# Building the Traffic Flow Network with Taxi GPS Trajectories and Its Application to Identify Urban Congestion Areas for Traffic Planning

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## Abstract

**:**

## 1. Introduction

- (1)
- A new traffic flow network model is built based on taxi GPS trajectories. The model is scalable and can reflect the traffic status. The traffic flow network can be employed to investigate traffic congestion problems and facilitate decision making in traffic planning.
- (2)
- The traffic flow network is applied to an actual case of identifying the congestion areas.
- (3)
- Several key problems pertaining to the traffic flow network are discussed.

## 2. Method: Building the Traffic Flow Network

#### 2.1. Data Source

#### 2.2. Overview of Building the Traffic Flow Network

#### 2.3. Procedures of Building the Traffic Flow Network

#### 2.3.1. Data Selection

#### 2.3.2. Building of the Primitive Traffic Flow Network

#### 2.3.3. Building of the Final Traffic Flow Network

## 3. Application: Identifying the Congestion Areas for Traffic Planning

#### 3.1. Overview

#### 3.2. Local Weighted Nodal Metrics

#### 3.2.1. Nodal Strength

#### 3.2.2. Average Strength

#### 3.2.3. Weighted Clustering Coefficient

#### 3.2.4. Weighted Companion Behaviors

#### 3.3. Results of Identifying the Congestion Areas

#### 3.3.1. Statistics of Nodal Attribute Values

#### 3.3.2. Illustration of the Congestion Nodes

#### 3.3.3. Comparative Evaluation When Using Four Nodal Metrics

## 4. Discussion

#### 4.1. Reliability of the Traffic Flow Network in Identifying the Congestion Areas

#### 4.2. Advantages in the Use of the Traffic Flow Network

#### 4.3. Disadvantages in the Use of the Traffic Flow Network

#### 4.4. Outlook and Future Work

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Selected taxi GPS trajectories in morning rush hours. (

**a**) Overview; (

**b**) An illustration of random 1000 trajectory nodes.

**Figure 2.**The V/C of the morning peak during weekdays in Beijing [29].

**Figure 3.**Merging of the primitive traffic flow network. (

**a**) Original network; (

**b**) Replacement of the indexes (

**c**) Merging of the network; (

**d**) Updating of the indexes.

**Figure 6.**Illustration of calculation process. (

**a**) $JC$ of edge $ij$; (

**b**) Companion behaviors of node i.

**Figure 7.**Frequency distributions of influential values using the local weighted metrics. (

**a**) Nodal Strength; (

**b**) Average Strength; (

**c**) Weighted clustering coefficient; (

**d**) Weighted companion behaviors.

**Figure 8.**Distributions of top 300 congestion nodes identified using the weighted metrics. (

**a**) Nodal Strength; (

**b**) Average Strength; (

**c**) Weighted clustering coefficient; (

**d**) Weighted companion behaviors.

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**MDPI and ACS Style**

Qin, J.; Mei, G.; Xiao, L.
Building the Traffic Flow Network with Taxi GPS Trajectories and Its Application to Identify Urban Congestion Areas for Traffic Planning. *Sustainability* **2021**, *13*, 266.
https://doi.org/10.3390/su13010266

**AMA Style**

Qin J, Mei G, Xiao L.
Building the Traffic Flow Network with Taxi GPS Trajectories and Its Application to Identify Urban Congestion Areas for Traffic Planning. *Sustainability*. 2021; 13(1):266.
https://doi.org/10.3390/su13010266

**Chicago/Turabian Style**

Qin, Jiayu, Gang Mei, and Lei Xiao.
2021. "Building the Traffic Flow Network with Taxi GPS Trajectories and Its Application to Identify Urban Congestion Areas for Traffic Planning" *Sustainability* 13, no. 1: 266.
https://doi.org/10.3390/su13010266