# Queue Spillover Management in a Connected Vehicle Environment

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## Abstract

**:**

## 1. Introduction

## 2. Method Principle

## 3. Intersection Signal Control Modeling

- The vehicle controller can provide spatial position and speed information to the intersection signal controller;
- Signal controllers of adjacent intersections can interact with each other;
- Keeping the same safety distance between the vehicles in line.

#### 3.1. Phase Maximum Control Distance

#### 3.2. Intersection Queue Overflow Judgment

- Step 1:
- The maximum control distance of intersection phase is assumed as ${L}_{c}$. The measured vehicle queue length of each cycle was ${L}_{r1}$, ${L}_{r2}$, ${L}_{r3}$, ….${L}_{ri}$. The queue length of the vehicle can be calculated by the position coordinates of the vehicle at the end of the queue; then get the vehicle queue length and maximum control distance differential record, as shown in Table 1.
- Step 2:
- In order to prevent the overflow of road queues, we need to find an optimal overflow control threshold value, which needs to be obtained according to the actual traffic conditions of the intersection roads. If 70% of the road length $L$ is the control limit $\left(0.7L\right)$, when the differential is reached $0.7L-{L}_{c}$, we need to take control measures.
- Step 3:
- When the control threshold of the intersection overflow is obtained, the overflow of the intersection can be judged according to the detection of the queue length. To eliminate the contingency of queuing, it is necessary to wait for at least one cycle, when the vehicle is detected at the overflow control limit. Then check whether the vehicle queuing trend continues to rise, if it continues, the judgment will be determined to overflow. Then the controller optimizes current phase and continues to detect the difference until it approaches zero.

#### 3.3. Signal Optimization Method

#### Current Phase Maximum Green Time Calculation

#### 3.4. Signal Optimization

## 4. Simulation Analysis

- (1)
- When the detector position was at 60% of the road, the blue line in the graph represents the change in the maximum queue length of the road. The initial moment of vehicle queuing was detected to be 320 s, and the arrival time of overflow was detected to be 500 s. The interval time was 180 s, that is, when the detecting point detects the vehicle, overflow will arrive after 1.5 cycles; so, the signal control needs to be implemented in 1.5 cycles.
- (2)
- When the detector position was at 70% of the road, the red line in the graph represents the change in the maximum queue length of the road. The initial moment of vehicle queuing was detected to be 440 s, and the arrival time of overflow was detected to be 500 s. The interval time was 60 s, that is, when the detecting point detects the vehicle, overflow will arrive after 0.5 cycles; so the signal control needs to be implemented in 0.5 cycles.
- (3)
- Similarly, when the detection point was located at 80% of the road, the signal control needs to be completed within 0.25 cycles.

## 5. Conclusions and Future Work

- (1)
- Establish vehicle travelling model;
- (2)
- Calculate the maximum control distance according to the model;
- (3)
- Queuing overflow logic judgment;
- (4)
- Optimal solution of queue overflow;
- (5)
- Simulation analysis.

## Author Contributions

## Funding

## Conflicts of Interest

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**Figure 1.**The schematic diagram of intersections between intersection and signal controller under Vehicle to Infrastructure (V2I).

${t}_{1}$ | ${t}_{2}$ | ${t}_{3}$ | ${t}_{i}$ | |

${L}_{r1}-{L}_{c}$ | ${L}_{r2}-{L}_{c}$ | ${L}_{r3}-{L}_{c}$ | $\dots $ | ${L}_{ri}-{L}_{c}$ |

Model and Lane Type | Design Speed (km/h) | |
---|---|---|

60 | ≤60 | |

Oversize vehicle or mixed lane (m) | 3.75 | 3.50 |

Passenger car lane (m) | 3.50 | 3.25 |

Direction | Vehicle Flowrate (V/h) |
---|---|

West | 600 |

East | 400 |

North | 400 |

South | 400 |

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**MDPI and ACS Style**

Ren, C.; Zhang, W.; Qin, L.; Sun, B. Queue Spillover Management in a Connected Vehicle Environment. *Future Internet* **2018**, *10*, 79.
https://doi.org/10.3390/fi10080079

**AMA Style**

Ren C, Zhang W, Qin L, Sun B. Queue Spillover Management in a Connected Vehicle Environment. *Future Internet*. 2018; 10(8):79.
https://doi.org/10.3390/fi10080079

**Chicago/Turabian Style**

Ren, Chuanxiang, Wenbo Zhang, Lingqiao Qin, and Bo Sun. 2018. "Queue Spillover Management in a Connected Vehicle Environment" *Future Internet* 10, no. 8: 79.
https://doi.org/10.3390/fi10080079