Mode Detection, Age Pattern Transition, andits Consequences on Carbon Emissions
Team 5
Deepank Verma (D3)
Neenu Thomas (D2)Omkar Deepak Karmarkar (D1)
INDIAN INSTITUTE OF TECHNOLOGY BOMBAY, INDIABACKGROUND: MODE CHOICE DETERMINANTS
Trip start time
Lesser number of Private mode

PurposePublic modes for majority of work

users during the working hours.

trips

Walk and bicycle least preferred

Private modes majorly for leisurefor late night trips.

trips.

GenderWalk and bicycle more preferredmode by female.
Public and private modes
more preferred mode by male.

.

Travel time
39.17% & 25.47% of short trips(less than 30 minutes travel) are madeon foot & by Car respectively.

Above 60% of long trips are madeon train.

Trip end timeAge

Private mode for late night trips

Most preferred mode

Walk and bicycle least preferredChildren aged less than 15 years - Walk
Working aged people – Public transport
Elderly - Private mode

for late night trips.

Data Analysis• PT Data

Objective 1 : Comparing
Mode: 1461514
Detection using• Total entries• After cleaning: 1441493.
Traditional and Machine
Learningtechniques.

• Data random split: 85:15, Training set
Predictor Variables entries: 1225269, Test set entries:216224
Purpose (5 categories)
Gender (2 categories)• PP Data
Age (17 categories)• Total entries : 1522
Start Time
• After cleaning : 1502
End Time• Data random split: 70:30, Training set
Travel Timeentries: 1051, Test set entries: 451
Target Variable• Target Variable (same)
Mode (4 categories)• Predictor Variables (same)MULTINOMIAL LOGIT MODEL (MNL MODEL)
Testing Set: 216224 nos.

• The most widely used mathematical form for choiceprobabilities in behavioural travel demand analyses

Totalsamples

Key Strengths:72885

Key Weaknesses:• Independence of Irrelevant Alternatives (IIA) property• No correlation between error terms (i.i.d. errors)• Random taste variation can not be represented,

63074

Actual• The MNL model is simple to perform• Computationally efficient• Permits a simple behavioural interpretation of its
parameters

Predicted

31675

48590

Log-Likelihood: -1464800
McFadden R2: 0.24345
Likelihood ratio: 942710

• A decision tree is a decision support tool that uses atree-like graph or model of decisions.
• RFs train each tree independently, using a random
sample of the data.

Key Strengths:• RF is much easier to tune. Only two hyperparameters (a)depth of trees (6) and (b) number of estimators (25).
• More robust than a single decision tree, and less likely toover fit on the training data.
• Does not require preparation of the input data.
• Works with unscaled data and missing values.
• Provides information on Feature importance.

Key Weaknesses:• For data including categorical variables with different
number of levels, RFs are biased against attributes withmore levels.

Age_50-54
Age_85 more
Age_70-74
Age_65-69
Age_35-39
Age_80-84
Age_25-29
Age_45-49
Age_55-59
Age_60-64
Age_30-34
Age_75-79
Age_40-44
Age_20-24
Purpose_LeisurePurpose_Shopping
Purpose_Ret. Home
Purpose_Others
Age_15-19
MaleFemale
Purpose_Work
Age_5-9
Age_10-14
StartTimeHr
EndTimeHr
TravelTimeMin

Predicted

72885

63074

ActualRandom Forests (RF)Testing Set: 216224 nos.

Totalsamples

3167548590

RF00.1

0.2

0.3

0.4

0.5

0.6

Extreme Gradient Boosting (XGB)Key Strength:• It performs the optimization which makes the use ofcustom loss functions much easier.
• Boosting focuses on unbalanced datasets bystrengthening the impact of the positive class.
• Provides information on Feature importance.

Key Weaknesses:• Training generally takes longer because of the factthat trees are built sequentially.
• XGB is harder to tune than RF. Three parameters totune: (a) number of estimators (100), (b) depth of trees(6) and (c) Learning rate (0.1)

FemaleAge_50-54
Age_55-59
Age_45-49
Age_30-34
Age_40-44
Age_60-64
Age_85 more
Age_35-39
Age_25-29
Age_80-84
Age_65-69
Age_20-24
Age_75-79
Age_70-74
Age_15-19
Age_10-14
Purpose_Others
Purpose_Shopping
Age_5-9
Purpose_LeisurePurpose_Ret.…
Purpose_Work
Male
StartTimeHr
EndTimeHr
TravelTimeMin

Predicted

72885

63074

Actual• XGB build trees one at a time, where each new tree
helps to correct errors made by previously trainedtree.

Testing Set: 216224 nos.

Totalsamples

31675

48590XGB
00.05

0.1

0.15

0.20.25

Artificial Neural Network (ANN)

Totalsamples

Testing Set: 216224 nos.

Predicted• ANN is based on a collection of connected units (nodes) called artificialneurons, which loosely model the neurons in a biological brain.

63074

Key Strengths:• Ability to learn and model non-linear and complex relationships.
• Efficiently processes large amount of training samples.

Key Weakness:••••

Hardware dependence
Unexplained behavior of the Model.
Comparatively sub-standard performance in smaller datasets.
Large number of hyperparameters to tune. (a) No. of Hidden Layers (3), (b)Activation functions(Relu, Relu, Softmax), (c) Loss functions (Crossentropy), (d) Dropout rate (0.8), (e) Optimizer (Adam), (f) Learning rate
(0.01).

31675

48590

Actual

72885

Evaluation Metrics
PT data• Overall Accuracy: The ratio of a total number of correctly identified
pixels to the total number of considered pixels.
• The overall accuracy metrics is influenced by unbalanced andprominent classes.
• F1-score: is a harmonic mean of Precision and Recall.
• Precision is the proportion of positive detections of theclassifier which were actually correct,• Recall refers to the proportion of actual positives which weredetected correctly.
• Kappa index of Agreement is used in assessing the performance ofdifferent models.

• The Kappa value (k) of model classifier suggests that the classifier is
k*100 percent better than random assignment of classes.

ClassesPublicPrivateBicycle
Walk

MNL
0.77
0.46
0.03
0.52

F1-score
RFXGB
0.79
0.79
0.5
0.52
0.02
0.3
0.51
0.58

Ov. Acc.
Kappa

54.72
0.368

57.030.388

ANN
0.79
0.51
0.34
0.59

60.71
0.451

60.44
0.45

ANN
0.69
0.55
0.52
0.48

64.75
0.499

58.76
0.406

PP dataClassesPublicPrivateBicycle
Walk

MNL
0.66
0.53
0.46
0.49

F1-score
RFXGB
0.76
0.72
0.63
0.66
0.52
0.57
0.49
0.51

Ov. Acc.
Kappa

56.09
0.379

64.52
0.491

Potential Use Cases
Real-time congestion estimation/pricing : By the useof mode detection models trained on telemetry datafrom various smartphone applications.
Advertising: Push Notifications influencing future mode
choice behavior by cab aggregators based on trainedmodel.
Health: Evaluation of exposure to pollution by estimating
the choice of mode.

Objective 2 : Analyzing the Mode Patternconsidering the Shifting Age Composition

10AGE PATTERN AND MODE CHOICEOBSERVATIONSHigher share of on-road motorised modes by elderly people

Mode use share
100%
90%
80%

70%

Increasing traffic volume of on-road vehicles
Increasing carbon share by elderly
Comfort and convenience of elderly people

60%

50%
40%
30%
20%

Measures to reduce carbon share by transportation
Measures to ease the travel for elderly10%0%0-14
RailBus

15-64
Car

Above 65

BicycleWalk

MODE USE AND CHANGING EMISSION LEVEL
Age wise share of different modes

Carbon Emission by Transport Modes

100%
90%
80%
70%

Hayashiya, H. Urban Rail Transit (2017) 3: 183.
https://doi.org/10.1007/s40864-017-0070-4

60%

50%
40%
30%

20%

Source: Hayashiya, H. Urban Rail Transit (2017) 3: 183. https://doi.org/10.1007/s40864-017-0070-4

10%

Age-wise Share of Carbon Emission through Transport Modes0%
Rail0-14

BusCar Bicycle Walk

15-64

Above 65

2010

5.70%

67.7
10%

20%

30%

40%26.6
50%

60%

70%

80%

90%

100%

0-1415-64
Above 65CARBON EMISSION THROUGH TRANSPORT MODES
Scenario 1Changing Age Pattern

a. Changing Population trend
b. Same Mode patternc. Same Mode based emission per PKM
35.7

33.2

29.6

80

27.8

22.5

90

17.4

100

70

a. Changing Population trend
b. Same Mode pattern
c. Changing Mode based emission per PKM(Reference: Decadal average)
53.6

55.8

59.2

60

40

64.1

68.1

60
50
30

14.6

13.4

12.2

11.3

1110.8

2000

2010

2020

2030

2040

2050

20100Age Group
0-1415-64

Scenario 2Above 65

Source: National Institute of Population and Social Security ResearchScenario 3

a. Changing Population trend
b. Same Mode pattern
c. Changing emission per PKM in car whileemission per PKM in other mode are constant

Scenario 4

a. Changing Population trend
b. Shifting Car users to Public transport (Bus)c. Changing Mode based Emission per PKM

CARBON EMISSION THROUGH TRANSPORT MODES
Scenario 1

a. Changing Population trend
b. Same Mode patternc. Same Mode based emission per PKM

a. Changing Population trend
b. Shifting Car users to Public transport (Bus)c. Changing Mode based Emission per PKM

Scenario 1

1040

Scenario
2Age GroupAbove
Scenario
3 65
15-64
Scenario
0-144

520
00 2010

2020

2030

2040

2050

2010
2020
2030
2040
2050

Scenario 4

60
15

2010
2020
2030
2040
2050

a. Changing Population trend
b. Same Mode pattern
c. Changing emission per PKM in car whileemission per PKM in other mode are constant

80
20

2010
2020
2030
2040
2050

Scenario 3(in billions)

2010
2020
2030
2040
2050

a. Changing Population trend
b. Same Mode pattern
c. Changing Mode based emission per PKM(Reference: Decadal average)

Carbon Emission
throughTransport
Scenario
2Scenario
3 Modes
Scenario 4Scenario 110025sharePercentage(g-CO2 /passenger-km)
CO2 Emission

Scenario 2Age-wise Share of Carbon Emission through Transport ModesFLEXIBLE TRANSPORT SERVICES FOR EASE OF TRANSPORT FOR ELDERLY PEOPLE
aa

FTS SYSTEMSb

Provide flexible transportation
service (FTS) for elderly people inthe city to access the metro
station to improve their mobility.

dMore detailed study of theecf

Route deviation
Fixed schedule and direction
Point deviation
No defined path or schedule, demand
responsive and deviates routes
between two points
Demand responsive connectors
Fixed route connectors
dRequest stops
Defined path and schedule
Flexible route segmentsfFixed schedule and path
Zone route
One or more end points decided bypassengers
Route terminus
Transfer pointbce

Scheduled bus stop
Bus stop served by request only

SCOPE & LIMITATIONS
Data
MODE PREDICTION

Sensitivity Analysis

Alternative specific variable
like cost and travel time notprovided for the PT data.

Scenario Analysis

MODE DETECTION

Age Pattern Transition

Discrete choice model VS Machine Learning
Multiple DCM

Use of MNL