# Understanding Spatio-Temporal Characteristics of Urban Travel Demand Based on the Combination of GWR and GLM

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## Abstract

**:**

## 1. Introduction

## 2. Literature Review

## 3. Methodology

#### 3.1. Terms Definition

^{2}. It is used to evaluate the prediction accuracy and the degree of model fitting, with a value ranging from 0 to 100%. For instance, an adjusted ${R}^{2}$ equal to 0.49 means that the model explains 49% of the variation in travel.

#### 3.2. Explanatory Variables

#### 3.3. GWR Model

#### 3.4. GLM

## 4. Experiment

#### 4.1. Study Area

#### 4.2. Data Sources

#### 4.3. Data Preprocessing

- Taxi trips that do not contain the locations of pick-ups are removed.
- According to the grid cell decomposition method, the number of occurrences of Uber vehicles in each ZCTA is counted, as shown in Equation (6).$$\{\begin{array}{l}{p}_{i}=\frac{{s}_{i}}{{S}_{k}}\\ {N}_{m}={\displaystyle \sum _{n=1}^{n}{N}_{uk}\times {p}_{i}}\text{\hspace{1em}}i=(1,2,\dots I);k=\left(1,2,\dots K\right);m=\left(1,2,\dots 168\right)\\ {\displaystyle \sum _{m=1}^{M}{N}_{m}={\displaystyle \sum _{k=1}^{K}{N}_{uk}}}\end{array}$$

## 5. Results Analysis

#### 5.1. Statistical Features of Variables

#### 5.2. Travel Demand Modeling

#### 5.2.1. Test of Variables

#### 5.2.2. GWR Estimation

#### 5.2.3. GLM Estimation

#### 5.3. Discussion of the Relation between Variables and Travel Demand

## 6. Discussion and Conclusion

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 3.**The study and spatial distribution of pick-ups. (

**a**) Study area; (

**b**) The spatial distribution of pick-ups on Oct 5, 2015.

**Figure 4.**Frequency analysis of taxi trips. (

**a**) Frequency distribution of number of trips. (

**b**) Frequency distribution of log values.

**Figure 5.**The spatial distributions of road density and subway accessibility. (

**a**) Road density. (

**b**) Subway accessibility.

**Figure 9.**The spatio-temporal feature of coefficients associated with commuting time. (

**a**) Weekdays. (

**b**) Weekend.

**Figure 10.**The spatio-temporal feature of coefficients associated with commercial area. (

**a**) Weekdays. (

**b**) Weekend.

**Figure 11.**The spatio-temporal feature of coefficients associated with taxi-related accidents. (

**a**) Weekdays. (

**b**) Weekend.

Category | Variable | Description |
---|---|---|

Socioeconomic & Demographic | BS Pop | The proportion of population (25 years and over) with bachelor’s degree or higher to population in zip code tabulation areas (ZCTAs) |

His Pop | The proportion of Hispanic population to total population in ZCTAs | |

Employment | The logarithm of the number of people in employed in ZCTAs | |

Mean Inc | The mean income of resident in ZCTAs ($10,000) | |

Land use | Res Area | The proportion of residential floor area to the total floor area in ZCTAs |

Com Area | The proportion of commercial floor area to the total floor area in ZCTAs | |

Traffic environment | Uber | The logarithm of the number of Uber vehicles trips in ZCTAs |

Road Den | The length of link per square meter in ZCTAs (m-1) | |

Sub Ace | The subway accessibility in ZCTAs, the higher the value means more convenient to take the subway station | |

Bus Ace | The bus accessibility in ZCTAs, the higher the value means more convenient to take the bus stop | |

Com Time | The average commute time for person in ZCTAs (min) | |

Accident | The logarithm of the number of taxi-related accidents in ZCTAs | |

Bike | The logarithm of the number of bike trips in ZCTAs | |

Social media | POIs | The logarithm of the number of Points of interest (POIs) in ZCTAs |

Variable | Employment | Com Area | Uber | Road Den | Sub Ace | Com Time | POIs | Accidents | VIF |
---|---|---|---|---|---|---|---|---|---|

PPC | |||||||||

Employment | 1 | 2.549 | |||||||

Com Area | 0.012 | 1 | 1.356 | ||||||

Uber | 0.211 | 0.397 | 1 | 1.995 | |||||

Road Den | 0.457 | 0.229 | 0.526 | 1 | 2.351 | ||||

Sub Ace | 0.173 | 0.251 | 0.462 | 0.415 | 1 | 1.677 | |||

Com Time | −0.240 | −0.559 | −0.594 | −0.335 | −0.273 | 1 | 2.219 | ||

POIs | 0.293 | 0.409 | 0.644 | 0.479 | 0.461 | −0.575 | 1 | 2.249 | |

Accident | 0.204 | 0.368 | 0.463 | 0.512 | 0.523 | −0.538 | 0.558 | 1 | 2.250 |

p-Value | |||||||||

Emplyment | |||||||||

Com Area | <0.01 | ||||||||

Uber | <0.01 | <0.01 | |||||||

Road Den | <0.01 | <0.01 | <0.01 | ||||||

Sub Ace | <0.01 | <0.01 | <0.01 | <0.01 | |||||

Com Time | <0.01 | <0.01 | <0.01 | <0.01 | <0.01 | ||||

POIs | <0.01 | <0.01 | <0.01 | <0.01 | <0.01 | <0.01 | |||

Accident | <0.01 | <0.01 | <0.01 | <0.01 | <0.01 | <0.01 | <0.01 |

Variable | Moran’ I | Predicting Index | z-Score | p-Value |
---|---|---|---|---|

Employment | 0.181 | −0.06 | 6.744 | 0.000 |

Com Area | 0.310 | −0.06 | 8.875 | 0.000 |

Uber | 0.336 | −0.06 | 9.548 | 0.000 |

Road Den | 0.442 | −0.06 | 12.566 | 0.000 |

Sub Ace | 0.317 | −0.06 | 9.079 | 0.000 |

Com Time | 0.537 | −0.06 | 15.414 | 0.000 |

POIs | 0.363 | −0.06 | 10.283 | 0.000 |

Accident | 0.174 | −0.06 | 5.022 | 0.000 |

residuals | 0.176 | −0.06 | 6.186 | 0.000 |

Independent Variable | Min | Lower Quartile | Median | Upper Quartile | Max | Mean | STD |
---|---|---|---|---|---|---|---|

Weekday | |||||||

Intercept | −3.501 | −2.020 | −0.725 | 0.169 | 0.834 | 0.949 | 1.245 |

Employment | 0.0001 | 0.0003 | 0.0006 | 0.0011 | 0.0015 | 0.0007 | 0.00004 |

Com Area | −0.501 | −0.241 | −0.123 | 0.004 | 0.368 | −0.105 | 0.213 |

Uber | 0.691 | 0.787 | 0.903 | 1.009 | 1.121 | 0.902 | 0.122 |

Road Den | 0.210 | 0.278 | 0.375 | 0.481 | 0.611 | 3.854 | 1.145 |

Sub Ace | 0.032 | 0.020 | 0.060 | 0.028 | 0.056 | 0.534 | 0.254 |

Com Time | −0.048 | −0.039 | −0.032 | −0.024 | −0.010 | −0.031 | 0.010 |

POIs | 0.114 | 0.227 | 0.384 | 0.459 | 0.522 | 0.344 | 0.125 |

Accident | 0.461 | 0.569 | 0.757 | 0.889 | 1.006 | 0.737 | 0.163 |

Akaike information criterion (AIC) | 550.946 | ||||||

AICc | 555.140 | ||||||

R^{2} | 0.947 | ||||||

Adjusted R^{2} | 0.925 | ||||||

Residual Squares | 212.811 | ||||||

Weekend | |||||||

Intercept | −2.917 | −2.127 | −1.378 | −0.900 | −0.323 | −1.512 | 0.699 |

Employment | 0.0001 | 0.00013 | 0.00017 | 0.00021 | 0.0003 | 0.0002 | 0.00004 |

Com Area | −0.077 | 0.248 | 0.687 | 0.929 | 1.111 | 0.604 | 0.363 |

Uber | 0.918 | 0.979 | 1.056 | 1.087 | 1.173 | 1.041 | 0.066 |

Road Den | 0.493 | 0.540 | 0.620 | 0.747 | 0.907 | 0.650 | 0.121 |

Sub Ace | 0.022 | 0.031 | 0.023 | 0.050 | 0.081 | 0.032 | 0.031 |

Com Time | −0.079 | −0.074 | −0.070 | −0.061 | −0.053 | −0.067 | 0.007 |

POIs | 0.055 | 0.138 | 0.212 | 0.278 | 0.347 | 0.207 | 0.078 |

Accident | −0.163 | −0.110 | −0.091 | −0.025 | 0.012 | −0.076 | 0.051 |

AIC | 559.202 | ||||||

AICc | 563.234 | ||||||

R^{2} | 0.932 | ||||||

Adjusted R^{2} | 0.908 | ||||||

Residual Squares | 224.404 |

Variable | Weekday | Weekend | ||||
---|---|---|---|---|---|---|

Coefficient | t-statistic | p-Value | Coefficient | t-statistic | p-Value | |

Intercept | −0.134 | −0.065 | <0.01 | −0.642 | −0.652 | <0.01 |

Com Area | 0.073 | 3.657 | <0.01 | 0.604 | 2.905 | <0.01 |

Uber | 0.746 | 9.067 | <0.01 | 1.215 | 11.734 | <0.01 |

Road Den | 0.578 | 3.608 | 0.012 | 0.642 | 3.483 | <0.01 |

Sub Ace | −0.634 | 2.159 | <0.01 | −0.684 | 3.904 | <0.01 |

POIs | 0.293 | 3.086 | <0.01 | 0.295 | 1.960 | <0.01 |

Accident | 0.344 | 4.098 | <0.01 | −0.771 | 5.092 | <0.01 |

AIC | 632.327 | 641.675 | ||||

AICc | 621.462 | 638.218 |

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## Share and Cite

**MDPI and ACS Style**

Tang, J.; Gao, F.; Liu, F.; Zhang, W.; Qi, Y.
Understanding Spatio-Temporal Characteristics of Urban Travel Demand Based on the Combination of GWR and GLM. *Sustainability* **2019**, *11*, 5525.
https://doi.org/10.3390/su11195525

**AMA Style**

Tang J, Gao F, Liu F, Zhang W, Qi Y.
Understanding Spatio-Temporal Characteristics of Urban Travel Demand Based on the Combination of GWR and GLM. *Sustainability*. 2019; 11(19):5525.
https://doi.org/10.3390/su11195525

**Chicago/Turabian Style**

Tang, Jinjun, Fan Gao, Fang Liu, Wenhui Zhang, and Yong Qi.
2019. "Understanding Spatio-Temporal Characteristics of Urban Travel Demand Based on the Combination of GWR and GLM" *Sustainability* 11, no. 19: 5525.
https://doi.org/10.3390/su11195525