Tour-based Travel Mode Choice Estimation based on

Data Mining and Fuzzy Techniques

Nagesh Shukla, Jun Ma, Rohan Wickramasuriya, Nam Huynh, Pascal Perez

Presented by:

Pascal PerezResearch Directorperez@uow.edu.au

Classification of literature in mode choice

Data Type

Trip Type

Discrete Choice Models Machine Learning

Crisp Data Crisp & Fuzzy Data Crisp DataCrisp & Fuzzy Data

Independent Trips

Gaudry, (1980); McFadden (1973); Daly & Zachary

(1979); Hensher & Ton (2000)

Dell'Orco et al. (2007)

Xie et al. 2003; Reggiani & Tritapepe 1998; Cantarella et al.,

2003; Shmueli et al. 1996; Edara 2003;

Hensher and Ton, 2000

Yaldi, G. (2005)

Linked Individual Trips (tour-based) Miller et al. (2005) - Biagioni et al., (2008) This

Study

Linked Household Trips Miller et al. (2005) - Future Work Future

Work

Machine learning methods

Input Layer Hidden Layer Output Layer

Back-propagation algorithms for ANN training - Scaled conjugate gradient (Moller 1993) -Levenberg-Marquardt optimization (Hagan and Menhaj, 1994)

(http://iasri.res.in/ebook/win_school_aa/notes/Decision_tree.pdf)

Decision trees - are easy to assimilate by humans thanks to their intuitive representation- do not require too much parameter settings - can be constructed fairly fast and its accuracy is comparable to other classification models.

DT algorithms such as C4.5 and Classification and Regression Technique (CART) have been identified as top 10 data mining algorithms in terms of its wider applicability.

• 3000-3500 household participants each year. Dataset covers 5 years.

• 14 variables include– Day of the week– Household type– Occupancy– Number of vehicles– Household income– Number of people holding a valid licence– Number of students– Working at home– Total number of residents– Trip time *– Trip purpose– Road distance travelled– Departure time *– Travel mode

Case study - HTS data for Sydney GMA

(http://www.bts.nsw.gov.au/Images/UserUploadedImages/86/hts-gma-map.jpg)

Data pre-processing

• Linking consecutive trips of an individual

Let (X,Y) be a survey dataset of trips made by L travellers, where (xlm,ylm) collectively represents information of the mth trip made by the traveller l, m Є {1, 2, ..., Ml}, l Є {1, 2, ..., L}.

is a collection of explanatory variables and ylm is the travel mode of the mth trip made by the traveller l.

To account for impact of consecutive trips, a new explanatory variable representing the mode of the previous trip is defined as

,

Data preprocessing (cont.)• Fuzzifying explanatory variables departure time

Four fuzzy sets of departure are defined, “2 hour am peak (7-9am), 6 hour inter-peak (9am-3pm), 3 hour evening peak (3-6pm) and the remaining evening/night period” (Sydney Strategic Travel Model – Modelling future travel patterns, February 2011 Release, Technical Documentation)

Data preprocessing (cont.)• Fuzzifying explanatory variables household income

Household income in survey data, ranging from AU$5006 to AU$402741, is classified into three fuzzy sets ‘low income’, ‘middle income’, and ‘high income’.

Low income: “Persons in the second and third income deciles”Middle income: “Persons in the middle income quintile”High income: “Persons in the top income quintile”

(Australian Bureau of Statistics – Household Income and Income Distribution, 6523.0, 2011-2012)

ExperimentsExperiment 1 (Base) Experiment 2 (Fuzzy variables) Experiment 3 (linked trip) Experiment 4 (Fuzzy variables and linked trips)

Day of the week Day of the week Day of the week Day of the week

Household type Household type Household type Household type

Occupancy Occupancy Occupancy Occupancy

Number of vehicles Number of vehicles Number of vehicles Number of vehicles

Household income Fuzzy household income Household income Fuzzy household income

Number of licences Number of licences Number of licences Number of licences

Number of students Number of students Number of students Number of students

Working at home Working at home Working at home Working at home

Number of residents Number of residents Number of residents Number of residents

Trip time Trip time Trip time Trip time

Trip purpose Trip purpose Trip purpose Trip purpose

Road distance travelled Road distance travelled Road distance travelled Road distance travelled

Departure time Fuzzy departure time Departure time Fuzzy departure time

Previous trip mode Previous trip mode

Results

Experiment Empirical Settings PCI (%)

Fuzzy sets Dependent trip DT ANN

1 No No 64.71 68.1

2 Yes No 67.67 68.7

3 No Yes 85.63 85.9

4 Yes Yes 86.17 86.8

Travel Modes HTS data DT Prediction ANN Prediction

Car_driver 40.95% 43.50% 43.11%

Car_passenger 20.65% 30.76% 19.05%

Public_transport 8.37% 7.54% 7.74%

Walk 29.26% 17.68% 29.55%

Bicycle 0.77% 0.53% 0.53%

Household travel survey data is partitioned into three subsets, a training dataset (30%), a testing dataset (35%) and a validation dataset (35%).

Conclusions• New methodology for travel mode choice using artificial

neural network and decision trees.• The methodology considers

– Expert judgements by using fuzzy sets instead of crisp data for some explanatory variables.

– Tour based model that accounts for the dependency of modes between trips

• Travel mode prediction using fuzzified explanatory variables combined with tour based model proved to out-perform predictions using crisp variables.

• Future work could involve more explanatory variables, new fuzzy sets, and account for dependencies between trips of individuals in the same household.

Questions