X vs. Y: an analysis of intergenerational differences in transport mode use among young adults

Transportation - Recent research has contrasted the travel patterns of young adults of Generation Y (or, synonymously, the Millennial Generation) with the travel patterns of earlier generations of...     

Easing or tightening control strategies: determination of COVID-19 parameters for an agent-based model

Some agent-based models have been developed to estimate the spread progression of coronavirus disease 2019 (COVID-19) and to evaluate strategies aimed to control the outbreak of the infectious disease. Nonetheless, COVID-19 parameter estimation methods are limited to observational epidemiologic studies which are essentially aggregated models. We propose a mathematical structure to determine parameters of agent-based models accounting for the mutual effects of parameters. We then use the agent-based model to assess the extent to which different control strategies can intervene the transmission of COVID-19. Easing social distancing restrictions, opening businesses, speed of enforcing control strategies, quarantining family members of isolated cases on the disease progression and encouraging the use of facemask are the strategies assessed in this study. We estimate the social distancing compliance level in Sydney greater metropolitan area and then elaborate the consequences of moderating the compliance level in the disease suppression. We also show that social distancing and facemask usage are complementary and discuss their interactive effects in detail.

     

Consideration of different travel strategies and choice set sizes in transit path choice modelling

Transportation - Path choice modelling is typically conducted by considering a subset of paths, not the universal set of all feasible paths as this is computationally challenging. This study...     

Estimation of bus dwell time using univariate time series models

A significant proportion of bus travel time is contributed by dwell time for passenger boarding and alighting. More accurate estimation of bus dwell time (BDT) can enhance efficiency and reliability of public transportation system. Regression and probabilistic models are commonly used in literatures where a set of independent variables are used to define the statistical relationship between BDT and its contributing factors. However, due to technical and monetary constraints, it is not always feasible to collect all the data required for the models to work. More importantly, the contributing factors may vary from one bus route to another. Time series based methods can be of great interest as they require only historical time series data, which can be collected using a facility known as automatic vehicle location (AVL) system. This paper assesses four different time series based methods namely random walk, exponential smoothing, moving average (MA), and autoregressive integrated moving average to model and estimate BDT based on AVL data collected from Auckland. The performances of the proposed methods are ranked based on three important factors namely prediction accuracy, simplicity, and robustness. The models showed promising results and performed differently for central business district (CBD) and non‐CBD bus stops. For CBD bus stops, MA model performed the best, whereas for non‐CBD bus stops, ARIMA model performed the best compared with other time series based models. Copyright © 2014 John Wiley & Sons, Ltd.     

Individual trip rate transferability analysis based on a decision tree approach

Transferring trip rates to areas without local survey data is a common practice which is typically performed in an ad hoc fashion using household-based cross-classification tables. This paper applies a rule-based decision tree method to develop individual-level trip generation models for eight different trip purposes as defined in the US National Household Travel Survey in addition to daily vehicle miles traveled. For each trip purpose, the models are obtained by finding the best fitted statistical distribution to each of the final decision tree clusters while considering the correlation between the trip rates for other trip purposes. The rule-based models are sensitive to changes in demographics. The performance of the models is then tested and validated in a transferability application to the Phoenix Metropolitan Region. These models can be employed in a disaggregate microsimulation framework to generate trips with different purposes at the individual or household level.     

Calibration of large-scale transport planning models: a structured approach

Transportation - Traditionally, transport planning model systems are estimated and calibrated in an unstructured way, which does not allow for interactions among included parameters to be...     

A novel approach for systematically calibrating transport planning model systems

Calibration of a transport planning model system is a complex process. While trial-and-error methods and modelling expertise are still the backbone of calibration of transport models, analytical approaches automating the calibration process can improve the accuracy of the models. Introducing a model to guide modellers in the calibration process of large-scale transport planning model systems is the core of this study, where a systematic model for choosing the most appropriate models and parameters is discussed. The effectiveness of the proposed model is investigated by comparing three scenarios which are built on the Travel/Activity Scheduler for Household Agents model as a large-scale agent-based model system.

     

A behavioral housing search model: Two-stage hazard-based and multinomial logit approach to choice-set formation and location selection

Residential location search has become an important topic to both practitioners and researchers as more detailed and disaggregate land-use and transportation demand models are developed which require information on individual household location decisions. The housing search process starts with an alternative formation and screening stage. At this level households evaluate all potential alternatives based on their lifestyle, preferences, and utilities to form a manageable choice set with a limited number of plausible alternatives. Then the final residential location is selected among these alternatives. This two-stage decision making process can be used for both aggregate zone-level selection as well as searching disaggregate parcel or building-based housing markets for potential dwellings. In this paper a zonal level household housing search model is developed. Initially, a household specific choice set is drawn from the entire possible alternatives in the area based on the average household work distance to each alternative. Following the choice set formation step, a discrete choice model is utilized for modeling the final residential zone selection of the household. A hazard-based model is used for the choice set formation module while the final choice selection is modeled using a multinomial logit formulation with a deterministic sample correction factor. The approach presented in the paper provides a remedy for the large choice set problem typically faced in housing search models.     

Travellers’ perceptions of travel time reliability in the presence of rare events

Transportation - Travellers account for variability in transport system performance when they make choices about routes, modes and destinations. Modellers try to quantify travel time reliability...