Efficiency Assessment of Transit-Oriented Development by Data Envelopment Analysis ... · 2019. 7. 30. · ResearchArticle Efficiency Assessment of Transit-Oriented Development by

Research ArticleEfficiency Assessment of Transit-OrientedDevelopment by Data Envelopment Analysis Case Study on theDen-en Toshi Line in Japan

Jing Guo 1 Fumihiko Nakamura 2 Qiang Li 3 and Yuan Zhou4

1Graduate School of Environmental Studies Nagoya University D2-1Furo-cho Chikusa-ku Nagoya 464-8603 Japan2Institute of Urban Innovation Yokohama National University 79-5 Tokiwadai Hodogaya Yokohama 240-8501 Japan3College of Resources Science and Technology Beijing Normal University 19 Xinjiekouwai Street Beijing 100875 China4Department of Geographical Sciences University of Maryland-College Park College Park MD 20742 USA

Correspondence should be addressed to Qiang Li liqiangbnueducn

Received 20 November 2017 Revised 28 March 2018 Accepted 15 April 2018 Published 15 May 2018

Academic Editor Taku Fujiyama

Copyright copy 2018 Jing Guo et alThis is an open access article distributed under the Creative Commons Attribution License whichpermits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Transit-Oriented Development (TOD) is an urban planning approach that encourages a modal shift from private to publictransportation This shift can generate additional benefits from a sustainability perspective This study aims to assess the efficiencyof TOD by applying the data envelopment analysis (DEA)methodThe ridership of public transportation is considered as the directoutput characteristic of TOD efficiency and nine indicators of ridership are selected as inputs on the basis of the core concepts ofTOD These concepts include density diversity and design (3Ds) The Tokyu Den-en Toshi Line in Japan is presented as a typicalcase of TODbecause this line includes TODandnon-TODstations Assessing and comparing the results of all railway stations revealthat almost all indicator values of non-TOD stations are higher than those of TOD stations The results suggest that TOD planningand programs are inefficient in terms of ridership generation This implication however may be attributed to the inadequacy ofthe selected indicators for TOD assessment The results obtained after adding operation year as input indicators and removingtransfer station show that TOD stations perform efficiently and in accordance with expectations This response indicates that theinclusion of influential factors is necessary for equitable TOD assessment Therefore other influential factors must be consideredwhen evaluating the efficiency of TOD-based stations with different inherent attributes In addition the design input with thelargest impact on all inefficient units was identified suggesting that management of bus service and railway system should be wellenhanced

1 Introduction

Transportation infrastructure is one of the most importantcomponents of urban development The efficient develop-ment and implementation of a transportation infrastructureplan are necessary to solve traffic congestion greenhousegas emissions and other environmental consequences thataccompany rapid urbanization and motorization [1] Fromthe perspective of sustainable and integrated urban devel-opment the imbalance between land use and transportationsystems is the most critical factor that affects the efficiencyof transportation infrastructure Transit-Oriented Develop-ment (TOD) strategy has been proposed as an efficientapproach to solve this imbalance [2 3]

TOD aims to encourage the use of public transportation[4] by increasing population density and mixed land usein areas surrounding transportation hubs and by promotingconnectivity between stations and trip originsdestinationsAlthough the formal term of TOD was not introduceduntil 1993 [5] its underlying principle has been appliedand implemented as a solution to urban growth challengessince the late 19th and early 20th centuries mainly in theUnited States and Europe [6] Later in 1997 the densitydiversity and design (3D) framework of TOD was proposedand explained [7] In several American cities the 3D-basedTOD plan has increased the ridership and modal sharesof public transport and has decreased vehicle mileage by3ndash5 [8 9] Accordingly the TOD concept has been

HindawiJournal of Advanced TransportationVolume 2018 Article ID 6701484 10 pageshttpsdoiorg10115520186701484

2 Journal of Advanced Transportation

applied with an emphasis on the 3D framework in urbanand transport planning worldwide However its efficiency inpractical applications may sometimes not meet expectations[10] The low utilization of public transport cannot achievethe sustainable cycle of landndashpeoplendashtransport [11] In otherwords whether or not the TOD strategy can play its duerole in urban planning and management is inconclusiveTherefore evaluating the efficiency of TOD is a necessarychallenge faced by urban administrators and policy makerswho aim to successfully implement enhance and improveTOD

To respond to such a demand this study selects theappropriate indicators that reflect the 3D concept of TODin practice This study assesses the efficiency of a railwaysystem that was developed on the basis of the 3D frameworkof TOD Furthermore it provides advice to improve thefactors that most significantly influence the efficiency ofTOD In this study data envelopment analysis (DEA) whichwas first proposed by Charnes et al in 1978 is applied[12 13] Since its introduction DEA has been widely usedto measure the relative efficiency of various organizationsreferred to as decision-making units (DMU) with multipleinputs and outputs [14ndash17] DEA can identify the inputwith the largest impact on an inefficient unit Such inputcan provide guidelines to policy makers One line of theJapanese railway system is presented as a case study In Japanthe integration of the transport system and land use hasbeen practiced since the 1910s This practice is consistentwith the concept of TOD After more than a century thecountry has recognized advancements in railway operationand management The efficiency evaluation of TOD cases inJapan can provide essential guidance or experiences for futuretransport and urban planning in other countries especially indeveloping countries experiencing rapid urbanization

2 Literature Review

The integration of transportation and land use dates back tothe post-World War II era when planners in Europe mostnotably in Stockholm [18] and Copenhagen [19] led subur-ban development into satellite areas along transit corridorsSince the 1990s a series of new concepts and approaches suchas the Smart Growth concept [20 21] and New Urbanismapproach [22] for the integration of transportation and landuse have emerged to combat uncontrolled urban sprawl [6]TOD is also gaining popularity as an approach to promotesmart growth and sustainable development in the metropoli-tan areas of many developed and developing countries [23ndash26] where mass transit systems have been implemented torelieve traffic congestion

A common definition or criterion remains to be estab-lished for TOD despite the substantial body of researchthat has discussed the concept For example Cervero et alstressed the contributions of the 3Ds to the TOD conceptfurthermore they explained the 3D concept as density in theform of residence and jobs diversity in the form of mixedland-use development and design in the form of good streetconnectivity for pedestrians [25] Other scholars interpretedTOD by borrowing words from its original explanation and

providing new empirical additions The various definitionsfor TOD generally state that TOD should promote compactand highly mixed-use development around transit hubs andshould include accessible and walkable neighborhoods [1819]

Existing studies have demonstrated that TOD providesnumerous advantages such as facilitating polycentric citiesand regions mitigating urban sprawl boosting public trans-port ridership increasing bike usage and walkability accom-modating economic growth and creating sustainable neigh-borhoods [6] The most important function of TOD is toreduce traffic congestion and increase public transit use inareas experiencing accelerated urban development [25 2728] TOD has attracted increasing attention as a solution tothe challenges of urban growth given its widely recognizedbenefits TOD implementation however is not always suc-cessful Thus several studies have evaluated the efficiency ofTOD (eg [9 21]) Most existing research applied single andseveral indicators to measure the outcomes or benefits thatTOD provides in terms of travel behavior economics andenvironment [28 29] Ridership is one of most representativeindicators of TOD outcome [25] Renne andWells monitoredthe success of TOD through a survey that included andsummarized the various indicators of the influences andthe most useful indicators of TOD [3] However this studyquantifiedTOD in the formof a single indicator and provideda limited explanation for the ridership efficiency of TODIn addition it lost meaningful information for the aspectsthat contribute to the success or failure of TOD The multi-indicator evaluation method involves weight setting for eachindicator However weight setting in existing studies is oftenempirical or less objective and could provide biased results

In the past decade DEA has been widely used to measurethe efficiency of different models of transportation systemoperations or management [30] The most significant advan-tage of DEA over traditional weight-based methods is thatDEA does not require weight parameters to measure effi-ciency Researchers have employed DEA to address twomainresearch topics One topic is the analysis and comparison ofthe efficiency of firms networks or nations in the field ofeconomics and management science [31ndash33] Only a smallpart of related works has focused on the evaluation of theoperational performance and management of railways [30]The other topic is the selection of the optimal solution foractual operations in vehicle dispatching and route planning[34 35] Up to now DEA has not been applied in theassessment of transportation strategy at least in terms ofTOD

21 DEA As mentioned above DEA identifies the frontier(envelopment surface) which is determined directly fromdata and quantifies the relative efficiency of a set of compa-rable DMUs with several inputs and outputs It solves a linearprogrammingmodel to detect the efficient inputs and outputsthat form the frontier and the inefficient inputs and outputsthat form nonfrontier units In addition to providing theefficiency level DEA can identify the sources of inefficiencyand the projection path to the frontier The projection pathto the envelope surface is determined by whether the model

Journal of Advanced Transportation 3

Table 1 Indicators for DEA method

Indicators Unit ExplanationDensity x1 Population density Personkm2 Population per square kilometer

Diversityx2 Land-use diversity - ShannonndashWiener Indexx3 Functional land area km2 Total residential commercial official public service land area per catchmentx4 High-building area km2 Area with high buildings (over 4 floors) per catchment

Design

x5 Bus stops - Bus stops per unit catchment areax6 Daily bus operation length km Bus operation length per day in each catchmentx7 Bus routes - Number of routes to and from railway stationx8 Road length km Road length per catchmentx9 Number of station exits - Number of exits per railway stationy Ridership Person Average daily commuters per station

is output-oriented or input-oriented the choice of whichdepends on the production process that characterizes theunits (ie the process minimizes the use of inputs to producea given output level or maximizes output level given aninput level) A large output (ie ridership) is preferred inthe evaluation of TOD efficiency given several certain inputsTherefore output-oriented DEA was selected in this study

The CCR model which was first proposed by Charnes etal in 1978 is the basic DEA model It functions under theassumption of constant returns to scale and reflects the factthat outputs change proportionally with inputs [12] Assumethat n DMUs (119895 = 1 119899) m inputs 119909119894119895 (119894 = 1 119898) and119904 outputs 119910119903119895 (119903 = 1 119904) exist for each DMUj The output-oriented CCR model can be formulated as follows

max 120579 + 120576( 119898sum119894=1

119904minus119894 +119904sum119903=1

119904+119903)

st119899sum119895=1

120582119895119909119894119895 + 119904minus119894 = 1199091198940119899sum119895=1

120582119895119910119903119895 minus 119904+119894 = 1205791199101199030119904minus119894 119904+119903 ge 0120582119895 ge 0

(1)

where 120579 is the efficiency score limited to less than 1 120576 is thenon-Archimedean infinitesimal and 1199091198940 and 1199101199030 refer to theinput and output of one certain unit DMU0 119904minus119894 and 119904+119903 are theinput and output slack which refers to the excess input ormissing output that exists after the proportional change inthe input or the output has reached the efficiency frontier120582119895 is the weight given to the DMUj to achieve its optimalperformance It is calculated automatically during themodel-solving process

The improvement target (projection) of each inefficientunit is the result of respective slack values added to pro-portional reduction amounts (formula (2) and (3) for theoutput-oriented DEA model) The improvement target pro-vides alternative ways to improve the performance of each

inefficient unit The difference between 1199091198940 and 11990910158401198940 1199101199030 and11991010158401199030 should receive focus when changing the input and output11990910158401198940 = 1199091198940 minus 119904minus119894 (2)

11991010158401199030 = 1205791199101199030 + 119904+119903 (3)

22 Indicator Selection As the output variable ridership (119910)is the number of passengers who ride a public transportsystem and directly reflects the efficiency of transporta-tion policy and urban planning such as TOD strategyPopulations living around transport hubs provide the tripdemand and have the potential to use the transportationinfrastructure mixed land use in the catchment area helpsgenerate trips by providing different services and convenientdesign encourages high numbers of people to use publictransportation All of these effects promote modal shift andincrease ridership Therefore from the core concept of TODthat is density diversity and design nine indicators wereselected as the input variables of ridership output (Table 1)

First density is defined as the number of buildings orpopulation per area Here population density (x1) in acatchment that is population per unit area is the primaryindicator that directly determines trip demand and economicactivity The residential population tends to generate theboarding ridership whereas the commercial and office pop-ulation is related to the alighting ridership via commercial oremployment attractiveness

Second diversity is reflected by land use and primarilyrepresents the level of mixed land-use development Thisindicator is obtained through the ShannonndashWiener indexwhich is widely applied to calculate species diversity in thefield of ecology It has been successfully applied in land-use research to estimate the number of land-use types andevenness [16] Given that residential land commercial landofficial land and public service land are the primary typesof urban functional land areas the functional land areawill affect ridership by providing for the different needs ordemands of passengers and other interested people Further-more areas with high buildings (over 4 floors) surroundingtransportation hubs affect trip demand generation and rep-resent the basic characteristic of regional development levelTherefore land-use diversity (x2) functional land-use area


RailwaysDon-en Toshi LineTOD station

Transfer TOD stationNon-TOD stationTransfer non-TOD station

(km)

N

6030150

Figure 1 Tokyu Den-en Toshi Line

(x3) and high-building area (x4) were selected as the threeindicators of diversity input

Third design schemes can influence travel demand byincreasing the accessibility of destinations by foot or byother public transportation systems [6] It contains differentimplications from the micro to macro level including thepresence of shade trees and overhead lighting design ofon-site stores and services and connections between worksites and worker residences In this study the design pointsto a basic transit infrastructure around rail stations thatshortens the distance or time required to reach a destinationA highly advanced design would have the potential tofacilitate public transit use and walking as well as promoteefficient energy use Therefore the number of bus stops (x5)daily bus operation length (x6) and bus routes (x7) wereselected to represent the connectivity between the rail stationand passenger destinations Connectivity eventually affectsridership choice andmode splitWith respect to the total roadlength (x8) we hypothesized that long roads in a certain areawould increase road density and facilitate passenger accessto railway stations Finally the number of railway stationexits (x9) also provides passengers with convenient accessto aboveground roads or buses and underground stores orsupermarkets

3 Case Study

31 Tokyu Den-en Toshi Line Japanrsquos urban transportationsystem is famous for its excellent accessibility efficiencyand punctualityThese characteristics resulted fromadvancedand reasonable transportationurban planning Moreoverin Japan cities are commonly planned on the basis of

TOD because the country has centered its land and citydevelopment along railroad construction for more than acentury Ensenkaihatsu (or ldquorailway corridor developmentrdquo)was proposed by as early as 1910 and has a longer history thanTODThis urban development concept focused on residentialdistrict development along railways

The Tokyu Den-en Toshi (DT) Line is a major com-muter line that connects Shibuya in Tokyo and the Chuo-Rinkanin terminal in the neighboring Kanagawa Prefecture(Figure 1) The earliest interval which is known as theMizonokuchi Line was opened in 1927 and connects theFutako-Tamagawa and Mizonokuchi Stations In the mid-20th century economic activity and employment rapidlyconcentrated in the central area of Tokyo thus drasticallyincreasing land and real estate prices and promoting ex-urban settlement In response to this situation the Japanesegovernment adopted a new TODplanTheDT Line was bornat the right moment and Tama New Town was subsequentlydeveloped In 1966 the DT Line began operating from theMizonokuchi Station to the Nagatsuta Station An expansionwas built as an access route to Tama Garden City [36] TheDT Line which spans Shibuya Station to Futako-TamagawaStation was completed in 1977 In 1984 the line was extendedfrom Nagatsuta Station to Chuo-Rinkan Station Stationsbetween theMizonokuchi and Chuo-Rinkan Stations exceptfor Nagatsuta Station (Figure 1) were developed on the basisof TOD To enable comparison in this study the earlierolderstations are identified as non-TODs (12 stations) and thelatternewer stations are considered as TODs (15 stations)

The DT Line was selected as a case study in this workfor two reasons First the DT Line and the Tama New Town


that it traverses provide examples of transportation and land-use integration that are intended to stimulate populationconcentration and ridership growth Second this line has arelatively long history and was constructed under previousand new urban development policies (ie some of its stationswere originally built with the birth of the line whereas othersare relatively new and were constructed through the TODproject) Therefore earlier and laternewer stations can becompared to identify the factors that affect ridership

32 Catchment Definition andData Collection Station catch-ment areas which influence indicator values need to bedetermined In the transportation research field the catch-ment area where local residents potentially use the railwayis identified through (1) placing a buffer circle around astation (eg buffers with a radius of 400 800 or 1200m todefine walkability to the public transportation facility) [37](2) network analysis [38] based on path networks wherein acatchment area can be an area with a fixed walking or drivingdistance or travel time (3) the boundary method which isbased on geo-code trip survey data (in this method howeveronly 90 of the trip data are used within the study to removethe outliers) [39] and (4) statisticalmodeling based on spatialtheory such as location and allocation method [40] Amongthese methods the boundary method is the most objectivemethod that does not require intensive expert experience

In this study the Japanese Nationwide Person Trip Sur-vey was used to identify station catchments through theboundary method This survey is the most fundamentaltransportation questionnaire survey and is conducted every10 years by the Ministry of Land Infrastructure Transportand Tourism to investigate the actual travel behaviors ofpeople in certain regions in Japan In the TokyoMetropolitanArea approximately 730000 individual questionnaires arecollected Survey items include individual trips personalattributes and travel behaviors The origin collected for thesurvey was recorded by a small zone geo-code and the surveyresults showed the station choices in each zone

An originndashdestination matrix from the zones of pas-senger residence (origin) to rail stations (destination) wasconstructed in accordance with the survey results and isshown in the flow map in Figure 2(a) Then zones thatgenerated few demands were removed on the basis of therule that a catchment area should cover at least 90 of theridership around the station (Figure 2(b)) A single catchmentarea nearly covers the buffer area with a 1500m radiusaround the railway Given that individuals from the samesmall zone may select different rail stations the catchmentareas of two or more adjacent rail stations may overlapOverlapped zones were divided into different stations by thenumber of station choices and distances to different stations(Figure 2(c)) Finally the catchment area for each rail stationwas identified Following the identification of catchmentboundaries all data for selected indicators (eg bus stop dataas an example shown in Figure 2(d)) were collected fromthe website of the Official Statistics of Japan and NationalLand Numerical Information The collected data were thenmasked reorganized and standardized

4 Results

41 Basic Statistical Characteristics The initial analysis pro-vided the basic statistical characteristics of all selected indi-cators for the 27 stations All of the indicator values of non-TODs are higher than those of TOD stationsTherefore TODstations do not provide any obvious advantages In detailthe mean population density of TOD stations is considerablylower than that of older stationsThis result may be attributedto the different development histories of non-TOD and TODstationsThe higher land-use diversity of non-TODs than thatof TOD stations can be attributed to the main function of theTama New Town Station along the DT Line The catchmentof the Tama New Town Station mainly functions to relievehousing problems in Tokyo Thus this area contains vasttracts of residential land By contrast the catchment areas ofolder stations contain relatively more commercial and officeareas than those of the Tama New Town Station Non-TODstations have higher functional land areas and numbers ofhigh buildings per unit area than TOD stations indicatingthat land use around old stations is better developed than thataround new stations TOD stations consistently have lowervalues for the five design indicators than non-TOD stationsThis result suggested that infrastructure with less accessibilitymay not serve a high proportion of riders Compared withnon-TOD stations TOD stations have lower average dailyridership which is the direct output of transportation opera-tion and management and land use However low ridershipand indicator values do not necessarily indicate low efficiencyand vice versa Thus an in-depth assessment is necessary toidentify the efficiency of TOD stations

42 TODEfficiency To explain whether different 3D patternscan generate correspondingly different ridership patternsthe DEA method was applied to calculate the efficiency ofeach station pattern The results are shown in Table 3 Ninestations with E equal to 1 are considered as relatively efficientHowever seven of these stations are non-TOD stations andonly two are TOD stationsThemean efficiency score of TODstations is lower than that of non-TOD stations indicatingthat the 3D-based TOD strategy does not generate sufficientridership as initially expected

This finding can be partly explained by the individualattributes of each station Station attributes include desig-nation as a transfer station opening date and operatinghistory For example the Shibuya Station is a transfer stationwith a long operating history in a catchment area with highpopulation density Accordingly its corresponding land useis well developed and its accessibility attracts high numbersof riders Furthermore non-TOD stations may achieve TODgoals given their specific attributes For example amongnon-TOD stations the Azamino Station is highly efficientdespite its lack of a long operating history because of itstransfer function The above result suggests that consideringonly 3D-related indicators cannot provide an adequatelycomprehensive assessment of TOD efficiency The inherentattributes of each station cannot be ignored because theyconstrain the comparability of stations


(km)

Tokyo

Kanagawa

N

864210

TODsNon-TODsDT LineSmall zone

Number of choices1

2ndash10

11ndash50

51ndash150

151ndash250

251ndash789

(a)

(km)

Tokyo

Kanagawa

N

864210


1500 BuffermPopulation mesh

Number of choices1ndash50

50ndash100

100ndash200

200ndash400

400ndash800

(b)

(km)

Tokyo

Kanagawa

N

864210

TODsNon-TODsDT LineCatchment

Population0ndash300

300ndash700

700ndash1000

1000ndash1400

1400ndash2800

(c)

(km)

Tokyo

Kanagawa

N

864210


Bus stops0ndash4

4ndash6

6ndash8

8ndash10

10ndash15

(d)

Figure 2 Generation of catchment area and distribution of a selected indicator for each catchment (a) Original flow map (b) deletion ofextra zones and magnification (c) finished catchment area (d) bus stops for each catchment Note In (a) and (b) the flow color is used todistinguish the populations of different stations The width of gray bar in legend represents the number of choices of separate stations

43 TOD Efficiency considering Inherent Station AttributesThe inherent attributes of each stationmay affect its ridershipproportion and efficiency Inherent station attributes includethe number of operation years designation as a transferstation number of trains running per day and durationbetween tripsThe former two attributes have been discussedabove to account for the relatively inefficient performance ofTOD stations To avoid the biases resulting from operatinghistory and transfer function operation yearwas added to theDEA model At the same time seven transfer stations wereremoved from the DMU list to improve the comparabilityof stations Hence the DEA model was recalculated with 20stations (6 non-TOD and 14 TOD stations) with 10 inputindicators including operation year

After removing transfer station as an input (Table 4) thedifferences between the input indicator values of TOD andnon-TOD stations have drastically decreased compared withthose shown in Table 2 As shown in Table 2 the averageridership of non-TOD stations is almost three times largerthan that of TOD stations However the average ridershipof non-TODs is approximately 20 higher than that of TODstationswhen only nontransfer stations are considered Giventhat ridership is the output indicator of the DEA modelchanges in ridership will inevitably lead to the differentefficiency results shown in Table 5 Most TOD stations arerelatively efficient and the mean values of efficiency E forTODs and non-TODs are not considerably different YogaStation a non-TOD station performed relatively inefficiently


Table 2 Means of the indicators of non-TOD and TOD stations

Indicators Non-TODs TODs DifferenceDensity Population density 1421 1039 382

DiversityLand-use diversity 097 064 033Functional land area 087 086 001High-building area 006 003 003

Design

Number of bus stops 737 605 132Daily bus operation length 57544 42351 15193Bus routes 450 173 277Road length 1936 2137 minus201Number of station exits 1208 787 421

Daily ridership 130459 46747 83712

Table 3 Efficiency of each station

Non-TOD stations TOD stationsStation 119864 Operation years Station 119864 Operation years

Kajigaya 0824 51Shibuyalowast 1 132 Miyazakidai 0536 51Ikejiri-Ohashi 0364 40 Miyamaedaira 0496 51Sangen-Jayalowast 0739 110 Saginuma 1 51Komazawa-Daigaku 0760 40 Tama-Plaza 0644 51Sakura-Shimmachi 0730 110 Azaminolowast 1 40Yoga 0487 110 Eda 0895 51Futako-Tamagawalowast 1 110 Ichigao 0507 51Futako-Shinchi 1 90 Fujigaoka 0513 51Takatsu 1 90 Aobadai 0905 51Mizonokuchilowast 1 90 Tana 0809 51Nagatsutalowast 1 109 Tsukushino 0286 49Chuo-Rinkanlowast 1 88 Suzukakedai 0274 45

Minami-Machida 0573 41Tsukimino 0257 41

Mean 0840 9325 Mean 0635 484Note lowast denotes a transfer station

Table 4 Means of indicators of nontransfer stations



Design

Number of bus stops 702 601 101Daily bus operation length 54205 43090 11115Bus routes 283 171 112Road length 2299 2171 128Number of station exits 1067 729 338

Operation year 80 49 31Daily ridership 52478 40566 11912

given its 110-year operation history The efficiency values of12 TOD stations with 40ndash50 years of operation history exceptfor Ichigao andTana Stations are all higher than those of YogaStationThis result suggested that TOD stations tend to reachtheir current efficiency more rapidly than non-TOD stations

44 Potential for Improving Efficiency The frontier projectreflects the improvement target that the inputs and outputsof each inefficient station should reach The distance (dif-ference) between the real point and its projection indicatesthe volume required for improvement Then the percentage


Table 5 Efficiencies of nontransfer stations considering operationyear

Non-TOD stations TOD stationsStation 119864 Station 119864

Kajigaya 1Miyazakidai 1Miyamaedaira 1Saginuma 1

Ikejiri-Ohashi 0702 Tama-Plaza 1Komazawa-Daigaku 1 Eda 1Sakura-Shimmachi 1 Ichigao 0708Yoga 0881 Fujigaoka 0958Futako-Shinchi 1 Aobadai 1Takatsu 1 Tana 0809

Tsukushino 1Suzukakedai 0894

Minami-Machida 0928Tsukimino 0999

Mean 0981 Mean 0955

Table 6 Difference between the average projected and real valuesof inefficient stations ()

Indicators Non-TODs TODsDensity Population density 633 1789

DiversityLand-use diversity 2039 2014Functional land area 604 867High building area 946 2789

Design

Number of bus stops 2868 2632Daily bus operation length 3831 4632Bus routes 3638 2808Road length 1927 2317Number of station exits 634 062

Operation year 2703 1384

of difference relative to the original input can be calculatedto discover the excess input indicator that contributes mostsignificantly to inefficiencyThe average difference percentageof each input for non-TOD stations and TOD stations (onlyincluding inefficient stations) is listed in Table 6

All inefficient railway stations have higher numbers ofredundant design indicators than the other two types ofstations The results indicated that bus location and servicesdo not increase the corresponding ridership proportion ofthe railway One possible reason is that passengersrsquo dailytransportation needs cannot be satisfied by the mutualindependence of the railway and bus systems Therefore busservices and their feeder roles should be enhanced to improvethe efficiency of the railway system Moreover diversity inthe land use of areas surrounding inefficient stations doesnot generate adequate ridership suggesting that the mixtureof land-use types should be promoted despite requiringlong-term effort Population density is more redundant forinefficient TOD stations than for non-TOD stations Thisresult can be attributed to the number of serviced people

and to age category (ie percentage of the elderly adultsand children) and social level (ie percentage of unem-ployed professionals and managers) of the residents of acertain catchment area For example high-class workers andthe elderly tend to use private transportation Accordinglyincreasing the density and optimizing the age and socialcategory of the target population may help improve TODefficiency All aspects listed above should receive additionalattention when planning future TOD stations

5 Discussion and Conclusions

This study focused on measuring the efficiency of the TODstrategy implemented in the DT Line TOD is a powerfulurban planning approach and concept that is applied toresolve various urban problems This study expounded onTOD assessment on the basis of the 3D framework In thisstudy the assessment procedure included the identificationof catchment areas and the selection of suitable indicatorsas input variables and ridership as output variables Theinput variables are then subjected to the DEA method tovalidate the successful performance of the TOD strategy orprogramand to identify possible solutions to strengthenTODimplementation

Existing TOD evaluations have failed to provide reliableconclusions regarding TOD performance For example asingle-indicator assessment cannot adequately characterizethe comprehensive efficiency level of a TOD strategy fromthe perspective of the 3D framework Meanwhile a simpleweight-based method following the 3D concept is likely tobe affected by weights with low objective values and maythus produce a biased result thatmay contradict realityThesesituations suggest the necessity of designing a procedure forthe comprehensive assessment of TOD strategies By consid-ering that density and diversity may generate transportationdemand and that design must provide convenient service tosatisfy such needs we employed DEA to reflect the relativeefficiency of TOD in accordance with 3D-based inputs andridership output Our DEA-derived results reflect the coreprinciple of the TOD concept which coordinates densitydiversity and design with ridership output

Many TOD applications have demonstrated that TODhas a significant role in regional development However thepresent results suggested that TOD strategies cannot providemore positive effects than non-TOD strategies because onthe one hand market power enables non-TOD strategies toachieve effects similar to those achieved by TOD strategiesMore importantly the current indicators selected for TODassessment do not adequately reflect the 3D concept In factthe current indicators for TOD assessment only highlightaspects related to 3D and some important factors that affectoutput are excluded from conventional TOD assessmentFor example operating history can also affect the output ofTOD stations along the DT Line Although TOD stations arecomparable with non-TOD stations in general developmenthistory results in the incomparable efficiency of new and oldstations Moreover other factors such as the age and socialclass of the catchment population designation as transferstation number of trains running per day and duration


between trips have been overlooked Adding operation yearas an input indicator and removing transfer station revealedthat the performance of TOD stations is relatively efficientand approaches expectations This result indicated that theinclusion of other influential factors is necessary for equitableTOD assessment Consequently taking such factors intoaccount is indispensable when evaluating the efficiency ofTOD stations with different inherent attributes In additionthe design input with the largest impact on all inefficientunits was identified Management of bus service and railwaysystem should be well enhanced and encouragement forpublic transportation usage should be emphasized

The applicability of the DEA-based TOD assessmentapproach is mainly dependent on indicator selection Theframework of the approach includes indicator selectionfrom the perspective of 3Ds catchment divisions and theDEA model and is applicable to other public transportationsystems (eg light rail metro bus and bus rapid transit)and to other regions or countries Nevertheless the selectedindicatorsmay vary across different applications Researchersand urban planners may have specific local or specializedpriorities and knowledge that differ from those emphasizedin the present caseThus other researchers should select indi-cators that are appropriate for their priorities and knowledge

Nevertheless the present study presents several criticalissues that should be considered to improve the evaluation ofTOD approaches First additional factors that affect the effi-ciency of TOD-based systems should be considered duringthe assessment and planning of a TOD project These factorsinclude the attributes of the target population (age structureoccupation and income level) and the transportation systemitself Second the design scheme should improve the con-venience of accessing destinations by foot bus or bicycleIn this study the selected indicators mainly characterizedconnectivity to railway stations by bus However such a char-acterization may be insufficient for design characterizationOther factors such as plating strips overhead lights andbicycle path lengths should be considered in future studiesFurthermore the definition of the analytical unit is crucialfor TOD assessment In this study the Nationwide PersonTrip Survey in Japan was used to identify station catchmentareas through the boundary method When such data areunavailable the effect of catchment size should be addressedby specifying a different buffer radius travel distance ortravel time

Conflicts of Interest

The authors declare that there are no conflicts of interestregarding the publication of this paper

References

[1] P Miller A G de Barros L Kattan and S C WirasingheldquoPublic transportation and sustainability A reviewrdquo KSCEJournal of Civil Engineering vol 20 no 3 pp 1076ndash1083 2016

[2] R Cervero ldquoTCRP Report 102 Transit-Oriented Developmentin the United States Experiences Challenges and Prospectsrdquo2004

[3] J L Renne and J S Wells ldquoEmerging European-style planningin the USA Transit oriented developmentrdquo World TransportPolicy amp Practice vol 10 no 2 2004

[4] A Nasri and L Zhang ldquoThe analysis of transit-oriented devel-opment (TOD) in Washington DC and Baltimore metropoli-tan areasrdquo Transport Policy vol 32 pp 172ndash179 2014

[5] P Calthorpe The Next American Metropolis Ecology Commu-nity and the American Dream Princeton Architectural PressNew York NY USA 1993

[6] E Papa and L Bertolini ldquoAccessibility and Transit-OrientedDevelopment in European metropolitan areasrdquo Journal ofTransport Geography vol 47 pp 70ndash83 2015

[7] R Cervero and K Kockelman ldquoTravel demand and the 3Dsdensity diversity and designrdquo Transportation Research Part DTransport and Environment vol 2 no 3 pp 199ndash219 1997

[8] R Ewing and R Cervero ldquoTravel and the built environment asynthesisrdquoTransportation Research Record vol 1780 pp 87ndash1142001

[9] Y J Singh P Fard M Zuidgeest M Brussel andM V Maarse-veen ldquoMeasuring transit oriented development A spatial multicriteria assessment approach for the City Region Arnhem andNijmegenrdquo Journal of Transport Geography vol 35 pp 130ndash1432014

[10] J E Evans and R H Pratt ldquoTCRP Report 95 ndash Chapter17mdashTransit Oriented Developmentrdquo 2007

[11] YHayashi and J RoyTransport Land-Use and the EnvironmentSpringer US Land-Use and the Environment 1996

[12] A Charnes W W Cooper and E Rhodes ldquoMeasuring theefficiency of decision making unitsrdquo European Journal of Oper-ational Research vol 2 no 6 pp 429ndash444 1978

[13] A Charnes W Cooper A Y Lewin and L M Seiford ldquoDataEnvelopment AnalysisTheoryMethodology andApplicationsrdquoJournal of the Operational Research Society vol 48 no 3 pp332-333

[14] S F Gomes Junior A P D S Rubem J C C B Soares deMelloand L AnguloMeza ldquoEvaluation of Brazilian airlines nonradialefficiencies and targets using an alternative DEA approachrdquoInternational Transactions in Operational Research vol 23 no4 pp 669ndash689 2016

[15] M Sama C Meloni A DrsquoAriano and F Corman ldquoA multi-criteria decision support methodology for real-time trainschedulingrdquo Journal of Rail Transport Planning and Manage-ment vol 5 no 3 pp 146ndash162 2015

[16] T Yoshida and K Tanaka ldquoLand-use diversity index a newmeans of detecting diversity at landscape levelrdquo Landscape andEcological Engineering vol 1 no 2 pp 201ndash206 2005

[17] Q He J Han D Guan Z Mi H Zhao and Q Zhang ldquoThecomprehensive environmental efficiency of socioeconomic sec-tors in China An analysis based on a non-separable bad outputSBMrdquo Journal of Cleaner Production 2017

[18] R Cervero ldquoSustainable new towns Stockholmrsquos rail-servedsatellitesrdquo Cities vol 12 no 1 pp 41ndash51 1995

[19] R D Knowles ldquoTransit OrientedDevelopment in CopenhagenDenmark from the Finger Plan toOslashrestadrdquo Journal of TransportGeography vol 22 pp 251ndash261 2012

[20] K Behan H Maoh and P Kanaroglou ldquoSmart growth strate-gies transportation and urban sprawl Simulated futures forHamilton Ontariordquo The Canadian Geographer vol 52 no 3pp 291ndash308 2008

[21] D L Winstead ldquoSmart growth smart transportation A newprogram to manage growth in Marylandrdquo Urban Lawyer vol30 no 3 pp 537ndash539 1998


[22] HDittmar andGOhlandTheNewTransit Town Best PracticesIn Transit-Oriented Development Island Press 2003

[23] L Bertolini C Curtis and J Renne ldquoStation area projects inEurope and beyond Towards transit oriented developmentrdquoBuilt Environment vol 38 no 1 pp 31ndash50 2012

[24] M Givoni and D Banister Integrated Transport From Policy toPractice Routledge New York NY USA 2010

[25] A Galelo A Ribeiro and L M Martinez ldquoMeasuring andEvaluating the Impacts of TOD Measures ndash Searching forEvidence of TOD Characteristics in Azambuja Train LinerdquoProcedia - Social and Behavioral Sciences vol 111 pp 899ndash9082014

[26] N L Widyahari and P N Indradjati ldquoThe Potential of Transit-Oriented Development (TOD) and its Opportunity in BandungMetropolitan Areardquo Procedia Environmental Sciences vol 28pp 474ndash482 2015

[27] B P Y Loo C Chen and E T H Chan ldquoRail-based transit-oriented development lessons from New York City and HongKongrdquo Landscape and Urban Planning vol 97 no 3 pp 202ndash212 2010

[28] C Curtis J L Renne and L Bertolini ldquoTransit OrientedDevel-opment Making it Happenrdquo Journal of Transport Geographyvol 17 no 6 p 312 2009

[29] C D Higgins and P S Kanaroglou ldquoA latent class methodfor classifying and evaluating the performance of station areatransit-oriented development in the Toronto regionrdquo Journal ofTransport Geography vol 52 pp 61ndash72 2016

[30] M Kamruzzaman D Baker S Washington and G TurrellldquoAdvance transit oriented development typology Case study inbrisbane australiardquo Journal of Transport Geography vol 34 pp54ndash70 2014

[31] D S Vale ldquoTransit-oriented development integration of landuse and transport and pedestrian accessibility Combiningnode-place model with pedestrian shed ratio to evaluate andclassify station areas in Lisbonrdquo Journal of Transport Geographyvol 45 pp 70ndash80 2015

[32] J L Renne and J Wells Transit-Oriented Development Devel-oping a Strategy to Measure Success Washington Wash USA2005

[33] L Cavaignac and R Petiot ldquoA quarter century of Data Envel-opment Analysis applied to the transport sector A bibliometricanalysisrdquo Socio-Economic Planning Sciences vol 57 pp 84ndash962017

[34] X Li J Yu J Shaw and Y Wang ldquoRoute-Level TransitOperational-Efficiency Assessment with a Bootstrap Super-Data-Envelopment Analysis Modelrdquo Journal of Urban Planningand Development vol 143 no 3 p 04017007 2017

[35] J Sanders ldquoLinking station node- and place functions to trafficflow a case study of the Tokyu Den-En Toshi line in TokyoJapanrdquo 2015

[36] R Cervero A Round T Goldman and K-L Wu Rail AccessModes and Catchment Areas for the BART System Berkeley1995

[37] J Lohmann E Andersen and A Landex ldquoGIS-basedApproaches to Catchment Area Analyses of Mass Transitrdquo inEsri International User Conference Proceedings pp 1ndash13 2009

[38] T Lin R Palmer J Xia and D A Mcmeekin ldquoAutomatic gen-eration of station catchment areas A comparison of Euclideandistance transform algorithm and location-allocation meth-odsrdquo in Proceedings of the 2014 11th International Conference onFuzzy Systems and Knowledge Discovery FSKD 2014 pp 963ndash967 China August 2014

[39] O Harrison and D O Connor ldquoMeasuring Rail Station Catch-ment Areas in The Greater Dublin Areardquo in Proceedings of theITRN 2012 2012

[40] D Wheeler and A Wang ldquoCatchment Area Analysis UsingGeneralized Additive Modelsrdquo Austin Biometrics Biostat vol 2no 3 pp 1ndash6 2015

International Journal of

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Submit your manuscripts atwwwhindawicom


applied with an emphasis on the 3D framework in urbanand transport planning worldwide However its efficiency inpractical applications may sometimes not meet expectations[10] The low utilization of public transport cannot achievethe sustainable cycle of landndashpeoplendashtransport [11] In otherwords whether or not the TOD strategy can play its duerole in urban planning and management is inconclusiveTherefore evaluating the efficiency of TOD is a necessarychallenge faced by urban administrators and policy makerswho aim to successfully implement enhance and improveTOD

To respond to such a demand this study selects theappropriate indicators that reflect the 3D concept of TODin practice This study assesses the efficiency of a railwaysystem that was developed on the basis of the 3D frameworkof TOD Furthermore it provides advice to improve thefactors that most significantly influence the efficiency ofTOD In this study data envelopment analysis (DEA) whichwas first proposed by Charnes et al in 1978 is applied[12 13] Since its introduction DEA has been widely usedto measure the relative efficiency of various organizationsreferred to as decision-making units (DMU) with multipleinputs and outputs [14ndash17] DEA can identify the inputwith the largest impact on an inefficient unit Such inputcan provide guidelines to policy makers One line of theJapanese railway system is presented as a case study In Japanthe integration of the transport system and land use hasbeen practiced since the 1910s This practice is consistentwith the concept of TOD After more than a century thecountry has recognized advancements in railway operationand management The efficiency evaluation of TOD cases inJapan can provide essential guidance or experiences for futuretransport and urban planning in other countries especially indeveloping countries experiencing rapid urbanization

2 Literature Review

The integration of transportation and land use dates back tothe post-World War II era when planners in Europe mostnotably in Stockholm [18] and Copenhagen [19] led subur-ban development into satellite areas along transit corridorsSince the 1990s a series of new concepts and approaches suchas the Smart Growth concept [20 21] and New Urbanismapproach [22] for the integration of transportation and landuse have emerged to combat uncontrolled urban sprawl [6]TOD is also gaining popularity as an approach to promotesmart growth and sustainable development in the metropoli-tan areas of many developed and developing countries [23ndash26] where mass transit systems have been implemented torelieve traffic congestion

A common definition or criterion remains to be estab-lished for TOD despite the substantial body of researchthat has discussed the concept For example Cervero et alstressed the contributions of the 3Ds to the TOD conceptfurthermore they explained the 3D concept as density in theform of residence and jobs diversity in the form of mixedland-use development and design in the form of good streetconnectivity for pedestrians [25] Other scholars interpretedTOD by borrowing words from its original explanation and

providing new empirical additions The various definitionsfor TOD generally state that TOD should promote compactand highly mixed-use development around transit hubs andshould include accessible and walkable neighborhoods [1819]

Existing studies have demonstrated that TOD providesnumerous advantages such as facilitating polycentric citiesand regions mitigating urban sprawl boosting public trans-port ridership increasing bike usage and walkability accom-modating economic growth and creating sustainable neigh-borhoods [6] The most important function of TOD is toreduce traffic congestion and increase public transit use inareas experiencing accelerated urban development [25 2728] TOD has attracted increasing attention as a solution tothe challenges of urban growth given its widely recognizedbenefits TOD implementation however is not always suc-cessful Thus several studies have evaluated the efficiency ofTOD (eg [9 21]) Most existing research applied single andseveral indicators to measure the outcomes or benefits thatTOD provides in terms of travel behavior economics andenvironment [28 29] Ridership is one of most representativeindicators of TOD outcome [25] Renne andWells monitoredthe success of TOD through a survey that included andsummarized the various indicators of the influences andthe most useful indicators of TOD [3] However this studyquantifiedTOD in the formof a single indicator and provideda limited explanation for the ridership efficiency of TODIn addition it lost meaningful information for the aspectsthat contribute to the success or failure of TOD The multi-indicator evaluation method involves weight setting for eachindicator However weight setting in existing studies is oftenempirical or less objective and could provide biased results

In the past decade DEA has been widely used to measurethe efficiency of different models of transportation systemoperations or management [30] The most significant advan-tage of DEA over traditional weight-based methods is thatDEA does not require weight parameters to measure effi-ciency Researchers have employed DEA to address twomainresearch topics One topic is the analysis and comparison ofthe efficiency of firms networks or nations in the field ofeconomics and management science [31ndash33] Only a smallpart of related works has focused on the evaluation of theoperational performance and management of railways [30]The other topic is the selection of the optimal solution foractual operations in vehicle dispatching and route planning[34 35] Up to now DEA has not been applied in theassessment of transportation strategy at least in terms ofTOD

21 DEA As mentioned above DEA identifies the frontier(envelopment surface) which is determined directly fromdata and quantifies the relative efficiency of a set of compa-rable DMUs with several inputs and outputs It solves a linearprogrammingmodel to detect the efficient inputs and outputsthat form the frontier and the inefficient inputs and outputsthat form nonfrontier units In addition to providing theefficiency level DEA can identify the sources of inefficiencyand the projection path to the frontier The projection pathto the envelope surface is determined by whether the model





Design




max 120579 + 120576( 119898sum119894=1

119904minus119894 +119904sum119903=1

119904+119903)

st119899sum119895=1

120582119895119909119894119895 + 119904minus119894 = 1199091198940119899sum119895=1

120582119895119910119903119895 minus 119904+119894 = 1205791199101199030119904minus119894 119904+119903 ge 0120582119895 ge 0

(1)




11991010158401199030 = 1205791199101199030 + 119904+119903 (3)







(km)

N

6030150




3 Case Study










4 Results





(km)

Tokyo

Kanagawa

N

864210


Number of choices1

2ndash10

11ndash50

51ndash150

151ndash250

251ndash789

(a)

(km)

Tokyo

Kanagawa

N

864210




50ndash100

100ndash200

200ndash400

400ndash800

(b)

(km)

Tokyo

Kanagawa

N

864210


Population0ndash300

300ndash700

700ndash1000

1000ndash1400

1400ndash2800

(c)

(km)

Tokyo

Kanagawa

N

864210


Bus stops0ndash4

4ndash6

6ndash8

8ndash10

10ndash15

(d)








Design











Design












Mean 0981 Mean 0955




Design
















References












































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia






Design




max 120579 + 120576( 119898sum119894=1

119904minus119894 +119904sum119903=1

119904+119903)

st119899sum119895=1

120582119895119909119894119895 + 119904minus119894 = 1199091198940119899sum119895=1

120582119895119910119903119895 minus 119904+119894 = 1205791199101199030119904minus119894 119904+119903 ge 0120582119895 ge 0

(1)




11991010158401199030 = 1205791199101199030 + 119904+119903 (3)







(km)

N

6030150




3 Case Study










4 Results





(km)

Tokyo

Kanagawa

N

864210


Number of choices1

2ndash10

11ndash50

51ndash150

151ndash250

251ndash789

(a)

(km)

Tokyo

Kanagawa

N

864210




50ndash100

100ndash200

200ndash400

400ndash800

(b)

(km)

Tokyo

Kanagawa

N

864210


Population0ndash300

300ndash700

700ndash1000

1000ndash1400

1400ndash2800

(c)

(km)

Tokyo

Kanagawa

N

864210


Bus stops0ndash4

4ndash6

6ndash8

8ndash10

10ndash15

(d)








Design











Design












Mean 0981 Mean 0955




Design
















References












































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia





(km)

N

6030150




3 Case Study










4 Results





(km)

Tokyo

Kanagawa

N

864210


Number of choices1

2ndash10

11ndash50

51ndash150

151ndash250

251ndash789

(a)

(km)

Tokyo

Kanagawa

N

864210




50ndash100

100ndash200

200ndash400

400ndash800

(b)

(km)

Tokyo

Kanagawa

N

864210


Population0ndash300

300ndash700

700ndash1000

1000ndash1400

1400ndash2800

(c)

(km)

Tokyo

Kanagawa

N

864210


Bus stops0ndash4

4ndash6

6ndash8

8ndash10

10ndash15

(d)








Design











Design












Mean 0981 Mean 0955




Design
















References












































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia







4 Results





(km)

Tokyo

Kanagawa

N

864210


Number of choices1

2ndash10

11ndash50

51ndash150

151ndash250

251ndash789

(a)

(km)

Tokyo

Kanagawa

N

864210




50ndash100

100ndash200

200ndash400

400ndash800

(b)

(km)

Tokyo

Kanagawa

N

864210


Population0ndash300

300ndash700

700ndash1000

1000ndash1400

1400ndash2800

(c)

(km)

Tokyo

Kanagawa

N

864210


Bus stops0ndash4

4ndash6

6ndash8

8ndash10

10ndash15

(d)








Design











Design












Mean 0981 Mean 0955




Design
















References












































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia



(km)

Tokyo

Kanagawa

N

864210


Number of choices1

2ndash10

11ndash50

51ndash150

151ndash250

251ndash789

(a)

(km)

Tokyo

Kanagawa

N

864210




50ndash100

100ndash200

200ndash400

400ndash800

(b)

(km)

Tokyo

Kanagawa

N

864210


Population0ndash300

300ndash700

700ndash1000

1000ndash1400

1400ndash2800

(c)

(km)

Tokyo

Kanagawa

N

864210


Bus stops0ndash4

4ndash6

6ndash8

8ndash10

10ndash15

(d)








Design











Design












Mean 0981 Mean 0955




Design
















References












































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia






Design











Design












Mean 0981 Mean 0955




Design
















References












































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia









Mean 0981 Mean 0955




Design
















References












































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia








References












































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia
























RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia




RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia


Documents

Efficiency Assessment of Transit-Oriented Development by Data Envelopment Analysis ... · 2019. 7. 30. · ResearchArticle Efficiency Assessment of Transit-Oriented Development by