Team Akhil Johnson (M2) Melna Jose (M2) Shanaraj V.K. (M2) Omkar Deepak Karmarkar (D2) DIAN INSTITUTE OF TECHNOLOGY BOMBAY, INDIA
•  Socio – demographic variables: Age, Gender • Women walk and bicycle more while men use car more • Car usage increases with age • Trip related variables: start time, purpose, travel time • Large work trips in morning while shopping trips are after that • Shopping and leisure trips are of shorter durations and dominated by specific modes •  Activity variable: Stay time • What role does tours' attributes play?
• A total of 1.46 Million data points (trips) •  More than 511,000 unique ‘trip chains’ in the data ■  Corresponds to set of trips per person in a day (3 AM to 11:59 PM) • Variable details: ■ Socio - economic variables: Age (groups of 5 years) and Gender . Trip related variables: Purpose, start time, end time, mode ■ Activity characteristic: Stay time at t rip destination
•  Trip chains - unique numbers correspond to series of trips performed by a person in a day • Are all of them a single tour? - Not necessary • How are we defining a tour then? Time Place 9:00:0 AM Home 9:15:00 AM Shop 1 9:30:00 AM Shop 9:45:00 AM Home 10:00:0 AM Home 10:30:00 AM Work 2:00:0 PM Work 2:10:00 PM Lunch 2:40:0 PM Lunch 3:00:0 PM Work 7:00:0 PM Work 7:20:00 PM Shop 2 7:40:00 PM Shop 2 7:50:00 PM Home 8:15:00 PM Home 8:30:0 PM Dinner 9:15:00 PM Dinner 9:30:00 PM Home
1. Tour type - Primary tour or secondary tour 2. Stop pattern - tour without stop v/s with one stop v/s multiple stops 3. Tour's main purpose - work or shopping or socializing and leisure 4. Trip in the tour - main trip v/s return trip v/s intermediate trip Trip characteristics: 5・ •  Start time - morning peak v/s evening peak v/s off peak •  Intermediate trip purpose - shopping or leisure
The most widely used mathematical form for choice probabilities in behavioural . travel demand analyses • Key Strengths: 。 Simple to perform 。 Computationally efficient Permits a simple behavioural interpretation of its parameters . • Key Weaknesses: . Independence of Irrelevant Alternatives (IIA) property . No correlation between error terms (i.i.d. errors) Random taste variation can not be represented .

Public Primary (main trip) Bicycle Walk Secondary
. A decision tree is a decision support tool that uses a tree-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 (10) and (b) number of estimators (20). .
 More robust than a single decision tree, and less likely to over 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 with more levels. .

。 XGB build trees one at a time, where each new tree helps to correct errors made by previously trained tree. • Key Strength: . It performs the optimization which makes the use of custom loss functions much easier. .
 。 Boosting focuses on unbalanced datasets by strengthening the impact of the positive class. Provides information on Feature importance. • Key Weaknesses: . Training generally takes longer because of the fact that trees are built sequentially. XGB is harder to tune than RF. .
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• Potential use • primary mode detection and future mode change prediction for same tour • Usage of home check - in for mode prediction through time of the day and purpose • Baseline framework for activity based model (can be enhanced with companionship and temporal data)
• Policies
• Promoting multiple trips per hour – reduced carbon emission • Decrement of exclusive shopping tours in future No. of trips per tour