Megha Aggarwal Dr. (Prof.) Sanjay Gupta School of Planning & Architecture, New Delhi

Megha Aggarwal Dr. (Prof.) Sanjay Gupta School of Planning & Architecture, New Delhi

Contents Introduction Delhi Scenario Literature Review Survey Details & Findings Scenario Development Conclusions

Introduction - Background Motorization Rate (India) 1991-2001 10% 2001-2005 12.3% 2005-2011 16-18% 2010 No. of Registered Motor Vehicles = 136.8 million. % Cars URBAN TRANSPORT SCENE 17% Registered MV other than Cars Cars Source: SIAM (2012) 83% Delhi Highest level of motorization; as well as highest no. of cars (106 cars / 1000 population; 0.31 cars per HH in 2011)

Research Need & Objectives Research Need There is an alarming increase in car ownership levels resulting in increased congestion levels, pollutions, safety issues, etc. While national policies do emphasize on public transport usage there is very little thrust on ways to achieve restraint on car ownership and its usage Insignificant empirical studies in mega cities like Delhi on measures to restraint car ownership and its use. Objectives of the Study To review the best practices for restraining car ownership & usage. To assess the socio-economic and mobility patterns of car-users. To study the attitudinal behavior of car users in selected case study locations in Delhi towards using of cars & the deterring factors prohibiting their public transit usage. To evolve alternate policies & strategies for restraining car ownership and its use.

No. of Cars (in Lakhs) 1990-91 1991-92 1992-93 1993-94 1994-95 1995-96 1996-97 1997-98 98-99 99-00 2000-01 2001-02 2002-03 2003-04 Car Ownership Scenario 2011 17.8 lakh cars (106 cars / 1000 pop n.) AAGR in no. of cars 1990s 8%p.a. 2000-2010 13%. Delhi - Scenario 14.00 12.00 10.00 8.00 6.00 4.00 2.00 0.00 Car Usage (Car Travel Characteristics) Car Ownership Trend - Delhi Year Mode Share no. of cars (in lakhs) Car constituted almost 40% of the total traffic but has only 9% modal share. Source: RITES (2007) Average Trip Length by Car 15.5 km. Av. Car Utilization Rate per day 36km. 45 km.

What is happening on Delhi Roads?? Lajpat Nagar Market Karol Bagh Market 2 lanes encroached upon by parking Only 1 lane left for moving vehicles Traffic Jam at Mathura Road More than half of the carriageway is under parking Traffic Jam at Ashram Flyover

Car ownership Literature Review - Car Ownership Restraint Policy Examples: 1. Car License Quota No intervention on car ownership policy Eg. Beijing Rigidly limited quota of car license Eg. Shanghai 1986 2009 1.4 cars / 1000 pop. 0.90 cars / 1000 pop. 220 cars / 1000 pop. 80 cars / 1000 pop. 2. Vehicle Quota System, Singapore Fixed growth rate of car/annum -1990 3% P.A. - 2009-1.9% P.A. Bidding Process Certificate of Entitlement (Coe) 3. Increase in Taxes on Car Price, Japan

Literature Review - Car Ownership Restraint Policy (cont.) Summary: Relevance in Delhi s Scenario: Car Ownership Restraint Policies 1. Fixing up of Saturation level can be done, but ensuring a transparent & equitable Licensing bidding System system will be a challenge. Taxation (Excise Increasing 2. Fixing up of quota according to availability Duty, of Road parking Tax, space Interest at residential Rate On area. Etc.) Car Loans 3. Based Increasing on fixed interest saturation rates level on for car price can only be feasible if the interest the city rate is increased annually according to the increase in per-capita income. Have only short-term effect, License plates are auctioned or Delays purchase of car, given Expert s on certain Opinion: other criteria like Leads to shift from higher to lower availability 1. Vehicle of Quota parking System, space at Resident Parking model Permit of particular Program make may prove to be residential area relevant and feasible for car ownership restraint in Delhi.

AREA LICENSING SCHEME, SINGAPORE CORDONED ZONE Car Use Restraint Examples: 1. Congestion Charging London, Singapore Relevance in Delhi s Scenario: 1. Congestion charging can be adopted in places like CP, Chandni Chowk, ITO which have good connectivity by metro & bus. 2. Park & Ride strategy is highly relevant. (Park & Ride sites can be proposed at various regional parks, stadiums, etc. and these should be complemented with shuttle service to the activity area.) Impact: 3. Enhancing PT supply & coverage would be highly relevant, if supported by 1. Total Traffic Volume feeder service, pedestrian pathways, state of art infrastructure, decreased multimodal integration, etc. Impact: by upto 14% in 1 year. Expert s Opinion: 1. Total Traffic 2. Modal share of car Volume decreased 1. Hiked & variable Impact: parking fees, park & Dedicated ride facility, bus enhancing service has from PT decreased park supply & ride & by sites to all activity by areas upto 37% in 1 coverage have - been 32% reduction rated as relevant in travel times policies by experts. over 36% segregated in the bus lanes, bus priority year. Whereas cordoned system congestion pricing, zone - 8.75 car pool, 10 lakhs etc. passenger were regarded CAMERAS / day as 2. not Modal efficient share policies of car PUBLIC TELEPHONES - 2 in 5 cars shifted 3. to PT the system usage (1.3 for restraining car has WITH - decrease use. INTERNET in air pollution WITHIN million - THE 43% car/day reduction less decreased in SOby 2, over 18% ZONE increased road) by over PAYMENT reduction OPTION NOALS 2, & RESTRICTED 12% reduction ZONE - Modal share in particulate of 70% bus 30% increased matters the ALS from WARNING SIGN - Fatality rate dropped by 50% 42%. to 80% (in 12 zone. years) 2. Oxford Park & Ride System CHANGES IN THE VOLUME OF INBOUND TRAFFIC DURING THE MORNING & EVENING PEAK HOURS SINCE 1975. SIGNAGE & USE OF TECHNOLOGY IN THE CORDONED ZONE CONGESTION CHARGING SIGNS AT THE EDGE OF THE ZONES LOCATION OF ALS RESTRICTED ZONE, SINGAPORE 3. Enhancing PT supply & coverage (Bogota s Transmillenio)

Survey Details and Findings Case Study Area Case Study Selection criteria Car Trip Attraction Intensities, access to Public Transport, Hierarchy of use zone. MAP: Car OD Pattern Delhi (Source: RITES, 2007) Table: Case Study Selection Use Type / Activity Access To PT Node Bus Only Bus + Metro South Ex. Lajpat Nagar Commercial Areas G.K. Market Connaught Place Work Zone Bhikaji Cama Place Case Study - Information Aspect I. P. Estate Nehru Place Saket Lodi Institutional Area Commercial Work Zones Centers Lajpat Bhikaji Nagar Cama Connaught I.P. Estate Place No. of Establishments 234 Place 462 Footfalls / day 16000 17500 250000 15000 Parking Space 2621 E.C.S. 10326 E.C.S.

Surveys Conducted & Sample Covered Table: Surveys Conducted S.No. Aspect Personal or HH Information (HH Income, Vehicle Ownership Details, 1 Car usage) 2 General Car Use (ATL, CU, Trip Purpose) Attitudinal Survey (Reason for choosing Car & not PT; willingness to 3 shift to PT) Pre- & Post- Metro Survey (Done Only For Connaught Place & I.P. 4 Estate) (Mode used to travel to the case location before & after Metro & frequency of visit) Table: Sample Covered S.No. Case Study Total footfalls Sample Covered / day General Visitor Car User 1 Lajpat Nagar 16000 45 116 2 Connaught Place 250000 47 64 3 Bhikaji Cama Place 17500 45 63 4 I.P. Estate 20000 50 63 Total 187 306 A total of 493 persons were covered of which 306 were car users.

Findings Socio-economic Characteristics 1. Threshold Income for a HH to own a car Rs. 20,000/month 2. Av. HH vehicular ownership 1.5-2 vehicles / HH 3. Av. HH car ownership 1-1.4 cars/hh (Almost 87% HHs owned car(s)) % HHs 4. Frequency of Car Use 25-35% 8-13% 1 Car Car 2 1 Cars - ~85% daily Car More 2 55% than 55-60% being 2 cars used 1-2 times a week Car 3 1-2 times a month Indicates that having multiple car is more of a social status thing and not a necessity

Findings Car Trip Information ATL of Car (in km.) 14-16 km. (during weekdays) - 16-17 km. (during weekends) 15.5 km. (for Delhi (RITES, 2007) Car Utilization Rate 32-34 km. (during weekdays) - 36-37 km. (during weekends) ~40 km. (for Delhi (CRRI, 2002) Modal Split observed Modal Share (Comm. Areas) 34% 2% 17% 7% 40% Twowheeler Car Auto Metro Bus 0% Modal Share (Work Zone - Bhikaji Cama Place) 7% 20% 34% 39% Not connected by metro 6% Modal Share (Work Zone - I.P. Estate) Two-Wheeler 30% 9% 9% 13% 33% Car Auto Metro Bus Metro+Bus

% Respondents % Respondents Findings Attitudinal Response Reasons for choosing car over other modes Reasons for Choosing Car over Other Modes 120 100 80 60 40 20 0 90 100 17 10 30 Reasons Comfortable Convenient Safer Lack of PT Saves Time Willingness to Shift to Bus or Metro 100 90 80 70 60 50 40 30 20 10 0 % Respondents not willing to shift to Bus or Metro 65 92 67 62 Bus 29 66 44 32 Metro Lajpat Nagar Connaught Place Bhikaji Cama Place I.P. Estate Willingness to pay extra for travelling by car (in the form of hiked parking fees or congestion pricing) 75-80% respondents are willing to pay upto Rs. 80-100 beyond which they would shift to other modes

PRE-METRO Pre- & Post-Metro 36% Analysis 22% Mode Used to commute before 0% and after coming up of Metro Connaught Place 36% 34% PRE-METRO 17% 22% 26% 16% POST-METRO 0% CAR 2-WHEELER TAXI BUS AUTO METRO I.P. Estate 30% 31% PRE-METRO 17% 8% 8% 2% 23% 30% CAR 2-WHEELER BUS AUTO METRO METRO + BUS POST-METRO 34% 17% 30% 26% 16% CAR 2-WHEELER TAXI BUS AUTO METRO POST-METRO PRE-METRO 31% 17% 8% 30% 8% 2% 23% CAR 2-WHEELER BUS AUTO METRO METRO + BUS 31% 10% POST-METRO 11% 23% 13% 12% In Case of commercial areas, Modal shift to metro was seen mainly from 2-wheeler, bus, and auto. Mode share in car has increased over time. In case of work areas, Modal shift to metro was seen from cars, 2-wheeler, bus, and auto. Availability of bus service from metro station to work areas played an important role in the shift

NO. OF CARS (IN LAKHS) Scenario 1: Delhi Car Ownership Business As Usual Scenario 1990-91 3,98,479 cars 2003-04 12,67,700 cars Model Development from Car Ownership Trend Based on time series trend, exponential curve best fitted. 14.00 12.00 10.00 8.00 6.00 4.00 2.00 Car Ownership Trend 0.00 1990 0 1995 5 200010 2005 15 TIME (T) y = 3.7314e 0.0855x R² = 0.9931 no. of cars (in lakhs) Expon. (no. of cars (in lakhs)) y = 3.7314e 0.0855x R² = 0.9931 BUSINESS AS USUAL SCENARIO If trend continues, by 2022 no. of cars will increase to 49.9 lakh cars. (» 245 cars per 1000 population as compared to 106 cars per 1000 population in 2011 2.3 times more)

Scenario 2: Policy 1- Vehicle Quota System Assessment of Desired Car Ownership Levels. Based on cross-sectional model formed from tpt. System characteristics of liveable cities (Perth, Stockholm, Munich, Sydney, Zurich, Vienna, Melbourne) Car Congestion Index = Total Car kilometer / Network Kilometer Asian Cities Livable Cities Car congestion index is lesser in liveable cities, even though they have higher car ownership rate lesser dependence on car Average Car congestion index for liveable cities = 4540 Accordingly, assuming % area under roads to be 21% by 2022, Delhi should limit to 210 cars /1000 population by 2022.

Scenario 2: Policy 1- Vehicle Quota System (cont.) No. of Cars (in lakhs) 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 For calculating motorization level per year: Where, M = S/(1+e (a bt) ) M = Motorization level at year T, S = Saturation level for Car ownership (i.e. 210 cars per 1000 population) T = Year for which motorization level is required. a & b are coefficient, from the model based on trend; i.e. y = 3.731 e (0.085x) ; therefore, a = 3.731 and b = 0.085 [From the exponential model above] 60 50 40 30 20 10 0 Projected No. of cars (BAU vs. VQS Scenario) BAU Year VQS By 2022 no. of cars will increase to 33.2 lakh (16.7 lakh lesser cars than BAU)

Scenario 2: Policy 1- Vehicle Quota System Implementation Mechanism Fixing up of Saturation Level Based on car congestion index of livable cities Calculation of total cars which can be sold per year Bidding for cars Bidding process based on current HH ownership levels for ensuring equity Market segmentation of cars 1.Car price upto 6 lakhs 70% 2. Car price b/w 6-10 lakhs 26% 3.Car price above 10 lakhs 4% HHs With No Cars HHs With 1 Cars HHs With 2 Or More Cars 70% quota 25% quota Subject to: 5% quota 1.Scrapping or selling Subject to: of the old car; 1.Scrapping or selling 2.More than 1 earning of the old car. member in the HH. Certificate of car ownership

Scenario 3: Policy 2 - Car Use Restraint Policy Assessment of Desired Car Usage Level Table: Primary Survey Data Income / household Average No. of CU (Km.) Predominant Trip Purpose Cars Owned Rs. 20000 - Rs. 40000 1 20 Social Rs. 40000 - Rs. 60000 1 28 Social, Shopping Rs. 60000 - Rs. 80000 2 30 Work, Social Rs. 80000 - Rs. 100000 > 2 34 Work, Social, Recreational, Shopping > Rs. 100000 > 2 37 Work, Social, Recreational, Shopping Av. CU for essential purposes 30 km. Average Emission levels for a liveable city 412.5 tonnes per day Achieving this would imply car utilization to be limited to 28 km. / day Desirable Car use level to be fixed at 30 km./day

Scenario 2: Policy 2 - Car Use Restraint Policy (cont.) Implementation Mechanism Car Mobility Card To be issued to all motor vehicle owning people Fixing up of total car usage or fuel consumption per car Card to be swiped each time while purchasing: 1. Petrol, 2. Diesel, 3. CNG If the points are enough, recharge every month or on year basis. If more points are reqd. Central controlling agency People using public transport can: 1. Either save their car points & use it in bulk anytime; 2. Sell back their car points to the issuing govt. agency & get their money back. 1. Next set of point at double the rate of the original card value

Impact Comparison No. of Cars (in Lakhs) No. of Cars / 1000 Population Total Car Kilometer / Day (in Lakh Km./Day) Use BAU VQS Ration Year Use Use BAU VQS BAU VQS BAU Ration Ration 2011 17.8 17.8 17.8 106 106 106 605.2 7868 GHG Emissions (Tonnes / Day) VQS Use Ration 2017 34.1 23.5 23.5 182 158 158 1159.4 799 705.9 15072 10387 9177 2022 49.9 33.2 33.2 245 207 207 1696.6 1128.8 994.8 22056 14674 12930 * Taking CU rate as 34 km./car/day from the Primary Survey ** At 34 km./car/day, one car will emit around 4.42 kg. GHG emissions/day (Assuming car average to be 10 km./litre) Impact Assessment: 1. Vehicle Quota System 33.5% reduction will be observed. 2. Car Use Rationing 12% further reduction will be observed.

Conclusion Implementing VQS likely to result in 33.5% lesser no. of cars than BAU scenario Implementing Car use rationing likely to result in 12% lesser car km. travelled, fuel consumption, GHG emissions Implementing strategies like Park & Ride System, enhancing PT coverage and supply result in only 9% lesser car km. travelled, emissions, but implementing these help in creating liveable environment in activity areas, by providing more walk space and reducing the space required for car parking. Other major recommendations include having public transit service connecting all markets and work places from each of the planning zones. These buses can be planned on the basis of HoHo buses running currently in Delhi for tourist destinations. Implementation of intelligent transportation system is also recommended. Advanced Traveller Information System (ATIS) Advanced Public Transportation System (APTS)