# Timeliness-Aware On-Site Planning Method for Tour Navigation

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## Abstract

**:**

## 1. Introduction

- Algorithm A (Time Series Greedy Algorithm) is a greedy algorithm that identifies, in order, the top three spots with the maximum scores, considering only the next spot.
- Algorithm B (Whole Single Greedy Algorithm) is a greedy algorithm that selects, in order, the top three spots from the pairs of spots and times with the highest scores, taking into account the overall tour time.
- Algorithm C (Whole Greedy Algorithm with Search Width) is an extended version of Algorithm B. It is a greedy algorithm that searches the choices k by k in a tree structure.

- First, we design three algorithms for on-site tourism planning that takes into account the dynamic tourist context and the expected satisfaction of tourists in the future (Section 4).
- Secondly, we apply the three proposed algorithms to 20 PoIs in Higashiyama, Kyoto, Japan (season: autumn) to confirm the effectiveness and on-site of the proposed algorithms. Compared with the tourist routes published in Kyoto tourist magazines, Algorithms B and C confirm that each spot could be visited at a time of high satisfaction (Section 6).
- Finally, we compare the method of recommending based on the satisfaction with the next spot (Algorithm A) and the method of recommending based on the expected satisfaction of the next and subsequent spots (Algorithms B and C) in terms of total satisfaction and number of spots visited. We confirm that overall satisfaction and the number of spots to be visited will be improved by taking into account the level of expected satisfaction (Section 6 and Section 7).

## 2. Related Work

#### 2.1. Existing Work

#### 2.2. Problem of Existing Work and Positioning of Our Work

## 3. Preliminaries

#### 3.1. Static Score Component

#### 3.2. Dynamic Score Component

#### 3.3. Future Expected Score Component

## 4. On-Site Tour Planning Algorithm

#### 4.1. Setting Time Slot Width to Simplify the Problem

#### 4.2. Overview of Three Algorithms

**Time Series Greedy Algorithm (Algorithm A)**This is a greedy algorithm that considers only the evaluation values obtained for the next spot. An outline of the algorithm’s application to the assumed environment is shown in Figure 1. In this algorithm, the arrival time at each spot in the set of unvisited spots S is calculated based on the current location, taking into account the duration of the stay (stay time) and the travel time. Algorithm A identifies, in order, the spot with the maximum score considering only the next spot, and determines the top three tour scores (assuming $k=3$).

**Whole Single Greedy Algorithm (Algorithm B)**This is a greedy algorithm that takes into account the evaluations obtained for all of the time slots. An outline of the algorithm’s application to the assumed environment is shown in Figure 2. With Algorithm B, the top three tour routes are selected by considering the travel time to each spot and the duration of the stay (stay time) in the list of spots to be visited, Z.

**Whole Greedy Algorithm with Search Width (Algorithm C)**This is a greedy algorithm that considers the evaluations which obtained up to the top k rank in all time slots. An outline of the algorithm’s application to the assumed environment is shown in Figure 3. With Algorithm C, the top three tour routes are determined by recursively selecting spots within the top k of the total tour time, taking into account the travel time and duration of stay for each spot in the list of spots to be visited, Z.

#### 4.3. Algorithm A (Time Series Greedy Algorithm)

#### 4.3.1. Details of Algorithm A

**Main**), the algorithm outputs the results of the route with the top three tour scores (list of spots to be visited), which is the sum of the evaluation value of the next spot calculated in Algorithm 1—P2 (

**GetOptRoutes**) and the future expected score calculated in Algorithm 1—P3 (

**GetEVRoutes**), and the tour score of each route. In Algorithm 1—P2 (

**GetOptRoutes**), the algorithm calculates the evaluation value (the sum of the static and dynamic scores) for each spot in the set of unvisited spots S. At this time, the selected tourist spot is stored in Z, the list of spots to be visited. Then, using the updated set of unvisited spots ${S}_{remain}$, the list of spots to be visited Z, and the tourism time T as arguments, the algorithm calculates the tour score in Algorithm 1—P3 (

**GetEVRoutes**). In Algorithm 1—P3 (

**GetEVRoutes**), the algorithm selects the spot with the lowest tourism time among the spots stored in Z. For each spot in set ${S}_{remain}$ (spots not yet visited), the evaluation value of the arrival time is calculated, taking into account the travel time and stay time for each spot. It then calculates the evaluation value of each spot and adds the spot with the highest evaluation value to Z, the list of spots to be visited. The spots added to Z are removed from the set of unvisited spots ${S}_{remain}$. The same process is repeated until ${T}_{end}$ at the end of the tour, or until the set of unvisited spots is empty. The total evaluation value of the spots in Z at the end of the tour is the tour score.

Algorithm 1 Algorithm A (Time Series Greedy Algorithm) |

#### 4.3.2. Example of Algorithm A

#### 4.4. Algorithm B (Whole Single Greedy Algorithm) and Algorithm C (Whole Greedy Algorithm with Search Width)

#### 4.4.1. Details of Algorithms B and C

**Main**) is identical to Algorithm 1—P1 (

**Main**). (Please refer to Section 4.3.)

**GetOptRoutes**), the algorithm calculates the evaluation value (the sum of the static and dynamic scores) for each spot in the set of unvisited spots S. At this time, the selected spot is stored in Z, the list of spots to be visited. Next, it calculates the tour score in Algorithm 2—P3 (

**GetEVRoutes**) as described below, with the search width k, current location $cp$, set of unvisited spots S, list of spots to be visited Z, and tourism time T as arguments. In Algorithm 2—P3 (

**GetEVRoutes**), the top k evaluated values of arrival time, spot are selected using the sort result $TS$ calculated. We then employ GetEVRoutes recursively with the search width k, current location $cp$, set of unvisited spots ${S}_{remain}$, replicated list of temporary spots to be visited ${Z}_{tmp}$, and tourism time T as arguments. $TS$ are sorted in descending order based on the tour time T, set of unvisited spots S, and list of spots to be visited Z. If more than one recurrence result is returned, the route with the best evaluation value among them is stored in ${Z}_{out}$. If there is no recurrence result, the result before the recurrence is stored in ${Z}_{out}$. When ${Z}_{out}$ is stored in ${R}_{out}$, if ${R}_{out}$ contains k routes, the iteration is terminated and ${R}_{out}$ at that time is returned. The total evaluation value of the spots stored in the list of spots to be temporarily visited at the end of the tour is the tour score.

Algorithm 2 Algorithm B (Whole Single Greedy Algorithm) and Algorithm C (Whole Greedy Algorithm with Search Width) |

#### 4.4.2. Example of Algorithms B and C

## 5. Experiment

#### 5.1. Objective of the Experiment

#### 5.2. Contents of the Experiment

## 6. Results

#### 6.1. Output Solutions

#### 6.2. Computation Times

#### 6.3. Setting the Width in Algorithm C

#### 6.4. Comparison with Model Routes

#### 6.5. Output Solution When the Experimental Environment Is Changed

## 7. Discussion

## 8. Conclusions and Future Work

## Author Contributions

## Funding

## Conflicts of Interest

## References

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Method | On-Site | Reflect Preferences | Timeliness | Future | ||
---|---|---|---|---|---|---|

Next-POI | Next-POI Based Route | Static | Dynamic | -Aware | Expection | |

Google Maps [34] | 🗸 | |||||

P-Tour [12,23] | 🗸 | 🗸(a) | ||||

CT-Planner [17,18] | 🗸 | |||||

Yuan et al [11] | 🗸 | 🗸 | ||||

The City Trip Planner [35,36] | 🗸 | 🗸 | 🗸(b) | |||

This Work | 🗸 | 🗸 | 🗸 | 🗸(c) | 🗸 | 🗸 |

Definition | Description |
---|---|

All spots set ${S}_{all}$ | A, B, C, D, E, F, G, H, I |

Set of visited spots ${S}_{visited}$ | B, H |

Set of unvisited spots S | A, C, D, E, F, G |

Tourism time T | 13:00–18:00 |

Time slot width $tl$ | 1h |

Current position $cp$ | I |

Current time $ct$ | 12:00 |

List of spots to be visited Z | $[ct,cp,0]$ |

List of spots to be visited on a temporary variable ${Z}_{tmp}$ | $\left\{\right\}$ |

Time | |||||||
---|---|---|---|---|---|---|---|

13:00 | 14:00 | 15:00 | 16:00 | 17:00 | 18:00 | ||

Spot | A | 7 | 3 | 4 | 5 | 6 | 7 |

B | 4 | 5 | 3 | 2 | 4 | 5 | |

C | 4 | 5 | 6 | 7 | 9 | 6 | |

D | 4 | 5 | 4 | 3 | 2 | 6 | |

E | 4 | 3 | 2 | 1 | 2 | 3 | |

F | 7 | 7 | 6 | 4 | 3 | 2 | |

G | 5 | 4 | 3 | 2 | 4 | 7 | |

H | 4 | 5 | 4 | 3 | 2 | 1 | |

I | 2 | 1 | 4 | 5 | 1 | 6 |

Symbol | Description | Symbol | Description |
---|---|---|---|

IK | Ishibe-Koji | SGR | Shore-in Gate Ruins |

RNT | Rokuhara Mitsuji Temple | KM | Kyoto Minamiza |

KCM | Kyoto Culture Museum | KYT | Kiyomizu Temple |

CIT | Chion-in Temple | CHT | Chorakuji Temple |

YK | Yasui Konpiragu | MP | Maruyama Park |

NM | Nishiki Market | KNT | Kenninji Temple |

KRGS | Kyoto Ryozan Gokoku Shrine | YS | Yasaka Shirine |

RD | Rokkakudo | TT | Tohukuji Temple |

HS | Hanamikoji Street | NZ | Ninenzaka |

KDT | Kodaiji Temple | SSD | Sanju Sangen Do |

Route | Result | Tour_Score | Count_Spot | Mean | |
---|---|---|---|---|---|

Algorithm A | Best | [13:20, KDT, 5.3], [14:00, KNT, 6.5], [14:50, SSD, 6.3], [16:10, CIT, 6.5], [16:50, SGR, 6.5], [17:30, MP, 5.6], [17:50, IK, 5.1] | 41.9 | 7 | 41.6 |

Second | [13:10, KNT, 6.6], [14:00, SSD, 6.3], [15:20, CIT, 6.1], [16:00, SGR, 6.0], [16:50, KDT, 5.7], [17:30, MP, 5.6], [17:50, IK, 5.1] | 41.4 | 7 | ||

Third | [13:20, SSD, 6.3], [14:30, KNT, 6.6], [15:20, CIT, 6.1], [16:00, SGR, 6.0], [16:50, KDT, 5.7], [17:30, MP, 5.6], [17:50, IK, 5.1] | 41.4 | 7 | ||

Algorithm B | Best | [13:10, HS, 5.0], [13:40, SGR, 5.8], [14:30, IK, 5.1], [15:00, SSD, 6.4], [16:30, KNT, 8.0], [17:30, CIT, 8.0] | 38.3 | 6 | 38.3 |

Second | [13:10, IK, 5.1], [13:40, SGR, 5.8], [14:30, HS, 5.1], [15:00, SSD, 6.4], [16:30, KNT, 8.0], [17:30, CIT, 8.0] | 38.3 | 6 | ||

Third | [13:10, YS, 5.0], [13:40, SGR, 5.8], [14:30, IK, 5.1], [15:00, SSD, 6.4], [16:30, KNT, 8.0], [17:30, CIT, 8.0] | 38.3 | 6 | ||

Algorithm C | Best | [13:10, YS, 5.0], [13:40, SGR, 5.8], [14:30, CHT, 2.3], [15:00, KNT, 6.6], [15:50, KDT, 5.3], [16:30, CIT, 7.5], [17:10, HS, 5.0], [17:30, MP, 5.6], [17:50, IK, 5.6] | 48.3 | 9 | 47.8 |

Second | [13:20, SGR, 5.8], [14:00, CHT, 2.3], [14:30, KNT, 6.6], [15:20, KDT, 5.3], [16:00, YS, 5.0], [16:30, CIT, 7.5], [17:10, HS, 5.0], [17:30, MP, 5.6], [17:50, IK, 5.6] | 48.2 | 9 | ||

Third | [13:10, KNT, 6.6], [14:00, SSD, 6.3], [15:20, SGR, 5.9], [16:00, YS, 5.0], [16:30, CIT, 7.5], [17:10, HS, 5.0], [17:30, MP, 5.6], [17:50, IK, 5.6] | 47.0 | 8 |

Computation Time (s) | ||||||
---|---|---|---|---|---|---|

First | Second | Third | Fourth | Fifth | Mean | |

Algorithm A | 1.8 | 2.0 | 1.9 | 1.8 | 1.8 | 1.9 ± 0.1 |

Algorithm B | 2.2 | 1.9 | 1.8 | 2.0 | 2.0 | 2.0 ± 0.1 |

Algorithm C | 29.9 | 25.5 | 26.3 | 25.1 | 28.0 | 27.0 ± 1.8 |

Route | Result | Tour_Score | Count_Spot |
---|---|---|---|

Model Route 1 | [13:20, KYT, 5.5], [14:40, KDT, 5.3], [15:20, MP, 4.6], [16:20, CIT, 6.5], [17:30, SGR, 5.5] | 27.3 | 5 |

Model Route 2 | [13:10, YS, 5.0], [13:30, IK, 5.1], [13:50, KRGS, 3.1], [15:20, KDT, 5.3], [16:00, CHT, 3.4], [16:30, MP, 4.9], [16:50, CIT, 7.0], [17:30, SGR, 5.5] | 39.8 | 8 |

Route | Result | Tour_Score | Count_Spot | Mean | |
---|---|---|---|---|---|

Algorithm C | Best | [13:10, HS, 3.0], [13:30, YS, 3.0], [14:00, CIT, 4.0], [14:50, KCM, 5.8], [16:40, KNT, 6.0], [17:30, IK, 3.1], [17:50, MP, 2.6] | 27.5 | 7 | 27.5 |

Second | [13:10, MP, 2.5], [13:30, YS, 3.0], [14:00, CIT, 4.0], [14:50, KCM, 5.8], [16:40, KNT, 6.0], [17:30, IK, 3.1], [17:50, HS, 3.0] | 27.5 | 7 | ||

Third | [13:10, YS, 3.0], [13:40, HS, 3.0], [14:00, CIT, 4.0], [14:50, KCM, 5.8], [16:40, KNT, 6.0], [17:30, IK, 3.1], [17:50, MP, 2.6] | 27.5 | 7 |

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## Share and Cite

**MDPI and ACS Style**

Isoda, S.; Hidaka, M.; Matsuda, Y.; Suwa, H.; Yasumoto, K. Timeliness-Aware On-Site Planning Method for Tour Navigation. *Smart Cities* **2020**, *3*, 1383-1404.
https://doi.org/10.3390/smartcities3040066

**AMA Style**

Isoda S, Hidaka M, Matsuda Y, Suwa H, Yasumoto K. Timeliness-Aware On-Site Planning Method for Tour Navigation. *Smart Cities*. 2020; 3(4):1383-1404.
https://doi.org/10.3390/smartcities3040066

**Chicago/Turabian Style**

Isoda, Shogo, Masato Hidaka, Yuki Matsuda, Hirohiko Suwa, and Keiichi Yasumoto. 2020. "Timeliness-Aware On-Site Planning Method for Tour Navigation" *Smart Cities* 3, no. 4: 1383-1404.
https://doi.org/10.3390/smartcities3040066