Transportation Planning and Analytics

Selected Works in

Transportation Planning and Analytics Himadri Shekhar Kundu

ICICI Bank Ltd., Mumbai, India Rutgers University, NJ

M.C.R.P., Urban Planning and Design, 2017 • Travel Characteristics of New Jersey • New Brunswick Vision Plan • Transit Network Improvement

SFMTA, San Francisco Student Design Trainee II, Summer 2016

Real Estate Mortgage Valuations, 2015

NICMAR, Pune, India

Graduate Degree, Construction & Project Management, 2014

Jadavpur University, Kolkata, India • • •

B.E. Civil, 2009 Research Fellow, 2011 Land Use & Transportation Planning

Consulting Engineering Services (Jacobs), New Delhi, India Highway Contract Management Trainee, Summer 2013

CONTENTS

FORECASTING SUSTAINABILITY

MOBILITY

COORDINATION AVIATION

PLANNING

TRAFFIC

CORRIDOR STUDIES ENVIRONMENTAL MANAGEMENT HIGHWAY DESIGN

NETWORK ANALYSIS ENVIRONMENT

DATA COLLECTION

TRANSIT

ROUTE PLANNING

EXPRESS LANES

TOLL ALTERNATIVES ANALYSIS

ENVIRONMENTAL IMPACT ASSESSMENT

ADDRESS GEOCODING

SPATIAL ANALYST ROUTING EFFICIENCY

GEOSTATISTICAL ANALYST NEAREST FACILITY

SCENARIO 360

DEMAND

LOCATION ALLOCATION

SCALE

CARTOGRAPHY

GEO-PROCESSING TRAFFIC ANALYSIS DISASTER PLANNING

DATA QUALITY NETWORK

ANALYSIS

TRANSPORTATION PLANNING Transit Network Improvement, Cubetown

4-5

Travel Characteristics of New Jersey

6 - 11

Transit Accessibility Analysis Recreational Facilities, Edison Township, New Jersey

SITE SUITABILITY ANALYSIS Recreational Facilities, Hudson County, New Jersey

13 - 15

Waste Disposal Facility, Lakewood Township, New Jersey

16 - 17

New Residential Development, Delaware Township, New Jersey

MARKET RESEARCH SUSTAINABILITY

MOBILITY

COORDINATION QUALITY

LEED

CONTRACTS

VISIONING

LAND DEVELOPMENT TRANSPORTATION PLANNING ECONOMIC DEVELOPMENT

PROJECT MANAGEMENT ENVIRONMENT

ZONING

DEMAND MANAGEMENT

REDEVELOPMENT & REHABILITATION

NEIGHBORHOOD DEVELOPMENT

SITE PLANNING & PROGRAMMING

12

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STATISTICAL ANALYSIS Geostatistical & 3D Analyst Spatial Statistics, Crime Statistics Analysis, Albany , New York

19 20 - 21

TRANSIT NETWORK IMPROVEMENT Cubetown

Academic Timeline: 2 weeks

C

ubetown is facing congestion problems at present and since expanding the road network has become politically infeasible, we have tried to improve the transit network to address these problems. We considered some changes to the Ctrans transit network and after incorporating these changes, we ran the models using Cube.

Table 1. Current Headways for Different CTrans Lines Line Type of Headways Transit 1 3 2 4 (AM Peak)

In order to ensure a better future for the transit system in Cubetown, a broad coalition of activists pushed for a quartercent sales tax on most items in the city to support the transit operations. This measure, known as Measure T, if implemented, would generate a meaningful amount of revenue to support the cause. There alternative scenarios are considered other than the Base Scenario 1: Scenario 2: Shift Buses Around Scenario 3: Add 6 new Buses and 6 drivers

Fig 1. CTrans Lines and Percentage of High Income Households (5th Income Quintile)

Fig 2. CTrans Lines and Percentage of Low Income Households (1st Income Quintile)

Scenario 4: Add 2 new Buses with drivers and 1 Rail Vehicle with 3 drivers Table 2. Current Operational Characteristics of CTrans Lines Line Round-Trip Mean Round-Trip Driver Minimum Total Length Operational Operation Recovery Time for Round-Trip (in miles) Speed Duration Time (in including Recovery (in mph) (in minutes) minutes) Time (in minutes)

Table 3. Vehicle and Personnel Needs of CTrans Lines

Table 4. Alternative Scenarios

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TRANSIT NETWORK IMPROVEMENT

Table 5. Scenario 2-Alternative Arrangement (1) of Transit (Measure T Fails) Line Number Minimum Total Theoretical New Previous of Vehicles Time for Round- Headway Headway Headway and Drivers Trip including Possible (in (in minutes) (in minutes) Recovery Time (in minutes) minutes)

Table 6. Scenario 3-Alternative Arrangement (2a) of Transit (Measure T Passes) Line Number Minimum Total Theoretical New Previous of Vehicles Time for Round- Headway Headway Headway and Drivers Trip including Possible (in (in minutes) (in minutes) Recovery Time (in minutes) minutes)

Fig 4. CTrans Lines and Percentage of Total Employment in Cubetown 160%

7000

140% 6000

Blue Line

3000

Flash

2000

Rail

1000 Total

0 Base

Alternative 1

Base Scenario

Measure T Fails

Alternative 2a

Alternative 2b

Red Line

100% Ridership Change

4000

144%

120%

Red Line

5000

No. of Trips

Table 7. Scenario 4-Alternative Arrangement (2b) of Transit (Measure T Passes) Line Number Minimum Total Theoretical New Previous of Vehicles Time for Round- Headway Headway Headway and Drivers Trip including Possible (in (in minutes) (in minutes) Recovery Time (in minutes) minutes)

Fig 5. CTrans Lines and Population Density

80%

95%

60%

Blue Line

40% 20%

28%

0% -20%

-30%

35% 10%

2%

-13% 0%

-33%

Flash -2%

-40% -60%

Rail Alternative 1 Measure T Fails

Measure T Passes

Fig 5. Scenario-wise Transit Ridership (AM Peak Hour) for CTrans Lines

46%

Alternative 2a

Alternative 2b Measure T Passes

Fig 6. Scenario-wise Percentage change in Transit Ridership (AM Peak Hour) for CTrans Lines

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5

TRAVEL CHARACTERISTICS OF NEW JERSEY Rutgers University, New Brunswick Academic Timeline: 2 weeks

Research Objective •

Study the demographics, travel characteristics, travel behavior (to a certain extent) and the relationship to land use in New Jersey In particular, residential land use, residential choice and how that influences the commuting behavior, mode choice behavior for non-work travel of individuals

•

Methodology • • •

2009 NHTS data Descriptive Statistics Spatial Analysis

Residential Densities were grouped into 5 different classes (units/sq. mile) • • • • •

Very Low: 0 -99 Low: 100 -499 Moderate: 500 -1999 High: 2000 -9999 Very High: 10000 -99999

Household family income was classified into 2 groups: < $ 40,000 p.a. -Low Income > $ 40,000 p.a. -Medium to High Income

INCOME DISTRIBUTION IN NEW JERSEY, 2011 ($'000) TOTAL

MALE

FEMALE

90000 80000

Median Household Income in 2011, NJ $71,093

70000 Population

Annual Income required to rent 0 BR Apartment statewide in NJ $39,514*

Source: 2010 Census Topological Integrated Geographic Encoding and Referencing (TIGER) files for the New Jersey transportation network.

60000 50000 40000

(U.S. Census, American Community Survey 2011 Estimates)

30000 20000 10000 0 35 - 50

50 - 65

65 - 75

75 - 100

100 & above

Income ($'000)

Source: U.S. Department of Transportation, Federal Highway Administration, 2009 National Household Travel Survey.

URL: http://nhts.ornl.gov

Source: U.S.Census Bureau 2010 Topological Integrated Geogrphic Encoding and Referencing (TIGER) files for New Jersey Transportation Network; U.S. Department of Transportation, Federal Highway Administration, 2009 National Household Travel Survey. URL: http://nhts.ornl.gov

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Transit Use v/s Residential Density TRAVEL CHARACTERISTICS OF NEW JERSEY

Results & Analysis

120%

Results & Analysis

100%

Residential Density (housing units/ sq. mile)

Time to work (mins)

Transit Use

# of Bike trip Last week

Personal VMT/day

Trip Distance

High

Very High

100 - 499 16.0

500 - 1999 8.6

2000 - 9999 5.3

10000 - 99999 N.A.

16.8 28.0

17.2 14.8

16.3 18.3

10.4 N.A.

29.5 0.0

30.7 0.3

29.4 0.1

32.3 2.0

32.8 1.4

3.4 7.2

2.0 2.8

2.3 3.0

3.8 2.6

7.4 4.7

4.3 86%

4.9 100%

3.8 95%

3.9 81%

4.8 0%

97% 0.7

96% 0.9

93% 0.7

87% 0.7

78% 0.2

1.2 0%

0.9 0%

0.8 0%

0.9 4%

0.7 100%

0% 0.3

1% 0.0

3% 0.2

2% 0.0

13% 0.0

0.5 10.5

0.5 7.8

0.4 5.0

0.4 3.6

0.0 2.5

Low

15.9 10.6

11.6 7.0

9.2 5.2

10.0 3.4

7.3 6.2

Medium - High

15.4

13.4

15.6

7.6

6.2

Low Medium - High

# of Walk trips in the past Low week Medium - High

Cars Ownership/HH Size

Moderate

18.9 15.6

Medium - High

# of Transit trips in the past Low week Medium - High

Auto Use (% of all modes)

Low

Low Medium - High Low Medium - High Low Medium - High Low Medium - High Low Medium - High

% of all modes

Distance to Work (miles)

80%

60%

Medium - High Income

40% 20% 0% Very Low

Low

Moderate

RESIDENTIAL DENSITY

High

Very High

Residential Density for Household Income 60% 50%

Relative Frequency

Travel Characteristics

Very Low HH Family Income Level 0 - 99 9.3 Low

Low Income

RESIDENTIAL DENSITY

Very Low

40%

Low 30%

Moderate High

20%

Very High 10% 0% Low Income

Medium - High Income

Source: U.S. Department of Transportation, Federal Highway Administration,5 2009 National Household Travel Survey. URL: http://nhts.ornl.gov

Source: U.S. Department of Transportation, Federal Highway Administration, 2009 National Household Travel Survey.

URL: http://nhts.ornl.gov

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TRAVEL CHARACTERISTICS OF NEW JERSEY

Source: Source: U.S.Census Bureau 2010 Topological Integrated Geogrphic Encoding and Referencing (TIGER) files for New Jersey Transportation Network; U.S. Department of Transportation, Federal Highway Administration, 2009 National Household Travel Survey. URL: http://nhts.ornl.gov

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TRAVEL CHARACTERISTICS OF NEW JERSEY

Source: Source: U.S.Census Bureau 2010 Topological Integrated Geogrphic Encoding and Referencing (TIGER) files for New Jersey Transportation Network; U.S. Department of Transportation, Federal Highway Administration, 2009 National Household Travel Survey. URL: http://nhts.ornl.gov

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TRAVEL CHARACTERISTICS OF NEW JERSEY

Source: Source: U.S.Census Bureau 2010 Topological Integrated Geogrphic Encoding and Referencing (TIGER) files for New Jersey Transportation Network; U.S. Department of Transportation, Federal Highway Administration, 2009 National Household Travel Survey. URL: http://nhts.ornl.gov

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TRAVEL CHARACTERISTICS OF NEW JERSEY

Conclusion • Similar trends to other travel behavior studies for New Jersey and others states • Built environment has considerable influence but so does other factors • Increase in density & diversity also likely to increase avg. travel time and congestion • Difficult to force people to change their preferential modes • Restricting further sprawling and providing residential mobility for low • income households with a preference for dense urban transit areas.

Source: Source: U.S.Census Bureau 2010 Topological Integrated Geogrphic Encoding and Referencing (TIGER) files for New Jersey Transportation Network; U.S. Department of Transportation, Federal Highway Administration, 2009 National Household Travel Survey. URL: http://nhts.ornl.gov

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TRANSIT ACCESSIBILITY ANALYSIS

Recreational Facilities, Edison Township, New Jersey, Fall 2015

Final Buffer Overlay Analysis

Source: Source: U.S.Census Bureau 2010 Topological Integrated Geogrphic Encoding and Referencing (TIGER) files for New Jersey Transportation Network

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SITE SUITABILITY ANALYSIS

Recreational Facilities, Hudson County, New Jersey, Fall 2016 Academic Timeline: 2 weeks

Context Increasingly open recreational facilities and green spaces are being sought for a premium by the urban dwellers due to the lack of quality accessible spaces within the city limits. Hudson County, New Jersey being one of the most densely urbanized county was chosen for a case study to determine the most suitable site for a new open recreational facility development

Existing Recreational Facilities

Methodology - Network Analyst Service Area - Coverage of existing facilities - 10 min walking distance Location Allocation - Candidate parcels that maximized walkable attendance based on population weights

Service Area for Existing Recreational Facilties

Source: New Jersey Department of Environmental Protection; URL: http://www.state.nj.us/dep New Jersey Geoographic Information Network; URL: https://njgin.state.nj.us U.S. Decinial Census; URL: https://www.census.gov

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SITE SUITABILITY ANALYSIS

Recreational Facilities, Hudson County, New Jersey Methodology

Methodology

- Spatial Analyst A circular area with radius of 1500' was selected for each candidate sites on the basis of the Location Allocation results to further analyze the parcel charateristics and 3 analysis areas were chosen for environmental suitability.

- Spatial Statistics Used to analyze the clustering and distributions of Class of Property, Value & Size of parcels

Allocated Site Analysis Areas Location Allocation of New Candidate Sites

Site Analysis Areas

Site Number 4 26 48 & 56 49 & 57 54 55 & 60 58

Weights

Value Rank 7 5 1 2 3 6 4 25%

Ranking Residential Demand Vacant Area Weighted Rank Rank Rank Rank Rank 7 2 5 3 1 6 4 25%

5 7 3 2 6 1 4 25%

1 5 3 4 7 2 6 15%

1 2 6 7 4 3 5 10%

5 4.45 3.3 3.05 3.95 3.85 4.4 100%

Source: New Jersey Department of Environmental Protection; URL: http://www.state.nj.us/dep New Jersey Geoographic Information Network; URL: https://njgin.state.nj.us U.S. Decinial Census; URL: https://www.census.gov

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SITE SUITABILITY ANALYSIS

Recreational Facilities, Hudson County, New Jersey Methodology Weighted Ranking - All the candidate areas ranked first on the basis of invididual variables analyzed and then under a combined rank where Population, % of Residential parcels and Value were each weighted 25%.

Suitable Facilitiy locations within Site Area 55 & 60

Suitable Facilitiy locations within Site Area 48 & 56

Recommended Area

- Environmental Suitability Four factor analysis - Proximity to Chromate contamination sites, - Proximity to Wetlands, - Vacancy of parcel, - Proximity to residential population demand Weighted Overlay function was performed with proximity to residential demand as the highest weight.

Suitable Facilitiy locations within Site Area 49

Parcel Statistics Mean Net Mean Site Population Parcel Value of Number Weight Parcels Area (SF) 4 $ 391,097 2084 99466 26 $ 236,294 1814 6387 48 & 56 $ 81,818 31586 3020 49 & 57 $ 105,915 70784 2921 54 $ 108,146 2062 4081 55 & 60 $ 245,625 100541 4237 58 $ 170,551 6245 3847 Limitation - In-depth analysis could be performed in future with higher number of factors, which was out of scope within the time avialble.

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SITE SUITABILITY ANALYSIS

Waste Disposal Facility, Lakewood Township, New Jersey, Fall 2016 Academic Timeline: 1 week

Lakewood Township Location

Distance to Wetlands

Land use 2012 Raster

Environmental Suitability Four Factor Analysis was carried out to find suitable sites for a new Waste Disposal Facility, based on the following factors: 1. Non - residential land use currently 2. Not in the floodprone zone

0

0.5

1

2

3

Miles

0

0.5

1

2

3

Miles

3. Within 1 mile of a major road 4. Not within 1000 feet of a wetland or water body

Source: New Jersey Department of Environmental Protection; URL: http://www.state.nj.us/dep New Jersey Geoographic Information Network; URL: https://njgin.state.nj.us

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SITE SUITABILITY ANALYSIS

Waste Disposal Facility, Lakewood Township, New Jersey, Fall 2016

Distance to Wetlands

Flood Prone Areas

Suitable Sites

(in ft)

0

0.5

1

2

3

Miles

0

0.5

1

2

3

Miles

0

0.5

1

2

3

Miles

Source: New Jersey Department of Environmental Protection; URL: http://www.state.nj.us/dep New Jersey Geoographic Information Network; URL: https://njgin.state.nj.us

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SITE SUITABILITY ANALYSIS

New Residential Development, Delaware Township, New Jersey, Fall 2016 Academic Timeline: 1 week

Context Based on the Goals and Objective provided by the Township of Delaware, a suitability analysis was performed for new residential land use as a part of the Comprehensive Plan for the Township

GOAL 1: Protect all agricultural land

GOAL 2: Protect evironmental quality

Protect existing agricultural land

Influe

nce

Multiple Utility Assignment 1

Containment of rural residential

Preserve open space & Natural Heritage sites Preserve the wetlands

nce

Influe

nce

30%

High/ Medium Density development Away from Airports, Industry, Contaminated Sites

30%

Multiple Utility Assignment 2

nce

Influe

Influe

nce

30%

Final Multiple Utility Assignment

40%

40%

Multiple Utility Assignment 3

nce

Influe

Influence 50%

30%

Influe

Preserve Steep Slopes

GOAL 3: Consolidate the new residential development

70%

Influence 20%

60%

Model Builder ArcMAP 10.4

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GEOSTATISTICAL & 3D ANALYST

Based on the data set from the U.S. Census via the New Jersey State Data Center website a point dataset was used to interpolate the poverty rate across the state by the help fo the Geostatistical Analyst Extension of the ArcGIS suite.

3D Map of Percentage Population below Poverty Line

Both these maps show slightly different variations of the predicted poverty or income rate distribution from the same point data set The first map was created using an Emperical Bayesian kriging method, while the second one was created using Radial Basis Function.

Source: U.S. Decinial Census; URL: https://www.census. gov New Jersey State Data Center: URL: http://lwd.dol. state.nj.us/labor/lpa/content/njsdc_index.html

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SPATIAL STATISTICS

Crime Statistics Analysis, Albany, New York, Fall 2016

)Poverty(

Source: Division of Criminal Justice Services, New York State; URL: http://www.criminaljustice.ny.gov/crimnet/ ojsa/stats.htm

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SPATIAL STATISTICS

Crime Statistics Analysis, Albany, New York

HotSpots

Source: Division of Criminal Justice Services, New York State; URL: http://www.criminaljustice.ny.gov/crimnet/ ojsa/stats.htm

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Photography by Himadri Shekhar Kundu

Himadri Shekhar Kundu New Brunswick, NJ, USA (732) 948-3243

https://www.linkedin.com/in/hskundu

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