# Characterizing Trafﬁc Conditions from the Perspective of Spatial-Temporal Heterogeneity

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## Abstract

**:**

## 1. Introduction

## 2. Data and Data Processing

## 3. Methodology

#### 3.1. Ratio of Areas in Rank-Size Plots

**Y**be a set of random numbers. Rank these numbers in increasing order, and then obtain $\mathrm{Y}=\left\{{y}_{1},{y}_{2},\dots ,{y}_{n}\right\}$, where ${y}_{1}\le {y}_{2}\le \dots \le {y}_{n}$. Let

**X**be the orders of these increasing numbers, where $\mathrm{X}=\left\{{x}_{1}=1,{x}_{2}=2,\dots ,{x}_{n}=n\right\}$. The calculation method of RA is as follows:

#### 3.2. Characterize the Traffic Condition of a Network

#### 3.3. Characterize the Traffic Condition of a Single Road

## 4. Traffic Conditions Characterized

#### 4.1. The Measuring Scale

#### 4.2. Traffic Conditions of Shenzhen in One Week

#### 4.3. Congestion-Prone Roads in Shenzhen

## 5. Experiments

#### 5.1. Effect of the Measuring Scale

#### 5.2. Comparison to Ht-Index/CRG Index

#### 5.3. Necessity of Interpolations

## 6. Discussion

## 7. Conclusions

## Supplementary Files

Supplementary File 1## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

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**Figure 1.**Frequency of reports from the 358 monitored roads in a one-day period (from 20:20, 14 October 2015 to 20:15, 15 October 2015).

**Figure 2.**An example rank-size plot. Two areas in this plot are used for the calculation of the defined index. One area is the polygon filled with color, and the other one is the triangle ABC.

**Figure 3.**Traffic conditions of Shenzhen on (

**a**) 19; (

**b**) 21; (

**c**) 23; and (

**d**) 25 October 2015. These four days are Monday, Wednesday, Friday, and Sunday, respectively. The smaller the RA, the more congested the traffic network.

**Figure 4.**Traffic conditions of Shenzhen on 19 October 2015 (Monday) characterized with different measuring scales: (

**a**) 10 km/h; (

**b**) 1 km/h; and (

**c**) 0.1 km/h.

**Figure 5.**Traffic conditions of Shenzhen on 19 October 2015 (Monday) characterized by using (

**a**) the ht-index; (

**b**) the CRG index; and (

**c**) the RA.

Order | Name of Road | RA | Order | Name of Road | RA |
---|---|---|---|---|---|

1 | Mei Long (S. to N.) | 28.22% | 6 | Chang Chun (S. to N.) | 51.22% |

2 | Hua Qiang (N. to S.) | 42.16% | 7 | Ji Hua (N. to S.) | 52.26% |

3 | Bai Shi (W. to E.) | 47.74% | 8 | Xin An (W. to E.) | 60.63% |

4 | Liu Xian San (S. to N.) | 48.43% | 9 | Song Ming Da Dao (W. to E.) | 61.32% |

5 | Luo Hu Tao Yuan (E. to W.) | 50.17% | 10 | Luo Hu Tao Yuan (W. to E.) | 65.85% |

© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons by Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Gao, P.; Liu, Z.; Tian, K.; Liu, G.
Characterizing Trafﬁc Conditions from the Perspective of Spatial-Temporal Heterogeneity. *ISPRS Int. J. Geo-Inf.* **2016**, *5*, 34.
https://doi.org/10.3390/ijgi5030034

**AMA Style**

Gao P, Liu Z, Tian K, Liu G.
Characterizing Trafﬁc Conditions from the Perspective of Spatial-Temporal Heterogeneity. *ISPRS International Journal of Geo-Information*. 2016; 5(3):34.
https://doi.org/10.3390/ijgi5030034

**Chicago/Turabian Style**

Gao, Peichao, Zhao Liu, Kun Tian, and Gang Liu.
2016. "Characterizing Trafﬁc Conditions from the Perspective of Spatial-Temporal Heterogeneity" *ISPRS International Journal of Geo-Information* 5, no. 3: 34.
https://doi.org/10.3390/ijgi5030034