Edward Robson

Research Centre for Integrated Transport Innovation (rCITI)

University of New South Wales

Supervisor: Dr Upali Vandebona

Consumer Benefit Model forDeveloping Public Transport Systems

• What happens when we want to improve the transport network?

• Is there a more efficient way to do this?

2/14

Introduction

Set of options

Transport modelling

Economic analysis

Development of final option

• Aim: to develop a model to integrate the economic evaluation of a transport network alteration with a transport demand model

• Purpose: to perform rapid appraisals of consumer benefit for transport network design

• Case study: evaluation of a metro network proposal for Sydney

3/14

Details of the study

• Who is affected by the transport network?• Consumers• Suppliers• Community

• Consumer surplus:

The transport market

4/14

P (price)

D (demand)

D (P)

P0

CS

0

Pm

P (price)

D (demand)

D (P)

P0

CS

0

Pm

P1

ΔCS

• What do consumers prefer?• Lower fares• Shorter travel time – in-vehicle time, waiting time,

access time• Better comfort etc.

• Price of transport generalised cost of transport (GC):

• To calculate consumer surplus at an aggregate level, we need:

• A demand model• Generalised costs across the network

5/14

Modelling consumer benefit

• Demand for each mode in each origin-destination pair can be predicted with the multinomial logit model:

• Consumer surplus is calculated using the logsum:

• Linear approximation (rule-of-a-half) is most common

6/14

Modelling transport demand

• For each origin-destination pair in the network:1. Measure generalised costs for each mode before the

network change

2. Measure generalised costs for each mode after the network change

3. Calibrate scale factor μ

4. Measure change in consumer surplus using the logsum

• Sum the changes in consumer surplus for every origin-destination pair

• Key assumption:• Origins and destinations of trips do not change

7/14

The final model

8

413 O-D pairs48,966 people

• Aim: to calculate the change in consumer surplus, per AM commute period, from introducing a metro network to Sydney

• Maroubra to Drummoyne; Strathfield to St Leonards

• 1km study area radius surrounding each station

• Analysis:• Base case analysis using logsum

• Sensitivity tests of metro network form; metro network service; generalised cost parameters; analytical procedure

• Logsum results compared with rule-of-a-half

9/14

Case study

10/14

Methodology

Generalised costs of existing network

Generalised costs of network with

metro

• Public transport: measured using Google Maps

• Other modes: back-calculated using Journey to Work data

• Public transport: metro, as modelled on Hong Kong network

• Other modes: kept identical to previous

• Scale factor calibration and consumer surplus calculation implemented with Microsoft Excel macro

LogsumΔCS#

Rule-of-a-half ΔCS#

Increase in public

transport usage

Base case* $63,997 $63,401 30.60%

Increase VOT by 100%

$127,997 $126,803 30.60%

Set μ = 0.1 $56,443 $56,414 9.00%

Set μ = 1 $77,636 $72,446 57.30%

11/14

Results

* Average μ in base case was 0.336

# Per morning commute period

• Rule-of-a-half results were around 1% lower than logsum results

• Sensitivity tests of network parameters behaved predictably

• Scale factor has a large influence – logsum results appear to converge towards rule-of-a-half results as the scale factor approaches 0+

• A number of simplifying assumptions were required to calculate consumer surplus with the available data, e.g. waiting times

• Accuracy could be improved by using more detailed models for generalised cost parameters

12/14

Discussion

• Can measure the consumer surplus of any changes to the network, as long as they are reflected in generalised costs, e.g.

• Fares• Travel time• Frequency• New routes

13/14

Uses of the model

• Final model rapidly calculates consumer surplus from a transport network change, using an integrated transport model

• Only requires a small amount of data and accounts for changes in consumer surplus across all modes, but

• Calculations increase exponentially as nodes increase

• Further research:• To devise an integrated economic and transport model

that can account for community benefits, including wider economic benefits

14/14

Conclusions and further research

Questions?