# How Does the Location of Transfer Affect Travellers and Their Choice of Travel Mode?—A Smart Spatial Analysis Approach

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## Abstract

**:**

## 1. Introduction

## 2. Literature Review

## 3. Study Area and Data

^{2}. The recent report by the Queensland Government revealed that from January to March 2016, 27.38 million trips were conducted by bus, followed by 12.21 million trips by train, 1.71 million trips by ferry and 1.93 million trips by tram [29]. Bus ridership consisted of more than 63% of total transit ridership. This shows that the bus is the dominant transit mode in Brisbane. The benefit of the bus, in comparison to the train, tram and ferry, is that it has the flexibility to access almost all locations where a road network is present. The nature of buses travelling on existing road networks gives more feasibility of adapting to change, such as the addition of new bus routes to serve more destinations. These considerations have steered the scope of this research towards bus ridership in Brisbane.

## 4. Transformation Mapping of Transfer Coordinate

#### 4.1. Processing for Single-Transfer Journey Itineraries

#### 4.2. Transformation

- $O,T,D$ = Interest points of the journey triangle, OTD
- $OD$ = Distance between origin point and destination point
- $OT$ = Distance between origin point and transfer point
- $TD$ = Distance between transfer point and destination point

#### 4.3. Transfer Location Map

#### 4.4. Grid-Based Hierarchical Clustering

## 5. Mode Choice Analysis

^{2}at 0.29, whereas Model II increased it to 0.31. McFadden suggested ρ

^{2}values of between 0.2 and 0.4 should represent a very good fit of the model [46]. The increase in ρ

^{2}by Model II demonstrates that with the inclusion of the new variable, Model II has a better explanatory power on mode choice as compared to Model I.

^{2}) test was conducted to investigate the statistical improvement between Model I and Model II, by gauging the change in the log-likelihood function relative to the change in degrees of freedom. The chi-squared, χ

^{2}value of 5.04 exceeds the critical chi-squared of 1 degree of freedom of 3.84, at the 0.05 significant level. This gives a sufficient evidence to reject the null hypothesis that Model II is no better than Model I. With the inclusion of the transfer location variable into Model II, it outperforms Model I (base model).

#### Transfer Location and Transit Travel Time

## 6. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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

Socioeconomic Attributes | |

Gender | Nominal variables: 0—male; 1—female |

Age | Age of individuals |

Individual weekly income | Individuals’ weekly income, given in different income bracket |

Number of cars | Total number of cars per household |

Household size | Number of persons in the household |

Journey Attributes | |

Car travel time (minutes) | Total time taken to travel from origins to destinations using private vehicle |

Bus travel time (minutes) | Total time taken to travel from origins to destinations using bus |

Initial wait time (minutes) | Total wait time for the next available bus service |

First mile walk time (minutes) | Walk time taken to access bus station from origination |

Last mile walk time (minutes) | Walk time taken from bus station to destination |

Transfer Attributes | |

Proportion of in-vehicle bus travel time | Proportion of a journey spent on two buses |

Proportion of transfer walk time | Proportion of a journey spent on walking for a transfer |

Proportion of transfer wait time | Proportion of a journey spent on waiting for a transfer |

Type of transfer | Nominal variables: 0—non-walking transfer; 1—otherwise |

Transfer location | Ordinal and nominal variables: The cluster developed using smart card data (i.e., Cluster A–F encoded to 0–5), of which individual transfer location falls into |

Variables | Model I | Model II | ||||
---|---|---|---|---|---|---|

Base Model | Expanded Model | |||||

Coefficient | Std. Err. | Exp. β | Coefficient | Std. Err. | Exp. β | |

Constant | −0.85 | 1.27 | 0.43 | 0.35 | 1.39 | 1.42 |

Socioeconomic Attributes | ||||||

Individual weekly income | −0.00 *** | 0.00 | 1.00 | −0.00 *** | 0.00 | 1.00 |

Household size | 0.43 *** | 0.14 | 1.54 | 0.40 *** | 0.14 | 1.49 |

Number of cars | −1.22 *** | 0.24 | 0.29 | −1.31 *** | 0.25 | 0.27 |

Journey Attributes | ||||||

Car travel time (minutes) | 0.04 *** | 0.02 | 1.04 | 0.06 *** | 0.02 | 1.07 |

Initial wait time (minutes) | −0.03 | 0.02 | 0.97 | −0.03 * | 0.02 | 0.97 |

First mile walk time (minutes) | −0.08 * | 0.04 | 0.93 | −0.09 ** | 0.04 | 0.92 |

Last mile walk time (minutes) | −0.07 * | 0.04 | 0.93 | −0.08 ** | 0.04 | 0.92 |

Transfer Attributes | ||||||

Proportion of in-vehicle bus travel time | 3.90 ** | 1.54 | 49.45 | 4.24 ** | 1.59 | 69.27 |

Transfer location | Not included | −0.28 ** | 0.13 | 0.75 | ||

Number of observation | 393 | 393 | ||||

Log-likelihood function value: Constant only model | −172.99 | −172.99 | ||||

Log-likelihood function value: Parameterised model | −122.40 | −119.88 | ||||

Goodness of fit (McFadden rho squared) | 0.29 | 0.31 | ||||

Model Improvement Test: −2 *(log-likelihood of basic model—log-likelihood of expanded model) | 5.04 | |||||

Chi-critical based on 1 degree of freedom | 3.84 |

Variables | Model I | Model II | ||||
---|---|---|---|---|---|---|

Base Model | Expanded Model | |||||

Coefficient | Std. Err. | Exp. β | Coefficient | Std. Err. | Exp. β | |

Constant | 0.16 | 1.37 | 1.18 | −0.98 | 1.53 | 0.38 |

Socioeconomic Attributes | ||||||

Age | −0.03 ** | 0.01 | 0.97 | −0.03 *** | 0.01 | 0.97 |

Individual weekly income | −0.00 *** | 0.00 | 1.00 | −0.00 *** | 0.00 | 1.00 |

Household size | 0.34 ** | 0.14 | 1.41 | 0.32 ** | 0.15 | 1.37 |

Number of cars | −1.32 *** | 0.24 | 0.27 | −1.48 *** | 0.26 | 0.23 |

Journey Attributes | ||||||

Car travel time (minutes) | −0.05 *** | 0.02 | 0.95 | −0.07 *** | 0.02 | 0.94 |

Initial wait time (minutes) | −0.02 * | 0.01 | 0.98 | −0.02 ** | 0.01 | 0.98 |

Last mile walk time (minutes) | −0.07 * | 0.04 | 0.93 | −0.08 ** | 0.04 | 0.92 |

Transfer Attributes | ||||||

Proportion of in-vehicle bus travel time | 4.00 *** | 1.51 | 54.52 | 4.52 *** | 1.59 | 91.84 |

Transfer LocationThe reference category: Cluster F | ||||||

Cluster A | Not included | 1.79 * | 1.05 | 5.99 | ||

Cluster B | 2.34 ** | 0.98 | 10.40 | |||

Cluster C | 2.31 ** | 0.96 | 10.04 | |||

Cluster D | 1.99 ** | 1.01 | 7.30 | |||

Cluster E | 1.21 | 0.95 | 3.36 | |||

Number of observation | 393 | 393 | ||||

Log-likelihood function value: Constant only model | −172.99 | −172.99 | ||||

Log-likelihood function value: Parameterised model | −121.58 | −116.20 | ||||

Goodness of fit (Nagelkerke R Square) | 0.39 | 0.43 | ||||

Goodness of fit (McFadden R Square) | 0.30 | 0.33 | ||||

Model improvement test (Chi-squared test, χ^{2}):−2 *(log-likelihood of basic model—log-likelihood of expanded model) | 10.76 | |||||

The critical chi-squared value with 5 degrees of freedom at the 0.10 α-level | 9.24 | |||||

The critical chi-squared value with 5 degrees of freedom at the 0.05 α-level | 11.07 |

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**MDPI and ACS Style**

Chia, J.; Lee, J.; Han, H.
How Does the Location of Transfer Affect Travellers and Their Choice of Travel Mode?—A Smart Spatial Analysis Approach. *Sensors* **2020**, *20*, 4418.
https://doi.org/10.3390/s20164418

**AMA Style**

Chia J, Lee J, Han H.
How Does the Location of Transfer Affect Travellers and Their Choice of Travel Mode?—A Smart Spatial Analysis Approach. *Sensors*. 2020; 20(16):4418.
https://doi.org/10.3390/s20164418

**Chicago/Turabian Style**

Chia, Jason, Jinwoo (Brian) Lee, and Hoon Han.
2020. "How Does the Location of Transfer Affect Travellers and Their Choice of Travel Mode?—A Smart Spatial Analysis Approach" *Sensors* 20, no. 16: 4418.
https://doi.org/10.3390/s20164418