“IMPACT OF COMMON MOBILITY CARD ON TRAVEL PATTERN” CASE STUDY –DELHI #mobility as a service #ease the mobility Anshula Gumber School of Planning and Architecture, Delhi

UP METRO Need of the Study

Different Modes Different Fare Structures Hassle free and seamless travel

Modern Technologies provide modern solutions • Facilitates multi-modal travel behavior. Commuters • Commuters just have to carry one card • Encourages faster boarding • Hassle free transaction. Enables commuters to enjoy the benefits of integrated fare policies.

• Collection of real time data Transport Authorities • Complex fare schemes. • Help operators to balance peak • Expandable to the other and off peak patronage. services like toll payment, • Faster reconciliation of revenue congestion pricing in CBD with recorded data. areas, and parking and further for retail shopping. • It saves manhandling hours • Elimination of fake currency • Increases the accountability and from the economy. transparency of the transactions. • Increase the accessibility. • Enables the authorities to maintain the extra sum of money Literature Review London -Oyster Number Populati Cards/1000 Technology Additional SchemeAtlanta - MARTA TFL OysterTransport DublinCardmodes LUAS + NationalFunctions ITS of cards on served head used %of depositinformation to - MBTACity Charlie Card Deposit MaximumToulouse- Pastel Fare TFLChicago Oyster - CTA43 Parameter Ventra8.17 5,263 Contactless Bus, Tube,Before Tram, LondonParis Docklands- Navigo Transit, RetailsAftermaximum and Concession fare for million- METRO million Q Card card with RFID Light Railway (Metro), the Emirates (Translink tourism)- OV Chipkaartchildren and students Number of cash transactions During the 1990’s to 2000, 30% of After the introduction of Oyster, the (2012) - LACMTA(2011) TAP Card and NFC Airline, London Overground and senior citizens bus journeys in London involved number of cash transactions on buses - MDT technology ,National Rail servicesEurope , Thames cash transactionsClippers River Bus services fell to just 1.4%. Montreal,London( Quebec -pounds)STM OPUS Card 5 12.5 40% Average 64% New Jersey - NJ TRANSIT Card Contactless Concession for 35 7.4 MTR, Light Rail, Tramways, Bus, HK Hong Kong (HK Dollar) card with RFID50 57 Transit, Retails, children88% and students million million 4,711 Ferry and Octopus and NFC parking and tourism and senior citizens (2018) (2018) Taxi technology North AmericaDelhi 50 60 83%Concession for Queues and ticket offices at Average ticket office queue time Average ticket office queuechildren time and was students underground stations Contactlesswas 129 seconds 78 seconds and senior citizens and EZ Link 17 5.7 card with RFID MRT, LRT, taxis, buses and road Transit, Retails, Singapore Armed PTENumber million of people million accommodated 2,935 15 people per minute 35 people per minute and NFC pricing parking and tourism Forces, Singapore (Singapore)at ticket(2018) gates (2018) technology Also, the deposit money charged is CivilhighAsia DefencewhenForce Boarding time approximately 2-3 seconds are saved per boarding in buses. and Singapore Police compare to the other global cities. Force Fraud and malpractices 2.5% of the total journeys made 1.5% of the total journeysThreemade type of cards:  Delhi being the most populousHong Kongcity - OctopusPersonnalof all OV thecard, OV 14.3 11.7 This also Japanlead to – costPiTaPa savingAnonymous of card and ChipkaartRio de Janeiro – Rio Card Contactless above mentioned ones still lags behind. The card is million million 1,222 Train, Bus, tram and metro servicesapproximatelyTransit( Suicaand tourismup/Pasimo to £40m)single per useyear. card. The (Netherland card Mendoza(2017) – Red (2017)Bus not multifunctional. It is limitedSingaporeto Transit- EZproject Link. tookEven long intime Santiagos)Public transport – Transantiago usage In 2000 In 2005 Africa in implementation Peru, Lima- Tarjeta Public transitTransportit :33%is limited to onlyPublicbus Transportand :37%Metro.(2005-2014) Delhi 29.2 19 Private Transport:45% Private Transport:41% 1537 Contactless ThereCapeMetro, TownisRapidno – lineMyincentive ,DIMTSConnect ,DTCschemeTransit. Concessional Fares Metro Million Million(2 card There was 38% reductionfor incard traffic users and Durban – Muvo Card (One (2019) South019) America after the scheme. This furtherConcessional has also fares for Delhi One Johannesburg – Gautrain Goldreduced journey times. Weekdays and Ride) Lagos – ETC card Weekends Aim, Objective and Methodology

AIM : To study the user Listing of public transport scenario in Delhi and finalizing the scope for further study behavior to ease the mobility using common mobility card Collection of Data Objectives: 1. To appreciate the types of 2. To review the global best practices of using smart card for mobility. Primary data Secondary Data 3. To study the existing travel behavior of users.  Socio-economic Data (Gender, Age, Vehicle  Ridership Data by 4. To evaluate the travel behavior of non - ownership, Income, Educational Background, DMRC and DIMTS, users by expanding the smart card profession) DTC services  Trip Characteristics (Trip Purpose, origin,  Income generated by destination, Trip length, Trip cost and Trip Time, card (DMRC,DTC) 5. To estimate the economic benefits to Frequency of travel) users.  Payment mode used and reason 6. To list out the financial benefits to  Rating of intermodal parameters stakeholders  Rating and Ranking of existing Card services

 Revealed Preference survey (PCA Analysis) Analysis of Data  Multi –Criteria Decision making analysis for intermodal parameters  Stated Preference survey, Design of Experiments Building up of Quantitative relationship between specific parameters By orthogonal analysis and indicators using various statistical tools  Cojoint Analysis Policy Evaluating the economic benefits of the scenarios so generated Recommendations & Proposals Introduction: Study Area: Delhi

Delhi Metro DTC AND DIMTS BUSES  The DTC runs a fleet of 3,882 buses, of which  Presently, the Delhi Metro network consists of 2,506 are low-floor non-AC buses, 1,275 are low- about 373 Km with 271 stations. floor AC buses and 101 green standard floor buses.  Currently, there are 8 lines and the airport Besides, there are 1,672 cluster (orange) buses express line plying on city roads.  Average Ridership of DMRC is 25.35 lakhs.  Currently, bus fares in Delhi for non-AC buses are (Data as per RTI filed on 27/2/19) Rs 5, Rs 10 and Rs 15. There is a flat fare of Rs 5  Deposit is of Rs. 50 , so initial amount totals for travel in non-AC DTC and cluster buses for upto Rs. 150 travel up to five kms. Fare slab for travel in AC MetroMetro: : Average Monthly Daily Average ridership Ridership buses is between Rs 10 and Rs 25.  Bus Passes are also available for students(Rs.100 28.00 28 27.61 per month), Normal Passes (Rs.800 per month for 27.50 27 Non AC and Rs. 1000 Per month for AC buses)and free pass for disabled persons and senior citizens. 2627.00 26.50 (Data as HT Times dated sept. 07, 2018) 25 25.90 26.00 Bus: Average Daily Ridership 24 25.38 25.35 25.50 46.77 43.47 23 50 38.87 42.25 Ridership (in lakhs) (in Ridership 35.37

2225.00 40 Ridership(in Lakhs) Ridership(in 2124.50 30 24.00 20

2015 2016 2017 2018 10

Jul-18

Jan-18 Sep-18 Oct-18 Jan-19

Feb-18 Dec-18

Jun-18

Apr-18 Nov-18

Aug-18 Mar-18 May-18 0 2012 2013 2014 2015 2016 Year

(Data as(Data per RTI from filed DMRC on 27/2/19) Website) Clakhs) (in Ridership Year (Data from The Asian age article dated Oct 19, 2017) Introduction: Study Area: Delhi Primary Data Collection S.N Area Abutting o. Landuse 1 Nehru Place Institutional 2 Kashmere Gate Commercial 6 2 3 Anand Vihar Commercial 9 5 4 3 4 New Delhi Commercial 8 12 5 Old Delhi Commercial 10 11 7 6 Delhi Vishvidhalaya Institutional 1 7 Lajpat Nagar Commercial Metro Bus Number of 8 Connaught Place Commercial samples collected 9 Rajauri Commercial Captive X 200 10 Durgabhai Deshmukh Institutional Captive Captive 100 11 Hazarat Nizammuddin Commercial Captive Choice 50 Metro Bus12 Dwarka Mor Residential Cash Cash Choice Captive 50 Data was collected from different Cash DTC pass activity places having different landuse Choice X 50 Cash Card so to have a rich mix of characters of different commuters. Choice Choice 50 Card Cash Different samples were collected for X Captive 200 Card DTC pass different set of commuters as per the frequency of there travelling through X Choice 50 Card Card different mode. Data Analysis: Revealed Preference: Captive Riders Bus Metro Bus + Metro

3 3 2

2

2 t

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n n 1

e

e

e 1 n

n

n Educational o

o

p o Employment 1 Educational

p p Payment mode_Bus Payment mode m

m 0 o m Reason 0 Age

C o Educational Employment o

Employment

C Payment mode

C

d Gender

n

d -1 d 0

Age o

n

n

Income c

o Reason for not using in bus

e Reason o -1 c Income Gender

c Age S Income

e

e

S -2 S -1 Gender

-2 -3 -4 -3 -2 -1 0 1 2 3 4 5 -2 -2 -1 0 1 2 3 4 -3 -2 -1 0 1 2 3 4 First Component First Component First Component Variable PC1 PC2 PC3 Variable PC1 PC2 Variable PC1 PC2 PC3 Age 0.572 -0.016 0.074 Age -0.337 -0.397 Age 0.463 -0.333 0.197 Gender 0.301 -0.565 -0.231 Gender -0.398 -0.343 Gender 0.202 -0.678 -0.176 Educational Level 0.475 0.133 0.017 Educational Level 0.456 0.029 Educational Level 0.055 0.218 0.862 Employment -0.456 0.198 -0.529 Employment Status 0.447 0.099 Employment Status -0.436 0.286 -0.272 Status Income Level 0.31 -0.558 Income Level -0.276 -0.208 0.256 Income Level -0.123 -0.629 -0.36 Payment mode 0.41 -0.015 Payment mode 0.504 0.323 -0.146 Payment mode 0.296 0.099 -0.599 Reason 0.235 -0.634 Reason 0.471 0.391 -0.163 Reason for not -0.214 -0.467 0.416 using the card in Total Cumulative Total Cumulative bus variance(>60%) 67.5% variance(>60%) 73% Kaiser-Meyer-Olkin Measure 0.558 Kaiser-Meyer-Olkin Measure 0.576 Total Cumulative of Sampling Adequacy(>0.5) of Sampling Adequacy(>0.5) variance(>60%) 75.4% Kaiser-Meyer-Olkin Measure of 0.61 Income and Reason for Mode and Reason for not Sampling Adequacy(>0.5) not using the card in bus using the card in bus are Income and Reason for not using are related. related. the card in bus are related. Data Analysis: Revealed Preference: Captive Riders Bus Metro Bus + Metro 2% 15% Payment Mode For Bus Trip 20% 7% 48% Metro Card 35% 20% 80% Cash 73% High Deposit Money DTC Pass Pass is Cheaper Not enough cash to recharge it today Lack of Awareness Fear of loosing the card Pass is more Topping Up Card is difficult 19% cheaper 33% Lack of awareness High2-3 Lakhs Per Annum 3-6 LakhsTopping Per Annum Up 6-9 Lakhs Per Annum 12 and above Lakhs Per 26% Mode/Income Deposit Pass is Lack of card is AnnumTopping up card is a difficulty DTCIncome/ReasonPass MoneyPass is cheaper:Cheaper 100%AwarenessPass is cheaper:Difficult 71% Pass is cheaper:21% Pass is cheaper: 100% 1-2 Lakhs Per 73% 20% 7%Lack of Awareness:- 29% 100% Machine Hangs Annum  For metro 100% of the Cash2-3 Lakhs Per Topping- Up card70% is a difficult30%Topping Up- card is a Machine hangs:60% Machine hangs:67% Annum sample used metro card. as no facility is available: difficult as no facility is Lack of Awareness:But the Lackirony ofis Awareness:same 33% 3-6 Lakhs Per - 12.5%% 37.5% 50% 44% available: 25% 40% people are not using metro Annum Machine hangs: 33% Machine hangs:50% card in bus. They are Lack of Awareness: 22% Lack of Awareness: 25% Income and Reason for paying via cash. not using the card in bus Income and Reason for not using are related. the card in bus are related. Data Analysis: Revealed Preference: Choice Riders

Non- Daily Bus Users Non- Daily Metro Users

Daily Metro Users Not using metro

Cash 64% 100% using Cash as the payment Daily Bus Users Not using Bus mode in Bus Metro card 27% Payment Mode Cash 28% DTC Pass 9% 3 100% Metro card 72% Reason for not using 2

t

n 80% 38%

e 50% the Card n 1

o

p 60% Pass is more Reason Educational m 16% 0

o

C Income cheaper 40% 13% d n -1 Employment o Gender c 46% 50% Lack of e Age 20%

S 25% -2 62% awareness 0% -3 Bus (Captive) Metro(Non-daily) Machine -3 -2 -1 0 1 2 3 4

Hangs First Component Cash Metro card DTC Pass Percentage of Commuters of Percentage Topping Payment Mode Vs Frequency Money gets Lack Fear of up card of Travel High locked up and we of loosing is a Reason for not using the Reason for not using 100% Income / Deposit cannot use it at awaren Machine the difficult card in bus the card in 22%metro 80% 56% Reason Money other things ess Hangs card y 60% 91% High deposit money : 0-1LPA 40% 40% 20% - - - Pass40% is cheaper: 35% 60% 78% 1-2LPA 33% 42% 25% - - - Machine20% Hangs:29% 44% 2-3LPA 33% 67%- - - - 0% 9% Once in a TwiceMoney in a getsOnce locked in a 3-6 LPA - 50% 25% 13% 13% High depositweek money: 26%monthup and we cannotmonth use 6-9 LPA - 42% 8% 25% 8% 17% Frequencyit ofat Travelother things: 9-12 Lack of awareness: 10% 40% LPA - - 20% 40% 40% Commuters of Percentage Cash Metro Card Data Analysis: Revealed Preference Rank Metro Captive Riders Mean Score 1 Boarding Time Difference 1.70 2 Fare 1.81 Calculate the Mean Ranks by Friedman Test 3 Elimination of Change 3.34 4 Less communication with staff 3.72 5 Incentives 4.43  Significance is less than 0.005 Fare Boarding Time Difference .482 Check the significance Fare Incentives .000 (less than 0.05) Fare Less communication with staff .000 Fare Elimination of Change .000 Make the pairs of different Boarding Time Difference Incentives .000 attributes and run Post hoc Boarding Time Difference Less communication with staff .000 test (Wilcoxon signed rank Boarding Time Difference Elimination of Change .000 tests) Incentives Less communication with staff .000 Incentives Elimination of Change .003 Less communication with staff Elimination of Change .013 Use the Bonferroni adjustment (0.05/Number In this case fare and boarding time difference stands at the same place if pairs = 0.005) for commuters or both the benefits attract commuters are at the same pace. Similarly, less communication with staff and elimination of Change problem stands at the same pace. But on the other hand, there is a vast difference between incentives and Check the statistically significance of the result the other benefits. This indicates that commuters today are receiving low or no incentive benefits. Data Analysis: Stated Preference Extension of services Incentives Users were willing to use the metro card in Multifunctionality of Card other modes also. Cashback Users preferred to get Shopping 54% 46% incentives in the form of Parking 87% 13% 28% Shopping Cashback. Tolls 84% 16% 80% of the users 68% vouchers Metro Feeder bus 91% 9% showed positive Other Govt. Taxi 92% 8% 5% Mileage response regarding Ola/Uber 91% 9% Points multiple recharge facility. 0% 20% 40% 60% 80% 100% • Multifunctionality of card Around 76% of the Yes No • Incentives users were not willing • Multiple Recharge options to attach their metro Payment Mode Shift • Reduction in deposit card to bank. Yet another 24% were money Modes willing to attach their cards to bank. Scenarios Generated: Bus Metro 1. Deposit Money Orthogonal Analysis of the Design • Rs.150 of experiments fetched these 8 • Rs.120 Cash to Pass to Cash to scenarios of four different 2. Multifunctionality of Card Card Card attributes with two levels each. the Card: Yes / No 3. Multiple Recharge Options : Yes / No 4. Incentives : Yes / No Data Analysis: Stated Preference BusMetro Users: Users: Cash Cash to toCard Card • Run the Conjoint Analysis in Correlations the on the ranks given by users Correlations • Run the Conjoint Analysis in Value Sig. Willingness to shift : in SPSS with the help of syntax Value Sig. the on the ranks given by users Pearson's R .879 .002 . Fare is 10%less • More the Utility of the level Pearson's R .907 .001 . Elimination of Change problem in SPSS with the help of syntax Kendall's tau .786 .003 • moreMore istheit’sUtilitylikingofbythethe leveluser Kendall's tau .546 .031 8085% of the users show willingness to and hence more is the shit. more is it’s liking by the user Check the Correlation value it should importanceand hence scoremore ofis the beCheckabove 0the.5 Correlation value it Utilities attribute should be above 0.5 Utilities importance score of the Check the significance (less than 0.05) Utility Check the significance (less than Utility attribute Attributes and Levels Estimate 0.05) Attributes and Levels Estimate Hence, it is found that deposit money is of least important parameter for the Incentives No -.996 ′ Deposit money 150 0.000 metro users. 푒′푢푡𝑖푙𝑖푡푦 표푓 푡ℎ푒 푠푐푒푛푎푟𝑖표 Yes .996 푃 푃푟표푏푎푏𝑖푙𝑖푡푦 표푓 푐ℎ표표푠𝑖푛푔 푡ℎ푒 푠푐푒푛푎푟𝑖표 = 120 1.900 As the importance score for the incentives is high and푒′푢푡𝑖푙𝑖푡푦 so is표푓 it’s푎푙푙 utility푠푐푒푛푎푟𝑖표푠 value.′ Multifunctionality No -.854 Multifunction No -.750 Yes .854 Yes .750 Multiple recharge No -.796 %% Multiple recharge No -.733 options Yes .796 shiftshift options Yes .733 Initial Deposit 150 0.000 againsagainst Incentives No -.383 Money 120 -.467 MultipleMultiple EstimatedEstimated t allall Yes .383 Constant 4.733 ScenScen InitialInitial MultifuncMultifuncti rechargerecharge UtilityUtility Exponential scenascenari Constant 3.550 arioario DepositDeposit tionalityonality facilityfacility IncentiveIncentive ValueValue ExponentialValue riosos Averaged Importance Score Averaged Importance Score 11 120120 YesYes NoNo YesYes 5.325.85 347.2204.5 26%9% Incentives 34 22 150150 NoNo NoNo YesYes 4.082.45 11.659.1 3%1% MultifunctionalityParameters Score27 33 150150 YesYes YesYes NoNo 5.384.65 104.6218.7 9%8% MultipleDeposit Recharge Money Options 3626 44 120120 YesYes NoNo NoNo 3.335.08 161.327.9 12%2% DepositMultifunctionality Money 2513 55 150150 YesYes YesYes YesYes 7.385.42 1602.3225.1 67%17% Multiple Recharge Options 24 66 120120 NoNo YesYes YesYes 5.205.82 335.9182.0 25%8% Incentives 14 77 120120 NoNo YesYes NoNo 3.215.05 156.024.8 12%1% 88 150150 NoNo NoNo NoNo 2.081.68 5.48.1 1%0% Economic Analysis

Fare savings Ridership Deposits

Boarding time difference Recharge Time Consumed Incentives User Shift 10,00,000 Card users Cash users 8,00,000 6,00,000 4,00,000 Willingness to shift to card 2,00,000 Number of Users Number - 5 1 Initial2 Deposit3 Money:4 5 Rs. 1506 7 Actually shifted users (As from probability) MetroMultifunctionalityBus Total : Yes Multiple Recharge Facility: Yes Net SavingsIncentive: Yes 6,000 5,000 4,000 3,000 2,000 Boarding Fare savings 1,000 Initial Deposits Year perRs. time - 1 2 3 4 5 6 7 difference Recharge Time Metro 5,413 5,381 5,490 5,296 5,607 5,639 5,522 Consumed Bus 605 573 628 546 686 718 659 Incentives Total 1,043 2,809 1,823 765 3,310 1,135 774 Metro Bus Total Benefits to operators Recommendations Operational Benefits Logistics Multifunctionality Ticket Counters Wages of workers involved in Cash Multiple Recharge Facility Ticket Collection Incentives A way Forward Reduction in People approaching TVM by 59% Therefore, No  The next model could be that bank debit further need to invest in huge capital amount in TVMs. card/credit card could be used as a transit Increase will be in service time. card. But as we noticed 76% of the users were not ready to link their transit cards to bank. Research in security is needed to win users confidence.  One might work as what will be the Reduction in People Currently, DMRC is spending estimated breakeven point for the authorities so approaching Ticket Counters involved. Concessions given to different by 62%. Therefore, Counters 5 crores per month on wages of these users can also be worked out. can be removed. This will incur  Therefore, it paves the way to introduce the wage savings. employees. integrated fare structure among different modes available in the urban transport and  Logistics Cost savings share of the input and output cost that  Wage savings follows.  No need of huge capital investments in terms of vending machines  Increase efficiency of the system