# Route Choice Behavior: Understanding the Impact of Asymmetric Preference on Travelers’ Decision Making

^{*}

## Abstract

**:**

## 1. Introduction

## 2. The Status Quo-Dependent Route Choice Model

R_{W} | the set of feasible routes for a given origin-destination (OD) pair w |

$r$ | the index for a route $r\in {R}_{w}$ |

$j$ | the index for a route $j\in {R}_{w}\backslash r$ |

${t}_{r}^{w}$ | the index for the travel time of route $r$ between OD pair $w$ |

${t}_{j}^{w}$ | the index for the travel time of route $j$ between OD pair $w$ |

${\tau}_{r}^{w}$ | the index for the monetary cost of route $r$ between OD pair $w$ |

${\tau}_{j}^{w}$ | the index for the monetary cost of route $j$ between OD pair $w$ |

${c}_{r}^{w}$ | the index for a pair of travel time and monetary cost of route $r$ between OD pair $w$ and ${c}_{r}^{w}=\left({t}_{r}^{w},{\tau}_{r}^{w}\right)$ |

## 3. Experiment Design

#### 3.1. Participants

#### 3.2. Experimental Design

#### 3.3. Procedure

- (1)
- At the initial time, each participant needed to input the answers about their WTP, i.e., how much you are willing to pay if travel time can save 10 min; how much you are willing to pay if travel time can save 25 min; and how much you are willing to pay if travel time can save 45 min.
- (2)
- During the experiment, in each test round, participants needed to choose one route from two non-dominated routes as their travel route, as can be seen in Figure 2. However, in order to reduce participants’ arbitrary choices as made by the continuous choice under the same scenarios, the three choice scenarios alternated at the computer screen. Moreover, the interval time between each test round was one minute.
- (3)
- The experiment ended, and each participant received a small gift as a show-up fee.

## 4. Results and Discussion

#### 4.1. The Values of Participants’ WTP and WTA

#### 4.2. Influence of Asymmetric Preference on Participants’ Route Choices

## 5. Model Parameters Estimation

#### 5.1. Estimation of ${\lambda}_{1}$ and ${\lambda}_{2}$

**Scene**

**1**

**Scene**

**2**

#### 5.2. Estimation of ${\eta}_{1}$ and ${\eta}_{2}$

## 6. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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Scenario 1 (Short) | Scenario 2 (Middle) | Scenario 3 (Long) | |||
---|---|---|---|---|---|

Route 1 | Route 2 | Route 1 | Route 2 | Route 1 | Route 2 |

(25 *, 3 **) | (10, ${\tau}_{2}^{1}+{\alpha}_{n}^{1}$) | (50, 10) | (30, ${\tau}_{2}^{2}+{\alpha}_{n}^{2}$) | (90, 20) | (60, ${\tau}_{2}^{3}+{\alpha}_{n}^{3}$) |

Scenario 1 (Short) | Scenario 2 (Middle) | Scenario 3 (Long) | |||
---|---|---|---|---|---|

Route 1 | Route 2 | Route 1 | Route 2 | Route 1 | Route 2 |

(24, ${\tau}_{1}^{1}+{\beta}_{k}^{1}$) | (12, ${\widehat{\tau}}_{2}^{1}$) | (45, ${\tau}_{1}^{2}+{\beta}_{k}^{2}$) | (25, ${\widehat{\tau}}_{2}^{2}$) | (85, ${\tau}_{1}^{3}+{\beta}_{k}^{3}$) | (65, ${\widehat{\tau}}_{2}^{3}$) |

Null Hypothesis | t-Test | Std. err | 95% Confidence Interval | Sig. | |
---|---|---|---|---|---|

Lower | Upper | ||||

H_{0}: WTP = WTA | 5.171 | 0.214 | 0.486 | 0.108 | 0.013 |

Case | Route A | Route B | |||||
---|---|---|---|---|---|---|---|

Settings | Number/Proportion | Settings | Number/Proportion | ||||

1 | (20, 10%) | 16 | 53.3% | (10, 20%) | 14 | 46.7% | |

2 | (20, 10%; 10, 80%) | 20 | 66.7% | (10, 100%) | 10 | 33.3% | |

3 | (20, 10%; 10, 20%) | 17 | 56.7% | (10, 40%) | 13 | 43.3% | |

4 | (20, 10%; 10, 70%) | 14 | 46.7% | (10, 90%) | 16 | 53.3% | |

5 | (20, 10%; 10, 30%) | 18 | 60.0% | (10, 50%) | 12 | 40.0% |

Case | Route R | Route S | |||||
---|---|---|---|---|---|---|---|

Settings | Number/Proportion | Settings | Number/Proportion | ||||

6 | (10, 10%) | 17 | 56.7% | (5, 20%) | 13 | 43.3% | |

7 | (10, 10%; 5, 80%) | 22 | 73.3% | (5, 100%) | 8 | 26.7% | |

8 | (10, 10%; 5, 20%) | 16 | 53.3% | (5, 40%) | 14 | 46.7% | |

9 | (10, 10%; 5, 70%) | 13 | 43.3% | (5, 90%) | 17 | 56.7% | |

10 | (10, 10%; 5, 30%) | 19 | 63.3% | (5, 50%) | 11 | 36.7% |

Scenario 1 (Short) | Scenario 2 (Middle) | Scenario 3 (Long) | ||||
---|---|---|---|---|---|---|

${\mathit{\eta}}_{1}^{1}$ | ${\mathit{\eta}}_{2}^{1}$ | ${\mathit{\eta}}_{1}^{2}$ | ${\mathit{\eta}}_{2}^{2}$ | ${\mathit{\eta}}_{1}^{3}$ | ${\mathit{\eta}}_{2}^{3}$ | |

Estimate | 0.462 | 1.513 | 0.574 | 1.421 | 0.632 | 1.357 |

Std. error | 0.214 | 0.476 | 0.246 | 0.432 | 0.284 | 0.407 |

t-Test | 2.040 | 4.741 | 2.268 | 4.470 | 2.441 | 4.132 |

P-Value | <0.001 | <0.001 | <0.010 | <0.001 | <0.001 | <0.001 |

$WTP={\eta}_{1}/{\lambda}_{2}{\eta}_{2}$ | 0.242 | 0.321 | 0.364 | |||

$WTA={\lambda}_{1}{\eta}_{1}/{\eta}_{2}$ | 0.427 | 0.570 | 0.647 |

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**MDPI and ACS Style**

Liu, K.; Xu, Y.
Route Choice Behavior: Understanding the Impact of Asymmetric Preference on Travelers’ Decision Making. *Symmetry* **2019**, *11*, 66.
https://doi.org/10.3390/sym11010066

**AMA Style**

Liu K, Xu Y.
Route Choice Behavior: Understanding the Impact of Asymmetric Preference on Travelers’ Decision Making. *Symmetry*. 2019; 11(1):66.
https://doi.org/10.3390/sym11010066

**Chicago/Turabian Style**

Liu, Kai, and Yuan Xu.
2019. "Route Choice Behavior: Understanding the Impact of Asymmetric Preference on Travelers’ Decision Making" *Symmetry* 11, no. 1: 66.
https://doi.org/10.3390/sym11010066