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Smartphones offer a potential alternative to collect high-quality information on the travel patterns of individuals without burdening the respondents with reporting every detail of their travel. Smartphone apps have recently become a common tool for travel survey data collection around the world, especially for multiday surveys. However, there still exists a lack of systematic assessment of issues related to smartphone app-based surveys, such as the impact of app design or the recruitment method on the collected data. Through a large-scale experiment (named the City Logger), this paper assesses the data produced by the City Logger app, to better understand recruitment avenues (targeted invitation versus crowdsourcing), and examine differences in respondents’ travel behavior recruited through crowdsourcing methods. The paper also examines how app design, and particularly the user input method for trip validation, influences participants’ responses. The results indicate that, while crowdsourcing recruitment is promising, it might not yet be the best way to capture a true representation of the population. For app design, a combination of real-time and travel diary approaches is recommended. An ideal app would prompt users real-time and create a travel diary, so users can validate, edit, or delete the recorded information.

Vehicle kilometers traveled (VKT) has been widely used in regional planning as a key sustainability performance indicator. Many regional growth plans for reducing work trip VKT have been proposed, with a focus on land use development in employment centers. Despite the potential impact of urban form on the reduction of VKT, the fundamentals of how this takes place remain unclear. This study analyzes the relationship between urban form, VKT, and mode shares by examining office commuting patterns in the Greater Toronto and Hamilton Area (GTHA) through a structural equation modeling approach. The model supports the substantial impact of urban form on the reduction of VKT; however, it indicates that such an impact is made mostly through shifting modes, rather than directly on reduced travel distances. This model is then used to evaluate critically a regional growth plan for the GTHA, finding that strategies focusing solely on increasing land use densities in employment centers are not likely to reduce regional VKT significantly without also easing commuting auto dependency. Thus, it is recommended that more sustainable travel alternatives for workers in employment centers should be provided to achieve a sufficient reduction in VKT.

With the emergence of new technologies, new data sources, and software, it is important to understand the current approaches used by transit agencies in ridership prediction. This study reports the results of a recent web-based survey conducted in 2018 among 36 Canadian transit agencies to understand their current state of ridership prediction practice. The study presents a wide range of results, starting from agencies’ used prediction methods to the challenges faced by transit agencies as a result of the observed changes in ridership estimates after the introduction of new automated data collection systems. The study also discusses the transit agencies’ level of satisfaction with the currently used methods and data inputs and factors that are incorporated in their methods. In addition, it develops a better understanding of the requirements of robust ridership prediction models from the transit agencies’ perspective. This paper provides planners and researchers with a comprehensive examination of the different aspects and issues that are related to the current state of transit agencies’ ridership prediction practices.

The completion progress of residential development projects and the length of construction are frequently discussed in the construction industry, but rarely studied by urban modellers. Nonetheless, a realistic reflection of housing supply processes is important for urban microsimulation and land use modelling. To predict the dwelling units generated over space and time, this paper decomposes the housing supply process into two major components: housing starts and completions, the nature and modelling logic of which are quite different. This paper deals with the latter segment, aiming to answer the question of: how long will it take to complete construction of new dwellings? A Cox Proportional Hazard (CPH) Model is employed to examine the “survival” rate of residential building projects and the probabilistic distribution of construction periods. Narrowing down the scope of research, this study investigates housing completions at the individual project level, and discusses the impact of structure type, surrounding land use, and accessibility on the housing completion rate. The Cities of Toronto, Hamilton, and Brampton in the Greater Toronto and Hamilton Area (GTHA) were selected to conduct the empirical study, with each representing different types of urban form to test model compatibility. The hazard models show good performance in replicating completion rates, and the impact of each factor on hazard ratio indicates that, single detached dwelling units with relatively homogeneous land use have the shortest completion time. This study could provide one component of a comprehensive framework for modelling housing supply, especially in urban microsimulation systems.

Isochrone analysis and assessments of cumulative opportunities are a common way to quantify accessibility. However, different time cut-offs have been used by different researchers, with little investigation into what is the ‘best’ cut-off time. Outstanding questions remain concerning the most effective or predictive cut-off time and the potential implications of choosing one time limit over another. The primary objective of this paper is to explore how different cut-off times affect the calculation of isochrone-based accessibility measurements and their potential to predict travel-mode choice. Fifty dissemination areas (DAs) within the Greater Toronto and Hamilton Area (GTHA) are selected to test the impact of different isochrone cut-off times in 5-minute intervals for public transit, automobile, and walking accessibility. The relative predictive power of 30- and 45-minute isochrones in modeling mode choice is also examined. This paper finds that different cut-off times do impact the interpretability of accessibility measurements in the isochrone approach, but a defined cut-off time for general use cannot be determined based on the analysis.

The two-level dynamic model presented in this paper was developed for scheduling work episodes within a 1-week planning period. The week-long time frame captures day-to-day variability in an individual’s work participation within a typical week. Two types of work episodes are modeled: those planned before the week (preplanned) and those scheduled during the week (unplanned). The first level of the model schedules preplanned work episodes, considering workers’ total time awake as their time budget. After the schedule of the preplanned episodes is known, the second level schedules unplanned work episodes. In this level the duration of preplanned episodes is subtracted from the first level’s time budget to define the individual’s time constraint. In each level of the framework, discrete-continuous econometric models are used to model jointly the decision of working on each day with the associated episode duration and start time. Results indicate that not only do work episodes have different attributes based on the time when they are added to the schedule but also there are interdependencies between preplanned and unplanned work episode scheduling. Working on previous days of the week increases the probability of scheduling work episodes on the following days; this setup is representative of the routine nature of much work activity. Workers with a fixed place of work schedule more preplanned work episodes, whereas they engage in fewer unplanned episodes. Flexible work duration increases time expenditure on preplanned episodes. Both models are estimated with computerized household activity scheduling survey data collected in Toronto, Ontario, Canada.

This paper briefly discusses the current state of the art of urban travel demand modeling and research needs in the field. Special emphasis is given to both the challenges and the opportunities posed by modern information technology and the data, transportation services, and travel behaviors that this technology is generating. Travel demand modeling has made very significant strides over the past 20 years, especially in the development of operational activity- and tour-based regional travel demand forecasting systems. These model systems represent first-generation agent-based microsimulation models. Considerable need, opportunity, and scope exist for the development of significantly more powerful second-generation agent-based microsimulation models that build upon emerging big data sets (among other information sources) and high-performance computing. This task, however, will involve the development of new behavioral representations and computational algorithms implemented within much more flexible software environments that both fully exploit available computing power and enable flexible experimentation with and extension of representations of new transportation modes and services and evolving travel behavior.

A buffer of ½ mi (805 m) is commonly used to define the service area of a subway (metro) station. This buffer is based on an approximation of the distance people are willing to walk to access the subway. This research compared the ½-mi pedestrian catchment area with the service areas reported in the Transportation Tomorrow Survey for access to the Toronto Transit Commission Subway System in Canada. This paper assesses the breakdown of access by mode to the subway and the pedestrian, bus–streetcar, and automobile catchment areas of stations in Toronto. This analysis finds two major drawbacks with the use of the ½-mi pedestrian catchment area. The service areas of buses and streetcars that connect to the subway are critical; they account for more than a third of all riders. Spatially, the size and the shape of the service area predicted by the ½-mi approach do not accurately represent what is observed in Toronto. Pedestrian catchment areas are commonly less than ½ mi in radius, and the bus–streetcar and automobile catchment areas are often many times larger.

In this paper we present a prism-based and gap-based approach to model shopping location choice. Location choice is a fundamental decision in the activity scheduling process. We propose a simple yet robust model to capture shopping location choice behaviour. In this model, an individual first chooses a time window (or gap); the choice of the shopping location depends on the gap chosen. This notion arises from our understanding that shopping location choice behaviour depends on shopping type, scheduling constraints, time of day, and day of week. Or quite simply, where you shop depends on when you shop. The gap-based approach to destination choice is envisioned as a small but significant step towards a more comprehensive location choice model in a dynamic scheduling environment.

The travel activity scheduler for household agents (TASHA) is an operational rule-based model that generates activity schedules and travel patterns for a 24-h typical weekday for all persons in a household. This paper reports the application and validation of the model in Changzhou, China. The data cleaning procedure of the traditional household survey and the verification results of the rules used in the TASHA are presented. After the results of the activity generation and scheduling models were analyzed, an iterative approach was applied to inflating the observed activity rates to calibrate the results. The model was shown to replicate observed activity frequency, tour frequency, and trip start time fairly accurately at the regional level. The activity duration of the model was underestimated by 13.1%, but this underestimate did not add much bias to the shape of the duration distribution by start time. The final model results show that the TASHA is an attractive alternative to conventional modeling syst...

The present studies examine whether information contained in medical records can be used to predict outcomes following two orthopedic procedures: repair of hip fracture and total knee replacement. Study 1 reports the acute, in-hospital recovery data from the medical records of 119 hip fracture patients. Study 2 is a prospective, longitudinal investigation of 3-month postoperative recovery of 110 total knee replacement patients. Patients characterized by a greater number of post-traumatic stress risk factors experienced poorer outcomes following orthopedic surgery. Our results suggest that patients at risk for negative outcomes can be identified by information readily available to medical personnel.

The Integrated Land Use, Transportation, Environment (ILUTE) model system is an agent-based microsimulation model for the greater Toronto–Hamilton, Ontario, Canada, area. The model system uses disaggregate models of spatial socioeconomic processes to evolve the state of the greater Toronto–Hamilton area from a known base case to a predicted end state in 1-year time steps. ILUTE has reached a state of operational implementation in which historical validation runs are being undertaken. The model runs start with 100% of the population of people, families, households, and dwelling units in the greater Toronto area that was synthesized for the year 1986. Twenty-year historical simulations (1986 to 2006) have been run, with model outputs being compared with Canadian census data and Transportation Tomorrow Survey data for 1991, 1996, 2001, and 2006. This paper presents recent findings from these historical validation tests and emphasizes the system's modeling of the demographic evolution of the population and the region's housing market.

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.

This paper investigates transit bus emissions in the city of Toronto, Ontario, Canada, by linking the results of a microsimulation transit assignment model, MILATRAS (microsimulation learning-based approach to transit assignment), with emission factors derived from Mobile6.2C. Emissions were estimated at the level of individual buses during idling conditions at bus stops and on roadway links between stops during the morning peak period. The busiest routes were associated with the highest total emissions as a result of a combination of high ridership and lower speeds; this association confirmed the common wisdom that newer, low-emitting buses should be first allocated to these routes. The highest dwell emissions occurred at intermodal transfer stations (bus to subway and vice versa). On a passenger kilometer basis, the highest-emitting routes were not the busiest, but rather were those with the lowest ridership. In fact, the highest emissions per passenger kilometer were associated with the Airport Rocket, a route that provided service to the airport and was characterized by low ridership in the morning peak period. On average, bus trips in Toronto were about three times more fuel efficient than were private car trips and created 20 times less carbon monoxide pollution. The effects of changing fuel types and fleet age on transit bus emissions were assessed. Implications for bus operations are discussed relative to fleet allocation to minimize total emissions.

The agent-based microsimulation modeling technique for transportation planning is rapidly developing, is being applied in practice, and is attracting considerable attention. Along with the conventional four-step modeling technique, MATSim and EMME/2 represent two genres of traffic assignment. They are built on different theoretical bases: dynamic stochastic stationary state assignment and static deterministic user equilibrium assignment, respectively. A study was done of the models' application with data from the Greater Toronto and Hamilton area network in Canada. Given the actual demand data, the models' assignment results are compared and validated on the basis of four indicators of the road network—travel time, travel distance, link volume, and link speed—to reflect both spatial and temporal variation of the traffic flow pattern. The comparison results show that numerical outputs produced by MATSim are not only compatible with those by EMME/2 but are also more realistic from a temporal point of view. The agent-based microsimulation model can be an appropriate alternative to the conventional model for transportation planning. Therefore, agent-based microsimulation models reflect a promising direction of next-generation transportation planning models.

This paper presents the hedonic analysis of office space rent in the Greater Toronto area of Canada. The effects of accessibility, quality, location, and market conditions on rent are explored. Data indicate a high degree of spatial heterogeneity and clustering effects. Spatial analysis techniques are incorporated within the hedonic framework to capture these effects. Results indicate that access to transport infrastructure, distance from the central business district, and vacancy rate are significant in explaining variation in the rent.

The public transportation system, a key part of a multimodal transportation network, has been widely viewed as an efficient way to reduce road congestion and pollution. Public transportation planners use transit assignment models to forecast travel demand and service performance. As technologies evolve and smart transit systems become more prevalent, it is important that assignment models adapt to new policies, such as traveler information provision. This paper investigates three transit assignment tools that represent three approaches to modeling transit trip distribution over a network of fixed routes. These tools are the EMME/2 Transit Assignment Module (Module 5.35), commonly used by planners; Toronto, Canada, Transit Commission's transit assignment tool, MADITUC; and the newly developed Microsimulation Learning-based Approach to Transit Assignment (MILATRAS). These approaches range from aggregate, strategy-based frameworks to fully disaggregate microscopic platforms. MILATRAS presents a stochastic process approach (i.e., nonequilibrium based) for modeling within-day and day-to-day variations in the transit assignment process in which aggregate travel patterns can be extracted from individual choices. Although MILATRAS presents a different standpoint for analysis in comparison with equilibrium-based models, it still gives the steady state run loads. MILATRAS performs comparatively well with EMME/2 and MADITUC. In addition, MILATRAS presents a policy-sensitive platform for modeling the effects of smart transit system policies and technologies on passengers’ travel behavior (i.e., trip choices) and transit service performance.

Microsimulation is becoming more popular in transportation research. This research explores the potential of microsimulation by integrating an existing activity-based travel demand model, TASHA, with a dynamic agent-based traffic simulation model, MATSim. Differences in model precisions from the two models are resolved through a series of data conversions, and the models are able to form an iterative process similar to previous modeling frameworks using TASHA and static assignment using Emme/2. The resulting model is then used for light-duty vehicle emission modeling where the traditional average-speed modeling approach is improved by exploiting agent-based traffic simulation results. This improved method of emission modeling is more sensitive to the effect of congestion, and the linkage between individual vehicles and link emissions is preserved. The results have demonstrated the advantages of the microsimulation approach over conventional methodologies that rely heavily on temporal or spatial aggregation. The framework can be improved by further enhancing the sensitivity of TASHA to travel time.

Office location has important impact on urban form and the transportation system in urban areas. One of the ways to study office location decisions uses surveys of managers and owners of office firms regarding the firm location decision process. The following article presents an analysis of the results gathered in Survey of Office Location Decisions (SOLD)—a Web-based retrospective survey, designed to provide some insight into location decision making of office firms. The main conclusion of the article is that office firms participating in the survey (mainly small and medium sized offices) exhibit a satisficing rather than utility maximizing location decision making. In addition, the results indicate that agglomeration has only a minor role in location decisions by office firms.

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.

Firm location models constitute a significant element of integrated land use and transportation models because the behavior of individuals and households is influenced by employment location. This paper examines office firm location models, both for new and relocating firms, to identify ways to improve their estimation and application. The results of the paper suggest that introduction of anchor points, such as the firm current location and the owner household location, significantly improve the estimation of office location models. A generation procedure for choice sets is then shown to improve the model's ability to replicate the decision behavior of office firms and thus provides a way to increase efficiency in the application of the model.

This paper presents a data collection effort designed to incorporate the social dimension in social activity-travel behavior by explicitly studying the link between individuals' social activities and their social networks. The main hypothesis of the data collection effort is that individuals' travel behavior is conditional upon their social networks; that is, a key cause of travel behavior is the social dimension represented by social networks. With this hypothesis in mind, and using survey and interview instruments, the respondents' social networks are collected using an egocentric approach that is constituted by the interplay between their individual social structures and their social activity behavior. More explicitly, individuals' networks are a context within which to elicit social activity-travel generation, spatial distribution, and information communication and technology use. The resultant dataset links aspects, in novel ways, that have been rarely studied together, and provides a sound base of theory and method to study and potentially give new insights about social activity-travel behavior.

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.

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 people's 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.

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.

Firm mobility is one of the processes a firm can go through during its time of operation. When aggregated, the mobility decisions of numerous individual firms affect the spatial distribution of economic activity and employment as well as the outputs of the transportation system in urban areas. Models of mobility of office business establishments that use hazard modeling and competing risk formulations are presented. These formulations explicitly account for the effect of the duration of the firm in its current location as well as forecast what type of stress or push factor (e.g., lack of space or excessive cost in its current location) will make the firm relocate. The results indicate that combining general hazard modeling with competing risk models could provide a foundation for modeling mobility decisions of firms as well as for estimating thresholds for the locations to which the firm may choose to move.

Hägerstrand's seminal argument that regional science is about people and not just locations is still a compelling and challenging idea when the spatial distribution of activities is studied. In the context of social activity–travel behavior (hosting and visiting), this issue is particularly fundamental as individuals’ main motivation in making social trips is mostly with whom they interact rather than where they go. A useful approach to incorporate the travelers’ social context is to study explicitly the spatial distribution of their social networks, focusing on social locations as emerging from their contacts, rather than analyzing social activity locations in isolation. In this context, this paper studies the spatial distribution of social activities, focusing on the home distances between specific individuals (egos) and the network members (alters) with whom they socialize—serving as a proxy to study social activity–travel location. Using data from a recent study of personal networks and social interaction, and multilevel models that account for the hierarchical structure of these networks, this paper provides empirical evidence on how the characteristics of individuals and their social context relate to the distance separating them. The results strongly suggest that, although the spatial distribution of social interaction has idiosyncratic characteristics, there are several systematic effects associated with the characteristics of egos, alters, and their personal networks that affect the spatial distribution of relationships, and they can contribute to an understanding of where people perform social activities with others.

An initial attempt is made to quantify vehicle emissions in the Greater Toronto Area (GTA) in Canada by exploiting travel information provided by activity-based 24-h models rather than conventional trip-based models. For this purpose, travel activity inputs to the Canadian version of the MOBILE6.2 model (MOBILE6.2C) are generated by relying on the travel demand modeling capabilities of the Travel Activity Scheduler for Household Agents (TASHA), a next-generation activity-based model of travel demand for the GTA. Additional input data supplied to MOBILE6.2C are obtained from Canadian sources and by running traffic assignment (using EMME/2) on the trip distribution matrix generated by TASHA. The integration of MOBILE6.2 with TASHA has provided estimates of the time of day that vehicle emissions occur. TASHA provides an explicit representation of trip starts and ends, which results in improved engine start emissions. Overall, because TASHA provides a better behavioral framework for modeling travel than conventional trip-based models, it is expected to lead to better emissions estimates. Such an effort also provides insight and experience that will be used later in the integration of TASHA with more advanced emission models, thus refining the reliability of practical tools that can be used to assess the environmental sustainability of policies.

Four sets of econometric models are presented for time use decisions (durations and start times) regarding the basic, regular, and committed components (skeleton) of workers' daily life: gap before work, work, gap after work, and night sleep. Two types of models are compared for each component: multilevel linear models and continuous-time hazard models. The multilevel models consider three-level random effects (temporal, personal, and household) and the hazard models consider individual-based unobserved heterogeneity. On the basis of performance in fitting observed data, hazard models are selected for the first three components, and a multilevel model is selected for the last component. For parametric hazard models, the Gompertz distribution shows promising performance in fitting activity data. The models are estimated by using 2002-2003 Toronto [Canada] Computerized Household Activity Scheduling Elicitor data.

This paper presents the results of the first phase of an ambitious research project aiming at modeling the changes over a 15-year period in the bus network of the city of Mississauga, Ontario, Canada, a fast-growing suburb in the greater Toronto area. Data for the Mississauga transit network, along with a host of demographic and socioeconomic variables, were analyzed. For each main route, a buffer zone representing its vicinity was constructed, and the relevant variables captured inside these zones were computed for inclusion in the proposed empirical models. Other global variables for the city were included as well to account for other effects. Results from multiple regression and simultaneous equation models attempting to relate transit supply to this group of demographic, socioeconomic, and route-specific variables are presented. Time and demand–supply interactions were taken into consideration in the simultaneous equation models. The models show that supply increases with demand and population density and decreases with number of schoolchildren in the vicinity.

A conceptual model of location decisions of small office firms is presented. Most firm location models consider all firms in a region; this process usually enables market segmentation only according to the industry classification of a firm. This approach introduces a high level of heterogeneity into the modeling system, since firms’ attributes vary greatly in multiple dimensions. Moreover, most firm location models assume utility maximization behavior and use random utility models. In this conceptual model, an effort is made to reduce heterogeneity through focusing on the relatively homogeneous population of small office firms. Small office firms are also theorized to be satisficers rather than maximizers or optimizers; consequently, more adequate models for the phenomenon may incorporate rule-based approaches rather than random utility techniques. The conceptual model depicted constitutes a first step toward the validation of the proposition that firm location models should be more sensitive to market segmentations and that different methods of decision-making models need to be applied to firms from different parts of the economy.

The procedure for estimating a household model of mode choice is described. The tour-based mode choice model incorporates interpersonal interactions within the household explicitly in an agent-based random utility modeling framework. Household interactions include vehicle allocation, ridesharing to joint activities, and drop-off and pickup. Because of the complex nature of the model decision structure, choice probabilities are simulated from direct generation of random utilities rather than through an analytical probability expression. The computational requirements for the simulation are large. Therefore a grid of computers is used in parallel to perform the necessary calculations and a genetic algorithm is used for parameter estimation. A brief description of the model, the full model results, and a discussion of the computational techniques used in parameter estimation are presented.

A comprehensive frequency and time allocation modeling system for shopping activities is described. The modeling system is person-based but explicitly considers fixed and random household effects. It has three components: a weekly shopping frequency model, a daily shopping frequency model, and a time allocation model for individual shopping episodes. The frequency models consider activity generation as a latent response—the propensity to participate in shopping activities. This latent response is modeled by using an ordinal response model. Both the weekly and daily frequency models are multilevel ordinal logit models, in which the household is the highest level and the individual is the lowest level. The multilevel ordinal logit models incorporate household-level influences on an individual's shopping behavior in terms of fixed effects and a random intercept. The time allocation models are hazard duration models that consider household-level random heterogeneity. The entire modeling system is sequential from the weekly frequency component to the time allocation component. The outputs of the earlier components enter as inputs to the later components: weekly frequency is the input to the daily frequency model; weekly and daily frequencies are input to the time allocation model.

Four sets of econometric models are presented for time use decisions (durations and start times) regarding the basic, regular, and committed components (skeleton) of workers’ daily life: gap before work, work, gap after work, and night sleep. Two types of models are compared for each component: multilevel linear models and continuous-time hazard models. The multilevel models consider three-level random effects (temporal, personal, and household) and the hazard models consider individual-based unobserved heterogeneity. On the basis of performance in fitting observed data, hazard models are selected for the first three components, and a multilevel model is selected for the last component. For parametric hazard models, the Gompertz distribution shows promising performance in fitting activity data. The models are estimated by using 2002–2003 Toronto [Canada] Computerized Household Activity Scheduling Elicitor data.

This paper presents a new funding model for urban transit systems. The model is performance driven in that it captures the performance of transit systems in attracting riders in a cost-effective manner and recognizes that transit system funding needs vary with transit systems’ scale of operations. The model also allocates funding on a weighted per capita basis and thereby addresses equity concerns. Recent data for Canadian transit systems are used to illustrate the application of this funding model to real-world operations.

The current state of operational practice in intercity travel demand modeling was critically reviewed. Conventional model structures and specifications as well as a range of theoretical and practical problems with current models were studied. These included problems with the total demand model specification, modeling intercity mode choice with nested logit models, modeling access and egress trips to and from common carrier line-haul modes, forecasting the demand for new modes of travel, dealing with highway congestion, aggregation issues in intercity models, and data collection issues. The study concluded with some suggestions about promising research and development directions for model improvement.

Traditional activity-travel diary surveys have for some time served as the primary source of data for understanding and modeling travel behavior. Recent changes in policy and forecasting needs have led to the development of an emerging class of activity-scheduling process surveys that focus on the underlying behavioral mechanisms that give rise to travel and condition future change. Many of these surveys involve the use of computers for data entry over multiday periods. These changes pose new challenges and opportunities for quality assessment. At this early stage it is more important than ever to document closely the quantity and quality of data provided by such surveys as well as the associated burden and experience of respondents. This study reviews existing quality standards and seeks to develop several new data-quality measures suitable to this emerging class of surveys. Data are used from a recent household activity-scheduling survey of 271 households in Toronto, Ontario, Canada. A detailed description of the survey instrument is provided, along with an in-depth examination of key results that shed light on data quality. Included are results from a separate survey of 31 respondents concerning their experiences and perceptions of the survey. Overall, although the survey was generally successful in tracking both observed patterns and underlying decision processes over a multiday period within a household, it did come at a price in terms of respondent burden. On the basis of these results several new data-quality guidelines are suggested that incorporate use of activity-trip rates, scheduling-step rates, planning-time horizons, and log-in durations. Further specific suggestions for reducing respondent burden are suggested.

An analysis of the spatial choice of housing builders in the greater Toronto area (GTA), Canada, is presented. A spatially disaggregate database of 126,462 new housing units built by 445 builders is used to analyze the determinants of the intrametropolitan location of new housing. Housing starts are classified into four types: single-family detached (SFD), semidetached, condominiums, and row-link housing. An accessibility analysis shows that the GTA remains a monocentric region where accessibility for most activities declines with distance from the central business district. The location choice of homebuilders differs by housing type. For instance, the construction of new condominiums is more likely to be in high-density areas with high accessibility to jobs. Similarly, the likelihood of construction of low-rise (SFD or semidetached) housing is higher in low-density areas with low accessibility to work and other activities. Neighborhood attributes help determine the type of housing likely to be built in the vicinity. Also, the location of low-rise residential units and planned residential construction is influenced by proximity to major transport corridors in the GTA. The location of condominiums is influenced by proximity to the subway system. Builders are attracted to zones with higher dwelling values, where they can obtain higher values for their products. Spatial inertia in housing markets is presented, which implies that the presence of a certain type of housing attracts more housing of that type to the vicinity.

An empirical investigation of household annual travel and housing expenditures within the greater Toronto area (GTA), Canada, during the period 1986-1996 is presented. The analysis is based on data obtained from local travel surveys, the Canadian census, and the Toronto Real Estate Board, which were combined with transportation network assignment models and travel cost models for the GTA to compute average annual travel and housing expenditures by zone within the region. Key findings include the following: ( a) both travel and housing expenditures tend to increase as one moves away from the city center; ( b) the city of Toronto is much more "efficient" than the surrounding suburban regions in that it has lower combined average travel plus housing expenditures; and ( c) combined housing plus travel expenditures have increased over time within the region (principally because of increased housing expenditures), with the result that GTA households on average spent just over a third of their total income on travel plus housing in 1996.

Background Proton magnetic resonance spectroscopy (1H-MRS) and neuropsychological tests may be useful for monitoring the effectiveness of highly active antiretroviral therapy (HAART) in HIV-associated brain injury. We aimed to evaluate whether brain abnormalities will improve 3 months after HAART. Method Thirty-three HIV patients naive to antiretroviral medications were evaluated before and 3 months after HAART using 1H-MRS and neuropsychological tests; results were compared with those of 26 seronegative control subjects. Results Despite significant improvement in CD4 counts, and suppression of plasma and cerebrospinal fluid (CSF) viral loads, elevated brain metabolites (choline compounds and myoinositol in the frontal lobes) and neuropsychological tests abnormalities (including the computerized tests [CalCAP®]) persisted after 3 months of HAART. In the basal ganglia, choline and myoinositol became elevated only after treatment. No interaction effect was observed between the number of CSF-penetrating drugs (one vs two) and changes (baseline vs 3 months) in any of the brain metabolites, cognitive performance or CSF viral load. Conclusions The persistent brain abnormalities suggest ongoing repair or reactive inflammatory processes in the brain after 3 months of HAART. Regimens with two CSF-penetrating antiretroviral medications do not appear to be more effective than those with one CSF-penetrating drug in treating HIV brain injury at 3 months.

The Toronto Area Scheduling Model for Household Agents (TASHA), a new prototype activity scheduling microsimulation model, generates activity schedules and travel patterns for a 24-h typical weekday for all persons in a household. The prototype model is based solely on conventional trip diary data and therefore is applicable in many urban areas where activity data may not be available. The model makes use of the concept of the project, a "container" of activities with a common goal, to organize activity episodes into the schedules of persons in a household. A heuristic, or rule-based, method is used to organize activities into projects and then to form schedules for interacting household members. The TASHA model is considered to be a successful first attempt to operationalize a generalized conceptual model of household decision making, with reasonable correspondence between model and observed trip rates and chain characteristics.

Automobiles play a pivotal role in daily life, which makes them a subject of interest in many academic fields. Transportation planners are interested in knowing how many and what types of automobiles are owned by households, how people adjust their fleet, and how they use their vehicles. The primary objective of this study was to develop a comprehensive dynamic model of household automobile transactions at a disaggregate level to be used in a dynamic microsimulation modeling framework that can provide a direct forecast of consumer demand for personal-use vehicles. A market-based decision-making process and a transaction approach were applied for this project because of their consistency with the actual processes followed by decision makers. In the proposed framework, each year a decision maker faces four choices: add a new vehicle to the fleet, dispose of one vehicle, trade one of the vehicles in the fleet, or do nothing. A mixed (random parameters) logit model was used to investigate the effects of heterogeneity in the dynamic transaction model and distinguish between heterogeneity- and state-dependence-based explanations for the observed persistence in choice behavior. In this study, the application of dynamic variables representing the occurrence of changes in household state and their impacts on the observed behavior were also investigated.

Over the past few years, machine-learning techniques have expanded enormously. These approaches are increasingly being applied to traffic and transportation problems formerly reserved for formal statistical approaches such as discrete choice models. Part of the reason for this has to do with research trends, but there are some potential advantages associated with such techniques, including the ability to model nonlinear systems; the ease with which symbolic, nominal, or categorical variables can be included; and the ability of these methods to deal with noisy data. The use of two modeling techniques, the nested logit model and the multilayer perceptron artificial neural network, was investigated in terms of their applicability to the household vehicle choice problem. Both methods generated strong results, although the multilayer perceptron artificial neural network yielded better predictive potential.

Recent work in the area of comprehensive transportation modeling systems in a microsimulation framework, more specifically auto ownership modeling, has recognized the need for increased experimentation with dynamic models. Implicitly, dynamic models require longitudinal data. A Toronto area car ownership study was conducted to design and administer a longitudinal survey to fulfill the data requirements for such a dynamic model, to validate the survey results, and to conduct preliminary analysis on those results. An in-depth retrospective telephone survey was conducted with the help of a computer aid in Toronto, Canada. Simple univariate analyses were conducted on the data to determine the relationship between characteristics of the household and the occurrence of vehicle transactions, the choice of vehicle type, the duration a vehicle is held, and the degree of consumer loyalty to different types of vehicles.

An empirical study of the temporal transferability properties of entropy-type trip-distribution models, applied within a fixed geographic region, is presented. Data for the study were drawn from three travel surveys conducted in 1964, 1986, and 1996 in the Toronto region. Very long transfer periods were examined, and a wide variety of model specifications were tested. These specifications ranged from the simple proportional flow model to an occupationally stratified, doubly constrained entropy model, for which spatial separation was measured by highway travel time. These models were estimated using data from 1964 and 1986. The estimated 1964 models were transferred to 1986 and 1996 to predict the distribution of travel. The 1986 models also were transferred to 1996 to predict the distribution of travel. Results showed that the travel-time parameter was not temporally stable. However, pragmatically, the transferred models were found to provide forecasts very comparable to those generated by models estimated in the respective application contexts. Improved model specification consistently resulted in improved precision of the forecasts obtained. In particular, stratification of the data by worker occupation category resulted in models with the best fit to estimation data, as well as forecasts. However, from a decision-oriented perspective, the improvement in precision obtained from additional complexity in specification did not warrant a recommendation for use of more complex models. Thus, the simple, doubly constrained entropy model with spatial separation measured by highway travel time appears satisfactory for use in practical modeling efforts.

The case is presented for a new, coordinated research and development (R&D) program designed to improve significantly the operational state of practice in integrated urban modeling. First, the authors’ view of an “ideal” integrated urban modeling system is described. If developed, this modeling system should be capable of incorporating the best possible understanding of transportation and land use interactions and of addressing planning and analysis needs. Also discussed briefly are current operational modeling capabilities as related to the proposed ideal model. Finally, a multiyear R&D program that was designed to move current practice significantly toward the operationalization of the ideal model is detailed.