Assessing the impacts of COVID-19 on activity-travel scheduling: A survey in the greater Toronto area

The COVID-19 lockdown provided many individuals an opportunity to explore changes in their daily routines, particularly when considered in combination with an ever-changing Information and Communication Technology (ICT) landscape. These new routines and alternative activities have the potential to be continued in the post-COVID era. Transportation planners must understand how routines vary to effectively estimate activity-travel scheduling. The purpose of this study is to determine the influence of the COVID-19 pandemic lockdown on activity-travel behavior and the adoption of ICT-based alternative options. A special emphasis is placed on predicting the long-term effects of this disturbance on activity-travel scheduling. This study examines the changes in the frequency and mode of completing five of the most repetitious tasks in the daily schedule (working, grocery and non-grocery shopping, preparing/eating meals, and visiting family/friends) during the lockdown and immediately after reopening. We find an increased preference for home meal preparation over online ordering and a reluctance to engage in in-person shopping until a substantial proportion of the population has acquired a vaccination against the virus. Respondents prefer to work from home if they have adequate access to home office materials (e.g., desk, chair, computer monitor). Individuals with children must also consider suitable childcare before considering a return to work

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.Comment: The paper was presented at 2022 Annual Meeting of Transportation Research Boar

A review of the housing market-clearing process in integrated land-use and transport models

The land-use/transport interaction (LUTI) modeling framework has become the current state of best practice for analyzing the interdependency between the land-use and transportation systems. This paper presents a comprehensive review of the housing market-clearing mechanisms used in operational LUTI models. Market clearing is a critical component of modeling housing markets, but a systematic review and critique of the current state of the art have not previously been undertaken. In the review paper, the theoretical foundations for modeling household location choice are reviewed, including bid-rent and random utility theories. Five LUTI models are discussed in detail: two equilibrium models, MUSSA and RELU-TRAN, and three dynamic disequilibrium models, UrbanSim, ILUTE, and SimMobility. The discussion focuses on the following key points: the assumptions embedded in the models, the aggregation level of households and locations, computational cost and operationalization of the models. One of the challenges is that there are rarely any empirical studies that compare the performance of equilibrium and dynamic models in the same study context. Future research is recommended to empirically investigate the pros and cons of the two modeling approaches and compare the model performances for their representativeness of real-world behavior, computational efficiencies, and abilities for policy analysis. More sophisticated studies about the impacts of agents’ behavior on the housing market-clearing process are also recommended

Comparing multiple data streams to assess free-floating carsharing use

ABSTRACT: Passive data streams are a great alternative to traditional travel surveys to assess the use and the change in behavior. Because of accessibility issues, researchers have harvested free-floating carsharing (FFcs) web-services in order to estimate the spatiotemporal demand. This paper presents the comparison of multiple data streams in the assessment of trip type distribution for FFcs service. While a full dataset of GPS traces may be considered a good approximation of the ground truth, harvesting of origin-destination data seems to estimate correctly the general trend of certain trip type distributions, while for other trip type estimations, a more extensive set of data is needed (member & stopover information) to fully assess it

Examining the difference between park and ride and kiss and ride station choices using a spatially weighted error correlation (SWEC) discrete choice model

This paper presents a novel discrete choice model formulation: the spatially weighted error correlation (SWEC) logit model for spatial location choices. This model captures both the correlation between spatially distinct alternatives based on the relative distance between them and the heteroskedasticity of the errors of the alternative as a function of their relative distance to the decision maker. The SWEC model is applied for the estimation of models of transit station choice for P-and-R as well as kiss and ride (dropped off at transit station) transit commuters in the Greater Toronto and Hamilton Area (GTHA). The kiss and ride model has particular relevance due to its ability to capture household tradeoffs made by both the driver and the passenger. The tradeoffs are captured by utilizing the subsequent trip made by the driver in the utility function specification. The proposed model structure provides additional insights into how station choice occurs for such complex trips. Finally, the application of the SWEC model for both choice contexts provides a fundamental improvement over conventional approaches, as it is able to capture non-proportional substitution patterns and heteroskedasticity inherent with spatial choices

Finding the Subway Disruption Regimes of Switching Subway to Uber in Toronto

The evolving relationship between public transit and transportation network companies (TNCs), such as Uber and Lyft, is of great interest to government agencies and has seen much critical attention in the academic literature. In this paper, we focus on the demand for TNC trips (also known as ride-hailing trips) during the disruption of the subway service. We combined a detailed dataset of Uber trips made in Toronto, Canada during the period September 2016 to August 2018 and subway disruption data provided by the Toronto Transit Commission. These data were used to examine the question: how long are subway users willing to wait during a disruption before switching modes? This question was framed as a threshold point, and an innovative structural threshold regression model was used to obtain an answer. Controlling for environmental and location-specific factors in the model, it was revealed that subway users in Toronto tend to switch to Uber after a service delay of as little as 3 min, with an average result of 7 min and an upper bound of 12 min

An econometric investigation of the influence of transit passes on transit users’ behavior in Toronto

The paper presents the results of an empirical investigation of the influence of transit pass ownership on daily transit service usage behavior under a flat fare system. The transit system of the City of Toronto is investigated to evaluate the factors that influence the choice of owning a transit pass and at the same time whether or not a pass ownership influences different transit ridership behavior. Data from a household travel survey are used to estimate a joint econometric model of the choice of owning a transit pass as a function of benefits/utility drawn from daily transit usage (trip rate and distance travel) along with different socio-economic and land-use variables. Results clearly show that a transit pass has a profound and segmenting influence on ridership behavior in terms of the daily frequency of transit trips and the total distance travel by transit. Non-pass owners seem to draw higher utility from daily transit usage than the pass owners. Pass owners seem to have utility of owning a pass over and above the utility drawn from the daily use of the transit service. A transit pass is often considered as a mobility tool, but this investigation clearly shows an empirical evidence of it. The results suggest that it works for small-size families, people with work/student status, high income and places where compact development makes transit service competitive to other modes of transportation. Empirical results show the influence of different variables can be exploited to cater to various policies that may encourage higher pass ownership rate and thereby reduce demand for private automobiles

Inferring origin and destination zones of transit trips through fusion of smart card transactions, travel surveys, and land-use data

This paper presents a data fusion method to infer the origin and destination zones of transit trips from smart card data. The fusion framework has disaggregate mixed multinomial logit models at its core that predict the most probable origin and destination zones of individual transit trips using smart card transaction records, land use data, and transit system characteristics. The logit models are estimated using revealed trip origin and destination responses from a travel survey that are augmented by land use and transit system data to provide contexts about the zones\u27 trip generation and attraction potentials. For empirical analysis, the methodology is applied to the smart card system of the Greater Toronto and Hamilton Area. Specifically, it is tested under different system configurations (tap-on-only and tap-on-and-off) and for networks with substantial shares of automobile and walk access/egress. When applied to transit trips constructed from the smart card transactions, the estimated models successfully capture the spatial distribution of trip origin and destination at the traffic analysis zone level. The empirical analysis also demonstrates that the proposed fusion method can be appropriately used to reconcile information provided by transit smart card and travel surveys to generate up-to-date transit demand data necessary for public transport planning and operations