Methods

4.2.1. Construction research framework

Improvement of parking configuration may lead to two entirely different results.

A good result can promote personal visit in a group; a bad one may lose visitors due to excessive traffic pressure and parking saturation. The overall research framework is divided into two parts: physical comprehensive traffic system indicators system and parking user' travel indicators system. The former indicator system was measured by motorized traffic indicators and parking lots use indicators. Motorized traffic indicators are further measured by relevant road loading capacity index and bus accessibility index. Parking user' travel indicators are extracted by a field survey which includes their parking characteristics and activity content in the historic district.

The evaluation system of impact is discussed base on four dimensions. (1) Related urban road loading capability condition: measurement of motor vehicles' traffic accessibility, including road loading capability, which calculates by two-way traffic volume of related vehicle roads; and bus accessibility in calculated area; (2) Parking characteristic: we obtained real-time parking space use data, parking spaces' turnover rate, parking volume in peak hours, actual parking volume, and cumulative parking volume through continuous measurement and discontinuous observation to evaluate the characteristics of parking lot use and parking behavior; (3) Public transport accessibility: an indicator system composed of bus stop indicators and bus line indicators; (4) Consumption characteristic of parking users: include activity

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duration in the district, consumption area, consumption content and subjective evaluation data of access behavior.

Figure 4.1 Research framework

4.2.2.Data collection and calculation 4.2.2.1 Data collection

Sources and collection rules of aforementioned research data are as follows:

·Longitudinal related urban road loading capability index. Width of the urban roads have not changed after RP implementation, so our statistic and measurement are based on the 1:2000 topographic file provided by the surveying department in 2012.

The collection objects of vehicle flow data are urban road sections adjacent to the historic district, which influence the vehicle flow at the entrance and exit of parking lots. The collected related urban vehicle roads include Zhongfa Road, Yan'an Road, and Xinhua West Road. Traffic signal control lights separate the road sections.

Zhongfa road makes statistics on Xinhua West-Nanchang Road section (S1) and Xinhua West-Xiuwen road section (S2). Yan'an Road (S3) and Xinhua West Road (S4) make statistics on one related road section (Figure 4.2). Due to a lack of real-time traffic monitoring data acquisition of urban roads, a manual acquisition method was

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adopted to extract traffic volume data (V) of each two-way (N-S and S-N) road section in nighttime peak hour (17:30-18:30).

Data collected comes from statistics of a single day the nighttime peak hour (17:30-18:30) traffic surveyed by the Zhangzhou Planning Bureau in 2015 and by author in 2017. The survey includes the observation and statistics of two-way traffic volume of urban roads around the entrance and exit of two parking lots in the historic district.

·Longitudinal bus accessibility index. Public transport statistics mainly collect the distribution of bus stations and routes in the surrounding closely connected with the historic district. The statistical period includes two-time points before and after the implementation of the project. Therefore, public transport statistical data for the special planning of the Zhangzhou public transport system in 2015 and public transport distribution data of the Baidu map in 2020 provides required bus data sources within the statistical scope. We expanded the scope of statistics and calculation from the management scope of the historic district (about 0.5 square kilometer) to the area covered by surrounding main roads area (about 0.85 square kilometer) (Figure 4.3).

·Real time change statistical data collection of parking lots. Considering that Zhangzhou ancient city historic district has the characteristics of tourism destination, work commuting trip may affect the result, so we collected the data of weekday and weekend. We adopted a continuous measurement method and discontinuous observation method, to achieve three consecutive days' real-time monitoring data of parking APP from 9:00 to 24:00 on a working day and two-day weekend in January 2018. The survey object is parking lots at West and North entrance in the historic district.

·Activity behavior index. We performed an oriented sample survey to investigate drivers' parking characteristics and activity behavior in the historic district. Parking users who have just finished their visit activity and were ready to drive away were eligible for the investigation. Through face-to-face field investigation, 100 valid questionnaires were formed, 50 for West entrance parking lot, and 50 for North entrance parking lot.

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Firstly, we followed the quasi-longitudinal design when conducting the sample survey. Respondents were requested to report their previous parking status, their estimates of parking configuration in the historic district, self-assessment of influence of parking convenience on personal visits, and their attitudes towards automobile travel. Secondly, we collected the activity behavior of parking users in the historic district, including consumption, activity area, and activity duration.

Furthermore, the demographic attributes were collected as the covariate, including age, gender, number of passengers, annual personal income, visit identity ,and visit frequency.

Figure 4.2 Traffic flow statistical road sections Figure 4.3 Calculation area of bus accessibility

4.2.2.2 Data calculation

(1) Before-after road loading capability is reflected by the traffic saturation index, which is the ratio of statistical traffic volume to basic road capacity. Road service level was then achieved based on the traffic saturation (TS) index concerning the corresponding evaluation standard (Table 4.1). The equation is:

TS=V/ C (4.1)

Where TS is traffic saturation, V is the traffic flow of statistical roads, and C is the basic traffic capacity. According to the width of current road and design speed, the corresponding theoretical traffic capacity value is calculated and modified by the

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method of China code for design of urban Road Engineering (CJJ37-2012)，then finally get the basic traffic capacity value.

Table 4.1 Evaluation Standard of road Service Level (LOS) Traffic

Saturation

Level of

service(LOS) Representative driving condition

<0.35 A Smooth

0.35-0.5 B Stable traffic stream with slightly delayed

0.5-0.75 C Stable traffic stream with slight congestion and acceptable delay

0.75-0.9 D Unstable traffic stream with near serious congestion and acceptable delay

0.9-1 E Unstable traffic stream with serious congestion and unacceptable delay

≥1 F Severe congestion

(2) Calculation of bus accessibility. The index system includes bus stop distribution index and supply index of bus lines. Calculation adopted the accessibility calculation method of Chen Y Y (2015), where 1-5 scores are taken as quantitative evaluation standards. The calculated area is 0.85km² (Table 4.2). Calculation data include: Bus stop density (D), stop coverage ratio (F), optimal coefficients of public transit network (L), and optimal coefficients of route distance (La). Then, the score of four indicators is weighted to get the value of bus accessibility (A). The equation is:

D=N/S (4.2)

^∑ (4.3)

(4.4)

∑ (4.5)

Where N is the number of bus stops in calculation area; S is calculation area;

is the service area of stops i; W is nonlinear coefficient; is repetition coefficient of bus lines, namely, the ratio of bus line length inside calculation area to the total length of its line network; is the distance between the edge points of the bus line and the centroid of calculation area; a is the influence coefficient of the route distance.

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The final bus accessibility value was obtained by weighted calculation based on corresponding scores of each indicator's calculation result.

A= 0.3*0.42*D+0.3*0.58*F+0.7*0.4*L+0.7*0.6*La

Table 4.2 Evaluation standard of bus accessibility

D F L La Score

<2 <60% <1 <2 1

(2,3] (60%,70%] (1,2] (2,3] 2

(3,4] (70%,80%] (2,3] (3,4] 3

(4,5] (80%,90%] (3,4] (4,5] 4

>5 >90% >4 >5 5

(3) Parking use characteristic index. This part of indicators was based on the real-time parking space use measurement data to extract actual parking volume (S), cumulative observed parking volume (S1) and peak hour parking volume(Nj), then calculate turnover rate (α) and peak parking index λ (including the peak and average data). The equation is:

C S /

  (4.6)

C N_j /

1 

 (4.7) )

· /(

1

2  S C X

 (4.8)

Where S is the actual parking volume; C is the total number of parking spaces. λ1

is the peak parking index; λ2 is the average parking centralized index; Nj is the peak hour parking quantity; S₁ is the cumulative parking volume during the statistical period. X is the number of hours in the statistical period.