Virtual Detection at Intersections using Connected Vehicle Trajectory Data

Li, Howell; Day, Christopher M.; Bullock, Darcy M.

doi:10.1109/itsc.2016.7795969

Cited by 16 publications

(20 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 2 illustrates how virtual detections were extracted from the trajectory data (8). First, a detection line was identified by denoting two points that represented the left and right sides of a line perpendicular to the roadway and stretched from the centerline to the edge of the right-of-way (D L and D R , respectively).…”

Section: Methodsmentioning

confidence: 99%

Detector-Free Optimization of Traffic Signal Offsets with Connected Vehicle Data

Day

Richardson

et al. 2017

Transportation Research Record

Self Cite

View full text Add to dashboard Cite

Signal offset optimization recently has been shown to be feasible with vehicle trajectory data at low levels of market penetration. Offset optimization was performed on two corridors with that type of data. A proposed procedure called “virtual detection” was used to process 6 weeks of trajectory splines and create vehicle arrival profiles for two corridors, comprising 25 signalized intersections. After data were processed and filtered, penetration rates between 0.09% and 0.80% were observed, with variations by approach. Then those arrival profiles were compared statistically with those measured with physical detectors, and most approaches showed statistically significant goodness of fit at a 90% confidence level. Finally, the arrival profiles created with virtual detection were used to optimize offsets and compared with a solution derived from arrival profiles obtained with physical detectors. Results demonstrate that virtual detection can produce good-quality offsets with current market penetration rates of probe data. In addition, a sensitivity analysis of the sampling period indicated that 2 weeks may be sufficient for data collection at current penetration rates.

show abstract

Section: Methodsmentioning

confidence: 99%

Detector-Free Optimization of Traffic Signal Offsets with Connected Vehicle Data

Day

Richardson

et al. 2017

Transportation Research Record

Self Cite

View full text Add to dashboard Cite

show abstract

“…Compared with the existing works (2,(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22), more practical and emerged variables are covered in the proposed Bi-PCD. In addition, a new probabilistic surrogate quantification combined with an offset optimization is proposed to improve coordination performances.…”

Section: Methodsmentioning

confidence: 99%

“…However, CV-based signal coordination, with potential features such as high mobility, would offer remarkable and profound benefits over existing traditional signal coordination systems ( 1 , 7 ). As such, several CV-based coordination approaches have been proposed in recent years from both online control ( 6 , 7 , 11 ) and offline offset optimization ( 2 , 12 – 15 ) perspectives.…”

mentioning

confidence: 99%

“…To overcome the issue of low CV market penetration rates, CV-based offline offset optimization has been increasingly investigated by integrating CV-like or CV-based data into coordination systems over the last decade ( 2 , 12 – 15 ). The first core component of this optimization is utilizing a coordination diagram which combines a detection event and a signal timing using fixed advanced detectors, called the Purdue coordination diagram (PCD) ( 16 , 17 ).…”

mentioning

confidence: 99%

“…The second component of this optimization is to replace traditional data with obtained CV-like or CV-based data for both visual and quantitative optimizations. A few CV-based coordination methods are proposed ( 2 , 12 – 15 ). Day et al ( 2 , 12 , 13 ) presented detector-free offset optimization works, where CV-based trajectories were used to generate virtual detections.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Platoon Priority Visualization Modeling and Optimization for Signal Coordination in the Connected Vehicle Environment

Qiu

Seraj

et al. 2019

Transportation Research Record

View full text Add to dashboard Cite

Although connected vehicle (CV)-based signal coordination has some proposed prototypes and has been investigated by several different strategies, the existing works have issues that require attention, including additional CV-based system uncertainties and platoon priority request considerations. Thus, a generalized framework consisting of a platoon-based bicyclic coordination diagram (Bi-PCD), a new probabilistic surrogate quantification, and a platoon priority-based offset optimization in the CV environment is proposed to improve the coordination performance. The proposed Bi-PCD extends the scope of the current Purdue coordination diagram (PCD) and its variants by covering extra practical and emerged variables. A prototype and field tests in a CV test pilot for general scenarios were implemented in a typical arterial road to verify performances of the proposed Bi-PCD and the offset optimization method. Field results demonstrated that using Bi-PCD could obtain explicit platoon features in both limited and full CV penetration conditions. The comprehensive analysis showed that the proposed Bi-PCD and the platoon priority-based offset optimization could further improve performances of the signal coordination.

show abstract

Real‐time predictive coordination based on vehicle‐triggered platoon dispersion in a low penetration connected vehicle environment

Peng

Qiu

2021

IET Intelligent Trans Sys

View full text Add to dashboard Cite

The connected vehicle (CV) technology can benefit signal coordination with fine-grained spatial and temporal vehicle and infrastructure data via real-time communication. Although CV-based signal coordination systems have been investigated from offline and online strategic perspectives, existing works have yet to address certain coordination performance issues, including the dynamic platoon dispersion effect and low penetration impact. Targeting at resolving these issues, this work proposes a real-time predictive coordination method consisting of a probabilistic single-vehicle-based dynamic platoon dispersion model, an extended link performance function, and a real-time model predictive control (MPC)-based coordination framework. The proposed coordination method was comprehensively investigated by a software-in-loop simulation platform with different practical corridor scenarios in the ACTIVE CV testbed in Canada. Results show the proposed coordination control continuously outperformed existing signal control with lower delays for major streets with different demand profiles and different CV penetration rates, even in low penetration conditions. In conclusion, the proposed CV MPC-based coordination can offer significant potential to further improve the system performance of signal coordination in a low penetration environment; therefore, it has the potential to enhance other CV-based signal control applications in the initial deployment stage of CV technology.

show abstract

Virtual Detection at Intersections using Connected Vehicle Trajectory Data

Cited by 16 publications

References 15 publications

Detector-Free Optimization of Traffic Signal Offsets with Connected Vehicle Data

Detector-Free Optimization of Traffic Signal Offsets with Connected Vehicle Data

Platoon Priority Visualization Modeling and Optimization for Signal Coordination in the Connected Vehicle Environment

Real‐time predictive coordination based on vehicle‐triggered platoon dispersion in a low penetration connected vehicle environment

Contact Info

Product

Resources

About