Effects of smartphone application usage on mobility choices

Transportation Research Part A 132 (2020) 932–947

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Effects of smartphone application usage on mobility choices Nazmul Arefin Khana, Muhammad Ahsanul Habibb, , Shaila Jamalc ⁎

T

a

Department of Civil and Resource Engineering, Dalhousie University, 1360 Barrington Street, Halifax, NS B3H 4R2, Canada School of Planning, and Department of Civil and Resource Engineering, Dalhousie University, 5410 Spring Garden Road, Halifax, NS B3H 4R2, Canada c Dalhousie Transportation Collaboratory, School of Planning, Dalhousie University, 5410 Spring Garden Road, Halifax, NS B3H 4R2, Canada b

ARTICLE INFO

ABSTRACT

Keywords: Smartphones Smartphone application usage Mobility choices Latent class random parameter logit model

This paper examines the impacts of smartphone application usage on mobility choice dimensions, such as individuals’ visits to new places, individuals’ trips planned in groups, their participation at social gatherings, and vehicle kilometers traveled. It uses data from the Smartphone Use and Travel Choice Survey 2015, which was conducted exclusively on smartphone users of Halifax, Canada. A latent class random parameter logit (LCRPL) modeling technique is applied in this study that provides a better understanding of the effects of smartphone application usage on mobility choices. The model results offer behavioral insights regarding the influence of individuals’ attitudes, travel characteristics, built environment and accessibility measures on the relationship between smartphone application usage and mobility choices. For instance, living in the higher mixed land-use areas, individuals are less likely to increase their vehicle kilometers traveled due to smartphone application usage, especially if they are tech savvy. Such individuals tend to increase their participation in social gatherings. One of the unique features of this study is that it explores the effects of individuals’ smartphone application usage on mobility choices in terms of their attitudes. The study reveals that in case of people with positive attitude towards sustainable travel, smartphone application usage tends to decrease vehicle kilometers traveled, new place visits and planned group trips, however, increase participation in social gatherings. Results of this study provide critical behavioral insights that could be useful for transportation planners and policy makers to develop flexible policy interventions.

1. Introduction The rapid advancement of technology is significantly influencing the transportation industry and mobility markets over the last decade (Giannopoulos, 2004). The next generation of emerging technologies are continuously changing the ways that we interact with one another. In particular, information and communication technology (ICT) is altering our mobility patterns by affecting daily travel related decisions (Mokhtarian and Tal, 2013). For example, ICT facilitates the reorganization of daily activities by creating alternative means of opportunities for work and non-work related activities (e.g. shopping and social networking), which influences a person’s everyday travel patterns (Couclelis, 2003). ICT provides readily available information that could change the nature and scope of daily travel and activities (Harvey and Taylor, 2000). Over the past few years, the smartphone, a revolutionary ICT device, has emerged as a powerful travel support solution for its users. For instance, real-time information on travel fares, schedules, and

⁎ Corresponding author at: School of Planning, and Department of Civil and Resource Engineering, Dalhousie University, 5410 Spring Garden Road, P.O. Box 15000, Halifax, NS B3H 4R2, Canada. E-mail addresses: [email protected] (N.A. Khan), [email protected] (M.A. Habib), [email protected] (S. Jamal).

https://doi.org/10.1016/j.tra.2019.12.024 Received 16 February 2018; Received in revised form 22 November 2019; Accepted 22 December 2019 0965-8564/ © 2020 Elsevier Ltd. All rights reserved.

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travel time to different destinations by multiple types of modes are easily accessible via various applications of smartphones. These applications also support different travel related activities by offering information on local restaurants, tourist places, and community events. In addition, alternative means of transportation such as shared mobility (e.g. ridesharing, carsharing) are accessible via smartphone applications which potentially can facilitate group trips. Such information has changed individuals’ daily movement and interactions among themselves. The usage of smartphone applications for individuals’ travel needs are continuously evolving. Research suggests that the usage of such applications has become one of the key means of travel in recent years, which has resulted in a higher usage of smartphones (Schwanen, 2015). For example, in 2016, 76% of Canadians owned a smartphone, representing a 38% rise in the market penetration rate in just two years (Catalyst, 2016). However, the smartphone market and its applications are still developing. Due to ongoing changes and improvements, it is not clear how smartphone applications are shaping daily person-level mobility and how this will evolve in future (Brazil and Caulfield, 2013). Therefore, exploring the relationship between daily smartphone application usage and mobility can provide insights on how the usage of smartphone applications could shape the nature of individuals’ mobility choices. Such investigation could help to formulate policy measures on smartphone related travel alternatives and potential improvements (e.g. carpool, real-time information, maps on shared mobility resources, etc.). Contemporary studies have found changes in daily activity and travel decisions (e.g. activity duration, start time, destination choice, mode choice, route choice, etc.) due to ICT device usage (Mokhtarian and Tal, 2013; Kaplan et al., 2017; Windmiller et al., 2014). The multi-purpose use of smartphone applications is influencing day-to-day travel. People may travel less or not travel at all for work, shopping, or banking; instead they can use different smartphone applications that serve such purposes. Smartphone applications are prominent sources of information on different places and attractions, as well as local festivals and community events, which may result in exploring new places and participating in social gatherings. Reviews and ratings of new places might help smartphone users to decide whether to visit those places. Readily available information on local events may encourage people to get involved in community events and different social activities. Furthermore, applications that help to facilitate the usage of e-commerce, e-shopping and online banking alternatives influence individuals’ daily travel by decreasing their possibility to travel physically to business establishments. Since smartphones and their application usage is increasing and changing the way people move, it is pertinent to focus on developing a better understanding of how smartphone application usage influences individuals’ daily movement. However, based on the length of technology ownership, generational diversity, and socio-demographic status, the usage of such applications and their impact on mobility choices may vary across smartphone users (Julsrud and Denstadli, 2017). This study contributes to the current literature by investigating how smartphone application usage shapes individuals’ daily mobility choices. In particular, the study explores following research questions: (1) what are the factors that influence individuals’ different mobility choices due to their smartphone application usage? (2) what variations exist among different individuals’ mobility choices? To address these research questions, this study develops four latent class random parameter logit (LCRPL) models that investigate how smartphone application usage is changing the scope and nature of different mobility dimensions, specifically, individuals’ visits to new places, planned group trips, participation in social gatherings and vehicle kilometers traveled in terms of individuals’ attitudes, travel characteristics, built environment and accessibility measures. The models also explore different smartphone users’ behavioral variations in case of the above-mentioned four mobility dimensions. The study develops the mobility choice models by utilizing data from ‘Smartphone Use and Travel Choice Survey 2015’ conducted in Halifax, Nova Scotia, Canada. 2. Literature review The review presented in this section offers a general understanding of the relationship between ICT and mobility choices. This section first presents a synopsis of earlier literature that explores the relationship between ICT and mobility, which is followed by a brief discussion on the factors affecting such relationship. The factors identified from previous literature on ICT and mobility provides guidance about how they can affect the usage of a particular type of ICT device, in other words, a smartphone. Finally, this section ends with a brief discussion of the studies related to smartphone applications in transportation and contribution of this study. Previous research on ICT-based travel behavior mostly focused on how ICT affects daily travel outcome indicators, for instance, vehicle kilometers traveled, number of total trips, and trip duration (Mokhtarian, 2009). In the earlier studies, there was an expectation that ICT might reduce daily travel (Salomon, 1986). However, recent studies indicate that ICT usage does not make any substantial net reduction in daily travel; rather, it often increases travel by replacing commute trips with other non-work trips (Wee, 2015). Arguably, the usage of ICT cannot fully compensate for the necessity of face-to-face communication and firsthand experience (Aguilera, 2008). For example, although internet-based shopping has the possibility to reduce individual-level travel, Cao (2012) found an increase in overall shopping trips due to online shopping. Perhaps, available online information influence buyers to go to stores to acquire more information and experience products, thus increasing their shopping trips. This finding is supported by another study in the Netherlands, where Hoogendoorn-Lanser et al. (2015) found that product delivery and return increases the overall shopping trips due to online shopping. ICT also provides more flexibility in travel decisions and planning by offering real-time traffic information (Mokhtarian, 2009) and access to location and service information (Chorus and Timmermans, 2008), which in turn produces additional travel. A special form of ICT usage, telecommuting, also increases the overall travel demand. Kim et al. (2015) found that telecommuting decreases individuals’ commute trips in terms of vehicle kilometers traveled. However, such individuals and their household members make more non-work trips compared to non-telecommuters and their household members, thus increasing the net vehicle kilometers traveled. Various socio-demographic and neighborhood characteristics are found to be associated with ICT’s impact on travel. For example, 933

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research explored that age, gender and income level have considerable impact on the ICT and travel relationship, and differences in such socio-demographic characteristics exhibit different effects on ICT and travel (Windmiller et al., 2014; Srinivasan and Athuru, 2004; Bhat et al., 2003). Researchers also investigated how residential location, and accessibility to various facilities and different types of establishments in neighborhood influence the relationship between ICT usage and travel. For instance, a study by Tang et al. (2011) found that regional accessibility and a higher number of institutional establishments positively influence the relationship between ICT and overall travel. A study on California workers found that living farther from workplaces increases individuals’ probability to use ICT-based telecommute services without a net change in their work-related travel (Mokhtarian et al., 2004). Hong and Thakuriah (2016) found a complementary relationship between the time spent on the internet and travel for the individuals who live in dense and more accessible urban neighborhoods. Research also indicated that individuals’ attitude towards travel has significant effects on their ICT device usage and daily travel. According to a study by Gripsrud and Hjorthol (2012), commuters’ positive attitude towards journeys by public transit increase their daily travel and cellphone/laptop usage since it facilitates multitasking while commuting. Although the existing literature investigated the relationship between ICT and travel, it is not evident how the usage of smartphone applications influences individuals’ daily mobility choices. Literature related to smartphone applications and travel are still evolving. Research found that generally smartphone applications are being used to improve different travel related activities, such as searching for travel information, arranging leisure trips (Chen and Tsai, 2017), promoting active transportation (Bopp et al., 2016), etc. For the last couple of years, smartphone applications have been widely used for travel data collection. Several studies focused on developing applications for the data collection process in different places, such as New Zealand (Safi et al., 2015), Sydney, Australia (Greaves et al., 2015), Singapore (Cottrill et al., 2013), and Barcelona, Spain (Delclòs-Alió et al., 2017). In addition, some studies explored the desirable features that people want in smartphone-based travel applications. For instance, the study of Bopp et al. (2016) suggested that route planning, travel time projection and weather forecasting are the primary features desired in an application to promote active travel among smartphone users. Studies also exist that investigated the reasons behind using smartphone applications for travel related information. The study of Brazil and Caulfield (2013) found that easy access of the information on real-time traffic conditions and travel time is the main reason to use smartphone applications while choosing an everyday travel mode. A study in California found that pro-transit attitudes and motivation to make less energy intensive travel are the primary reasons behind individuals’ intention to use smartphone applications that offer travel information for multiple modes (Shaheen et al., 2016). Alemi et al. (2018) explored that individuals’ younger age, tech-savviness, pro-environmental attitude, and desire to live in higher mixed land-use areas are the reasons of their higher usage of smartphone-based ridesharing applications. Furthermore, preference of specific travel mode is also found to be a critical reason to use different types of smartphone application usage. For example, preference of auto mode results in more navigational applications, whereas, preference of transit mode increases a person’s usage of transit scheduling applications (Walker, 2019). In addition to that, existing research investigated which group of people are the primary users of smartphones and its applications. It has been found that young and middle-aged urban dwellers are the most frequent smartphone application users (Ettema, 2018; Julsrud and Denstadli, 2017; Jamal and Habib, 2019). In recognition of the above review, it is evident that there is a gap in understanding the relationship between individuals’ daily smartphone app usage and mobility choices. None of the existing studies have empirically explored such relationship and associated factors thereof. Essentially, the impact of smartphone application usage on overall mobility is dependent on how an individual utilizes the applications. Availability of information from different sources, including social media and user-friendly navigation systems offered through smartphone applications, could influence individuals’ mobility in various ways. Smartphone applications could provide opportunities to perform activities online with instant access and readily available information rather than physically traveling to places, which in turn, could affect their daily movement. Interactions among individuals on social media might result in social travel in a group (Delbosc and Mokhtarian, 2018) that eventually influences their participation in social gatherings and group trip making. Moreover, the ease of communications and smartphone-based shared mobility services (e.g. carsharing, ridesharing, carpooling) might encourage traveling in groups. This study aims to establish an understanding of how the usage of smartphone applications influences individuals’ mobility choices; in particular, their visits to new places, planned group trips, participation at social gatherings, and vehicle kilometers traveled. It also hypothesizes that different person-level and neighborhood-level attributes, such as socio-demographic factors, attitudes, travel characteristics, built environment and accessibility measures shape individuals’ mobility choices due to smartphone application usage. The study proposes a flexible choice modeling approach following a latent class random parameter logit (LCRPL) modeling technique to examine the relationship between smartphone application usage and different mobility dimensions. The LCRPL models capture behavioral variations across population by allocating individuals into different latent classes. 3. Data 3.1. Smartphone use and travel choice survey 2015 A number of smartphone-based applications are available today that provide travel and social assistance to smartphone users. For instance, Google Maps, TripSee, Triplt, Transit360, Uber, Lyft and related applications contribute to shaping individuals’ everyday travel. Social networking applications, such as Facebook, Twitter, Instagram, WhatsApp and Snapchat help to socialize individuals with one another. These smartphone-based applications can be used for several transportation and socialization purposes. For example, prior to visiting a new place, people can get an understanding of the place and its adjacent attractions through Google Maps Street View, by visiting Facebook or Instagram pages, or by using travel planners such as TripSee. Social networking applications and 934

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trip planning applications allow individuals to share trip information and photos of attractive new locations with family/friends to plan group trips. They can also instantly arrange social gatherings by quick invitations through social networking applications. Uber and Lyft offer assistance in the form of carpooling and car-sharing. People can coordinate their trips using such carpooling and carsharing applications. While driving, Google Maps can provide directions to the user’s destination. Also, regular transit riders can get precise transit schedules from the Transit360 app. Considering the role of smartphone and its applications in daily activities and travel, a web-based survey known as ‘Smartphone Use and Travel Choice Survey 2015’ was implemented in Halifax, Canada, between March and April 2015. The survey was promoted through multimedia campaign. Promotional pamphlets were prepared for distribution at key locations in Halifax. The promotional materials were also distributed at local events by volunteers. To reach a wider audience, social media was used for survey promotion (e.g. paid advertisements were placed through Facebook). Additionally, all Dalhousie Transportation Collaboratory’s (DalTRAC) online channels such as the blog, website, Twitter, and other social media accounts were utilized for survey dissemination. The survey collected information on respondents’ weekday and weekend travel behavior. Respondents were specifically asked how smartphone application usage has impacted their mobility choices and how frequently they use smartphone applications for trip planning activities and travel purposes. The survey also collected respondents’ socio-demographic information: age, gender, annual income, employment, and student status. This survey was conducted exclusively on smartphone users of Halifax. Literature suggests that millennials are more connected and technology-oriented (Catalyst, 2015). According to ‘2012 Cellphone Consumer Attitudes Study’ conducted by the Canadian Wireless Telecommunications Associations (CWTA), the adoption of smartphones is significantly higher among 18–34 years old (69%) Canadians. The Halifax ‘Smartphone Use and Travel Choice Survey 2015’ showed that 66.49% of the respondents are between 20 and 34 years old, which we assumed to be a reasonable representation of Canadian smartphone users. The survey yielded a sample of 386 respondents. The details of the survey can be found in Jamal and Habib (2019). 3.2. Data preparation Preparation of the datasets involves several processing steps. After survey data collection, data are cleaned carefully, and all identifiable information are replaced with anonymous codes. Socio-demographic and travel characteristics of the respondents are first collected from the survey data. Then, home locations of the respondents are geocoded by an online service, BatchGeo. Using the Halifax Regional Municipality (HRM) map and coordinates found from BatchGeo, respondents’ dissemination areas are identified in ArcGIS. The study uses the 2011 Canadian Census and Halifax Regional Municipality (HRM) Geodatabase for built environment attributes. After identifying dissemination areas, respondents’ home locations are joined with the census database that includes the size of a dissemination area, the number of people and dwellings in a dissemination area, dwelling type (i.e. percentage of singledetached, semi-detached, row houses and apartments) and dwelling status (i.e. percentage of owned and rented houses) in the neighborhoods. Similarly, land-use information for each respondent is derived from the HRM land-use database. Finally, accessibility measures such as distance from home to central business district (CBD), nearest shopping mall and bus stop are determined through ArcGIS using respondents’ home location, and location information of activity points and transportation services from Desktop Mapping Technologies Inc. (DMTI) database. During the survey, respondents were asked how smartphone application usage influences their mobility choices, i.e. visits to new places, trips planned in groups, participation at social gatherings and vehicle kilometers traveled. They were given three alternatives to describe the effects of smartphone application usage on mobility choices. These alternatives are used as the dependent variables in each model. The alternatives are: (a) smartphone application usage “INCREASE” mobility, (b) smartphone application usage has “NO IMPACT” on mobility, and (c) smartphone application usage “DECREASE” mobility. Respondents were also asked about their attitudinal preferences. They responded to the following statements using a three-point Likert scale with options Agree, Neutral and Disagree: (1) adapt emerging technologies easily, (2) making sustainable lifestyle choices whenever possible, (3) limit driving because it is bad for environment, (4) households should be fined for exceeding daily greenhouse gas limit, (5) proximity to shop and service is important, (6) travel time generally is wasted time, and (7) smartphones improve daily life. A correlation test shows that the statements are highly correlated. For instance, a positive correlation of 72% is found between ‘travel time generally is wasted time’ and ‘proximity to shop and service is important’. Additionally, ‘travel time generally is wasted Table 1 Principal Component Analysis (PCA). Statement Variables

Component 1 (ICT component)

Component 2 (Sustainable travel component)

Adapt emerging technologies easily Smartphones improve daily life Shop and service proximity is important Travel time generally is wasted time % Variance Explained

0.6594 0.6546 −0.0877 −0.3591 33%

−0.0960 −0.0490 0.8718 0.4778 55%

935

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time’ has positive correlations with ‘making sustainable lifestyle choices whenever possible’ (98.5%) and ‘limit driving because it is bad for environment’ (98%). Due to these high correlations, a Principal Component Analysis (PCA) is conducted with selected statements. The varimax rotation method is applied to extract the components (Brown, 2009). Two components are identified, ICT component and sustainable travel component, which exhibit total explainable variance of 88% in the sample. Table 1 shows the principal component analysis with component loading on each statement variable. Component loadings are then multiplied with the corresponding statements’ responses and the sum is taken to obtain the attitudinal variables ‘positive attitude towards ICT’ and ‘positive attitude towards sustainable travel’. The three remaining attitudinal variables, such as ‘highly dependent on smartphone apps for trip planning purposes’, ‘smartphone has effects on social trip frequency’ and ‘no smartphone effects on social trip frequency’ are collected directly from the survey data. The descriptive statistics of variables retained in the final mobility choice models are presented in Table 2. Table 2 Descriptive statistics of the variables used. Variable Socio-demographic Variables Young Income below $15,000 Student Full time employed Pro-smartphone user Travel Characteristics Commute mode_auto Commute mode_transit Weekdays travel distance Weekend travel distance Number of vehicles in the household Attitudes Highly dependent on smartphone apps for trip planning purposes Smartphone has effects on social trip frequency No smartphone effects on social trip frequency Positive attitude towards ICT Positive attitude towards sustainable travel Built Environment Population density Dwelling density Land-use index Percentage of rented houses Percentage of owned houses Percentage of single-detached houses Percentage of apartments Accessibility Measures CBD distance from home Nearest shopping mall distance from home Nearest bus stop distance from home

Description

Mean/Distribution

Standard Deviation

Dummy, if individual’s age below 25 years = 1, 0 otherwise Dummy, if individual’s annual income below $15,000 = 1, 0 otherwise Dummy, if individual is a student = 1, 0 otherwise Dummy, if individual is full time employed = 1, 0 otherwise Individuals using smartphones for more than 5 years = 1, 0 otherwise

40.7% 47.4%

– –

68.0% 41.8% 32.1%

– – –

Dummy, if primary commute mode is auto = 1, 0 otherwise Dummy, if primary commute mode is transit = 1, 0 otherwise Distance traveled in a typical weekday (kilometer) Distance traveled in a typical weekend (kilometer) Number of private vehicles in the household

39.3% 28.5% 12.03 22.5 1.01

– – 28.1 41.5 1.06

Dummy, if individual reports high dependency on smartphone applications for trip planning = 1, 0 otherwise Dummy, if individual agrees that smartphone has effects on social trip frequency = 1, 0 otherwise Dummy, if individual disagrees that smartphone has effects on social trip frequency = 1, 0 otherwise Individual’s positive attitude towards ICT (PCA-derived) Individual’s positive attitude towards sustainable travel (PCAderived)

22.1%

–

43.0%

–

10.0%

–

2.3 3.3

0.7 0.6

Population density in the neighborhood (per square kilometer) Dwelling density in the neighborhood (per square kilometer) Land-use index of the neighborhood Percentage of rented houses in the neighborhood Percentage of owned houses in the neighborhood Percentage of single-detached houses in the neighborhood Percentage of apartments in the neighborhood

22.18 13.35 0.16 52.1 46.9 40.4 21.3

35.06 22.40 0.2 28.7 27.9 32.6 26.4

Individual’s home to central business district (CBD) distance (kilometer) Individual’s home to nearest shopping mall distance (kilometer) Individual’s home to nearest bus stop distance (kilometer)

5.3

7.2

4.4 0.8

5.1 3.2

4. Methodology 4.1. Latent class random parameter logit model This study utilizes a random utility-based discrete choice modeling technique, specifically the latent class random parameter logit (LCRPL) formulation that can capture heterogeneity across population. In the previous studies of ICT-travel behavior, preference heterogeneity of individuals was often ignored. In other words, it is not evident what differences exist between different classes of individuals’ mobility choices due to smartphone application usage. Researchers have found that inconsistent and biased estimations are obtained if taste preference heterogeneity across population is not considered (Provencher and Bishop, 2004). To address this issue, the LCRPL model is applied in this study where the latent class formulation can capture the preference variation across individuals with different characteristics by developing class allocation models in each mobility choice models, namely (1) visiting 936

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new places, (2) planned group trips, (3) attending social gatherings, and (4) vehicle kilometers traveled. Each class allocation model is defined by a set of smartphone user’s individual characteristics in such a way that the classes can be characterized distinctly to best describe the taste variation between different types of individuals with respect to their mobility choices. In addition, it is highly unlikely that all smartphone users with similar class allocation characteristics within a class have the same choice preferences, instead there is a higher probability of taste preference variation among individuals within the same class. To accommodate this within-class taste variation, random parameters are specified in each class within the modeling framework. These random parameters allow another layer of preference heterogeneity across smartphone users by assuming that each member within a class has a different set of utility parameters. The class allocation models developed in this study probabilistically allocate individuals into different latent classes (Hess et al., 2011). Probabilities to be allocated into classes are determined by using individuals’ different characteristics, Yj (e.g. socio-demographic, smartphone usage and built environment attributes) that confirm the flexibility of class allocation models. Thus, the probability of the individual j falling into class s can be written as: S js (Yj,

) = exp( s +

s Yj )

exp(

c

+

c Yj )

(1)

c=1

Here, υs and φ′s are the latent class-specific constant and parameter vector, respectively. To identify the model, one of the latent classes is considered as the reference class by fixing the value of υs and φ′s as ‘zero’. Within the latent class formulation, usually it is assumed that all individuals belonging to the same class will behave in the same way. However, as stated earlier, there is strong possibility that all members within a class might not behave the same and they might show variations in taste preference. To capture this heterogeneity, a continuous variation of parameters is allowed within each class. Let, βi|s is the parameter vector for individual j in class s. Heterogeneity within each class can be given by: j

s=

s

+

(2)

js

j s ~E [ j s

Xji ] = 0,

Variance [

js

Xji ] =

s

For heterogeneity within classes, this study assumes a normally distributed density function with mean 0 and covariance δ. The choice probability of an individual j belongs to class s choosing alternative i is written as: I

g [Zji (

+

s

j s ),

I

Xji ] = exp

Zji (

+

s

j s)

Xji

I

exp

i=1

i=1

Zji (

s

+

j s)

Xji

i = 1, 2, ....,I

i=1

(3)

Here, Xji denotes the column vector of individual j’s observed attributes (i.e. travel characteristics, attitudes, built environment, etc.) and Zji is the choice by the individual j from the given set of alternatives I. Since the parameters to be estimated are not known, an unconditional probability is required to estimate the mobility choice models. This probability is expressed as: S

R (Zji Xji ,

1,

....., S , Yj, ,

1,

...., S ) =

js (Yj,

)

s=1

g [Zji (

s

js

+

j s ),

Xji ] F (

js

s )d j s

(4)

where F is assumed as a normally distributed density function, and Zji is the choice representing 1 when an alternative i is chosen by an individual j, and 0 for all others. The log-likelihood function to estimate the models can be given by: J

LLu =

log[R (Zji Xji ,

1,

....., S , Yj, ,

1,

...., S )]

(5)

j=1

Eq. (5) is a multivariate integral that does not have any closed form. Simulation methods are generally utilized to estimate such integrals (Revelt and Train, 1998). Therefore, this study applies maximum simulated likelihood to evaluate the integral. The loglikelihood function that contributes to the maximum simulated likelihood can be written as: S

L=

js (Yj,

)

s=1

1 Q

Q

g [Zji (

s

+

q

j s ),

Xji]

(6)

q= 1

Here, j s is the q-th random draws on the random vector j s , which is repeated total Q times. Finally, the simulated loglikelihood function (SL) to estimate the models can be obtained by taking the logarithm of equation (6): q

J

SL =

S

log j=1

js (Yj, s=1

)

1 Q

Q

g [Zji (

s

+

q

j s ),

Xji]

(7)

q= 1

The Halton sequence is used in this study as it requires a substantially lower number of draws. All the models are converged, and stable covariates are found at 200 Halton draws. The goodness-of-fit measures for each of the models are evaluated on the basis of log-likelihood value at convergence, Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC). 937

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4.2. Determination of number of latent classes The modeling process begins with the identification of the number of classes in all four mobility choice models. The models are tested for multiple numbers of classes, and the appropriate number of classes is determined in this study based on AIC and BIC values. Comparatively, models with lower AIC and BIC values are considered as better models. Results suggest that AIC and BIC values increase as the number of classes is increased in all four mobility choice models. Furthermore, the higher the number of classes, the less statistically significant the models become, since the new classes introduce multiple dimensions within the models. Therefore, the final mobility choice models in this study are developed by assuming two classes, which have the smallest AIC and BIC values. Table 3 exhibits the log-likelihood at convergence, AIC and BIC values of the models with two and three classes. Table 3 Number of class determination. Goodness-of-fit measures

Log-likelihood (convergence) AIC BIC

Visiting new places

Planned group trips

Attending social gatherings

Vehicle kilometers traveled

No. of class 2

No. of class 3

No. of class 2

No. of class 3

No. of class 2

No. of class 3

No. of class 2

No. of class 3

−191.05 1.56 2.43

−225.29 2.00 3.23

−158.82 1.41 2.34

−194.74 1.88 3.31

−228.19 1.79 2.68

−247.24 1.90 2.79

−135.39 1.43 2.21

−193.74 1.87 3.30

5. Result discussion This section describes the parameter estimation results of the mobility choice models. A variety of socio-demographic, attitude, travel characteristic, built environment and accessibility measure variables are tested in all four models. The class allocation results of the LCRPL models are presented first in the model results tables (see Tables 4–7), where the positive coefficient values indicate the higher probability for an individual’s membership in a certain class, and vice versa. The parameter estimation results of two latent classes are presented after the class allocation models. A positive coefficient value represents ‘higher probability’ and a negative coefficient value represents ‘lower probability’ of choices. Preference variation (i.e. heterogeneity) within each class is demonstrated through the standard deviations of random parameters in the models. If a variable exhibits a standard deviation along with its mean value within a class, this would suggest the variable’s heterogeneous effect on the individuals within the corresponding class. The majority of the variables retained in the final models reveal statistically significant relationships. Some variables with statistical significance below 90% confidence interval are kept during the model analysis. These variables provide key insights about the effects of smartphone application usage on mobility choices and have critical policy implications. Hence, such variables are retained in the final models with an assumption that if larger datasets were available, they may reveal statistically significant coefficient values. Detailed results of four mobility choice models are discussed below. 5.1. Visiting new places The class allocation model results of the ‘visiting new places’ model (Table 4) is developed considering class two as the reference class. Results suggest that young pro-smartphone users, who earn below $15,000 annually and prefer to live in a neighborhood with higher percentage of apartments, are less likely to be allocated in class one as indicated by the negative coefficient values. Jamal and Habib (2019) found that technologies are quickly adopted by the individuals who are young, students and long-term users of smartphones. As per this finding, class one is assumed as a class of ‘non-tech savvy’ individuals. Conversely, class two can be identified as the class of ‘tech savvy’ individuals. For the brevity of result discussion, such identification of classes is considered throughout the paper. Individuals’ attitudes demonstrate expected outcomes in the case of visiting new places. First, individuals who are highly dependent on smartphone applications for trip planning purposes are less likely to decrease visiting new places than those less dependent on smartphones for trip planning. Since apps can help users find new places, it makes sense that high usage would not decrease users’ exploratory travel. Interestingly, heterogeneous behavior across classes is observed in the case of ‘increase new place visits’. In both classes, the 1% statistically significant (i.e. 99% confidence interval) standard deviations of the variable for the ‘increase’ alternative suggests that heterogeneity exists within the classes. The second variable ‘smartphone has effects on social trip frequency’ exhibits negative signs for ‘no impact’ and positive signs for ‘decrease’ in both tech savvy and non-tech savvy classes. This means individuals who agree that smartphone affects their social trip frequency have impacts of smartphone application usage on visiting new places. Such individuals tend to decrease their visits to new places, perhaps indicating inclination towards making social trips to familiar places rather than visiting new places for recreational or other purposes. Third, individuals having a positive attitude towards ICT increases the probability that smartphone application usage has an impact on visiting new places across both classes, which is to increase individuals’ visits to new places. Positive attitude towards ICT might suggest regular usage of the latest ICT-based platforms (e.g. smartphone applications) that make their travel-activity easier. The higher frequency of visiting new places for these individuals is consistent with the existing literature that claims that ICT usage significantly facilitates non-work travel and activities (Ben-Elia et al., 2014). Finally, a positive attitude towards sustainable travel is related to a higher tendency for smartphone 938

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Table 4 Results of visiting new places model. Class allocation model results

Class allocation probability Constant Pro-smartphone user (> 5 years) Young (< 25 years) Income below $15,000 Full time employed Percentage of apartments in the neighborhood

Non-tech savvy

Tech savvy

0.70 −1.135 −8.730** −8.005* −2.518*** 0.363** −0.204*

0.30 – – – – – –

Parameter estimation results Variables

Constant Attitudes Highly dependent on smartphone applications for trip planning purposes Smartphone has effects on social trip frequency Positive attitude towards ICT Positive attitude towards sustainable travel Travel Characteristics Primary commute mode_auto Primary commute mode_transit Weekdays travel distance Weekend travel distance Number of vehicles in the household Built Environment Population density Dwelling density Land-use index Percentage of rented houses in the neighborhood Percentage of owned houses in the neighborhood Accessibility Measures CBD distance from home Nearest shopping mall distance from home Nearest bus stop distance from home Standard Deviation of Random Parameters Highly dependent on smartphone applications for trip planning purposes Smartphone has effects on social trip frequency

Increase

No impact

Decrease

Tech Savvy

Non-tech Savvy

Tech Savvy

Non-tech Savvy

Tech Savvy

Non-tech Savvy

1.387**

−6.056**

−2.628*

0.192

Reference

Reference

2.550

−2.103**

−7.730***

−4.675**

2.874** −0.034**

5.821* −0.013*

1.516

4.555*

3.372**

4.359

3.982* 3.940*

1.459** −1.204***

−6.716

−1.328

1.349*** 2.610*

9.741*** 0.024**

2.336 −0.631* −2.583*

0.558* −0.243 −0.654**

−1.320***

0.160

2.253*

−0.190

1.099

1.589**

3.140 −0.446 −3.638***

−4.189** 1.473 −2.616

−4.128*** 4.811*

0.178 1.911***

4.867* 1.748

−0.221** −0.449**

0.006***

0.014*** 0.026

0.017***

−0.185*** −5.620**

−0.822 −1.669*

−1.573* −0.161**

9.435* −0.003*

1.820 −2.940*

0.34 0.844***

0.062*

0.010

−0.757

0.041*

−3.555**

−1.551**

0.050***

0.039

Note: *** 1% significance level. ** 5% significance level. * 10% significance level.

application usage to decrease visiting new places for all individuals. These individuals are less likely to increase their new place visits due to smartphone application usage. Sustainable travel (e.g. walking, biking and traveling by transit) offers opportunities to increase social contacts by increasing virtual and face-to-face interactions, which may encourage individuals to make more social and community meetings instead of visiting new places. The majority of the travel characteristics retained in the final model exhibit behavioral similarity across tech savvy and non-tech savvy classes. For instance, results suggest that in both classes, auto commute and higher number of vehicles in the household are more likely to increase and less likely to decrease individuals’ visits to new places due to smartphone application usage. Primarily commuting by auto might indicate individuals’ vehicle ownership that offers more opportunities for visiting places. Also, a higher number of vehicles in the households essentially provides more flexibility for traveling to new places. Smartphone applications may assist such users to find information, reviews, and directions to newer places. Hence, individuals may have both the means and information for increased visits of new places. Similar behavior is observed in the case of weekend travel distance. Individuals with higher weekend travel exhibit inclination towards increasing their visits to new places due to smartphone application usage. Higher weekend travel might indicate individuals’ leisure trips, which can be facilitated by the information about new places provided by smartphone applications. As expected, weekday travel distance shows a negative relationship with ‘no impact’ and a positive 939

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parametric relationship with ‘decrease’ alternative for visiting new places due to smartphone application usage. Weekday travel often relates to regular activities that arguably leave less time to explore new places to visit. Heterogeneity across classes is found in the case of transit commute mode. Results suggest that tech savvy transit commuters have higher propensity to increase their visits to new places, whereas non-tech savvy people tend to have no impact of smartphone application usage on new place visits. Built environment and accessibility measure variables provide valuable insights. For example, tech savvy urban area dwellers (represented by higher population density) are less likely to increase visiting new places due to smartphone usage. They exhibit a higher likelihood to decrease visiting new places. Usually, high density urban areas are full of activities. Tech savvy individuals may find information regarding recreational places nearby through smartphone applications that might not necessitate visiting new places. Non-tech savvy urban area dwellers, on the other hand, exhibit opposite relationships for both ‘increase’ and ‘decrease’ alternatives. Similar results are observed in case of the land-use index. Tech savvy individuals living in higher mixed land-use areas exhibit higher inclination towards decreasing their new place visits due to smartphone application usage. On the other hand, similar non-tech savvy individuals are found to be less inclined to decrease their new place visits due to smartphone application usage. Individuals living in suburban areas (represented by a higher percentage of owned houses in the neighborhood) show no impact of smartphone application usage on their visits to new places. This could be because suburban dwellers are less inclined towards ICT usage during their activity and travel (Bhat et al., 2003; Farag et al., 2007). Interestingly, living farther away from the CBD increases tech savvy individuals’ probability to decrease their new place visits due to smartphone application usage. Presumably, a higher distance between home and the CBD increases such individuals’ probability to work from home (Mokhtarian et al., 2004). This decreases their daily travel commitments, thus may limit their distance traveled that reduces the probability to visit more new places. 5.2. Planned group trips Table 5 presents the class allocation model results for the planned group trips model. Results demonstrate positive coefficient values for young pro-smartphone users, who earn less than $15,000 annually and prefer to live in a neighborhood with higher percentage of apartments in class one, which indicate these individuals’ higher probability of belonging in that class. Following the assumption made in the visiting new places model, class one is defined here as the class of ‘tech savvy’ individuals. In contrast, class two (reference class) is characterized as the ‘non-tech savvy’ class. Model results suggest that individuals’ attitudes have significant effects on how smartphone application usage affects their group trip planning. Tech savvy individuals who are highly dependent on smartphone applications for trip planning are more likely to plan more group trips due to smartphone application usage than less reliant tech savvy individuals. For non-tech savvy individuals, being dependent on smartphones is linked with app usage tending to have no impact on their planned group trips. It is possible that tech savvy people are accustomed to using smartphone applications to plan trips through virtual interactions, whereas non-tech savvy individuals are less likely to do so. The variable also exhibits statistically significant standard deviations for both ‘no impact’ and ‘increase’ alternatives that confirm heterogeneity within classes. As expected, both tech savvy and non-tech savvy individuals who agree that smartphone usage influences their social trip frequency, tend to have a positive relationship with increased planned group trips. Expected outcomes are also observed in the cases of positive attitude towards ICT. Both tech savvy and non-tech savvy individuals who have a positive attitude towards ICT have a lower tendency to decrease planned group trips due to smartphone application usage. Moreover, a positive attitude towards sustainable travel tends to decrease individuals’ planned group trips for both classes, which is in-line with the finding of visiting new places model. Most of the travel characteristics retained in the final model are found to be significant and exhibit similar behavior across classes in the ‘planned group trips’ model. For example, all individuals who use auto as their primary commute mode are less prone to increase planned group trips due to smartphone application usage. Perhaps these individuals use smartphone applications more for assistance with their daily commute rather than trip planning, thus decreasing their planned group trips. With an increase in weekend travel distance, individuals are less likely to decrease and more likely to increase their planned group trips due to smartphone application usage, which is expected since weekend travel is more likely to be planned in groups. Moreover, a higher number of vehicles in the households might bring more flexibility while planning trips in groups. Thus, with an increase in number of vehicles per household, individuals tend to increase their planned group trips through smartphone application usage. However, the statistically significant standard deviation indicates the heterogeneous nature of ‘number of vehicles’ in the case of increasing planned group trips. Heterogeneous behavior across classes is observed for transit commuters. In the tech savvy class, commuting by transit is related to a higher probability of increasing trips planned in groups due to smartphone usage, whereas non-tech savvy transit commuters are less likely to increase their group trips due to smartphone usage. Potentially, a tech savvy transit commuter heavily relies on transit and trip planning apps, as well as social networking apps that might lead to more group trips planned with others. Among the built environment variables used in this study, land-use index exhibits substantial impact on individuals’ planned group trips due to smartphone application usage. Living in higher mixed land-use areas, both tech savvy and non-tech savvy individuals exhibit higher negative coefficient values (−2.289 and −5.879 respectively) for the alternative ‘decrease’, which indicates their lower tendency to decrease planned group trips due to smartphone application usage irrespective of classes. Higher mixed landuse areas provide a variety of activity amenities and transportation choices. The smartphone applications may provide information regarding those amenities, opportunities and virtual interactions that could encourage individuals not to decrease their planned group trips. Population density exhibits a negative relationship with smartphone usage ‘increases’ planned group trips in the tech savvy class and ‘decreases’ planned group trips in non-tech savvy class, which is similar to the ‘visiting new places’ model. Although tech savvy individuals living in suburban areas (represented by neighborhoods with a higher percentage of owned houses) are more likely to have no impact of smartphone application usage on their planned group trips, some individuals might behave differently 940

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Table 5 Results of planned group trips model. Class allocation model results

Class allocation probability Constant Pro-smartphone user (> 5 years) Young (< 25 years) Income below $15,000 Student Percentage of apartments in the neighborhood

Tech Savvy

Non-tech savvy

0.69 2.291** 0.938* 1.021* 1.151** −1.663* 0.012***

0.31 – – – – – –

Parameter estimation results Variables

Constant Attitudes Highly dependent on smartphone applications for trip planning purposes Smartphone has effects on social trip frequency Positive attitude towards ICT Positive attitude towards sustainable travel Travel Characteristics Primary commute mode_auto Primary commute mode_transit Weekdays travel distance Weekend travel distance Number of vehicles in the household Built Environment Population density Dwelling density Land-use index Percentage of rented houses in the neighborhood Percentage of owned houses in the neighborhood Accessibility Measures CBD distance from home Nearest shopping mall distance from home Nearest bus stop distance from home Standard Deviation of Random Parameters Number of vehicles in the household Highly dependent on smartphone applications for trip planning purposes Smartphone has effects on social trip frequency Percentage of owned houses in the neighborhood

Increase

No impact

Decrease

Tech Savvy

Non-tech Savvy

Tech Savvy

Non-tech Savvy

Tech Savvy

Non-tech Savvy

−1.458***

1.265**

8.899*

2.959

Reference

Reference

6.122**

−1.717**

−3.765*

3.399***

4.185**

0.397**

−0.024**

−0.456*

−7.696** −0.530

−4.228* 1.696**

−0.455** 3.448*

−0.216*** 1.549**

−0.169** 0.857**

−0.858** −4.609**

−3.462* 0.108

9.888* −0.407

−0.013** −2.133

−0.350 −2.675*

0.730** −0.451

0.69 −4.421**

0.110

−0.159**

−2.289**

−5.879

−0.038*

0.103

0.592**

−0.201

**

1.519 0.973**

1.437 10.532*

−0.400**

0.153

0.153*

1.283

0.822*** −0.326* −1.468*

−2.146* 1.247** 1.913*

0.001** 0.008***

0.153* 0.061**

0.015

***

−0.047*

−0.242

0.940

3.945

−2.054*

0.074*

0.011***

0.096*** 0.697*

0.239** 0.001**

Note: *** 1% significance level. ** 5% significance level. * 10% significance level.

given that the standard deviation is larger than the mean (mean, 0.015 and standard deviation, 0.697). In contrast, non-tech savvy individuals are less likely to have ‘no impact’ on planned group trips, however, a significant standard deviation at a 95% confidence interval indicates the heterogeneous nature of the effects across non-tech savvy individuals living in suburban areas. Accessibility measures also exert significant effects of smartphone application usage on individuals’ planned group trips. For example, the higher the tech savvy individuals’ home to CBD distance, the higher is their probability to increase planned group trips due to smartphone application usage. These individuals might prefer working from home due to living farther from the CBD, which saves time that can be used for discretionary trips. In contrast, non-tech savvy individuals living farther from the CBD show less inclination to increase their planned group trips due to app use, as expected. In the case of living farther from the nearest shopping malls, tech savvy individuals tend to decrease their planned group trips due to smartphone application usage, perhaps indicating their tendency to use online shopping applications rather than physically traveling to malls. On the other hand, non-tech savvy individuals are more prone to increase their planned group trips while living farther from shopping malls.

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5.3. Attending social gatherings The class allocation model of ‘attending social gatherings’ (Table 6) exhibits positive coefficient values for socio-demographic, smartphone usage and built environment attributes that are used to define the class probability. This suggests that young students, who are essentially pro-smartphone users, earn less than $15,000 annually, and prefer to live in neighborhoods with a higher percentage of apartments have a higher probability to be allocated in class one. Class two (reference class) can be identified as the opposite. Accordingly, class one is intuitively branded as the ‘tech savvy’ class and class two as the ‘non-tech savvy’ class. The model results suggest that being highly dependent on smartphone applications for trip planning purposes, both tech savvy and non-tech savvy individuals tend to increase and are less likely to decrease their participation in social gatherings due to higher smartphone application usage, which is expected. Although the variable ‘highly dependent on smartphone applications for trip planning purposes’ does not exhibit heterogeneity across classes, significant standard deviations for both alternatives (i.e. increase and decease) indicate the existence of behavioral variations within the classes. Heterogeneity across classes is observed for the variable ‘smartphone has effects on social trip frequency’. Tech savvy individuals who agree that smartphones affect their social trip frequency are more likely to increase their participation in social gatherings due to smartphone application usage. Social trips are derived from the social and community activities that result in social gatherings (Kelly et al., 2017) to discuss the process of Table 6 Results of attending social gatherings model. Class allocation model results

Class allocation probability Constant Pro-smartphone user (> 5 years) Young (< 25 years) Income below $15,000 Student Percentage of apartments in the neighborhood

Tech Savvy

Non-tech Savvy

0.79 1.501** 0.333*** 1.449*** 1.266 1.251* 0.131**

0.21 – – – – – –

Parameter estimation results Variables

Constant Attitudes Highly dependent on smartphone applications for trip planning purposes Smartphone has effects on social trip frequency Positive attitude towards ICT Positive attitude towards sustainable travel Travel Characteristics Primary commute mode_auto Primary commute mode_transit Weekend travel distance Number of vehicles in the household Built Environment Population density Dwelling density Land-use index Percentage of rented houses in the neighborhood Percentage of owned houses in the neighborhood Accessibility Measures CBD distance from home Nearest shopping mall distance from home Nearest bus stop distance from home Standard Deviation of Random Parameters Weekend travel distance Highly dependent on smartphone apps for trip planning purposes Smartphone has effects on social trip frequency

Increase

No impact

Decrease

Tech Savvy

Non-tech Savvy

Tech Savvy

Non-tech Savvy

Tech Savvy

Non-tech Savvy

2.375*

9.577

4.584

−2.136

Reference

Reference

0.579***

6.055*

−0.095*

−1.236**

0.243**

−3.078

2.199*

1.279**

−0.200**

−3.517

−2.046 0.222** 0.467* 0.132**

−2.089** −4.608** −6.379 3.905*

2.836**

2.122

−0.156 −0.943

0.038 −8.014**

−0.31

−0.27

2.005* 0.429* −2.439

7.798* 1.266 4.598*

−0.690* −5.828*

0.410** −3.431**

0.097** −3.522**

−2.900 0.558**

−0.167**

−1.644*

0.002** 0.027*

0.060** 0.147***

Note: *** 1% significance level. ** 5% significance level. * 10% significance level. 942

−0.170** −2.264

3.909* 2.886*

−1.142**

2.484**

0.480**

−0.122***

0.02

−0.099

0.087* −4.172

1.737** 1.187

0.051*

0.023**

0.427

**

0.039***

−0.267

0.980*

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performing such activities. Smartphones offer a variety of applications that provide information about such social and community events and activities. Since tech savvy individuals who agree that smartphones impact their social trip frequency might be more familiar with those applications, their higher probability to increase attending social gatherings is plausible. Non-tech savvy individuals, however, exhibit opposite relationships. The significant standard deviation of the variable for the ‘no impact’ alternative indicates that heterogeneous nature of the effects not only exists across the classes, but also within each class. Furthermore, individuals with a positive attitude towards sustainable travel tend to increase their participation at social gatherings due to smartphone application usage in both tech savvy and non-tech savvy classes. Presumably, sustainable travel (e.g. traveling by public transport, walking and biking) promotes social inclusion by connecting people and increasing social contacts, which may enable further virtual interactions via smartphone-based social networking applications (Kenyon et al., 2002). This may lead to more social gatherings. In the case of travel characteristics, model results suggest that commuting by auto increases both tech savvy and non-tech savvy individuals’ probability to decrease social gathering participation. Although smartphones increase virtual social interactions through social networking application usage, auto-dependency reduces individuals’ face-to-face social interactions (Litman, 2002) that might lead to participating fewer social gatherings. Non-tech savvy transit commuters tend to have no impact of smartphone applications on attending social gatherings. Tech savvy transit commuters, on the other hand, are more likely to increase their participation at social gatherings due to smartphone application usage. This result perhaps indicates such individuals’ inclination to using multiple smartphone-based social networking applications that increase their social interactions, thus increasing the probability of participating more social gatherings. As expected, higher weekend travel distances are related to tech savvy individuals’ inclination towards ‘increase attending social gatherings’ due to smartphone application usage, and the opposite inclination in non-tech savvy individuals. Weekend travel distance exhibits significant heterogeneity for the alternative ‘increase’ in both classes as evidenced by statistically significant standard deviations of the variable. As the higher number of vehicles in the household offers household members more flexibility in everyday travel, the positive coefficient values of the variable ‘number of vehicles’, for ‘increase attending social gatherings’ and negative coefficient values for ‘decrease attending social gatherings’ in both tech savvy and non-tech savvy classes are reasonable. Among all the built environment variables retained in the final model, land-use index is found to be the most influential. Living in higher mixed land-use areas, both non-tech savvy and tech savvy individuals are less likely to decrease and more likely to increase their participation at social gatherings due to smartphone usage. This might be reasonable since higher mixed land-use areas offer various facilities that provide opportunities for social gatherings by interacting with each other and the larger community. In the case of accessibility measures, higher CBD distance from home is negatively related with ‘decrease in attending social gatherings’ in both tech savvy and non-tech savvy classes. These individuals’ smartphone application usage is more likely to have no impact on attending social gatherings while living farther away from the CBD. Perhaps these people tend to work from home and are dependent on technology, which might increase their inclination towards virtual social interactions rather than face-to-face meetings. Thus, smartphone application usage might not affect their social gatherings. 5.4. Vehicle kilometers traveled Table 7 shows the class allocation probability of the ‘vehicle kilometers traveled’ model. It indicates that young students who are essentially pro-smartphone users, earn less than $15,000 annually, and prefer to live in neighborhoods with a lower percentage of single-detached houses have higher probability to be included in class one. On the other hand, class two (reference class) can be represented as the class of individuals who are not students and pro-smartphone users, over 25 years old, earn more than $15,000 annually, and prefer to live in a neighborhood with a higher percentage of single-detached houses. Like previous models, class one is ‘tech savvy’ and class two is ‘non-tech savvy’ in the following discussion. Parameter estimation results indicate the existence of heterogeneity across classes regarding attitudes while exploring individuals’ smartphone application usage effects on vehicle kilometers traveled (VKT). For example, tech savvy individuals who are highly dependent on smartphone applications for trip planning purposes tend to increase their VKT due to smartphone application usage, whereas similar non-tech savvy individuals are more likely to show a decrease in VKT with smartphone use. The results might indicate that tech savvy individuals’ vehicle travel is facilitated by various trip planning and transportation related smartphone applications. However, non-tech savvy individuals are less familiar with such smartphone applications, so exhibit the opposite relationships. Furthermore, tech savvy individuals with a positive attitude towards ICT are more prone to reduce distance traveled by vehicles due to smartphone application usage, whereas, non-tech savvy individuals with positive attitude towards ICT tend to have no impact of smartphone application usage on their vehicle kilometers traveled. Transportation ICT, specifically transportation related smartphone applications, encourage and support sustainable transport choices. Tech savvy individuals with a positive attitude towards ICT might become inclined towards using sustainable transportation alternatives, which are very likely to decrease their daily VKT. This is also supported by the positive relationship between ‘decrease in VKT’ and ‘positive attitude towards sustainable travel’ in the tech savvy class. Similar behavior is observed in the case of non-tech savvy individuals. A positive attitude towards sustainable travel tends to decrease non-tech savvy individuals’ VKT due to smartphone application usage. As expected, both tech savvy and nontech savvy individuals who agree that ‘smartphone has no effects on social trip frequency’ exhibit positive relationships with ‘no impact’ on individuals’ VKT irrespective of classes. Statistically significant standard deviations for ‘no impact on VKT’ indicate the existence of behavioral variations across sampled individuals in both classes. Travel characteristics are also found to significantly influence individuals’ vehicle kilometers traveled. Results suggest that tech savvy auto commuters (represented by the variable primary commute mode_auto) are less likely to increase and more likely to 943

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Table 7 Results of vehicle kilometers traveled model. Class allocation model results

Class allocation probability Constant Pro-smartphone user (> 5 years) Young (< 25 years) Income below $15,000 Student Percentage of single-detached houses in the neighborhood

Tech savvy

Non-tech savvy

0.59 2.491** 0.473*** 1.091* 0.158*** 2.074** −0.024**

0.41 – – – – – –

Parameter estimation results Variables

Constant Attitudes Highly dependent on smartphone applications for trip planning purposes No smartphone effects on social trip frequency Positive attitude towards ICT Positive attitude towards sustainable travel Travel Characteristics Primary commute mode_auto Primary commute mode_transit Number of vehicles in the household Built Environment Population density Dwelling density Land-use index Percentage of rented houses in the neighborhood Percentage of owned houses in the neighborhood Accessibility Measures CBD distance from home Nearest shopping mall distance from home Nearest bus stop distance from home Standard Deviation of Random Parameters Primary commute mode_transit Number of vehicles in the household No smartphone effects on social trip frequency Population density Percentage of owned houses in the neighborhood

Increase

No impact

Decrease

Tech Savvy

Non-tech Savvy

Tech Savvy

Non-tech Savvy

Tech Savvy

Non-tech Savvy

1.864

−7.395**

11.672***

5.517*

Reference

Reference

6.419*

−0.313***

−8.369*

3.348***

−1.693 0.024** 1.439***

−3.200* −0.068** 2.427

0.894**

−0.553*

−0.364***

−0.776

−4.258 −0.404*

2.788** −0.671

−0.720

0.165*

−5.828* −1.67**

1.827** 0.113

−0.306*** −0.921*

0.700*** 0.622

0.015***

0.116***

2.446* −4.573**

1.263** 1.52

−2.426 −0.219**

1.167** −1.909**

0.100* −2.557

−0.278 −7.007*

0.016**

2.336

***

1.589*

0.640**

−0.144

−0.01

−2.341*

1.735**

−0.176*

0.310**

0.959**

−0.176

2.146* 0.376

−1.139 −0.156**

0.036***

0.089*

0.015**

0.047***

0.029***

0.012***

0.002**

0.824

Note: *** 1% significance level. ** 5% significance level. * 10% significance level.

decrease VKT due to smartphone application usage. This might indicate tech savvy individuals’ higher usage of smartphone-based carpooling and car-sharing applications for commuting, which assists to decrease their vehicle kilometers traveled. Such findings indicate changes in travel behavior due to smartphone application usage, since previous studies found that auto commute usually increases individuals’ vehicle kilometers traveled (Dutzik and Baxandall, 2013). In contrast, non-tech savvy individuals exhibit a higher propensity to increase VKT, perhaps suggesting their lack of familiarity with carpooling and car-sharing applications, and increased usage of personal vehicles for commuting. Interestingly, higher vehicle ownership in the households tends to decrease all individuals’ VKT due to smartphone application usage. Possibly, the availability of multiple transportation-related smartphone applications encourages people to choose other modes to travel. Existing studies suggested that a higher number of vehicles in the households is associated with higher vehicle kilometers traveled (Yang et al., 2016). Therefore, the finding confirms changes in individuals’ travel behavior as a result of smartphone application usage. However, along with mean values, the ‘number of vehicles’ exhibits statistically significant standard deviation for ‘decrease VKT’ that indicates its heterogeneous nature of effects in both classes. In the case of commuting by transit, individuals in both classes are less prone to increase VKT due to smartphone application usage, which is expected. However, the variable ‘primary commute mode_transit’ for ‘increase VKT’ shows statistically significant standard deviation in both classes at 99% confidence interval that confirms heterogeneity within each class. 944

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Land-use index is found to be most influential among the built environment variables retained in the final VKT model. Negative coefficient values of land-use index in both classes for ‘no impact on VKT’ indicate that the land-use index may influence individuals’ VKT due to smartphone application usage. While tech savvy individuals are less likely to increase their VKT, individuals in the nontech savvy class exhibit a higher likelihood to increase VKT while living in higher mixed land-use areas. This result is plausible, since higher mixed land-use areas include a variety of sustainable travel alternatives and tech savvy individuals are more familiar with transportation related smartphone applications that provide sustainable transportation choices. Similar behavior is observed in the case of individuals living in the neighborhoods with higher population density and higher percentage of rented houses. Higher population density in the neighborhoods increases tech savvy individuals’ probability to reduce VKT. These neighborhoods can essentially be labeled as urban areas that might generate traffic congestion. Individuals can get real-time traffic information ahead of their journey by using transportation related advanced smartphone applications. Such information possibly discourages tech savvy individuals to increase their travel by vehicles. On the other hand, the opposite relationship is observed in the non-tech savvy class, perhaps indicating that they do not get such advanced real-time information due to less smartphone usage and less familiarity with smartphone applications. However, ‘population density’ not only shows heterogeneous behavior across the classes for the alternative ‘decrease VKT’, but also its standard deviation indicates that heterogeneity exists within the tech savvy and non-tech savvy classes. Accessibility measures such as distance from home to the CBD, nearest shopping mall and nearest bus stop have heterogeneous effects on individuals’ vehicle kilometers traveled in both classes. For example, as the distance from home to CBD increases, tech savvy individuals tend to show a decrease in VKT, whereas, non-tech savvy individuals exhibit a higher propensity to have no impact of smartphone usage on VKT. This finding might indicate tech savvy individuals’ inclination towards telecommuting due to living farther from the CBD, thus decreasing their vehicle kilometers traveled. A similar finding was presented by Mokhtarian et al. (2004), which explored individuals’ preference for telecommuting while living farther from the CBD. As the distance from home to the nearest shopping mall increases, tech savvy individuals are less likely to increase their VKT, whereas non-tech savvy individuals tend to increase their VKT. This might suggest tech savvy individuals’ higher online shopping frequency using smartphone applications rather than traveling to the malls physically. In contrast, non-tech savvy individuals might be less familiar with online shopping applications, hence, exhibit a higher likelihood to increase VKT while living farther from a mall. 5.5. Comparisons of model results and policy implications In the case of latent class random parameter logit models, a comparison of results is challenging due to multidomain variations that are observed and accounted for. However, comparisons of the mobility choice model results of this study yield interesting behavioral insights that could assist transportation planners and practitioners to develop flexible policy interventions. For example, although tech savvy transit commuters are less likely to increase VKT due to smartphone application usage, they have a higher probability to increase their participation in social gatherings, visits to new places and planned group trips due to smartphone application usage. Lessons learned from such findings may lead transportation planners to focus on placemaking strategies around transit stations that foster social, recreational and other discretionary activities. On the other hand, the usage of smartphone applications has a lower probability of affecting non-tech savvy transit commuters, as indicated by the positive parameters of the ‘no impact’ alternative in all four models. This suggests that transportation related smartphone application usage varies between tech savvy and non-tech savvy individuals. Such result informs the necessity of developing smartphone applications that cater to the demands of both types of users in supporting transit ridership. App developers should concentrate on developing more smartphone applications that would provide real-time vehicle information (e.g. automatic vehicle location-based transit schedule, transit frequency, network condition, etc.) so that the benefits of using transit services for multiple mobility purposes by different groups of people can be maximized. Furthermore, individuals with a positive attitude towards sustainable travel are prone to reduce VKT, visiting new places, planned group trips and increase social gathering participation due to their smartphone application usage. Planners and policymakers should take advantage of targeting this specific group to further promote active living strategies, and to provide a well-planned built environment that is conducive to healthier and vibrant lifestyles. The land-use index is found to be the most influential among all the built environment variables retained in the final models. Higher mixed land-use areas offer less car intensive lifestyles and diverse facilities by promoting sustainable travel opportunities (e.g. cycling, bike sharing, carpooling, carsharing, etc.) and sustainable community. This might encourage social interactions through social and community gatherings by including a variety of individuals in groups. Results suggest that tech savvy individuals who live in higher mixed land-use areas are less prone to increase their VKT. They exhibit their inclination to increase participation in social gatherings due to smartphone application usage. In contrast, non-tech savvy individuals who live in higher mixed land-use areas tend to increase their VKT, as well as participation in social gatherings. These findings can assist planners in multiple ways; for instance, to develop policies that integrate different types of land uses and activities in one place, to create neighborhoods with attractive and vibrant streets that provide infrastructure for dynamic activities and sustainable transportation facilities (e.g. dedicated active transportation lane), and to make such diversified and sustainable information readily accessible through multiple smartphone-based travel and related applications. Moreover, with the distance from home to the central business district (CBD), tech savvy individuals tend to decrease VKT and increase planned group trips due to smartphone application usage. Nevertheless, non-tech savvy individuals are more likely to have no impact of smartphone application usage on their VKT and planned group trips. These results encourage transportation planners and practitioners to develop strategies for promoting telecommuting (i.e. work from home) opportunities via smartphone applications. Such opportunities would give individuals freedom since it offers flexibility in their work schedule, thus would increase job satisfaction and productivity. Adopting telecommuting strategy would also decrease individuals’ commute travel, which saves time to be used for social interactions to plan group trips. These interactions may assist in building a healthy and livable community. 945

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6. Conclusion This study presented the findings of an investigation into the impacts of smartphone application usage on mobility choices, specifically, individuals’ visits to new places, planned group trips, participation in social gatherings and vehicle kilometers traveled. One of the unique contributions of this study is that it explored the effects of individuals’ smartphone application usage on mobility choices in terms of their attitudes; in addition to travel characteristics, built environment and accessibility measures. Four latent class random parameter logit (LCRPL) models were developed in this study to estimate individuals’ mobility choices. The model results revealed that individuals’ travel characteristics, built environment attributes and attitudes are the key factors that influence the relationship between mobility choices and smartphone application usage. For example, results found that individuals who primarily commute by auto tend to increase visiting new places due to smartphone application usage. Also, such application usage increases individuals’ willingness to plan more group trips and attend more social gatherings with the increase in number of vehicles in their households. As expected, individuals’ positive attitude towards sustainable travel was found to be negatively associated with the increase in vehicle kilometers traveled. Furthermore, the mobility choice models probabilistically identified two latent classes, tech savvy and non-tech savvy, which demonstrated considerable preference variations across population. In each class, random parameters were found to have standard deviation along with their mean coefficient values that indicated individuals’ preference variations within the corresponding latent class. For instance, tech savvy auto commuters were found to be prone to decrease their vehicle kilometers traveled due to smartphone application usage, whereas, non-tech savvy auto commuters demonstrated an opposite behavior. Tech savvy urban area dwellers (represented by the variable ‘population density’) showed less willingness than the non-tech savvy people to increase their mobility due to smartphone application usage. Such finding differs from the traditional travel behavior patterns (i.e. higher mobility in urban areas due to more mobility opportunities), hence, indicates that individuals’ travel behavior are changing due to the usage of smartphone applications, which has been argued by past studies (Dutzik and Baxandall, 2013; Yang et al., 2016; Colak et al., 2016). Moreover, although transit commuters were found less inclined to increase vehicle kilometers traveled, standard deviations of the parameter in both latent classes suggest that some transit commuters may increase their vehicle kilometers traveled due to smartphone application usage. The study had certain limitations. For example, it utilized self-reported smartphone application usage data to evaluate how it affects different mobility choices. Observational studies and direct monitoring of smartphone application usage could be used to obtain better information for modeling, although collecting long-term observational data would be costly and time consuming. Future research should address this while collecting multiyear observational data of smartphone application usage and analyzing individuals’ mobility choice behavior. Nevertheless, results found in this study could be useful for policy implication that may contribute to the smart growth of the communities. Alternative policy interventions, such as mixed-use development with sustainable and technology-oriented transportation system, and placemaking strategies to create comfortable and interactive places for everyone, could be implemented to build a viable community and enhance personal interactions. In addition, since model results indicated that variations in choices exist across tech savvy and non-tech savvy people, target marketing could be applied by focusing into specific market segments. For example, transit ridership can be popularized among tech savvy people by developing efficient and technology-based transit infrastructure. Moreover, transportation planners and policy makers should concentrate on developing smartphone applications that would serve all types of smartphone users’ needs in terms of their daily mobility. App developers should focus on developing mobility-supported smartphone applications with different types of adjustable features for diverse population groups, such as tech savvy and non-tech savvy groups, urbanite and suburbanite groups, student and worker groups, etc. Social marketing strategies, which anticipate behavioral changes, could be applied to reflect such variations in campaign strategies, slogans and community engagements to reach different types of individuals in this complex digital era. CRediT authorship contribution statement Nazmul Arefin Khan: Conceptualization, Data curation, Investigation, Methodology, Software, Formal analysis, Validation, Writing - original draft, Writing - review & editing. Muhammad Ahsanul Habib: Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Writing - review & editing. Shaila Jamal: Data curation, Writing - original draft, Writing review & editing. 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