Available via license: CC BY 4.0
Content may be subject to copyright.
Citation: Zhang, B.; Zhou, L.; Yin, Z.;
Zhou, A.; Li, J. Study on the
Correlation Characteristics between
Scenic Byway Network Accessibility
and Self-Driving Tourism Spatial
Behavior in Western Sichuan.
Sustainability 2023,15, 14167.
https://doi.org/10.3390/su151914167
Academic Editor: Hariklia D.
Skilodimou
Received: 28 July 2023
Revised: 21 September 2023
Accepted: 22 September 2023
Published: 25 September 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
sustainability
Article
Study on the Correlation Characteristics between Scenic Byway
Network Accessibility and Self-Driving Tourism Spatial
Behavior in Western Sichuan
Bo Zhang 1, Liangyu Zhou 2, Zhiwen Yin 1, Ao Zhou 2and Jue Li 2,*
1School of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, China;
zhangbocq@cqjtu.edu.cn (B.Z.); zw.yin2021@mails.cqjtu.edu.cn (Z.Y.)
2School of Traffic & Transportation, Chongqing Jiaotong University, Chongqing 400074, China;
622190950037@mails.cqjtu.edu.cn (L.Z.); a.zhou2022@mails.cqjtu.edu.cn (A.Z.)
*Correspondence: lijue1207@cqjtu.edu.cn
Abstract:
The scenic byways in Western Sichuan are some of the most popular self-driving tourism
destinations in China. However, the current network of scenic byways in the region is not well-
coordinated with the level of regional tourism development. This paper, based on travel digital
footprints, uses methods such as spatial design network analysis, GIS spatial analysis, social network
analysis models, and spatial econometric models to analyze the accessibility and self-driving tourism
spatial behavior characteristics in Western Sichuan. The main research results are as follows: (1) the
accessibility level of scenic byways in Western Sichuan exhibits significant spatial variation, with
the majority of areas demonstrating moderate to poor accessibility; (2) the network structure of
self-driving tourism spatial behavior displays characteristics of low overall network density, but
with a high clustering coefficient and relatively short average path length, indicating a significant
small-world phenomenon. All network node indicators exhibit significant heterogeneity, with the
core nodes displaying clear clustering characteristics; (3) the accessibility of scenic byways and
self-driving tourism spatial behavior exhibit significant spatial spillover effects. This study analyzes
the relationship between the accessibility of scenic byways and self-driving tourism spatial behavior
in Western Sichuan, providing valuable insights for the planning and construction of scenic byways
and the development of self-driving tour routes.
Keywords:
accessibility; self-driving tourism spatial behavior; spatial analysis; scenic byway;
Western Sichuan
1. Introduction
Transportation plays a crucial role in supporting the tourism industry, serving as
a fundamental factor for its success [
1
]. The integrated development of transportation
and tourism is based on the accessibility provided by transportation infrastructure. A
well-designed transportation network enhances the convenience of tourist travel and
promotes the comprehensive and harmonious development of transportation, tourism,
culture, and economy in a region [
2
,
3
]. Research has shown that systematic transportation
planning and policies contribute to improving the overall travel experience and satisfaction
of tourists [4,5].
In recent years, scenic byways have garnered increased attention as significant in-
frastructural elements that integrate transportation and tourism, particularly due to the
rapid development of China’s economy and society [
6
]. In 2016, the Chinese government
introduced the “National Ecotourism Development Plan”, which outlined the construction
of 25 national scenic byways. Subsequently, in 2017, “Opinions on Promoting the Inte-
grated Development of Transportation and Tourism” was published, with the objective of
establishing a comprehensive system of tourism scenic byways and actively promoting
their construction [7].
Sustainability 2023,15, 14167. https://doi.org/10.3390/su151914167 https://www.mdpi.com/journal/sustainability
Sustainability 2023,15, 14167 2 of 24
Among the Chinese provinces, Sichuan stands out for its abundant tourism resources
and well-developed tourism industry. In July 2023, Sichuan Provincial Government un-
veiled the “Plan for Building World-renowned Tourist Destinations in Sichuan (2023–2035)”,
which aims to establish a world-class scenic byway network. The western region of Sichuan
Province is characterized by its high-altitude mountainous terrain and abundant tourism
resources, making it one of the most popular destinations for self-driving tourism in China.
Related studies indicate that Western Sichuan is rich in natural and historical cultural
resources. In order to effectively utilize these resources, the establishment of an ecological
and cultural heritage tourism corridor spatial system is recommended. This system should
revolve around the main axes of National Highway 317 and National Highway 318, with
core nodes centered around cities such as Chengdu, Kangding, Litang, and Garze [8,9].
The natural geographical environment is a major determinant of transportation infras-
tructure, and, compared to plain areas, mountainous regions face challenges in establishing
robust transportation networks. Due to the extremely complex natural environment and
harsh geological conditions, the density of scenic byways, road capacity, and overall service
quality in Western Sichuan are relatively low, and their support for the development of
tourism resources along these routes is limited [
10
,
11
]. Essentially, these problems stem
from the unbalanced and uncoordinated development between the transportation infras-
tructure of scenic byways and regional tourism. Analyzing the accessibility and self-driving
tourism spatial behavior in Western Sichuan’s scenic byways is of great significance for
further expanding the tourism market, improving the level of integration between trans-
portation and tourism, and promoting regional economic and social sustainability. This
study can also provide valuable insights for government agencies, tourism destination plan-
ners, and tourism service providers in areas such as transportation network and tourism
infrastructure development.
Previous research has mainly focused on aspects such as the planning and construction
of scenic byways [
12
], landscape assessment and design [
13
], and tourism development [
14
],
with relatively limited studies on the impact of scenic byways on tourism spatial behavior.
To address the challenges posed by the imbalance between tourism transportation supply
and demand, improve the accessibility of scenic byways, and improve the coordination
between the development of self-driving tourism and regional transportation networks in
Western Sichuan, this paper investigates the relationship between the accessibility of scenic
byways and self-driving tourism.
This study uses methods such as GIS spatial analysis and social network analysis mod-
els to construct models for measuring the accessibility of scenic byways and the network
structure of self-driving tourism in the Western Sichuan. It examines the accessibility of
scenic byways and the level of self-driving tourism development in different regions of
Western Sichuan and analyzes the characteristics of self-driving tourism spatial behavior.
This research contributes to the understanding of the current layout of the scenic byway
network (route system and node system) and the development level of self-driving tourism
in Western Sichuan. It also provides insights for the scientific and rational layout of the
scenic byway network in this region.
2. Literature Review
2.1. Scenic Byway
Researchers generally agree that the definition of scenic byways can be broadly catego-
rized into two types: a broad definition refers to roads with dual functions of transportation
and scenic appreciation, while a narrow definition refers to high-quality roads that possess
scenic, natural, cultural, historical, recreational, and archaeological values within or visible
from their surroundings [
15
,
16
]. The United States is the birthplace and practical ground
for scenic byways. In 1991, the United States introduced The National Scenic Byways
Program (NSBP). As of 2021, there were more than 180 nationally designated byways in the
United States, including 150 National Scenic Byways and 37 All-American Roads, covering
48 states [17].
Sustainability 2023,15, 14167 3 of 24
Scenic byways in different countries have different names, including Scenic Road,
Scenic Route, Tourist Road, Tourist Route, etc. Norway has 18 National Tourist Routes,
mainly featuring natural landscapes. The “Scenic Routes project” in Norway has achieved
significant success in attracting tourists and promoting Norwegian architecture [
18
]. Ger-
many has introduced various theme routes such as the Romantic Road, the Wine Road,
and Castle Road, which have been well-received by tourists [
19
,
20
]. Japan’s “Historical
Routes Policy” emphasizes the protection of the routes themselves and the related heritage
along the routes. Scenic byways were introduced to China around 2000. Since 2016, the
Chinese government has issued several policies encouraging the integration of transporta-
tion and tourism, which has accelerated the planning and construction of scenic byways in
China [21].
Compared to conventional tourist roads that primarily facilitate transportation be-
tween tourist destinations, scenic byways represent a distinct type of tourism road that
integrates multiple values, including scenic, transportation, recreational, ecological, and
cultural, all within a visible range. In essence, scenic byways function as self-contained
tourist destinations. By adopting a collaborative development model for tourist attractions,
scenic byways undergo a transition from isolated “nodes” to interconnected “routes”,
effectively encompassing a broader “area” and breaking away from the isolation typi-
cally associated with traditional destination-centric tourism nodes. This approach holds
significant potential in fostering comprehensive tourism experiences [22].
2.2. Accessibility
Transportation, as an important carrier of spatial flows such as passenger flows, logis-
tics, information flows, and capital flows, is an integral part of the tourism system. Due
to the immobility of tourism resources, the spatial displacement of tourism flows relies
heavily on the regional transportation system, which provides essential support for the
development of the tourism industry [
23
]. Scholars have studied the impact of transporta-
tion on the tourism industry from the perspective of transportation accessibility [
24
]. They
believe that transportation is crucial to the development of tourism because it directly links
supply and demand, providing accessibility to tourist destinations.
In 1959, Hansen [
25
] first introduced the concept of accessibility, defining it as the
degree of mutual influence between different transportation nodes. Since then, many
scholars have expanded the concept of accessibility. Lenntorp [
26
] views accessibility as the
physical spatial environment that can be reached under temporal and spatial constraints,
emphasizing the constraints in both time and space dimensions. Ma´ckiewicz [
27
] and
Shen [
28
] have pointed out that accessibility is a crucial factor contributing to regional
spatial development disparities and serves as an indicator of the depth and breadth of
geographic relationships between regions. Transportation accessibility refers to the ease of
overcoming obstacles and establishing connections between different locations by different
modes of transport. It is a critical reference indicator for measuring the level of smoothness
of transportation between different regions [29].
As scholars continue to expand the application areas of accessibility, the methods and
models for accessibility evaluation have also been continuously enriched. Luo et al. [
30
]
used the weighted average travel time method and gravity model to calculate and analyze
the accessibility of railway transportation in the Greater Bay Area of Guangdong, Hong
Kong, and Macau. Based on spatial syntax and network analysis theory, Yang et al. [
31
]
considered various aspects of public transportation network configuration, such as service
frequency, topological structure, and geographical layout, as well as potential travel de-
mand and attractiveness. The study comprehensively used centrality and gravity models
to evaluate the accessibility of public transportation. Gu et al. [
32
] conducted a compre-
hensive evaluation of the layout and accessibility of green spaces in Nanjing using spatial
syntax models, considering global integration, local integration, and intelligibility. From the
perspective of spatial design network analysis, Zhou et al. [
33
] calculated the accessibility
Sustainability 2023,15, 14167 4 of 24
and centrality of the road network in the Chengdu-Chongqing Economic Circle based on
four indicators: closeness, traversability, detour rate, and efficiency.
Many scholars have studied the impact of accessibility on spatial patterns and their
evolution. The evolution of regions and the development of transportation networks are
spatially interactive processes, with the transportation system determining the spatial
accessibility of regions. Cao et al. [34] pointed out that regions with higher transportation
accessibility exhibit stronger spatial agglomeration effects. Zhou et al. [
35
] indicated that
expanding the construction of internal road networks in transportation-disadvantaged
counties is beneficial for promoting spatial equity in the distribution of transportation
resources. Through empirical research in Indian villages, Sarkar et al. [
36
] found that road
networks located at the center of blocks or neighboring urban villages had higher centrality
and efficiency, and there was a significant positive correlation between road network
connectivity and accessibility. Wu et al. [
37
] used data from high-grade scenic spots in
Inner Mongolia and the nearest neighbor index, kernel density, and spatial autocorrelation
to systematically analyze the spatial distribution patterns, accessibility. Their research
indicated that the spatial distribution of tourist attractions in this region shows a pattern of
“small agglomeration and large dispersion”.
2.3. Self-Driving Spatial Behavior
Research on self-driving spatial behavior mainly focuses on tourists’ consumption be-
havior, spatial differentiation characteristics, and travel chains. Gronau et al. [
38
] analyzed
questionnaire-based statistical data and found a strong positive correlation between the
layout of tourism transportation networks and travel behavior choices. Gardner [
39
] and
Vance [
40
] conducted a questionnaire survey of self-driving tourists, considering factors
such as road travel costs, travel time, and destination choices. They found that travel costs
play a critical role in self-driving travel decisions. Eby et al. [
41
], through a questionnaire
survey of American self-driving tourists, identified accessibility and distance as the main
influencing factors. Can et al. [
42
] analyzed the consumption and decision-making char-
acteristics of self-driving tourists and found that the ratio of travel cost per kilometer to
income is a key factor in their travel route decisions.
With the development of Internet technology, researchers have shifted from traditional
survey methods such as questionnaires and travel diaries to using big data approaches
such as online travel journals, photographs, and other “tourism digital footprints” to study
the spatial structure of self-driving travel, spatial patterns of self-driving tourism, and
macro-level behavioral characteristics such as tourist flows.
Zhao et al. [
43
] conducted research on the self-driving tourism market and network
structure characteristics in Fujian Province, based on data mining from online travelogues.
They used a combination of social network analysis methods, GIS, and mathematical
statistics techniques. The study indicated that, within the research area, there was a
strong imbalance in network node statuses, with significant polarization. The interaction
frequency among core nodes was much higher than that among peripheral nodes. The
spatial distribution showed a pattern of “overall dispersion with local concentration”.
Luo et al. [
44
] examined the spatiotemporal characteristics of self-driving tourist
passenger flows in Yunnan Province using two types of “digital footprints”, namely online
travelogues and photos. The research showed that self-driving passenger flows exhibited a
high degree of concentration in terms of travel times, with peak flows occurring during
winter and summer vacations, as well as during the National Day holiday. The source of
tourists was mainly distributed around Yunnan and eastern China. The self-driving space
exhibited an overall distribution pattern characterized by multiple cores, multiple linear
shapes, and multiple regions. The network had a relatively low density, and its structure
showed clear stratification, with core tourist areas not exerting strong driving forces on
peripheral tourist areas.
Zheng et al. [
45
] collected digital footprints of self-driving tourists in western Hunan
and, in conjunction with OSM road network data and tourist attraction POI data, used grid
Sustainability 2023,15, 14167 5 of 24
calculators, standard deviation ellipse analysis, and buffer analysis methods. This research
investigated the layout of the regional scenic byways. It was found out that tourist points
of interest were prominently distributed along the roads, forming a scenic byway layout
characterized as “one main route with one subsidiary and multiple branches”.
Liu et al. [
46
] conducted a study on the self-driving tourism hotspots in Tibet using
GPS trajectory mining, OSM road network data, and tourist attraction POI data. The
study found that the natural scenery around national highways had a strong attraction
for self-driving tourists. Self-driving tourists tended to cluster around tourist attractions,
with visitors stopping and resting at parking lots, service stations, scenic road sections with
beautiful views, and tourist spots. The overall rhythm of tourist activities combined both
mobility and rest, with most of the time spent in a mobile state.
2.4. Transportation and Tourism
Regarding the impact of transportation on the spatial structure of tourist destina-
tions, Smallwood et al. [
47
] analyzed the relationship between tourism spatial patterns
and regional transportation networks, and their study showed that improving the layout
of the tourism transportation network significantly increases the efficiency of road travel.
Pellegrini et al. [
48
] considered that land transportation is an important factor influenc-
ing tourism demand and tourists’ length of stay. Huang et al. [
49
] pointed out that the
development of high-speed railway has a greater impact on the tourism system of urban
agglomerations, strengthening the core–periphery structure of the urban agglomeration
tourism system, with the peripheral cities being more affected. Li et al. [50] indicated that
transportation development has significantly compressed time and space within the region,
resulting in the evolution of the spatial structure of the regional tourism system from a
“strip” pattern to a combined “spot-axis-surface” form known as “blocks”.
In terms of the coupling and coordination mechanism between transportation and
tourism, Wang et al. [
51
] pointed out that optimizing transportation networks and promot-
ing coordinated development of tourist destinations can lead to synchronized optimization
of the coupling degree of both, facilitating a positive interactive relationship between
tourist destinations and transportation networks. Wang et al. [
52
] emphasized the dynamic
and mutually reinforcing coupling relationship between transportation networks and the
development of tourism spatial structures. Mao et al. [
53
] found that the spatial coupling
relationship between tourism formats and transportation networks is the result of element
aggregation and functional spillover effects in the tourism process.
In terms of the coordinated relationship between tourism development and trans-
portation level, Zou et al. [
54
] pointed out that transportation accessibility and the length
of highways are important factors influencing tourism development. Xu et al. [
55
] em-
phasized that regions with relatively developed tourism industries should maintain the
development of transportation, while regions with a well-established foundation in tourism
development will experience a significant increase in transportation flow, thus promoting
the development of transportation level.
The theories of new economic geography indicate that the heterogeneity and prox-
imity of geographical space influence and determine the industrial collaboration and
development spillovers between different regions, and the existence of spillover effects
will promote regional coordinated growth [
56
,
57
]. Given the inherent spatial coupling
between transportation and tourism development, scholars have conducted research on
the spatial spillover effects arising from transportation on regional tourism development
and the spatial structure of tourist flows.
Guo’s et al. [
58
,
59
] research on the Yangtze River Economic Belt and Yunnan shows
that high-value tourism economic zones are highly dependent on regions with highly
developed road transportation. The level of self-driving tourism development shows
remarkable spatial heterogeneity, and its spillover and diffusion simultaneously promote
the development of transportation levels. Wang et al. [
60
] pointed out that the uneven
Sustainability 2023,15, 14167 6 of 24
regional tourism development and infrastructure construction not only have a negative
direct impact but also show significant negative spatial spillover effects.
In summary, researchers have conducted extensive studies on various aspects of
self-driving tourism, including scenic byway planning, accessibility measurement, and
spatial behavior patterns. However, there is still a need for further research to explore
the correlation characteristics and the coordinated relationship between scenic byway
network accessibility and self-driving tourism. This paper focused on Western Sichuan
as the study area and employed spatial design network analysis, GIS spatial analysis,
and social network analysis models to assess the level of scenic byway accessibility and
examine the characteristics of self-driving tourism behavior in the region. In addition,
this study employed spatial econometric methods to empirically investigate the impact of
self-driving tourism spatial behavior on scenic byway accessibility and the resulting spatial
spillover effects. By integrating these analytical approaches, the research aimed to provide
valuable insights for regional tourism transportation planning and the development of
self-driving tourism routes in the area. The findings of this study will contribute to a
better understanding of the relationship between self-driving tourism and scenic byway
accessibility, offering practical implications for the optimization of tourism transportation
networks and the enhancement of self-driving tourism experiences.
3. Materials and Methods
3.1. Study Area
The Western Sichuan region examined in this study encompasses the geographical area
outlined in the “Overall Plan for National Highway 317/318-Sichuan-Tibet World Tourism
Destination (Sichuan Section)”. The study area specifically includes three counties within
Chengdu City, namely Qionglai, Dujiangyan, and Wenjiang, as well as eight counties within
Ya’an City, namely Yucheng, Mingshan, Yingjing, Tianquan, Hanyuan, Shimian, Baoxing,
and Lushan. Additionally, it encompasses the entirety of Garze Tibetan Autonomous
Prefecture and Ngawa Tibetan and Qiang Autonomous Prefecture (Figure 1).
Sustainability 2023, 15, x FOR PEER REVIEW 7 of 25
Figure 1. The geographical location of Western Sichuan.
3.2. Data Sources
There are five airports in the research area, all of which are located at an altitude of
over 3000 m and are classified as high-altitude airports, primarily for general aviation
purposes. In terms of railways, the region is currently only served by the under-construc-
tion Sichuan-Tibet Railway. Consequently, tourism activities in this area are primarily re-
liant on road transportation.
The road vector data utilized in this study for the study area is derived from Open-
StreetMap, a publicly accessible and collaborative mapping platform. To ensure data ac-
curacy and reliability, the obtained road data undergoes meticulous topological relation-
ship checks and editing modifications. Through manual interpretation, the data is com-
bined and cleaned, ensuring the generation of a comprehensive road network specific to
the study area. This cleaning process involves the removal of empty values, interchanges,
disconnected lines, and duplicate features. The resulting road network represents the re-
quired infrastructure for the study. Figure 2 provides the scenic byways network within
the Western Sichuan region.
Figure 1. The geographical location of Western Sichuan.
Western Sichuan has abundant tourism resources, including several world natural
and cultural heritage sites, biosphere reserves, geological parks, and intangible cultural
heritage. Landmarks such as Giant Panda National Park, Jiuzhaigou, Mount Gongga,
Sustainability 2023,15, 14167 7 of 24
and Shangri-La are world-class tourist attractions and highly favored destinations for
self-driving tourists in China. This region serves as a vital link between Sichuan Province
and Tibet, characterized by a wide range of elevations spanning from over 500 m to over
7500 m. Notably, it boasts diverse terrain and landforms and a well-preserved ecosystem
and represents the most comprehensive area in the Northern Hemisphere in terms of the
vertical distribution of plant species. Furthermore, it possesses abundant and distinct
resources, making it the most intricate and diverse high-mountain ecosystem corridor
globally. Additionally, it stands as the oldest pathway for ethnic migrations, a trade route
facilitating cultural integration, and an area teeming with natural and cultural heritage
sites. National Highway 317/318, which traverses this area, holds significant importance
as it serves as a crucial route leading to Tibet. Renowned for its picturesque landscapes,
rich cultural heritage, and abundant resources along its path, this highway has earned
the reputation of being the “scenic byway for Chinese people” and is the most favored
self-driving route in China. Consequently, the Western Sichuan region has emerged as a
highly sought-after destination for self-driving tourists within China.
3.2. Data Sources
There are five airports in the research area, all of which are located at an altitude
of over 3000 m and are classified as high-altitude airports, primarily for general aviation
purposes. In terms of railways, the region is currently only served by the under-construction
Sichuan-Tibet Railway. Consequently, tourism activities in this area are primarily reliant on
road transportation.
The road vector data utilized in this study for the study area is derived from Open-
StreetMap, a publicly accessible and collaborative mapping platform. To ensure data
accuracy and reliability, the obtained road data undergoes meticulous topological rela-
tionship checks and editing modifications. Through manual interpretation, the data is
combined and cleaned, ensuring the generation of a comprehensive road network specific
to the study area. This cleaning process involves the removal of empty values, interchanges,
disconnected lines, and duplicate features. The resulting road network represents the
required infrastructure for the study. Figure 2provides the scenic byways network within
the Western Sichuan region.
Sustainability 2023, 15, x FOR PEER REVIEW 8 of 25
Figure 2. The scenic byways network in Western Sichuan.
The scenic byway network nodes are classified into three categories based on their
aributes: tourism resource nodes, transportation-related nodes, and socio-economic and
comprehensive tourism service nodes. Tourism resource nodes, classified according to
“Classification, Investigation, and Evaluation of Tourism Resources” (GB/T 18972-2017,
[61]), are primarily divided into natural resource nodes and cultural resource nodes. Tour-
ism resource nodes are the core tourist aractions in the region, capable of aracting and
generating tourism trips, providing various self-driving and recreational activities, and
driving regional tourism economic and social benefits.
Transportation-related nodes serve self-driving tourism and encompass three major
categories: automobile service nodes (gas stations, charging stations, vehicle rescue
points, etc.), transportation facility service nodes (parking lots, long-distance bus stations,
etc.), and road-related facility nodes (service areas, toll booths, etc.).
Socio-economic and comprehensive tourism service nodes include locations of tour-
ist cities, central towns, tourist towns, and tourist characteristic villages, as well as self-
driving camping sites, observation platforms, and other tourism service facilities.
To gather textual data for the travelogues, the Mafengwo website was selected as the
primary source. Mafengwo is widely recognized as one of the most popular travel web-
sites in China. Python web scraping techniques were employed to extract the travelogue
text data from the Mafengwo website. The data collection process involved scraping trav-
elogue texts related to Sichuan Province, with travel dates ranging from 1 January 2000,
to 1 April 2022. In total, 7863 travelogues were collected.
The collected travelogues were imported into an Excel spreadsheet, which included
essential information such as the travelogue title, author, place of origin, departure time,
travel duration, travel companions, and the main text content. While big data techniques
facilitated the rapid collection of a large sample, it was necessary to address certain issues
in the collected data, such as duplicates and blank entries. Therefore, data filtering was
performed.
The data filtering rules were as follows: (1) Exclude travelogues with multiple desti-
nations that do not focus on self-driving tours in Western Sichuan; (2) Exclude travelogues
that only provide information about scenic spots or promote hotels, guesthouses, or travel
agencies, as well as those with mostly pictures and minimal text content; (3) Exclude trav-
elogues with missing travel itineraries; (4) Merge serialized and duplicate travelogues.
Figure 2. The scenic byways network in Western Sichuan.
Sustainability 2023,15, 14167 8 of 24
The scenic byway network nodes are classified into three categories based on their
attributes: tourism resource nodes, transportation-related nodes, and socio-economic
and comprehensive tourism service nodes. Tourism resource nodes, classified according
to “Classification, Investigation, and Evaluation of Tourism Resources” (GB/T 18972-
2017, [61]), are primarily divided into natural resource nodes and cultural resource nodes.
Tourism resource nodes are the core tourist attractions in the region, capable of attracting
and generating tourism trips, providing various self-driving and recreational activities, and
driving regional tourism economic and social benefits.
Transportation-related nodes serve self-driving tourism and encompass three major
categories: automobile service nodes (gas stations, charging stations, vehicle rescue points,
etc.), transportation facility service nodes (parking lots, long-distance bus stations, etc.),
and road-related facility nodes (service areas, toll booths, etc.).
Socio-economic and comprehensive tourism service nodes include locations of tourist
cities, central towns, tourist towns, and tourist characteristic villages, as well as self-driving
camping sites, observation platforms, and other tourism service facilities.
To gather textual data for the travelogues, the Mafengwo website was selected as the
primary source. Mafengwo is widely recognized as one of the most popular travel websites
in China. Python web scraping techniques were employed to extract the travelogue text
data from the Mafengwo website. The data collection process involved scraping travelogue
texts related to Sichuan Province, with travel dates ranging from 1 January 2000, to 1 April
2022. In total, 7863 travelogues were collected.
The collected travelogues were imported into an Excel spreadsheet, which included
essential information such as the travelogue title, author, place of origin, departure time,
travel duration, travel companions, and the main text content. While big data techniques
facilitated the rapid collection of a large sample, it was necessary to address certain is-
sues in the collected data, such as duplicates and blank entries. Therefore, data filtering
was performed.
The data filtering rules were as follows: (1) Exclude travelogues with multiple destina-
tions that do not focus on self-driving tours in Western Sichuan; (2) Exclude travelogues
that only provide information about scenic spots or promote hotels, guesthouses, or travel
agencies, as well as those with mostly pictures and minimal text content; (3) Exclude
travelogues with missing travel itineraries; (4) Merge serialized and duplicate travelogues.
By applying these filtering rules, travelogues specifically related to self-driving tours in
Western Sichuan were isolated. Additionally, travelogues with missing data and duplicates
were eliminated, resulting in a total of 4712 valid travelogues. Finally, from each travelogue
text, self-driving travel chains were manually extracted. Travelogues with incomplete
travel routes that primarily focused on a single destination were removed, leaving a final
count of 2411 self-driving travel chains.
3.3. Research Methods
3.3.1. Accessibility Calculation
In terms of accessibility measurement indicators for scenic byways, this study adopted
the proximity measure from the sDNA (spatial design network analysis) model [
26
]. Prox-
imity represents the ease or difficulty of access compared with the rest of the road network
within a given search radius. Road networks with high proximity tend to have higher
accessibility and centrality, making them more attractive for regional transport flow. The
description and calculation method are as follows.
NQPD(x) = ∑
y∈RX
(W(y)P(y))nqpdn
dM(x,y)nqpdd (1)
In the formula:
W(y)
represents the weight of chain
y
,
P(y)
represents the weight of
node
y
within the search radius
R
. This study conducts continuous analysis, so
P(y)∈[
0, 1
]
.
dM(x
,
y)
represents the shortest topological distance from node
x
to node
y
.
nqpdn
and
nqpdd are often taken as 1 for the analysis radius.
Sustainability 2023,15, 14167 9 of 24
Node accessibility refers to the level of smoothness in the regional scenic byway system
and reflects the ease of reaching each node within the scenic byway network. A smaller
value indicates better node accessibility. The formula used to calculate node accessibility is
as follows.
Ki=∑dij
n(2)
In the formula:
∑dij
represents node accessibility, and
n
represents the total number
of nodes.
3.3.2. Social Network Analysis (SNA)
Social network analysis is an important analysis method used to describe the overall
morphology and structure of the self-driving travel chain network. The structure of the
self-driving travel flow network is composed of destination nodes for self-driving trips.
Social network analysis methods are applied to analyze the importance of nodes in the self-
driving travel network [
62
]. Based on the study of the spatial patterns in self-driving travel,
social network analysis methods are applied to analyze various indicators such as the
importance, centrality, structural holes, and core–periphery structure of self-driving travel
nodes. This analysis aims to further understand the relationships between self-driving
travel nodes and the basic characteristics of the self-driving travel chain network.
1. Centrality
Degree centrality (
CA
) represents the number of nodes directly connected to a specific
node in the network. A higher number indicates a closer connection between the node
and others, primarily calculated by counting the number of connections between a node
and other nodes in the network. Betweenness centrality (
CB
) represents the number of
shortest paths that pass through a node, reflecting its intermediary role in the network. A
higher betweenness centrality indicates stronger control and mediation ability over other
tourism nodes in the network. Closeness centrality reflects the proximity between a node
and other nodes in the network. A higher closeness centrality indicates shorter paths from
the node to all other nodes, indicating less dependence on other nodes. Outward closeness
centrality (
Cc,out
) reflects a node’s diffusion capability, while inward closeness centrality
(Cc,in) reflects a node’s aggregation capability.
2. Structural holes
Structural holes represent areas in the network structure where there are no connec-
tions between nodes, indicating fragmented regions. Structural holes can reflect competitive
relationships. In a tourism route network, the higher the network density and the fewer the
structural holes, the greater the competitive advantage of the nodes that span across the
structural holes. Efficiency scale (
ES
) measures the non-redundant connections between
the nodes in each travel chain and reflects the competitiveness of nodes in the network. A
higher efficacy scale indicates greater competitiveness.
3.3.3. Bivariable Moran’s I
The bivariate global Moran’s I is used to explore the coupling relationship between
the density of tourism resources and the density of sDNA measurement values at different
scales. The calculation of the global Moran’s I index, which assesses the overall spatial
relationship between spatial features, is as Formula (3), and its value ranges from
−
1 to
1. A value greater than 0 indicates positive spatial correlation, and the closer it is to 1, the
higher the degree of spatial clustering. A value of 0 indicates no spatial autocorrelation,
indicating a random distribution.
I=
ni=1
∑
n
j=1
∑
nwij zxizy j
i=1
∑
n
j=1
∑
nwij
i=1
∑
nzxi zyj
(3)
Sustainability 2023,15, 14167 10 of 24
In formula, I represents the bivariate Moran’s I index,
n
represents the number of
counties,
Zxi
and
Zyj
represent the standardized values of the kernel density of tourism
resources and sDNA measurement values, respectively, and
w
represents the spatial weight
matrix based on geographical distance.
3.3.4. Spatial Durbin Model
The spatial Durbin model and the spatial Durbin error model can not only measure
the local area but also assess the influence of various road network variables in surrounding
areas on the spatial distribution of tourism resources in the area through the multiplication
of the weight matrix and explanatory variables. The spatial dependence effect in the spatial
Durbin error model is captured within the error term, which describes the impact of the
error shocks of neighboring regions on the explanatory variables [63,64].
The spatial Durbin model and spatial Durbin error model can not only measure the
spatial average effects of different driving spatial behavioral variables and scenic byway
network accessibility levels in the local area but also measure the spatial average effects
of various self-driving spatial behavioral variables in surrounding areas and their level of
scenic byway accessibility through the multiplication of the weight matrix and explanatory
variables. The spatial Durbin error model further reflects the impact of error shocks
from neighboring areas on the accessibility level and the influence on the explanatory
variables [65].
Based on this, in this study, the spatial Durbin model and spatial Durbin error model
are used systematically to explore the spatial spillover effects between the parameters
of self-driving tourism flow and the level of scenic byway accessibility. In selecting the
spatial weight matrix, following the research by Wang H.L. et al. [
65
], the expression for
the geographical distance spatial weight matrix is as follows:
W=(1
d2ij i6=j
0i=j(4)
In the formula:
iand jrepresent the regions, counties, or cities;
d
represents the Euclidean distance between region (district or city)
i
and region
(district or city) j;
d2represents the squared Euclidean distance.
The formula for the spatial Durbin model (SDM) is as follows:
Y=ρWY +Xβ+W Xθ+ε(5)
The formula for the spatial Durbin error model (SDEM) is as follows:
Y=Xβ+WXθ+µ
µ=λWµ+ε(6)
In the formula, the dependent variable is represented by
KLX
,
KJD
, and
KFJ D
, the
explanatory variables are represented by
Cc,in
,
Cc,out
,
CB
, and
ES
.
W
represents the spatial
weight matrix (
N∗N
, where
N
is the number of county units).
θ
,
λ
, and
β
represent the
parameter vectors for the explanatory variables,
µ
represents the random error vector
following a normal distribution, and εrepresents the vector of random error terms.
4. Results
4.1. Calculation of Scenic Byway Accessibility
The evaluation of scenic byway accessibility in Western Sichuan encompasses both a
local perspective and an overall perspective, as the scenic byway network exhibits a compo-
sitional relationship between these levels. The overall perspective focuses on assessing the
quality of accessibility, which serves as an indicator of the development level of the entire
Sustainability 2023,15, 14167 11 of 24
scenic byway network in the region. Conversely, the local perspective emphasizes the
evaluation of the accessibility of individual nodes, representing the quality of individual
access. To effectively evaluate the accessibility of scenic byway routes and nodes in Western
Sichuan, two distinct methodologies are employed: the sDNA (Spatial Design Network
Analysis) model and the ArcGIS Network Analyst extension module. These approaches
enable the calculation and measurement of both route and node accessibility, providing a
comprehensive assessment.
To examine the spatial variations in scenic byway network accessibility, the spatial join
tool in ArcGIS is utilized to aggregate calculations based on road network line features into
county-level geographic units. As the accessibility of scenic byway nodes is derived from
average travel time calculated through GIS network analysis, its numerical significance is
inversely related to the accessibility obtained through sDNA calculations. Thus, it becomes
necessary to normalize the global proximity of scenic roads and the accessibility of scenic
byway nodes. To achieve this, expert scoring is employed, considering the sensitivity of
route accessibility and node accessibility as equal. Both aspects are assigned weights of
0.5 each
. The results are visualized in Figure 3using the ArcGIS reclassification tool and the
natural break classification method. This visualization facilitates the clear representation of
the overall assessment of scenic byway accessibility, encompassing both route and node
accessibility aspects.
Sustainability 2023, 15, x FOR PEER REVIEW 12 of 25
clear representation of the overall assessment of scenic byway accessibility, encompassing
both route and node accessibility aspects.
(a) (b) (c)
Figure 3. Network accessibility level of scenic byways in Western Sichuan ((a) Node Accessibility,
(b) Route Accessibility, (c) Scenic Byway Accessibility).
Results show that the overall accessibility of scenic byways in Western Sichuan ex-
hibits a distinct spatial distribution paern. It can be observed that there is a gradual de-
crease in accessibility from the core areas of Dujiangyan City and Wenjiang District, form-
ing an “east strong, west weak, circle-layer distribution” paern. This implies that the
eastern regions of Western Sichuan have higher accessibility compared to the western re-
gions, and the accessibility gradually decreases in a circular paern from the core areas.
Ngawa County Prefecture displays a “core–periphery” spatial structure, where Wen-
chuan County, Mao County, and Li County stand out with good accessibility and spatial
agglomeration. These areas serve as the core regions with high accessibility, while the
surrounding areas exhibit relatively lower accessibility. In Garze Prefecture, the overall
accessibility is assessed as moderate, and it also demonstrates a “core–periphery” spatial
structure. Kangding serves as the core area, with relatively higher accessibility, and the
accessibility gradually decreases towards the periphery of the prefecture. Significant spa-
tial differences in accessibility are observed across different regions, indicating a dispersed
paern. This means that there are notable variations in accessibility levels within Western
Sichuan, with some areas having beer accessibility and others having poorer accessibil-
ity. Overall, the level of scenic byway accessibility in Western Sichuan is evaluated as mod-
erate. Only two counties (or districts) demonstrate a high level of accessibility, while the
majority of areas have moderate or poor accessibility, accounting for approximately
65.85% of the total area.
4.2. Analysis of Self-Driving Travel Spatial Behavior on Scenic Byways
4.2.1. Overall Network Analysis
In this study, high-frequency tourism nodes (occurring more than 20 times) identified
in the self-driving tourism flow network in Western Sichuan are considered as network
nodes, and the flow trajectory of tourists between nodes represents the network relation-
ships. A total of 2411 self-driving travel chains and 105 tourism nodes were selected. First,
the directed self-driving routes between each pair of nodes were obtained by traversing
the data to represent the self-driving tourism flow network in Western Sichuan. For ex-
ample, if there is a one-way flow from node A to B, it is represented by the number 1,
while if there is no flow from B to A, it is represented by the number 0. This information
Figure 3.
Network accessibility level of scenic byways in Western Sichuan ((
a
) Node Accessibility,
(b) Route Accessibility, (c) Scenic Byway Accessibility).
Results show that the overall accessibility of scenic byways in Western Sichuan exhibits
a distinct spatial distribution pattern. It can be observed that there is a gradual decrease in
accessibility from the core areas of Dujiangyan City and Wenjiang District, forming an “east
strong, west weak, circle-layer distribution” pattern. This implies that the eastern regions
of Western Sichuan have higher accessibility compared to the western regions, and the
accessibility gradually decreases in a circular pattern from the core areas. Ngawa County
Prefecture displays a “core–periphery” spatial structure, where Wenchuan County, Mao
County, and Li County stand out with good accessibility and spatial agglomeration. These
areas serve as the core regions with high accessibility, while the surrounding areas exhibit
relatively lower accessibility. In Garze Prefecture, the overall accessibility is assessed as
moderate, and it also demonstrates a “core–periphery” spatial structure. Kangding serves
as the core area, with relatively higher accessibility, and the accessibility gradually decreases
towards the periphery of the prefecture. Significant spatial differences in accessibility are
observed across different regions, indicating a dispersed pattern. This means that there are
notable variations in accessibility levels within Western Sichuan, with some areas having
better accessibility and others having poorer accessibility. Overall, the level of scenic byway
Sustainability 2023,15, 14167 12 of 24
accessibility in Western Sichuan is evaluated as moderate. Only two counties (or districts)
demonstrate a high level of accessibility, while the majority of areas have moderate or poor
accessibility, accounting for approximately 65.85% of the total area.
4.2. Analysis of Self-Driving Travel Spatial Behavior on Scenic Byways
4.2.1. Overall Network Analysis
In this study, high-frequency tourism nodes (occurring more than 20 times) identified
in the self-driving tourism flow network in Western Sichuan are considered as network
nodes, and the flow trajectory of tourists between nodes represents the network relation-
ships. A total of 2411 self-driving travel chains and 105 tourism nodes were selected. First,
the directed self-driving routes between each pair of nodes were obtained by traversing the
data to represent the self-driving tourism flow network in Western Sichuan. For example, if
there is a one-way flow from node A to B, it is represented by the number 1, while if there
is no flow from B to A, it is represented by the number 0. This information was used to
construct a weighted directed matrix of 105
×
105 in size. Ucinet was used to construct
the network structure diagram of the self-driving tourism nodes in Western Sichuan based
on travelogue data, as shown in Figure 4, where the arrows represent the direction of
tourist flow.
Sustainability 2023, 15, x FOR PEER REVIEW 13 of 25
was used to construct a weighted directed matrix of 105 × 105 in size. Ucinet was used to
construct the network structure diagram of the self-driving tourism nodes in Western Si-
chuan based on travelogue data, as shown in Figure 4, where the arrows represent the
direction of tourist flow.
Figure 4. Network structure diagram of self-driving tour nodes in Western Sichuan.
Based on the calculation of relevant indicators for the self-driving tourism flow net-
work in Western Sichuan, the following observations can be made:
(a) Overall Density and Clustering Coefficient: The overall density of the tourism flow
network is 0.233, indicating a relatively low density. The clustering coefficient is
0.423, suggesting a higher level of clustering within the network.
(b) Coverage and Connectivity: The theoretical number of tourism flow paths in the net-
work is 11,025, but only 2638 paths were observed in reality, accounting for 23.93%.
This indicates that the coverage of the network is extensive but relatively concen-
trated in a few core cities. The network exhibits a low level of connectivity and weak
links between nodes and towns.
(c) Degree Centrality: The outward degree centrality is 85.41%, the inward degree cen-
trality is 83.65%, and the intermediate degree centrality is 26.83%. The higher out-
ward degree centrality indicates an imbalance in the overall network structure, with
significant aggregation and diffusion effects of core tourism nodes. The diffusion ef-
fect is beer than the aggregation effect, and there is a clear spatial clustering trend
in the network. Most nodes have one-way tourism connections, and there are several
core nodes in the network.
(d) Intermediate Degree Centrality: The relatively low value of the intermediate degree
centrality suggests that most nodes are connected to only a few core nodes in terms
of tourism flows. These nodes hold strong control over the tourism connections of
other districts and counties. However, such nodes are relatively few, indicating a
weak overall transit capacity in the self-driving tourism flow network in Western Si-
chuan. Transfers require the aggregation and diffusion of multiple intermediate
nodes, which are generally the core nodes in the network structure. This indicates a
core–periphery structure in the network.
In summary, the self-driving tourism flow network in Western Sichuan has a low
density and a loose overall network structure. It exhibits a high clustering coefficient and
relatively short average path length, indicating a small-world phenomenon. The network
Figure 4. Network structure diagram of self-driving tour nodes in Western Sichuan.
Based on the calculation of relevant indicators for the self-driving tourism flow net-
work in Western Sichuan, the following observations can be made:
(a)
Overall Density and Clustering Coefficient: The overall density of the tourism flow
network is 0.233, indicating a relatively low density. The clustering coefficient is 0.423,
suggesting a higher level of clustering within the network.
(b)
Coverage and Connectivity: The theoretical number of tourism flow paths in the
network is 11,025, but only 2638 paths were observed in reality, accounting for 23.93%.
This indicates that the coverage of the network is extensive but relatively concentrated
in a few core cities. The network exhibits a low level of connectivity and weak links
between nodes and towns.
(c)
Degree Centrality: The outward degree centrality is 85.41%, the inward degree cen-
trality is 83.65%, and the intermediate degree centrality is 26.83%. The higher outward
degree centrality indicates an imbalance in the overall network structure, with signifi-
cant aggregation and diffusion effects of core tourism nodes. The diffusion effect is
better than the aggregation effect, and there is a clear spatial clustering trend in the
Sustainability 2023,15, 14167 13 of 24
network. Most nodes have one-way tourism connections, and there are several core
nodes in the network.
(d)
Intermediate Degree Centrality: The relatively low value of the intermediate degree
centrality suggests that most nodes are connected to only a few core nodes in terms of
tourism flows. These nodes hold strong control over the tourism connections of other
districts and counties. However, such nodes are relatively few, indicating a weak
overall transit capacity in the self-driving tourism flow network in Western Sichuan.
Transfers require the aggregation and diffusion of multiple intermediate nodes, which
are generally the core nodes in the network structure. This indicates a core–periphery
structure in the network.
In summary, the self-driving tourism flow network in Western Sichuan has a low
density and a loose overall network structure. It exhibits a high clustering coefficient and
relatively short average path length, indicating a small-world phenomenon. The network
structure shows an imbalance with significant aggregation and diffusion effects of core
tourism nodes. Most nodes have one-way tourism connections, and there are a few core
nodes that hold strong control over the tourism connections. The network exhibits a
core–periphery structure.
4.2.2. Self-Driving Tourism Network Centrality Analysis
Centrality analysis was conducted on network nodes to quantify and evaluate the
status of self-driving tourism nodes within the structure of the self-driving tourism network.
Three major indicators, namely degree centrality, closeness centrality, and betweenness
centrality, were calculated for the self-driving tourism flow network in Western Sichuan.
The centrality indicators exhibited relatively high variance values, indicating the presence
of significant network imbalance.
Degree centrality, which encompasses outward degree centrality and inward degree
centrality, is utilized to assess the proximity of a self-driving tourism node to other nodes
within the self-driving tourism network. Outward degree centrality reflects the node’s
external connections with other nodes, while inward degree centrality represents its internal
connections. The average value of inward and outward degree centrality was calculated
to be 25.12, indicating that, on average, each of the 105 self-driving tourism nodes in the
network exhibited diffusion or aggregation relationships with approximately 25.12 other
tourism nodes within the Western Sichuan self-driving tourism flow network.
Closeness centrality, an indicator of the proximity between self-driving tourism nodes,
encompasses outward closeness centrality (
Cc,out
) and inward closeness centrality (
Cc,in
).
Cc,out
represents the comprehensive measure of the difficulty for tourists to reach other self-
driving tourism nodes from a specific node, while
Cc,in
measures the difficulty for tourists
from other self-driving tourism nodes to reach the specific node. The calculation results of
closeness centrality are presented in Figure 5. The node with the highest closeness centrality
is Chengdu, with an outward closeness centrality value of 99.048. Upon comparing the
values of inward and outward centrality, it is observed that outward centrality generally
surpasses inward centrality. Notably, Xiangcheng County, Dujiangyan City, Seda County,
Danba County, and Luding County exhibit the largest disparities between inward and
outward centrality values, with absolute differences of 12.105, 7.128, 6.899, 6.233, and
5.559, respectively. Approximately 57.14% of the self-driving tourism nodes function as
outward-oriented nodes, indicating that the overall self-driving tourism flow network in
Western Sichuan is predominantly characterized by outward-oriented tourism connections.
Betweenness centrality assesses the control capability of self-driving tourism nodes in
their interactions with other nodes within the network. It serves as a valuable complement
to degree centrality and closeness centrality, validating the results obtained from the node
centrality analysis mentioned earlier. The calculation results of betweenness centrality
are depicted in Figure 6. The total sum of betweenness centrality for each self-driving
tourism node in the study area is 8463.998. However, the sum of betweenness centrality
for the top ten self-driving tourism nodes amounts to 5531.259, accounting for 65.34% of
Sustainability 2023,15, 14167 14 of 24
the total. This indicates significant disparities in betweenness centrality within Western
Sichuan, with the majority of tourism connections being facilitated by the top ten ranked
self-driving tourism nodes. This concentration of control may not be conducive to the
coordinated development of regional transportation and tourism. Chengdu exhibits notably
higher betweenness centrality compared to other cities in the province, highlighting its
core position within the overall network and its close tourism connections with other
nodes. Kangding County, Danba County, Barkam County, and Jiuzhaigou County also play
significant roles as tourism cores, tourism distribution centers, and secondary tourism cores
and distribution centers within the network. Among the self-driving tourism nodes, only
16 counties possess a betweenness centrality exceeding 100, accounting for 15.23% of the
total. Approximately 57.14% of the betweenness centrality values fall within the range of 10
to 100, indicating that most nodes possess a moderate level of control capacity. Additionally,
there are 29 nodes with a betweenness centrality below 10, indicating a relatively weak
global control capacity.
Sustainability 2023, 15, x FOR PEER REVIEW 14 of 25
structure shows an imbalance with significant aggregation and diffusion effects of core
tourism nodes. Most nodes have one-way tourism connections, and there are a few core
nodes that hold strong control over the tourism connections. The network exhibits a core–
periphery structure.
4.2.2. Self-Driving Tourism Network Centrality Analysis
Centrality analysis was conducted on network nodes to quantify and evaluate the
status of self-driving tourism nodes within the structure of the self-driving tourism net-
work. Three major indicators, namely degree centrality, closeness centrality, and between-
ness centrality, were calculated for the self-driving tourism flow network in Western Si-
chuan. The centrality indicators exhibited relatively high variance values, indicating the
presence of significant network imbalance.
Degree centrality, which encompasses outward degree centrality and inward degree
centrality, is utilized to assess the proximity of a self-driving tourism node to other nodes
within the self-driving tourism network. Outward degree centrality reflects the node’s ex-
ternal connections with other nodes, while inward degree centrality represents its internal
connections. The average value of inward and outward degree centrality was calculated
to be 25.12, indicating that, on average, each of the 105 self-driving tourism nodes in the
network exhibited diffusion or aggregation relationships with approximately 25.12 other
tourism nodes within the Western Sichuan self-driving tourism flow network.
Closeness centrality, an indicator of the proximity between self-driving tourism
nodes, encompasses outward closeness centrality ( ,cout
C) and inward closeness centrality
(,cin
C). ,cout
C represents the comprehensive measure of the difficulty for tourists to reach
other self-driving tourism nodes from a specific node, while ,cin
C measures the difficulty
for tourists from other self-driving tourism nodes to reach the specific node. The calcula-
tion results of closeness centrality are presented in Figure 5. The node with the highest
closeness centrality is Chengdu, with an outward closeness centrality value of 99.048.
Upon comparing the values of inward and outward centrality, it is observed that outward
centrality generally surpasses inward centrality. Notably, Xiangcheng County, Dujiang-
yan City, Seda County, Danba County, and Luding County exhibit the largest disparities
between inward and outward centrality values, with absolute differences of 12.105, 7.128,
6.899, 6.233, and 5.559, respectively. Approximately 57.14% of the self-driving tourism
nodes function as outward-oriented nodes, indicating that the overall self-driving tourism
flow network in Western Sichuan is predominantly characterized by outward-oriented
tourism connections.
Figure 5. The node closeness centrality of self-driving tourism in Western Sichuan.
Figure 5. The node closeness centrality of self-driving tourism in Western Sichuan.
Sustainability 2023, 15, x FOR PEER REVIEW 15 of 25
Betweenness centrality assesses the control capability of self-driving tourism nodes
in their interactions with other nodes within the network. It serves as a valuable comple-
ment to degree centrality and closeness centrality, validating the results obtained from the
node centrality analysis mentioned earlier. The calculation results of betweenness central-
ity are depicted in Figure 6. The total sum of betweenness centrality for each self-driving
tourism node in the study area is 8463.998. However, the sum of betweenness centrality
for the top ten self-driving tourism nodes amounts to 5531.259, accounting for 65.34% of
the total. This indicates significant disparities in betweenness centrality within Western
Sichuan, with the majority of tourism connections being facilitated by the top ten ranked
self-driving tourism nodes. This concentration of control may not be conducive to the co-
ordinated development of regional transportation and tourism. Chengdu exhibits notably
higher betweenness centrality compared to other cities in the province, highlighting its
core position within the overall network and its close tourism connections with other
nodes. Kangding County, Danba County, Barkam County, and Jiuzhaigou County also
play significant roles as tourism cores, tourism distribution centers, and secondary tour-
ism cores and distribution centers within the network. Among the self-driving tourism
nodes, only 16 counties possess a betweenness centrality exceeding 100, accounting for
15.23% of the total. Approximately 57.14% of the betweenness centrality values fall within
the range of 10 to 100, indicating that most nodes possess a moderate level of control ca-
pacity. Additionally, there are 29 nodes with a betweenness centrality below 10, indicating
a relatively weak global control capacity.
Figure 6. The node betweenness centrality of self-driving tourism in Western Sichuan.
In summary, (1) Chengdu, Kangding, Dujiangyan, Danba, Barkam, Jiuzhaigou, and
other areas are the core nodes of the scenic byway network in Western Sichuan. These
places are the most renowned tourist nodes in Western Sichuan, known for their conven-
ient transportation, well-developed tourism infrastructure, and high tourist traffic, mak-
ing them popular destinations in the region. (2) The distribution of nodes in the scenic
byway network in Western Sichuan is uneven, with tourism flows in the region primarily
controlled by a few core nodes. In self-driving tourism, tourists disperse and converge
through these core nodes.
Figure 6. The node betweenness centrality of self-driving tourism in Western Sichuan.
Sustainability 2023,15, 14167 15 of 24
In summary, (1) Chengdu, Kangding, Dujiangyan, Danba, Barkam, Jiuzhaigou, and
other areas are the core nodes of the scenic byway network in Western Sichuan. These
places are the most renowned tourist nodes in Western Sichuan, known for their convenient
transportation, well-developed tourism infrastructure, and high tourist traffic, making
them popular destinations in the region. (2) The distribution of nodes in the scenic byway
network in Western Sichuan is uneven, with tourism flows in the region primarily controlled
by a few core nodes. In self-driving tourism, tourists disperse and converge through these
core nodes.
4.2.3. Node Structural Characteristics Analysis
Structural holes in the self-driving tourism network structure in Western Sichuan
represent the relationships between different nodes, reflecting the status of each node
within the network. By employing the structural hole model to calculate the structural
hole indicators for self-driving tourism nodes in Western Sichuan, as depicted in Figure 7,
we can gain insights into the network characteristics. The results reveal that counties
under Chengdu City, Kangding City, Barkam County, Dujiangyan City, and Danba County
exhibit relatively higher efficacy scale (
ES
), efficiency (
EF
), and lower constraint (
CT
). This
suggests that these regions located at the center of the network serve as bridges and exhibit
strong autonomy in tourism development, with a high level of tourist attraction.
Sustainability 2023, 15, x FOR PEER REVIEW 16 of 25
4.2.3. Node Structural Characteristics Analysis
Structural holes in the self-driving tourism network structure in Western Sichuan
represent the relationships between different nodes, reflecting the status of each node
within the network. By employing the structural hole model to calculate the structural
hole indicators for self-driving tourism nodes in Western Sichuan, as depicted in Figure 7,
we can gain insights into the network characteristics. The results reveal that counties un-
der Chengdu City, Kangding City, Barkam County, Dujiangyan City, and Danba County
exhibit relatively higher efficacy scale ( ES ), efficiency ( EF ), and lower constraint (CT
). This suggests that these regions located at the center of the network serve as bridges and
exhibit strong autonomy in tourism development, with a high level of tourist araction.
On the other hand, Ngawa County, Baiyu County, Shiqu County, Shimen County,
and Rangtang County have relatively lower efficacy scale and efficiency, indicating fewer
effective connections. Tourism development in these peripheral areas is constrained, and
tourist aractions face a disadvantage in aracting visitors. From Figure 7, we can see that
Barkam County is an important transportation node, serving as an intermediate node con-
necting other areas. It is relatively far from other nodes in terms of transportation, lacking
alternative tourism towns and large scenic spots along this distance. Therefore, the control
capability of transportation conditions on the overall self-driving tourism flow is much
greater than the aractiveness of tourism resources. Visitors must first consider the service
facilities of the transportation node upon arrival.
Figure 7. Structural hole analysis of self-driving tourism in Western Sichuan.
Within the self-driving tourism flow network, cities and towns located within
Chengdu and Ya’an enjoy several advantages. Firstly, they benefit from relatively short
distances between each other, facilitating convenient travel. Additionally, these areas
boast comprehensive supporting facilities, ensuring a seamless and comfortable experi-
ence for tourists. Moreover, they possess strong scenic byway accessibility, allowing for
easy exploration of various aractions. Furthermore, a larger number of nodes within
Chengdu and Ya’an exhibit structural hole advantages, indicating a higher level of net-
work openness and autonomy. This abundance of structural holes provides ample oppor-
tunities for small groups to explore diverse routes, promoting decentralization and flexi-
bility in selecting self-driving tourism nodes.
Figure 7. Structural hole analysis of self-driving tourism in Western Sichuan.
On the other hand, Ngawa County, Baiyu County, Shiqu County, Shimen County,
and Rangtang County have relatively lower efficacy scale and efficiency, indicating fewer
effective connections. Tourism development in these peripheral areas is constrained, and
tourist attractions face a disadvantage in attracting visitors. From Figure 7, we can see
that Barkam County is an important transportation node, serving as an intermediate node
connecting other areas. It is relatively far from other nodes in terms of transportation,
lacking alternative tourism towns and large scenic spots along this distance. Therefore, the
control capability of transportation conditions on the overall self-driving tourism flow is
much greater than the attractiveness of tourism resources. Visitors must first consider the
service facilities of the transportation node upon arrival.
Within the self-driving tourism flow network, cities and towns located within Chengdu
and Ya’an enjoy several advantages. Firstly, they benefit from relatively short distances
between each other, facilitating convenient travel. Additionally, these areas boast compre-
Sustainability 2023,15, 14167 16 of 24
hensive supporting facilities, ensuring a seamless and comfortable experience for tourists.
Moreover, they possess strong scenic byway accessibility, allowing for easy exploration
of various attractions. Furthermore, a larger number of nodes within Chengdu and Ya’an
exhibit structural hole advantages, indicating a higher level of network openness and
autonomy. This abundance of structural holes provides ample opportunities for small
groups to explore diverse routes, promoting decentralization and flexibility in selecting
self-driving tourism nodes.
4.3. Bivariate Global Autocorrelation Tests
The spatial Durbin model and the geographically weighted regression model are
used to study the spatial spillover effects and spatial non-stationarity of variables in
different geographical locations. They effectively analyze global average effects and local
spatial heterogeneity. Therefore, before constructing global and local regression models,
spatial effect tests are necessary. Moran’s I index is commonly used to perform spatial
autocorrelation tests on variables, s, which reflect the significance of the spatial distribution
differences of the variables.
Prior to using spatial econometric models to measure the spatial relationship between
scenic byway accessibility and self-driving spatial behavior, the bivariate global Moran’s I
index was applied to reveal the global spatial correlation patterns between the two variables.
Geoda V1.20 spatial econometric software was used to calculate the bivariate global Moran’s
I index for county-level units in the study area. The results are shown in Table 1, where all
variable parameters passed the significance level test, and the Euclidean distance threshold
for adjacent county units was 144.248 km. The bivariate Moran’s I index for inward
closeness centrality and road accessibility was 0.108, while, for outward closeness centrality,
it was 0.124. For node accessibility and overall accessibility, the bivariate Moran’s I index
increased to 0.115 and 0.129, and 0.149 and 0.161, respectively. This indicates that there
is a positive spatial correlation between outward closeness centrality and scenic byway
accessibility, and it is statistically significant at least at the 5% level. It was also observed
that, compared to road accessibility, node accessibility and scenic byway accessibility had a
more pronounced mutual influence on inward closeness centrality, showing a significant
“resource-node orientation” feature.
Table 1. Bivariate global Moran index results.
Outward Closeness
Centrality
Inward Closeness
Centrality
Betweenness
Centrality
Efficacy
Scale
scenic byway route
accessibility 0.124 0.108 0.164 0.220
scenic byway node
accessibility 0.149 0.115 0.305 0.303
scenic byway global
accessibility 0.161 0.129 0.293 0.314
The Moran’s I index of betweenness centrality with route accessibility, node accessibil-
ity, and global accessibility are 0.164, 0.305, and 0.293, respectively, showing an inverted
“V”-shaped pattern. These values were statistically significant at least at the 5% level, indi-
cating clear overall spatial dependence and correlation characteristics. Therefore, spatial
effects cannot be ignored when studying the spatial relationship between the accessibility
level of scenic roads and the spatial behavior of self-drivers in Western Sichuan.
4.4. Spatial Durbin Models Empirical Analysis
The empirical analysis using the spatial Durbin model reveals a significant spatial
relationship between the scenic byway accessibility level and self-driving spatial behavior
in Western Sichuan. When studying the spatial relationship between the two variables,
the presence of spatial spillover effects cannot be ignored. Using STATA 15 software
Sustainability 2023,15, 14167 17 of 24
as the computational platform, the spatial Durbin model was used to estimate and test
the relationship between scenic byway accessibility level and self-driving development
level. To compare the performance of different models, this study also used both the
spatial Durbin model (SDM) and the spatial Durbin error model (SDEM) for the maximum
likelihood estimation. Table 2presents the empirical results obtained using the method of
constructing spatial autoregressive models in STATA 15.
Table 2. Regression analysis of SDM model and SDEM model.
Route Accessibility Node Accessibility Scenic Byway Accessibility
SDM SDEM SDM SDEM SDM SDEM
CC,in 0.941 ** 0.919 ** −0.787 * −0.759 * −0.096 −0.088 *
CC,out −1.180 ** −1.087 *** 0.959 ** 0.917 ** 0.137 0.124
Cb0.179 *** −0.171 *** 0.137 ** 0.169 ** 0.032 ** 0.038 *
ES 0.961 *** 0.836 *** −0.144 * −0.173 * 0.224 *** 0.222 **
W*CC,in 6.331 2.160 * −20.109 *** −18.178 *** −10.944 ** −10.058 **
W*CC,out −9.951 ** −4.930 * 21.366 *** 19.760 *** 10.660 ** 9.784 **
W*Cb−1.658 ** −1.540 *** 2.778 *** 3.239 *** 1.371 * 1.427 **
W * ES 9.616 *** 7.172 *** −4.548 ** −4.808 *** −0.098 0.022 *
Log-L 37.942 36.625 36.012 36.468 43.401 43.421
R20.771 0.758 0.813 0.826 0.838 0.882
AIC −53.855 −51.251 −50.025 −52.935 −64.803 −64.843
BIC −34.771 −34.136 −30.910 −31.821 −45.689 −45.728
In the table, ***, **, and * represent significance levels at 1%, 5%, and 10%, respectively.
From the results, it can be observed that the natural logarithm of the likelihood
function (log likelihood) of the spatial Durbin error model is generally higher than that of
the spatial Durbin model. In addition, the Akaike information criterion (AIC) and Bayesian
information criterion (BIC) values of the spatial Durbin error model are generally lower
than those of the spatial Durbin model. Therefore, this study will continue the analysis
based on the results of the spatial Durbin error model.
The spatial lag term alone cannot directly determine the magnitude of the spatial
spillover effects. In order to further explore the spatial spillover effects between the specific
parameters of self-driving spatial behavior and accessibility, this study decomposes the
spatial effects of the main model. By using STATA’s spatial effects decomposition, the
direct, indirect, and total effects values are obtained. Table 3shows the results of the spatial
decomposition of the direct and indirect effects of the Durbin model.
Table 3. Direct effect, indirect effect, and total effect of SDM model and SDEM model.
Route Accessibility Node Accessibility Scenic Byway Accessibility
SDM SDEM SDM SDEM SDM SDEM
Direct effect
CC,in 0.791 * 0.919 * −1.367 * −0.759 * −0.167 −0.088 *
CC,out −0.929 ** −1.087 *** 1.577 * 0.917 *** 0.206 * 0.124 *
Cb −0.136 *** −0.171 *** 0.218 ** 0.169 *** 0.0414 * 0.038 *
ES 0.708 *** 0.836 *** −0.274 −0.173 0.2234 ** 0.222 **
Indirect effect
CC,in 1.799 1.998 −36.251 * −16.815 *** −12.0294 * −9.303 **
CC,out −3.022 * −4.560 38.624 * 18.277 *** 11.7254 * 9.050 **
Cb −0.515 * −1.425 ** 5.032 * 2.995 *** 1.5104 ** 1.320 **
ES 3.037 *** 6.633 *** −8.168 * −4.447 *** −0.067 −0.020 *
Total effect
CC,in 2.590 * 2.917 −37.618 * −17.573 *** −12.1964 * −9.391 **
CC,out −3.951 *** −5.647 * 40.201 * 19.194 *** 11.9314 * 9.174 **
Cb −0.651 ** −1.596 *** 5.250 ** 3.165 *** 1.5514 ** 1.357 **
ES 3.745 *** 7.469 *** −8.443 * −4.620 *** 0.156 0.202 *
In the table, ***, **, and * represent significance levels at 1%, 5%, and 10%, respectively.
Sustainability 2023,15, 14167 18 of 24
5. Discussion
5.1. Scenic Byway Accessibility
According to the results of the scenic byway accessibility calculation in Western
Sichuan (Figure 3), the overall scenic byway network in the region exhibits a “two-axis and
four-belt” road network structure with a spatial pattern characterized by block aggregation
and axial extension. The “two axes” are centered around Chengdu and consist of two
main east–west development axes based on the National Highway 317 passing through Du-
jiangyan and Barkam and the National Highway 318 passing through Ya’an and Kangding.
Based on the “point-axis” theory, Zhang et al. [
8
] conducted research on the spatial system
of tourism corridors in this region from green space ecological systems, spatial structure
systems, and transportation accessibility systems and also reached the similar conclusions.
The regions traversed by the “two axes and four belts” are characterized by high
values of closeness and betweenness (Figures 5and 6), indicating firstly that these regions
have good accessibility and centrality on a global scale, making them more attractive within
the overall road network. Second, these areas have a higher level of network traversability,
which accommodates a greater volume of traffic and is consistent with the actual high-level
road network that carries a greater volume of long-distance traffic flows [
55
]. This confirms
Li’s [
11
] research, which points out that, from the perspective of tourism support elements,
tourism transportation is the most important aspect of tourism public services. Highly
accessible tourist attractions in Western Sichuan are mainly distributed along National
Highway 317 and National Highway 318.
The analysis of local scale betweenness and efficiency indicates that the high accessibil-
ity areas exhibit a spatial structure characterized by “two cores and multiple nodes” overall.
The “two cores” refer to the two core tourism cities in the research area, Chengdu and
Ya’an. The “multiple nodes” include cities such as Kangding, Barkam, and Garze [
8
,
9
]. This
conclusion is consistent with the spatial distribution characteristics of multi-core spatial
structures in other regions [37,44].
5.2. Network Structure Characteristics of Self-Driving Tourism Flows on Scenic Byways
Overall, the network density of self-driving tourism flows in Western Sichuan is
relatively low, indicating a loosely connected network structure. However, it exhibits a
high clustering coefficient and relatively short average path lengths, indicating a significant
small-world phenomenon in the self-driving tourism flow network in Western Sichuan.
This suggests that self-driving tourism in Western Sichuan is characterized by a distant
source market, longer travel times, and a preference for closed-loop tour modes. According
to the “Research Report on Self-Driving Tourism of the Sichuan-Tibet Highway (2020)” [
9
]
most tourists in this region choose travel modes that combine air travel or railway travel
with self-driving. The majority of these travelers originate from Chengdu and rent or
drive their own vehicles for the journey, and their travel duration typically ranges from
5 to 15 days. The source market is mainly composed of cities from China’s economically
developed regions, including the Beijing-Tianjin-Hebei urban cluster, the Yangtze River
Delta urban cluster, and the Pearl River Delta urban cluster. These urban clusters are more
than 1000 km away from the research area. The composition of tourist source areas is
similar to that of Yunnan Province, which borders Western Sichuan [44].
5.3. Spatial Spillover Effects between Scenic Byway Network Accessibility and Self-Driving
Spatial Behavior
Comparing the calculated scenic byway accessibility results with the model’s self-
driving spatial behavior parameters, the efficacy scale has a positive direct effect on scenic
byway accessibility and a negative spillover effect. For every 1% increase in the efficacy
scale index of local structural holes, the accessibility levels of the local area and adjacent
areas will increase by 0.222% and −0.02%, respectively.
Regional self-driving competitiveness is an important factor that promotes the im-
provement of local scenic byway infrastructure and the potential increase in scenic byway
Sustainability 2023,15, 14167 19 of 24
accessibility [
38
]. Under the combined influence of spatial interaction theory and distance
decay law, the improvement of regional self-driving competitiveness in the local and adja-
cent areas will relatively weaken the development advantages of scenic byway supporting
services in adjacent areas. This, in turn, facilitates the improvement of local scenic byway
planning and construction, effectively improving the local scenic byway accessibility level
while inhibiting the improvement of scenic byway accessibility in adjacent areas. High
efficacy scale regions refer to areas with competitive advantages in tourism. On the one
hand, these regions have favorable tourism resources that support the improvement of
regional transportation facilities. On the other hand, they generate a “siphon effect” on
adjacent regions with lower transportation advantages [58].
Inward centrality has a positive direct effect and a positive spillover effect on the acces-
sibility of scenic byways. For every 1% increase in local inward centrality, the accessibility
levels of the local and adjacent areas will increase by 0.124% and 9.050%, respectively. On
the other hand, the outward centrality has a negative direct effect and negative spillover
effect on scenic byway accessibility. For every 1% increase in local outward centrality, the
accessibility levels of the local and adjacent areas will decrease by 0.088% and 9.303%,
respectively. Compared to outward centrality, high inward centrality regions are areas with
a high influx of tourist flows, and they have a strong promoting effect on the development
potential of accessibility in neighboring areas.
Betweenness centrality has a significant positive direct effect and positive spillover
effect on the distribution of tourism resources. For every 1% increase in local betweenness
centrality, the development potential of accessibility in the local area and adjacent areas will
increase by 0.038% and 1.320%, respectively. This indicates that improving betweenness
centrality is crucial for promoting comprehensive tourism. The improvement of local
tourism intermediary capability and road service level contributes to the transition from
“weak relationships” to “strong relationships” between regions. The presence of high-
grade scenic byways, such as expressways and national highways, forms a strong tourism
axis, transforming the spatial structure of scenic byways from “points” to “areas” [
50
].
This promotes the development of “comprehensive tourism” in the regions along the
scenic byways in Western Sichuan and accelerates the integration of transportation and
tourism [51].
6. Conclusions
6.1. Conclusions
This paper took the scenic byway network and self-driving tourism behavior in
Western Sichuan as the research subjects. It constructed self-driving travel chains using
travelogue texts from the Mafengwo website and analyzed the road network accessibility of
the study area and the spatial behavior of self-driving tourism using mathematical statistics
and complex network analysis methods. The main conclusions are as follows:
(1) The high accessibility areas of the scenic byways in Western Sichuan exhibited a spatial
structure of “two axes and four belts”. The coordination of accessibility among the
“core–edge” regions varied significantly. While the core cities of Chengdu and Ya’an
had relatively well-developed scenic byway accessibility, most areas in the region
required further connectivity and optimization of the scenic byway road network.
The accessibility of scenic byway nodes also followed a “core–edge” spatial structure,
gradually decreasing outward from Chengdu and Ya’an. There was significant spatial
variation in the accessibility level within the study area, with the majority of regions
having moderate to low accessibility levels, accounting for approximately 65.85%.
(2)
The spatial behavior network of self-driving tourism in Western Sichuan exhibited
characteristics of relatively low overall network density, high clustering coefficient,
and short average path length, indicating a significant small-world phenomenon.
There was an observable imbalance in the indicators of each network node, with
core nodes showing significant clustering tendencies. Nodes with strong outward
centrality also exhibited strong inward centrality.
Sustainability 2023,15, 14167 20 of 24
(3)
The overall association pattern between the accessibility of scenic byways in Western
Sichuan and the spatial behavior of self-driving tourism demonstrated clustering
and dependence characteristics, with spatial effects playing a crucial role. There
was a spatial spill-over effect between the level of scenic byway accessibility and
self-driving tourism behavior. Regional efficacy scale had a significant positive direct
effect and a negative spillover effect. For every 1% increase in local efficacy scale, it
promoted a 0.222% increase in local accessibility and a
−
0.02% decrease in neighboring
areas’ accessibility. Local efficacy scale was an important factor in promoting the
improvement of local scenic byway infrastructure and the potential growth of scenic
byway accessibility. However, it had an inhibiting effect on improving accessibility in
neighboring areas. Betweenness centrality had a positive direct effect on accessibility
and a significant positive spillover effect. For every 1% increase in local betweenness
centrality, it promoted a 0.038% increase in local accessibility and a 1.320% increase in
neighboring areas’ accessibility, strengthening the connectivity between regions from
“weak relationships” to “strong relationships”.
6.2. Implications
This paper presents a research framework for the accessibility of scenic byway net-
works and self-driving tourism behavior using GIS spatial analysis techniques and tourist
digital footprint data. This framework combines traditional spatial quantitative analy-
sis methods with complex network analysis, providing a new perspective for studying
the interaction between transportation infrastructure and tourism behavior. In terms of
theoretical research, the authors made the following efforts:
Firstly, in terms of research methods, traditional accessibility measurement methods
are mainly conducted through weighted average travel time [
30
], gravity models [
31
],
spatial syntax [
32
], spatial autocorrelation [
37
], and other approaches. Considering the
compatibility of sDNA tools and GIS platforms, this paper uses network analysis and
spatial design network analysis to break through the traditional spatial syntax axial line
model, which defines the shortest path based on a single topological depth and adopts
a measurement approach that combines “minimum angular distance” and “shortest Eu-
clidean distance”, providing new possibilities for measuring regional accessibility. The
shift from a discrete spatial search radius to a continuous one allows for a change from
the original binary values (0 or 1) to a continuous range from 0 to 1. This method not only
improves computational accuracy but also supports subsequent research on the spatial
behavior and spatial correlations of self-driving tourism, providing new insights into the
integration of transportation and tourism research.
Secondly, in terms of research perspective, previous studies on the spatial behavior of
self-driving tourists have mainly focused on analyzing aspects such as route patterns, spatial
patterns of travel chains, and the overall spatial structure of networks [
43
,
46
]. There has been
a lack of analysis on the relationship between the accessibility of transportation infrastructure
and the spatial behavior of self-driving tourists. This paper explores a research approach that
includes “scenic byway spatial patterns research—accessibility measurement—analysis of the
spatial behavior network structure of self-driving tourism—correlation characteristics”. It
achieves this by using spatial Durbin models and geographic weighted regression models
combined with analyses of centrality and structural holes. It considers both global and local
perspectives, investigating the spatial spillover effects and spatial heterogeneity character-
istics between scenic road accessibility and the spatial behavior of self-driving tourism.
Based on the above research and considering the practical needs of sustainable de-
velopment in transportation and tourism in the research area, this paper proposes the
following recommendations:
Firstly, promote the development of scenic byway construction from corridor devel-
opment to a “regional perspective”, creating a three-dimensional scenic byway network.
In the context of self-driving tourism development in Western Sichuan, emphasis should
be placed on improving the hierarchical structure of scenic byway networks and accel-
Sustainability 2023,15, 14167 21 of 24
erating the development of road technology. Priority should be given to upgrading the
technical standards of roads with high tourism demand and improving the construction of
supporting service facilities. Efforts should be made to improve the existing radial layout
of the scenic byway network, which is mainly centered around Chengdu. Emphasis should
be placed on developing secondary cores in places such as Kangding, Barkam, Daofu,
Jiuzhaigou, and Daocheng. These secondary cores should play a pivotal role and radiate
outward, expanding the scenic byway network to cover the northwest and southwest
regions of Graze Prefecture, as well as the northeast region of Aba Prefecture.
Secondly, create distinctive self-driving tourism routes, optimize tourism-related
transportation facilities, and promote integrated regional transportation and tourism devel-
opment. Establish premium self-driving tourism routes such as the “Most Beautiful Scenic
Byways in China” along National Highway 317 and National Highway 318, the Giant Panda
Ecotourism Scenic Byway, and the Gongga Mountain Scenic Byway Loop. In addition, it is
important to accelerate the planning and construction of supporting transportation and
tourism facilities, including service areas, self-driving campgrounds, observation platforms,
scenic byway interpretation systems, distinctive hotels and guesthouses, museums, and
unique traffic signs. This will facilitate the integration of transportation and tourism for
comprehensive development.
Currently, this research remains at the level of single spatial factor analysis. In the
future, the study can collect time-series data of the road network and self-driving activities
in the Western Sichuan scenic byway region. By combining space–time voxel models and
space–time cube models, multidimensional grid elements can be constructed to explore
spatiotemporal evolution characteristics in-depth. Additionally, the impact of various
control variables, such as road network morphology, economic foundation, and industrial
structure, on the development of regional self-driving tourism can be investigated. By
simultaneously applying the geographical detectors method to examine spatiotemporal
evolution driving factors, a multidimensional approach can be utilized to explore the
relationship between road networks and the spatial behavior of self-driving tourism.
Author Contributions:
Conceptualization, B.Z. and L.Z.; methodology, L.Z.; software, Z.Y.; valida-
tion, Z.Y. and A.Z.; formal analysis, Z.Y.; investigation, B.Z. and L.Z.; resources, A.Z.; data curation,
L.Z. and Z.Y.; writing—original draft preparation, B.Z. and J.L.; writing—review and editing, J.L. and
L.Z.; visualization, J.L.; supervision, L.Z.; project administration, A.Z.; funding acquisition, B.Z. All
authors have read and agreed to the published version of the manuscript.
Funding:
Please add: This study is supported by National Social Science Fund of China [Grant
No. 21CMZ010].
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement:
The data that were used to support the findings of this study are
included within the article.
Conflicts of Interest: The authors declare no conflict of interest.
References
1.
Jangra, R.; Kaushik, S.P.; Singh, E.; Kumar, P.; Jangra, P. The role of transportation in developing the tourism sector at high altitude
destination, Kinnaur. Environ. Dev. Sustain. 2023, 1–27. [CrossRef] [PubMed]
2.
Haller, A.-P.; Butnaru, G.I.; Tacu Hâr
s
,
an, G.-D.; ¸Stefănică, M. The relationship between tourism and economic growth in the
EU-28. Is there a tendency towards convergence? Econ. Res. Ekon. Istraž. 2021,34, 1121–1145. [CrossRef]
3.
Banerjee, A.; Duflo, E.; Qian, N. On the road: Access to transportation infrastructure and economic growth in China. J. Dev. Econ.
2020,145, 102442. [CrossRef]
4.
Cheng, G.; Wang, J. How Underlying Attitudes Affect the Well-Being of Travelling Pilgrims—A Case Study from Lhasa, China.
Sustainability 2023,15, 11268. [CrossRef]
Sustainability 2023,15, 14167 22 of 24
5.
Li, B.; Ye, Z.; Li, J.; Shao, S.; Wang, C. Analyzing the Passenger Waiting Tolerance during Urban Rail Transit Service Interruption:
Using Stated Preference Data in Chongqing, China. Computation 2023,11, 33. [CrossRef]
6.
Wang, Z.Y.; Li, L.; Wang, H.W.; Lin, Q. Study on Route Selection of Scenic Byway Based on Comprehensive Evaluation of
Visual Quality and Land Suitability: The Case of Bortala Mongol Autonomous Prefecture. Chin. Landsc. Archit.
2022
,38, 60–65.
[CrossRef]
7.
Qi, J.; Bai, Z.C.; Lyu, G.Y.; Tang, X.Q. Visual landscape quality of scenic road: Applying image recognition to decipher human
field of view. Acta Geogr. Sin. 2022,77, 2817–2837. [CrossRef]
8.
Zhang, Y.R.; Song, B.; Fan, X.Y. Study on the Spatial System of Ecological and Cultural Heritage Tourism Corridor of the
Sichuan-Tibet Highway. Highway 2021,66, 249–253.
9.
Qiu, P.; Si, W.; Mao, Y. Report on Self-Driving Travel along the Sichuan-Tibet Highway (2020); Social Science Academic Press: Beijing,
China, 2020; pp. 49–52.
10.
Zhang, B.; Tang, B.M.; Zhou, L.Y. Research on the Optimization of Campsite Location for Self-Driving Tourism in Western Sichuan
Based on Spatial Suitability. Highway 2021,66, 218–223.
11.
Li, R.; Deng, W.; Peng, L. A Matching Relationship between Tourism Development and Tourism Public Services in Sichuan
Province, China. Mt. Res. 2019,37, 899–909. [CrossRef]
12.
Wang, X.; Zhang, H.; Liu, S.; Wang, J.; Wang, Y.; Shangguan, D.H. Path planning of scenic spots based on improved A* algorithm.
Sci. Rep. 2022,12, 1320. [CrossRef] [PubMed]
13.
Qin, X.; Cui, S.; Liu, S. Linking Ecology and Service Function in Scenic Road Landscape Planning: A Spatial Analysis Approach.
J. Test. Eval. 2018,46, 20170451. [CrossRef]
14.
Zhang, W.; Wen, L. Analysis of the Coordination Effects and Influencing Factors of Transportation and Tourism Development in
Shaanxi Region. Sustainability 2023,15, 9496. [CrossRef]
15.
Yao, P.; Sun, Y.H.; Xi, Q.H. Research on Ecological Scenic Byways Planning Coupled with Multiple Values-Taking Ulanqab Four
Horizontal Traffic Belt as an Example. Chin. Landsc. Archit. 2019,35, 101–106. [CrossRef]
16.
Yu, Q.; Wu, B.H.; Liu, Z.M. Literature review on scenic byway research and planning practice. Geogr. Res.
2007
,26, 1274–1284.
[CrossRef]
17.
Federal Highway Administration. National Scenic Byway 2021 Designation Booklet. Available online: https://www.fhwa.dot.
gov/hep/scenic_byways/designations/fhwadesignationsbooklet.pdf (accessed on 28 July 2023).
18.
Pasgaard, J.C.; Hemmersam, P.; Nielsen, T. New Nordic stereotypes: In search of alternative design practices for tourism in
peripheral landscapes. J. Landsc. Archit. 2020,15, 6–19. [CrossRef]
19.
Zillinger, M. Tourist routes: A time-geographical approach on German car-tourists in Sweden. Tour. Geogr.
2007
,9, 64–83.
[CrossRef]
20.
López-Guzmán, T.; Vieira-Rodríguez, A.; Rodríguez-García, J. Profile and motivations of European tourists on the Sherry wine
route of Spain. Tour. Manag. Perspect. 2014,11, 63–68. [CrossRef]
21.
Sun, P.P.; Yu, Q. Scenic Byway Policy Evolution in United States: Process, Characteristics, and Enlightenment. Trop. Geogr.
2020
,
40, 1136–1149. [CrossRef]
22.
Yan, K.W.; Chen, J. A Study of Mechanisms of Promoting the Integrated Construction of Scenic Byways in the Yangtze River
Delta. Jiangsu Soc. Sci. 2021,2, 224–231+244. [CrossRef]
23.
Ma, Y.; Tang, H.Y. Research on High-Speed Rail and High-Quality Tourism Collaborative Development in the Context of
Integrated Transportation and Tourism. Tour. Trib. 2021,36, 10–12. [CrossRef]
24. Papatheodorou, A. A review of research into air transport and tourism. Ann. Tour. Res. 2021,87, 103151. [CrossRef]
25. Hansen, W.G. How Accessibility Shapes Land Use. J. Am. Inst. Plan. 1959,25, 73–76. [CrossRef]
26.
Lenntorp, B. Paths in space-time environments: A time-geographic study of movement possibilities of individuals. Lund Stud.
Geogr. Ser. B Hum. Geogr. 1977,44, 961–972. [CrossRef]
27.
Ma´ckiewicz, A.; Ratajczak, W. Towards a new definition of topological accessibility. Transp. Res. Part B Methodol.
1996
,30, 47–79.
[CrossRef]
28.
Shen, Q. Location Characteristics of Inner-City Neighborhoods and Employment Accessibility of Low-Wage Workers. Environ.
Plan. B 1998,25, 345–365. [CrossRef]
29.
Shi, Y.; Blainey, S.; Sun, C.; Jing, P. A literature review on accessibility using bibliometric analysis techniques. J. Transp. Geogr.
2020,87, 102810. [CrossRef]
30.
Luo, J.G.; Zhang, B.; Liu, S.M. Relationship between Traffic Accessibility and Tourism Economic Contact of Guangdong-Hong
Kong-Marco Greater Bay Area. Econ. Geogr. 2020,40, 213–220.
31.
Yang, R.Q.; Liu, Y.L.; Liu, Y.F.; Liu, H.; Gan, W.X. Comprehensive Public Transport Service Accessibility Index—A New Approach
Based on Degree Centrality and Gravity Model. Sustainability 2019,11, 5634. [CrossRef]
32.
Gu, K.; Liang, B. Research on the Accessibility of the Parks along the CityWall of Ming Dynasty in Nanjing Based on Space Syntax.
Mod. Urban Res. 2020,8, 11–17. [CrossRef]
33.
Zhou, L.Y.; Zhang, B.; Tang, B.M. On Spatial Characteristics of Highway Traffic Network in Chengdu-Chongqing Economic Circle
Based on Multi-Scale sDNA Model. J. Southwest China Norm. Univ. Nat. Sci. Ed. 2021,46, 72–81. [CrossRef]
Sustainability 2023,15, 14167 23 of 24
34.
Cao, X.S.; Ouyang, S.S.; Yang, W.Y. Transport Accessibility and Spatial Connections of Cities in the Guangdong-Hong Kong-Macao
Greater Bay Area. Chin. Geogr. Sci. 2019,29, 820–833. [CrossRef]
35.
Zhou, Q.; Wu, B.Y.; Li, C.K. Spatial Coupling and Coordination Degree Analysis on Road Network Accessibility and Economic
Development Level of Changsha-Zhuzhou-Xiangtan Urban Agglomeration. Geomat. World 2020,27, 70–76.
36.
Sarkar, T.; Sarkar, D.; Mondal, P. Road network accessibility analysis using graph theory and GIS technology: A study of the
villages of English Bazar Block, India. Spat. Inf. Res. 2021,29, 405–415. [CrossRef]
37.
Wu, X.; Chen, C. Spatial Distribution and Accessibility of High Level Scenic Spots in Inner Mongolia. Sustainability
2022
,14, 7329.
[CrossRef]
38.
Gronau, W.; Kagermeier, A. Key factors for successful leisure and tourism public transport provision. J. Transp. Geogr.
2007
,15,
127–135. [CrossRef]
39.
Gardner, B.; Abraham, C. What drives car use? A grounded theory analysis of commuters’ reasons for driving. Transp. Res. Part F
Psychol. Behav. 2007,10, 187–200. [CrossRef]
40.
Vance, C.; Hedel, R. The impact of urban form on automobile travel: Disentangling causation from correlation. Transportation
2007,34, 575–588. [CrossRef]
41.
Eby, D.W.; Molnar, L.J. Importance of scenic byways in route choice: A survey of driving tourists in the United States. Transp. Res.
Part A Policy Pract. 2002,36, 95–106. [CrossRef]
42.
Can, V.V. Estimation of travel mode choice for domestic tourists to Nha Trang using the multinomial probit model. Transp. Res.
Part A 2013,49, 149–159. [CrossRef]
43.
Zhao, W.; Li, R.; Yan, J. The geographical distribution and network structure of self-driving tourist flowing Fujian Province. World
Reg. Stud. 2017,26, 165–174. [CrossRef]
44.
Luo, Q.J.; Liang, S.X. Temporal and Spatial Characteristics of Self-driving Tourist Flows Based on Tourism Digital Footprints: A
Case Study in Yunnan Province. Tour. Trib. 2016,31, 41–50. [CrossRef]
45.
Zheng, Q.M.; Huang, Y.Q.; Yang, X.Y.; Shen, M.Z.; Hu, J.H. Research on Route Selection of Scenic Byway in Western Hunan:
Based on Tourism Digital Footprint. J. Nat. Sci. Hunan Norm. Univ. 2021,44, 9–16.
46.
Liu, Y.; Bao, J.G.; Huang, Y.H.; Zhang, Z.A. Study on Spatio-Temporal Behaviors of Self-driving Tourists Based on GPS data: A
Case Study of Tibet. World Reg. Stud. 2019,28, 149–160.
47.
Smallwood, C.B.; Beckley, L.E.; Moore, S.A. An analysis of visitor movement patterns using travel networks in a large marine
park, north-Western Australia. Tour. Manag. 2012,33, 517–528. [CrossRef]
48.
Pellegrini, A.; Scagnolari, S. The relationship between length of stay and land transportation mode in the tourism sector: A
discrete–continuous framework applied to Swiss data. Tour. Econ. 2021,27, 243–259. [CrossRef]
49.
Huang, T.; Xi, J.C.; Ge, Q.S. Spatial Differentiation and Integration Optimization of an Urban Agglomeration Tourism System
under the Influence of High-Speed Railway Network Evolution. Appl. Spat. Anal. Policy 2017,12, 349–376. [CrossRef]
50.
Li, Y.M.; Xiu, C.L.; Kong, X. Influence of Land Transportation Network Evolution on Spatial Structure and Development of
Regional Tourism in Zhejiang Province. Sci. Geogr. Sin. 2018,38, 2066–2073. [CrossRef]
51.
Wang, Z.; Zhao, S.; Yang, X. Study on the evolvement of coupling coordination between transportation network and tourism
resort of urban agglomeration-A case study of Chang-Zhu-Tan urban agglomeration. World Reg. Stud. 2020,29, 962–972.
52.
Wang, F. Study on the Correlation Mechanism and Effect of Traffic Network and Tourism Spatial Structure Evolution in Southwest
Frontier Mountainous Area—A Case Study of Yunnan Province. J. Yunnan Minzu Univ. Philos. Soc. Sci. Ed. 2020,37, 2+161.
53.
Mao, R.; Chen, F.; Ji, X. Spatial Coupling of the Public Transport Network and the Tourism and Leisure Industry against the
Background of Tourism Urbanization: A Case Study of Lijiang. Trop. Geogr. 2022,42, 659–673. [CrossRef]
54.
Zou, Y.; Li, Q.; Zhu, R. Differences and Influencing Factors of Tourism Development in Cities with Isomorphic Locations.
J. Huaqiao Univ. Philos. Soc. Sci. 2022,1, 49–63. [CrossRef]
55.
Xu, J.; Jin, C. Research on Relationship between Tourism Development and Traffic Flow at County Level in Jiangsu Province.
Areal Res. Dev. 2020,39, 82–87. [CrossRef]
56. Krugman, P. Increasing Returns and Economic Geography. J. Political Econ. 1991,99, 483–499. [CrossRef]
57. Proost, S.; Jacques, F.T. What Can Be Learned from Spatial Economics? J. Econ. Lit. 2019,57, 575–643. [CrossRef]
58.
Guo, X.Y.; Mu, X.Q.; Ming, Q.Z. Research on the impact and spatial effects of high-speed transportation on tourism development
from the dual perspective of “performance”: A case study of the Yangtze River Delta. Hum. Geogr.
2022
,37, 172–182. [CrossRef]
59.
Guo, X.Y.; Mu, X.Q.; Ding, Z.S. Spatial Spillover Effect and Its Influence Mechanism of Traffic Service Function on Tourism
Efficiency: A Case Study of Yunnan Province. Geogr. Geo Inf. Sci. 2021,37, 126–134. [CrossRef]
60.
Wang, J.Y.; Zhang, H. Tourism Development, Spatial Spillover and Regional Development Imbalance. Tour. Sci.
2021
,35, 73–94.
[CrossRef]
61.
GB/T 18972-2017; Classification, Investigation, and Evaluation of Tourism Resources. People’s Republic of China General
Administration of Quality Supervision, Inspection and Quarantine, China National Standardization Administration: Beijing,
China, 2017.
62.
Li, Q.; Qu, L.Y. The network structure and influencing factors of the tourist flow within the city: The case of Shanghai. World Reg.
Stud. 2021,30, 114–124. [CrossRef]
63.
Teng, T.W.; Lin, H.L.; Hu, L.S. Development of the Yangtze River Delta: Progress, Spatial Differentiation and Spatial Correlation.
J. Univ. Camb. Philos. Soc. Sci. 2020,44, 134–145. [CrossRef]
Sustainability 2023,15, 14167 24 of 24
64.
Whitacre, B.E.; Meadowcroft, D.; Gallardo, R. Firm and regional economic outcomes associated with a new, broad measure of
business innovation. Entrep. Reg. Dev. 2019,31, 930–952. [CrossRef]
65.
Wang, H.L.; Hu, W.P.; Wu, C. Analyzing The Effect of Spatial Weighted Matrix on Spatial Autocorrelation-Taking Hunan’s Income
Gap Between Urban And Rural Areas As a Case. J. South China Norm. Univ. Nat. Sci. Ed. 2010,1, 110–115.
Disclaimer/Publisher’s Note:
The statements, opinions and data contained in all publications are solely those of the individual
author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to
people or property resulting from any ideas, methods, instructions or products referred to in the content.
