Edith Cowan University€¦ · 2003 2005 2007 2009 2011 2013 2015 2017 Travel Mode Trends - Mount Lawley Public Transport Walk/Cycle Drive Alone Carpool. Edith Cowan University 2017

Edith Cowan University

2017 Travel Surveys

Prepared forEdith Cowan University

22 May 2017

Edith Cowan University2017 Travel Surveys

CW973300-TR-R001-A-2017 ECU Travel Survey.docx Cardno ii

Contact InformationCardno (WA) Pty LtdTrading as CardnoABN 77 009 119 000

11 Harvest Terrace, West Perth WA 6005

Telephone: 08 9273 3888Facsimile: 08 9486 8664International: +61 8 9273 3888

[email protected]

Author(s):Jacob MartinTeam Leader, Transport Planning

Document InformationPrepared for Edith Cowan UniversityProject Name 2017 Travel SurveysSite Name Edith Cowan UniversityClient Reference N/A

Project No. CW973300Document Title Edith Cowan UniversityDiscipline Code Traffic & TransportDocument Type ReportDocument Sequence 1Date 22 May 2017Revision Number A

Approved By: Ray Cook Manager – Traffic and TransportPlanning

Document HistoryRevision Date Description of Revision Prepared by: Reviewed by:

A 22 May 2017 For Review Danny Sriono Jacob Martin

© Cardno. Copyright in the whole and every part of this document belongs to Cardno and may not be used, sold, transferred,copied or reproduced in whole or in part in any manner or form or in or on any media to any person other than by agreement withCardno.

This document is produced by Cardno solely for the benefit and use by the client in accordance with the terms of the engagement.Cardno does not and shall not assume any responsibility or liability whatsoever to any third party arising out of any use or relianceby any third party on the content of this document.


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Executive SummaryCardno has been commissioned by Edith Cowan University (ECU) to conduct a travel survey and collateresults for the three (3) ECU campuses – Mount Lawley, Joondalup and Bunbury.

A summary of the survey outcomes for the travel diary component of the survey is shown below:

ECU Student Mode Choice (All Campuses) ECU Staff Mode Choice (All Campuses)

In addition, Cardno has compared the results from the 2017 student survey to previous surveys undertakenbetween 2003 and 2014, as shown below:

ECU Student Travel Mode Trends

The results of these surveys show a significant decrease in private vehicle mode share over the last 10years, at an average of 0.7% p.a.

However, Mount Lawley has seen no significant growth in the walk/cycle transport modes across the long-term survey period, with all mode shift shown to be towards public transport. The most recent surveys showa slight increase in drive-alone modes, though the trend is still downwards.

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In general, Joondalup travel mode shift appears to be towards all forms of non-car active transport, with thegreatest change in cycling and walking mode shares. This may be the result of residential development inthe area, allowing a greater proportion of students to live closer to university. The most recent survey showsan increase in drive alone modes, although the trend is still downwards.

Private vehicle modes share of staff respondents at Mount Lawley increased between 2014 and 2017however there has been a reduction to private vehicle mode share for staff respondents at Joondalup, asshown in the charts below:

Changes in Staff Primary Transport Mode Share

Mount Lawley – 2014 Mount Lawley – 2017

Joondalup – 2014 Joondalup – 2017

The results of these surveys show a significant increase in private vehicle mode share over the last twoyears in the order of 2.5% p.a. at Mount Lawley campus. However the results also show a significantdecrease in private mode share in the order of 2.5% p.a. at Joondalup campus.

The cause of this increase in staff drive alone mode share for Mount Lawley campus does not appear to beexplained by the 2017 survey results. However, this behaviour shows a departure from the ECU vision,suggesting that an update to the Travel Plan may be required to evaluate the success of existing traveldemand management (TDM) policies, and to implement a new range of TDM policies. This Travel Planwould support ECU in their sustainable transport goals and recommend cost-effective measures for reducingcar dependency.

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Table of Contents1 Introduction 12 Demographics and Sample Rates 2

2.1 Population Demographics 22.2 Survey Validation 2

3 2017 Survey 43.1 Student Survey 4

3.1.1 Student Respondent Demographics 43.1.2 Student Travel Behaviour 43.1.3 Student Trip Chaining 73.1.4 Student Attendance, Time of Arrival and Departure 83.1.5 Importance of Travel Factors for Students 10

3.2 Staff Survey 123.2.1 Staff Respondent Demographics 123.2.2 Staff Travel Behaviour 123.2.3 Staff Trip Chaining 153.2.4 Staff Attendance, Time of Arrival and Departure 163.2.5 Importance of Travel Factors for Staff 17

3.3 Location Analysis for Transport Mode Share 17

4 Behavioural Change 184.1 Travel Management 18

4.1.1 Trip chaining 184.1.2 Potential of Active Transport Modes 18

4.2 Transport Initiatives 194.2.1 Cycling 194.2.2 Public Transport 20

5 Comparison to Previous Years 235.1 Trends 235.2 Mode Shift 2014-2017 23

5.2.1 Students 235.2.2 Staff 25

6 Greenhouse Gas Emissions 277 Conclusions 29

AppendicesAppendix A Travel Survey InstrumentAppendix B Travel Survey DataAppendix C Location Analysis

TablesTable 2-1 Student Population Demographics and Surveyed Key Variables 2Table 2-2 Staff Population Demographics and Surveyed Key Variables 3


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Table 6-1 Student Greenhouse Gas Emissions 27Table 6-2 Staff Greenhouse Gas Emissions 27

FiguresFigure 3-1 Student respondent demographics 4Figure 3-2 ECU Student Mode Choice (All Campuses) 5Figure 3-3 ECU Student Mode Choice – Mount Lawley 5Figure 3-4 ECU Student Mode Choice – Joondalup 6Figure 3-5 ECU Student Mode Choice - Bunbury 6Figure 3-6 ECU Student Mode Choice – With Trip Chaining (All Campus) 7Figure 3-7 ECU Student Mode Choice – Without Trip Chaining (All Campus) 8Figure 3-8 Student attendance levels by campus 8Figure 3-9 Student arrival and departure times by campus 9Figure 3-10 Importance of travel factors (students) 10Figure 3-11 Difference in values between non-car active transport users and private vehicle users

(students) 11Figure 3-12 Staff respondent demographics 12Figure 3-13 ECU Staff Mode Choice (All Campuses) 13Figure 3-14 ECU Staff Mode Choice – Mount Lawley 13Figure 3-15 ECU Staff Mode Choice – Joondalup 14Figure 3-16 ECU Staff Mode Choice – Bunbury 14Figure 3-17 ECU Staff Mode Choice – With Trip Chaining (All Campuses) 15Figure 3-18 ECU Staff Mode Choice – Without Trip Chaining (All Campuses) 15Figure 3-19 Staff attendance by campus 16Figure 3-20 Staff arrival and departure times by campus 16Figure 3-21 Importance of travel factors (staff) 17Figure 4-1 Impact of End-Of-Trip Facility Improvements – Student Responses 19Figure 4-2 Impact of End-Of-Trip Facility Improvements – Staff Responses 19Figure 4-3 Impact of Public Transport Frequency Improvements – Student Responses 20Figure 4-4 Impact of Public Transport Frequency Improvements – Staff Responses 20Figure 4-5 Impact of Public Transport Availability Travelling to Mount Lawley Campus 22Figure 4-6 Impact of Extending the Joondalup CAT that Services the Joondalup Campus 22Figure 5-1 ECU Student Travel Mode Trends 23Figure 5-2 Changes in Students’ Primary Transport Mode Share (Mount Lawley) 24Figure 5-3 Changes in Students’ Primary Transport Mode Share (Joondalup) 24Figure 5-4 Changes in Students’ Primary Transport Mode Share (Bunbury) 24Figure 5-5 Student Attendance Trends 25Figure 5-6 Changes in Staff Primary Transport Mode Share (Mount Lawley) 26Figure 5-7 Changes in Staff Primary Transport Mode Share (Joondalup) 26Figure 5-8 Changes in Staff Primary Transport Mode Share (Bunbury) 26


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1 Introduction

Cardno has been commissioned by Edith Cowan University (ECU) to conduct a travel survey and collateresults for the three (3) ECU campuses – Mount Lawley, Joondalup and Bunbury. In addition, Cardno hascompared the results from the 2017 survey to previous surveys undertaken between 2003 and 2014.

Previous travel information was gathered by students of the university and involved on-site intercept surveysof students. The first comprehensive survey was undertaken in 2010 by Cardno and used sample interceptsfor calibration, in addition to a freely available “on-line” survey distributed via email to staff and students. For2012, 2014, and 2017 surveys, the intercept survey was omitted and all responses were obtained throughon-line submission.

The 2017 survey was sent to all staff working at the three ECU campuses and 20,795 students. The Cardnoonline survey approach elicited a good level of response from both staff and students, with over 2,300responses.

For the purpose of this report, and to reflect ECU’s Vision Statement and KPIs, the term Active Transport hasbeen chosen to reflect the terminology used internally at ECU. Active Transport therefore includes alltransport modes except for “Drive Alone” and “Motorcycle”, i.e. carpooling, public transport, walking andcycling.

Active Transport in this definition is often referred to in other contexts as Alternative Transport or SustainableTransport.



2 Demographics and Sample Rates

2.1 Population DemographicsInformation regarding population demographics is taken from data disseminated by ECU for 2017. For thepurpose of determining whether the respondent mix matches the overall population, the following factorshave been compared:

> Gender;

> Student Load/Enrolment Type or Staff Contract; and

> Campus.

2.2 Survey ValidationStudent demographics have been determined from information provided by ECU. Table 2-1 shows thepercentage mix for the selected student demographic variables, and the comparison between population andonline surveys.

Table 2-1 Student Population Demographics and Surveyed Key VariablesDemographic Population

(number)Population(% of Total)

Survey Responses(number)

Survey ResponseRate(% of Population)

Student Load/Enrolment Type

Local

· Full Time 17,089 84% 1,403 8%

· Part Time 3,327 16% 279 8%

InternationalStudents

4,034 20% 208 5%

Gender

Male 8,222 40% 451 5%

Female 12,187 60% 1161 10%

Other 7 0% - -

Campus

Mount Lawley 6,424 31% 537 8%

Joondalup 12,906 63% 1193 9%

Bunbury 971 5% 111 11%

External 115 1% 18 16%

TOTAL 20,416 - 1,853 9%

The results show that there was a good response rate by students, with approximately 9% of the studentscompleting the survey across the three campuses. It should be noted that the 2014 student travel survey hada participation rate of 13%. The survey was also distributed to all internal and affiliated external students,rather than 33% of the population sampled for the 2014 survey.



Staff demographics have been determined from information provided by ECU. The variable nature of FixedTerm and Ongoing contracts has resulted in the need to combine Full time and Part time staff members.Table 2-2 compares the results of the online responses with the population data.

Table 2-2 Staff Population Demographics and Surveyed Key VariablesDemographic Population

(number)Population(% of Total)

Survey Responses(number)

Survey ResponseRate (% ofPopulation)

Staff Contract

Casual 1,028 41% 46 4%

Fixed Term andOngoing

1,489 59% 346 23%

Gender

Male 932 37% 128 14%

Female 1,585 63% 276 17%

Campus

Mount Lawley 783 31% 74 9%

Joondalup 1,652 66% 319 19%

Bunbury 82 3% 15 18%

TOTAL 2,517 - 408 16%

The response rate for staff was excellent and is reflected in the quality of the results. In total just over 16% ofall staff responded to the survey with the results indicating that all demographics are well represented in thedata, with the exception of casual workers.

It is also noted that staff responses from the Mount Lawley campus were approximately half that of othercampuses. It would be worth exploring how to engage more staff from this campus for future surveys.

It is noted that the technical definitions of staff contract details may not fully reflect the self-selecteddefinitions from the survey. Casual staff may, in fact, describe themselves as part-time – which may result ina skewed understanding of the demographics profile.



3 2017 Survey

An online survey was completed by both staff and students across a one-week period between 31 March2017 and 7 April 2017. The survey questionnaire was retained from the 2014 survey with minor changes toensure consistency and to better assess key performance indicators. An example of the survey is included inAppendix A.

Survey results have been presented primarily in graphical form and separated as seems pertinent for thedesired outcomes. For most of the part, demographic information has been displayed for ECU as a whole,while travel behaviour is shown for each campus individually. Staff and student information has beendisplayed separately throughout.

3.1 Student Survey

3.1.1 Student Respondent Demographics

Figure 3-1 Student respondent demographics

The graphs in Figure 3-1 show demographic information for the student population across the ECUcampuses. The low percentage of respondents who live on campus will limit the impact of these students onoverall transport mode share. For the purpose of assessment, the travel mode data gathered will beassumed to reflect the behaviour of the student population as a whole.

3.1.2 Student Travel Behaviour

The following graph shows the travel mode share for all ECU students. The proportion of active transportmodes has been separated out for clarity, with 54% of all respondents’ trips undertaken by active modes.

For the purpose of this study, all trips to and from campus have been surveyed. This included all substantialparts of the trip chain. For example, a typical park ‘n’ ride might include carpool driver, train and bus modes.Respondents were asked to log all trips to and from campus undertaken during the 5 weekdays prior to thesurvey. Given that many trips may constitute a journey to or from campus, and students do not necessarilyattend campus for all 5 weekdays, the total number of trips may only be loosely related to the studentpopulation at any one campus or over the course of a typical week.



Figure 3-2 ECU Student Mode Choice (All Campuses)

A similar graph can be shown for each campus, as follows:

Figure 3-3 ECU Student Mode Choice – Mount Lawley



Figure 3-4 ECU Student Mode Choice – Joondalup

Figure 3-5 ECU Student Mode Choice - Bunbury

The graphs above illustrate the range of transport options available to students, and their willingness to useeach mode of transport. Driving alone is generally equivalent for Joondalup and Mount Lawley students,showing a small change since the 2014 survey, with a slightly higher drive alone mode share for Joondalup



(see Figure 5-1). Further investigation of the survey data suggest that the proportion of male and femaledriving alone was found to be similar for both Joondalup and Mount Lawley campus however there was ahigher proportion of female driving alone for the Bunbury campus when compared with male driving alone.There were no significant trends observed for the different age groups.

Carpool Driver trips are shown to be approximately equivalent to Carpool Passenger trips for all three ECUcampuses.

Unsurprisingly, students at the Bunbury campus are much more likely to drive, with both higher drive-aloneand carpooling modes. They are also less inclined to walk to university. The location of the campus adjacentto two major roads and removed from residential neighbourhoods minimises the attractiveness of walkingmodes. The implementation of proposed improvements to the Bunbury cycle network may have resulted in asmall uptake in cycling mode share, based on limited results obtained through the study.

It should be noted that only 111 Bunbury students completed the survey, so the results may not be fullyrepresentative of the student population. However, with 9% of the total population responding to the survey,the results can be viewed with some confidence.

3.1.3 Student Trip Chaining

Respondents were asked whether they stop off anywhere on their way to or from campus. The followinggraphs shows the mode choice based on whether they are required to stop off anywhere when travelling toor from campus.

Figure 3-6 ECU Student Mode Choice – With Trip Chaining (All Campus)



Figure 3-7 ECU Student Mode Choice – Without Trip Chaining (All Campus)

The survey results suggest that if the respondent is required to stop off anywhere when they travelled toECU then they are more likely to drive alone instead of using other mode. This behaviour is mainly attributedto the convenience and travel time savings of driving alone when compared with other mode choice.

3.1.4 Student Attendance, Time of Arrival and Departure

Students, particularly part-time students, do not necessarily attend university every day due to timetabling ofclasses, tutorials and laboratories. The number of days students attended the campus over the survey periodwas logged as part of the travel survey. This should be considered as a guide only, as students who attendfewer classes may be less interested in participating in a survey of this type.

The following graphs shows attendance across the week, for each campus. These graphs are shown withthe total number of responses shown on the y-axis to provide a sense of scale.

Mount Lawley Joondalup Bunbury

Figure 3-8 Student attendance levels by campus

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On average, approximately 59% and 56% of respondents attended Mount Lawley and Joondalup campuseach day, respectively. Approximately 50% of respondents attended the Bunbury campus on any given day.

As expected, full time students were more likely to attend campus, with approximately 65% of full timerespondents in attendance on average, compared to 43% of part time students.

Students were asked to note their arrival and departure times for a week of travel. This was used todetermine the relative impact of student travel on congestion, public transport demand and car parkingoccupancy.

The following graphs show average time of arrival and departure for all days surveyed, for each campus.


Figure 3-9 Student arrival and departure times by campus

The behaviour illustrated by these graphs is relatively similar for the Mount Lawley and Joondalup campuswith a marked peak arrival time between 8am and 9am, coinciding with the roadway AM peak hour. Thereare also significant proportion of arrivals after 10am, presumably reflecting later classes or independentstudy. Departure times are also similar with departures spread throughout the day and evening, with a smallproportion occurring during roadway peak periods.

Mount Lawley respondents generate a large proportion of departure trips after 6pm. This behaviour mayrepresent a conscious effort by students to avoid traffic congestion, or may be a result of class scheduleswhich extend into the evening.

The results show that the main contribution of students to peak hour traffic is during the morning roadwaypeak. This coincides with both the start of classes, and the need to arrive at campus in time to get parking.Interesting, at Bunbury, where parking is abundant and peak hour congestion low, students tend to arrive

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within a very short time period, when compared with other campuses. However, the impact of class timescannot be accounted for in this dataset.

Students utilising public transport are less likely to stay late than those employing private vehicle modes,presumably a result of a lack of public transport options outside of peak times.

3.1.5 Importance of Travel Factors for Students

Survey respondents were asked to assign values between 1 to 5 to various travel factors corresponding totheir perception of importance in their choice of travel mode where 1 is Not Important and 5 is VeryImportant. The graphs below show the aggregated results for each campus.

Mount Lawley

Joondalup

Bunbury

Figure 3-10 Importance of travel factors (students)

On the whole, responses across all campuses are relatively similar, with students consistently rating thefollowing as high priority:

> convenience,

> travel time,

> safety and

> security.

Other valuations varied significantly, and were closely related to mode choice. For example, public transportfrequency and cost was very important for students using these modes, but of no importance for drivers. Thisfact is reflected by the large majority of Bunbury campus students placing a low priority on the cost of publictransport.

Responses to fuel and parking cost questions was less polarising, suggesting that some demand for activetransport modes is driven by the financial costs of driving and parking. In addition, responses by driversindicate a level of demand inelasticity to parking and particularly fuel prices. These findings are welldocumented in transport economics. The graphs below show the differences in values between non-caractive transport users and private vehicle users, respectively.

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Non-car Active Transport Modes

Private Vehicle Transport Modes

Figure 3-11 Difference in values between non-car active transport users and private vehicle users(students)

Of interest is the proportion of active transport users who place a significant importance on fuel cost andparking pricing. This indicates that these respondents, cost is a significant factor in their decision to useactive transport modes. There is also a large proportion of respondents using sustainable transport modesthat focus on the non-cost components of transport: fitness and environment. This confirms that there are awide range of reasons for students to use sustainable transport modes.

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3.2 Staff Survey

3.2.1 Staff Respondent DemographicsThe following graphs show demographic information for the staff population across the ECU campuses.

Figure 3-12 Staff respondent demographics

3.2.2 Staff Travel Behaviour

The following graph shows the travel mode share for all ECU staff. The proportion of active transport modeshave been separated out for clarity, with 27% of all staff trips undertaken by active modes, including 5% bycarpool. This compares with 54% for students across all campuses.

As stated previously, all trips to and from campus have been surveyed including all substantial parts of thetrip chain. Given that many trips may constitute a journey to or from campus, and staff doo not necessarilyattend campus for all 5 weekdays, the total number of trips is only loosely related to the staff population.



Figure 3-13 ECU Staff Mode Choice (All Campuses)

A similar graph can be shown for each individual campus, as follows:

Figure 3-14 ECU Staff Mode Choice – Mount Lawley



Figure 3-15 ECU Staff Mode Choice – Joondalup

Figure 3-16 ECU Staff Mode Choice – Bunbury

The transport statistics shown above indicate the various transport options available, and staff willingness touse each of transport. As shown, the majority of staff trips are taken by private vehicle modes, with 75% and71% of all trips undertaken by private vehicle for staff at Mount Lawley and Joondalup campusesrespectively. The majority of respondents commute to work by car to the Bunbury campus, presumablyaccommodated by easily available parking and relatively poor public transport services. Further investigationon the survey data suggest that females are more likely to drive alone when compared with their malecounterparts. There were no significant trends observed for the different age groups.



It is noted that the drive alone proportion for staff at Mount Lawley campus has increased by 5% between2014 and 2017. The drive alone proportion for staff at Joondalup and Bunbury campus has decreased by 6%and 11% respectively between 2014 and 2017.

3.2.3 Staff Trip ChainingRespondents were asked whether they stop off anywhere on their way to or from campus. The followinggraphs shows the mode choice based on whether they are required to stop off anywhere when travelling toor from campus.

Figure 3-17 ECU Staff Mode Choice – With Trip Chaining (All Campuses)

Figure 3-18 ECU Staff Mode Choice – Without Trip Chaining (All Campuses)



The survey results suggest that if the respondent is required to stop off anywhere when they travelled toECU then they are more likely to drive alone instead of using other mode choice. This behaviour is mainlyattributed to the convenience and travel time savings of driving alone when compared with other modechoice.

3.2.4 Staff Attendance, Time of Arrival and Departure

Staff members, particularly part time staff, do not necessarily attend university every day, with some staffworking from home. The following graph show attendance across the week, for each campus, with staff notin attendance either working from home or away from work for other reasons. These graphs are shown withthe total numbers of responses shown on the y-axis to provide a sense of scale.


Figure 3-19 Staff attendance by campus

As shown in the above graphs, attendance was relatively consistent across the week, for all campuses.

Staff were asked to note their arrival and departure times for a week of travel. This was used to determinethe relative impact of travel on congestion, public transport demand and car parking occupancy.

The following graphs show average time of arrival and departure for all days surveyed, for each campus.


Figure 3-20 Staff arrival and departure times by campus

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The behaviour illustrated by these graphs is relatively similar for all three with a marked peak arrival timebetween 8am and 9am, coinciding with the roadway AM peak hour. Departure times are also similar,occurring primarily between 4pm and 6pm. A large percentage of staff based on Mount Lawley left campusafter 6pm, coinciding with a similar statistics for students. This is most likely representative of classesoccurring later in Mount Lawley than the Joondalup and Bunbury campus.

3.2.5 Importance of Travel Factors for Staff

Survey respondents were asked to assign values to various travel factors corresponding to their perceptionof importance in their choice of travel mode. The graphs below shown the aggregated results for eachcampus.

Mount Lawley

Joondalup

Bunbury

Figure 3-21 Importance of travel factors (staff)

On the whole, responses across the Perth metropolitan campuses are relatively similar, with staffconsistently rating convenience, travel time, safety and security as a high priority. Public transport priceshave elicited a similar response across all three university grounds, with staff considering this factor a lowpriority.

Surprisingly, fuel costs and parking prices also showed a wide range of importance.

Other valuations varied significantly, and were closely related to mode choice. For example, public transportfrequency was very important for staff using these modes, but of no importance for drivers.

3.3 Location Analysis for Transport Mode ShareAn analysis of the 2017 data has been undertaken of the Metropolitan campuses, and Appendix Cillustrates the geographical spread of residential suburbs for the staff and students. These maps show thedemand for a given mode related to residential origin. This illustrates opportunities for mode shift within thesuburbs surrounding the campuses. Data has been compiled for the combined staff and student populationfor the combined campus profile, and for each of the Joondalup and Mount Lawley campuses.

For the purpose of this analysis, a sensitivity variable was introduced to improve the quality of the data. Inthis instance, a minimum number of trips per suburb was required, set to include 80% of the total responses.

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1 2 3 4 5

Environment

1 2 3 4 5

Convenience

1 2 3 4 5

Fitness

1 2 3 4 5

Safety

1 2 3 4 5

Security



4 Behavioural Change

A range of questions were asked to provide information on how staff and students manage their travelrequirements and what initiatives might influence their mode choice.

4.1 Travel Management

4.1.1 Trip chaining

Respondents were asked whether they had, or were planning to stop off on the way to campus (tripchaining). Approximately 40% of all respondents were planning to undertake at least one extra task on theirway to or from campus. This increased to 45% for solo driving modes, but decreased to 28% for publictransport users, pedestrians, and cyclists. This reflects increased mobility constraints and capacity to carryitems, and potentially a lack of facilities adjacent to the end points of their trips.

4.1.2 Potential of Active Transport Modes

Two baseline questions were asked to determine respondent’s level of awareness for active transportmodes. Analysis of the results of these questions are provided in the following subsections.

4.1.2.1 Public Transport

The first of these questions asked whether respondents had used public transport at all in the last 12months. This question can highlight a bias against public transport or a significant lack of services in theirresidential area. Approximately 75% of all respondents had used some form of public transport in the lastyear, including 76% of students and 73% for all staff. Even amongst those respondents who did not takepublic transport to ECU within the survey timeframe, 66% had used some form of public transport in theprevious 12 months.

These results were similar to the previous survey, with a slight increase across staff and student responses.

4.1.2.2 End of Trip facilitiesThe second question concerned the availability of bicycle end of trip facilities (e.g. showers, lockers andparking) on campus. The lack of common knowledge about these facilities has been shown to reduce theprevalence of cycling. Results show that while the majority of staff are aware of on-campus facilities (83%),only of 52% students were aware of these facilities. However, only 4% of staff commuting to ECU do so on abicycle. Separating students by campus shows awareness was 39% at Mount Lawley, 57% at Joondalupand 53% at Bunbury campuses. This lack of awareness seems to be consistent across the population as theresults for existing cyclists showed the same characteristics.

This result suggests a potential avenue for improvement. The provision and quality of end-of-trip facilitieshave been shown to be a primary determinant for active transport use (pedestrian and cycling trips). While alarge majority of staff may be aware of the end of trip facilities available, the type, quality and accessibility offacilities available at the various ECU campuses may be a deterrent to staff commuting by pedestrian andcycling trips.



4.2 Transport InitiativesFor the purpose of predicting the effectiveness of potential transport initiatives, those students utilising thetravel mode three or more times in the week have been removed from the sample.

4.2.1 Cycling

In response to the question “Would you be more likely to cycle if there were better end of trip facilities?” theresponse for non-cyclists (where 1 is not likely to cycle and 4 is highly likely to cycle) was as follows:


Figure 4-1 Impact of End-Of-Trip Facility Improvements – Student Responses


Figure 4-2 Impact of End-Of-Trip Facility Improvements – Staff Responses

These results show a significant willingness to change to cycling modes; 36-55% of students and 21-36%staff willing to shift to cycling at least some of the time if facilities were improved.



4.2.2 Public TransportIn response to the question “Would you be more likely to take public transport if frequencies wereimproved?” the response from non-public transport regulars (where 1 is not likely to take public transport and4 is highly likely to take public transport) was as follows;


Figure 4-3 Impact of Public Transport Frequency Improvements – Student Responses


Figure 4-4 Impact of Public Transport Frequency Improvements – Staff Responses

From these results we can see that there is significant level of latent demand for public transport, withapproximately 33-44% of students and 21-34% of staff willing to increase their public transport mode share ifservice frequencies were increased. This may reflect a perceived environment of infrequent service, as it iscommon for the public to underestimate the availability of public transport options.



In response to the question “Would you be more likely to take public transport if travel times were improved?”the response from non-public transport regulars (where 1 is not likely to take public transport and 4 is highlylikely to take public transport) was as follows;

Impact of Public Transport Travel Time Improvements – Student Responses


Impact of Public Transport Travel Time Improvements – Staff Responses


The responses to this question reflect closely those for public transport frequency, reinforcing the conceptthat there remains latent demand for public transport that is currently unsatisfied, either through insufficientservices in and around the university campus, or at the respondents’ place of residence (or both).

The results from the Bunbury are unusual, with evidence that the travel time component for public transportis a significant barrier for students, and that a more direct service would have the potential to attract studentpatronage.



In response to the question “Would you be more likely to use public transport to the Mount Lawley campus ifit were made more readily available?” the response from non-public transport regulars (where 1 is not likelyto take public transport and 4 is highly likely to take public transport) was as follows;

Student Responses Staff Responses

Figure 4-5 Impact of Public Transport Availability Travelling to Mount Lawley Campus

From these results we can see that there is significant level of latent demand for local area transportservices, with approximately 52% of students and 44% of staff willing to consider using the service ifprovided.

In response to the question “Would you be more likely to use the Joondalup CAT service if the route wereextended to include the adjacent car park?” the response from non-public transport regulars (where 1 is notlikely to take public transport and 4 is highly likely to take public transport) was as follows;

Student Responses Staff Responses

Figure 4-6 Impact of Extending the Joondalup CAT that Services the Joondalup Campus

From these results we can see that there is a significant level of potential demand by students who woulduse the CAT service if it were extended to the nearby car park. Approximately 53% of student respondentswould consider this service in this instance. In contrast, it is unlikely that there would be any significantsupport from staff with respect to the potential CAT extension, with only 23% of staff likely to use theextended CAT. This is hypothesised to be a result of the proximity of staff versus student parking areas.



5 Comparison to Previous Years

As part of the evaluation of existing travel mode share, the 2017 data was compared to previous years’. Thefollowing graphs show the changes in travel mode from 2003 to 2017, with information gathered throughintercept surveys for all previous years. Note that the data for 2006 has not been included in this comparisondue to the low number of samples (approximately 95 from each campus).

5.1 Trends

Figure 5-1 ECU Student Travel Mode Trends

The above trend lines show that car use was relatively static prior to 2008, but a steady decline since 2008.

Mount Lawley has seen no significant growth in the walk/cycle transport modes across the long-term surveyperiod, with all mode shift shown to be towards public transport. The most recent surveys show a slightincrease in drive alone modes, though the trend is still downwards.

In contrast, Joondalup travel mode shift appears to be towards all forms of non-car active transport, with thegreatest beneficiaries being cycling and walking mode shares. This may be the result of residentialdevelopment in the area, allowing a greater proportion of students to live closer to university. The mostrecent surveys shows an increase in drive alone modes, though the trend is still downwards.

5.2 Mode Shift 2014-2017

5.2.1 StudentsThe following charts show a simplified view of the transport mode choice data obtained from the 2014 and2017 student surveys. It illustrates that despite differences in the transport environment at each of thecampuses, mode choice remains similar for the two Metropolitan campuses. The results of these surveysshow a significant decrease in private vehicle mode share over the last 10 years, at an average 0.7% p.a.

It should be noted that at both Metropolitan campuses, carpooling trips constitute a higher proportion than inother comparable universities in Perth and may present additional opportunities for the existing car-sharing/pooling initiatives undertaken by ECU.

Bunbury students seem to be shifting towards carpooling and public transport and away from driving alone.However, the smaller sample size of the Bunbury campus does result in some additional variability in thedataset, which may account for some observed differences in behaviour.

0%

10%

20%

30%

40%

50%

60%

2003 2005 2007 2009 2011 2013 2015 2017

Travel Mode Trends - Joondalup


0%

10%

20%

30%

40%

50%

60%

2003 2005 2007 2009 2011 2013 2015 2017

Travel Mode Trends - Mount Lawley




The results of the 2017 travel survey assessment for Bunbury campus shows that while student transportmode decisions continue to progress towards more sustainable modes, the primary shift in studentbehaviour appears to be a reduction in overall attendance. This may be a result of the prevalence of onlinetutorials, lectures and notes, as well as the ubiquity of high-speed internet access which allows students anopportunity to undertake more of their study from home.

2014 2017

Figure 5-2 Changes in Students’ Primary Transport Mode Share (Mount Lawley)

2014 2017

Figure 5-3 Changes in Students’ Primary Transport Mode Share (Joondalup)

2014 2017

Figure 5-4 Changes in Students’ Primary Transport Mode Share (Bunbury)

41%

9%

39%

11%


40%

9%

37%

14%


47%

8%

33%

12%


44%

8%

32%

16%


78%

13%

4% 5%


68%

24%

6%

2%




The student attendance rate is shown in the following graph. The survey results suggest that for bothJoondalup and Mount Lawley campus, the percentage of student attendance has been decreasing from2010 to 2014 and then increasing again in 2017. It is noted that for Bunbury campus, the percentage ofstudent attendance was increasing between 2010 and 2012 and then reducing to 2017.

Figure 5-5 Student Attendance Trends

5.2.2 StaffSimilar charts are shown for staff travel in 2014 and 2017. The results of these surveys show a significantincrease in private vehicle mode share over the last two years in the order of 2.5% p.a. at Mount Lawleycampus. However the results also show a significant decrease in private vehicle mode share in the order of2.5% p.a. at Joondalup campus.

The results from the Bunbury campus surveys show a shift away from driving alone and towards activetransport modes. Again, this may be a result of the smaller sample size, or may have been an environmentalcause resulting from differences in weather, class schedules or other unrelated events.

2014 2017

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

2010 2011 2012 2013 2014 2015 2016 2017

Student Attendance Trends


71%

4%

16%

9%


76%

6%

11%

7%




Figure 5-6 Changes in Staff Primary Transport Mode Share (Mount Lawley)

2014 2017

Figure 5-7 Changes in Staff Primary Transport Mode Share (Joondalup)

2014 2017

Figure 5-8 Changes in Staff Primary Transport Mode Share (Bunbury)

77%

8%

7%

8%


72%

7%

11%

10%


94%

6%

0%0%


83%

10%

1%6%




6 Greenhouse Gas Emissions

The CO2 emissions resulting from staff and student travel has been calculated according to the travel modesindicated by the survey, estimates of distances travelled and factors for vehicle emissions determined by theAustralian Greenhouse Office (AGO). The methodology used is consistent with National Greenhouse andEnergy Reporting (NGERS) requirements.

The emission results of this analysis for students in 2017 are shown in Table 6-1 (in tonnes of CO2).

Table 6-1 Student Greenhouse Gas EmissionsCampus Private

VehiclesBus Train Total per

campusTotal percapita

% per capitachange from2014

Mount Lawley 2,091t 103t 203t 2,397t 373kg 11%

Joondalup 5,325t 126t 308t 5,759t 446kg 2%

Bunbury 683t 5t - 688t 709kg -6%

Total per mode 8,099t 234t 511t 8,844t 436kg 5%

Total percapita

1,397kg 109kg 290kg

The results show that student per capita CO2 production increased for both Mount Lawley and Joondalupwhilst reducing for Bunbury. The overall result was an increase in CO2 emissions by 5%.

As expected, the Bunbury campus shows the highest per capita emissions due to a reliance on privatevehicle modes.

The emission results of this analysis for staff in 2017 are shown in Table 6-2 (in tonnes of CO2).

Table 6-2 Staff Greenhouse Gas EmissionsCampus Private

VehiclesBus Train Total per

campusTotal percapita

% per capitachange from2014

Mount Lawley 697t 5t 8t 710t 907kg -14%

Joondalup 2,129t 8t 27t 2,164t 1,310kg 0%

Bunbury 114t - - 114t 1,390kg 20%

Total per mode 2,940t 13t 35t 2,988t 1,187kg -3%

Total percapita

1,797kg 144kg 253kg

Travel emissions by staff are shown to be consistently and significantly higher than for students. This is likelycaused by staff members living further away from campus, commuting more often by car, and attendingcampus more days of the year.

For the purpose of the 2017 analysis staff attendance was taken directly from survey results, consistent withthe 2014 assessment regime. The results show that staff per capita CO2 production increased for Bunburywhilst reducing for Mount Lawley. The overall result was a decrease in CO2 emissions by 3%.



The calculations used were as follows:

Private Vehicles

[(# drive alone trips) + ½ (# carpool trips)] x (# days attending campus) x (ave. distance travelled) x (ave. fuelconsumption = 0.113L/km) x (CO2 produced = 2.4kg/L)

Bus Transit

(# bus trips) x (# days attending campus) x (ave. distance travelled) x (ave. LPG consumption = 0.457L/km)x (CO2 produced = 1.6kg/L) / (# people on bus)

Train Transit

(# train trips) x (# days attending campus) x (ave. distance travelled) x (CO2 per passenger km =0.0303g/km)

All figures for emissions and average vehicle consumption are taken from information provided by the Centrefor Climate Change and the Australian Greenhouse Office. Only direct emissions have been calculated,neglecting the contribution of fuel production and transport.



7 Conclusions

The results of student travel surveys show a significant decrease in private vehicle mode share over the last10 years, at an average of 0.7% p.a. However, Mount Lawley has seen no significant growth in thewalk/cycle transport modes across the long-term survey period, with all mode shift shown to be towardspublic transport. The most recent surveys show a slight increase in drive-alone modes, though the trend isstill downwards.

In contrast, Joondalup travel mode shift appears to be towards all forms of non-car active transport, with thegreatest change in cycling and walking mode shares. This may be the result of residential development inthe area, allowing a greater proportion of students to live closer to university. The most recent surveys showan increase in drive alone modes, though the trend is still downwards.

The results of staff travel surveys show a significant increase in private vehicle mode share over the last twoyears in the order of 2.5% p.a. at Mount Lawley campus. However the results also show a significantdecrease in private vehicle mode share in the order of 2.5% p.a. at Joondalup campus.

The cause of this increase in staff drive alone mode share for Mount Lawley campus does not appear to beexplained through the 2017 survey results. However, this behaviour shows a departure away from the ECUvision, suggesting that an update to the Travel Plan may be required to evaluate the success of existingtravel demand management (TDM) policies, and to implement a new range of TDM policies. This Travel Planwould support ECU in their sustainable transport goals and recommend cost-effective measures for reducingcar dependency.



2017 Travel Surveys

APPENDIX

ATRAVEL SURVEY INSTRUMENT





2017 Travel Surveys

APPENDIX

BTRAVEL SURVEY DATA





2017 Travel Surveys

APPENDIX

CLOCATION ANALYSIS





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Documents

Edith Cowan University€¦ · 2003 2005 2007 2009 2011 2013 2015 2017 Travel Mode Trends - Mount Lawley Public Transport Walk/Cycle Drive Alone Carpool. Edith Cowan University 2017