Darren M. Scott

Social influence on travel behavior: a simulation example of the decision to telecommute

This paper addresses interagent interactions, an issue that has received limited attention in travel behavior research. Drawing upon the theory of externalities and the sociological notion of social networks, we develop a discrete choice model that incorporates elements of social influence in addition to more conventional factors such as the attributes of alternatives and the characteristics of decisionmakers. Using simulation, we apply the model to the case of telecommuting—that is, the decision to telecommute or not—over two waves. The experiment suggests that some marginal adopters of telecommuting are influenced heavily in the second wave by the decisions of others in the first wave. Furthermore, the example illustrates the importance of social influence on new adopters of telecommuting in the second wave.

Elderly Mobility: Demographic and Spatial Analysis of Trip Making in the Hamilton CMA, Canada

Recent interest in the urban transport challenges posed by the demographic outlook of ageing societies has prompted a growing body of scholarship on the subject. The focus of this paper is on the topic of elderly trip generation and the development of models to help formalise some important relationships between trip-making behaviour and personal, household and contextual variables (such as location). The case study is the Hamilton Metropolitan Area-an important functional component of Greater Toronto, itself one of the regions in Canada where the impact of ageing is expected to be most strongly felt. Using data from Torontos Transport Tomorrow Survey and mixed ordered probit models, the study investigates the question of spatial and demographic variability in trip-making behaviour. The results support the proposition that trip-making propensity decreases with age. However, it is also found that this behaviour is not spatially homogeneous and in fact exhibits a large degree of variability-a finding that highlights both the challenges of planning transport for the elderly and the potential of spatial analytical approaches to improve transport modelling practice.

Impacts of commuting efficiency on congestion and emissions: case of the Hamilton CMA, Canada

Abstract This study uses IMULATE (Integrated Model of Urban LAnd use, Transportation, energy and Emissions) to examine the impacts of commuting efficiency on congestion and automobile emissions—specifically, non-methane hydrocarbons, carbon monoxide and nitrogen oxides—in the Hamilton Census Metropolitan Area. Estimates of these externalities are compared for two commuting scenarios: a base scenario of estimated commuting flows for 1991 and an optimal scenario in which the mean commuting time for all workers is minimized. The findings indicate that significant reductions in congestion and automobile emissions are possible by advocating policies that encourage greater commuting efficiency in the locational choices of workers. The analysis of jobs–housing balance as one such means suggests that a considerable proportion of commuting cannot be explained by geographical imbalances in the distributions of jobs and housing, and that workers consider many factors besides commuting costs in their locational choices. It is concluded that policies promoting jobs–housing balance as the principal strategy for facilitating more efficient commuting may not meet the expectations of policy-makers.

A discrete-choice approach to modeling social influence on individual decision making

Individual decision making is commonly studied using discrete choice models. Models of this type are applied extensively to the study of travel behavior, residential location, and employment decisions, among other topics of interest. A notable characteristic of the underlying economic theory is the assumption that individuals seek to maximize utility on the basis of their personal attributes and the attributes of the alternatives available to them. This approach ignores the interrelated nature of decision making in social situations—in other words, the role that social structures play in shaping behavior. In this paper we describe a multinomial discrete choice approach to analyzing individual behavior in social situations where position in a social network may encourage or discourage different courses of action. By means of a simulation example, we explore some properties of the model, in particular the effect of network topology.

Weight matrices for social influence analysis : An investigation of measurement errors and their effect on model identification and estimation quality

Weight matrices, such as used in network autocorrelation models, are useful to investigate social influence processes. The objective of this paper is to investigate a key topic that has received relatively little attention in previous research, namely the issues that arise when observational limitations lead to measurement errors in these weight matrices. Measurement errors are investigated from two perspectives: when relevant ties are omitted, and when irrelevant ties are erroneously included as part of the matrix. The paper first shows analytically that these two situations result in biased estimates. Next, a simulation experiment provides evidence of the effect of erroneously coding the weight matrix on model performance and the ability of a network autocorrelation test to identify social influence effects. The results suggest that depending on the level of autocorrelation and the topology attributes of the underlying matrix, there is a window of opportunity to identify and model social influence processes even in situations where the ties in a matrix cannot be accurately observed.

Human Capital Location Choice: Accounting for Amenities and Thick Labor Markets

A growing literature has found a positive association between human capital and long‐run employment growth across cities. These studies have increased interest in understanding the location choices of university degree holders, a group often used as a proxy measure of human capital. Based on data from the 2001 Canadian Census of Population, this paper investigates determinants of the location choices of degree and nondegree holders. With a multinomial logit model, it tests a series of hypotheses about the differential effects of thick labor markets and amenities on the location choice of these groups across metropolitan and nonmetropolitan areas in Canada.

Location Choice Modeling for Shopping and Leisure Activities with MATSim

The activity-based multiagent simulation toolkit MATSim adopts a coevolutionary approach to capturing the patterns of peoples activity scheduling and participation behavior at a high level of detail. Until now, the search space of the MATSim system was formed by every agents route and time choice. This paper focuses on the crucial computational issues that have to be addressed when the system is being extended to include location choice. This results in an enormous search space that would be impossible to explore exhaustively within a reasonable time. With the use of a large-scale scenario, it is shown that the system rapidly converges toward a systems fixed point if the agents’ choices are per iteration confined to local steps. This approach was inspired by local search methods in numerical optimization. The study shows that the approach can be incorporated easily and consistently into MATSim by using Hägerstrands time–geographic approach. This paper additionally presents a first approach to improving the behavioral realism of the MATSim location choice module. A singly constrained model is created; it introduces competition for slots on the activity infrastructure, where the actual load is coupled with time-dependent capacity restraints for every activity location and is incorporated explicitly into the agents location choice process. As expected, this constrained model reduces the number of implausibly overcrowded activity locations. To the authors’ knowledge, incorporating competition in the activity infrastructure has received only marginal attention in multiagent simulations to date, and thus, this contribution is also meant to raise the issue by presenting this new model.

Understanding the Impact of the Modifiable Areal Unit Problem on the Relationship between Active Travel and the Built Environment

This study examines how the relationship between active travel (AT) and the built environment is influenced by the modifiable areal unit problem (MAUP). Characteristics of the built environment are measured at 14 geographical scales. At each scale, these characteristics are related to a binary measure of AT using a logit model. The data for this study come from the Space–Time Activity Research (STAR) project, which was conducted in Halifax, Canada, between April 2007 and May 2008. The variable coefficients and their significance are examined for MAUP influences. The results suggest that the relationship between AT and the built environment is indeed impacted by the MAUP. The results are used to suggest guidelines for selecting an appropriate scale at which to measure the built environment in order to minimise MAUP effects.

GIS-based Map-matching: Development and Demonstration of a Postprocessing Map-matching Algorithm for Transportation Research

This paper presents a GIS-based map-matching algorithm that makes use of geometric, buffer, and network functions in a GIS – to illustrate the suitability of a GIS platform in developing a postprocessing mapmatching algorithm for transportation research applications such as route choice analysis. This algorithm was tested using a GPS-assisted time-use survey that involved nearly 2,000 households in Halifax, Nova Scotia, Canada. Actual routes taken by household members who travelled to work by car were extracted using the GPS data and the GIS-based map-matching algorithm. The algorithm produced accurate results in a reasonable amount of time. The algorithm also generated relevant route attributes such as travel time, travel distance, and number of left and right turns that serve as explanatory variables in route choice models. The ease and flexibility of the Python scripting language used in developing the GIS-based mapmatching algorithm make this tool easy to develop and implement. It can be improved to suit data inputs and specific fields of application in transportation research.

Mapping commuter cycling risk in urban areas

Cycling is becoming an increasingly important transportation option for commuters. Cycling offers exercise opportunities and reduces the burden of motor vehicle travel on society. Mapping the risk of collision between cyclists and motor vehicles in urban areas is important to understanding safe cyclist route opportunities, making informed transportation planning decisions, and exploring patterns of injury epidemiology. To date, many geographic analyses and representations of cyclist risk have not taken the concept of exposure into account. Instead, risk is either expressed as a rate per capita, or as a count of events. Using data associated with the City of Hamilton, Canada, we illustrate a method for mapping commuter cyclist collision risk per distance travelled. This measure can be used to more realistically represent the underlying geography of cycling risk, and provide more geographically and empirically meaningful information to those interested in understanding how cycling safety varies over space.