# Evaluation of Land-Use Layout of the Rail Station Area Based on the Difference in Noise Sensitivity to Rail Transit, Taking a Suburb of Tokyo as an Example

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Methods and Materials

#### 2.1. Research Scope

#### 2.2. Research Method

- (1)
- Hedonic Price Method and Multiple Regression Analysis

_{i}is the price of the “i” land parcel, and it reflects the value of the asset (unit: Yen/m

^{2}); U

_{ki}is the value of the $\mathrm{k}$ attribute of the “i” land parcel; ${\mathsf{\epsilon}}_{\mathrm{i}}$ is the random error term; and ${\mathrm{a}}_{0},{\mathrm{a}}_{\mathrm{k}}$ is the coefficient to be estimated. The model that is selected will directly affect the calculation result. Firstly, it is necessary to make a selection based on whether the independent variable can be taken as the logarithm; such content already existed in the last process in which the precondition was analyzed. Secondly, it is necessary to make a judgement by conducting every regression estimate according to the data obtained from the investigation. Through the calculation, the model adopted in this paper is the semi-logarithmic model, which will be described in detail later.

- (2)
- Acoustic environment simulation

#### 2.3. Data Acquisition

_{walking}and U

_{station}are alternative variables for characterizing the station’s accessibility. Later, one of them will be selected through correlation analysis to represent station accessibility for further regression analysis.

_{noise}is the level of noise loudness, and its rule is as follows: starting from 45 dB, the noise level will increase one level if the noise increases 5 dB every time. In other words, if L ≤ 45 dB, then U

_{noise}= 0; if 45 dB < L ≤ 50 dB, then U

_{noise}= 1; if 50 dB < L ≤ 55 dB, then U

_{noise}= 2……, and so on.

## 3. Analysis Process

#### 3.1. Regression Analysis of the Influence of Noise on Land Prices

_{R}” by taking the logarithm of the residential land price “P

_{R}”, it can be seen that the variable “lnP

_{R}” meets the requirements of normal distribution and meets the conditions of using stepwise regression analysis (Figure 4).

_{PS}, U

_{JHS}, U

_{rail}, U

_{FAR}, and U

_{station}are screened out.

_{noise}, U

_{station}, U

_{walking}, U

_{Yamanote}and U

_{commerce}as the final variables, and we obtained their coefficients (Table 6). Figure 5 shows that the standardized residual of this regression analysis conforms to the normal distribution.

_{k}refers to other characteristic variables other than track noise, and a

_{k}is its coefficient.

#### 3.2. Regression Analysis of the Influence of Station Accessibility on Land Price

#### 3.2.1. Commercial Plot

_{B}” in SPSS (Figure 7), it can be seen that the commercial land price is roughly normally distributed, so the characteristic variable model in a semi-logarithmic form can be used for further analysis.

#### 3.2.2. Residential Plot

_{R800}) for verification to confirm whether the impact of accessibility on residential land prices needs to be taken into account in the construction of the model. The Pearson correlation analysis between various factors and land prices shows that, within the 800 m radius of suburban rail transit stations, both the walking distance between the plot and the station and the straight-line distance between the plot and the station have little correlation with the residential land prices (Table 10).

#### 3.3. Evaluation of the Land-Use Layout Scheme Based on the Noise Impact

#### 3.3.1. Case Introduction

#### 3.3.2. Multiple Scheme Generation and Comparison

_{SUM}means the total land price losses caused by the impact of noise and the distance to the rail transit station. ΔP

_{Ri}means the area of the ith residential plot. S

_{j}is the area of the j-th commercial plot. ΔP

_{Bj}is the value of the loss in land price caused by the distance from the station of the j-th commercial plot.

## 4. Results and Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 3.**Schematic diagram for the selection of the measuring point of the noise loudness data at the land price data point. The blue line represents the nearest distance between the measured parcel and the track line, and gray thick lines means track lines.

**Figure 11.**Different schemes and noise simulation. “●” means the location of station; ①,②,③,④ means the serial number of blocks in schemes; Yellow indicates residential plots, while others are commercial office plots.

**Table 1.**Acoustic environment classification and requirements in Japan [31].

Type of Areas | Standard Value | |||
---|---|---|---|---|

Grade | Description | Difference | Daytime | Night |

AA | Area in which medical facilities and social welfare facilities were provided | 50 db | 40 db | |

A | Dedicated areas for residence | Base value | 55 db | 45 db |

Roads facing two or more carriageways | 60 db | 55 db | ||

B | Areas mainly for residence | Base value | 55 db | 45 db |

Roads facing two or more carriageways | 65 db | 60 db | ||

C | Mainly for commerce or industry Area in which a few residential buildings were constructed | Base value | 60 db | 50 db |

Facing roads with carriageways | 65 db | 60 db | ||

Special notes: | Regardless of the above table, the road approaching major traffic should observe the standard value. | 70 db | 65 db | |

Simultaneously, if a certain residential building is located at one side that is prone to noise, when their windows are closed, the noise transmitted indoors must meet the standard value. | 45 db | 40 db |

Name | Noise Volume | Name | Noise Volume | Name | Noise Volume |
---|---|---|---|---|---|

Chūō Line | 98 | Ikebukuro Line | 88 | Nambu Line | 79 |

Sagamihara Line | 96 | Haijima Line | 85 | Tomono Line | 78 |

Keiō Line | 95 | Hachikō Line | 83 | Tama Lake Line | 78 |

Den-en-toshi Line | 95 | Yokohama line | 81 | Itsukaichi Line | 78 |

Odawara line | 95 | Musashino Main line | 81 | Ōme Line | 77 |

Tama Line | 93 | Inokashira Line | 80 | Seibuen Line | 73 |

Toei Shinjuku Line | 92 | Tamagawa line | 79 | Racecourse Line | 65 |

Model Name | Function Expression |
---|---|

Linear form | ${\mathrm{P}}_{\mathrm{i}}={\mathrm{a}}_{0}+{\displaystyle \sum _{\mathrm{k}=1}^{\mathrm{m}}{\mathrm{a}}_{\mathrm{k}}}\cdot {\mathrm{U}}_{\mathrm{ki}}+{\mathsf{\epsilon}}_{\mathrm{i}}$ |

Semi-logarithmic form | ${\mathrm{lnP}}_{\mathrm{i}}={\mathrm{a}}_{0}+{\displaystyle \sum _{\mathrm{k}=1}^{\mathrm{m}}{\mathrm{a}}_{\mathrm{k}}}\cdot {\mathrm{U}}_{\mathrm{ki}}+{\mathsf{\epsilon}}_{\mathrm{i}}$ |

Log-linear form | ${\mathrm{lnP}}_{\mathrm{i}}={\mathrm{a}}_{0}+{\displaystyle \sum _{\mathrm{k}=1}^{\mathrm{m}}{\mathrm{a}}_{\mathrm{k}}}\cdot \mathrm{ln}{\mathrm{U}}_{\mathrm{ki}}+{\mathsf{\epsilon}}_{\mathrm{i}}$ |

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

Geographical position data | U_{Yamanote} | Shortest straight-line distance to the planar graph of the Yamanote Line |

U_{walking} | Walking distance to the nearest station | |

U | U_{station} | Straight-line distance to the nearest rail line |

U_{rail} | Straight-line distance to the nearest rail line | |

Supporting service data | U_{PS} | Straight-line distance to the planar graph of the nearest primary school |

U_{JHS} | Straight-line distance to the planar graph of the nearest junior high school | |

U_{commerce} | Percentage of the commercial land area within a 1 km range | |

Population data | U_{2015} | Population density of the local area in 2015 as published by the Ministry of Land, Infrastructure, Transport and Tourism, Japan |

U_{2020} | Population density of the local area in 2020 as published by the Ministry of Land, Infrastructure, Transport and Tourism, Japan | |

U_{growth} | Average population density growth rate of the local area between 2015 and 2020 | |

Land-use data | U_{FAR} | Plot ratio: ratio between residential community’s above-ground total building area and land parcel’s area |

U_{BD} | Building density: percentage of the total sum of the basal area of the buildings in a certain range and the total area of the occupied land parcel’s area | |

Station data | U_{PF} | Data of passenger flow in the station |

Noise date | U_{noise} | Noise level obtained from the measured noise value |

**Table 5.**Correlation and significance of the characteristic variable of the price of the land parcels within 200 m of the rail.

lnP_{R} | U_{noise} | U_{station} | U_{walking} | U_{BD} | U_{FAR} | U_{Yamanote} | U_{rail} | U_{PS} | U_{JHS} | U_{commerce} | ||
---|---|---|---|---|---|---|---|---|---|---|---|---|

Pearson correlation | lnP_{R} | 1.000 | −0.310 | −0.195 | −0.283 | 0.210 | 0.165 | −0.783 | 0.095 | −0.097 | −0.014 | 0.431 |

U_{noise} | 1.000 | −0.094 | −0.070 | 0.029 | −0.013 | 0.267 | −0.652 | −0.066 | −0.170 | −0.149 | ||

U_{station} | 1.000 | 0.591 | −0.132 | −0.098 | 0.059 | −0.037 | 0.098 | 0.129 | −0.113 | |||

U_{walking} | 1.000 | −0.160 | −0.164 | 0.058 | 0.024 | 0.098 | 0.111 | −0.133 | ||||

U_{BD} | 1.000 | 0.931 | 0.093 | −0.038 | −0.114 | −0.151 | 0.323 | |||||

U_{FAR} | 1.000 | 0.091 | −0.014 | −0.067 | −0.111 | 0.293 | ||||||

U_{Yamanote} | 1.000 | −0.065 | −0.143 | −0.199 | −0.116 | |||||||

U_{rail} | 1.000 | −0.013 | −0.004 | −0.005 | ||||||||

U_{PS} | 1.000 | 0.747 | −0.230 | |||||||||

U_{JHS} | 1.000 | −0.156 | ||||||||||

U_{commerce} | 1.000 | |||||||||||

Significance | lnP_{R} | 0.000 | 0.003 | 0.000 | 0.001 | 0.010 | 0.000 | 0.091 | 0.088 | 0.421 | 0.000 | |

U_{noise} | 0.092 | 0.164 | 0.344 | 0.430 | 0.000 | 0.000 | 0.176 | 0.008 | 0.018 | |||

U_{station} | 0.000 | 0.031 | 0.084 | 0.203 | 0.300 | 0.085 | 0.035 | 0.056 | ||||

U_{walking} | 0.012 | 0.010 | 0.208 | 0.367 | 0.083 | 0.059 | 0.030 | |||||

U_{BD} | 0.000 | 0.096 | 0.298 | 0.054 | 0.017 | 0.000 | ||||||

U_{FAR} | 0.102 | 0.422 | 0.172 | 0.059 | 0.000 | |||||||

U_{Yamanote} | 0.179 | 0.022 | 0.002 | 0.051 | ||||||||

U_{rail} | 0.427 | 0.480 | 0.472 | |||||||||

U_{PS} | 0.000 | 0.001 | ||||||||||

U_{JHS} | 0.014 |

Unstandardized Coefficient-B | Standard Error | Standardized Coefficient-Beta | t | Significance | |
---|---|---|---|---|---|

(constant) | 12.916 | 0.091 | 142.276 | 0.000 | |

U_{noise} | −0.038 | 0.014 | −0.094 | −2.786 | 0.006 |

U_{walking} | 0.000 | 0.000 | −0.186 | −5.645 | 0.000 |

U_{BD} | 0.008 | 0.002 | 0.170 | 4.904 | 0.000 |

U_{Yamanote} | −3.658 × 10^{−5} | 0.000 | −0.734 | −21.671 | 0.000 |

U_{commerce} | 5.838 × 10^{−7} | 0.000 | 0.252 | 7.225 | 0.000 |

**Table 7.**Correlation and significance of the characteristic variable of the price of commercial land within 800 m of the rail.

lnP_{B} | U_{FAR} | U_{walking} | U_{station} | U_{Yamanote} | U_{PF} | U_{2015} | U_{2020} | U_{growth} | U_{noise} | ||
---|---|---|---|---|---|---|---|---|---|---|---|

Pearson correlation | lnP_{B} | 1.000 | 0.664 | 0.030 | −0.166 | −0.469 | 0.735 | 0.134 | 0.146 | 0.273 | −0.072 |

U_{FAR} | 1.000 | 0.125 | −0.213 | −0.018 | 0.558 | 0.097 | 0.094 | 0.064 | −0.017 | ||

U_{walking} | 1.000 | 0.038 | 0.129 | 0.041 | −0.073 | −0.074 | −0.088 | 0.065 | |||

U_{station} | 1.000 | 0.018 | 0.188 | 0.138 | 0.136 | −0.017 | −0.375 | ||||

U_{Yamanote} | 1.000 | −0.238 | −0.403 | −0.421 | −0.494 | 0.080 | |||||

U_{PF} | 1.000 | 0.091 | 0.096 | 0.165 | −0.090 | ||||||

U_{2015} | 1.000 | 0.999 | 0.398 | −0.200 | |||||||

U_{2020} | 1.000 | 0.432 | −0.202 | ||||||||

U_{growth} | 1.000 | −0.183 | |||||||||

U_{noise} | 1.000 | ||||||||||

Significance | lnP_{B} | 0.000 | 0.336 | 0.009 | 0.000 | 0.000 | 0.029 | 0.019 | 0.000 | 0.154 | |

U_{FAR} | 0.038 | 0.001 | 0.400 | 0.000 | 0.085 | 0.091 | 0.182 | 0.406 | |||

U_{walking} | 0.294 | 0.033 | 0.280 | 0.151 | 0.146 | 0.106 | 0.180 | ||||

U_{station} | 0.399 | 0.004 | 0.025 | 0.026 | 0.404 | 0.000 | |||||

U_{Yamanote} | 0.000 | 0.000 | 0.000 | 0.000 | 0.129 | ||||||

U_{PF} | 0.099 | 0.086 | 0.009 | 0.100 | |||||||

U_{2015} | 0.000 | 0.000 | 0.002 | ||||||||

U_{2020} | 0.000 | 0.002 | |||||||||

U_{growth} | 0.004 |

Variables before regression analysis | U_{FAR}, U_{station}, U_{Yamanote}, U_{PF}, U_{2020}, U_{growth} |

Variables after regression analysis | U_{FAR}, U_{station}, U_{Yamanote}, U_{PF} |

Unstandardized Coefficient-B | Standard Error | Standardized Coefficient-Beta | t | Significance | 95.0% Confidence Interval of B | ||
---|---|---|---|---|---|---|---|

Lower Limit | Upper Limit | ||||||

(constant) | 12.802 | 0.127 | 100.438 | 0.000 | 12.551 | 13.054 | |

U_{PF} | 1.095 E−05 | 0.000 | 0.496 | 11.058 | 0.000 | 0.000 | 0.000 |

U_{station} | −0.001 | 0.000 | −0.180 | −4.905 | 0.000 | −0.001 | 0.000 |

U_{Yamanote} | −3.100 E−05 | 0.000 | −0.342 | −9.893 | 0.000 | 0.000 | 0.000 |

U_{FAR} | 0.002 | 0.000 | 0.343 | 7.838 | 0.000 | 0.002 | 0.003 |

**Table 10.**Correlation analysis on the land prices in the residential area within an 800 m radius of the rail transit station and all factors.

LnP_{R800} | U_{walking} | U_{BD} | U_{FAR} | U_{station} | U_{PS} | U_{JHS} | U_{rail} | U_{Yamanote} | U_{commerce} | ||
---|---|---|---|---|---|---|---|---|---|---|---|

Pearson correlation | LnP_{R800} | 1.000 | −0.054 | 0.198 | 0.165 | −0.085 | −0.014 | 0.056 | −0.080 | −0.804 | 0.424 |

U_{walking} | 1.000 | −0.127 | −0.130 | 0.769 | −0.035 | 0.040 | 0.210 | 0.034 | 0.112 | ||

U_{BD} | 1.000 | 0.915 | −0.083 | −0.010 | −0.118 | −0.074 | 0.072 | 0.366 | |||

U_{FAR} | 1.000 | −0.086 | −0.041 | −0.111 | −0.064 | 0.071 | 0.357 | ||||

U_{station} | 1.000 | −0.038 | 0.012 | 0.310 | 0.038 | 0.093 | |||||

U_{PS} | 1.000 | 0.646 | −0.088 | −0.147 | −0.152 | ||||||

U_{JHS} | 1.000 | −0.023 | −0.238 | −0.139 | |||||||

U_{rail} | 1.000 | 0.061 | −0.088 | ||||||||

U_{Yamanote} | 1.000 | −0.096 | |||||||||

U_{commerce} | 1.000 | ||||||||||

Significance | LnP_{R800} | 0.182 | 0.000 | 0.003 | 0.075 | 0.407 | 0.173 | 0.087 | 0.000 | 0.000 | |

U_{walking} | 0.015 | 0.014 | 0.000 | 0.275 | 0.248 | 0.000 | 0.282 | 0.029 | |||

U_{BD} | 0.000 | 0.079 | 0.433 | 0.022 | 0.104 | 0.112 | 0.000 | ||||

U_{FAR} | 0.073 | 0.243 | 0.030 | 0.140 | 0.116 | 0.000 | |||||

U_{station} | 0.261 | 0.419 | 0.000 | 0.262 | 0.057 | ||||||

U_{PS} | 0.000 | 0.067 | 0.006 | 0.005 | |||||||

U_{JHS} | 0.347 | 0.000 | 0.009 | ||||||||

U_{rail} | 0.149 | 0.068 | |||||||||

U_{Yamanote} | 0.052 |

Residence | Office | Commercial Service |

50 | 33 | 17 |

Scheme | Commercial Area | Residential Area | ${\Delta \mathbf{P}}_{\mathbf{sum}}$(100 Million Yen) | ||||||
---|---|---|---|---|---|---|---|---|---|

Land Occupied Number | U_{station} | ${\mathbf{S}}_{\mathbf{Bj}}$ (m ^{2}) | ${\Delta \mathbf{P}}_{\mathbf{Bj}}$ (Yen/m ^{2}) | Land Occupied Number | U_{noise} | ${\mathbf{S}}_{\mathbf{Ri}}$ (m ^{2}) | ${\Delta \mathbf{P}}_{\mathbf{Ri}}$ (Yen/m ^{2}) | ||

Original scheme | ① | 180 | 31,200 | −104,018 | ③ | 0 | 31,400 | 0 | −167.5 |

② | 330 | 53,700 | −176,268 | ||||||

④ | 559 | 15,200 | −265,426 | ||||||

Scheme 1 | ① | 180 | 31,200 | −104,018 | ② | 1 | 26,200 | −15,155 | −204.3 |

③ | 345 | 27,500 | −182,579 | ||||||

④ | 522 | 46,600 | −252,522 | ||||||

Scheme 2 | ① | 180 | 31,200 | −104,018 | ③ | 2 | 27,500 | −29,746 | −203.0 |

② | 318 | 26,200 | −170,664 | ||||||

④ | 522 | 46,600 | −252,522 | ||||||

Scheme 3 | ② | 330 | 53,700 | −176,268 | ① | 4 | 31,200 | −57,317 | −230.2 |

③ | 522 | 46,600 | −252,522 |

**Table 13.**Station–city integration mode of some stations in the suburbs of Tokyo. In the picture given in the table, crimson means the station, orange means the business, and brown means the office.

Name | Plane Figure | Type | Line Name | Connection Facilities |
---|---|---|---|---|

Mizonokuchi | Transfer Station | Den-en-toshi Line | Square + Footbridge | |

Tama Square | Midway Station | Den-en-toshi Line | Complex + Footbridge | |

Aobadai | Midway Station | Den-en-toshi Line | Complex + Square + Footbridge | |

Nagatsuta | Transfer Station | Den-en-toshi Line | Square + Footbridge | |

Central forest | Transfer Station | Den-en-toshi Line | Complex + Footbridge + Underground passage | |

Kokuryo | Midway Station | Keiō Line | Complex + Street | |

Fuchu | Midway Station | Keiō Line | Complex + Square + Underground passage | |

Bubaigawara | Transfer Station | Keiō Line | Square + Footbridge | |

Seiseki Sakuragaoka | Transfer Station | Keiō Line | Complex + Footbridge | |

Takahata Fudo | Transfer Station | Keiō Line | Complex + Square + Street | |

Kyohachioji | Transfer Station | Keiō Line | Complex + Square + Underground passage | |

Kichijoji | Midway Station | Inokashira Line | Square + Street | |

Keio Horinouchi | Midway Station | Sagamihara Line | Complex | |

Minami Osawa | Transfer Station | Sagamihara Line | Complex + Square | |

Kyodo | Midway Station | Odawara line | Complex | |

Front of Seijo Gakuen | Midway Station | Odawara line | Complex | |

Shinyuri Hill | Transfer Station | Odawara line | Square + Street + Footbridge | |

Tsurukawa | Midway Station | Odawara line | Square + Street | |

Machida | Transfer Station | Odawara line | Complex + Square + Street + Footbridge | |

Sagamiono | Transfer Station | Odawara line | Complex + Square + Street + Footbridge | |

Odaen | Transfer Station | Odawara line | Complex + Square + Footbridge + Underground passage |

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## Share and Cite

**MDPI and ACS Style**

Zhai, Z.; Yao, M.; Li, Y.
Evaluation of Land-Use Layout of the Rail Station Area Based on the Difference in Noise Sensitivity to Rail Transit, Taking a Suburb of Tokyo as an Example. *Sustainability* **2022**, *14*, 7553.
https://doi.org/10.3390/su14137553

**AMA Style**

Zhai Z, Yao M, Li Y.
Evaluation of Land-Use Layout of the Rail Station Area Based on the Difference in Noise Sensitivity to Rail Transit, Taking a Suburb of Tokyo as an Example. *Sustainability*. 2022; 14(13):7553.
https://doi.org/10.3390/su14137553

**Chicago/Turabian Style**

Zhai, Zhijunjie, Minfeng Yao, and Yueying Li.
2022. "Evaluation of Land-Use Layout of the Rail Station Area Based on the Difference in Noise Sensitivity to Rail Transit, Taking a Suburb of Tokyo as an Example" *Sustainability* 14, no. 13: 7553.
https://doi.org/10.3390/su14137553