# Influential Factors and Determination Method of Unconventional Outside Left-Turn Lanes Based on a BP Neural Network

^{*}

## Abstract

**:**

## 1. Introduction

#### 1.1. Background

- Qualitative analysis was made on the influencing factors of the setting of external left-turn lane in this paper. Factors such as traffic flow, left-turn traffic flow, weaving section length, and the number of through lanes are considered, and a thorough analysis was made about their impact on the delay of external entrance lane in the left-turn lane. To some extent, the findings in this paper fill the gap of the lack of comprehensive consideration of multiple factors in previously published literature.
- Comparisons are made between the simulated delay value and the predicted delay value, which is the feasibility of BP neural network prediction. Such an attempt, to some degree, makes up for the prediction research of solving traffic delays based on a BP neural network.

#### 1.2. Literature Review

## 2. Case Study

#### Traffic Survey Data

## 3. Methodology

#### 3.1. Analysis and of Influential Relationship

#### 3.2. BP Neural Network Delay Model

#### 3.2.1. Data Normalization

_{i}represents the normalized sample data, that is, the input data of the neural network model; x′

_{i}represents the original sample data; x′

_{min}represents the minimum value in the original sample data; x′

_{max}represents the maximum value in the original sample data.

#### 3.2.2. Network Structure Construction

#### 3.2.3. Network Training

_{ij}represents the connection weight value from the input layer to the hidden layer; θ

_{j}represents the hidden layer’s threshold value.

_{j}of the hidden layer can be obtained by substituting Formula (4) into the activation function, as shown in Formula (6). When the hidden layer is mapped to the output layer, the input value of the output layer is calculated according to Formula (7).

_{j}represents the connection weight value from the hidden layer to the output layer; α represents the threshold value of the output layer. Similarly, the output value of output layer is calculated according to Formula (8).

_{j}of the hidden layer is calculated according to Formula (15).

## 4. Simulation Experiment

#### 4.1. Influential Factor

#### 4.2. Experimental Scheme

- Import the base map into VISSIM and set the correct scale;
- Set the road sections according to the direction of actual traffic, and then connect the entry and exit routes by connectors in accordance with the way that vehicles pass in real life;
- Input the survey data into the system of VISSIM traffic to calibrate parameters, including traffic volume, traffic composition, path decision setting, signal timing scheme, and traffic signal lights;
- Set the data detector and then select result output items, such as delay time, journey time, and traffic data in the system of VISSIM.

#### 4.3. Parameter Calibration

- a.
- Routing Decision

- b.
- Traffic Compositions

- c.
- Desired Speed

#### 4.4. Experiment Conditions

## 5. Results

#### 5.1. BP Neural Network Prediction Results

#### 5.2. Simulation Verification with VISSIM

## 6. Discussion

## 7. Conclusions

- (1)
- From the perspective of delay, the outside left-turn lane is suitable for the following situation: more left-turn vehicles in the outside lane, more straight-going vehicles on the section, a shorter length of upstream weaving section, and more straight-going lanes at the intersection entrance.
- (2)
- According to the number of lanes, straight-going vehicles, left-turn vehicles, and the length of the weaving section, the delay model of the BP neural network can be used to calculate the traffic delay corresponding to the scheme of inside or outside left-turn lanes, respectively, and the analysis results of the model are in good agreement with the simulation results. The appropriate location of the left-turn lane can be determined through delay comparison.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Road conditions at the four intersections. (

**a**) The intersection of Southwest Rd. and Nansha St. (inside). (

**b**) The intersection of Southwest Rd.and Huanghe Rd. (inside). (

**c**) The intersection of Xinggong St. and Shenliao Rd. (outside). (

**d**) The intersection of Xinlong St. and Shiji Rd. (outside).

**Figure 2.**The influence of different numbers of left-turn vehicles and lengths of the weaving section on the delay. (

**a**) The number of straight-going vehicles is 600 veh/h. (

**b**) The number of straight-going vehicles is 750 veh/h. (

**c**) The number of straight-going vehicles is 900 veh/h. (

**d**) The number of straight-going vehicles is 1050 veh/h.

Location | Longitude Coordinates | Latitude Coordinates | Date | Period | Investigator (Individual) |
---|---|---|---|---|---|

The intersection of Southwest Rd. and Nansha St. | 121.57147 | 38.914697 | 12 July 2019 13 July 2019 | 7:00–8:00 17:00–18:00 | 8 |

The intersection of Southwest Rd. and Huanghe Rd. | 121.575113 | 38.920412 | 12 July 2019 13 July 2019 | 7:00–8:00 17:00–18:00 | 8 |

The intersection of Xinlong St. and Shiji Rd. | 123.46434 | 41.724777 | 5 July 2019 6 July 2019 | 7:00–8:00 17:00–18:00 | 8 |

The intersection of Xinggong St. and Shenliao Rd. | 123.38505 | 41.788174 | 5 July 2019 6 July 2019 | 7:00–8:00 17:00–18:00 | 8 |

Number of Straight-Going Vehicles | Curve Projection Equation |
---|---|

600 | $y=\left(1.005e-5\right){x}^{4}-0.0045{x}^{3}+0.768{x}^{2}-56.62x+1597$ |

750 | $y=0.00061{x}^{3}-0.2182{x}^{2}+26.54x-985.3$ |

900 | $y=1.685x-50.4$ |

Road Traffic Conditions | Lower Limit | Upper Limit | Steps | Number of Groups |
---|---|---|---|---|

Number of straight-going lanes | 2 | 3 | 1 | 2 |

Length of weaving section/m | 70 | 150 | 20 | 5 |

Number of left-turn vehicles/(veh/h) | 100 | 300 | 40 | 6 |

Number of straight-going vehicles/(veh/h) | 600 | 1050 | 150 | 4 |

Location of left-turn lane | inside | outside | - | 2 |

Entrance Road Number | Number of Left-Turn Vehicles (veh/h) | Number of Straight-Going Vehicles (veh/h) | Location of Left-Turn Lane | Length of Weaving Section (m) | Number of Straight-Going Lanes | ||
---|---|---|---|---|---|---|---|

Car | Large Vehicles | Car | Large Vehicles | ||||

1 | 480 | 48 | 609 | 123 | inside | 160 | 2 |

2 | 235 | 24 | 1809 | 131 | inside | 93 | 2 |

3 | 153 | 19 | 845 | 13 | outside | 90 | 2 |

4 | 391 | 41 | 1346 | 97 | outside | 100 | 3 |

Entrance Road Number | Inside Delay(s) | Outside Delay(s) | ||
---|---|---|---|---|

Model Value | Simulation Value | Model Value | Simulation Value | |

1 | 40.6 | 39.4 | 48.7 | 50.7 |

2 | 60.3 | 58.7 | 73.6 | 71.5 |

3 | 86.6 | 88.7 | 59.0 | 59.6 |

4 | 98.5 | 96.5 | 70.9 | 71.6 |

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**MDPI and ACS Style**

Cao, Y.; Jiang, D.; Li, X. Influential Factors and Determination Method of Unconventional Outside Left-Turn Lanes Based on a BP Neural Network. *Appl. Sci.* **2022**, *12*, 6026.
https://doi.org/10.3390/app12126026

**AMA Style**

Cao Y, Jiang D, Li X. Influential Factors and Determination Method of Unconventional Outside Left-Turn Lanes Based on a BP Neural Network. *Applied Sciences*. 2022; 12(12):6026.
https://doi.org/10.3390/app12126026

**Chicago/Turabian Style**

Cao, Yi, Dandan Jiang, and Xuetong Li. 2022. "Influential Factors and Determination Method of Unconventional Outside Left-Turn Lanes Based on a BP Neural Network" *Applied Sciences* 12, no. 12: 6026.
https://doi.org/10.3390/app12126026