NORTHWESTERN UNIVERSITYTRANSPORTATION CENTER

Researcher(s): Amanda Stathopoulos, Jason Soria
Year: 2023

On-demand mobility platforms play an increasingly important role in urban mobility systems. Impacts are still debated, as these platforms supply personalized and optimized services while also potentially exacerbating sustainability challenges. To alleviate these concerns, microtransit projects have emerged, promising to combine the advantages of pooled on-demand rides with more sustainable fixed-route public transit services. Specifically, microtransit provides, dynamic rider-driver matching to serve demand with fewer vehicles and design optimal routes if riders accept to wait to board vehicles at curbside boarding locations. The shift to microtransit calls for new research on user behavior, motivations, and acceptability to understand demand and its role in existing mobility systems. The COVID-19 pandemic context adds an additional layer of complexity. This study investigates the potential demand for microtransit options against the background of the pandemic. We use a pivoted efficient choice experiment to study the decision-making of Israeli public transit and car commuters when offered to use novel microtransit options (sedan vs. passenger van). By estimating commuter group-specific Integrated Choice and Latent Variable models with error component terms for the microtransit alternatives, we investigate the tradeoffs related to traditional fare and travel time attributes, along with microtransit features: walking time to the pickup location, vehicle sharing, waiting time, minimum advanced reservation time, and shelter at designated boarding locations. We analyzed two latent constructs: the attitudes toward sharing and the experiences and risk perceptions related to the COVID-19 pandemic. The results reveal three key takeaways. (1) New modal attributes significantly affect the utility of the microtransit alternatives, with a notable aversion to walking and waiting among drivers; (2) car and transit commuters have structural differences in attribute elasticities; (3) significant differences are noted for the magnitude of the latent variable effects. Sharing experience and COVID Comfort play a key role for drivers evaluating the choice of microtransit.

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Researcher(s): Ian Savage
Year: 2010

Van Reeven (2008) argues that the Mohring effect is not relevant to the determination of transit subsidies because a profit-maximizing monopolist would supply frequencies that are the same as, or greater than, those that are socially optimal. We find that his results depend on the reduction or elimination of the effect of fares on demand, causing optimal prices to be indeterminate within broad ranges. Consequently, his model is an unsatisfactory tool for discussing subsidies in general, and the optimal combination of fare and frequency in particular.

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Researcher(s): Joel Horowitz, Richard Blundell
Year: 2007

This paper presents a test for exogeneity of explanatory variables that minimizes the need for auxiliary assumptions that are not required by the definition of exogeneity. It concerns inference about a non-parametric function "g" that is identified by a conditional moment restriction involving instrumental variables (IV). A test of the hypothesis that "g" is the mean of a random variable "Y" conditional on a covariate "X" is developed that is not subject to the ill-posed inverse problem of non-parametric IV estimation. The test is consistent whenever "g" differs from "E"("Y"|"X") on a set of non-zero probability. The usefulness of this new exogeneity test is displayed through Monte Carlo experiments and an application to estimation of non-parametric consumer expansion paths.

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Researcher(s): Joel Horowitz, J. Huang, S. Ma
Year: 2008

We study the asymptotic properties of bridge estimators in sparse, highdimensional, linear regression models when the number of covariates may increase to infinity with the sample size. We are particularly interested in the use of bridge estimators to distinguish between covariates whose coefficients are zero and covariates whose coefficients are nonzero. We show that under appropriate conditions, bridge estimators correctly select covariates with nonzero coefficients with probability converging to one and that the estimators of nonzero coefficients have the same asymptotic distribution that they would have if the zero coefficients were known in advance. Thus, bridge estimators have an oracle property in the sense of Fan and Li [J. Amer. Statist. Assoc. 96 (2001) 1348–1360] and Fan and Peng [Ann. Statist. 32 (2004) 928–961]. In general, the oracle property holds only if the number of covariates is smaller than the sample size. However, under a partial orthogonality condition in which the covariates of the zero coefficients are uncorrelated or weakly correlated with the covariates of nonzero coefficients, we show that marginal bridge estimators can correctly distinguish between covariates with nonzero and zero coefficients with probability converging to one even when the number of covariates is greater than the sample size.

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Researcher(s): Frank S. Koppelman
Year: 2006

Abstract: Random utility maximization discrete choice models are widely used in transportation and other fields to represent the choice of one among a set of mutually exclusive alternatives. The decision maker, in each case, is assumed to choose the alternative with the highest utility to him/her. The utility to the decision maker of each alternative is not completely known by the modeler; thus, the modeler represents the utility by a deterministic portion which is a function of the attributes of the alternative and the characteristics of the decision-maker and an additive random component which represents unknown and/or unobservable components of the decision maker's utility function.

Early development of choice models was based on the assumption that the error terms were multivariate normal or independently and identically Type I extreme value (gumbel) distributed (Johnson and Kotz, 1970). The multivariate normal assumption leads to the multinomial probit (MNP) model (Daganzo, 1979); the independent and identical gumbel assumption leads to the multinomial logit (MNL) model (McFadden, 1973). The probit model allows complete flexibility in the variance-covariance structure of the error terms but it's use requires numerical integration of a multi-dimensional normal distribution. The multinomial logit probabilities can be evaluated directly but the assumption that the error terms are independently and identically distributed across alternatives and cases (individuals, households or choice repetitions) places important limitations on the competitive relationships among the alternatives. Developments in the structure of discrete choice models have been directed at either reducing the computational burden associated with the multinomial probit model (McFadden, 1989; Hajivassiliou and McFadden, 1990; Börsch-Supan and Hajivassiliou, 1992; Keane, 1994) or increasing the flexibility of extreme value models.

Two approaches have been taken to enhance the flexibility of the MNL model. One approach, the development of open form discrete choice models is discussed by Bhat in another chapter of this handbook. This chapter describes the development of closed form models which relax the assumption of independent and identically distributed random error terms in the multinomial logit model to provide a more realistic representation of choice probabilities.

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Researcher(s): P. Vovsha, J. Gliebe, E. Petersen, F.S. Koppelman
Year: 2005

Intra-household interactions constitute an important aspect in modeling activity and travel-related decisions. Recognition of this importance has recently produced a growing body of research on various aspects of modeling intra-household interactions and group decision making mechanisms as well as first attempts to incorporate intra-household interactions in regional travel demand models. This paper presents an attempt to build a general framework for incorporation of intra-household interactions in the regional travel demand model. The approach distinguishes between three principal levels of intra-household interactions: 1) Coordinated principal daily pattern types, 2) Episodic joint activity and travel, 3) Intra-household allocation of maintenance activities. The adopted models are discrete choice constructs of the Generalized Extreme Value class. These models together create an analytical framework for integrative modeling of the daily activity and travel of multiple household members, taking into account their interactions.

Researcher(s): Justin D. Siegel, Joaquın De Cea, Jose Enrique Fernandez, Renan E. Rodriguez, David Boyce
Year: 2006

Detailed analyses and comparisons of urban travel forecasts prepared by applying the state-of-practice sequential procedure and the solution of a combined network equilibrium model are presented. The sequential procedure for solving the trip distribution, mode choice and assignment problems with feedback is the current practice in most transportation planning agencies, although its important limitations are well known. The solution of a combined model, in contrast, results from a single mathematical formulation, which ensures a well converged and consistent result. Using a real network, several methods for solving the sequential procedure with feedback are compared to the solution of the combined model ESTRAUS. The results of these methods are shown to have various levels of instability. The paper concludes with a call for a new paradigm of travel forecasting practice based on an internally consistent model formulation that can be solved to a level of precision suitable for comparing alternative scenarios.

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Researcher(s): George P. Baker, Thomas N. Hubbard
Year: 2004

We investigate how contractual incompleteness affects asset ownership in trucking by examining cross-sectional patterns in truck ownership and how truck ownership has changed with the diffusion of on-board computers (OBCs). We find that driver ownership of trucks is greater for long than short hauls, and when hauls require equipment for which demands are unidirectional rather than bidirectional. We then find that driver ownership decreases with OBC adoption, particularly for longer hauls. These results are consistent with the hypothesis that truck ownership reflects trade-offs between driving incentives and bargaining costs, and indicate that improvements in the contracting environment have led to less independent contracting and larger firms.

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Researcher(s): Ian Savage
Year: 2008

This paper investigates the choice of fare and service frequency by urban mass transit agencies. A more frequent service is costly to provide but is valued by riders due to reduced waiting times at stops, and faster operating speeds on less crowded vehicles. Empirical analyses in the 1980s found that service frequencies were too high in most of the cities studied. For a given budget constraint, social welfare could be improved by reducing service frequencies and using the money to lower saved fares. The cross-sectional nature of these analyses meant that researchers were unable to address the question of when and why the oversupply occurred. This paper seeks to answer that question by conducting a time series analysis of the bus operations of the Chicago Transit Authority from 1953 to 2005. The paper finds that it has always been the case that too much service frequency was provided at too high a fare. The imbalance between fares and service frequency became larger in the 1970s when the introduction of operating subsidies coincided with an increase in the unit cost of service provision.

Researcher(s): Hani Mahmassani
Year: 2009

Funded as part of Northwestern’s Initiative on Sustainability and Energy, the objective of this study is to develop a methodology for analyzing and setting user prices (under marginal cost pricing) to better reflect vehicle greenhouse gas emissions and energy consumption.

Congestion results in increased greenhouse emissions and wasted fuel. Pricing is considered along with other operational strategies, such as signal timing, lane use controls and real-time traffic management, as a mechanism to influence user behavior and the resulting flow patterns towards less congested and more energy and environmentally sustainable states.
Outcomes of this ongoing study are to:

1. extend congestion pricing principles to account explicitly for emissions and fuel consumption costs, in addition to travel time costs;
2. provide a reliable basis and network-level methodology for analyzing pricing mechanisms for reducing CO2 emissions and fuel consumption.

Researcher(s): Yu Nie, Yang Liu
Year: 2010

This paper considers a static congestion pricing model in which travelers select a mode from either, driving on highway or taking public transit, to minimize a combination of travel time, operating cost and toll. The focus is to examine how travelers’ value of time (VOT), which is continuously distributed in a population, affects the existence of a pricing-refunding scheme that is both self-financing (i.e. requiring no external subsidy) and Pareto-improving (i.e. reducing system travel time while making nobody worse off). A condition that insures the existence of a self-financing and Pareto-improving (SFPI) toll scheme is derived. Our derivation reveals that the roll authority can select a proper SFPI scheme to distribute the benefits from congestion pricing through a credit-based pricing scheme. Under mild assumptions, we prove that an SFPI toll always exists for concave VOT functions, of which the linear function corresponding to the uniform distribution is a special case. Existence conditions are also established for a class of rational functions. These results can be used to analyze more realistic VOT distributions such as log-normal distribution. A useful implication of our analysis is that the existence of an SFPI scheme is not guaranteed for general functional forms. Thus, external subsidies may be required to ensure Pareto-improving, even if policy-makers are willing to return all toll revenues to road users.

Researcher(s): John O. Ledyard, Mark Olson, David Porter, Joseph A. Swanson, David P. Torma
Year: 2002

Combined-value auctions (CVAs) allow participants to make an offer of a single amount for a collection of items. These auctions provide value to both buyers and sellers of goods or services in a number of environments, but they have rarely been implemented, perhaps because of lack of knowledge and experience. Sears Logistics Services (SLS) is the first procurer of trucking services to use a CVA to reduce its costs. In 1993, it saved 13 percent over past procurement practices. Experimental economics played a crucial role in the development, sale, and use of the CVA.

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Researcher(s): Robert J. Gordon, Darryl Jenkins
Year: 1999

This paper investigates the “hub premium” hypothesis that major carriers with the major share of traffic in and out of a hub exploit so-called "monopoly power." The hypothesis states that these carriers charge hub-city residents higher fares for travel originating or terminating at the hub than they charge other passengers traveling on the rest of their systems. Some have even gone so far as to claim that consumers living in hub cities live in “pockets of pain.”

By contrast, virtually everyone agrees that consumers who choose to take one stop flights enjoy the full benefits of competition. If a passenger is traveling, say, from Newark to Los Angeles or Seattle and is willing to include a stop in the itinerary, that person has a choice of flying perhaps seven or eight different airlines – including all of the major carriers. Those flying shorter distances, even from Washington to Chicago, have the choice of connecting through cities like Cleveland, Pittsburgh, Detroit, and Cincinnati, rather than going nonstop. This rich array of choices for connecting traffic guarantees a competitive fare to the passenger willing to make a connection.

The surprising result of this study is that the passenger originating or terminating his or her trip in the three major Northwest Airlines hub cities actually enjoys the same competitive fare as the connecting passenger, holding constant the effect of mileage on fares. And yet this study makes no adjustment whatsoever to the benefit to the hub-originating passenger of his or her freedom from the inconvenience or time penalty of connecting or stopping enroute.

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Researcher(s): Hani Mahmassani
Year: 2009

Dr. Mahmassani served as PI for Northwestern University Transportation Center’s participation through a subcontract to PB Americas, Inc., contracted by the National Academy of Science to implement a research synthesis on “Improved Framework and Tools for Highway Pricing”.

The current state of U.S. practice for highway pricing decisions is characterized by the following tendencies:

• Incorporation of road pricing in the general framework of regional travel demand models and planning processes by state DOTs and MPOs. These include 4‐step models based on static assignment procedures as well as recent practical implementations of activity‐based models and dynamic traffic assignment.
• Financial analyses by consultants hired for bond‐rating agencies, resulting in revenue and demand projections often based on simplified modeling tools, such as corridor‐level traffic assignment using aggregate demand elasticities.

Public agencies seek to improve the overall performance of regional transportation networks, so their interest in toll road profitability may be bounded by opportunities for re-investing net revenues in further system enhancements and/or pursuing other welfare-enhancing policies for their community of travelers, including both transit and highways users. In order to adequately reflect the number of potential behavioral shifts (for example, destination and time-of-day choice) and policy strategies that may apply under significant implementations of roadway pricing (for example, variable and/or occupancy-based tolls), travel models should be comprehensive. They should reflect regional networks and modes, times of day and traveler types. To permit robust evaluation of welfare impacts, they should be behaviorally founded. The ultimate purpose of these models is a description of travel behavior and demand response.

Understanding, planning, and managing road pricing as part of a regional system’s operation is a critical objective of this study. NUTC’s role focused on the development of network-based methodologies to capture the short and long term term responses of users to different pricing schemes, as part of decision-support capabilities for public and private entities.

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Researcher(s): Hani Mahmassani
Year: 2010

Dr. Mahmassani serves as co-PI on this study, awarded by the National Academy of Science to PB Americas, Inc. (with Northwestern University Transportation Center). The work plan can be conceptualized in three interconnected levels of behavioral rigor and practical application, with varying levels of sophistication:

Level 1 – Behavioral Foundations. The first level corresponds to behavioral models intended for a deep understanding and quantitative exploration of travel behavior. These models seek to address the full range of possible short and long‐term responses, but also may focus on select choice dimensions (for example, route and departure time choices, or usual workplace location choice).

Level 2 – Advanced Operational. The second level relates to relatively advanced, yet operational, Activity‐Based (AB) models, integrated with state of the art DTA (Dynamic Traffic Assignment) models. These models allows for the incorporation of a wide range of possible short‐ and long‐term responses that are embedded in the choice hierarchy. The integrity of operational models requires that each and every choice dimension should be allocated a proper “slot” in the hierarchy, with upward and downward linkages to related choices. Operational/computing time requirements often limit the total number of choice dimensions and alternatives, but this restriction is lessening with time.

Level 3 – Opportunities for Prevailing Practice. The third level relates to existing model systems used by most of MPOs and state DOTs, in the form of aggregate trip-based models (frequently referred to as 4-step models). Though rather restrictive in design, such models offer opportunities for meaningful and immediate contributions to the state of travel demand modeling practice. A serious restriction of 4‐step models is that these rely on static assignment procedures. Static assignments generate only crude average travel time and cost variables, and reliability can be incorporated only through certain proxies.

The SHRP 2 C04 project has completed an inventory of available datasets to support the research, and demonstrated an integrated application of user response models with a simulation-based DTA platform for the New York region Best Practice Model network.

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Researcher(s): Thomas N. Hubbard
Year: 2003

Productivity reflects not only how efficiently inputs are transformed into outputs, but also how well information is applied to resource allocation decisions. This paper examines how information technology has affected capacity utilization in the trucking industry. Estimates for 1997 indicate that advanced on-board computers (OBCs) have increased capacity utilization among adopting trucks by 13 percent. These increases are higher than for 1992, suggesting lags in the returns to adoption, and are highly skewed across hauls. The 1997 estimates imply that OBCs have enabled 3-percent higher capacity utilization in the industry, which translates to billions of dollars of annual benefits

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Researcher(s): Ronald R. Braeutigam
Year: 1999

This chapter examines the progress made during the past few decades in understanding transportation costs. Specifically, the chapter focuses on the costs incurred by carriers in providing railroad, motor carrier, airline, or other transportation services. Many other types of costs are not addressed here, such as congestion costs, pollution costs and other externalities, and other costs to users of transport services, such as the value of time in travel. Progress in understanding has come on three fronts. First, there have been significant advances in the theoretical understanding of costs. For example, early cost studies did not recognize the proper role of factor prices in a cost function. Researchers such as McFadden and Nerlove showed the importance in empirical work of specifying cost functions that are consistent with production theory, including not only a proper treatment of factor prices, but also variables that might contribute to a change in technology over time. Although these principles are now part of the material covered in standard graduate and even undergraduate courses in microeconomics, they helped define a renaissance in empirical studies of costs and production functions.

Second, improvements in empirical techniques have made it possible to learn more than ever about the underlying structure of technology in an industry. Early cost studies were often based on simple functional forms that embodied very strong assumptions about the nature of technology. They also were highly aggregated, effectively treating transportation firms as single product enterprises, and they often paid little attention to the quality of services provided. Empirical work in the past two decades has been advanced by the introduction of more flexible functional forms that contain as special cases many of the more specialized functional forms used by early investigators. Researchers have also improved techniques for studying the costs of multiproduct firms, allowing at least some degree of disaggregation of products.

Finally, as regulatory reform has been implemented, researchers have asked new kinds of questions about technology. For example, in the past regulators often studied costs to determine whether a firm’s revenues would cover its costs or to measure the extent to which total costs could be divided into fixed and variable costs. Over time researchers have learned the importance of incorporating features of the transportation network into cost studies, for example, by distinguishing economies of size from economies of density. As regulatory reform became a real possibility, researchers began to ask whether is was likely to lead to an industry structure compatible with competition.

To understand the evolution of transportation cost studies, it is useful to begin with a brief discussion of the kinds of cost studies that the Interstate Commerce Commission (ICC) commonly used before regulatory reform. Because issues of rail rate making were important even before the turn of the century, much of the early effort to measure transport costs focused on the railroad industry. Several academic researchers succeeded in pointing out the limitations of regulatory costing procedures and inspired a generation of improved studies. From that beginning point, I follow the flow of literature through a series of improvements in the use of theory and empirical techniques.

After discussing several studies that have made important methodological contributions to the literature, I summarize findings from several of them about the major characteristics of selected transport modes, including economies of scale, density, size, and scope. At the outset, however, I note that this chapter is not intended to provide a comprehensive survey of transportation cost studies, an effort well beyond the scope of this paper and also one that has attempted elsewhere, including the recent excellent survey by Oum and Waters.

Researcher(s): George P. Baker, Thomas N. Hubbard
Year: 2003

Explaining patterns of asset ownership is a central goal of both organizational economics and industrial organization. We develop a model of asset ownership in trucking, which we test by examining how the adoption of different classes of on-board computers (OBCs) between 1987 and 1997 influenced whether shippers use their own trucks for hauls or contract with for-hire carriers. We find that OBCs' incentive-improving features pushed hauls toward private carriage, but their re- source-allocation-improving features pushed them toward for-hire carriage. We con- clude that ownership patterns in trucking reflect the importance of both incomplete contracts and of job design and measurement issues.

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Researcher(s): J.P. Gliebe, F.S. Koppelmann
Year: 2005

The daily activity-travel patterns of individuals often include interactions with other household members, which we observe in the form of joint activity participation and shared rides. Explicit representation of joint activity patterns is a widespread deficiency in extant travel forecasting models and remains a relatively under-developed area of travel behavior research. In this paper, we identify several spatially defined tour patterns found in weekday household survey data that describe this form of inter-agent decision making. Using pairs of household decision makers as our subjects, we develop a structural discrete choice model that predicts the separate, parallel choices of full-day tour patterns by both persons, subject to the higher level constraint imposed by their joint selection of one of several spatial interaction patterns, one of which may be no interaction. We apply this model to the household survey data, drawing inferences from the household and person attributes that prove to be significant predictors of pattern choices, such as commitment to work schedules, auto availability, commuting distance and the presence of children in the household. Parameterization of an importance function in the models shows that in making joint activity-travel decisions significantly greater emphasis is placed on the individual utilities of workers relative to non-workers and on the utilities of women in households with very young children. The model and methods are prototypes for tour-based travel forecasting systems to represent the complex interaction between household members in an integrated model structure.

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Researcher(s): Joel Horowitz, S. Lee
Year: 2007

We consider nonparametric estimation of a regression function that is identified by requiring a specified quantile of the regression "error" conditional on an instrumental variable to be zero. The resulting estimating equation is a nonlinear integral equation of the first kind, which generates an ill-posed-inverse problem. The integral operator and distribution of the instrumental variable are unknown and must be estimated nonparametrically. We show that the estimator is mean-square consistent, derive its rate of convergence in probability, and give conditions under which this rate is optimal in a minimax sense. The results of Monte Carlo experiments show that the estimator behaves well in finite samples.

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Researcher(s): Ian Savage
Year: 2010

This paper estimates congestion feeds for arriving flights at Chicago O’Hare Airport. The analysis finds that the level of congestion is only about a fifth of the magnitude of the congestion associated with departing flights. Congestion is much worse in poor weather conditions, and mitigating these weather delays is a primary objective of the current program to reconfigure the airfield. The analysis finds that the nonlinearities inherent in models of congestion mean that even a very modest change in flight patterns reduces delays and congestion fees quite considerably.

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Researcher(s): Dirk Brockmann, Lars Hufnagel
Year: 2006

The dispersal of individuals of a species is the key driving force of various spatiotemporal phenomena which occur on geographical scales. It can synchronize populations of interacting species, stabilize them, and diversify gene pools.1–3 The geographic spread of human infectious diseases such as influenza, measles and the recent severe acute respiratory syndrome (SARS) is essentially promoted by human travel which occurs on many length scales and is sustained by a variety of means of transportation4–8. In the light of increasing international trade, intensified human traffic, and an imminent influenza A pandemic the knowledge of dynamical and statistical properties of human dispersal is of fundamental importance and acute.7,9,10 A quantitative statistical theory for human travel and concomitant reliable forecasts would substantially improve and extend existing prevention strategies. Despite its crucial role, quantitative assessment of human dispersal remains elusive and the opinion that humans disperse diffusively still prevails in many models.11 In this chapter we will report on a recently developed technique which permits a solid and quantitative assessment of human dispersal on geographical scales.12 The key idea is to infer the statistical properties of human travel by analysing the geographic circulation of individual bank notes for which comprehensive datasets are collected at online bill-tracking websites. The analysis shows that the distribution of traveling distances decays as a power law, indicating that the movement of bank notes is reminiscent of superdiffusive, scale free random walks known as L´evy flights.13 Secondly, the probability of remaining in a small, spatially confined region for a time T is dominated by heavy tails which attenuate superdiffusive dispersal. We will show that the dispersal of bank notes can be described on many spatiotemporal scales by a two parameter continuous time random walk (CTRW) model to a surprising accuracy. We will provide a brief introduction to continuous time random walk theory14 and will show that human dispersal is an ambivalent, effectively superdiffusive process.

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