Feasibility Analysis of Green Travel in Public Transportation: a Case Study of Wuhan

sustainability

Article Feasibility Analysis of Green Travel in Public Transportation: A Case Study of Wuhan

Junjun Zheng, Yi Cheng, Gang Ma *, Xue Han and Liukai Yu

Economics and Management School, Wuhan University, Wuhan 430072, China; [email protected] (J.Z.); [email protected] (Y.C.); [email protected] (X.H.); [email protected] (L.Y.) * Correspondence: [email protected]

Received: 3 July 2020; Accepted: 11 August 2020; Published: 12 August 2020

Abstract: The demand to alleviate urban traffic and reduce air pollution puts forward high requirements for green travel in public transportation. Thus, study of the feasibility of urban green travel in public transportation is necessary. This study focuses on it from two aspects: City level by complex network and individual level by structural equation model. As for the former, point of interest data on the spatial distribution of urban public transportation in Wuhan city are quantitatively analyzed. Then, a complex network of public transportation in Wuhan is constructed by using the Space L method, and the network characteristics are analyzed. Results show that accessibility coverage is mainly concentrated in the central urban area, and two significant central nodes exist, namely, Linshi and Zhaohu stations. At the individual level, 354 valid questionnaires and the structural equation model were used to explore the factors affecting individual intention of public transportation. Behavioral perceptual outcome, behavioral attitudes, and subjective norms have positive influences on the behavioral intention of public transportation, among which the behavioral attitudes are the most significant, and the subjective norms had the lowest influence. Some suggestions are proposed for Wuhan to improve urban accessibility and for individuals to increase green travel in public transportation.

Keywords: complex network; green travel; structural equation model; theory of planned behavior

1. Introduction

1.1. Context Public transportation can alleviate urban air pollution problems and has attracted extensive attention from governments of various countries [1]. Urban areas, urban population density, public facilities construction, and spatial distribution constantly develop and change, followed by the diversification and complexity of people’s value orientation, desire evolution, and behavior choices in aspects, such as food, clothing, housing, and transportation. How to guarantee public transportation at a city level effectively is a difficult problem. As shown in Figure1, China’s urbanization is in a period of rapid development, but the supply growth of urban public facilities and infrastructure services does not fully match with urbanization development.

Sustainability 2020, 12, 6531; doi:10.3390/su12166531 www.mdpi.com/journal/sustainability Sustainability 2020, 12, 6531 2 of 22

Sustainability 2020, 12, 6531 2 of 22 Sustainability 2020, 12, 6531 2 of 22

Figure 1. China’s urban area and urban population density. (Source: China Statistical Yearbook). Figure 1. China’s urban area and urban population density. (Source: China Statistical Yearbook). GivenFigure the 1. China’srapid increase urban area of andurban urban areas population and po denspulation,ity. (Source: urban China facilities Statistical and publicYearbook). transport servicesGiven have the developed rapid increase gradually, of urban as shown areas in and Figure population, 2. Moreover, urban as facilities shown in and Figure public 3, the transport growth servicesrate Givenof havetotal the developedurban rapid public increase gradually, transport of urban as shown passenger areas in and Figure volume po2pulation,. Moreover, has urbanslowed as shown facilities significantly in Figureand public 3(in, the blue), transport growth and ratepassengerservices of total have volume urban developed public of rail transport gradually,transit remarkably passenger as shown volumeincr in Figureeased has (in2. slowed Moreover, grey) significantlybut asa marked shown (in indecrease blue), Figure and 3,happened the passenger growth in volumetherate passenger of oftotal rail transit urbanvolume remarkablypublic of public transport increasedbus and passenger tram (in grey) traffic volume but (in a red) marked has in detail,slowed decrease from significantly happened 2014 to 2017. in (in the Onblue), passenger the andone volumehand,passenger the of phenomenonvolume public bus of rail and of transit urban tram remarkably traroadffic congestion (in red) increased in became detail, (in grey)increasingly from but 2014 a marked to serious, 2017. decrease and On the happened demand one hand, ofin theurbanthe phenomenon passenger residents volume for of urbanbuses of public roaddecreased. congestionbus and On tram the became othertraffic increasinglyhand, (in red) given in detail, serious,the increase from and 2014 theof urbanto demand 2017. subway, On of the urban onerail residentstransithand, thelines, for phenomenon and buses train decreased. schedules, of urban On traveling road the othercongestion has hand, become became given highly the increasingly increaseconvenient of serious, urbanand punctual. subway, and the More raildemand transitpeople of lines,preferurban and residentstram train public schedules, for transpotation,buses traveling decreased. such has On become as the the other highlysubway. hand, convenient However, given the and the increase punctual. subway of Moreisurban in need people subway, of prefer more rail traminvestemnttransit public lines, transpotation,than and trainbus schedules,public such transpotation, as traveling the subway. has which be However,come leads highly the many subway convenient cities is into and needhave punctual. of fewer more Moretram investemnt peoplepublic thantranspotation.prefer bus tram public public The transpotation, above transpotation, analysis which shows such leads asan theimbalance many subway. cities between However, to have the fewer supplythe subway tram of urban public is in public transpotation.need transport of more Theservicesinvestemnt above and analysis than the developmentbus shows public an imbalance transpotation, of urbanization between which thein China. supply leads Therefore, ofmany urban cities public scientific to transporthave and fewer reasonable services tram and publicspace the developmentnetworktranspotation. planning of The urbanization isabove crucial analysis for in China.improving shows Therefore, an urban imbalance scientificenvironmental between and reasonable the quality supply [2]. space of urban network public planning transport is crucialservices for and improving the development urban environmental of urbanization quality in [China.2]. Therefore, scientific and reasonable space network planning is crucial for improving urban environmental quality [2].

Figure 2. Changes of urban public transport services in China. (Source: China Statistical Yearbook).

Figure 2. Changes of urban public transport services in China. (Source: China Statistical Yearbook). Sustainability 2020, 12, 6531 3 of 22 Sustainability 2020, 12, 6531 3 of 22

Figure 3. Passenger volume change of urban public transport in China. (Source: China FigureStatistical 3. Passenger Yearbook). volume change of urban public transport in China. (Source: China Statistical Yearbook). Obviously, the construction of urban rail lines requires a large amount of social resources andObviously, government the financial construction revenue. of urban Therefore, rail lines resources requires in a urban large amount rail public of social transport resources are mainly and governmentconcentrated financial in the central revenue. urban Therefore, areas of first-tierresources and in second-tier urban rail citiespublic and transport radiate toare the mainly remote concentratedurban areas. in However, the central the urban lines currently areas of first-tier in operation and aresecond-tier still limited. cities Residents and radiate in large-sized to the remote and urbanmedium-sized areas. However, cities have the lines a large currently demand in for operat conveniention are still and limited. punctual Residents public transport in large-sized resources; and medium-sizedtherefore, the cities distribution have a oflarge urban demand public for transport convenient resources and punctual is unbalanced public transport and geographically resources; therefore,different tothe a certaindistribution extent of [3 ].urban Spatial public distribution transport features resources are essential is unbalanced [4,5], but and previous geographically studies on differentspatial analysis to a certain are fewextent and [3]. mostly Spatial from distribution the macro features description, are essential such as satellite[4,5], but maps. previous Ren etstudies al. [1 ] onused spatial the China–Brazilanalysis are few Earth and Resources mostly from Satellite-02B the macro to description, analyze land-use such as change. satellite Moreover, maps. Ren complex et al. [1]network used the theory China–Brazil can analyze Earth the reliability Resources of Satell networksite-02B and to evaluate analyze node land-use importance change. [6 ].Moreover, However, complexfor the construction network theory of green can travelanalyze network, the reliability it is generally of networks only simulation, and evaluate not node combined importance with actual [6]. However,data, used for to the construct construction an actual of green effective travel network. network, Zheng it is etgenerally al. [7] employed only simulation, a scale-free not networkcombined to withsimulate actual the data, group used choice to construct behavior an in actual green effe travel.ctive Alexander network. Zheng [8] proposed et al. [7] a seedemployed network a scale-free using the networkNash equilibrium to simulate allocation the group method choice from behavior the perspective in green of travel. game theoryAlexander and defined[8] proposed the conditions a seed networkof an optimal using greenthe Nash travel equilibrium and transportation allocation network.method from The the deepening perspective of urbanization of game theory to urban and definedpublic transportthe conditions service of presents an optimal new green requirements travel and and transportation challenges.China’s network. urbanization The deepening lagand of urbanizationregional distribution to urban imbalance public transport significantly service reduce presents the willingnessnew requirements of urban and residents challenges. to green China’s travel. urbanizationA vicious cycle lag isand shown, regional where distribution the inconvenience imbalance of significantly public transportation reduce the leads willingness to the decrease of urban of residentsgreen travel to green intention, travel. then A vicious the congestion cycle is ofshown, urban where roads, the and inconvenience finally the aggravation of public oftransportation urban air and leadsenvironmental to the decrease pollution. of green travel intention, then the congestion of urban roads, and finally the aggravationThe annual of urban reports air ofand China’s environmental motor vehicle pollution. environmental management (2018) and mobile source environmentalThe annual management reports of China’s (2019) releasedmotor vehicle by the ministryenvironmental of ecology management and environment (2018) and showed mobile that sourceChina environmental has been the world’s management largest producer(2019) released of motor by vehiclesthe ministry in the of past ecology 10 years. and Motorenvironment vehicle showedemission that has China become has thebeen first the element world’s oflargest urban producer air pollution of motor in China vehicles [9]; in it the affects past urban 10 years. air qualityMotor vehicleand is emission a key factor has ofbecome city residents’ the first element health; itof also urba reducesn air pollution gasoline in consumption China [9]; it affects and greenhouse urban air qualitygas emissions and is a [10 key]. Studiesfactor of have city shown residents’ that reducinghealth; it thealso number reduces of gasoline private carsconsumption can positively and greenhousepromote green gas travelemissions in public [10]. Studies transportation have shown [4]. Therefore, that reducing controlling the number the use of ofprivate motor cars vehicles can positivelyand encouraging promote urbangreen greentravel travelin public in publictransporta transportationtion [4]. Therefore, are the cont keyrolling to solve the the use problem of motor of vehiclesair pollution. and encouraging urban green travel in public transportation are the key to solve the problem of air Influencedpollution. and restricted by external conditions, people who are willing to travel green in public transportationInfluenced and sometimes restricted have by extern to chooseal conditions, non-green people travel who [11]; are for willing example, to travel they green have toin public choose transportationto travel by air sometimes because of have the distance,to choose asnon-green well as othertravel reasons [11]; for [ 12example,]. However, they personalhave to choose attitudes to travelare important by air because to public of the transportation distance, as well [13, 14as]. other For reasons example, [12]. some However, people personal may lessen attitudes their tripsare importantbecause they to public are unable transportation to travel green[13,14]. in For public example, transportation some people [12]. may Therefore, lessen their on the trips basis because of the Sustainability 2020, 12, 6531 4 of 22 theory of planned behavior (TPB), the factors influencing individuals’ green travel intentions in public transportation were analyzed, and an empirical study on the above factors was conducted using the structural equation model. In terms of the research on individuals’ green travel intentions in public transportation, on the basis of the TPB, Jia et al. [15] studied the relationship between environmental factors and green travel intentions using a questionnaire survey and showed that the government needs to take new measures on relevant policies and transportation systems to increase the proportion of green travel. Ru et al. [16] researched the interaction effects of norms and attitudes on green travel intention. Shi et al. [17] found that individuals’ subjective norm and attitude are important in affecting factors of individuals’ pro-environment intention and further influence actual behavior. Wu [18] adopted the structural equation modeling and showed that nostalgia enhances tourists’ pro-environmental behavior through subjective attitude, perceived behavioral control, subjective norms, and meaning in life. To sum up, the key to urban air pollution control lies in the prevention and control of urban motor vehicle pollution, and the adoption of green travel mode by urban residents is vital to reduce the emission of motor vehicles and control air pollution. Thus, the present study contributes to two aspects. First, on the basis of the spatial distribution data of various functional facilities in Wuhan, the spatial distribution and correlation of various functional facilities were quantitatively analyzed by using ArcGIS technology. This study discusses the spatial distribution and accessibility of urban public transport service systems from the perspectives of 13 administrative divisions and central and remote districts to provide policy suggestions for the optimization of the urban public transport network. Second, on the basis of the index system constructed by TPB, a survey on the permanent residents of Wuhan was conducted, and their willingness to go by public transportation way was analyzed. A total of 724 valid survey samples were obtained. AMOS was used to construct the corresponding structural equation model of Wuhan residents’ willingness to green travel in public transportation, which further identified the deep-seated psychological driving factors of urban residents’ willingness to green travel in public transportation. Finally, the strategies and ways to guide urban residents to green travel were given.

1.2. Overview of Wuhan City In Wuhan, the new energy vehicles used in public transportation take account of 66.1% in 2018, and the rail traffic is also pro-environmental [19], which belongs to green travel compared with private car travel. The scientific and reasonable supply of the urban public transport service system is the prerequisite for urban residents to adopt behavior of green travel in public transportation. However, the insufficient supply of public transport, insufficient coverage, and unsatisfactory accessibility of public transport are important reasons for the low willingness to green travel in public transportation of urban residents and the difficulty in promoting green behavioral patterns. Therefore, the spatial distribution of the urban public transport service system provides a preliminary basis for this study. This study takes Wuhan as the case and divides it into the core and far cities. The core city comprises Jiangan, Qingshan, Jianghan, Qiaokou, Wuchang, Hanyang, and Hongshan Districts, whereas the far city comprises Huangpi, Xinzhou, Dongxihu, Caidian, Hanan, and Jiangxia Districts, as shown in Figure4. Sustainability 2020, 12, 6531 5 of 22 Sustainability 2020, 12, 6531 5 of 22

FigureFigure 4. 4.Thirteen Thirteen administrative administrative districts districts in in Wuhan Wuhan city. city. The research data were collected from the point of interest (POI) data of March 2019 on Baidu Map. The research data were collected from the point of interest (POI) data of March 2019 on Baidu Each record contained the name, address, administrative division location, classiﬁcation, and longitude Map. Each record contained the name, address, administrative division location, classification, and and latitude of Baidu coordinates of a POI. Wuhan currently operates a total of 456 bus lines and 9 longitude and latitude of Baidu coordinates of a POI. Wuhan currently operates a total of 456 bus subway lines. lines and 9 subway lines. After reweighing, sorting, and classifying data, 3903 records about bus stations in daily operation, After reweighing, sorting, and classifying data, 3903 records about bus stations in daily and 206 records about subway stations in daily operation are selected. The spatial distribution of urban operation, and 206 records about subway stations in daily operation are selected. The spatial public transport stations provides the research background for the study on the green travel behavior distribution of urban public transport stations provides the research background for the study on the of urban residents in Wuhan in the following paragraphs. The stations that have a practical impact on green travel behavior of urban residents in Wuhan in the following paragraphs. The stations that the residents’ travel behavior are mainly built stations. Thus, the planning and construction of public have a practical impact on the residents’ travel behavior are mainly built stations. Thus, the planning transport stations are excluded from this study. and construction of public transport stations are excluded from this study. This paper is organized as follows. Section2 depicts the network of public transportation and This paper is organized as follows. Section 2 depicts the network of public transportation and extension of TPB. In Section3, data and method are shown, in which we also consider city level and extension of TPB. In Section 3, data and method are shown, in which we also consider city level and individual level. Section4 analyzes some results. Some concluding remarks are shown in Section5. individual level. Section 4 analyzes some results. Some concluding remarks are shown in Section 5. In Section6, some discussions have been given. In Section 6, some discussions have been given. 2. Conceptual Model 2. Conceptual Model 2.1. Network of Public Transportation by ArcGIS 2.1. Network of Public Transportation by ArcGIS The POI data of Wuhan bus stations and subway stations were collected on Baidu Map in MarchThe 2019 POI to understanddata of Wuhan the bus spatial stations distribution and subway of Wuhan’s stations public were collected transport on system. Baidu Map Each in record March contained2019 to theunderstand name of thethe POI spatial point, distribution location of the of administrative Wuhan’s public division, transport address, system. category, Each latitude, record andcontained longitude. the Thename data of werethe POI converted point, intolocation the WGS84of the administrative coordinate system division, andimported address, intocategory, the ArcGISlatitude, as and shown longitude. in Figure The5. data The subwaywere converted stations into in Wuhan the WGS84 mainly coordinate have core system areas. and Huangpi, imported Xinzhou,into the and ArcGIS Jiangxia as Districtsshown in only Figure have 5. a fewThe stationssubway close stations to the in core Wuhan urban mainly areas, whereas have core Caidian areas. andHuangpi, Hanan DistrictsXinzhou, have and almost Jiangxia no stations.Districts Moreonly bushave stops a few exist stations in the city,close and to theythe core are more urban widely areas, whereas Caidian and Hanan Districts have almost no stations. More bus stops exist in the city, and Sustainability 2020, 12, 6531 6 of 22 SustainabilitySustainability 2020 2020, 12, ,12 6531, 6531 6 of6 of22 22

they are more widely distributed than subway stations. These bus stations make up for the shortage theydistributed are more than widely subway distributed stations. than These subway bus stations stations. make These up forbus the stations shortage make of subwaysup for the and shortage are the of subways and are the main means to connect the core and far cities. ofmain subways means and to connectare the main the core means and to far connect cities. the core and far cities.

(a)( a) (b()b )

FigureFigureFigure 5. 5. 5.(a( a)( a)Spatial) Spatial Spatial distribution distribution distribution of of ofsubway subway subway stations stations stations in in inWuhan Wuhan Wuhan city; city; city; (b ( b()b )spatial) spatial spatial distribution distribution distribution of of ofbus bus bus stationsstationsstations in in inWuhan Wuhan Wuhan city. city. city.

WuhanWuhanWuhan waswas was divideddivided divided into into 9740into 9740 compartments,9740 compartments, compartments, and the and busand the andthe bus subway bus and and stations subway subway in eachstations stations compartment in in each each compartmentwerecompartment counted. were Thewere resultscounted. counted. are The shown The results results in Figure are are shown6 .shown The colorin in Figure Figure of the 6. small 6.The The gridcolor color is of the of the deepestthe small small ingrid grid Jianghan is isthe the deepestanddeepest Qiaokou in in Jianghan Jianghan Districts, and and indicating Qiaokou Qiaokou Districts, that Districts, these indicating districts indicating have that that the these these most districts districts bus stations. have have the Hongshan,the most most bus bus Wuchang,stations. stations. Hongshan,Hanyang,Hongshan, Huangpi,Wuchang, Wuchang, andHanyang, Hanyang, Jiangxia Huangpi, Huangpi, Districts and have and Jiangxia Jiangxia a red grid,Districts Districts indicating have have a thatreda red grid, the grid, bus indicating indicating stations that in that these the the busdistrictsbus stations stations are in dense.in these these districts The districts spatial are are dense. quantitydense. The The distributionspatial spatial quantity quantity of subway distribution distribution stations of of subway insubway Wuhan stations stations is also in in Wuhan mainly Wuhan isconcentrated isalso also mainly mainly inconcentrated concentrated the core area in ofin the thethe core city.core area Asarea shownof of the the city. in city. Figure As As shown 6shown, the subwayin in Figure Figure stations 6, 6,the the subway are subway concentrated stations stations are on are concentratedShengliconcentrated Street, on Yangsigangon Shengli Shengli Street, Street, Fast Track,Yangsigang Yangsigang Dingziqiao Fast Fast Track, Road, Track, andDingziqiao Dingziqiao Dongfeng Road, Road, Avenue. and and Dongfeng Most Dongfeng of the Avenue. areasAvenue. in MosttheMost far of of citythe the areas have areas almostin in the the far no far city subway city have have stations. almost almost no no subway subway stations. stations.

(a()a ) (b()b )

FigureFigureFigure 6. 6. 6.(a( a)( )aSpatial Spatial) Spatial quantity quantity quantity distribution distribution distribution of of bus bus stationsstations stations inin Wuhan;inWuhan; Wuhan; ( b(b) () spatialb spatial) spatial quantity quantity quantity distribution distribution distribution of ofsubway ofsubway subway stations stations stations in in Wuhan. inWuhan. Wuhan. 2.2. Extension of TPB 2.2.2.2. Extension Extension of ofTPB TPB According to the TPB, when an individual chooses whether to green travel in public transportation, AccordingAccording to to the the TPB, TPB, when when an an individual individual chooses chooses whether whether to to green green travel travel in in public public his behavioral intention directly affects his behavior occurrence. The stronger the willingness to act, transportation,transportation, his his behavioral behavioral intention intention directly directly affects affects his his behavior behavior occurrence. occurrence. The The stronger stronger the the the more likely he takes action. The core factor of individual behavior is individual behavioral intention, willingnesswillingness to to act, act, the the more more likely likely he he takes takes action. action. The The core core factor factor of of individual individual behavior behavior is isindividual individual which depends on the comprehensive effect of behavioral attitude, subjective norm, and perceived behavioralbehavioral intention, intention, which which depends depends on on the the comprehe comprehensivensive effect effect of of behavioral behavioral attitude, attitude, subjective subjective norm,norm, and and perceived perceived behavioral behavioral control. control. An An individual’s individual’s behavioral behavioral intention intention of of green green travel travel in in public public Sustainability 2020, 12, 6531 7 of 22 Sustainability 2020, 12, 6531 7 of 22 behavioraltransportation control. can Anaffect individual’s his behavior behavioral only when intention he can of decide green travelwhether in publicor not transportation to green travel can in public transportation. That is, the individual is constrained by external opportunities and resources affect his behavior only when he can decide whether or not to green travel in public transportation. and can have actual control over the behavior only when the necessary opportunities and resources That is, the individual is constrained by external opportunities and resources and can have actual control are available. People’s choice of green travel in public transportation is also influenced by their own over the behavior only when the necessary opportunities and resources are available. People’s choice of and external factors and perception of behavioral results. Previous studies have proven that, due to green travel in public transportation is also influenced by their own and external factors and perception the callback effect of behavioral outcome perception on green travel behavior and intention, the of behavioral results. Previous studies have proven that, due to the callback effect of behavioral outcome corresponding outcome perception is mainly reflected in three aspects, namely, economic saving, perception on green travel behavior and intention, the corresponding outcome perception is mainly spiritual satisfaction, and health improvement. reflected in three aspects, namely, economic saving, spiritual satisfaction, and health improvement. Based on the above analysis, this study mainly analyzes the influencing factors of choice of Based on the above analysis, this study mainly analyzes the influencing factors of choice of individual behavior of green travel in public transportation with bounded rationality and establishes individual behavior of green travel in public transportation with bounded rationality and establishes the conceptual model shown in Figure 7. the conceptual model shown in Figure7.

Figure 7. Conceptual model of individual green travel behavior. Figure 7. Conceptual model of individual green travel behavior. 3. Data and Method 3. Data and Method 3.1. Data Sources 3.1. DataTo build Sources a complex public transport network in Wuhan, in addition to the POI data, this study capturedTo build the road a complex distribution public information transport fromnetwork Baidu in Map.Wuhan, Given in addition that completed to the POI stations data, will this have study a realcaptured impact the on road residents’ distribution travel information behavior, the from planning Baidu andMap. construction Given that completed of public transport stations will stations have werea real not impact considered. on residents’ Moreover, travel abehavior, road with the multiple planning turns and was construction considered of public as a road transport with multiple stations connections.were not considered. To unify theMoreover, geographical a road location with multiple information turns andwas projection considered state as a of road various with POI multiple data, allconnections. data were To transformed unify the geographical into the form location in the WGS-84information coordinate and projection system. state After of sortingvarious dataPOI data, and refiningall data thewere classification, transformed this into study the form selected in the the WGS-84 information coordinate on operating system bus. After and subwaysorting data stations and (3903),refining subway the classification, stations (206), this and study roads selected (10189). the information on operating bus and subway stations (3903),Moreover, subway to stations analyze (206), the influencing and roads factors(10189). of individual green travel behavior choice, a five-point LikertMoreover, scale was to used analyze to investigate the influencing and analyze factors theof individual resident population green travel of Wuhan.behavior Achoice, total ofa five- 746 questionnairespoint Likert scale were was distributed used to investigate at bus stations, and anal subwayyze the stations, resident and population business districts of Wuhan. of Jiedaokou A total of and746 thequestionnaires Nanhu community. were distributed In order to at close bus to stat theions, structure subway of Wuhan stations, residents, and business we referred districts to data of fromJiedaokou Wuhan and statistical the Nanhu yearbook community. in 2018, andIn order collected to cl 354ose valid to the questionnaires. structure of Wuhan The age residents, distributions we ofreferred the statistical to data yearbookfrom Wuhan and statistical respondents yearbook are shown in 2018, in and Table collected1. Table 3542 shows valid thequestionnaires. questions and The indicatorsage distributions affecting of willingness the statistical to greenyearbook travel and in re thespondents questionnaire. are shown in Table 1. Table 2 shows the questions and indicators affecting willingness to green travel in the questionnaire. Table 1. Age distribution of people in Wuhan. Table 1. Age distribution of people in Wuhan. Proportion 19 [20,35) [35,50) [50,65) [65,80) 80 ≤ ≥ StatisticalProportion ൑19 [20,35) [35,50) [50,65) [65,80) ൒80 17.80% 23.35% 23.07% 22.08% 10.93% 2.77% YearbookStatistical Yearbook 17.80% 23.35% 23.07% 22.08% 10.93% 2.77% RespondentsRespondents18.16% 18.16% 28.82% 28.82% 25.36% 25.36% 18.44% 18.44% 8.36% 8.36% 0.86% 0.86%

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Table 2. Investigation indicators of individual green travel choice behavior.

Factor Research Indicators and Questions Behavior intention I would like to travel in a green way.(Q12) I think air quality is very important, and pay attention to the emission Individual attitude of pollutants.(Q1) Sustainability 2020, 12, 6531 8 of 22 (IA) Green travel is better than other forms of travel.(Q2) I’m under a lot of financial pressure right now.(Q3) Table 2. InvestigationMany indicators people of recommended individual green travel me to choice travel behavior. by bus or subway.(Q4) Subjective FactorI am willing to consider Research the Indicators suggestions and Questions of people around me and NormsBehavior intention I wouldadjust like tomy travel travel in a green style.(Q5) way.(Q12) I think air quality is very important, and pay attention to the (SN)Individual attitudeTV, Internet and other mediaemission often of pollutants.(Q1) recommend me to travel in public (IA) Green travel istransportation.(Q6) better than other forms of travel.(Q2) It is not difficultI’m for under me a lotto of green ﬁnancial travel pressure alone right without now.(Q3) other people’s Many people recommended me to travel by bus or subway.(Q4) Perceived behavioralSubjective Norms I am willing to considercompany the suggestions or help.(Q7) of people around me and control (SN) Greenadjust mytravel travel is style.(Q5) safe.(Q8) (BC) TV, Internet and other media often recommend me to travel in public Green traveltransportation.(Q6) is convenient.(Q9) Behavioral outcome It’sIt cheaper is not diﬃ forcult forgreen me to travel green travel in public alone without transportation.(Q10) other people’s Perceived behavioral control company or help.(Q7) perception (BC) Choosing green travel canGreen protect travel the is safe.(Q8) environment, which makes me (OP) Greenfeel travel very is happy.(Q11) convenient.(Q9) Behavioral outcome perception It’s cheaper for green travel in public transportation.(Q10) (OP) Choosing green travel can protect the environment, which makes me 3.2. Method feel very happy.(Q11)

3.2.1. Complex3.2. Method Network Analysis of Public Transportation In3.2.1. the ComplexSpace L Networkmodeling Analysis method, of Public the Transportationactual road network station corresponded to the model node [20]. IfIn the the Spacetwo nodes L modeling can reach method, each the other, actual roadan edge network is established station corresponded at the corresponding to the model location of the model.node [20 ].Figure If the two 8 is nodes a sche canmatic reach diagram each other, of an the edge Space is established L mode atling. the corresponding The network location constructed by the Spaceof the L model.method Figure can8 represent is a schematic the diagramline transforma of the Spacetion L modeling.of the public The networktransportation constructed network. All by the Space L method can represent the line transformation of the public transportation network. stations on the same line can be directly connected, forming a line cluster in the figure. The nodes All stations on the same line can be directly connected, forming a line cluster in the ﬁgure. The nodes that thethat two the clusters two clusters are are connected connected to to representrepresent the the actual actual transformation transformation site. site.

Figure 8. Schematic diagram of Space L modeling method. Figure 8. Schematic diagram of Space L modeling method. The Space L method is the most common method for building complex public transportation networks [21,22]. This method fully considers the relative position relation of adjacent stations on the The Space L method is the most common method for building complex public transportation public transport route and can reﬂect the service order relation of public transport on the actual operating networksroute. [21,22]. Therefore, This the method collected fully POI considers information the on relative bus and position subway stations relation represents of adjacent the public stations on the public transporttransport nodes route in complex and can networks. reflect The the public service transportation order relation operation of route public of adjacent transport nodes on was the actual operatingused route. to show Therefore, the network the connection collected relationship, POI inform whichation was on used bus to constructand subway public stations transportation represents the public intransport Wuhan’s complexnodes in networks complex and networks. analyze complex The networkpublic transportation topology and characteristics. operation route of adjacent nodes was used to show the network connection relationship, which was used to construct public transportation in Wuhan’s complex networks and analyze complex network topology and characteristics. Sustainability 2020, 12,, 65316531 9 of 22

Using the Space L method, the node information in the complex network of public Using the Space L method, the node information in the complex network of public transportation transportation in Wuhan was sorted out. The node name, ID, longitude, and latitude were included, in Wuhan was sorted out. The node name, ID, longitude, and latitude were included, which total which total 4109 pieces of information. Given that the majority of public transport routes are two- 4109 pieces of information. Given that the majority of public transport routes are two-way, this study way, this study did not consider the directionality of the connection relationship in the complex did not consider the directionality of the connection relationship in the complex network of public network of public transport. The result contains 4109 points and 4914 edges, one of which represents transport. The result contains 4109 points and 4914 edges, one of which represents the adjacent the adjacent relationship between two stations on a public transport route. Two adjacent stations may relationship between two stations on a public transport route. Two adjacent stations may exist on exist on multiple public transport routes. Therefore, this study introduced the weight concept of multiple public transport routes. Therefore, this study introduced the weight concept of edges in the edges in the complex network to represent the number of public transport routes where these edges complex network to represent the number of public transport routes where these edges are located. are located. The existence of a certain edge on N public transport routes shows that residents in The existence of a certain edge on N public transport routes shows that residents in Wuhan can reach Wuhan can reach neighboring stations by taking buses or subways on N lines. The weight of this neighboring stations by taking buses or subways on N lines. The weight of this edge is denoted as N. edge is denoted as N. The topological structure relationship of this complex network is shown according to the Force The topological structure relationship of this complex network is shown according to the Force Atlas and Fruchterman–Reingold diagram in physics. The speciﬁc results are shown in Figure9a,b. In a Atlas and Fruchterman–Reingold diagram in physics. The specific results are shown in Figure 9a,b. complex network, the set node size is positively correlated with its degree size. Therefore, the nodes in In a complex network, the set node size is positively correlated with its degree size. Therefore, the the complex network with many edge connections are represented by large points. The distribution nodes in the complex network with many edge connections are represented by large points. The map of the public transport complex network based on Force Atlas shows two prominent central distribution map of the public transport complex network based on Force Atlas shows two prominent nodes in the network, namely, Linshi and Zhaohu stations in Huangpi District. These two nodes central nodes in the network, namely, Linshi and Zhaohu stations in Huangpi District. These two have many side connections, which play a decisive role in the normal operation of Wuhan’s public nodes have many side connections, which play a decisive role in the normal operation of Wuhan’s transport system. public transport system.

(a) (b)

Figure 9.9. ((aa)) ComplexComplex network network of of public public transport transport based based on Forceon Force Atlas; Atlas; (b) complex (b) complex network network of public of publictransport transport based onbased Fruchterman–Reingold. on Fruchterman–Reingold.

The Fruchterman–Reingold diagramdiagram of of the the public public transportation transportation complex complex network network places places nodes nodes in inthe the middle middle of the of circle,the circle, andtheir and positionstheir positions extend extend outward outward with the with decrease the decrease of node degree. of node Therefore, degree. Therefore,the distribution the distribution diagram shows diagram that the shows size distribution that the size of eachdistribution node degree of each moves node outward degree from moves the outwardcircular center. from the As circular shown incenter. the Figure As shown9, a few in nodesthe Figure in the 9, center a few ofnodes the circle in the have center a large of the degree, circle haveand most a large of degree, the nodes and in most the networkof the nodes have in athe small network degree. have Therefore, a small degree. the complex Therefore, public the transport complex publicnetwork transport in Wuhan network presents in Wuhan some scale-free presents networksome scale-free features, network that is, features, a small numberthat is, a ofsmall nodes number have ofmore nodes edge have connections more edge in connect the complexions in network. the complex network. To analyze thethe index,index, somesome inductionsinductions areare shownshown asas following:following:

1. Degree distribution 1. Degree distribution = ( ) If the network’s adjacency matrix is A aij N N, the degree of node i, ki can be described as: If the network’s adjacency matrix is Aa= ()×× , the degree of node i, ki can be described as: ijX N N ki = aij (1) ka= j N iij∈ (1) jN∈ Sustainability 2020, 12, 6531 10 of 22

Further, if n(k) is used to represent the number of nodes with degree k, the degree distribution P(k) can be expressed as: n(k) P(k) = (2) P ∞ n(j) j=1 Degree of nodes can reflect the influence of themselves in the network. The more adjacent nodes a node has, the more important its position in the network will be. Degree distribution refers to the probability distribution of node degrees in the network, that is, the ratio of the number of nodes with the same degree to the total number of nodes. Moreover, degree distribution can describe the dispersion degree of the overall network. The overall structural characteristics and dynamic behavior of complex networks are closely related to degree distribution, so degree distribution is often used in quantitative analysis of complex networks. 2. Clustering coefficient The phenomenon that two nodes of a network are connected to each other is called clustering. The clustering coefficient is the ratio of the actual number of edges between nodes connected to a certain node to the most possible number of edges between them. The larger the clustering coefficient is, the more information exchange channels there are between them, which reflects the tight degree of connection between nodes in the local network. We use Ci to denote clustering coefficient of node i, ki is degree, and Ei is the number of edges between adjacent nodes of ki. Moreover, ki nodes can have as ki(ki 1) many edges as 2− . The clustering coefficient can be written as 2E C = i (3) i k (k 1) i i − The clustering coefficient of the network is the average value of the local clustering coefficient, i.e.,

N 1 X C = C (4) N i i=1

If the clustering coefficient of one node is greater than the clustering coefficient of the network, then the network is considered to have clustering phenomenon. 3. Eigenvector Centrality The importance of each node in the network is different, and centrality can measure whether a single node is in the center of the network, in which eigenvector centrality is one of the indexes. A node in network with higher igenvector centrality should be connected with more nodes, and its adjacent nodes are also of high importance. For any node i, the eigenvector centrality is:

N 1 X 1 X x = x = a x (5) i λ j λ ij j j M(i) j=1 ∈ where aij is an element of node i’s adjacency matrix, and it denotes whether the node i is connected to the node j. λ is a constant. 4. Modularity class value Modularity class value is a quantitative index that can be used to evaluate the structural strength of the community. If the value is big, it indicates that eﬀect of community division is good. The calculation is shown as following: 1 X X kikj Q = δ δ (A ) (6) 2n ci cj ij − 2n c C i,j V ∈ ∈ Sustainability 2020, 12, 6531 11 of 22

where Aij is the adjacent matrix of networks, ki and kj denote degree of node i and node j, respectively. C is a set of communities on the network, and c represents one of the community structures. n is the number of edges. δci is an indicator variable, which means if the node i is inside of c, δci equals to 1; otherwise, it is 0.

3.2.2. Structural Equation Model of Individual Behavior of Green Travel in Public Transportation This section models individual green behavioral intention on the basis of the TPB. According to a reasonable survey index system, the reliability and validity of the questionnaire and survey data are analyzed, which ensures the eﬀectiveness of the structural equation model of residents’ green travel behavioral intention in public transportation. The initial model was built, and total statistics of questionnaire were shown in Table3. Moreover, in Cronbach’s alpha was used to analyze the reliability of the survey results. KMO (Kaiser-Meyer-Olkin) and Bartlett’s tests were used to analyze their validity. The results show that the validity of the questionnaire data is 0.823, indicating good internal consistency and high reliability (Tables3 and4).

Table 3. Reliability analysis.

Cronbach’s Alpha Based on Cronbach’s Alpha N of Items Standardized Items 0.830 0.823 12

Table 4. Questionnaire index statistics.

Scale Mean Corrected Squared Cronbach’s Scale Variance if if Item Item-Total Multiple Alpha if Item Item Deleted Deleted Correlation Correlation Deleted Q1 44.69 24.029 0.663 0.488 0.802 Q2 44.70 23.903 0.709 0.548 0.799 Q3 46.98 33.498 0.501 0.354 0.881 − Q4 44.79 24.438 0.624 0.456 0.806 Q5 44.90 24.673 0.591 0.384 0.809 Q6 44.75 25.247 0.561 0.352 0.812 Q7 44.52 24.715 0.559 0.331 0.811 Q8 44.69 26.896 0.334 0.167 0.829 Q9 44.69 26.152 0.382 0.203 0.826 Q10 44.67 24.800 0.576 0.394 0.810 Q11 44.62 24186 0.669 0.521 0.802 Q12 44.63 24.098 0.754 0.711 0.797

From Table5, the result of the validity test shows that the value of KMO is 0.930, which is greater than 0.5. Therefore, the research result is suitable for factor analysis. Therefore, the hypothesis that all variables are independent should be rejected, and the questionnaire has structural validity.

Table 5. KMO (Kaiser-Meyer-Olkin) and Bartlett’s Test.

Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.930 Approx. Chi-Square 1690.500 Bartlett’s Test of Sphericity df 66 Sig. 0.000 Sustainability 2020, 12, 6531 12 of 22

Table 5. KMO (Kaiser-Meyer-Olkin) and Bartlett’s Test.

Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.930 Approx. Chi-Square 1690.500 Bartlett’s Test of Sphericity df 66 Sig. 0.000 Sustainability 2020, 12, 6531 12 of 22 According to the concept of the extended TPB in Section 2.2, Wuhan residents’ green travel behavioral intentionAccording tois theinfluenc concepte ofby the behavioral extended TPB attitude, in Section subjec 2.2, Wuhantive norm, residents’ perceived green travel behavioral control, behavioraland behavioral intention perceptual is inﬂuence byoutcome. behavioral More attitude,over, subjective behavioral norm, attitude perceived can behavioral be measured control, by Q1, Q2, andand Q3; behavioral subjective perceptual norms outcome.can be measured Moreover, behavioral by Q4, Q5, attitude and can Q6; be perceived measured by behavioral Q1, Q2, and Q3;control can subjective norms can be measured by Q4, Q5, and Q6; perceived behavioral control can be measured be measuredby Q7, by Q8, Q7, and Q9;Q8, and and behavioral Q9; and perceptual behavioral outcome perceptual can be measured outcome by can Q10 be and measured Q11, which by are Q10 and Q11, whichalso shownare also in Figureshown 10 in. Figure 10.

FigureFigure 10. Initial 10. Initial model model of of behavioral behavioral intention intention to green to green travel. travel. Moreover, the initial model was built by Amos software on the basis of survey data. The ﬁtting Moreover,results of the initialinitial structural model equationwas built model by Amos can be obtained,software as on shown the inbasis Table of6. survey The model data. of the The fitting results ofchi-square the initial value structural is 234.167. equation CFI and GLI model values can are be close obtained, to 0.9. IFI as and shown TLI values in Table are 0.957 6. andThe 0.942, model of the chi-squarewhich value are greateris 234.167. than 0.9.CFI and GLI values are close to 0.9. IFI and TLI values are 0.957 and 0.942, which are greater than 0.9. Table 6. Initial model ﬁtting degree of green travel intention.

2 2 Tableχ 6. Initialdf model χfitting/df degreeRMSEA of green IFI travel CFI intention. GFI TLI Intention 234.167 52 4.503 0.097 0.957 0.868 0.896 0.942 2 2 χ df χ / df RMSEA IFI CFI GFI TLI IntentionThe initial 234.167 model of green52 travel 4.503 behavioral intention 0.097 in public 0.957 transportation0.868 0.896 corresponds 0.942 to the path coefficient, load coefficient, and significance ratio, as shown in Table7. The path coe fficient of perceived behavioral control (BC) on green travel behavioral intention in public transportation is The initial model of green travel behavioral intention in public transportation corresponds to the 0.029. The SE and CR values are 0.069 and 0.423, respectively. The P value is 0.672 and greater than − − path coefficient,0.01, which load shows coefficient, there is no di andfference significance between the ratio, path coeas ffishowncient and in 0Table under the7. The confidence path coefficient level of perceivedof 99%.behavioral The previous control analysis (BC) shows on green that the travel questionnaire behavioral and dataintention have met in thepublic requirements transportation of is - 0.029. Thereliability SE and and CR validity. values Combined are 0.069 with and the −0.423, fitting analysisrespectively. results ofThe the P initial value model is 0.672 of green and travel greater than 0.01, whichbehavioral shows intention there is in no public difference transportation, between this sectionthe path considers coefficient eliminating and 0 the under factor the of perceptual confidence level behavioral control from the initial structural equation model. The modified model of green travel of 99%. The previous analysis shows that the questionnaire and data have met the requirements of behavioral intention is shown in Figure 11. reliability and validity. Combined with the fitting analysis results of the initial model of green travel behavioral intention in public transportation, this section considers eliminating the factor of perceptual behavioral control from the initial structural equation model. The modified model of green travel behavioral intention is shown in Figure 11.

Sustainability 2020, 12, 6531 13 of 22

Table 7. Estimated results of the initial structural equation model.

Estimate S.E. C.R. P Intention <--- IA 1.000 Sustainability 2020, 12, 6531 13 of 22 Intention <--- SN 0.327 0.053 6.182 ***

IntentionTable <--- 7. Estimated BC results of the−0.029 initial structural0.069 equation −0.423 model. 0.672 Intention <--- OP 0.412 0.064 6.459 *** Estimate S.E. C.R. P Q1 <--- IA 1.000 Intention <— IA 1.000 IntentionQ2 <---<— IA SN 1.135 0.327 0.099 0.053 11.422 6.182 *** *** Intention <— BC 0.029 0.069 0.423 0.672 − − IntentionQ3 <---<— IA OP −1.006 0.412 0.088 0.064 −11.397 6.459 *** *** Q1Q4 <---<— SN IA 1.000 1.000 Q2 <— IA 1.135 0.099 11.422 *** Q3Q5 <---<— SN IA 1.01.00609 0.122 0.088 8.20011.397 *** *** − − Q4Q6 <---<— SN SN 0.8 1.00007 0.101 7.990 *** Q5 <— SN 1.009 0.122 8.200 *** Q6Q7 <---<— BC SN 1.000 0.807 0.101 7.990 *** Q7 <— BC 1.000 Q8Q8 <---<— BC BC 0.843 0.843 0.198 0.198 4.257 4.257 *** *** Q9Q9 <---<— BC BC 1.043 1.043 0.254 0.254 4.108 4.108 *** *** Q10 <— OP 1.000 Q11Q10 <---<— OP OP 1.000 1.178 0.195 6.033 *** Q11Q11 <---<— IntentionOP 1.178 1.000 0.195 6.033 *** ***: p > 0.01. Q11 <--- Intention 1.000

FigureFigure 11. Modification 11. Modification model model of of behavioralbehavioral intention intention to green to green travel. travel. The P values in Table8 are all less than 0.05, indicating that all the estimated parameters in the Themodified P values model in Table are significant 8 are all atless the than 95% confidence0.05, indicating level, which that all can the be consideredestimated as parameters having a in the modifiedsignificant model impact.are significant Moreover, at the the standardized 95% confiden resultce (ST) level, of the which path coe canfficient be ofconsidered the modification as having a significantmodel impact. of green Moreover, travel behavior the standardized intention is also shownresult in(ST) Table of8 .the path coefficient of the modification model of(1) greenThe travel path coe behaviorfficient between intention the is behavioral also shown attitude in Table and green8. travel intention in public transportation is 0.89. Therefore, the behavioral attitude has a significant positive influence on Table 8.green Coefficient travel behavioral estimation intention, results and of its the influence green ontravel green in travel public behavioral transportation intention is greater behavior intentionthan modification the two factors model. of subjective norms and behavioral perceptual outcome. (2) The path coefficient from subjective norm to green travel intention in public transportation is 0.35. Therefore, behavioral attitude has a positiveEstimate influence onS.E. green travel C.R. intention, P butST its influence is lowerIntention than behavioral <--- attitude IA and the 1.000 result of behavioral perception. *** 0.89 (3) The path coefficient from a behavioral perceptual outcome to green travel behavioral intention in publicIntention transportation <--- is 0.41. SN Therefore, 0.3the23 subjective 0.053 norm has6.120 a positive *** influence 0.35 on green travelIntention behavioral <--- intention, OP but its influence 0.409 is lower than0.064 the behavioral6.420 perceptual*** 0.41 outcome. Q1 <--- IA 1.000 0.58 Q2 <--- IA 1.140 0.100 11.374 *** 0.65 Q4 <--- SN 1.000 0.47 Q5 <--- SN 1.008 0.122 8.281 *** 0.48 Q6 <--- SN 0.807 0.101 7.980 *** 0.35 Q10 <--- OP 1.000 0.40 Sustainability 2020, 12, 6531 14 of 22

Table 8. Coeﬃcient estimation results of the green travel in public transportation behavior intention modiﬁcation model.

Estimate S.E. C.R. P ST Intention <— IA 1.000 *** 0.89 Intention <— SN 0.323 0.053 6.120 *** 0.35 Intention <— OP 0.409 0.064 6.420 *** 0.41 Q1 <— IA 1.000 0.58 Q2 <— IA 1.140 0.100 11.374 *** 0.65 Q4 <— SN 1.000 0.47 Q5 <— SN 1.008 0.122 8.281 *** 0.48 Q6 <— SN 0.807 0.101 7.980 *** 0.35 Q10 <— OP 1.000 0.40 Q12 <— Intention 1.000 0.74 Q11 <— OP 1.182 0.197 5.985 *** 0.76 Q3 <— IA 1.012 0.089 11.368 *** 0.65 − − − ***: p < 0.05.

4. Results

4.1. Network Analysis

4.1.1. Analysis of Topological Parameter in Complex Networks To characterize quantitatively the topological structure of the complex public transport network in Wuhan, this section calculates and analyzes the topological structure parameters of the network, including degree distribution, network path, clustering coeﬃcient, feature vector centrality, and modularization. The degree distribution of all nodes in the complex network is shown in Table9 and Figure 12. A total of 2275 nodes have a degree of 3. Using node degree distribution, the number of nodes in the node degrees of 1 to 3 has a proportion of more than 70% of the summary points, the number of nodes in the node degree not more than 5 has a proportion that is as high as 92.5%, and the number of nodes in the node degree not less than 9 has a proportion of 1.095%. Therefore, Wuhan’s public traﬃc complex network with a small number of node degree is large. The vast majority of the node degree is small, which indicates that the corresponding complex networks present obvious characteristics of scale-free networks. The calculation shows that the average value of the complex network of bus-subway stations in Wuhan is 2.392 and the average weighted value is 4.14. Therefore, each bus-subway station in Wuhan is connected with two to three other stations on average, which provides guarantee for people to travle nearby palces by public transportation.

Table 9. Node statistics of Wuhan public transportation complex network.

Degree Number Degree Number 1 328 9 22 2 275 10 12 3 2275 11 6 4 547 12 1 5 376 13 1 6 152 16 1 7 75 19 1 8 36 35 1 Sustainability 2020, 12, 6531 15 of 22

Sustainability 2020, 12, 6531 15 of 22 Sustainability 2020, 12, 6531 15 of 22

Figure 12. Degree distribution of Wuhan public transportation network.

Figure 12. Degree distribution of Wuhan public transportation network. The calculationFigure results 12. Degree show distribution that the diameterof Wuhan puof blicthe transportation complex public network. transport network in WuhanThe is calculation 103, the resultsaverage show path that length the diameter is 24, and of thethe complex values are public relatively transport large. network Therefore, in Wuhan the istransmission 103,The the calculation average performance path results length and show is circulation 24, th andat the the efficiencydiameter values are ofof relatively thethe complexcomplex large. networkpublic Therefore, transport in the the field transmission network of public in performanceWuhantransport is are103, and low, the circulation which average brings epathfficiency trouble length of to the ispeople 24, complex and wh enthe network they values choose in are the to fieldrelatively travel of publicin publiclarge. transport transportation,Therefore, are low,the whichtransmissionand the brings whole performance trouble complex to peoplepublic and whentransportcirculation they network chooseefficiency tostill travelof has the room incomplex public for further transportation,network optimization. in the andfield the Theof wholepublic value complextransportcorresponding public are low, transportto whicheach node brings network in trouble the still middle has to people room of the for wh ce furtherennter they degree optimization. choose distribution to travel The in valueis public shown corresponding transportation, in Figure 13. to eachandMost the node of wholethe in nodes the complex middle in the public of middle the centertransport of the degree center network distribution valu steill degree has is room shown are low.for in further FigureOnly a 13optimization. handful. Most ofof thenode The nodes valuesvalue in thecorrespondingare middlehigh, as of shown the to centereach in nodeWuhan’s value in degree the public middle are traffic low. of the Onlycomplex center a handful degreenetwork. of distribution node A few values sites is are exist,shown high, such in as Figure shownas Linshi 13. in Wuhan’sMoststation, of whichthe public nodes shows tra inffi c thethe complex middleshortest network. of path the forcenter A the few valucirculation sitese degree exist, and suchare effective low. as Linshi Only utilization station,a handful of which allof publicnode shows values traffic the shortestareresources. high, path as shown for the circulationin Wuhan’s and public effective traffic utilization complex ofnetwork. all public A tra fewffic sites resources. exist, such as Linshi station, which shows the shortest path for the circulation and effective utilization of all public traffic resources.

Figure 13. Centrality distribution in Wuhan public transportation network (except score = 0, count Figure 13. Centrality distribution in Wuhan public transportation network (except score = 0, count around 750). around 750). TheFigure clustering 13. Centrality coeffi distributioncient index in ofWuhan the complexpublic tran networksportation can network be used (except to measurescore = 0, clusteringcount characteristics.aroundThe clustering 750). The analysis coefficient results index show of thatthe complex the average network clustering can coebe ffiusedcient to of measure Wuhan’s clustering complex publiccharacteristics. transport The network analysis is 0.083, results which show is lowerthat the than average Qingdao clustering 0.734 [23 coefficient]. Therefore, of Wuhan’s complexcomplex publicpublicThe transport transport clustering network network coefficient is ofis poor0.083, index clustering which of the is andcomplex lower collectivity, than network Qingdao which can causes be0.734 used more [2 3].to di Therefore,measurefficulties forclustering Wuhan’s people tocharacteristics.complex travel by public public The transport transportation. analysis resultsnetwork On show theis oneofthat poor hand, the averageclustering the lower clustering theand value collectivity, coefficient of the index, ofwhich Wuhan’s the morecauses complex likely more it ispublicdifficulties thta peopletransport for face people network inconvenient to travel is 0.083, by problems public which transp of is public loortation.wer transportation, than On Qingdao the one which 0.734hand, leads [2the3]. lower peopleTherefore, the to value have Wuhan’s lowerof the willingnesscomplexindex, the public more of green likelytransport travel it is in thtanetwork public people transportation. is faceof poor inco nvenientclustering On the other problems and hand, collectivity, of just public owing transportation,which to the inconveniencecauses whichmore ofdifficultiesleads green people travel for to inpeople have public lowerto transportation,travel willingness by public demand oftransp greenortation. for travel improving inOn public the the one transportation. present hand, situationthe lower On and thethe travelling valueother ofhand, the in publicindex,just owing transportationthe more to the likely inconvenience conveniently it is thta people of of green people face travel isinco high. innvenient public The growing transportation,problems willingness of public demand of transportation, green for travel improving in publicwhich the transportationleadspresent people situation to promotes have and lower travelling the willingness improvement in public of transporta green of public traveltion transportation, in conveniently public transportation. which of people reflects is On high. people’s the Theother demandgrowing hand, andjustwillingness owing the government’s to ofthe greeninconvenience response.travel in of The greenpublic clustering travel transp in coeortation publicfficient transportation, promotes of each station the demand improvement in Wuhan for improving is statistically of public the analyzed,presenttransportation, situation and the which and specific travelling reflects results inpeople’s arepublic shown transportademand in Figure andtion 14 theconveniently. Givengovernment’s that of the people clusteringresponse. is high. coeThe Theffi cientclustering growing of a singlewillingnesscoefficient station ofof caneach green be station used travel to in measure Wuin hanpublic the is statistically density transp ofortation other analyzed, public promotes and transport the the specific stationsimprovement results around are theof shown station,public in thetransportation,Figure high 14. clustering Given which that coe the ffireflectscient clustering of people’s a single coefficient stationdemand of indicates aand single the thatstation government’s more can stations be used response. exist to measure around The thetheclustering station,density andcoefficientof other the tra public ffiofc each stations transport station in the stations in area Wu arehan around relatively is statistically the station, dense. analyzed, Figurethe high 14 clustering andshows the that specific coeffi the clusteringcient results of a are single coe shownfficient station ofin mostFigureindicates nodes 14. thatGiven (i.e., more morethat stations the than clustering 3200) exist inaround thecoefficient complex the station, of public a single and transport stationthe traffic can network stations be used of in Wuhanto the measure area is 0, are andthe relatively density 80% of theofdense. other public Figure public transport 14transport shows stations stationsthat is the poor. around clustering On thethe contrary,station,coeffici entthe the highof clustering most clustering nodes coe ffi coeffi(i.e.,cient morecient of approximately ofthan a single 3200) station in 100the stationsindicatescomplex is publicthat 1, which more transport indicates stations network thatexist the around of connectivity Wuhan the station,is 0, of and approximately and 80% the of trafficthe public 2.4% stations oftransport the in stations the stations area in are this is relatively complexpoor. On networkdense.the contrary, Figure is good. the14 Basedshowsclustering on that the coefficient the spatial clustering distribution of approximately coeffici ofent bus of and 100most subway stations nodes stations is(i.e., 1, whichmore in Chapter thanindicates 3200) 3, these that in 100thethe complex public transport network of Wuhan is 0, and 80% of the public transport stations is poor. On the contrary, the clustering coefficient of approximately 100 stations is 1, which indicates that the Sustainability 2020, 12, 6531 16 of 22

Sustainability 2020, 12, 6531 16 of 22 connectivity of approximately 2.4% of the stations in this complex network is good. Based on the Sustainabilityspatial distribution 2020, 12, 6531 of bus and subway stations in Chapter 3, these 100 stations are mainly16 of 22 connectivity of approximately 2.4% of the stations in this complex network is good. Based on the concentrated in the following regions: The central urban area of Wuhan; the core areas of Jiangxia, spatial distribution of bus and subway stations in Chapter 3, these 100 stations are mainly connectivityXinzhou,Sustainability and2020 of Huangpi, 12approximately, 6531 District; 2.4% the junction of the stations of Dong inxihu this and complex Caidian network Districts; is and good. the Based central on 16urban ofthe 22 concentrated in the following regions: The central urban area of Wuhan; the core areas of Jiangxia, spatialarea. distribution of bus and subway stations in Chapter 3, these 100 stations are mainly concentratedXinzhou, and in Huangpi the following District; regions: the junction The central of Dong urxihuban andarea Caidian of Wuhan; Districts; the core and areas the central of Jiangxia, urban Xinzhou,area.stations areand mainly Huangpi concentrated District; the in junction the following of Dong regions:xihu and The Caidian central Districts; urban area and of the Wuhan; central the urban core area.areas of Jiangxia, Xinzhou, and Huangpi District; the junction of Dongxihu and Caidian Districts; and the central urban area.

Figure 14. The clustering coefficient distribution of Wuhan public transportation network (except score = 0, count around 3300). Figure 14. The clustering coefficient distribution of Wuhan public transportation network (except scoreFigure = 14.0, countThe clusteringaround 3300). coeffi cient distribution of Wuhan public transportation network (except score Figure 14. The clustering coefficient distribution of Wuhan public transportation network (except The= 0, countnodes around in the 3300).complex network have eigenvector center degree values, which can be used to measurescore the= 0, centricitycount around of adjacent3300). nodes. If the eigenvector of a node center degree is big, it means The nodes in the complex network have eigenvector center degree values, which can be used to otherThe nodes nodes adjacent in the the complex node centricity network are have also eigenvector big. Wuhan’s center public degree traffic values, complex which network can be analysis used to measure the centricity of adjacent nodes. If the eigenvector of a node center degree is big, it means resultsmeasureThe show nodes the centricityeach in the node complex ofof adjacentthe eigenvectornetwork nodes. have center If theeigenvec eigenvectordegreetor has center huge of adegree nodedifferences, centervalues, degreeas which shown iscan big,in be Figure itused means 15.to other nodes adjacent the node centricity are also big. Wuhan’s public traffic complex network analysis measureOnlyother nodesa few the adjacenteigenvectorcentricity the of nodecenteradjacent centricity nodes nodes. are are If big, alsothe whicei big.genvectorh Wuhan’s includes of public a Linshi node tra centerandffic complexZhaohu degree networkstations,is big, it analysismeansQindai results show each node of the eigenvector center degree has huge differences, as shown in Figure 15. otherstationresults nodes showof Parrot adjacent each Avenue node the node of subway, the centricity eigenvector Zhuye are Mountain centeralso big. degree Wuhan’sof Huangxiaohe has hugepublic di traffic ffRoad,erences, complex and as Yima shown network Farm in Figureof analysis Wuluo 15 . Only a few eigenvector center nodes are big, which includes Linshi and Zhaohu stations, Qindai resultsRoad.Only a show few eigenvector each node centerof the nodeseigenvector are big, center which deg includesree has Linshi huge differences, and Zhaohu as stations, shown Qindai in Figure station 15. station of Parrot Avenue subway, Zhuye Mountain of Huangxiaohe Road, and Yima Farm of Wuluo Onlyof Parrot a few Avenue eigenvector subway, center Zhuye nodes Mountain are big, of Huangxiaohewhich includes Road, Linshi and and Yima Zhaohu Farm ofstations, Wuluo Road.Qindai stationRoad. of Parrot Avenue subway, Zhuye Mountain of Huangxiaohe Road, and Yima Farm of Wuluo Road.

Figure 15. Eigenvector centrality distribution of Wuhan public transportation network (except score Figure 15. Eigenvector centrality distribution of Wuhan public transportation network (except score = = 0, count around 340). Figure0, count 15. around Eigenvector 340). centrality distribution of Wuhan public transportation network (except score = 0, count around 340). FigureThe modularity 15. Eigenvector of Wuhan’s centrality public distribu traffiction of complex Wuhan public network transpor is 0.903,tation which network can (except be divided score into The modularity of Wuhan’s public traﬃc complex network is 0.903, which can be divided into 934 communities= 0, count around and 340). is shown in Figure 16. The seven largest communities are shown in Figure 17. 934 communitiesThe modularity and of is Wuhan’s shown in public Figure traffic 16. The complex seven largestnetwork communities is 0.903, which are can shown be divided in Figure into 17 . The largest red community has biggest scale and good connectivity that has better condition of green 934The communities largest red community and is shown has biggestin Figure scale 16. andThe goodseven connectivity largest communities that has betterare shown condition in Figure of green 17. travelThe in modularitypublic transportation. of Wuhan’s However, public traffic the gray complex communities network inis which0.903, whichthe gray can nodes be divided are located into Thetravel largest in public red community transportation. has biggest However, scale the and gray good communities connectivity in whichthat has the better gray condition nodes are of located green 934have communities poor connectivity. and is shown These in communities Figure 16. The are se importantven largest areas communities to improve are shownthe integration in Figure and17. travelhave poorin public connectivity. transportation. These However, communities the gray are communities important areas in which to improve the gray the nodes integration are located and Theconnectivity largest red of communityWuhan’s complex has biggest public scale transportation and good connectivity network. that has better condition of green travelhaveconnectivity poorin public connectivity. of transportation. Wuhan’s complexThese However, communities public transportationthe gray are importantcommunities network. areas in which to improve the gray the nodes integration are located and haveconnectivity poor connectivity. of Wuhan’s complexThese communities public transportation are important network. areas to improve the integration and connectivity of Wuhan’s complex public transportation network.

Figure 16. The community scale of Wuhan public transportation network. Figure 16. The community scale of Wuhan public transportation network.

Figure 16. The community scale of Wuhan public transportation network.

Figure 16. The community scale of Wuhan public transportation network. Sustainability 2020,, 12,, 65316531 17 of 22 Sustainability 2020, 12, 6531 17 of 22

Figure 17. Well-connectedWell-connected communities in Wuha Wuhann public transportation network. Figure 17. Well-connected communities in Wuhan public transportation network. 4.1.2. Accessibility of Bus-Subway Station Network 4.1.2. Accessibility of Bus-Subway Station Network After analyzinganalyzing thethe topological topological structure structure of of the the complex complex public public transportation transportation network network of bus of andbus After analyzing the topological structure of the complex public transportation network of bus subwayand subway stations stations in Wuhan, in Wuhan, the accessibility the accessibilit of eachy stationof each was station discussed was todiscussed show the to accessibility show the and subway stations in Wuhan, the accessibility of each station was discussed to show the andaccessibility coverage and of publiccoverage transportation of public transportation in each street in and each micro street area. and The micro collected area. andThe screenedcollected roadand accessibility and coverage of public transportation in each street and micro area. The collected and informationscreened road of information Wuhan was importedof Wuhan into was the imported geographic into space,the geographic and the road space, network and the dataset road obtainednetwork screened road information of Wuhan was imported into the geographic space, and the road network isdataset shown obtained in Figure is 18shown. in Figure 18. dataset obtained is shown in Figure 18.

Figure 18. SchematicSchematic diagram of Wuhan road network. Figure 18. Schematic diagram of Wuhan road network. The information of 206 subway stations was importedimported into the above network dataset, and the The information of 206 subway stations was imported into the above network dataset, and the tratrafficﬃc accessibilityaccessibility ofof eacheachsubway subway station station within within 2000 2000 m m was was calculated. calculated. The The accessibility accessibility coverage coverage of traffic accessibility of each subway station within 2000 m was calculated. The accessibility coverage subwayof subway stations stations shown shown in Figurein Figure 19 19can can be be obtained. obtained. of subway stations shown in Figure 19 can be obtained. Sustainability 2020, 12, 6531 18 of 22 Sustainability 2020, 12, 6531 18 of 22 Sustainability 2020, 12, 6531 18 of 22

Figure 19. Wuhan subway station network 2000 m accessibility coverage. Figure 19. Wuhan subway station network 2000 m accessibility coverage. Figure 19. Wuhan subway station network 2000 m accessibility coverage. TheThe analysis analysis shows shows that that the sixthe subwaysix subway stations stations are are mainly mainly distributed distributed inWuhan in Wuhan city city center. center. Thus,Thus,The accessibility analysis accessibility shows is mainly is thatmainly concentrated the concentrated six subway in the stationsin centralthe ce arentral urban mainly urban area, distributed area, speciﬁcally specifically in knownWuhan known ascity Jianghan as center. Jianghan District,Thus,District, accessibility which which is within isis mainlywithin 1000 concentrated1000 m from m from the subway.inthe the subway. central Within Within urban 2000 area,2000 m, the m,specifically Jinaghan,the Jinaghan, known Jiangan, Jiangan, as Qiaokou,Jianghan Qiaokou, andDistrict,and Wuchang Wuchangwhich Districts is within Districts can 1000 allcan bem all accessed.from be accessed. the subway. The The subway subwayWithin stations 2000stations of m, three ofthe three Jinaghan, above above districts Jiangan,districts can coverQiaokou,can cover the the mostand mostWuchang space. space. As Districts forAs otherfor other can areas, all areas, be accessibility accessed. accessibility The is also issubway al mainlyso mainly stations concentrated concentrated of three in above Dongfeng in Dongfeng districts Road, can Road, Chaoyang cover Chaoyang the Road,mostRoad, space. Panlong Panlong As for Road, other Road, South areas, South Road, accessibility Road, and and so on.is so al However,on.so mainly However, concentrated the accessibilitythe accessibility in Dongfeng of Hongshan of Hongshan Road, District Chaoyang District and and QingshanRoad,Qingshan Panlong District District Road, is poor, Southis poor, which Road, which shows and shows so these on. these However, districts districts are the theare accessibility keythe key areas areas forof Hongshan subwayfor subway planning District planning and and constructionQingshanconstruction District to strengthen to is strengthen poor, thewhich connectivitythe showsconnectivity these of Wuhan’s distof Wuhan’sricts public are publicthe transportation key transportation areas for network. subway network. planning and constructionAs forAs busfor to busstrengthen stations, stations, Wuhan’s the Wuhan’s connectivity bus bus network network of Wuhan’s accessibility accessibility public withintransportation within 2000 2000 m m network. isis shownshown in Figure 20.20. The Thebus As for site bus networknetwork stations, accessibilityaccessibility Wuhan’s bus isis bigger biggernetwork than than accessibility that that of of the the within subway subway 2000 site sitem coverage. is coverage.shown Jiangan, in FigureJiangan, Jiangxia, 20. Jiangxia, The Hanyang,bus Hanyang,site network Qiaokou, Qiaokou, accessibility Qingshan, Qingshan, Wuchang,is bigger Wuchang, andthan Hongshan andthat Hongshan of the districts subway districts are site almost are coverage. almost within within 2000Jiangan, m 2000 in Jiangxia, terms m in ofterms accessibility,Hanyang,of accessibility, Qiaokou, and there and Qingshan, arethere many are Wuchang, many bus stations. bus and stations. Hongshan In other In other words, districts words, the are bus the almoststation bus station within network network 2000 connectivity m connectivityin terms andof accessibility,and integration integration and in these inthere these six are core six many core areas bus areas have stations. have been been ideal.In other idea In l.words, terms In terms of the accessibility, of bus accessibility, stationthe network greenthe green connectivity travel travel needs needs ofand residents ofintegration residents in these inin thesethese areas sixareas can core becan areas met. be met.have been ideal. In terms of accessibility, the green travel needs of residents in these areas can be met.

FigureFigure 20. Wuhan 20. Wuhan bus stationbus station network network 2000 2000 m accessibility m accessibility coverage. coverage. Sustainability 2020, 12, 6531 19 of 22

Moreover, Huangpi, Xinzhou, and Jiangxia Districts form a considerably accessible area, and the number of bus stations is mainly concentrated in the core areas of these districts and counties. At the junction of Caidian and Xinzhou Districts and the central urban area, considerable accessibility coverage is formed, which can be regarded as the extension of public transport resources in the core region within the scope of two districts and counties. Dongxihu District forms a considerably reachable area but does not form highly concentrated accessibility coverage in Huangpi district, which indicates that the spatial distribution of bus resources in Dongxihu District is balanced. However, the accessibility coverage of bus stations in Hannan District is low. The number of bus stations is small, which is not conducive to the development of public transport in Hannan District and restricts the connection of public transport between Hannan District and other districts and counties in Wuhan.

4.2. Analysis of Factors on Green Travel Behavioral Intention The overall Cronbach’s alpha of the 724 survey data is 0.795. The Friedman chi-square test value is 836.675. The significance of the chi-square test is 0.000. Therefore, the internal consistency of the questionnaire is good, and the reliability is high. The KMO value is 0.803. The chi-square value of Bartlett’s test is 857. The corresponding significance is 0.000, which is less than 0.001. Therefore, the hypothesis that all variables are independent should be rejected. The questionnaire has structural validity. The above reliability and validity analysis results show that the research data are suitable for factor analysis and structural equation model analysis. Based on the TPB, the green travel behavioral intention of the initial model fitting results show that the model fitting results are highly reasonable, but it can be modified further. In our analysis, the variable of perceived behavioral control has no significant difference with the latent variable of green travel behavioral intention under the confidence degree of 99%. Thus, the path coefficient is removed when the above initial model is modified. The modified model of green travel behavioral intention showed significant improvement in all aspects of the fitting degree. The analysis results show that all the estimated parameters in the modified model are significant at 95% confidence level. The standardized results of the path coefficient of the green travel behavioral intention modification model are as follows. The behavioral perceptual outcome has a significant positive influence on the green travel behavioral intention, and its influence on the green travel behavioral intention is greater than behavioral attitude and subjective norms. Behavioral attitude has a significant positive influence on the behavioral intention of green travel, but its influence is lower than the behavioral perceived result. The subjective norm has a positive influence on the behavioral intention of green travel, but its influence is lower than the behavioral perceived result and behavioral attitude.

5. Discussion and Suggestions The Space L method is used on the basis of the distribution of the Wuhan’s public transportation complex network. On the one hand, public transportation in Wuhan has complex network topologies. On the other hand, combined with Wuhan city road information, Wuhan’s public transportation accessibility and complicated network coverage are analyzed. Moreover, the influencing factors from the perspective of individual green travel intention in public transportation are analyzed. The research results show the following: City level: (1) Two significant central nodes exist in the complex network of Wuhan’s public transport. They are Linshi and Zhaohu stations located in Huangpi District. These two nodes have the highest side connection. Moreover, each site in Wuhan is connected with two to three other sites on average. The degree of most nodes in the network is small, showing a relatively obvious scale-free network feature. (2) From the anylysis of topological parameter, we can find each bus and subway station in Wuhan is connected with two to three other stations on average and the clustering coefficient is low. Therefore, the transmission performance and circulation efficiency in the field of public transport are low, and the agglomeration and collectivity are poor, which shows the complex public transport network has room for further optimization. Furthermore, the whole complex network Sustainability 2020, 12, 6531 20 of 22 of public transport in Wuhan is divided into 934 communities, among which are seven significant and large-scale associations with good integration and connectivity. (3) The accessibility of Wuchang District’s subway network coverage is big, which indiciates the district has better public transportation feasibility. However, the subway accessibility of Hongshan and Qingshan Districts in the central urban area is poor. They are the key areas for the layout and construction of the subway network. Furthermore, the accessibility coverage of the bus station network is larger than that of the subway station. Individual level: (1) Behavioral attitude has a significant positive impact on the behavioral intention of green travel, which is greater than that of behavioral perceptual outcome and subjective norms. (2) Behavioral perceptual outcome also has a significant positive influence on the behavioral intention of green travel, but its influence is lower than the behavioral attitude. (3) Subjective norms have a positive influence on the behavioral intention of green travel, but their influence is lower than the behavioral perceived result and behavioral attitude. The following policy suggestions are given at the city level: (1) The stability of the daily operation of Linshi and Zhaohu stations must be increased. The two stations are connected to many stations. They play a pivotal role in the normal operation of the public transport system in Wuhan, especially in Huangpi District. The situation of unstable travel has a great impact on the public transport operation in Huangpi District and the public transport connection between Huangpi District and the central city. (2) The complex network of public transport must be further optimized. For example, the intermediate centrality and clustering coefficient of Linshi station, which is in the core hub of the whole complex public transport network, play an important role in circulation connection. Therefore, the management of these stations can be strengthened. (3) A reasonable layout of public transport stations must be created according to spatial characteristics. For example, the accessibility coverage of bus stations in Hannan District is limited, and the public transport network needs to be constructed and optimized. However, the East-West Lake areas have formed an important accessible area. Thus, the public transport resources in the East-West Lake areas are evenly distributed. Increasing optimization effort is unnecessary. The following suggestions are given at the individual level: (1) Attention should be given to relevant pro-environment information to improve the awareness of green travel in public transportation (i.e., the behavioral attitude). (2) The spiritual satisfaction brought by green travel and environmental protection should be improved, and more attention should be given to the behavioral results. (3) Individuals should consider suggestions put forwarded by people around him, and actively adopt relevant aspects of the corresponding media, which can improve subjective norms.

6. Conclusions The feasibility analysis of urban green travel was conducted from two levels, the city level and the individual level. (1) City level: Wuhan’s spatial distribution of public transportation network, its topology, and accessibility were studied, which shows some nodes that have a significant impact on the public transportation network. The results show the connectivity and circulation efficiency of 13 administrative districts of network are low, but 7 core districts where stations are distributed can meet the residents’ demand for green travel in public transportation, otherwise the far districts. (2) Individual level: Based on TPB, Wuhan residents’ intention to green travel behavior and its influencing factors were analyzed, whose data is from questionnaires on Wuhan residents. Then, according to the reliability and validity of the survey data, the intention of Wuhan residents to green travel behavior was analyzed using the structural equation model. The corresponding influence factors and path were discussed. Behavioral perceptual outcome, behavioral attitude, and subjective norm had a positive effect on green travel intention, but subjective norms were minimally affected. This study has the following limitations. Many new sites did not consider the rapid development of Wuhan, and behavioral intention is influenced by various factors [24]. Although several aspects were emphasized in this study, other significant factors should be added in future studies. Furthermore, Sustainability 2020, 12, 6531 21 of 22 as for public transport network, scheduling is also important and nedded to be supplemented, because waiting times make the difference.

Author Contributions: Conceptualization and supervision: J.Z. and G.M.; methodology: Y.C., L.Y., and X.H.; software: X.H.; validation: M.Y.; writing—original draft preparation: Y.C. and G.M.; writing—review and editing: G.M., L.Y., and X.H.; project administration: J.Z. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the National Natural Science Foundation of China, grant number 71771181. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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