THE HETEROGENEOUS PERCEPTION OF TRANSIT SERVICE … · THE HETEROGENEOUS PERCEPTION OF TRANSIT...

THE HETEROGENEOUS PERCEPTION

OF TRANSIT SERVICE QUALITY

by Laura Eboli and Gabriella Mazzulla

Scientific Seminar SIDT 2011:

“ENERGY, ENVIRONMENT AND

INNOVATION IN SUSTAINABLE

TRANSPORT SYSTEMS"

http://www.sidt.it/

http://www.sidt.it/

Private car and public transport

Demand distribution among transport modes - 2000-2008

Percentages

2000 2001 2002 2003 2004 2005 2006 2007 2008

Walk or bike 25,1 26,9 26,8 25,7 26,0 24,3 22,0 20,5 21,0

Motorized modes 74,9 73,1 73,2 74,3 74,0 75,7 78,0 79,5 79,0

Motorcycle 7,2 7,8 6,1 6,9 7,3 6,2 5,3 5,6 5,7

Private car 80,0 79,3 79,7 79,8 79,0 81,4 83,2 82,3 81,4

Public transport 12,8 12,9 14,2 13,3 13,7 12,4 11,5 12,1 12,9

Total 100,0 100,0 100,0 100,0 100,0 100,0 100,0 100,0 100,0

Source: Isfort (Istituto Superiore di Formazione e Ricerca per i Trasporti, Observatory “Audimob” about mobility.

Impacts on the environment

Road accidents

Noise pollution

Air pollution

Traffic congestion

Transit service quality

Overcrowded bus

Long waiting times at bus stops

Old vehicles

Service quality attributes

Service quality aspect Description Determinants

Service performanceBasic coverage of the service by geography, time, and

mode

path, number of bus stops and distance between bus stops,

location of the bus stops, service frequency, daily service time,

travel time, need for transfers

Service reliability Ability of the transit system to adhere schedulingpunctuality of runs that come on time, regularity of runs that come

on schedule, wait time when transferring

ComfortPhysical comfort regarding vehicles and ambient

conditions

availability of seating on board, smoothness of the bus ride,

comfort of the seats, degree of crowding on bus, comfortable

temperatures on bus, availability of handrails/grab bars,

availability of shelter, benches and illumination at stops

Cleanliness Physical condition of vehicles and facilitiescleanliness of the bus interior, cleanliness of the bus exterior, buses

clean of graffiti, cleanliness of seating, shelters clean of graffiti

Safety

Degree of safety from crime or accidents and the feeling

of security resulting from that and other psychological

factors

safety from crime while riding, safety from crimes at bus stops,

safety related to the behaviour of other persons, safety related to

the bus operation, safety from accidents

FareCharacteristics regarding the monetary cost of journey

by bus

cost of a one-way ride, cost of a transfer, availability of discounted

fares (e.g. students), availability of volume discounts (e.g. monthly

passes), cost of parking at bus stops

InformationAvailability of information pertinent to the planning

and execution of a journey

availability of schedule/maps and announcements on bus,

availability of schedule/maps at bus stops, availability of printed

schedules, availability of information by telephone or internet

Customer careElements needed to make the journey easier and more

pleasant

courtesy/helpfulness of ticket agents, courtesy/knowledge of

conductors/drivers, personnel appearance, ease of purchasing

tickets, ease of paying fare

Environmental impactsEffects on the environment resulting from transit in

terms of pollution, infrastructure and natural resources

emissions, noise, visual pollution, vibration, dust and dirt, odour,

waste, effect of vibrations on road, capacity demand, disruption,

energy, space

Transit service quality

The reasons of

heterogeneity

in passengers’

perceptions

Different attitudes of the users towards the

use of transit services

Qualitative nature of some service aspects

User social background

Passengers’ heterogeneity

Discrete choice models

jjnInformatio

jFarejsCleanlinesjCrowding

jStopjyReliabilitjFrequencyj

nInformatio

FaresCleanlinesCrowding

StopyReliabilitFrequencyU

)

(

jnnj XXXU )...( 2211

Service quality measure:

General utility function:

Different ways

for accounting

for passengers’

heterogeneity

Recognising that the underlying distribution

of preferences is continuous

Mixed Logit models (ML)

Introducing some socio-demographic

characteristics as attributes in the utility

functions

Multinomial Logit models (MNL)

Considering individual behaviour

depending on latent heterogeneity that

varies with unobserved factors

Latent Class models (LCM)

Passengers’ heterogeneity

The proposed research work

Summary

1Literature review of the studies analysing heterogeneity in bus

passengers’ perceptions about service quality factors

2 Experimental context

3 Proposed Logit models: MNL, ML, LCM

4 Discussions of the results and conclusions

Literature study Methodology Findings

Prioni, P. and Hensher, D.A. (2000)

Measuring service quality in scheduled bus

services. Journal of Public Transportation,

3(2), 51-74.

Hensher, D.A.; Prioni, P. A service quality

index for a area-wide contract performance

assessment regime. J Transport Econ Policy

2002, vol 36(1), 93-113.

An SP experiment was proposed to the passengers of some

private bus operators in New South Wales (Australia).

In this experiment the interviewed users made a choice

between the bus service currently used by the interviewed

person and some alternative services characterized by some

service quality attributes.

The choice data were used to calibrate an MNL model.

The authors introduced in the models some service quality

aspects such as reliability, frequency, walking distance to the

bus stops, travel time, comfort on board, safety, and so on.

Heterogeneity among users was considered by introducing

socio-economic characteristics such as personal income, age,

gender and car availability.

The authors found that individuals on higher incomes

and of more years were more likely to prefer the

levels of service offered by the existing trip than by

the alternative packages.

What this suggests is that as individuals age and

increase their income, they see existing service quality

as increasingly satisfying their requirements for service

quality.

Alternatively it is the younger and those on lower

incomes that see a greater need for improved service

quality.

Car availability was not statistically significant.

Service quality analysis based on discrete choice modelling

Heterogeneity by introducing socio-economic characteristics in the MNL models


dell’Olio L, Ibeas A, Cecìn, P. 2010a. The

quality of service desired by public transport

users. Transport Policy 18(1): 217-227.

Different groups of users as a function of the socio-economic

characteristics are considered.

The work evaluates how bus users perceive the quality of their

public transport service.

The perception of quality is shown to change with the

category of user.

The variables with greatest weight are: reliability of

service (27.6%), waiting time (23.8%) and comfort

(17.1%).

dell’Olio L, Ibeas A, Cecìn P. 2010b.

Modelling user perception of bus transit

quality. Transport Policy 17: 388-397.

The analysis is carried out with different categories of users

and potential users (those people not currently using public

transport).

Waiting time, cleanliness and comfort are shown to be

the public transport variables that users most valued,

but the degree to which they are valued varies

according to the category of user.

Variables such as driver kindness, bus occupancy and

journey time are generally given less weight.

The first two attributes vary little by user category,

but some variability appears for journey time.

For potential users the more important variables when

defining expected quality from public transport are

waiting time, journey time and above all, level of

occupancy.

Heterogeneity by considering different groups of users



Hensher, D.A. (2001) Service quality as a

package: what does it mean to

heterogeneous consumers. Proceedings of the

9th World Conference on Transport

Research. Seoul, Korea, 22-27 July.

The same SP data adopted in Prioni and Henher (2000) and

Hensher and Prioni (2002) were used for calibrating ML

models proposed in Hensher (2001) in order to explore

observed and unobserved heterogeneity among users. Three

specifications of ML models were proposed; each presented a

different distribution for the random parameters (normal,

uniform and triangular).

In addition the author explored the possibility that the

unobserved heterogeneity might be ‘converted’ to observed

heterogeneity around the mean estimates by making the

distribution a function of passenger characteristics.

Four attributes were found to be best represented by

random parameters: service reliability, access time,

travel time and ‘very safe’ while waiting for the bus.

He found that the (relative) marginal disutility of bus

travel time decreases as an individual ages; in

contrast, the relative marginal disutility of access time

increases as an individual ages. Likewise, a female

with a higher income tends to have a higher relative

marginal disutility for reliability that a male with a

higher income. As personal income increases for both

males and females, the marginal disutility of reliability

increases.

Eboli L, Mazzulla G. 2008. Willingness-to-

Pay of Public Transport Users for

Improvement in Service Quality. European

Transport 38:107-118.

ML models were proposed in order to investigate on the

heterogeneity across individuals.

The heterogeneity about the perceptions of reliability,

bus crowding, information and helpfulness of personnel

was investigated. The standard deviation values

obtained from the model calibration suggest that there

is a notable difference in user perception of these

attributes.

Cirillo, C.; Eboli, L.; Mazzulla, G. On the

Asymmetric User Perception of Transit

Service Quality. Int J Sust Transport.

The same survey data adopted in Eboli and Mazzulla (2008)

were used for treating the heterogeneity through a mixed logit

model with a non-parametric distribution of the coefficients,

allowing the asymmetry in user perception heterogeneity to be

considered.

Service characteristics analysed in this study such as

reliability, comfort on board, information, personnel

manners showed an asymmetrical behaviour when B-

spline distribution was applied.

Yang C, Fang W, Duann L, Chen Y. 2003.

Intercity bus choice models with choice set

generation and heterogeneity. Journal of the

Eastern Asia Society for Transportation

Studies 5.

Yang et al. (2003) formulated a ML model within a two-stage

choice structure that accommodated taste heterogeneity of bus

users. The first stage is a probabilistic choice set generation

model, while the second stage is a probabilistic alternative

choice model conditioned on the choice set.

They considered some service attributes, such as

reliability, convenience, comfort, fare, etc. The only

individual heterogeneity identified in the alternative

choice stage was the response heterogeneity to actual

fare.

(Heterogeneity by adopting ML models)


The experimental context

Urban bus service

1. Covered distance 11 km

2. Number of stops 19

3. Daily service time 17 hour (from 7.30 a.m. till 12.30 p.m.)

4. Service frequency2 runs/hour in peak hours (7.30-8.30 a.m.;

11a.m.-2 p.m.; 6-9 p.m.) 1 run/hour (no peak hours)

5. Ticket cost

(one-way)0.77 cents

6. Daily passengers 8,000

The transit services analysed

The experimental context

Socio-economic characteristic Results

1. Sex 52% of public transport users are women

2. Age

11% are over 24 years old, 43% are under 20 years old, and the

most numerous group is formed of users aged between 21 and 24

representing 46% of the total

3. Driving license 83% have a driving license

4. Car ownership92% do not have their own car, therefore they have to use public

transport

5. Incomethe most representative group belongs to a middle income level,

while only 15% of people belongs to an upper income class

The sample (320 university students)

The experimental contextThe SP experiment

Service quality attributes Levels of variation

Walking distance to bus stop same as now (1); 10 minutes more (0) measured in minutes

Service frequency every 15 minutes (1); same as now (0)

Schedule reliability on time (1); late (0)

Bus stop facilities bus shelter, seats and lighting (1); no shelter, no seats, no lighting (0)

Bus crowding No overcrowded (1); overcrowded (0)

Cleanliness of interior clean enough (1); not clean enough (0)

Fare 25% more than the current fare (1); same as now (0) measured in Euros

Information at the bus stoptimetable, map, announcement of delays (1); no timetable, no map, no

announcement (0)

Personnel attitude very friendly (1); very unfriendly (0)

The experimental contextThe SP exercise

Attributes Current service Service bus A Service bus B

Walking distance to

the bus stopsame as now 10 minutes more same as now

Frequency same as now same as now every 15 minutes

Reliability on time late late

Bus stop facilitiesno shelter, no seats, no

lighting

Bus shelter, seats and

lighting

no shelter, no seats, no

lighting

Bus crowding overcrowded overcrowded no overcrowded

Cleanliness clean enough clean enough not clean enough

Fare same as now same as now25% more than the

current fare

Informationno timetable, no map, no

announcement of delays

timetable, map,


no timetable, no map, no


Transit personnel

attitudevery friendly very friendly very unfriendly

ModelsMathematical formulation

Multinomial Logit Model:

jjPerjInfjFare

jCleanjCrowjStopjReljFreqjWTimeij

PerInfFare

CleanCrowStopRelFreqWTimeU

)

(

j

iji

iji

ijx

xP

exp

exp

ModelsResults

Variable est. (t-stat) est. (t-stat)

Walking distance to bus stop -0.147 (-8.5) -0.098 (-4.1)

Service frequency 2.693 (11.4) 2.645 (11.2)

Schedule reliability 1.411 (9.2) 1.226 (7.7)

Bus stop facilities 0.623 (4.0) 0.562 (3.7)

Bus crowding 0.905 (4.8) 0.619 (3.3)

Cleanliness of interior 0.909 (6.3) 0.748 (5.0)

Fare -8.549 (-8.4) -5.349 (-4.0)

Information at bus stops 0.561 (3.6) 0.535 (3.5)

Personnel attitude 0.515 (3.6) 0.420 (2.9)

Gender 0.314 (1.7)

Car Availability 0.432 (2.0)

Log-likelihood at zero -695.421 -695.421

Log-likelihood (final) -456.56 -457.22

R-squared 0.357 0.343


ModelsResults

MNL Gender

Female Male



Service frequency 2.584 (8.3) 2.794 (7.9)



Bus crowding 0.897 (3.4) 0.984 (3.7)


Fare -8.576 (-6.4) -7.910 (-5.3)

Information at bus stops 0.831 (3.9) 0.307 (1.3)


Log-likelihood at zero -369.134 -333.978

Log-likelihood (final) -238.624 -220.769

R-squared 0.354 0.339

n. obs. 336 304


ModelsResults

MNL Income

High income Medium income Low income

Variable est. (t-stat) est. (t-stat) est. (t-stat)

Walking distance to bus stop -0.072 (-1.5) -0.179 (-6.1) -0.132 (-5.3)

Service frequency 3.226 (4.9) 3.184 (8.4) 2.281 (6.4)

Schedule reliability 2.002 (4.7) 1.896 (7.0) 0.899 (4.0)

Bus stop facilities 0.381 (n.s.) 0.816 (3.2) 0.645 (2.7)

Bus crowding 1.159 (2.2) 1.006 (3.0) 0.904 (3.5)

Cleanliness of interior 1.553 (3.6) 0.626 (2.7) 1.034 (4.8)

Fare -6.354 (-2.2) -8.999 (-5.4) -7.997 (-5.5)

Information at bus stops 1.279 (2.6) 0.483 (2.0) 0.444 (1.9)

Personnel attitude 0.335 (0.8) 1.131 (4.7) 0.206 (n.s)

Log-likelihood at zero -107.664 -321.893 -274.653

Log-likelihood (final) -68.022 -179.450 -202.103

R-squared 0.368 0.442 0.264

n. obs. 98 293 250



Mixed Logit Model:

jjPerjCrowjStop

jInfjFarejCleanjReljFreqjWTimeij

PerCrowStop

InfFareCleanRelFreqWTimeU

)(

)(

Y

kj

k

iksj

s

sij

l

ij

ij

ij

xx

dgP

,

)(exp

exp

ModelsResults

Variable est. (t-stat)

Walking distance to bus stop m. -0.191 (-6.7)

Service frequency m. 3.963 (7.4)

Schedule reliability m. 2.128 (6.9)

Bus stop facilities m. 0.833 (3.4)

s.d. 1.109 (1.3)

Bus crowding m. 1.377 (4.2)

s.d. 1.559 (2.5)

Cleanliness of interior 1.357 (5.1)

Fare m. -11.476 (-6.9)

Information at bus stops m. 0.786 (3.4)

Personnel attitude m. 0.907 (3.3)

s.d. 2.359 (4.0)

Log-likelihood at zero -695.421

Log-likelihood (final) -447.107

R-squared 0.357

Mixed Logit Model:

From -0.276 to 1.942;

positive for 77% of users, negative for 23%

From -0.182 to 2.936;


From -1.452 to 3.266;



Latent Class Model:

jjPerjInfjFare


PerInfFare


)

(

111

1111111

jjPerjInfjFare


PerInfFare


)

(

222

2222222

ModelsResults

class 1 class 2



Service frequency 1.690 (2.7) 35.487 (n.s.)



Bus crowding 2.613 (4.5) -0.007 (n.s.)


Fare -14.741 (-4.9) -11.144 (-6.1)

Information at bus stops 2.045 (4.6) -0.888 (-2.6)


Class probability 0.586 (23.9) 0.414 (9.7)

Log-likelihood at zero -695.421

-418.861

0.398

Log-likelihood (final)

R-squared

Latent Class Model:

Conclusions

1

Gender and car availability influence users’ choices of the bus services.

We found users’ heterogeneity linked to gender and income.

We found users’ taste heterogeneity for the attributes linked to bus stop facilities,

bus crowding, and personnel attitude, which are more qualitative service aspects

There is a class of users who give more importance to the attributes that more

properly describe a bus service (localization of the stops, punctuality, fare,

information), and a class of users who mostly consider the qualitative characteristics

(stop facilities, cleanliness, personnel)

2

Each of the proposed models explains the heterogeneity of users’ perceptions from

different perspectives.

Considering different groups of users provides information about the differences of

perceptions in terms of belonging to classes such as male or female, classes of income.

Introducing random coefficients (in ML models) provides information about differences

of perceptions user by user based on users’ different tastes and attitudes towards

service aspects.

Latent Class Models provide information about the differences of perceptions in terms

of belonging to classes of users which are unobservable, differently from the

predefined classes based on gender, income, etc.

THE HETEROGENEOUS PERCEPTION

OF TRANSIT SERVICE QUALITY

by Laura Eboli and Gabriella Mazzulla

Scientific Seminar SIDT 2011:

“ENERGY, ENVIRONMENT AND

INNOVATION IN SUSTAINABLE

TRANSPORT SYSTEMS"

http://www.sidt.it/

http://www.sidt.it/

Main publications of the authors about

transit service quality

Eboli, L. and Mazzulla, G. (2010) How to capture the passengers’ point of view on a transit service through rating and

choice options, Transport Review, 30(4), pp. 435-450, Taylor and Francis, London, UK.

5

6

7

8

9

Eboli L., Mazzulla G. (2009), An ordinal logistic regression model for analysing airport passenger satisfaction,

Euromed Journal of Business, 4(1), pp. 40-57, Emerald, Bradford, UK.

Eboli L., Mazzulla G. (2008), An SP Experiment for Measuring Service Quality in Public Transport,

Transportation Planning and Technology, 31(5), pp. 509-523, Taylor & Francis, London, UK.

Eboli L., Mazzulla G. (2008), Willingness-to-Pay of Public Transport Users for Improvement in Service Quality,

European Transport, International Journal of Transport Economics, Engineering and Law, 38, pp. 107-118, ISTIEE, Trieste.

Eboli L., Mazzulla G. (2007), Service Quality Attributes Affecting Customer Satisfaction for Bus Transit,

Journal of Public Transportation, 10(3), pp. 21-34, Center for Urban Transportation Research (CUTR), Tampa, Florida.

Eboli L., Mazzulla G. (2009), A New Customer Satisfaction Index for Evaluating Transit Service Quality,

Journal of Public Transportation, 12(3), pp. 21-37, Center for Urban Transportation Research (CUTR), Tampa, Florida.

3Cirillo C., Eboli L., Mazzulla G. (2011), On the Asymmetric User Perception of Transit Service Quality,

International Journal of Sustainable Transportation, 5(4), pp.216-232, Taylor & Francis, London, UK.

4

1Eboli, L. and Mazzulla, G. (2011) Transit Passenger Perceptions: Face-to-Face Versus Web-based Survey,

Journal of the Transportation Research Forum, Transportation Research Forum, 50(1), pp.19-36, Fargo, North Dakota, USA.

Eboli L., Mazzulla G. (2011), A methodology for evaluating transit service quality based on subjective and objective

measures from the passenger’s point of view, Transport Policy, 18(1), pp.172-181, Elsevier, Amsterdam.2

10 Eboli L., Mazzulla G. (2007), Behavioural and non-behavioural models for customer satisfaction measure,

International Journal of Management Cases, 9(3/4), pp. 129-139, Access Press UK, Lancashire, UK.

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