Modelling mode switch associated with the change of residential location

Travel Behaviour and Society 9 (2017) 21–28

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Travel Behaviour and Society journal homepage: www.elsevier.com/locate/tbs

Modelling mode switch associated with the change of residential location b,⁎

a

MARK

Mahmudur Rahman Fatmi , Muhammad Ahsanul Habib a

Department of Civil & Resource Engineering, Room D215, D Building, Dalhousie University, 1360 Barrington Street, P.O. Box 15000, Halifax NS B3H 4R2, Canada School of Planning, and Department of Civil and Resource Engineering, Room B105, B Building, Dalhousie University, 1360 Barrington Street, P.O. Box 15000, Halifax NS B3H 4R2, Canada b

A R T I C L E I N F O

A B S T R A C T

Keywords: Mode switch Residential relocation Past travel behaviour Changes in household state Attitude Random parameters logit model

This paper presents the findings of a random parameters logit model (RPL) of mode switching behaviour as a longer-term decision using retrospective survey data. This study focuses on whether or not individuals switch their commute modes following residential relocation. The study accommodates the panel effects of the repeated mode switching decisions of individuals during their life-time and captures unobserved heterogeneity within the RPL modelling framework. This study extensively examines the effects of past travel behaviour, changes in household state due to life-cycle events, attitude, and accessibility measures. Factor analysis is performed to generate the following six attitudinal factors: pro-high density, pro-environment, travel satisfaction, travel stress, pro-car, and pro-active transportation. The model results suggest that past travel experiences significantly influence the mode switch decisions. Individuals are found to show preference in persisting with their past mode. In the case of change in household state, individuals are more likely to switch mode if household size increases. Among the attitudinal variables, pro-high density, pro-active transportation, and travel stress are found to influence mode switch. Individuals living closer to transit stops show preference to change mode; in contrast, individuals living farther from work locations reveal a lower likelihood to change mode. The model results reveal that heterogeneity exists among the individuals. For example, individuals with pro-high density attitude show significant variation during mode switching decisions. This study provides important behavioural insights, which will assist transportation planning and policy making that focuses to influence longer-term shifts in travel behaviour and choice of residential location.

1. Introduction The increasing use of private car for commuting causes urban traffic congestion during peak periods, which also posits the concern for high environmental pollution and associated health-related complicacies. To effectively manage the congestion and protect the urban environment, a change towards more sustainable choice of commute mode is required. Basically, commute mode choice is a habitual phenomenon, since it is the most frequent trip made by an individual (Gardner, 2009). Due to the inherent habit persistency nature of humans, a change in commute mode is generally associated with a change in the long-term state such as change in location or change in vehicle ownership (Stanbridge et al., 2004). Residential location is closely tied with the mode choice (Cao et al., 2009). A change in residential location changes the built environment, and accessibility to different activity points and transportation services, among others; which might influence a change in the choice of commute mode (Chen et al., 2008; Wang and Chen, 2012). Hence, the phenomenon of whether individuals intend to switch commute modes or not has a longer-term dimension, which is not well ⁎

understood. This study investigates commute mode switch as a longer-term decision and conceptualizes that individuals evaluate their commute mode following a change in residential location along their life-course. For adequate understanding of the behaviour, a dynamic modelling approach is required to address the temporal dimension of the individuals’ life-time (Nurdeen et al., 2007; Wang and Chen, 2012). To address the temporal dynamics within the modelling framework, this study utilizes a random parameters logit (RPL) modelling technique. The RPL model developed in this study captures the unobserved heterogeneity among the sample population during mode switching decisions. The model is developed using data from a retrospective Household Mobility and Travel Survey (HMTS) administered in Halifax, Canada. One of the unique features of this study is to examine the effects of a wide array of factors influencing mode switching behaviour. For example, this study addresses how past travel behaviour influences mode switch decisions. The study accommodates the effects of life-cycle events that causes a change in the household state, such as an increase in the household size, an increase in the number of driver’s licence, a

Corresponding author. E-mail addresses: [email protected] (M.R. Fatmi), [email protected] (M.A. Habib).

http://dx.doi.org/10.1016/j.tbs.2017.07.006 Received 5 February 2015; Received in revised form 27 July 2017; Accepted 27 July 2017 Available online 23 August 2017 2214-367X/ © 2017 Hong Kong Society for Transportation Studies. Published by Elsevier Ltd. All rights reserved.

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M.R. Fatmi, M.A. Habib

Travel behaviour has a temporal dimension of individuals’ life-time, as individuals’ previous experience and occurrence of life-cycle events influence the future choice. For example, in the case of residential location choice, Chen and Lin (2011) revealed that the choice of prior locations has an influence on the choice of the subsequent locations. Bamberg et al. (2003) investigated the commuting behaviour of the college students and argued that the past commuting behaviour significantly influences later behaviour. Moreover, life-cycle events occurring at different stages of the life-time influence travel behaviour (Verhoeven et al., 2005). For instance, Oakil et al. (2011) performed a panel analysis and argued that the birth of the first child significantly influences mode switching behaviour. van der Waerden et al. (2003) revealed that critical events such as getting a driver’s licence affects mode choice decisions. Rashidi and Mohammadian (2011) developed a hazard-based system of equations to jointly model vehicle transaction, residential mobility, and employment relocation timing decisions. They revealed that residential and job relocation influences vehicle transaction decisions. Residential relocation was found to positively influence environmentally concerned individuals to use car less frequently (Verplanken et al., 2008). Clark et al. (2015) suggested that commute mode is influenced by changes in the distance to work place. Relocation of workplace to a restricted parking facility was found to reduce car use and increase train use (Walker et al., 2015). Mohammadian and Miller (2003) examined how dynamic variables representing life-cycle events (i.e. household size increase, household size decrease) influence automobile transaction decisions. Travellers’ attitude has a close relationship with mode choice (Parkany et al., 2004; Sunkanapalli et al., 2000). According to the attitude theory, attitude refers to evaluation of a behaviour, which disposes a person to behave in a certain way (Dawes and Smith, 1985; Ajzen, 1987). Many disciplines, including social psychologists and transportation researchers describe attitude as a part of the decisionmaking process. Attitude has significant impact on travel choices (Outwater et al., 2003); particularly, the choice of travel mode (Marketa, 2009; Domarchi et al., 2008). For instance, in the case of choosing public transportation, attitude was found to be a major indicator, more than demographics and travel needs (Garling et al., 1998; Fuji and Garling, 2003). Paulssen et al. (2014) revealed that the effects of attitude regarding flexibility, comfort, convenience, and ownership on mode choice is higher than the effects of conventional level-of-service attributes. Cao et al. (2009) developed a seemingly unrelated regression equation model to examine the influence of attitude on mode choice behaviour using data from a travel survey in Northern California. The survey collected 32 attitudinal statements regarding travel on a 5-point scale from “strongly agree” to “strongly disagree”. Using the attitudinal statements, they (Cao et al. 2009) performed factor analysis to generate six attitudinal factors: pro-bike/walk, pro-transit, pro-travel, travel minimizing, car dependent, and safety to car. They revealed that car dependent factor is positively associated with higher frequency of car trips and lower frequency of transit trips. Pro-transit and pro-bike/walk factors were found to be related with higher use of transit and active-transportation modes. Recently, researchers have shown interest to investigate travellers’ mode switching behaviour with a focus to promote the use of sustainable travel modes. Some studies have focused on short-term mode switch behaviour. Athena et al. (2010) investigated traveller’s mode switch behaviour based on specific travel information acquisition, such as an incident on the roadway or sudden road closure using data from travel diaries in the Puget Sound Region (PSRC) in 2000. Hess et al. (2007) examined the relative sensitivity of mode and time of day choice to changes in travel times and costs using stated preference survey data from the United Kingdom and Netherlands. They revealed that short term mode switching can occur depending on the time of the day. Nurdeen et al. (2007) examined the mode switch behaviour from car to public transportation using both revealed and stated preference survey data in response to temporary incentives. On the other hand, mode

decrease in the number of private vehicle, and a decrease in the number of bedroom in the dwelling. Another important dimension of this study is to examine the effects of attitudes in two broad categories: a) attitude towards land use, environment and commute experiences, and b) attitude towards travel modes. Factor analysis is performed to generate the following six attitudinal factors: pro-high density, pro-environment, travel satisfaction, travel stress, pro-car, and pro-active transportation. The influence of accessibility measures to major activity points and transportation services, such as work place and transit stops, are tested as well. This paper is organized as follows: the next section reviews the relevant literatures; then the data used in the empirical application is briefly described. The subsequent section discusses the independent variables considered in the study, followed by the discussion on modelling approach. The successive section discusses model results. Finally, a summary of contributions and future research directions are presented. 2. Literature review Choice of commute mode tends to be a habitual decision due to the frequent nature of the trip (Gardner, 2009). As a result, individuals prefer to continue with their existing commute mode. This remains true unless there is a major longer-term change of state, such as a change in residential location, a change in work location, or a change in vehicle ownership; which may trigger a change in the commute mode choice (Zhang, 2006; van der Waerden et al., 2003). Among the longer-term changes, change in residential location considerably influences a change in commute mode (Dargay and Hanly, 2007; Stanbridge et al., 2004). A survey concerning recent movers (home owners) in Bristol, England, suggested that 27% of the survey respondents changed their commute mode following a residential relocation (Stanbridge et al., 2004). Analysis of the British Household Panel Survey revealed that a high proportion of the population changes commute mode following residential relocation (Dargay and Hanly, 2007). The reason for a close association between relocation and mode switch is attributed by the fact that a change in residential location changes the built environment, and accessibility characteristics of the home neighbourhood; which influences the choice of commute mode. Locations with higher mixed land use encourages the use of transit and active transportation, and discourages the use of car (Cao et al., 2009). Individuals living in urban areas walk more (Coogan et al., 2007) and choose to take more transit trips (Kitamura et al., 1997). On the other hand, individuals who prefer single-detached dwellings and single occupancy vehicles (SOV), live in sub-urban areas (Mae, 1997) and own cars (Wachs and Crawford, 1992). Brownstone and Fang (2014) developed a Bayesian multivariate ordered probit and tobit model to endogenously examine the effects of residential density on vehicle choice and usage. They revealed that an increase in the residential density slightly reduces the utility of truck choice. Although built environment such as, higher population and employment density positively influences the use of transit and active transportation (Zhang, 2004); it is argued that such densities are basically proxies of accessibility related variables (Crane and Crepeau, 1998). Distance to the CBD was found to explain vehicle kilometers travelled behaviour better than density (Miller and Ibrahim, 1998). Among the accessibility related variables, accessibility to transport services is a critical predictor. For example, Kitamura et al. (1997) revealed that distance to the nearest bus stop and rail stop are significant predictors for car trips and transit trips. Chen et al. (2008) revealed that longer distance to the nearest transit stop from home and job location are associated with higher likelihood of car use. They found that shorter accessibility to job is positively associated with transit use and longer accessibility to job is positively associated with car use. Individuals with longer commute were found to prefer SOV and less likely to carpool (Wang and Chen, 2012). 22

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M.R. Fatmi, M.A. Habib

2015). It was found that majority of the sample characteristics lie within a few percentage points of the census statistics. Thus, the HMTS can be considered as a representative sample. For example, in the case of gender distribution in the HMTS, 49% of the respondents are female and 51% are male. According to the census 2011, 51% of the Halifax population are female and 49% are male. Therefore, the gender distribution of the HMTS shows a 2% variability with census 2011. In the case of age distribution, age groups 25–34 years and 35–44 years show a 4% variability with the census 2011 population. A similar trend is found for other characteristics, such as household income, and household size. In the case of the primary mode choice, 41%, 20%, and 35% of the respondents are found to choose car, transit, and active transportation respectively. Comparing with the census 2011, the HMTS sample under-represents car and over-represents transit and active transportation mode for commute. For example, the HMTS over-represents the choice of transit mode by 7.5%. This biasness might be attributed by the higher share of urban dwellers (around 50%) in the sample. Additional data sources used in this study include 2011 census tabulations from the Statistics Canada, land use data from the Halifax Regional Municipality (HRM), and location of activity points and transportation services from the Desktop Mapping Technologies Inc.

switch is conceptualized as a longer-term decision as well. For example, Oakil et al. (2011) focused on how individuals’ switch commute mode to and from car. Idris et al. (2015) investigated the mode switch behaviour from car to transit and other modes. Clark et al. (2015) analyzed the commute mode switch behaviour regarding switching to and from car and active transportation. Wang and Chen (2012) examined the effects of attitude and built environment on mode switching behaviour between single occupancy vehicle and carpool. Although it is important to investigate the mode switch as a longer-term decision; how a change in commute mode is associated with a change in residential location is not well understood in the existing literature. This study tries to contribute in the literature by investigating commute mode switch as a longer-term decision. The phenomenon is conceptualized as a longitudinal process, where individuals’ evaluate commute modes following residential relocation along the life-course. To address the life-trajectory dynamics of the individuals’ life-time, the study adopts a random parameters logit (RPL) modelling technique. The RPL model assumes correlated sequence of choices due to the repeated mode switching instances and captures unobserved heterogeneity among the individuals. One of the unique features of this study is to examine hypotheses related to a wide array of factors, including past travel behaviour, change in household state, attitudes, accessibility measures, and socio-demographic characteristics. For example, the study examines whether there is an influence of past travel behaviour on mode switch decisions or not. In the case of change in household state, the study tests how critical life-cycle events such as birth of a child, and death of a member, resulting a change in household state such as change in household size, change in vehicle ownership level, and getting a driver’s licence, affect mode switch decisions.

4. Independent variables used 4.1. Past travel behaviour and socio-demographic characteristics The effects of past travel behaviour is accommodated by considering individuals’ past commute modes, such as car, transit, walk, and bicycle. Past commute mode refers to the commute mode in the immediate preceding residential location. Among the socio-demographic characteristics, the study considers a number of variables, including age of the respondent, household income, number of driver’s licence in the household, household owning monthly transit pass, education of the primary worker, dwelling type, and monthly rent, among others.

3. Data This study utilizes retrospective survey data obtained from the wave 1 of the Household Mobility and Travel Survey (HMTS), conducted in Halifax, Nova Scotia from September 23rd to October 17th 2012. The total number of survey respondents was 295. In terms of content, the survey included detailed questions regarding the location, and household and dwelling characteristics of the three most recent residential locations of the respondents. Respondents were also asked to identify their choice of commute mode at each residential location. This information is utilized to derive the dependent variable for this study, by comparing the commute mode choices in two consecutive residential locations. In addition, the survey collected information regarding household and employment compositional size changes, vehicle ownership history, employment records, and daily travel activities. The survey included attitudinal statements as well. Following the cleaning of the data, a total sample size of 289 is considered in this study. The exploratory analysis of the sample reveals that among the total 289 observations, 165 chose to switch mode with the change in residential location (57.09%) and 124 chose to continue with their previous mode (42.91%). The statistics of the mode-specific mode switch are presented in Table 1. Aggregate statistics of the data from the survey was compared with the 2011 Canadian Census statistics for the Halifax (Salloum and Habib,

4.2. Change in household state The dynamics of the changes in the household state at different stages of the life-time are accommodated in this study. A change in household state refers to a change in household composition, sociodemographic characteristics, and dwelling characteristics, etc. A change in household state is triggered by life-cycle events. For instance, an increase in household size occurs due to birth of a child or member move-in, and a decrease occurs due to death or member move-out. Since this study focuses on the mode switching behaviour associated with residential relocation, a change between two subsequent locations is considered as a change in the household state. The variables considered in this category include: change in the household size, change in the number of car, change in the number of driver’s licence, and change in the number of bedroom in the dwelling, among others. A change in household state includes both an increase and decrease. 4.3. Attitudinal variables The HMTS survey contains 33 statements concerning attitudes. The respondents were asked attitudinal questions on a three point agreedisagree likert scale to specify their level of agreement or disagreement. This study accounts for the attitudinal effects by subdividing the attitudinal statements into the following two groups: attitude towards land use, environment and commute experiences, and attitude towards travel modes. Among the 33 attitudinal statements in the survey, 18 statements were directly related to the attitude towards land use, environment and commute experiences. Alternatively, 8 statements in the survey correspond directly to attitude towards travel modes. The statements which are not related to land use, environment and commute experiences, and travel modes, for example, “I invest a lot of time

Table 1 Percentages of mode-specific mode switch with the change in residential location (among the 165 respondents who switches mode). To (Percentages)

From (Percentages)

Mode

Car

Transit

Bicycle

Walk

Car Transit Bicycle Walk

0 10.90 4.80 10.40

15 0 1.9 12.10

6.7 6.7 0 7.90

7.90 12.1 3.60 0

23

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M.R. Fatmi, M.A. Habib

Table 2 Factor analysis for attitude towards land use, environment and commute experiences (N = 289).a Statements

Pro-high density

I love to live in the inner city A suburban environment offers the best quality of life for families It is important for children to have a backyard to play in I am fully satisfied with my commute My commute makes me feel stressed My commute offers a good transition between home and work Travel time is generally wasted time I feel less stressed when taking transit than when driving Taking good transit is an enjoyable experience I consider global warming a major concern I limit my driving because it’s bad for air quality Households should be fined if their greenhouse gas emissions exceed a set daily limit Households that generate less greenhouse gas emissions should get a tax credit Proximity to shops/services is important to me Living in a multiple family unit does not provide enough privacy I consider transit an essential service Increasing residential density is good city planning More highways are required to reduce traffic congestion

0.7852 −0.5142 −0.6756

Pro-environment

Travel satisfaction

−0.8147 0.7626 −0.7702 0.4236

−0.2739 0.6926 0.7327 0.3128

Travel stress

0.5334 0.7379 0.7647 0.6724

0.5249 −0.5851 0.2915 0.4864 −0.2919

0.4971 −0.5988

Note: Loading less than 0.27 are suppressed for ease of interpretation. a Pattern matrix for principal component analysis with oblique rotation.

into the community where I live” are not considered in this study.

Table 3 Factor analysis for attitude towards travel modes (N = 289).a Statements

4.3.1. Attitude towards land use, environment and commute experiences Factor analysis is performed on 18 statements with a focus to explain attitude towards land use, environment and commute experiences. Factors are extracted using principal component analysis with an oblique rotation (Redmond, 2000). The analysis yields a four dimensional solution, which are identified as pro-high density, pro-environment, travel satisfaction, and travel stress (see Table 2). Standardized scores for each respondent on these factors have been used as candidate explanatory variables. The pro-high density factor relates to those who prefer to live in the vibrant lifestyle of the inner city and can access many facilities with relative ease in a high density environment. Thus, individuals with high scores in relation to this factor are likely to be less car dependent and more transit-oriented. A high negative loading for suburban lifestyle and houses with backyard for this factor is also expected. A positive loading on global warming statement reveals their environmental consciousness and support their less car dependent attitude. The proenvironment factor represents those individuals who are extremely conscious about the environment. The high positive loading on statements regarding global warming and greenhouse gas emission confirms their environmental consciousness. The high positive loading to limit driving for the sake of air quality reveals their specific consciousness about air quality (Schwanen and Mokhtarian, 2007). The factor representing travel satisfaction refers to those who gain utility from travelling. These individuals live in the inner city, extract maximum benefit from transit system, and find commuting enjoyable. Lastly, travel stress reflects those individuals who find travelling as waste of time. These individuals are generally unsatisfied and stressed with their commute.

I enjoy riding a bicycle I prefer walking to driving whenever possible I take pride in owning a car Driving provides me with freedom Workplaces should provide free parking to all employees Owning a vehicle is necessary when you have a family I consider walking as part of my daily exercise I limit my driving because it’s bad for air quality

Pro-car

Pro-active transportation 0.2107 0.7194

0.5485 0.4756 0.4592 0.6443 0.2015

0.3532 0.4641

Note: Loading less than 0.21 are suppressed for ease of interpretation. a Pattern matrix for principal component analysis with oblique rotation.

4.4. Accessibility measures The accessibility measures to major activity points and transportation services are represented by variables such as the distance from home to work location, and distance from home to the nearest transit stop, and shopping center, among others. The accessibility measures are determined on the basis of the respondents’ residential locations. Road network distance-based accessibility measures are generated utilizing the network analyst tool of ArcGIS. The appropriate temporal scale is maintained during generating accessibility measures and the measures vary temporally as households change their location along the life-time. 5. Modelling approach The mode switch model developed in this paper considers two possible choice options: (1) switch of commute mode after relocation, and (2) no switch of commute mode after relocation. The study follows a random utility-based discrete choice modelling technique. According to the random utility theory, an individual maximizes utility in choosing a particular alternative in a given choice situation. The utility derived from choosing alternative i by individual j at t choice situation is given by:

4.3.2. Attitude towards travel modes There are 8 statements in the survey which are directly related to attitude towards travel modes. Factor analysis is again conducted using principal component analysis considering two dimensional oblique rotations (see Table 3). The two attitudinal dimensions are: pro-active transportation and pro-car. The pro-active transportation factor reflects the attitude of those individuals who are environmentally concerned, enjoy walking or bicycling and consider it to be good for health. Pro-car individuals find pride in owning cars, as cars provide them travel freedom and aid them in completing multiple activities in one trip.

Uijt = βj Xijt + εijt 24

(1)

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M.R. Fatmi, M.A. Habib

correlation exhibit values closer to 0 in most cases. Table 5 shows the parameter estimation results and goodness-of-fit measures for the MNL and RPL models. In terms of goodness-of-fit measures, the RPL model outperforms the MNL model with an improved adjusted pseudo Rho-square value of 0.2478. In addition, a likelihood ratio test is performed, where a chi-square statistics of 118.72 with 20 degrees of freedom indicate that the RPL model performs reasonably better; at least at the 5% significance level. Therefore, the RPL model is considered as the final model. A detailed discussion of the RPL model results is given below.

Here, Xijt is the vector parameter of the observed attributes, βj is the associated parameter to be estimated, and εijt is the random error term. Assuming that the random error term is independently and identically distributed (IID), a logit model can be formulated as:

Pijt =

e βj Xijt I ∑i = 1

e βj Xijt

(2)

Here, Pijt represents the probability of individual j choosing alternative i at t choice situation, and I is the total number of alternatives considered in the choice set. However, this conventional logit formulation (Eq. (2)) is restricted to the IID assumption and does not capture unobserved heterogeneity among the sample individuals. As a result, the parameter estimates of the conventional logit model might be biased if heterogeneity exists among the sample individuals. Hence, the conventional logit has been extended to several flexible model formulations, such as random parameters logit (RPL) model to capture the unobserved heterogeneity in the decision processes. Moreover, in the case of this study, repeated mode switching decisions of the same individual exists due to the longitudinal nature of the study. To capture the repeated mode switching decisions along the individuals’ life-time, let’s assume that cjt is the alternative chosen by individual j at t choice situation from the sequence of choices = cj1,cj2,…………cjT . The repeated choice probability can be written as: T

Pj (βj ) =

∏ t=1

e βj X jcjt t I

∑i = 1 e βj Xijt

6.1. Random parameters logit (RPL) model results Among the socio-demographic characteristics, low income individuals with household income below $25,000 show a higher probability of switching commute mode following relocation. The budgetary constraint of the low income individuals might trigger a re-evaluation of commute mode following a change in the residential location. Older individuals, aged above 55 years, exhibit a lower probability to switch mode. Due to the habit persistency, older individuals might prefer to continue as long as possible with the type of mode they are accustomed to (Tacken, 1998). Hence, they show preference to not switching the mode. Individuals without a driver’s licence within the household show a strong negative relationship, as they have limited options to switch mode. Individuals who purchase monthly transit passes are less likely to switch mode. Individuals with such characteristics might represent transit users who have a higher probability of persisting with their current mode upon residential relocation. Among the dwelling characteristics, individuals living in single-detached dwellings show negative relationship. This variable might represent high income group, who live in owned properties and are less likely to switch mode. On the other hand, individuals living in rented dwellings with rent below $1200 show a strong positive relationship. These individuals can be considered as low income group, who have a higher probability to switch mode during relocation to reduce travel-related costs and stresses. Among the variables representing past travel behaviour, individuals whose commute mode at the previous residence was car, show a strong negative relationship with a coefficient value of 1.299. This result suggests that individuals prefer to continue with their past commute mode following relocation if their mode was car, which might be an indicator of habit persistence (Bamberg et al., 2003). Similar strong negative relationship is found for the variable representing individuals whose commute mode in the previous residence was bicycle. This variable shows a two times stronger negative relationship than the variable representing commute mode at the previous residence was car. In the case of the effects of changes in household state, majority of the variables reveal positive relationships, with the exception of the variable representing the downsizing of dwelling. This variable exhibits a strong negative relationship. In contrast, an increase in the household size shows a positive relationship. An increase in household size due to the birth of a child, or new members moving in generates additional travel needs, which might trigger a switch in commute mode. An increase of driver’s licence in the household shows a higher likelihood for switching mode, as addition of driver’s licence in the household implicates more reliance on car. Furthermore, the positive relationship with the decrease of private car in the household is expected. This result possibly reflects adjustments in accordance to the availability of mobility tools (Roorda et al., 2000). Among the attitudinal variables, individuals who belong to the prohigh density group are more likely to switch their mode for commute upon relocation. This can be explained by the fact that pro-high density individuals might live or move to the inner city, which is well supported by multi-modal options, such as, transit, walk or bike. This reflects the scenario of Halifax, as the inner city of Halifax is a highly mixed land use area, which is well connected by the transit system and supports

. (3)

Now the unconditional choice probability can be derived by integrating Pj (βj ) over all values of βj , as shown below:

Pj =

∫ Pj (βj) g (βj |μ,υ) dυ.

(4)

Here, g (βj |μ,υ) is the density function assumed to be normally (βj ∼ (μ,υ)) distributed (Revelt and Train, 1998). The log-likelihood function is constructed based on the above probability expression and can be written as: J

LL =

∑

lnPj. (5)

j=1

This likelihood function cannot be evaluated in the closed form as it is a multivariate integral. Therefore, the integral of the choice probabilities is approximated by a Quasi-Monte Carlo (QMC) simulation using 500 Halton draws. The QMC estimation with Halton draws uses cleverly crafted non-random and uniformly distributed sequences in the realm of integration and outperforms random draws with improved accuracy (Bhat, 2011). The choice probability is calculated through taking several draws from g (βj |μ,υ) for each individual. This process is repeated N times. Finally, the integration over g (βj |μ,υ) is approximated by averaging the N draws. The simulated log likelihood function can be expressed as: J

S=

∑ j=1

ln

1 N

N

∑

Pj (βjn )

n=1

(6)

Finally the goodness-of-fit measures of the RPL model is evaluated in terms of adjusted pseudo Rho-square and likelihood ratio test. 6. Model results For comparison purposes, a traditional multinomial logit (MNL) model is developed in addition to the RPL model. Table 4 shows the summary statistics of all the exploratory variables retained in the MNL and RPL models. A correlation coefficient test is performed to check the co-linearity among the variables considered in this study. The test confirms that substantial co-linearity does not exist among the variables considered in the final model specification as the coefficients of the 25

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Table 4 Summary statistics of explanatory variables used in the mode switch model. Variables

Description

Mean/Percentages

Std. Dev.

Socio-demographic Characteristics Age above 55 years (Dummy) Household income below $25,000 (Dummy) Minimum education under graduation (Dummy) No driver’s licence in the household (Dummy) Monthly transit pass ownership (Dummy) Current dwelling type single-detached (Dummy) Rent below $1200 (Dummy)

Respondent’s age above 55 years Respondent’s household income less than $25000/year Respondent’s highest education is minimum under graduation Respondent with no driver’s licence in the household Respondent purchases monthly transit pass Respondent lives in a single detached dwelling Respondent’s house rent less than $1200/month

21.11% 15.92% 60.55% 1.73% 22.49% 49.83% 87.20%

– – – – – – –

35.29%

–

Commute mode bicycle in previous residential location (Dummy)

Respondent’s primary mode of transportation was car in the previous residential location Respondent’s primary mode of transportation was bicycle in the previous residential location

7.96%

–

Change in Household State Increase in the household size Increase in the household driver’s licence Decrease in the household car ownership Decrease in the number of bedroom

Increase in the household size Increase in the number of driver’s licence in the household Decrease in the number of private car in the household Decrease in the number of bed room

1.04 1.03 1.39 1.60

3.54 3.58 1.24 2.62

Attitudinal Variables Pro-high density Travel stress Pro-active transportation Pro-car

Pro-high density Travel stress Pro-active transportation Pro-car

1.51 −1.25 2.07 3.23

2.07 1.61 1.68 2.66

Accessibility Characteristics Home to transit stop dist. below 200 m (Dummy) Home to work dist. above 500 m (Dummy) Reason for location change is to be closer to work (Dummy)

Respondent’s home to transit stop distance less than 200 m Respondent’s home to work place distance greater than 500 m Respondent’s reason of residential location change is to be closer to work

89.97% 56.05% 8.65%

– – –

Past Travel Behaviour Commute mode car in previous residential location (Dummy)

in the final model, as they do not confirm prior hypotheses with reasonable statistical significance. The final model considered few parameters, which are below the threshold t-statistics value with an assumption that they might show statistical significance if a larger dataset were available. Moreover, these variables provide important behavioural insights and have policy implications.

convenient access to nearby shops and services by walking or biking. Travel stress shows a positive relationship, since individuals who find stress in travelling are expected to evaluate different modes in search of a mode that reduces the stress. Both the variables representing attitudes toward travel modes show expected relationships. For instance, individuals with pro-car attitude are less likely to switch mode, since a car facilitates pride, freedom and the ability to conduct multi-activities through trip chaining. In the case of accessibility characteristics, individuals living within 200 m from a transit stop are more likely to switch mode. A dummy variable representing individuals living more than 500 meters away from the work place exhibit a lower probability of switching mode. This result suggests that individuals living farther away from their work locations, presumably in suburban areas, exhibit habit persistence in the case of commute mode choice. The model results suggest that unobserved heterogeneity exists among the sample individuals. Two variables representing pro-high density and travel stress are retained as random parameters. The parameter estimates suggest that the variables representing pro-high density and travel stress show a variance value of 0.5259 and 0.0107 with a mean value of 0.0527 and 0.2696 respectively. The results suggest that heterogeneity exists among the individuals during mode switching decisions. For example, in the case of the pro-high density, the large variance value suggests that a sizable variation exists among the pro-high density individuals during mode switch decisions. Therefore, individuals with such attitude might not always prefer to switch commute mode, they might choose to continue with the same mode as well. The pro-high density group is diverse, both socio-economically and spatially, thereby representing varying propensities of switching mode upon relocation. Other variables are also tested as random parameters; however, standard deviations with reasonable statistical significance could not be confirmed in most cases. Furthermore, the model tests a wide range of hypotheses utilizing different types of variables. Interestingly, none of the neighbourhood characteristics (average household income, average number of rooms and bedrooms, dwelling type) could be incorporated

7. Conclusion This paper employs a random parameters logit (RPL) model to investigate individuals’ mode switching behaviour as a longer-term decision. Particularly, this study focuses on the commute mode switch behaviour upon residential relocation. The RPL model accounts for repeated choice decisions and captures unobserved heterogeneity among the sample individuals. The study exclusively considers the effects of past travel behaviour, changes in the household state due to lifecycle events, attitudes, accessibility measures, and socio-demographic characteristics. The model results suggest that past travel behaviour significantly influences mode switch decisions. Individuals reveal preference to persist with their past mode such as, car and bike. A change in household state is found to considerably affect the decision of mode switch. For example, individuals are more likely to switch mode if the household size increases. On the other hand, individuals are less likely to switch mode if they downsize dwelling through relocation. In the case of attitudinal variables, individuals with pro-high density attitude are more likely to switch mode. Among the other attitudinal variables, pro-active transportation and travel stress show positive relationships. Individuals with pro-car attitude show lower likelihood to switch mode. In the case of socio-demographic characteristics, individuals in the 55+ age group are less likely to switch mode following relocations. The probability of switching mode is lower if there is a lack of driver’s licence in the household. Low income individuals have a higher probability of switching mode with relocation, whereas, high income individuals are less likely to switch mode. Finally, heterogeneity is found among the individuals during mode switch. For example, individuals with pro-high density attitude show varying propensity of switching 26

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Table 5 Parameter estimation results of multinomial logit and random parameters logit model for mode switching behaviour associated with residential relocation. Variables

Multinomial Logit

Random Parameters Logit

Coefficient

t-stat

Coefficient

t-stat

Socio-demographic Characteristics Age above 55 years (Dummy) Household income below $25,000 (Dummy) Minimum education under graduation (Dummy) No driver’s licence in the household (Dummy) Monthly transit pass ownership (Dummy) Current dwelling type single-detached (Dummy) Rent below $1200 (Dummy)

−0.7669 1.2801 0.8058 −2.9315 −0.5356 −0.7378 1.2604

−1.917* 2.409** 1.989** −2.025** −1.290 −2.236** 2.414**

−1.3093 1.5326 1.0346 −3.2621 −0.7330 −1.1823 1.9325

−2.154** 2.210** 1.979** −1.810* −1.345 −2.325** 2.443**

Past Travel Behaviour Commute mode car in previous residential location (Dummy) Commute mode bicycle in previous residential location (Dummy)

−0.8885 −2.248

−2.472** −3.701**

−1.2996 −3.1383

−2.576** −3.028**

Change in Household State Increase in the household size Increase in the household driver’s licence Decrease in the household car ownership Decrease in the number of bedrooms

0.5179 0.3775 0.2029 −1.3751

2.005** 1.283 1.234 −1.724*

0.5062 0.6577 0.2883 −2.0964

1.494 1.588 1.278 −1.826*

Attitudinal Variables Pro-high density Travel stress Pro-active transportation Pro-car

−0.0233 0.1820 0.1982 −0.0142

−0.237 1.530 0.952 −0.130

0.0527 0.2626 0.3565 −0.0730

0.331 1.552 1.154 −0.465

Accessibility Measures Home to transit stop dist. below 200 m (Dummy) Home to work dist. above 500 m (Dummy) Reason for location change is to be closer to work (Dummy)

1.0702 −0.8333 −0.8951

2.080** −2.452** −1.573

1.1698 −0.9941 −1.5439

1.725* −2.223** −1.942*

Standard Deviation of Random Parameters Pro-high density Travel stress Log-likelihood at constant Log-likelihood at convergence Rho square Adjusted Pseudo Rho square

– – −197.4041 −140.9608 0.2859 0.2242

– –

0.7252 0.1032 −197.4041 −200.3195 0.3077 0.2478

2.033** 1.154

** Statistical significance at 5% level. * Statistical significance at 10% level.

modelling system, if a dynamic, longitudinal, and evolutionary-based modelling framework is of interest.

mode upon relocation. The model results have important policy implications. For example, individuals living farther from work location reveal a lower likelihood to change mode. Moreover, distance to the nearest transit stop is found to significantly influence the mode switch decisions. Therefore, policies with a focus to promote a shift from car to transit/active transportation should offer housing opportunities closer to transit stops and work locations. Policies may include transit-oriented development and incentives to live closer to work. Such findings are expected to assist in transportation planning and policy making, which aims to influence longer-term shifts in travel behaviour and choice of residential location. This study has certain limitations. It uses a small sample size of 289. A mode-specific mode switch (e.g. car to transit) model could not be achieved due to the sample size hindrance. Future researches should investigate mode-specific mode switch decisions, when a second wave dataset with a larger sample size becomes available. In the case of modelling methodology, future studies should focus on developing latent class models (LCM), since LCM offers behavioural insights that are directly interpretable for policy making. A latent class model could not be developed in this study, since analyzing different domains of contrasts with respect to multiple choice dimensions and latent segmentation are challenging with a small sample size. Moreover, transit service frequency could not be accommodated within the model due to unavailability of such information. Nevertheless, this study significantly contributes in understanding how individuals evaluate the choice of commute mode at different life-stages in response to change in household state, past travel experience, and attitude. This can be a beneficial dimension to incorporate in the integrated transport and land use

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