incorporating social influence into hybrid choice models

Urban Transport & Energy Lab

24 November 2016Choice Modelling Centre, Institute for Transport Studies, University of Leeds

Incorporating Social Influence into Hybrid Choice ModelsMaria Kamargianni, Lecturer in Transport & Energy,

Head of the Urban Transport & Energy Group

Background Several factors affect mobility choices

The decision maker, the social environment and the information The hypothesis is based on the latest findings of neuroscience and the biobehavioural sciences stating that:

Individuals’ decisions are indirectly influenced by their social environment, as it affects their psychological state (van den Bos et al., 2013;

Homberg, 2012); Individuals filter the information they receive

from their social environment, so that the latter shapes their opinions towards them (Hochbaum, 1954; Hegselmann and Krause, 2002).

Modeling framework

Social influence is one more latent variable that represents the social interaction between the decision maker and her/his social environment.

It is added into the latent variable regarding the decision maker.

University of the Aegean, Department of Shipping, Trade & Transport

5

Equations

23 December 2013

Structural Model:

Social environment:

Decision maker:

Utility:

Measurement model:

Choice model: Choice probability:

Likelihood function:

THE CASE STUDY

Case StudyThe hypothesis is: - If a teenager anticipates that his/her parents and friends have “Walking-lover” behaviours, then these increase the probability that he/she too will have a “Walking-lover” attitude and in turn choose to walk to school.

Data Collection: 2012Sample: 9,704 individuals aged between 11-18 years oldHold-out sample: 20%

Modeling framework

Specification of the Model Social environment:

Decision maker:

Likelihood function

Parents Friends

Choice model:

Indicators of Latent Variables

Mod

elin

g fra

mew

ork

Mode Choice Model• Distance is one of the most significant

factors in the choice of walk;• Walking is preferred when the

distance is less than 2.0km;• Even if the distance is less than

2.0km, females do not prefer walking;• For distances between 2.0 and 5.0km

escorted by private motorized vehicle is preferred;

• Wide sidewalks encourages the decision to walk;

• Existence of trees and flowers favors the choice of walking significantly;

• The latent variable encourages the choice of walking to school.

Structural Models’ Estimation Results• PWL and FWL are the

most statistically significant variables in the structural equation of WL.

• Close friends seem to affect more WL than parents.

• When decision maker anticipates that their parents and friends love to walk, it has a positive effect on their own attitude towards walking.

Findings• A more real-world behavioural representation that includes the

social interaction effect. • HCM with social influence displays choice probabilities closer to

the actual choices.• The data required to apply this methodology is easy to collect:

attitudinal questions regarding the travel behavior of the social environment.

• Strong influence of friends and parents (social environment) on the development of their children’s attitudes towards walking.

Next steps: London Mobility Survey• Step 1: Create your account at https://london.fmsensing.com

• Step 2: Answer the pre-questionnaire survey Most of the questions have been taken from LTDS questionnaire Extra questions have been added to customise it to the purpose of our

study:– Car-Sharing– Parking– Journey planners

• Step 3: Download the FM Sensing app and start tracking your activities for one week (activity diary)

• Step 4: After a week of tracking and validating your activities, go to the post survey page to check your statistics and tell us your opinion about a new mobility service for London

Open APIs have been linked to the back-end of the system to enhance predictions, record more information automatically and reduce response burden.

Google Places API (transit stations locations)

TfL StopPoint API(transit stations IDs needed for

fare calculation)

TfL Fares API(fares for tube and rail based on

zone and time of the day)

Oyster Zones(shapefiles for pricing zones)

TfL Capping Rates(shapefiles for pricing zones) In

real

tim

e fo

r eac

h tri

p

Google Polyline Decoder (determine trace segments

leading up to each stop)

London Mobility Survey

Information from Open APIs is linked to each tripStatus of the PT services API

Disruptions API

TfL Embarkation Points’ Facilities API

Car Parks Occupancy API

Bike Points & Cycle Hire API

TfL Traffic Cameras API

for e

ach

trip

Weather API

Twitter API

National Rail API

Uber & Google API

London Mobility Survey

London Mobility Survey• Example of trips verification

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Thank you!

Maria Kamargiannim.kamargianni@ucl.ac.uk

• Kamargianni, M. (2014). "Development of Hybrid Models of Teenagers' Travel Behavior to School and to After-School Activities." University of the Aegean.

• Kamargianni, M., M. Ben-Akiva, and A. Polydoropoulou (2014). Incorporating Social Interaction into Hybrid Choice Models. Transportation, Vol.41(6), pp. 1263-1285.

• Kamargianni, M., S. Dubey, A. Polydoropoulou, and C. Bhat (2015). Investigating the subjective and objective factors influencing teenagers’ school travel mode choice – An integrated choice and latent variable model. Transportation Research Part A: Policy and Practice, Vol.78, 473–488.

Model Validation

Actual Share MNL HCM HCM with social interaction

Car 49% 53% 47% 44%

Bus 35% 28% 31% 32%

Walk 16% 19% 25% 19%

• 80% of the sample was used for model estimation• 20% is used for model validation