the effect of parking charges at transit stations on 'park ...docs.trb.org/prp/13-3521.pdf ·...

The Effect of Parking Charges at Transit Stations on ‘Park and Ride’ Mode Choice: 1

Lessons Learned from a Stated Preference Survey in Greater Vancouver 2 3 4

5 6 7

Khandker Nurul Habib, PhD, P.Eng 8 Assistant Professor 9

Department of Civil Engineering 10 University of Toronto 11

[email protected] 12 13

14 15

Mohamed S. Mahmoud, M.Sc. 16 PhD Candidate 17

Department of Civil Engineering 18 University of Toronto 19

[email protected] 20

21 22

23 Jesse Coleman, M.A.Sc, P.Eng 24

IBI Group, Toronto 25

[email protected] 26

27 28 29

30 31

32 33

34 35 Number of Words (Maximum 7500): 6170 + 3 Figures (750) + 1 Table (250) = 7170 words 36 37 38

39 40

41 42 43 44

45

TRB 2013 Annual Meeting Paper revised from original submittal.

Abstract 1

2 The objective of this study was to investigate the effect of increasing parking charges at park and 3 ride stations on mode choice for current park and ride users. To address this objective, a Stated 4

Preference (SP) survey was designed to study commuters’ willingness to pay for parking at park 5 and ride transit stations. The SP survey was conducted at the 14 busiest park and ride transit 6 stations in the Greater Vancouver Region (GVR). The survey data was then used to model mode 7 choice for longer distance commuting trips considering three major options: ‘automobile all-8 way’, ‘transit all-way’ and ‘park and ride’. A heteroskedastic multinomial logit model for stated 9

preference of modal choices was estimated. The model included several major factors that are 10 found to influence mode choice at park and ride stations. The estimated model parameters were 11 then used to investigate direct and cross elasticities of parking charges at park and ride stations to 12 mode choices. The model results show that increasing parking charges at park and ride stations is 13

more likely to divert current park and ride users to the transit all-way option compared to the 14 private car all-way option. 15

16


Introduction 1

2 Modal integration to facilitate multiple options for travel is often a desirable objective in urban 3 transportation planning. Typically, modal integration for an urban transportation system refers to 4

facilities encouraging combinations of transit and other motorized or non-motorized modes. 5 Among all of these, the use of private automobile for transit access, especially the park and ride 6 option is the most flexible form of modal integration (Cairns 1998). Well-designed park and ride 7 facilities can efficiently increase the capacity of the whole urban transportation system as well as 8 optimally utilize transit facilities (García and Marín 2002). From the users’ perspective, 9

opportunities from the efficient integration of transit services with other modes (e.g. private car) 10 literally increase their transportation options and therefore provide more sustainable 11 transportation choices (Foote 2000; Bos et al. 2005b; Liao et al. 2010; Vijayakumar et al. 2011). 12 13

So, park and ride facilities are an important component of major urban transit systems across 14 North America. They act as a key bridge between the road network and the transit system and 15

play a core role is facilitating cross-modal integration and in providing access to transit for users 16 who may not otherwise consider transit as a travel alternative. Moreover, parking restrictions 17

and/or the high costs of parking at destinations, such as downtown or city centres, can make the 18 park and ride mode option a viable alternative. Evidence also suggests that park and ride options 19 can influence the effectiveness of highway congestion reduction strategies (Supernak et al. 20

2002). This is why park and ride is becoming more popular in large cities (Meek et al. 2009). For 21 example, in the Greater Toronto Area (GTA), around 56 percent of the regional rail service users 22

access the system by automobiles (Wells 2004). Van der Waerden et al. (2011) reported a case 23 where the introduction of eleven park and ride facilities close to rail and bus stations in Northern 24 Holland reduced approximately 6 million vehicle kilometres (VKT) of travel demands. 25

26

While park and ride is able to provide easy access to the transit network, there are some 27 unwanted side-effects that can occur from relying too heavily on park and ride. Relying on park 28 and ride to bring users to the transit network can ultimately limit the overall capacity of the 29

transit system by discouraging people from walking or using local transit services to access rapid 30 transit which is more efficient from a network perspective. In systems that are primarily served 31

by park and ride, the overall capacity is limited by the amount of parking that can be provided 32 and as a result overall land available near stations. When this is the case further expansion can 33

require expensive parking structures and can cause significant traffic problems in the local area 34 surrounding the park and ride station. 35 36 In order to more effectively manage the demand for park and ride facilities, transit agencies are 37 more frequently looking towards implementing parking charges at park and ride facilities. This is 38

often a reaction to capacity limitations at park and ride facilities as well as a method of 39 recovering some of the costs of operation the park and ride. For example, previously fully free 40

local transit park and ride facilities are now fully charged in Toronto. In the case of Vancouver, 41 parking charges exist at the largest most heavily used transit stations while smaller outlying park 42 and ride stations are still free. Like any other economic good, free parking at park and ride 43 facilities often results in over-consumption and the inefficient use of the parking lots (Hendricks 44 and Outwater 1998). Also, there are other examples showing some negative effects of park and 45 ride options in terms of mode shift from transit all-way option to park and ride as well as 46


increasing travel demand as a result of latent demand generation (Parkhurst 2000). Introducing 1

parking charges for park and ride facilities can be an effective way of controlling the utilization 2 of parking facilities, but excessive charges can have the negative side effect of reducing overall 3 transit usage and encouraging passengers to drive all the way to their destinations. That is, 4

supply levels of parking facilities (i.e., space and cost) can effectively define the popularity of 5 the park and ride option for commuters. 6 7 Therefore, introducing parking charges for park and ride parking facilities are becoming 8 common practice for many large transit agencies. Numerous studies have been conducted on 9

commuting mode choice considering the parking cost for park and ride as an access cost. 10 However, studies dedicated to investigating the effects of increasing parking cost on park and 11 ride and other travel mode options for longer distance commuting trips, and commuter rail in 12 particular are not as common. More evidence-based studies are necessary to enrich our 13

understanding of the impact of parking charges on overall park and ride mode choice. 14 15

To contribute to this body of literature, this paper presents a Stated Preference (SP) survey-based 16 study on commuters’ willingness to pay for parking at park and ride transit stations in the Greater 17

Vancouver Region. Parking charges currently exist at many of the larger, higher-usage lots in 18 Greater Vancouver while many of the smaller outlying lots currently being free. The survey was 19 conducted to better-understand the implications of expanding parking charges across the Greater 20

Vancouver park and ride system, and to understand the impact of raising parking fees at lots that 21 currently are charging for parking. In order to develop a better understanding of willingness-to-22

pay for parking, A SP survey was conducted at the 14 busiest park and ride transit stations in the 23 Greater Vancouver Region (GVR). The resulting data was used to model mode choice for longer 24 distance commuting trips considering three major options: ‘automobile all-way’, ‘transit all-way’ 25

and ‘park and ride’. The estimated model parameters reveal the influence of parking charges at 26

park and ride stations on commuting mode choice. 27 28 The paper is organized as follows: the next section presents a brief literature review on modal 29

integration and park and ride mode choice. This is followed by a section explaining the SP 30 survey for parking cost sensitivity data collection at park and ride facilities. The later sections 31

explain the econometric modelling framework and discussions on the empirical model. The 32 paper concludes with key findings and recommendations for future study. 33

34

Literature Review 35 36 Park and ride is a complex modal option that has been the subject of a wide range in research. 37 The park and ride mode choice option is often treated under the umbrella of accessibility to 38

transit. Researchers have investigated various types of transit access options; in particular, the 39 automobile access to transit, including park and ride, kiss and ride, etc. (Tsang et al. 2005; 40

Washbrook et al. 2006; Li et al. 2007). However, park and ride itself is a complicated modal 41 option that requires focused investigation (Fan et al. 1993). 42 43 In one of the earliest studies, Florian and Los (1980) studied the impact of parking supply for 44 park and ride on overall transportation system performance. They considered the interaction 45 between demand and supply using maximum entropy formulations and the explicit consideration 46


of the capacity constraints on parking spaces. They found that parking supply at park and ride 1

stations can affect the overall transportation system performance significantly. Cairns (1998) 2 showed that providing permanent facilities for park and ride at transit stations can have an 3 immediate impact on increasing transit modal share and in the long run, can also influence urban 4

form and land use. Merriman (1998) investigated the effects of increasing free parking spaces at 5 transit stations on park and ride mode choice in Chicago. His investigation revealed that 6 increasing free parking spaces at some transit stations may reduce ridership of adjacent non-park 7 and ride transit stations, however the system-wide transit ridership would still increase. This 8 research was from the context where free parking is made available at some transit stations in 9

order to counter free or employer-subsidized parking in downtown or city centres. Hendricks and 10 Outwater (1998) investigated the demand for park and ride parking facilities in King County, 11 Washington. They found that capacity constraints along with parking charges can significantly 12 influence park and ride mode choices. 13

14 Parkhurst (2000) investigated eight bus-based short range park and ride facilities in the UK and 15

found that the congestion-reducing impact of park and ride is mixed. Although it can be effective 16 in reducing congestion in city centres, it may not necessarily reduce total travel demand. Bos et 17

al. (2003) investigated the cognitive construct of park and ride choice and found that transit 18 service reliability is a more important factor than cost in influencing the choice of a park and ride 19 mode option. Olsson (2003) conducted a stated preference survey on factors affecting mode 20

choice considering the park and ride mode and found that free parking for park and ride is the 21 strongest positive factor affective park and ride usage. The study showed that females and elders 22

are willing to pay more for parking charges at park and ride stations. Bos et al. (2004) presented 23 a stated preference survey-based study for evaluating factors influencing the choice of the park 24 and ride mode option. They found that social safety and the quality of transit service are the 25

major determinants of park and ride mode choice in the Netherlands. Bos et al. (2005a) used a 26

park and ride choice model to test several policy measures by a series of simulations. Their study 27 focused on individuals’ preferences regarding parking attributes rather than their choice of park 28 and ride as a mode of travel. Their investigation revealed that auto drivers are more attracted to 29

parking locations where safety aspects and better connections to public transit are well 30 established. 31

32 Shirgaokar and Deakin (2005) conducted an exploratory investigation of park and ride facilities 33

and users in the San Francisco Bay Area. Their focus was on investigating users’ concerns with 34 using park and ride facilities. The study relied on a focus group survey at three Bay Area Rapid 35 Transit (BART) stations. They found that transit service quality, parking lot security and the 36 provision of proper waiting areas were the major factors encouraging users to pay higher parking 37 charges. However, they did not investigate the influence of parking costs on the choice of the 38

park and ride mode. Washbrook et al. (2006) used stated preference survey data to show that 39 parking availability largely affects commuters’ mode choice as well as destination choice 40

according to their trip purpose. Kelly and Clinch (2006) discussed the impacts of parking cost 41 variations on park and ride mode choice for different trip purposes. Syed et al. (2009) conducted 42 a study at two heavily-used park and ride facilities on BART. Results showed that the 43 introduction of parking fees did not significantly change individuals’ preferences for access 44 mode or parking location choices. Further, the overall ridership at the two investigated stations 45 remained unchanged before and after introducing parking charges. Xiong (2011) studied the 46


psychological reactions to parking charge variations using the method of Price Sensitivity 1

Measurement (PSM). The range of reasonable prices was determined based on data collected 2 from a travel survey in Shenzhen, China. This range was defined by two price thresholds, 3 namely; indifference and optimal price points. 4

5 In a recent paper, Van der Waerden et al. (2011) investigated commuting and shopping travelers’ 6 willingness to use park and ride facilities using discrete choice models. Results indicated that a 7 high level of heterogeneity exists among different groups of users. Their study clearly identifies 8 that the total trip travel time and costs are the most influential factors affecting park and ride 9

mode choice. Liao et al. (2010) presented a methodology to accommodate a park and ride mode 10 choice option into activity-based travel demand modelling. Holguin-Veras et al (2012) proposed 11 a decision-making framework that identifies potential locations for park and ride facilities in 12 New York. 13

14 This literature review has shown that park and ride has been of considerable interest to 15

researchers and has been the subject of many research studies with diverse objectives. However, 16 few of these studies focused primarily on users’ response to parking charges for park and ride 17

trips. Although parking charges for park and ride facilities have been investigated, in many 18 cases, all trip lengths are considered together and do not focus on better-understanding the longer 19 trip commuter market. To complement to this body of knowledge, this paper focuses only on 20

longer distance commuting trips and investigates how parking charges for park and ride and the 21 provision of reserved parking spots can influence users’ mode choice. An SP survey was 22

designed to collect users’ response to a wide range of parking change variations along with other 23 factors. Data from the SP survey was then used to develop a discrete choice mode choice model, 24 where parking cost park and the availability of reserved parking spaces are some of the deciding 25

factors. Empirical models are used to enhance our understanding on how higher parking charge 26

can influence park and ride mode choice option for long distance commuting trips. 27 28

The Survey 29 30 As part of a large survey for collecting information on the characteristics of park and ride users 31

in Greater Vancouver, a stated preference survey was designed and conducted to collect 32 responses to hypothetical scenarios of different parking price levels at selected park and ride 33

stations across Greater Vancouver. The survey objective was to investigate the effect of 34 increasing parking charges on the mode choice of current park and ride users, as well as to 35 understand the impact reserved parking would have on this willingness-to-pay. Since surveyed 36 locations included a mix of stations that charge for parking (ranging from $3-$6 per day) and 37 stations that offer free parking, the resulting data set is able to examine the impact of shifting 38

away from free parking and compare this to the overall elasticity of parking cost changes at lots 39 that already charge for parking. The survey was conducted as face-to-face intercept surveys at 40

park and ride stations where travellers were either waiting for transit (at shelters/platforms) or 41 walking towards the station after parking their cars. The total survey duration was 7-10 minutes, 42 and was conducted using hand-held tablet computers. The questionnaire was divided into three 43 parts; current trip-related information, socioeconomic information and stated preference 44 scenarios to estimate willingness-to-pay for parking and the effects on mode choice. The 45 variables includes are in each of these sections are shown below. 46


1

Current trip information: 2

Origin location 3

Destination location 4

Access station distance from origin 5

Total distance between origin and destination 6

Destination purpose of the trips: work, school, shopping, visiting friends/family and others 7

Destination parking cost 8

Parking cost at the park and ride station 9

Access mode 10

11 Socioeconomic information: 12

Age 13

Gender 14

Household income 15

16 Stated preference scenarios: The stated preference scenarios were designed based on a random 17 design procedure considering two basic factors: parking cost and the option of reserved parking 18

at park and ride stations. The stated preference scenarios included current total travel times and 19 costs by three modes; park and ride, transit all-way and private car all-way which are fixed 20 across all scenarios. The parking cost and availability were varied as follows: 21

Parking cost: 5 levels are considered in $1 increments per level starting at $1 above the 22 current parking price at each station. 23

Parking availability: two levels were consider, one for reserved parking spaces and the 24 other for non-reserved parking spaces 25

26 Scenarios were designed by randomly choosing one parking cost level and one parking 27

availability level. Attribute level balance was maintained for the reserved parking factor, while 28 for the parking cost factor, the levels were drawn randomly with the condition that the same level 29

was not shown more than once to a respondent. Scenarios were designed on the fly using a 30 computer program while conducting the survey. The total travel time, total travel cost and access 31 travel time etc. for all modes were estimated using a predefined travel time and cost matrix for 32

the study area as well as origin and destination information reported by the respondent. In each 33 scenario, the respondent was asked to choose one alterative from the 4 presented options; one of 34 the three modes: park and ride, transit all-way, private car all-way or the option of abandoning 35

the trip. Respondents who have chosen ‘abandoning the trip’ option for any scenario are 36 excluded from the econometric investigation. This screening left 249 completed responses for 37 modelling individuals’ mode choice. 38

39 The survey was conducted at each of the 14 busiest stations of Greater Vancouver’s TransLink 40 transit system. Survey quotas from each station were determined based on the preliminary lot 41 size and lot utilization data available at the onset of the survey. Quotas were determined by 42

targeting an approximate 20 percent sample rate at each facility. 43 44

Econometric Model 45 46


A subset of all collected data was selected to ensure that there was no missing information of all 1

variables of concern for econometric modelling. The final dataset included 249 individuals with 2 each respondent completing four SP scenarios. All of these respondents have three feasible 3 modes, drive all-way, transit all-way and park and ride 4

5 Considering that individual respondents draw a certain level of utility in selecting a specific 6 mode from any SP scenario, the utility function (U) for each mode is composed of systematic 7 (V) and random (ε) components. The systematic component explains the deterministic utility of 8 choosing the corresponding alternative mode as a function of linear-in-parameter of the observed 9

variables and their corresponding coefficients (βx).The random utility component explains the 10 unobserved random variations in choice. 11 12

mmmmmxVU )(

(1) 13 where the subscript m indicates one of the three modes. 14

15

In order to derive the probability function for mode choice, distributional function of the random 16 error component needs to be assumed. Considering that the Independent and Irrelevant 17 Distribution (IID) of Type I Extreme Value distribution results in a Multinomial Logit (MNL) 18

model of mode choice (Ben-Akiva and Lerman 1985), we assume that the random error term is 19 IID Type I extreme value distributed. Unlike classic models with unit scale, the error term is 20

assumed to have a specific scale parameter of μ so that the heteroskedasticity in SP mode choice 21 behaviour can be captured. The Cumulative Density Function (CDF) of such an extreme value 22 distribution takes the form (Johnson et al. 1994) : 23

parameter scalea0),6/()Var( Variance,

ConstantsEuler'is,)E( Mean,

with exp)(

22

/

eF

(2) 24

Such an assumption results in a heteroskedastic MNL model of the form(Habib et al. 2012): 25

M

m

m

m

V

Vm

1/

/)exp(

)exp()Pr(

(3) 26

Here the subscript m indicates any mode and M indicates the maximum number of modes under 27 consideration. In order to capture the heteroskedasticity in SP responses the scale parameter was 28

parameterized as an exponential function of the attributes of the respondents: 29

)exp( z (4) 30

where, z refers attributes of that can explain scale variation and γ refers corresponding 31

coefficients. 32 33 The exponential function ensures that the scale parameter is positive. The scale parameter of SP 34

data-based discrete choice models are important to estimate as it allows for response biases to be 35 accommodated (Louviere et al 2000). In the case of MNL models, estimating the scale parameter 36 is important as it allows the utility measurement reliability to be captured when explaining 37 choice behaviour as function of systematic variables (Swait and Louviere 1993). In this paper, 38 the scale parameter has been explained systematically so that perception variations in utility can 39


be explained as a function of the observed variables. This allows us to interpret the certainty of 1

the SP choices in real life. For example, variables that increase the scale parameter value 2 indicated that the SP choice will be made with more certainty in real life and vice versa. 3 4

In the case of the current study, each respondent is exposed to 4 scenarios. Therefore, for any 5 individual respondent (i), the likelihood (Lt) of one SP scenario response is: 6 7

t

M

m

m

m

it

V

VL

1

/

/)exp(

)exp(

(5) 8

Thus, the likelihood of all SP responses for the individual is: 9

4

1

1/

/)exp(

)exp(

T

T

M

m

m

m

i

V

VL

(6) 10 In this paper, the empirical models were estimated using code written in GAUSS using the 11 MAXLIK component for maximum likelihood estimation (Aptech-Systems 2012). 12

13

Empirical Model 14 15 The empirical model is reported in Table 1. A total of 16 parameters were estimated using a 16 subset sample of 249 individuals. Most of the reported parameters are found to be highly 17

significant (i.e., t-statistics greater than 1.96). Some parameters were retained with t-statistics 18

less than 1.96 as their corresponding variables matched the expected signs and the variables are 19 able to provide insights into the behavioural processes. Model goodness of fit is measured by 20 estimating the Rho-Square value. The reported Rho-Square value is 0.19, which is considered a 21

reasonable value for a small dataset as was used in this study. 22 23

Alternative specific constant values of the model are relatively low in value indicating that the 24 model captures much of the choice behaviour through the observed variables. In general all 25

variable coefficients were found to have the expected signs (e.g. negative cost and travel time 26 parameters). Two types of cost variables were introduced, in-vehicle cost (or transit fare) and 27 parking cost. For the drive all-way option, the parking cost at the destination was considered 28 separately from the parking cost at the park and ride station. Separate travel time and cost 29 coefficients were estimated for work and non-work trip purposes. Interestingly, in-vehicle cost 30

was found to be insignificant with an unexpected positive sign for non-work trips. This indicates 31 that the respondents were not very sensitive to in-vehicle cost or fare for non-work trips. 32

33 Non-work trips were found to be more sensitive to in-vehicle travel time than work trips. In the 34 case of total travel time, non-work trips were found to be slightly more sensitive than work trips 35 again. In other words, considering only the in-vehicle cost, the value of travel time savings 36 would be slightly higher for non-work trips than for work trips. This apparently counter-intuitive 37


finding is perhaps reflecting the effects of higher captivity of commuters than the non-work trip 1

makers. 2

3

Table 1: Empirical Model 4 5

6 7 Work trips were found to be much more sensitive to parking costs at both the final destination 8 (for drive all-way trips) and at the park and ride station than non-work trips. As parking cost is 9 an out-of-pocket cost, it was found to be perceived differently than the in-vehicle cost by 10 travellers. An additional interesting finding is that it is clear that users are more sensitive to the 11

parking cost at the park and ride station than the parking cost at the destination. Comparing the 12 parking cost to in-vehicle cost, parking cost at park and ride stations is perceived 3 times more 13 negatively than in-vehicle cost or fare. However, parking cost at destination is perceived 2.5 14

Loglikelihood of Full Model -757.14

Loglikelihood of Null Model -930.53

Rho-square value 0.19

Systematic Utility Function:

Variables Parameter t-Statistics

Alternative Specific Constant

Park and Ride 1.8011 3.098

Private Car All-Way 0.422 2.476

Transit All-Way ---

In-Vehicle Cost/Fare

Work trip -0.1137 -1.749

Total Travel Time

Work trip -0.0128 -2.565

Non-work trip -0.0138 -1.858

Parking Cost at Station: Park and Ride

Work trip -0.3462 -2.984

Non-work trip -0.0887 -3.447

Parking Cost for Parking at Destination: Private Car All-Way

Work trip -0.2897 -2.838

Non-work trip -0.0241 -1.735

Researved Parking Space for Park and Ride 0.167 1.322

Ratio of Total Travel Distance to Access to Station distance

Park and Ride 0.0017 0.682

Transit All-Way -0.005 -1.075

Exponential Function of Scale Parameter

Currently Free Parking in Weekends -0.4247 -1.216

Current Parking Cost/Proposed Parking Cost 0.8882 1.859

Auto Occupancy for Provate Car All-Way 0.5791 2.709

Total Access Distance to Transit Station -0.0153 -1.161


times more negatively than in-vehicle cost. On the other hand, a guaranteed (reserved) parking 1

space is perceived positively by the traveller even in when the traveller is charged for parking at 2 the park and ride station. The ratio of the total travel distance to access distance to a park and 3 ride station was considered as a variable in the model. A higher value of this variable indicates a 4

longer portion of the trip is spent accessing the transit station. Intuitively, the coefficient of this 5 variable is positive for the park and ride option and negative for the transit all-way option (where 6 access to station is done by walking). This refers to the fact that park ride option is normally 7 considered for access stations that are far from trip origin. 8 9

One interesting finding looking at the model scale parameter is that for the stations where 10 parking is free on weekends, travellers are less confident about their SP choices. The ratio of 11 current parking and proposed parking costs was considered as a variable. Stations that currently 12 have free parking have a zero value for this variable. For currently charged stations, a wide 13

variation of any proposed parking price from the current price lowers the confidence of SP 14 choice. However, travellers with higher reported auto occupancy for the private car all-way 15

mode were found to have higher confidence in the SP choice. Longer access distance to the 16 transit station was found to reduce that confidence. 17

18 In order to investigate park and ride station parking price sensitivity to various mode choices and 19 mode switching behaviour, direct and cross elasticities were estimated for each individual in the 20

dataset. Average parking cost elasticities were estimated for each individual in the dataset by 21 taking the average of four elasticity values corresponding to each of the four scenarios. 22

Estimated elasticity values were averaged and kernel densities plotted. The direct elasticity of 23 park and ride cost for the park and ride mode choice was estimated and its cross elasticity to the 24 private car all-way and transit all-way mode choices (equations for direct and cross elasticity 25

calculations are available in Train (2009)). The sample average direct elasticity of the park and 26

ride station parking cost to the park and ride mode choice was -0.94 with a standard deviation of 27 0.34. This indicates that the park and ride parking cost is inelastic. However, considerable 28 heterogeneity in park and ride parking cost elasticity exists among the respondents. The 29

estimated kernel density of average parking cost elasticity is plotted and presented in Figure 1. It 30 clearly shows this direct elasticity has a bi-modal distribution with one modal value of -1 31

(perfectly elastic) and the other modal value of -0.5 (inelastic). The application of an advanced 32 discrete choice model allowed such a variation in parking cost elasticity for park and ride mode 33

options to be captured. The model clearly indicates that two distinct park and ride mode user 34 population segments exist, captive users (who are inelastic) and choice transit users (who are 35 elastic with respect to parking costs). 36 37 To understand the mode switching patterns created by increasing parking costs at park and ride 38

stations, the average cross elasticity of parking cost was estimated for the private car all-way and 39 transit all-way mode choice options for each individual respondent. The sample mean cross 40

elasticity for park and ride parking cost to the drive all-way option is 0.44 with a variance of 41 0.34. The kernel density of the cross elasticity values are plotted and presented in Figure 2. In 42 this case, the kernel density clearly shows a uni-modal distribution. The cross elasticity of 43 parking cost at park and ride stations for the transit all-way option was found to have a sample 44 mean of 0.51 and a variance 0.28. Figure 3 shows the kernel density plot of this cross elasticity 45


which clearly shows a bi-modal distribution. This is consistent with the bimodal distribution 1

found for the direct elasticity of parking price for the park and ride mode choice option. 2 3 In general, the results show that increasing parking costs at park and ride station will influence 4

travellers to switch more towards the transit all-way option than the private car all-way option. 5 The bimodal density of cross elasticity for the transit all-way option indicates that choice users 6 are more likely to switch (higher modal cross elasticity) and captive users are less likely to 7 switch (lower modal cross elasticity). 8 9

Figure 1: Parking Cost Direct Elasticity for Park and Ride 10

11

0.5

11

.5

Den

sity

-2 -1.5 -1 -.5 0Parking Cost Elasticity for Park and Ride Mode


Figure 2: Parking Cost Corss Elasticity for Auto Driving All-Way 1

2 3 4

Figure 3: Parking Cost Cross Elasticity for Transit All-Way 5

6

0.5

11

.52

Den

sity

0 .5 1 1.5 2Park and Ride Parking Cost Cross Elasticity for Auto Driving All-Way

0.5

11

.5

Den

sity

0 .5 1 1.5Park and Ride Parking Cost Cross Elasticity for Transit All-Way


1 In general, results show that increasing parking cost at park and ride station will influence 2 traveller to switch more towards the transit all-way option than the private car all-way option. 3 Bimodal density of cross elasticity for transit all-way option indicates that for the choice users it 4

is more likely to switch (higher modal cross elasticity) and for the captive users it is less likely to 5 switch (lower modal cross elasticity). 6 7

Conclusions and Recommendations for Future Study 8

9 This paper has presented an investigation of the influences of introducing parking charges at park 10 and ride stations on mode choice behaviour in Greater Vancouver. A stated preference survey 11 was conducted at the 14 busiest park and ride stations in Greater Vancouver, some of which are 12 currently free and some have parking charges ranging from $3 to $6/day. The survey collected 13

information on the trip in-progress and presented 4 stated preference scenarios to each 14 respondent. The stated preference scenarios presented combinations of parking charging schemes 15

and asked the respondent to make a mode choice in each context. Such stated preference 16 information along with the current trip information is able to provide a rich dataset for testing 17

parking price sensitivities for mode choice, especially for long distance trips (in which only park 18 and ride, private car all-way and transit all-way are the feasible travel modes). 19 20

The cleaned dataset was used to estimate a heteroskedastic multinomial logit model of the stated 21 preference for mode choices. The model explicitly included a scale parameter function as a 22

function of respondents’ characteristics and current trip information. This scale parameterization 23 was helpful in correcting stated response biases. The estimated model parameters were used to 24 investigate the direct and cross elasticities of parking charges at park and ride stations on the 25

choice of mode. The model showed that there are two clear segments of travellers currently using 26

the park and ride option in Greater Vancouver. One segment is choice users for whom parking 27 cost is perfectly elastic with respect to the park and ride mode choice while the other relatively 28 smaller group represents captive users for whom the parking cost is inelastic with respect to the 29

park and ride mode choice. In terms of mode switching behaviour, increasing parking charges at 30 park and ride stations is more likely to switch current park and ride users to the transit all-way 31

option than the private car all-way option. 32 33

This study was based on a small dataset collected from current park and ride users and includes 34 some limitations that should be noted. Considering the focused objective of this study to 35 investigate the parking price sensitivity for mode choice for long distance trips, only three modes 36 were considered for the empirical study. Further, in this paper, parking price changes were only 37 investigated in the design of stated preference scenarios. It is anticipated that combinations of 38

fuel price changes, transit fare changes and parking price changes would have different impacts 39 on mode choice behaviour. However, the scope of such investigations is beyond that of the 40

current study and hence is considered for future work. 41 42 Acknowledgements: 43 The authors acknowledge TransLink for sharing the data collected for research and educational 44 purposes. The views and findings presented in this paper are those of the authors and do not 45 necessarily reflect those of TransLink. 46


1 References: 2 Aptech-Systems, 2012. Gauss Engine and Maxlik. http://www.aptech.com. 3 Ben-Akiva, M. E. and S. R. Lerman, 1985. Discrete Choice Analysis: Theory and Application to 4

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