arXiv subjects

Khandker Nurul Habib

Publications and source records attributed to Khandker Nurul Habib.

5 recordsLinked to original sources

arXiv2026

A Framework for Transportation and Land Use Integration as a Parallel Constrained Multiple Discrete-Continuous Extreme Value (PC-MDCEV) Home Production Model

Integrated urban models (IUM) typically rely on a measure of accessibility or travel time to form the link between the transportation and land use systems. Such integration does not fully capture the trade-offs made by households in how they spend their limited temporal and monetary budgets. We propose a microeconomic foundation for transportation and land use choice model integration based on the theory of home production. A utility function is developed that considers both household monetary expenditure and individual time use. We address several limitations in previous home production functions. First, the introduction of a parallel constrained multiple discrete-continuous extreme value (MDCEV) structure that allows for the inclusion of multi-person households in the model. Second, travel time is defined as the minimum time required to conduct an activity and deducted from the temporal budget. This assumption has several appealing features. It defines the minimum time to complete an activity as a measure of accessibility. An empirical application is provided for the Greater Toronto Area using a validated synthetic dataset. Empirical results demonstrate an economy of scale in time devoted to home production, analogous to scaling exhibit in market production. It was found that the mix of dwelling types (detached, apartment, etc.) has a significant influence on both time use and consumption. Finally, we provide several directions for future research to advance the practice of urban modelling and better capture the complex dynamics of household decision-making.

econ.EM↗

arXiv2023

Deriving Weeklong Activity-Travel Dairy from Google Location History: Survey Tool Development and A Field Test in Toronto

This paper introduces an innovative travel survey methodology that utilizes Google Location History (GLH) data to generate travel diaries for transportation demand analysis. By leveraging the accuracy and omnipresence among smartphone users of GLH, the proposed methodology avoids the need for proprietary GPS tracking applications to collect smartphone-based GPS data. This research enhanced an existing travel survey designer, Travel Activity Internet Survey Interface (TRAISI), to make it capable of deriving travel diaries from the respondents' GLH. The feasibility of this data collection approach is showcased through the Google Timeline Travel Survey (GTTS) conducted in the Greater Toronto Area, Canada. The resultant dataset from the GTTS is demographically representative and offers detailed and accurate travel behavioural insights.

econ.GN↗

arXiv2022

Modelling the Frequency of Home Deliveries: An Induced Travel Demand Contribution of Aggrandized E-shopping in Toronto during COVID-19 Pandemics

The COVID-19 pandemic dramatically catalyzed the proliferation of e-shopping. The dramatic growth of e-shopping will undoubtedly cause significant impacts on travel demand. As a result, transportation modeller's ability to model e-shopping demand is becoming increasingly important. This study developed models to predict household' weekly home delivery frequencies. We used both classical econometric and machine learning techniques to obtain the best model. It is found that socioeconomic factors such as having an online grocery membership, household members' average age, the percentage of male household members, the number of workers in the household and various land use factors influence home delivery demand. This study also compared the interpretations and performances of the machine learning models and the classical econometric model. Agreement is found in the variable's effects identified through the machine learning and econometric models. However, with similar recall accuracy, the ordered probit model, a classical econometric model, can accurately predict the aggregate distribution of household delivery demand. In contrast, both machine learning models failed to match the observed distribution.

econ.EM↗

arXiv2020

How Large is too Large? A Review of the Issues related to Sample Size Requirements of Regional Household Travel Surveys with a Case Study on the Greater Toronto and Hamilton Area (GTHA)

The paper presents a review of sample size issues related to regional household travel surveys. A review of current practices reveals that different perspectives and, as a result, different practices exist in Canada, US, and abroad on sample size. The paper uses data from the Transportation Tomorrow Survey (TTS) - a household travel survey conducted every five years in the Greater Toronto and Hamilton Area (GTHA) - for a set of empirical investigations that asses the adequacy of household travel survey samples. The empirical investigations reveal that even with a 5% sample size, a full representation of the population and its corresponding travel behaviour may be difficult (at the 95% confidence level). Therefore, based on the results of the empirical investigations and the literature review, the paper proposes a flexible framework for household travel survey sample size determination, especially for Canadian municipalities.

stat.AP↗

arXiv2020

On the Factors Influencing the Choices of Weekly Telecommuting Frequencies of Post-secondary Students in Toronto

The paper presents an empirical investigation of telecommuting frequency choices by post-secondary students in Toronto. It uses a dataset collected through a large-scale travel survey conducted on post-secondary students of four major universities in Toronto and it employs multiple alternative econometric modelling techniques for the empirical investigation. Results contribute on two fronts. Firstly, it presents empirical investigations of factors affecting telecommuting frequency choices of post-secondary students that are rare in literature. Secondly, it identifies better a performing econometric modelling technique for modelling telecommuting frequency choices. Empirical investigation clearly reveals that telecommuting for school related activities is prevalent among post-secondary students in Toronto. Around 80 percent of 0.18 million of the post-secondary students of the region, who make roughly 36,000 trips per day, also telecommute at least once a week. Considering that large numbers of students need to spend a long time travelling from home to campus with around 33 percent spending more than two hours a day on travelling, telecommuting has potential to enhance their quality of life. Empirical investigations reveal that car ownership and living farther from the campus have similar positive effects on the choice of higher frequency of telecommuting. Students who use a bicycle for regular travel are least likely to telecommute, compared to those using transit or a private car.

econ.EM↗