Muhammad Ahsanul Habib

Reference-Dependent Residential Location Choice Model Within a Relocation Context

This paper presents a reference-dependent model for residential location choice. The key contribution of the model is its incorporation of reference dependence that explicitly recognizes the role of the status quo and captures asymmetric responses toward gains and losses in making location choice decisions. The study uses a retrospective residential search survey and a dwelling supply data set from the Toronto Real Estate Board in Ontario, Canada, to estimate the model at the elemental level of individual dwelling units. The study applies a mixed logit formulation that captures unobserved heterogeneity and avoids imposing independence of irrelevant alternatives restrictions on the choice probabilities. Several types of variables, including dwelling characteristics, land uses and other zonal attributes, accessibility measures, and household socio-demographics, are tested in the model. Although the current dwelling is assumed to be the reference point in framing evaluation of alternative dwellings, all gains and losses are measured by a comparison of current and prospective dwellings in the modeling framework. The results reveal that households prefer gains in the number of bedrooms, but they are more sensitive to the equal amounts of losses. A similar loss aversion attitude is observed for the percentage of open areas and unemployment rate. It is also found that decision makers are sensitive only to the losses for the level of service attributes. The reference-dependent model performs better than a conventional location choice model in terms of model fit and provides important behavioral insights.

Influence of Transportation Access and Market Dynamics on Property Values: Multilevel Spatiotemporal Models of Housing Price

This paper presents housing price models by using multilevel modeling techniques. The key motivation of using the multilevel modeling technique is that it clearly identifies and differentiates between-cluster heterogeneity (i.e., intrinsic differences across aggregated units) and heterogeneity between units of analysis that are nested within aggregated clusters. Two different specifications are tested: two-level spatial and mixed two-level spatiotemporal random effects models. Whereas the first specification assumes that dwelling units are nested within spatial clusters (i.e., neighborhoods), the second specification hypothesizes that dwelling units are nested within spatiotemporal clusters (neighborhoods in a given time period). The unique contribution of this paper is that it accounts for temporal heterogeneity simultaneously with spatial heterogeneity in the housing price models. The study uses an extensive sample of more than 250,000 housing property transactions in 1987–1995 in the Greater Toronto Area of Canada. The paper examines the functional form of the hedonic price model and chooses a semilogarithmic model for subsequent multilevel housing price modeling. The results suggest that the spatiotemporal model performs better in terms of explanatory power and parameter estimates.

Modeling of Job Mobility and Location Choice Decisions

This paper presents a comprehensive framework for modeling continuous decisions about changing jobs that individuals make over the course of their lives. The key objective of the research was to develop disaggregate econometric models for decision making about job mobility and location choice to implement longitudinal job mobility behavior of people within a dynamic microsimulation-based integrated urban modeling system. The paper includes two behavioral model components: (a) a job mobility model and (b) a job location choice model. The models were implemented empirically with a retrospective survey of the greater Toronto and Hamilton area in Ontario, Canada. The first component investigated the timing of job mobility with the use of a competing risk duration modeling approach for four event types: a job switch, a return to school, short-term unemployment, and withdrawal from the labor force. The second component, job location choice, was empirically estimated by applying the discrete choice methodology. One of the key features of the model was that it examined the influence of current employment in making decisions about the next job location and specified a gain–loss utility structure by the prospect–theoretic, reference-dependent choice modeling approach. A mixed logit model was developed to account for unobserved heterogeneity in the location preferences. These models were expected to be implemented in the Integrated Land Use, Transportation, Environment (ILUTE) modeling system, which had recently been updated with comparable disaggregate behavioral residential location models.

Life-Oriented Approach of Modeling Commute Mode Loyalty and Transition Behavior

This study developed a dynamic model for individuals’ commute mode choice over their lifetime by using retrospective survey data. The study conceptualized that individuals reassessed their choice of commute mode when they relocated to a new residential location. Following the re-appraisal, people either continued using the same mode, which was considered mode loyalty, or made a transition to a new mode, which was considered mode transition in this study. The study developed a panel-based random-parameters logit model. One key feature of this study is a life-oriented approach to accommodate the effects of life-cycle events, longer-term changes, life-oriented sociodemographic transitions, and accessibility transitions. The model results suggest that the high-income group tends to be car loyal. No car ownership over the lifetime and the addition of a job increase the probability of transit loyalty. Individuals with no children in the household and residing in an area with high walk and bike usage have a higher probability to be loyal to active transportation. A decrease in household income and tenure transition from owned to rental are likely to trigger a transition from car to transit. However, the presence of children and the addition of a car increase the transition propensity from transit to car. The model results suggest that the use of life-oriented characteristics to explain longer-term commute mode loyalty and transition behavior provides important behavioral insights into the dynamics of individuals’ travel behavior over their lifetime.

Modeling mobility tool ownership of youth in Toronto, Ontario, Canada

This paper presents the findings of the modeling of mobility tool ownership of the youth population in the greater Toronto and Hamilton area (GTHA) in Ontario, Canada. Research on mobility tool ownership is limited. The key contribution of the research is to enhance the understanding of how neighborhood characteristics and accessibility affect mobility tool ownership among GTHA youth. The study exclusively considered youth between the ages of 17 and 19 years; at this age the subjects passed through a transitional stage in the GTHA because of opportunities for ownership of types of mobility tools. This study used data from the 2006 Transportation Tomorrow Survey, a household-based travel survey of the GTHA. The study used a latent class choice modeling approach to account for unobserved heterogeneity, which was often ignored in traditional choice modeling. The results suggest that the latent class logit model outperforms the conventional multinomial logit model according to model fit and its ability to evaluate various parameters across latent classes. Several sociodemographic characteristics, trip attributes, accessibility measures, and neighborhood characteristics were found to explain different types of mobility tool ownership of the youth population. Finally, the results revealed that latent heterogeneity existed in the sampled population. The research offers important behavioral insights into the formation of travel habits of youth that could be useful in shaping the travel behavior of Torontos young adults.

Vulnerability in Transport Network during Critical Infrastructure Renewal: Lessons Learned from a Dynamic Traffic Microsimulation Model.

Abstract: This paper presents a Dynamic Traffic Assignment (DTA)-based microsimulation model that assesses traffic impacts on transport network during sudden closure of a critical infrastructure (CI) in Halifax, Canada. DTA models can replicate actual traffic conditions considering long queues and spillback. The model is calibrated and validated using field traffic data and used to predict traffic flows in the network. The model suggests an increment of 6 minutes in average traffic delay and 24.5% reduction in the number of arrived vehicles during the given incident. The model also reports spillover effects in the network.

Longitudinal Vehicle Transaction Model: Assessment of Lead and Lagged Effects of Longer-Term Changes and Life-Cycle Events

This paper presents a dynamic vehicle transaction model developed with data from a retrospective survey conducted in Halifax, Canada. The study investigated four types of vehicle transaction decisions, including first-time vehicle purchase, vehicle acquisition, vehicle disposal, and vehicle trade. A panel-based latent segment logit (PLSL) model was developed to account for repeated transaction decisions and to capture unobserved heterogeneity among the sample households. The study took a life-oriented approach by examining the lead and lagged effects of longer-term changes and life-cycle events. The PLSL model was estimated for two latent segments, and the model results suggest that profound heterogeneity exists, as evident in parametric values of the two segments. The life-cycle event represented by the birth of a child might trigger vehicle acquisition in Segment 1. In contrast, that life-cycle event might deter an acquisition decision in Segment 2. The model confirmed historical deposition effects of key life events. For instance, a 2-year lagged effect on vehicle acquisition was seen with the birth of a child. Moreover, the model results revealed that first-time vehicle purchase behavior was considerably different from the decision to acquire a vehicle. For example, for the longer-term change represented by the addition of a job, significant heterogeneity across the two segments for a first-time vehicle purchase decision was seen and a 3-year lagged effect was confirmed. A positive relationship for the decision to acquire a vehicle in both segments was found for the same variable, and a 1-year lagged effect was confirmed. Finally, the study provided important behavioral insights for targeting specific groups of the population to promote sustainable travel behavior.

Pedestrian Injury Severity Levels in the Halifax Regional Municipality, Nova Scotia, Canada: Hierarchical Ordered Probit Modeling Approach

Pedestrians are particularly vulnerable road users within the urban environment. Many studies have examined the factors that contribute to the frequency and severity of collisions, but limited research has examined the influence of the built environment on pedestrian injury severity. This study used the Halifax regional municipality in Nova Scotia, Canada, as a case study to examine the effect of the built environment on the injury severity of pedestrians. Two ordered response models were used: a conventionally ordered probit model and a hierarchical ordered probit (HOPIT) model, which accommodated unobserved heterogeneity because the thresholds could vary across observations. In the HOPIT model fit in this study, the threshold covariates varied with whether the collision occurred at an intersection and with the number of walking commuters in the neighborhood. Built environment contributing factors, including a variety of street pattern classifications, land use types, transit supply, and demographic characteristics, were examined with other variables (e.g., pedestrian and driver characteristics, collision characteristics, environmental conditions). Nova Scotia Collision Record Database data were used for the years 2007 to 2011 to develop the ordered response models of the injury severity of pedestrians. The study found personal and collision characteristics to be significant to explain the injury severity outcomes of pedestrians. In addition, environmental characteristics (e.g., land use type, presence of activity centers, demographic attributes) were found to influence injury severity outcomes. The study results may help inform policy development to improve pedestrian safety in Nova Scotia.

Modeling Frequency and Duration of Out-of-Home Participation in Physical Activity by School-Age Children

This paper investigates the frequency and the duration of participation in physical activity by children who go to school in the city of Dhaka, Bangladesh. The study uses data from a 2012 activity-based travel diary survey of 245 students in Grades 6 to 10 in the Dhaka metropolitan area. To explore the trade-offs between factors that affect the frequency of out-of-home physical activity, including the use of active transportation, this study used a zero-inflated Poisson (ZIP) regression model. Additionally, parametric hazard models were estimated to examine the duration of physical activities. The results of the ZIP count model suggest that personal and household characteristics, attributes of the built environment, and time commitments for other mandatory and discretionary activities influence the frequency of participation in physical activity. For example, an increase in the number of children in the household and the presence of sidewalks increased the frequency of out-of home, out-of-school physical activity if everything else was held equal. In contrast, travel time to school, the presence of traffic intersections, and the time commitment for tutoring sessions negatively affected participation in physical activity. In the modeling of the duration of out-of-home physical activity, this study found that a Weibull parametric hazard model outperformed a log logistic model. The duration was influenced by sociodemographic characteristics, spatial context, and escort arrangements. For instance, car ownership and a parental escort increased the duration of physical activity. The paper offers an in-depth behavioral understanding of childrens physical activities in a developing country.

Microsimulation of life-stage transitions and residential location transitions within a life-oriented integrated urban modeling system

Abstract This paper presents microsimulation of life-stage transitions and residential location transition processes within a life-oriented agent-based integrated Transport Land Use and Energy (iTLE) modeling system. The iTLE assumes that individuals and households are the agents, and parcels are the objects. It is conceptualized following the life-course perspectives and theories to address the evolution of multi-domain decision interactions over the life-course of the agents. Residential location is simulated as a two-stage process of residential mobility, and location choice. Life-stage simulation includes aging, death, birth, out-migration, in-migration, and household formation. The iTLE is implemented at a yearly time-step from 2006 to 2021 for Halifax, Canada. A 100% synthetic population is generated for the base year 2006, which shows a SRMSE value of 0.37 and APE measures of less than 5% for 89% of the DAs. The simulation results are validated with the 2011 Census information. The validation results suggest that the iTLE generates reasonably satisfactory population estimates. For example, around 52% of the DAs show an APE value of less than 30%, and 37% of the DAs show a difference in the number of households of less than ±50. The predicted results regarding the spatio-temporal evolution of Halifax suggests an increase of around 14% population in 2021 compared to 2007. Younger population residing closer to the CBD are predicted to be more frequent movers than older population residing farther away from the CBD. Higher proportions of the households are predicted in the locations within 25 km from the CBD over the years. Proportion of households in these high density neighborhoods are predicted to increase from 68% in 2007 to 71% in 2021. In 2021, a higher density of single person households are predicted in the urban core. Density is predicted to be more variable and skewed towards suburban neighborhoods as household composition changes through marriage and child birth.