Khandker Nurul Habib

Social Context of Activity Scheduling: Discrete–Continuous Model of Relationship Between “with Whom” and Episode Start Time and Duration

Activity-based approaches to travel demand modeling are increasingly moving from theoretical to operational models. Agent-based microsimulation models are a promising approach as they explicitly conceive travel as an emergent phenomenon from peoples activity characteristics and, more explicitly, from their activity-scheduling processes. Activity-scheduling processes are influenced by individuals’ characteristics as well as by the people with whom they interact. Thus, the activity-scheduling process has an intrinsic social context. With social activities used as a case study, the objective of this paper is to investigate empirically the relationship between social context (measured by with whom respondents interacted) and two key aspects of activity scheduling: start time and duration. Econometric models of the combined decisions of with whom to participate and when to start or how much time to spend are estimated to investigate the correlations between “with whom” and start time and duration decisions. Data collected by a 7-day activity diary survey were used for model development. Findings suggest that social context has a relevant role in activity-scheduling processes. For example, with whom people socialize influences social activity-scheduling processes more than do travel time or distances to social travel. In addition to theoretical understanding of the questions investigated here, the models serve as an empirical support for agent-based microsimulation models that could incorporate the role of social networks in activity-scheduling attributes.

Modelling activity generation: a utility-based model for activity-agenda formation

This article presents an econometric modelling framework for activity-agenda formation. The activity-agenda is referred to the collection of different types of activities that are to be scheduled within a specific time period (time budget). The concept of activity utility is used to model frequencies of all individual activity types under consideration within a specific time budget constraint. Contrary to univariate modelling approach for individual activity types separately, this approach deals with all activity types together in a unified econometric modelling framework. The specification of the model also ensures the scope for unplanned (or not defined a priori) activities within the time budget. Kuhn–Tucker optimality condition is used to ensure the probability of having zero frequency of any specific activity type. Each individual activity-specific utility has two components: baseline utility and additional utility. The logarithmic function of additional utility ensures the satiation effect with increasing frequency. The heterogeneity in activity behaviour is also considered by incorporating error correlation in baseline utility. Data from the 2002–2003 CHASE survey, collected in Toronto are used to test the model specifications. Application of this modelling framework in an activity-based travel demand model will greatly enhance behavioural validity as well as sensitivity to subtle transportation policies of travel demand models.

Using Smartphones and Sensor Technologies to Automate Collection of Travel Data

This paper presents a data collection framework and its prototype application for personal activity–travel surveys through the use of smartphone sensors. The core components of the framework run on smartphones backed by cloud-based (online) services for data storage, information dissemination, and decision support. The framework employs machine-learning techniques to infer automatically activity types and travel modes with minimum interruption for the respondents. The three main components of the framework are (a) 24-h location data collection, (b) a dynamic land use database, and (c) a transportation mode identification component. The location logger is based on the smartphone network and can run for 24 h with minimal impact on smartphone battery life. The location logger is applicable equally in places where Global Positioning System signals are and are not available. The land use information is continuously updated from Internet location services such as Foursquare. The transportation mode identification module is able to distinguish six modes with 98.85% accuracy. The prototype application is conducted in the city of Toronto, Ontario, Canada, and the results clearly indicate the viability of this framework.

Investigating the Role of Social Networks in Start Time and Duration of Activities: Trivariate Simultaneous Econometric Model

In the context of improving understanding and modeling the capabilities of activity-scheduling processes in travel behavior, this paper explores the role of social networks in the start time and the duration of social activities. The study was performed with a trivariate joint econometric model that was capable of capturing the correlation between unobserved influential factors causing the endogeneity of these three key decisions. The model captures the relevance not only of sociodemographic variables but also of the social network dimension for travelers, or the with whom variable, that is, individuals with whom travelers perform social activities. A particularly relevant case is the role of travel time to social activities, which has a positive effect on longer durations and late start times and which acts as a link between these two basic dimensions (start time and duration) of activity scheduling. The results confirm the relevance of the social context in an episodes temporal characteristics and illustrate aspects that future activity-based travel demand models should incorporate to be able to capture the socializing side of mobility decisions.

A joint discrete-continuous model considering budget constraint for the continuous part: application in joint mode and departure time choice modelling

This article presents an econometric modelling framework for a discrete-continuous choice structure. The proposed model ensures random utility maximisation (RUM) approach for both discrete and continuous decisions and explicit correlation between the two choices. The continuous choice is modelled as RUM under budget limitation. The model is applied for joint mode choice and departure time choice modelling. The empirical application considers continuous time scale for departure time under daily 24 h time budget constraint. The empirical model is estimated by using data collected in Toronto, Canada. The estimated parameters reveal many behavioural details of commuters’ mode and departure time choices. The RUM-based discrete-continuous model with the incorporation of time budget constraints for the continuous part is a generic econometric model. This article considers modelling ‘mode and departure time’ choice bundle for empirical application of the model. However, the model can be applied for any other similar choice bundle situation, for example, in the case of activity-based travel demand and land use modelling.

Weekly Rhythm in Joint Time Expenditure for All At-Home and Out-of-Home Activities: Application of Kuhn-Tucker Demand System Model Using Multiweek Travel Diary Data

This paper uses the Kuhn-Tucker demand system modeling technique to investigate the capacity of a typical week in capturing rhythms in activity-travel behavior. It considers all possible activity types within a weeklong modeling time frame. Complex interactions in time expenditure between at-home and out-of-home activities and among different out-of-home activities are captured by introducing behavioral elements in the model in terms of baseline preference, time translation, and satiation effects. The Kuhn-Tucker demand system model used in this paper is a random utility maximization model with the inherent assumption that every individual maximizes total utility in allocating time to the activities under consideration within the modeling time frame. Models are developed for each individual week of a 6-week travel diary drawn from the MobiDrive data set for Karlsruhe and Halle, Germany. Each model contains 83 variables and reveals behavioral details of complex activity-travel behavior. Based on the performances of the models in terms of fitting observed data and parameter values of specific variables, it is clear that a modeling time frame for a typical week is capable of capturing the rhythms of activity-travel behavior sufficiently. The paper concludes with the recommendation that the availability of activity diary data for a multiweek time period would further enhance understanding on this issue.

Investigating the factors affecting transit user loyalty

Public transit agencies are constantly looking for ways to increase their ridership. While many studies have attempted to identify the factors affecting new customer attraction, the issue of transit user loyalty has been far less researched. In addition to being a good indicator of a transit agency’s performance, customer loyalty provides several added benefits. Loyal customers are more likely to use the transit agency’s services and recommend them to potential new users. Furthermore, attracting users usually involves additional customer acquisition costs (e.g. marketing) not required in order to retain existing loyal users. This study used data provided by a mixed Stated Preference/Revealed Preference survey to identify some of the factors that affect customer loyalty in the context of public transit. Factors examined include service attributes, trip characteristics, as well as socioeconomic and psychological attributes of the individual. The findings suggest that service quality attributes play a critical role in transit user loyalty, while initiatives such as the provision of real-time information panels or making park and ride facilities available have a less determinant effect on the customers’ mode shifting decisions, irrespective of their emotional response to public transit.

Unraveling the relationship between trip chaining and mode choice: evidence from a multi-week travel diary

Abstract Trip chaining (or tours) and mode choice are two critical factors influencing a variety of patterns of urban travel demand. This paper investigates the hierarchical relationship between these two sets of decisions including the influences of socio-demographic characteristics on them. It uses a 6-week travel diary collected in Thurgau, Switzerland, in 2003. The structural equation modeling technique is applied to identify the hierarchical relationship. Hierarchy and temporal consistency of the relationship is investigated separately for work versus non-work tours. It becomes clear that for work tours in weekdays, trip-chaining and mode choice decisions are simultaneous and remain consistent across the weeks. For non-work tours in weekdays, mode choice decisions precede trip-chaining decisions. However, for non-work tours in weekends, trip-chaining decisions precede mode choice decisions. A number of socioeconomic characteristics also play major roles in influencing the relationships. Results of the investigation challenge the traditional approach of modeling mode choice separately from activity-scheduling decisions.

Temporal transferability of work trip mode choice models in an expanding suburban area: the case of York Region, Ontario

This paper presents an investigation of the transferability of home-based work mode choice models in the context of a rapidly growing suburban area: the Regional Municipality of York in the Greater Toronto Area. Between 2001 and 2006, York Region experienced a rapid change in population and saw the introduction of new transit mode. With a wealth of revealed-preference household survey data from the Transportation Tomorrow Survey, there is an obvious opportunity to investigate whether there were any structural changes in travel behaviour amongst the regions residents. Three heteroskedastic generalised extreme value (GEV)-class choice models are estimated: one for 2001, one for 2006 and a model estimated using data pooled from the 2001 and 2006 data sets. Disaggregate and aggregate transferability tests are conducted. Disaggregate transferability refers to the ability of a model to predict the individual choices observed in the context of application and is measured, in absolute terms, by the transfer log-likelihood. Aggregate transferability refers to the ability of a model to predict overall trends in the data (e.g. mode share). It becomes clear that the sets of estimation parameters are statistically different before and after the new bus transit system introduction. This implies that even advanced heteroskedastic GEV models are not fully transferable. However, interestingly, when assessing aggregate transferability, it is found that the transferred models perform quite well; in some cases, the transferred models fit the data better than the original estimated model. This suggests that disaggregate choice models capable of addressing both the systematic and random effects of transportation and land-use changes on choice-making behaviour should be developed.

Joint modelling of propensity and distance for walking-trip generation

The paper presents an investigation on walking-trip generation for commuting. It uses advanced econometric models to investigate the importance of considering walking distance jointly with walking propensity in the walking-trip-generation models. Empirical models are estimated by using large-scale household travel survey data collected in the Greater Toronto and Hamilton Area (GTHA) in 1996, 2001 and 2006. Empirical models clearly validate the proposal that for walking, travel distance should be considered jointly with the propensity. The relationship between auto-ownership and walking-trip generation is proved to be very strong, referring that high auto-dependent household and neighbourhoods discourage walking. The empirical models reveal that the baseline walking propensity and distance remain unchanged over the years despite significant efforts to encourage active transportation through mixed land-use policies in the GTHA. This implies that, in addition to land-use policies, more rigorous applications of public education and social marketing are necessary.