Intelligent urban planning on smart city blocks based on bicycle travel data sensing

Journal Pre-proof Intelligent urban planning on smart city blocks based on bicycle travel data sensing Quanhua Hou, Weijia Li, Xiaoqing Zhang, Yinnan Fang, Yaqiong Duan, Lingda Zhang, Wenqian Liu

PII: DOI: Reference:

S0140-3664(19)32120-6 https://doi.org/10.1016/j.comcom.2020.01.066 COMCOM 6187

To appear in:

Computer Communications

Received date : 30 December 2019 Revised date : 27 January 2020 Accepted date : 28 January 2020 Please cite this article as: Q. Hou, W. Li, X. Zhang et al., Intelligent urban planning on smart city blocks based on bicycle travel data sensing, Computer Communications (2020), doi: https://doi.org/10.1016/j.comcom.2020.01.066. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier B.V.

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Intelligent Urban Planning on Smart City Blocks based on Bicycle Travel Data Sensing Quanhua Hou1, Weijia Li1, Xiaoqing Zhang1, Yinnan Fang2, Yaqiong Duan1*, Lingda Zhang1, Wenqian Liu1 1 2

School of Architecture, Changan University, Xian 710061, Shaanxi, China

China Construction International Investment (Xian) Co., Ltd, Xian 710075 , Shaanxi, China

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Corresponding author: Yaqiong Duan ([email protected])

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0 Introduction

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Abstract: The block is an important spatial unit of urban built-up areas. The investigation of the relationship between bicycle travel mode and land use characteristics at the scale of the block can effectively address the contradiction between the supply and demand for transportation under the background of stock characteristics. Herein, 21 typical blocks in Xian are applied as the research object, Mobike bicycle data, mobile phone signaling data and traditional survey data are employed. The correlation analysis and multiple linear regression analysis methods are used to identify the relationship between bicycle travel and land use. As a result, land use characteristics under the influence of bicycle travel are obtained. The results show that the land use indicators significantly related to street bicycle travel are composed of building mixing degree, floor area ratio and riding connectivity. Each index has a positive effect on street bicycle travel. The building mixing degree has the greatest impact on bicycle travel, followed by the floor area ratio and riding connectivity. In different periods, the impact of three indicators on bicycle travel varies. Especially, the impact on weekend bicycle travel is more obvious. This study can provide a reference for the optimization and transformation of land use in blocks under the influence of bicycle travel. Key words: bicycle travel, land use characteristics, Xian city blocks, multiple linear regression

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Due to the rapid development of urbanization in China, the combined effect of the high-density development, urban scale expansion and gradual popularization of cars, the disease in big cities characterized by traffic congestion becomes increasingly serious, and the contradiction between traffic and land use turns out to be prominent[1][2]. As Chinas urban built-up areas enter the stage of the stock development, the coordinated development of residents’ travel modes and land use characteristics can be regarded as one of the core issues of current research[3]，Meanwhile, the block is an important spatial unit of urban built-up area. The investigation of the relationship between the travel mode of residents and land use characteristics at this scale can untangle the contradiction between the traffic supply and demand under the background of stock characteristics[4][5]. Previous reports show that bicycle travel is the main way for short-distance travel of residents in the block, and its travel characteristics have a two-way constraint relationship with the land use characteristics of the block. Specifically, the characteristics of land use affect the traits of bicycle travel. The spatial form, land use type and development intensity of different blocks greatly influence the residents c hoice of a bicycle travel mode. Moreover, bicycle travel will have a demand and influence on the changes in land use characteristics. The block itself needs bicycle travel to solve the short-and medium-distance traffic inside. Therefore, it is worthwhile to fundamentally identify the relationship between bicycle travel and land use characteristics in the block, and promote the intensive land use of the block. However, this paper makes up for the lack of research in this area, focuses on the block scale, and deeply studies the land use characteristics under the influence of bicycle travel, so as to provide reference basis for the next block land use optimization. At present, many scholars have investigated the relationship between bicycle travel mode and land use characteristics, which results in achievements in theory and model methods. In theory, the study of this relationship 1

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focuses on three aspects, including land use function, land use intensity and land use form. A basic point of view is that the land use under the influence of bicycle travel features a small scale, high road network connectivity, high floor area ratio and mixed functions[6][7]Error! Reference source not found.Error! Reference source not found..In terms of land use function, Pucher (2008)，Cervero（2003）and Zhang (2003) found that the land use function was highly complex in those areas with a high sharing rate of bicycle traffic modes. Residents were more likely to travel by bicycle with an increased mixing degree of land use function[10][11][12]。Hou found that "mixed functions" and "dense road network" are beneficial to residents low -carbon travel, non-motorized travel and regional bicycle travel[13].In terms of land use intensity, Madera (2009) and Zhang (2003) studied the relationship between urban bicycle travel and land use, and concluded that the urban land development featured high intensity and compactness in cities with large bicycle travel[12][13]. FRANK (1995), Pu (2007), Cervero (1996), Litman (2009) and others revealed that there was a negative correlation between density and urban carbon emissions. In areas with higher urban density, the level of ownership and use of cars are lower[15], which is conducive to low-carbon travel and thus effectively promotes bicycle trave[16][17][18][19]. Zhou (2005) summarized the impact of foreign urban land development models on urban traffic, and analyzed and compared them with the situation in China. It suggested that the impact of traffic supply on its demand in high-intensity land development areas was more obvious than that in low-intensity areas, and the travel distance was shorter. Residents were more likely to use non-motor vehicles such as bicycles[20]. As for land use patterns, Southworth (2005) and Cervero (2003) showed that street connectivity had a moderate impact on promoting bicycle travel that is less than 5 miles away. It should be noted that the stronger the street connectivity, the higher the non-motorized traffic travel rate[11][21]. Cui (2014) and others pointed out that there is a positive correlation between traffic accessibility and bicycle traffic[22]. As for model methods, the current model methods about that relationship mainly include direct questioning[23],statistical controlError! Reference source not found.,instrumental variable modelError! Reference source not found.,sample selection modelError! Reference source not found.,discrete choice model, cross-sectional structural equation model[27] and longitudinal model[28]. In addition to the discrete choice model, other models ignore their real-world application and operation difficulty, while the discrete choice model[29], has advantages of easy interpretation, implementation, conciseness, stability as well as generality. As a major discrete choice model, the Logit model has been widely applied[30]. It is used to study the factors affecting bicycle riding, including high land mix and short travel distance. Few people study the relationship between building environment factors and riding behavior. Herein, 21 typical blocks in Xian are selected as the research object and multi -source data such as mobile phone signaling[31], mobike data, and field research are applied to analyze the correlation and establish a multiple linear regression model. Consequently, the relationship between bicycle travel and land use is verified. Moreover, the characteristics of land use under the influence of bicycle travel are obtained, which provides a reference for the optimization and transformation of land use in the block.

1 Research Object and Data Source

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1.1 Selection of Research Objects

Through combing the relevant literature, the connotation of the block is clarified, that is, the area enclosed by several streets is the basic constituent unit of the urban structure. Combined with the travel characteristics of bicycles, it is determined that the block to be studied in this paper refers to the completed area of the city bounded by the edges of streets, lanes, squares, etc. As can be seen from the foregoing, the land use characteristics under the influence of bicycle travel mainly include three aspects: “land use function”, “land use intensity” and “land use form”. Therefore, the differences in these three aspects, the location factors of the selected block and the feasibility of the research should be taken into account when selecting sample blocks. At the same time, the practical experience 2

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of dividing blocks in Xiamen, Hong Kong, Guangzhou and Singapore is used for reference. The radius of each block is controlled between 500 m and 1500 m, and the block size is controlled at 2.5km2 Twenty-one typical block samples in Xi an are selected (Fig.1, Table 1).

D E F G Q R U A B H I J K

Function

Construction status

Size（hm2）

Mixed

Medium floor area ratio + High building density

27.69

Commercial + Residential

High floor area ratio + High building density

10.26

Commercial

Medium floor area ratio + High building density

60.81

Commercial

Medium floor area ratio + High building density

53.67

Residential

Low floor area ratio + High building density

77.68

Industrial + Residential

Medium floor area ratio + Medium building density

36.95

Mixed

High floor area ratio + High building density

38.62

Mixed

Low floor area ratio + Medium building density

157.39

Office + Residential

High floor area ratio + Low building density

22.12

Residential

High floor area ratio + Medium building density

76.78

Commercial + Residential

Medium floor area ratio + Low building density

200.13

Industrial + Residential

Medium floor area ratio + Medium building density

40.42

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Block number

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Fig.1 Sample block location map Table 1 List of present situation of sample districts in Xi an city

Office

High floor area ratio + High building density

102.16

Commercial

Low floor area ratio + Low building density

74.49

Mixed function

Medium floor area ratio + Low building density

154.22

Residential

High floor area ratio + Low building density

76.38

M

Office

Low floor area ratio + Medium building density

74.84

P

Industrial + Residential

Low floor area ratio + High building density

252.00

N

Industrial

Low floor area ratio + Low building density

38.98

S C L

3

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Residential

Medium floor area ratio + Low building density

37.34

T

Residential

Medium floor area ratio + Low building density

47.07

1.2 Data Acquisition

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This study mainly uses two types of data such as block bicycle travel and land use. Among them: the land use data is derived from the 2017 Xian land use status vector, as well as the Xian building vector image obtained from the a map platform, extracting the land use property and building area of the blocks in the neighborhood, and analyzing and calculating Land use indicators such as building density, floor area ratio, building mix, road network density, bus line density.( Table 2) Table 2 List of land use indicators Indicator name

Formula

Formula schematic

Building density

𝜌𝜌 = 𝑓𝑓/𝐴𝐴

F is the building base area; a is the total land area.

building mix Road network

μ=l/A

F is the total building area of the block;A is the total land area of the block. "P" refers to the proportion of residential buildings, commercial services, public management and public service buildings in the total building area; K refers to the type of buildings. L is the total mileage of the road network; A is the total area of the area.

δ=L⁄F

L is the total length of the central line of the road with bus lines; F is the area of urban land with public transportation services. T is the total score of intersections; A is the total land area.

R=F/A

n H=- ∑k=1 Pk ln Pk

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Floor area ratio

Bus line density Ride connectivity

𝜑𝜑 = 𝑇𝑇/𝐴𝐴

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density

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Bicycle travel has obtained Mobike cycling data, mobile phone signaling data and traditional survey data for two consecutive weeks from November 5th to November 18th, 2017. The indicators mainly include bicycle travel ratio and travel traffic. Among them, the mobile signaling data is mainly Unicom mobile signaling data, which is provided by Xian Dingcheng Digital Technology Co., Ltd. and split out the bicycle travel proportion through calculation; the Traditional research data is used to correct the bicycle travel proportion preliminarily obtained from the mobile signaling data; Mobike data is provided by smart footprint data Technology Co., Ltd. and is used to assist the mobile signaling data Calculate the bicycle traffic, and check the mobile signaling data (Fig.2, Table 3) . Mobile signaling data

Traditional research data

Mobike data

Preliminary bicycle travel ratio

Adjusted bicycle travel ratio

Corrected Actual Bicycle Trip

Fig. 2 Flow chart of multi-source data fusion processing Table 3 Current situation of bicycle travel and land use in sample districts Block

Bicycle travel

A B C D E F G H

Bicycle flow（Train number /h*hm2） Week Weekend

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Bicycle travel ratio （%） Week Weekend 4.94% 7.13% 16.51% 5.65% 15.35% 27.45% 3.63% 2.67%

2.73% 7.63% 9.99% 6.73% 5.12% 8.49% 1.08% 3.96%

4.09 6.73 3.03 4.19 8.04 3.62 4.13 3.64

1.63 2.73 4.59 0.93 1.09 0.81 0.93 0.65

Buildin g density (%) 26.07 21.45 13.99 34.97 62.58 39.53 42.17 31.83

Floor area ratio 2.15 0.68 0.75 1.86 2.1 2.33 2.22 3.26

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Block land use Buildin g mixing degree 1.38 0.69 1.86 1.45 0.91 1.54 1.96 0.69

Road network density （km/km2） 0.0070 0.0090 0.0060 0.0100 0.0160 0.0140 0.0120 0.0070

Bus line density （km/km2 ） 0.032 0.08 0.05 0.111 0.135 0.154 0.137 0.056

Ride connectivit y（score/ hm2） 1.35 1.53 0.81 1.82 1.94 1.84 1.91 1.41

Journal Pre-proof 1.68% 36.20% 14.75% 13.38% 8.94% 9.40% 22.01% 12.61% 22.62% 27.33% 1.22% 14.23% 3.65%

1.61% 7.06% 12.72% 8.42% 11.00% 17.00% 6.04% 8.61% 12.11% 8.84% 0.96% 12.64% 1.06%

4.08 5.02 4.65 5.02 4.22 2.64 2.51 1.58 2.63 3.55 3.1 4.87 8.22

0.86 0.85 0.77 0.84 0.75 0.78 0.78 0.49 0.65 0.91 0.52 0.76 0.92

30.92 35.62 27.44 18.43 25.39 37.85 23.00 33.91 33.26 34.17 29.65 21.00 42.00

1.93 1.91 2.39 1.67 0.9 0.99 1.49 1.58 2.21 1.59 1.3 1.49 2.23

0.0070 0.0080 0.0120 0.0080 0.0070 0.0050 0.0060 0.0040 0.0070 0.0100 0.0120 0.0060 0.0130

0.074 0.052 0.13 0.054 0.037 0.022 0.05 0.036 0.04 0.098 0.075 0.04 0.07

1.37 1.32 1.44 0.84 0.87 0.68 0.77 0.82 1.97 1.22 1.08 0.81 2.04

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2 Correlation Analysis

1.73 1.58 1.22 0.82 0.97 1.49 0.8 1.55 1.32 1.25 1.79 1.33 1.18

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I J K L M N O P Q R S T U

2.1 Technical Route

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First of all, the research objective is to explore the land use characteristics of the block under the influence of bicycle travel. By using the method of combining multi-source data, the bicycle travel data and the land use characteristics of the block are systematically analyzed. Through correlation analysis, the land use characteristics significantly related to bicycle travel are screened out, and a regression model is established. Finally, the land use characteristics of the block under the influence of bicycle travel and the relationship between bicycle travel and the land use of the block are determined（Fig.3）.

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Research target of land use characteristics under the influence of bicycle travel Sample of 21 blocks in Xi'an

Object layer

Raw data sampling

Data layer

Bicycle travel data

Land use data

Relational data extraction

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Target layer

Analysis layer Correlation analysis

Establish regression model Block land use characteristics

Conclusion layer

Fig.3 Technology roadmap

2.2 Index Selection Based on the data of bicycle travel and the current situation of land use in the streets, SPSS software is used to 5

Journal Pre-proof analyze the correlation between the proportion of bicycle travel and the characteristics of land use during the week and weekend. The results of the analysis not only consider the impact of travel on the indicators, but also consider the decorrelation between the indicators. Finally, it is concluded that the land use indicators under the impact of bicycle travel are floor area ratio, building mixing degree and riding connectivity (Table 4, Table5). Table 4 List of correlation analysis between bicycle proportion and land use characteristics in weeks Floor area ratio .438*

Building mixing degree .254

Road network density .616**

Pearson 1 correlation Significance .047 .266 (bilateral) Pearson .438* 1 .092 Floor area correlation ratio Significance .047 .692 (bilateral) Pearson .254 .092 1 Building correlation mixing Significance .266 .692 degree (bilateral) Pearson .616** .359 .257 Road correlation network Significance .003 .110 .261 density (bilateral) Pearson -.003 .265 .425 Bus line correlation Significance .989 .246 .055 density (bilateral) Pearson .391 .409 .023 Riding correlation Significance .080 .065 .920 connectivity (bilateral) Pearson .452* .776** .442* Weekly correlation Significance .040 .000 .050 ratio（Y1） (bilateral) *. Significant correlation was found at 0.05 level (bilateral). **. Significant correlation was found at the 0.01 level (bilateral).

.003 .359

Riding connectivity

-.003

.391

Weekly ratio （Y1） .452*

.989

.080

.040

.265

.409

.776**

.246

.065

.000

.425

.023

.442*

.261

.055

.920

.050

1

.470*

.527*

.598**

.032

.014

.004

1

.272

.449*

.233

.041

1

.801**

.110

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.257

.470*

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Building density

Bus line density

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Building density

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.032

.527*

.272

.014

.233

.598**

.449*

.801**

.004

.041

.000

.000 1

Table 5 List of Correlation Analysis between Weekend Bicycle Proportion and Land Use Characteristics

Floor area ratio Building mixing degree Road network density Bus line density Riding

Pearson correlation

Floor area ratio

Building mixing degree

Road network density

Bus line density

Riding connectivity

Weekend ratio （Y2）

1

.438*

.254

.616**

.003

.391

.487*

.047

.266

.003

.989

.080

.025

1

.092

.359

.265

.409

.735**

.692

.110

.246

.065

.000

1

.257

.425

.023

0.496*

.261

.055

.920

.021

1

.470*

.527*

.558**

.032

.014

.009

1

.272

.381

.233

.088

1

.872**

Significance (bilateral)

Pearson correlation

.438*

Significance (bilateral)

.047

Pearson correlation

.254

.092

Significance (bilateral)

.266

.692

Pearson correlation

.616**

.359

.257

Significance (bilateral)

.003

.110

.261

Pearson correlation

-.003

.265

.425

.470*

Significance (bilateral)

.989

.246

.055

.032

Pearson correlation

.391

.409

.023

.527*

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Building density

Building density

6

.272

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Significance (bilateral)

.080

.065

.920

.014

.233

Weekend ratio （Y2）

Pearson correlation

.487*

.735**

0.496*

.558**

.381

.872**

.021

.009

.088

.000

Significance .025 .000 (bilateral) *. Significant correlation was found at 0.05 level (bilateral). **. Significant correlation was found at the 0.01 level (bilateral).

.000 1

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From Tables 3 and 4, it can be seen that the bicycle proportion in the week is significantly correlated with building density, building mixing degree and bus network density at 0.05 level, and is significantly correlated with floor area ratio, road network density and riding connectivity at 0.1 level. Weekend bicycle proportion is significantly correlated with building density and building mixing degree at 0.05 level, and is significantly correlated with floor area ratio, road network density and riding connectivity at 0.1 level. Since building density is significantly related to floor area ratio and road network density, and road network density is significantly related to bus network density and riding connectivity, in order to ensure the non-correlation between the indicators, building density, road network density and bus network density are removed, and it is concluded that the land use characteristic indicators under the influence of bicycle travel include floor area ratio, building mixing degree and ride connectivity.

2.3 Model Establishment

be expressed as:

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Most scholars adopt linear regression or discrete selection model in the study of the characteristics of block land use under the influence of bicycle travel. The core is the study of land use under the direction of block travel selection. Therefore, Logit model is used as the probability model of traveler 𝑞𝑞 to option 𝑖𝑖 as the applicable model for the study. Based on the theory of multinomial Logit model, it is assumed that the block population as a whole is taken as the behavior decision unit, and in a set of travel modes that can be selected and the selection modes are mutually independent, the block population will choose the travel mode that he thinks is most effective for himself. This hypothesis is called the utility maximization behavior hypothesis, which is the basis of Logit model. Based on this, the model takes individual, family or block units as the basic research units to reflect the characteristics and differences of individual choices and to truly explain and reflect the travel behavior of the research object. If Ui is the utility of selecting branch 𝑖𝑖 for the block, and C is the set of branches corresponding to the block, then when Ui , Uj∈C（i≠j）, and Ui is greater thanUj , the block will select 𝑖𝑖.According to the random utility theory, Ui can Ui =Vi +εi

（1）

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In the formula Vi -The fixed term of utility when the block chooses branch 𝑖𝑖; εi -The unobservable factor vector and the probability variation term of utility caused by unobservable preferences peculiar to the block population have different impacts on residents' bicycle travel choices for different blocks. Ui-The effect of the factors to be studied on the travel of the block population, the main factors to be studied refer to three factors related to travel decisions as independent variables, namely “floor area ratio, building mix degree and ride connectivity”. Assuming that εi and Vi are independent of each other, εi and obey Gumbel distribution, the distribution function of its density function is F(εi )-exp(-e-λεi ), where 𝜆𝜆 is the parameter corresponding to the variance εin , then the selection probability formula of the model, that is the probability of block selection 𝑖𝑖 selection branch, can be obtained: exp(λVi ) Pi = （2） ∑j∈C(λVi ) 7

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The block unit is used as the basic unit. A selection behavior usually includes the following elements: （1）Decision Makers. That is the main body that makes the choice. Mainly to the crowd in the block as a unified whole, 10 working days, 4 non-working days. That is, each block produces 10 working day samples and 4 non-working day samples. Therefore, there are 21 blocks, including 210 working days and 84 non-working days. （2）Alternatives. There are usually two options for decision makers to choose from. The main options are mainly “Travel within the week” and “Weekend travel”. （3）Attributes of Alternatives. That is the land use index factors include “floor area ratio”, “building mixing degree” and “ riding connectivity”. It should be noted that the decision makers’ own attributes will also have an impact on the selection results. Even in the face of the same set of alternatives, different decision makers will make different choices, so there will be a random term 𝜀𝜀𝑖𝑖 . （4）Decision Rules. Different policy makers do not have the same code of conduct when making program choices. The travel mode includes two travel modes: “Travel within the week” and “Weekend travel”. These two modes of travel have also become two choices for building models. The model that needs to be built in this paper is mainly to fix the fixed function in the utility function of different travel modes. The Vi utility function can be expressed in various function forms. Currently, the linear function form is widely used: K

Vi = � βik xki k

（3）

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In the formula xki-The k-th variable value of the selection branch i of the block is the land use index value; ßik-The undetermined coefficient of the k-th variable value of the selection branch i of the block can be calibrated according to survey statistical data. Take logarithm to both sides of formula (2) to obtain formula (4): K

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K

ln Pin = � βik xki - � � βik xki

（4）

γ � Nc ≥Gi +Ai

（5）

k

j∈Cn -i k

The Pi on the left of formula (4) is the current value of the sharing rate of bicycle travel modes under different travel modes. The right contains the utility function of the travel mode, in which the selected branch-type variables and the newly added land-use variables can be obtained through current situation investigation and data access, while the individual characteristic variables of travelers need to be obtained through field investigation or data access. At the same time, the bicycle traffic volume of the block must be within the road capacity range of the block, so the capacity of non-motorized lanes for each block must meet the constraint boundary of the total bicycle traffic volume, and the formula is as follows: c

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In the formula γ-Reduction factor of bicycle travel in the block; Nc -The design traffic volume of non-motorized lane C in the block, unit veh/h; G-Bicycle trip volume in the block, unit veh /h; A-Bicycle travel attraction in block, unit veh /h. The γ in the left side of formula (5) is the bicycle travel reduction factor of different blocks, which represents the travel sharing rate of non-motor vehicle lanes in the block. It can be obtained by the statistics of mobike cycling combined with the field survey data of the block roads, Nc is the design traffic of each non-motor vehicle lane in 8

Journal Pre-proof the block, which can be calculated by the non-motor vehicle lane capacity design standard; the right side contains the total number of bicycle trips in the block, and the bicycle trip can be obtained by the mobike bicycle statistics.

2.4 Model Calculation Results The stepwise regression method was adopted to solve the coefficient, and the factors that had no significant influence on the travel mode were eliminated. The simulation results of the relationship model between land use and travel mode in 21 blocks of Xi ’an City were obtained (6). J

ln yi = � βij Xji +θi

(6)

j

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According to multiple regression results of bicycle travel proportion and three types of land use indicators in 21 districts of Xi an, the relationship between travel and land use can be obtained, as shown in formula.

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ln y1 =-4.022+0.152X1 +0.369X2 +0.115X3

(7)

ln y2 =-4.845+0.163X1 +0.706X2 +0.162X3

(8)

X1 =x21

(9)

X2 =x22

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X3 =x23

(10) (11)

In the formula yi -Bicycle travel proportion of block i travel mode, unit%;

i-Travel mode,i=1 for within a week, i=2 for weekend travel; xj -Index Value of Class j Land Use in Blocks；

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j-Classification of land use indicators，j = 1 is the floor area ratio, j = 2 is the building mixing degree, J = 3 is the riding connectivity； Table 6 List of regression results of sample block model Model R R2 P y1 0.834 0.695 0.013 y2 0.881 0.776 0.011

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From the results of regression models (Table 6), the complex correlation coefficients between Xj and yi are R=0.834/0.881, R2 =0.695/0.776. Therefore, the regression equation fits well on the whole 69.5% and 77.6% of the variation of yi can be explained by the variation of yi . At the same time, at the test level of 0.05, the regression equation fitted by Xj and yi is significant (P<0.05), and there is at least one non-zero regression coefficient in the fitted regression equation. From the results, it can be seen that whether in the week or on the weekend, the proportion of bicycle trips in the block has a positive correlation with the building mixing degree, floor area ratio and ride connectivity. Through the standardized regression coefficient, it can be found that the degree of building mixing has the greatest impact on bicycle travel, followed by the floor area ratio, and the degree of riding connectivity has the least impact on bicycle travel. In addition, compared with the week, the weekend land use characteristics have a more obvious impact on bicycle travel.

3 Empirical Results and Discussions According to the above literature review and the analysis of the related indexes of bicycle travel and land use in blocks, it can be seen that the indexes of land use under the influence of bicycle travel have the building mixing 9

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degree that characterizes the land use function. Plot ratio representing the intensity of land use; Riding connectivity that characterizes land use patterns, etc. From the analysis of the land use characteristics of the block, the floor area ratio is the basis of the bicycle travel in the block. The building mix is the necessary condition to promote the bicycle travel, and the riding connectivity will affect the accessibility of the block, which is a sufficient condition for promoting bicycle travel. When the building mixing degree, floor area ratio, and riding connectivity of the block reach a reasonable range, the block bicycle travel will reach an optimal state. Under the constraints of bicycle travel, residents have a variety of needs, which will require and guide the land use intensity, mixing degree, and riding connectivity of the block, so that the land use of the block is the most suitable for bicycle travel. Regarding the influence of building mix on bicycle travel, the degree of influence of block building on bicycle travel is the most obvious, which is a positive impact. The greater the mix of buildings, the more types of activities in the block, the more complete the function, the residents of the block. The willingness to travel by bicycle will be greatly enhanced. Compared with other land use characteristics, whether it is commuting trips such as work and school, or noncommuting trips on weekends, the building mixing degree of the block can effectively affect the number and proportion of bicycle trips of the residents in the block. During the week, residents mainly traveled by commuting, and travel will choose fast and convenient travel modes. Therefore, people are more willing to choose private cars or public transportation. For weekends, residents mainly travel non-commuting, and most of them for flexible travel, there are more ways for residents to travel, so when the mix of buildings in the block is high, the promotion of weekend bicycle travel and travel volume is more obvious. As for the influence of floor area ratio on bicycle travel, the influence of block floor area ratio on bicycle travel is lower than that of buildings, showing a secondary positive influence. The proportion and amount of bicycle travel mainly come from the population density of the block. Population density is the basis of bicycle travel in the block. Population density comes from the intensity of land development in the block. A certain amount of construction area is the source of bicycle travel proportion and amount of travel in the block. When the floor area ratio of the block is higher, it indicates that the number of people traveling in the block is larger, which will greatly increase the proportion and flow of bicycle travel. Compared with the week, the weekend considers the non-commuting factor as the main factor, and the high floor area ratio can promote non-commuting travel. Therefore, during the weekend, the influence of the floor area ratio on bicycle travel is more obvious than in the week. In terms of the impact of riding connectivity on bicycle travel, it has the weakest impact on block bicycle travel, with a positive impact on the third level. The higher the riding connectivity of the block, indicating that the block has a good riding environment, the block road the accessibility is higher, the more convenient the bicycle is, and the willingness of residents to use bicycles is significantly increased. Compared to the week and weekend, the increase in riding connectivity is beneficial to bicycle travel (Fig.4) .

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Travel within weeks

Weekend travel

Commuting predominates

Non-commuting predominates

Land use characteristics

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Travel proportion Travel flow Travel proportion

First level positive impact Secondary positive influence Three levels of positive influence

Building mixing degree

Function

Floor area ratio

Strength

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Bicycle travel characteristics

Riding connectivity

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Travel flow

Form

Fig.4 Block map of land use characteristics and bicycle travel relationship

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Considering the land use characteristics of the block under the influence of bicycle travel, and referring to the time distribution of travel, it can be concluded that the land use characteristic indicators under the influence of bicycle travel are building mixing degree, floor area ratio along with riding connectivity. All land use characteristics affect the proportion of travel and travel of bicycles in the block, and the performance is enhanced. Due to the impact of commuting, the degree of influence of building mixing degree, floor area ratio and riding connectivity on bicycle travel are more obvious during the weekend.

4 Conclusion

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Based on the research on bicycle travel and land use, this study puts forward a set of research ideas on the relationship between bicycle travel and land use characteristics of blocks. Although numerous studies have been done on this subject, only a few reports have focused on the block level. In this study, the Xian block is applied as an example to conduct an empirical study. In addition, an appropriate supplement to the current study of land use characteristics under the influence of bicycle travel at the block level is conducted. This can help us comprehensively grasp the relationship between bicycle and land use on the street scale. Moreover, this study provides a reference for relevant planning and planning decisions such as urban renewal and old city reconstruction. In this regard, the main conclusions of this study are described as follows: First，at the block scale, highly functional composite buildings, high-intensity developed land, and highly connected road networks have become the incentives for bicycle travel. This conclusion can be used as a reference for other cities to study the relationship between them. Second, for blocks in different cities, the impact of land use variables on bicycle travel is distinct. This is determined by the characteristics of each city block itself. This is also the most crucial background difference between urban travel and land research in China and developed countries. The research on the relationship between bicycle travel and land use in Xian block level provides a reasonable explanation for the in-depth study of other regional cases, provides support for the next block land use optimization, guides them to take bicycle travel into account in the optimization of block land use, and also provides ideas for the study of other travel modes. Some findings also prompt the reflection on the concept and method of land use planning in blocks. For example, during the process of land use planning in blocks, bicycle travel should be considered to meet the requirement of the change of land use pattern caused by the change of traffic flow pattern for bicycle travel, which can optimize land use indicators. Albeit land use variables show significant correlation with bicycle travel, it should be noted that the impact of land use on individual travel is more complex and 11

Journal Pre-proof multifaceted. The conclusion of individual cases can provide a reference for other cities. To provide more explicit theoretical research and methods for planning, further exploration of precise impact mechanisms is required.

Acknowledgement

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This work is supported by the Soft Science Research Program of Innovation Capability Support Plan Project in Shaanxi Province (Grant No.2018KRM166)，the Major Theoretical and Practical Problems of Shaanxi Social Science Projects in 2018 (Grant No.2018Z026), the Major Theoretical and Practical Research Project in the Social Sciences in Shaanxi (Grant No.2019GZL013)，the Fundamental Research Funds for the Central Universities (Natural Sciences) Projects (Grant No.310841172001),and the Fundamental Research Funds for the Central Universities of China (Social Sciences) Projects(Grant No.300102419631).

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Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

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Quanhua Hou Supervision Weijia Li Data curation Xiaoqing Zhang Formal analysis Yinnan Fang Methodology Yaqiong Duan Roles/Writing – original draft Lingda Zhang Writing – review & editing Wenqian Liu Visualization