I and I2V) and vehicles and handheld devices (V2D), enabling the access to detailed and instantaneous vehicle information such as its speed and location [1, 2]. Such real-time information can be used to design signal control strategies that can efficiently respond to the variations in traffic conditions. Various traffic scenarios were tested assuming 100% CV penetration (CVP)
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