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Travel demand models, the next generation
Chapter 3
Travel demand models, the next generation: boldly going where no-one has gone before Eric J. Miller Department of Civil and Mineral Engineering, University of Toronto, ON, Canada; University of Toronto Transportation Research Institute, ON, Canada
Chapter outline 1. 2. 3. 4.
Introduction Disruptions The four pillars of modeling Provocation 1: travel behavior theory 5. Provocation 2: microsimulation 5.1 A holistic approach to modeling cities: urban scale laws
29 30 33 33 36
5.2 Bridging the gap between micro & macro 6. Provocation 3: social Heisenberg uncertainty principle 7. Provocation 4: computing 8. Toward the next generation References
39 41 42 43 44
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1. Introduction Find the beginning, the slight silver key to unlock it, to dig it out. Here then is a maze to begin, to be in. Michael Ondaatje (1970)
IATBR can trace its origins back to a June 1973 conference in Maine titled “Issues in Behavioral Demand Modeling and Valuation of Travel Time”. During the intervening 45 years between this first conference and the 2018 Santa Barbara conference, many advances in travel demand modeling have occurred, notably the widespread adoption of random utility-based modeling methods, increasing implementation of activity-based (or, more typically, tourbased) models, and acceptance of microsimulation (in many quarters at least) as a practical computational approach for dealing with disaggregate, heterogeneous trip-makers (Miller, 2003, 2018a,b). Mapping the Travel Behavior Genome. https://doi.org/10.1016/B978-0-12-817340-4.00003-6 Copyright © 2020 Elsevier Inc. All rights reserved.
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30 PART | I Retrospective and prospective survey of travel behavior research
At the same time, one can argue that the pace of this change has been slow. After all, McFadden’s seminal work on random utility theory was already largely in place in the 1970’s (Domencich and McFadden, 1975; McFadden, 1978), as were other seminal concepts such as Hagerstrand’s time-space constraint paradigm (Hagerstrand, 1970) and Manheim’s definitive foundations for transportation systems analysis (Manheim, 1978). Similarly, the value of adopting an activity-based approach to travel modeling has been recognized at least as long (Jones, 1979). In particular, the adoption of improved methods in operational practice has been very incremental, and has typically considerably lagged the research state-of-the-art. Further, this slow evolution of modeling practice has occurred during an era of relative stability in terms of transportation technology and services, policy issues, urban form and, arguably, basic travel behavior. We are now, however, in a very different era, in which the transportation fielddalong with many other sectors of societydis being disrupted in ways that have not been experienced during the past 100 years. Not only are these disruptions fundamentally changing our transportation systems, but they are also posing new and very difficult challenges to even the best of our current travel demand models. Indeed, the basic premise of this chapter is that our current models are inadequate to analyze this “brave new world” of new technologies and services. The policy need to be able to model and evaluate these disruptions, however, is great. Thus, it is urgently required to develop and test new travel demand models that are capable of credibly dealing with this emerging new world and to help guide policies that will ensure that these coming changes will, in fact, result in more equitable and sustainable urban regions, rather than the reverse. Section 2 of this chapter briefly discusses a few of these key disruptions and some of the challenges that they pose to the current travel demand modeling state of practice/art. To frame the discussion of travel demand modeling R&D needs to address these challenges, Section 3 defines the “four pillars” of modeling. Based on these four pillars, four “provocations” concerning modeling challenges and needs are introduced that are then discussed in detail in Sections 4e7, respectively. Section 8, concludes the chapter by summarizing the preceding discussions in terms of recommendations concerning key steps that need to be taken toward development of a next generation of advanced travel demand models. Taking these steps will, indeed, require bold actions that, if successful, will take us into new policy analysis and modeling territories.
2. Disruptions Transportation is facing at least three major disruptive forces that are already changing the field and that can be expected with great confidence to continue to do so at an accelerated pace going forward. Two are technological in origins, while the third is an even more pervasive socio-economic trend concerning global urbanization. It is already a truism that the looming potential of connected and autonomous vehicles (CAVs) and the burgeoning of new mobility services (ride-
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hailing, car-sharing, etc.) are disrupting transportation in ways not seen since the dawn of the automobile age. While considerable hype currently exists concerning the ultimate capabilities of these technologies and services, as well as the timing and distribution of their implementations, there is no doubt that they will change the status quo in very significant ways. The impacts of new ride-hailing and ride-sharing services provided by new private sector “Transportation Network Companies” (TNCs) on travel behavior within many cities is readily apparent and undoubtedly foreshadow even great impacts in the years to come. The uncertainty surrounding these new technologies and services is also immense, in terms of both how exactly they will “play out” in the coming years and what their impacts on travel demand, transportation system performance, urban form and many other policy concerns will be. One can easily imagine both utopic and dystopic futures for our urban regions depending on how events actually transpire, and, in particular, how governments, public transit authorities and other public sector agencies react to these emerging challenges. A hundred years ago no-one anticipated the profound impacts that the automobile would have on transportation, urban form and our economic and social systems. This time around it is critical that we are proactive and thoughtful in our policy responses to these new technologies and services if we are to going to maximize their potential to improve urban life rather than the opposite. The role of travel demand modeling in addressing these issues is both obvious and essential. The challenge facing us, however, is that our current generation of travel demand models (even the most advanced) are not adequate to the task of dealing with the new technologies and services. Current models did not envision the existence of CAVs or new forms of mobility services, etc. Further, even within their current design context, they are typically weak in areas that are of considerable importance in the analysis of emerging issues. These areas include: modeling “auto passenger” modes (carpooling, conventional taxi, etc.), auto ownership, and non-homebased and non-work/school travel. Thus, a “next generation” of travel demand models is urgently needed in order to provide the policy analysis capabilities needed to understand, explore and choose from among the myriad options, possibilities and scenarios with which we are currently confronted. The second related, but also independently important, disruption is that of the continuing and accelerating growth in Information and Communications Technologies (ICT) and computing capabilities (notably various forms of High Performance ComputingdHPC) and the associated emergence of the Internet of things (IoT), extraordinarily powerful Artificial Intelligence (AI) methods, and increasing ubiquity of massive (and passive) “big data”. ICT, IofT and AI are, of course, the enabling technical foundations upon which CAVs and new
32 PART | I Retrospective and prospective survey of travel behavior research
mobility services are built. But they also are providing new sources of data for travel demand analysis and modeling that are: l l
Very large samples compared to anything we previously had to work with. Dynamic, time-series data, potentially providing observations over days, weeks and even months, compared to the static, one-day surveys that current models are based upon.
At the same time, new AI modeling methods (such as Deep Neural Nets, among others) and cost-effective access to HPC provide us with the tools needed to work with these massive new datasets. The opportunity exists, therefore, to “see” the city and its travel behavior in very new and different ways, opening up new avenues for research and for the development of new model formulations. These new datasets, however, are not without their challenges. Most notably, they are inevitably anonymized, so that we know nothing about the trip-makers being observed. Further, the data generally provides space-time traces of trip-maker movements, but does not directly observe the mode of travel nor the purpose of the trip. Depending on the sensor technology being used (e.g., cellphone traces, GPS tracking, etc.), the spatial-temporal precision of these traces can also vary considerably. Thus, considerable manipulation of such data, typically involving data cleaning and fusion methods, among others, is required to make the data useable for travel demand modeling purposes. Nevertheless, it is clear that the future development of next generation travel demand models will be tied closely to the availability and quality of these new big datasets. The third major challenge for next generation travel demand modeling is to deal much better with the analysis and design of global mega-cities. The history of human civilization has been tied directly to the development and growth of cities (Mumford, 1961). But throughout much of history cities necessarily remained generally small in size due to technology limitations. The emergence of the Industrial Revolution in the late 18th and early 19th Centuries, however, unleashed an expansion of urban living that has been continuing ever since, and which will continue throughout the current century. More than 50% of the world’s population now lives in urban regions (Urbanet, 2018), and our social, economic and political futures depend fundamentally on how well these urban regions function in terms of efficiency, equity, energy use and productivity. Transportation is not the only factor affecting urban wellbeing, but it is a very critical and fundamental one. Evidence-based planning and design will be critical to the addressing the major challenges facing urban transportation systems worldwide. Travel demand models obviously have their part of play in this process. And, again, current methods must improve and adapt considerably to meet this analysis challenge, especially in fast-growing urban regions in developing countries.
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Inducon (Descripon/Exploraon)
DATA
THEORY Deducon (Hypothesis Tesng)
METHODS
COMPUTING FIG. 3.1 The 4 Pillars of modeling.
3. The four pillars of modeling All models (travel demand or otherwise) are operational implementation of theory, built within the limitations of current data, methods and computational capabilities. As illustrated in Fig. 3.1, all useful models are based upon at least a loose balance among these four “pillars” of modeling. Great theory in the absence of methods (software, estimation techniques, simulation algorithms, etc.) with which to implement the theory cannot be operationalized. Similarly, without empirical data to test one’s theories they cannot be verified or improved. In particular, theory can only emerge and evolve over time through the “ying and yang” of both inductive and deductive reasoning/analysis, both of which depend upon the availability of appropriate, good quality data. And, certainly in the case of large-scale regional travel demand models, computing power is essential to the practical development and application of these models. If a next generation of travel demand models is to be developed, this will require advances in all four pillars. This chapter briefly explores a few issues facing travel demand modeling with respect to each of these pillars. It does so through a set of four “provocations” concerning current weaknesses and potential opportunities with respect to each pillar.
4. Provocation 1: travel behavior theory We know a tremendous amount about how the world works, but not nearly enough. Our knowledge is amazing; our ignorance even more so. Donella H. Meadows (2008)
34 PART | I Retrospective and prospective survey of travel behavior research
On the one hand, it can be argued that travel demand modelers have a relatively rich theoretical base upon which to draw. Random utility theory (Domencich and McFadden, 1975; Ben-Akiva and Lerman, 1985; Hensher and Johnson, 1981; Train, 2009; Ortuzar and Willumsen, 2011), in particular, provides a very powerful and (generally) operationally tractable basis for modeling most aspects of travel behavior. Much of the strength of current operational models derive in large part from their random utility foundations. Recent extensions/alternatives to random utility theory emerging out of behavioral economics (Kahneman, 2011; Thayler and Sunstein, 2008), such as regret minimization (Chorus, 2014a,b), also represent potentially promising avenues for improving our models, although these have not yet seen widespread operational implementation. In addition Information Theory also provides a very sound theoretical basis for statistical modeling (Anas, 1983; Shannon, 1948; Webber, 1977; Wilson, 1967), although its explicit application is not nearly so common in most current models.1 At the same time, it is also arguable that random utility theory is largely a methodological framework that provides a calculus for computing “how” we make decisions (maximize utility) within which we still need to “pour” our understanding of the “what” and the “why” and the “when” underlying these decisions (e.g., “What” defines utility? “Why” do we travel?). That is, there is lot of “behavioral theory” about which random utility theory is silent. To illustrate this point, consider the classic multinomial logit model (Ben-Akiva and Lerman, 1985): ebXit Pit ¼ P bX 0 t e i
(3.1)
i0 εCt
where: Pit ¼ Probability that person t chooses alternative i Ct ¼ Choice set for person t Xit ¼ Vector of explanatory variables defining the systematic utility of alternative i for person t; potentially includes attributes of both alternative i and person t b ¼ Row vector of parameters Random utility theory provides the theoretical derivation of Eq. (3.1), but it provides no insights into the specification of Xit, nor the determination of the elements of Ct. These must come “from elsewhere”. While we have both considerable empirical experience in the specification of such models in a variety of contexts (notably mode and destination choice), as well as a plethora of travel behavior research papers on a multitude of topics, it is arguable that 1. Although every time a “gravity model” of trip distribution is employed, its theoretical roots (whether the modeller knows it or not) lie in information theory.
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we still have very little consensus on many issues of practical importance within our models, including: l l l l l l
Generalizable values of time. Standardized specifications of socio-economic effects. How to model destination choice effectively. Mode-route choice interactions. Elastic trip/activity generation. Learning, adaptation and dynamics within travel decision-making.
The complexity (heterogeneity) of travel behavior makes testable generalized theory difficult to construct. As a result, we usually frame our “theory” in terms of a specific model for empirical testing. But these models are themselves extremely complicated constructs of many hypotheses and assumptions, many of which are very difficult to independently test. How you frame the problem, however, constraints the solutions found. We need a much more open process to encourage experimentation, new ideas, the testing of multiple specifications and hypotheses, and the opportunity to fail. It is not feasible within this chapter to have a detailed discussion of specific areas for travel demand model improvement. A few, representative research areas include: l
l
l l
l
l
l l
l
Moving beyond static models to incorporate dynamics and information flows (memory, inertia, state dependencies, adaptation, etc.). Better handling of heterogeneity in both trip-makers and choice contexts, particularly with respect to practical model implementation. Much better modeling of spatial choice (activity episode locations). Better, more comprehensive modeling of auto passenger models and mobility services. Dealing with the multi-dimensionality and sequencing of activity/travel decisions. Representing inter-agent interactions (within and between households; individual- vs. household-based models). Better specification of activity episode utilities (why do we travel?). Mobility auto ownership and other “mobility tools” (driver’s licenses, transit passes, car/bike-sharing service membership, etc.), interconnected with daily activity/travel modeling. Moving beyond daily out-of-home travel: l Multi-day (week-long) models. l Modeling in-home activity. l Modeling intercity (long-distance) travel.
A few personal propositions for approaching the development of next generation models include: l
People are “rational” but not global optimizers. Therefore, myopic, boundedly-rational decision-making should be assumed.
36 PART | I Retrospective and prospective survey of travel behavior research l
l
l
l
l
l
Maslow’s Hierarchy of Needs provide a useful starting point for thinking about activity participation utility. Take the activity-based approach seriously: build activity-scheduling, not merely tour-based, models. Take human agency seriously: l Use an explicit agent-based approach to model formulation. l Get the context and structure of decision-making “right”. l Decompose processes to manage complexity (object-orientation). Model implementations can then follow: l Model structures should be both behaviorally sound and feasible to implement. l Build a flexible/extensible framework. Computing efficiency is critical (run times matter): l Keep it simple, stupid; incorporate detail where needed, not for detail’s sake. We must respect data (and computing) constraints, but design for what is needed, not what is currently feasible.
5. Provocation 2: microsimulation Virtually all current best-practice operational activity/tour-based travel demand models use some form of microsimulation as their computational framework. This involves synthesizing a disaggregate population for the forecast year using one of many available methods (Muller and Axhausen, 2010) and then applying disaggregate choice models to determine the daily activity/travel behavior for each person in the synthesized population. Total road link and transit line flows, congested travel times, etc. emerge out of the interactions of this population of trip-makers within the road and transit network assignment models (Castiglione et al., 2015). This use of microsimulation is a natural (indeed, inevitable) outcome of the consistent trend in travel demand modeling since the 1960s toward increased disaggregation to deal with trip-maker heterogeneity combined with non-linear decision functions (such as Eq. 3.1, above), as well as to account for inter-agent and intertrip interactions and other contextual factors (Miller, 2018b). The microsimulation approach has enabled practical implementations of more behaviorally-based and more policy-sensitive travel demand models than are possible with “classic” four-step models (Spear, 1994). This has represented a very significant advancement in the modeling state of practice. The question arises, however, as to whether more parsimonious, holistic approaches might be conceivable in next generation models. Specifically, as computing power and increasing detailed datasets become increasingly available, there is a tendency for models to become ever more detailed and “micro” in design. Up to a point, this may continue to be welcome
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in order to deal even more effectively with issues of heterogeneity, etc. But, inevitably there comes a point where increasing disaggregation is difficult or even impossible to support with available data (and possibly theory as well), computation burden increases exponentially, and the “signal to noise ratio” declines precipitously. The great land use modeler and pioneer microsimulator, Michael Wegener, warns of the “Spitfire syndrome”, in which over-zealous microsimulators strive to achieve a level of micro representation that is well beyond want is supportable or needed.2 This represents taking to the extreme the reductionist approach to modeling (in which the system whole is modeled as the emergent outcome of the actions of its individual parts/agents), which has been the standard approach to travel demand modeling for the past 60 years. But the question arises as to whether a more holistic approach (in which system behavior is modeled directly) might, in some cases at least, be feasible and a better approach. As Hofstadter (1979) observes in his Pulitzer Prize-winning book, Go¨del, Escher, Bach: an Eternal Golden Braid, the question is do we model the anthill or the ants? The following two sub-sections provide several examples of more macro/holistic approaches to travel demand modeling.
5.1 A holistic approach to modeling cities: urban scale laws It is now clear that urban regions worldwide exhibit very strong, statistically significant scaling laws, similar to those observed in nature. These take the general form: logðYi Þ ¼ a þ b logðPOPi Þ
(3.2)
where Yi is some output/input/activity measure for urban region i (GDP, total road lane-miles, total energy consumed, etc.) and POPi is the population of the region. Eq. (3.2) is called a scaling law, since it states that an urban region’s inputs and output scale with its size (as measured by its population). Further, as illustrated in Fig. 3.2, the values of the scale parameter b fall into three distinct categories, depending on the measure Y under consideration (Bettencourt et al., 2007; Bettencourt and West, 2010): l
b < 1.0: All infrastructure-related variables, such as total road lane-miles, display sub-linear scaling. This implies economies of scale: larger urban regions can more efficiently meet their infrastructure needs than smaller cities; i.e., the amount of infrastructure required per person declines as the city grows. This is the standard relationship that is observed in the
2. The name comes from the analogy of someone wanting to build a model of a Spitfiredperhaps the most beautiful plane ever built. The modeller keeps adding detail: movable ailerons, elevators and rudders, cockpit details, etc., etc., until eventually he no longer has a model of a Spitfire but an actual, full-scale airplane.
38 PART | I Retrospective and prospective survey of travel behavior research
log(Y)
β>1 β=1 β<1
log(Pop)
FIG. 3.2 Urban scale Relations.
l
l
biological world: all biological activity scales sub-linearly with animal body mass. Thus, an elephant is more efficient than a mouse. But, this also means that an elephant is in many respects a very large mousedit obeys the same biological scale law. b z 1.0: All “consumption” variables, such as energy consumption, scale approximately linearly with population size. b > 1.0: All “economic”/“social”/“innovation” variables, such as GDP, patents generated per year, crime, disease, etc. scale super-linearly. This means that larger cities are not just more efficient than smaller ones, but that they also display agglomeration economies: they are more productive (for both good and ill) than smaller ones. This super-linear behavior is a purely human invention: it is unknown in the biological, “natural” world. It represents a pervasive “positive feedback” process in cities, in which growth begets more growth at accelerated rates over time (just as interest in a bank account compounds exponentially over time).3 It is what explains the growth in knowledge, wealth and other key attributes of our economies, societies and cultures.
The question of relevance to this discussion is what the implications of scaling are for models/theories of travel behavior. Bettencourt (2013) argues that this super-linear behavior is the result of non-linear increases in social network interactions as cities grow larger. If this is the case, then the connections between super-linear scaling and travel and, in particular the transportationdland usedeconomic system of systems may be very direct. In 3. Of course, this “positive” feedback loop can run in the other direction. Population decline will generate super-linear declines in economic activity, etc. as well.
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Initial location advantage (e.g., a natural port)
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Activity 1 attracted to this location
“Customers” of Activity 1 attracted to this location
Infrastructure Improvements
More of activity 1 attracted
Activity 2 attracted
More activities attracted
FIG. 3.3 Accessibility & agglomeration.
particular, the rather surprisingly illusive concept of accessibility (which is so central to all of transportation) and yet is arguable still poorly defined and measured (Miller, 2019) surely is central to the agglomeration processes driving the super-linear behavior. Fig. 3.3 attempts a quick sketch of accessibility-driven agglomeration/positive-feedback effects.
5.2 Bridging the gap between micro & macro If holistic, macro relationships such as the scaling laws discussed in the previous sub-section are to be of practical use in most transportation policy analyses then some linkage to the more micro processes which are generating them may well be essential.4 For example, truly holistic models of transportation system behavior are probably not feasible at a level that would permit decisions concerning individual major infrastructure investments (e.g., build this subway line here vs. that freeway there). One approach (among possibly others) for addressing this problem might be to find ways to analytically “bridge the gap” between a “behaviorally sound” micro representation and much more computationally efficient macro representation which is of direct policy relevance. Three examples of such an approach are briefly presented below to illustrate this concept. The first is drawn from traffic flow theory. Consider the classic carfollowing model shown in Eq. (3.3), in which the acceleration of car n þ 1 4. Establishment of this linkage is also probably essential to establish these relationships as actual “laws” rather than, in the extreme, just interesting empirical “quirks”.
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(which is following car n) in the next time step, x€nþ1 ðt þ DtÞ, is a function of _ and their current locations within the the current speeds of the two cars (x) road (x). . x€nþ1 ðt þ DtÞ ¼ l0 x_nþ1 ðtÞM ½x_n ðtÞ x_nþ1 ðtÞ ½xn ðtÞ xn ðtÞL (3.3) It is well known that, under appropriate assumptions, this dynamic, micro car-following model can be integrated to yield the steady-state fundamental equation of traffic flow theory that describes the relationship between average flow (q), density (k) and speed (v) for a roadway section (Eq. 3.4): q ¼ kv
(3.4)
This ability to move analytically from the micro to macro provides strong theoretical support for both the micro and macro models and deeper insights into traffic flow behavior at both levels. Might something similar exist in travel behavior? A second, simple example of analytical aggregation is the tour-based mode choice (TBMC) model in the TASHA (Travel/Activity Scheduler for Household Agents) agent-based microsimulation model. In this model, a random utility (U) is defined for each trip mode m in the tth trip in tour k for a person i, with a normally distributed error term in the usual way, consisting of a systematic utility component (V) and a random term (ε) (Miller and Roorda, 2003; Miller et al., 2005): UimðtjkÞt ¼ VimðtjkÞt þ εimðtjkÞt
(3.5)
The total utility of a person’s tour is then simply defined as the sum of the tour’s trip utilities (Eq. 3.6). Since the sum of normal random variables remains normal, the tour utility is also normally distributed and the person’s choice of a tour is a multinomial probit model. Ueik ¼
T X t¼1
UimðtjkÞt ¼
T X t¼1
VimðtjkÞt þ
T X
εimðtjkÞt
(3.6)
t¼1
This formulation permits arbitrarily complex tours to be modeled without needing to pre-specify the possible set of tours that can be modeled or any “nesting” structure for the trips within the tour (as would be the case in a typical nested model formulation). Complex multinomial probit models are rarely used in practice (or even research) due to their computational requirements. But with modern computing power, they are, in fact, computationally practical. Given their ability to directly integrate a micro process to a macro one (including the possibility of estimating the micro parameter using macro data) (Daganzo, 1979), might we not usefully explore the use of probit models in appropriate applications?
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The third, and much more elaborate example of establishing micro support for an emergent macro phenomenon is provided by Martinez (2016, 2018), in which he demonstrates that it is possible to derive a scale law relationship (such as discussed in the previous section) between housing rents (prices) and city population from his disaggregate bid-choice urban housing market models: Under plausible conditions of the economy, such as nonnegative economies of scale in production, the main result . is the emergence of rent and welfare superlinear power laws with population from standard microeconomic assumptions. Compared with Bettencourt’s (2013) macroscale model, . [this] approach allows simulation of the urban system’s microeconomic interactions to test the simultaneous emergence of all market prices and the scale law of the growth of cities. This . allows the estimation of bids’ parameters from observed data of households’ and firms’ locations and the associated rents using standard maximum likelihood estimators. Martinez (2016)
6. Provocation 3: social Heisenberg uncertainty principle There is no longer much of an excuse to ignore many of the measurable properties of cities. Cities across the globe and through time are now knowable like never before, across many of their dimensions: social, economic, infrastructural and spatial. Luis Bettencourt (2013)
We are a very empirically driven field. But we have always faced significant limitations on what we can and cannot observe, due to a variety of factors. These include latent variables (utility being a prime example), which are inherently unobservable, and dependency on limited, expensive, relatively small-sample surveys for much of our data. Further, such survey data is more often than not static and cross-sectional in nature. This long-standing dependence on small-sample, cross-sectional survey data, however, is potentially changing. New, “big” datasets are providing the opportunity to observe massive amounts of passive, revealed preference data concerning trip-making within urban regions. Such data includes cellphone traces, GPS location tracking from a variety of sources, including customdesigned smartphone apps, public transit smartcard transaction data, credit card transaction data, etc. These data are: l
Potentially relatively consistent across urban regions, since they are often collected using common methods. A cellphone trace, for example, is, by and large, a cellphone trace whether it is collected in Santa Barbara or Toronto or Montevideo.
42 PART | I Retrospective and prospective survey of travel behavior research l
l
Continuously collected, day after day, week after week, providing timeseries observations of the same trip-makers over extended periods of time, thereby facilitating much more dynamic models of travel behavior than previously possible to construct. VERY large-sample, thereby potentially providing much more comprehensive characterization of trip-making than is possible from small-sample surveys. Such data, of course, are also not without their challenges. These include:
l
l
l
l
Spatial precision of the location traces is often problematic, most notably for cellphone traces. The data are inevitably anonymized to preserve the privacy of the people being tracked, and so are lacking trip-maker attributes. Similarly, trip purpose (and often mode) generally are not collected and need to be inferred. The data usually track an individual’s movements and information concerning the household within which the person resides is usually lacking.
Thus considerable processing of the raw data and/or fusing of the data with other data sources (census data, GIS place of interest (POI) data, etc.) is typically required to make the tracking data fully useable for travel demand modeling purposes. Despite these challenges, there is very likely that future models will make much more use of such data. Is cellphone trace data (for example), fused with census and POI data, collected across very large segments of the population over extended periods of time “any worse” than the small-sample, cross-sectional survey data that w have been working with all these years? Might it not be much better?
7. Provocation 4: computing The history of travel demand modeling is tied directly to the history of digital computing (Meyer and Miller, 2013). Travel demand modelers are as much in the software business as they are the travel behavior and econometrics business. Even in today’s computing world, big urban travel demand models are computationally big, with network modeling (road and transit assignment) being the primary culprit. But to be of practical use, travel demand model systems need to run within reasonable time limits. Arguably many/most current activity-based travel demand models run much too slowly to be truly useful to planning agencies. And so, computational efficiency remains a very important design criterion for operational models. Despite this dependency upon the computer as our “lab”, as a field we arguably have not exploited advances in High Performance Computing (HPC), especially cluster-based and other parallelized/distributed computing capabilities, as we might have to deal more efficiently with the large, complex, computationally-intensive problems which we are studying.
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Further, it is arguable that we could often do much better in both our theoretical conceptualizations and our software implementations to design for efficient computation. Parsimonious model formulations, in which the model formulation is as simple as possible to adequately model the behavior of interest, can go a long way toward improving model system run times. It is also arguable that, as a field, we suffer from a proliferation of bespoke software that often: l l
l
l
Represents expensive and inefficient reinvention of wheels. Presents barriers to the sharing of data and ideas (due to lack of common data standards, semantic, etc.). Is overly “hard-wired” to deal with a particular model specification, with little flexibility to incorporate new ideas, specifications, etc. Is either expensive proprietary commercial software or else poorly supported open source software.
In other words, we do not share and collaborate enough with each other in terms of software development and usage. We often discuss the “My Model” syndrome in our field, in which each modeler views his/her model as “the best” and we see relatively little convergence around best practice and similarly little rejection of poor practice. But there is arguably also a “My Software” problem, in which little convergence in software is occurring. Can/should we be standardizing within a common, open source software “environment” which everyone can use and to which everyone can contribute? This is not a new idea, but it has not yet come to pass in our fielddalthough it does exist in many other fields. This proposal might be viewed as creating “a big (virtual) lab” within which testing and experimentation by many researchers can proceed in a systematic, controlled useful way. It would encourage comparison of alternative methods and theories within a common environment that would lead to rejection of weaker ideas and convergence on stronger ones. It would reduce “barriers to entry” (especially for younger researchers) since less time would be spent on developing one’s own software and more time would be spent on using the existing, common software to actually build models and test hypotheses. And it would also reduce barriers to the testing of new approaches to modeling, since a highly modularized software “lab” would facilitate rapid prototyping of new procedures, etc., rather than “locking” researchers into hard-wired computational structures.
8. Toward the next generation Our mission: to go boldly where no-one has gone before. James T. Kirk.
Model complicatedness, computaonal burden, …
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Acvity-scheduling? What’s next?
Tour-based 4-Step
Behavioural representaon, policy sensivity, … FIG. 3.4 Technological growth curves.
Virtually all technologies follow growth curves in which: l
l
l
l
Performance increases within a given technology come at exponentially increasing costs. At some point additional growth in performance is restricted by the highly non-linear growth in cost (energy, etc.). For further improvements to occur, innovation must occur to replace the old technology with a more efficient new technology. The process of growth can then continue.
As illustrated in Fig. 3.4, travel demand modeling is subject to this same general principle. Although arguably the “efficiency gains” in shifts in modeling paradigms to date have been modest at best. This chapter has argued that there is a critical need for travel demand modeling to undertake such a “technology shift”, to a much more powerful, much more efficient next generation of models. These models will exploit as best as possible the new, large sources of data that are increasingly available from a variety of sources. They will be much more computationally efficient that the typical current generation model, exploiting much more fully advanced hardware and software capabilities. And they will have a much more fundamental theoretical foundation that will enable them to robustly and insightfully address the many technological, operational and societal challenges facing 21st Century urban transportation systems worldwide.
References Anas, A., 1983. Discrete choice theory, information theory, and the multinomial logit and gravity models. Transportation Research B 17, 13e23.
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Ben-Akiva, M., Lerman, S.R., 1985. Discrete Choice Analysis: Theory and Application to Predict Travel Demand. MIT Press, Cambridge MA. Bettencourt, L.M.A., 2013. The origins of scaling in cities. Science 340, 1438e1441. Bettencourt, L.M.A., West, G.B., 2010. A unified theory of urban living”. Nature 467, 912e913. Bettencourt, L.M.A., Lobo, J., Helbing, D., Kuhnert, C., West, G.B., 2007. Growth, innovation, scaling, and the pace of life in cities”. Proceedings of the National Academy of Sciences 104 (17), 7301e7306. Castiglione, J., Bradley, M., Gleibe, J., 2015. Activity-based Travel Demand Models: A Primer, Report S2-C46-RR-1. Transportation Research Board, Washington, DC. Chorus, C.G., 2014a. A generalized random regret minimization model. Transportation Research Part B 68, 224e238. Chorus, C.G., 2014b. Risk aversion, regret aversion and travel choice inertia: an experimental study. Transportation Planning and Technology 37 (4), 321e332. Daganzo, C.F., 1979. Multinomiql Probit. Academic Press, New York. Domencich, T., McFadden, D., 1975. Urban Travel Demand: A Behavioral Analysis. North Holland, Amsterdam. Hagerstrand, T., 1970. What about people in regional science? Regional Science Association Papers 24 (1), 6e21. Hensher, D.A., Johnson, L.W., 1981. Applied Discrete-Choice Modelling. Routledge, London. Hofstadter, D.R., 1979. Go¨del, Escher, Back: An Eternal Golden Braid. Vintage Books, New York. Jones, P.M., 1979. New approaches to understanding travel behaviour: the human activity approach. In: Hensher, D., Stopher, P. (Eds.), Behavioral Travel Modeling. Redwood Burn Ltd, London, pp. 55e80. Kahneman, D., 2011. Thinking, Fast and Slow. Doubleday Canada, Toronto. Manheim, M.L., 1978. Fundamentals of Transportation Systems Analysis Volume 1: Basic Concepts. MIT Press, Cambridge, MA. Martinez, F.J., 2016. Cities’ power laws: the stochastic scaling factor. Environment and Planning B 43 (2), 257e275. Martinez, F.J., 2018. Microeconomic Modeling in Urban Science. Academic Press, New York. McFadden, D., 1978. Modeling the choice of residential location. In: Karlquist, et al. (Eds.), Spatial Interaction Theory and Residential Location. North Holland, Amsterdam, pp. 75e96. Meadows, D.H., 2008. In: Wright, D. (Ed.), Thinking in Systems: A Primer. Chelsea Green Publishers, White River Junction, VT. Meyer, M.D., Miller, E.J., 2013. Urban Transportation Planning, A Decision-Oriented Approach, third ed. Available at: http://mtsplan.com/services.html. Miller, E.J., 2003. Microsimulation. In: Goulias, K.G. (Ed.), Chapter 12 in Transportation Systems Planning Methods and Applications. CRC Press, Boca Raton, FL, pp. 12-21e12-22. Miller, E.J., 2018a. The case for microsimulation frameworks for integrated urban models. Journal of Transport and Land Use 11 (1), 1025e1037. Miller, E.J., 2018b. Agent-based activity/travel microsimulation: what’s next? In: Briassouli, et al. (Eds.), Spatial Analysis: Tools and Land Use, Transport and Environmental Applications. Springer, pp. 119e150. Miller, E.J., 2019. Accessibility: measurement and application in transportation planning (editorial, forthcoming in). Transport Reviews. Miller, E.J., Roorda, M.J., 2003. A prototype model of household activity/travel scheduling. Transportation Research Record, Journal of the Transportation Research Board 1831, 114e121.
46 PART | I Retrospective and prospective survey of travel behavior research Miller, E.J., Roorda, M.J., Carrasco, J.A., 2005. A tour-based model of travel mode choice. Transportation 32 (4), 399e422. Muller, K., Axhausen, K.W., 2010. Population Synthesis for Microsimulation: State of the Art. ETH, Zurich. http://www.strc.ch/2010/Mueller.pdf. Mumford, L., 1961. The City in History. Harcourt, New York. Ondaatje, M., 1970. The Collected Works of Billy the Kid. Vintage Books, New York. Ortuzar, J., de, D., Willumsen, L.G., 2011. Modeling transport, 4th edition. John Wiley and Sons, New York. Shannon, C.E., 1948. A mathematical theory of information”. Bell System Technical Journal 27, 379e423, 623e656. Spear, B.D., 1994. New Approaches to Travel Demand Forecasting Models, A Synthesis of Four Research Reports, DOT-T-94-15. U.S. Department of Transportation, Washington, D.C. Thayler, R.H., Sunstein, C.R., 2008. Nudge: Improving Decisions About Health, Wealth and Happiness. Yale University Press, New Haven. Train, K., 2009. Discrete Choice Methods with Simulation, second ed. Cambridge University Press, Cambridge. Urbanet, 2018. https://www.urbanet.info/world-urban-population. Webber, M.J., 1977. Pedagogy again: what is entropy? Annals of the Association of American Geographers 67 (2), 254e266. Wilson, A.G., 1967. A statistical theory of spatial distribution models (Chapter 3). In: Quandt, R. (Ed.), The Demand for Travel: Theory and Measurement. Lexington Books, Lexington, MA, pp. 55e82.
Travel demand models, the next generation: boldly going where no-one has gone before Eric J. Miller Department of Civil and Mineral Engineering, University of Toronto, ON, Canada; University of Toronto Transportation Research Institute, ON, Canada
Chapter outline 1. 2. 3. 4.
Introduction Disruptions The four pillars of modeling Provocation 1: travel behavior theory 5. Provocation 2: microsimulation 5.1 A holistic approach to modeling cities: urban scale laws
29 30 33 33 36
5.2 Bridging the gap between micro & macro 6. Provocation 3: social Heisenberg uncertainty principle 7. Provocation 4: computing 8. Toward the next generation References
39 41 42 43 44
37
1. Introduction Find the beginning, the slight silver key to unlock it, to dig it out. Here then is a maze to begin, to be in. Michael Ondaatje (1970)
IATBR can trace its origins back to a June 1973 conference in Maine titled “Issues in Behavioral Demand Modeling and Valuation of Travel Time”. During the intervening 45 years between this first conference and the 2018 Santa Barbara conference, many advances in travel demand modeling have occurred, notably the widespread adoption of random utility-based modeling methods, increasing implementation of activity-based (or, more typically, tourbased) models, and acceptance of microsimulation (in many quarters at least) as a practical computational approach for dealing with disaggregate, heterogeneous trip-makers (Miller, 2003, 2018a,b). Mapping the Travel Behavior Genome. https://doi.org/10.1016/B978-0-12-817340-4.00003-6 Copyright © 2020 Elsevier Inc. All rights reserved.
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30 PART | I Retrospective and prospective survey of travel behavior research
At the same time, one can argue that the pace of this change has been slow. After all, McFadden’s seminal work on random utility theory was already largely in place in the 1970’s (Domencich and McFadden, 1975; McFadden, 1978), as were other seminal concepts such as Hagerstrand’s time-space constraint paradigm (Hagerstrand, 1970) and Manheim’s definitive foundations for transportation systems analysis (Manheim, 1978). Similarly, the value of adopting an activity-based approach to travel modeling has been recognized at least as long (Jones, 1979). In particular, the adoption of improved methods in operational practice has been very incremental, and has typically considerably lagged the research state-of-the-art. Further, this slow evolution of modeling practice has occurred during an era of relative stability in terms of transportation technology and services, policy issues, urban form and, arguably, basic travel behavior. We are now, however, in a very different era, in which the transportation fielddalong with many other sectors of societydis being disrupted in ways that have not been experienced during the past 100 years. Not only are these disruptions fundamentally changing our transportation systems, but they are also posing new and very difficult challenges to even the best of our current travel demand models. Indeed, the basic premise of this chapter is that our current models are inadequate to analyze this “brave new world” of new technologies and services. The policy need to be able to model and evaluate these disruptions, however, is great. Thus, it is urgently required to develop and test new travel demand models that are capable of credibly dealing with this emerging new world and to help guide policies that will ensure that these coming changes will, in fact, result in more equitable and sustainable urban regions, rather than the reverse. Section 2 of this chapter briefly discusses a few of these key disruptions and some of the challenges that they pose to the current travel demand modeling state of practice/art. To frame the discussion of travel demand modeling R&D needs to address these challenges, Section 3 defines the “four pillars” of modeling. Based on these four pillars, four “provocations” concerning modeling challenges and needs are introduced that are then discussed in detail in Sections 4e7, respectively. Section 8, concludes the chapter by summarizing the preceding discussions in terms of recommendations concerning key steps that need to be taken toward development of a next generation of advanced travel demand models. Taking these steps will, indeed, require bold actions that, if successful, will take us into new policy analysis and modeling territories.
2. Disruptions Transportation is facing at least three major disruptive forces that are already changing the field and that can be expected with great confidence to continue to do so at an accelerated pace going forward. Two are technological in origins, while the third is an even more pervasive socio-economic trend concerning global urbanization. It is already a truism that the looming potential of connected and autonomous vehicles (CAVs) and the burgeoning of new mobility services (ride-
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hailing, car-sharing, etc.) are disrupting transportation in ways not seen since the dawn of the automobile age. While considerable hype currently exists concerning the ultimate capabilities of these technologies and services, as well as the timing and distribution of their implementations, there is no doubt that they will change the status quo in very significant ways. The impacts of new ride-hailing and ride-sharing services provided by new private sector “Transportation Network Companies” (TNCs) on travel behavior within many cities is readily apparent and undoubtedly foreshadow even great impacts in the years to come. The uncertainty surrounding these new technologies and services is also immense, in terms of both how exactly they will “play out” in the coming years and what their impacts on travel demand, transportation system performance, urban form and many other policy concerns will be. One can easily imagine both utopic and dystopic futures for our urban regions depending on how events actually transpire, and, in particular, how governments, public transit authorities and other public sector agencies react to these emerging challenges. A hundred years ago no-one anticipated the profound impacts that the automobile would have on transportation, urban form and our economic and social systems. This time around it is critical that we are proactive and thoughtful in our policy responses to these new technologies and services if we are to going to maximize their potential to improve urban life rather than the opposite. The role of travel demand modeling in addressing these issues is both obvious and essential. The challenge facing us, however, is that our current generation of travel demand models (even the most advanced) are not adequate to the task of dealing with the new technologies and services. Current models did not envision the existence of CAVs or new forms of mobility services, etc. Further, even within their current design context, they are typically weak in areas that are of considerable importance in the analysis of emerging issues. These areas include: modeling “auto passenger” modes (carpooling, conventional taxi, etc.), auto ownership, and non-homebased and non-work/school travel. Thus, a “next generation” of travel demand models is urgently needed in order to provide the policy analysis capabilities needed to understand, explore and choose from among the myriad options, possibilities and scenarios with which we are currently confronted. The second related, but also independently important, disruption is that of the continuing and accelerating growth in Information and Communications Technologies (ICT) and computing capabilities (notably various forms of High Performance ComputingdHPC) and the associated emergence of the Internet of things (IoT), extraordinarily powerful Artificial Intelligence (AI) methods, and increasing ubiquity of massive (and passive) “big data”. ICT, IofT and AI are, of course, the enabling technical foundations upon which CAVs and new
32 PART | I Retrospective and prospective survey of travel behavior research
mobility services are built. But they also are providing new sources of data for travel demand analysis and modeling that are: l l
Very large samples compared to anything we previously had to work with. Dynamic, time-series data, potentially providing observations over days, weeks and even months, compared to the static, one-day surveys that current models are based upon.
At the same time, new AI modeling methods (such as Deep Neural Nets, among others) and cost-effective access to HPC provide us with the tools needed to work with these massive new datasets. The opportunity exists, therefore, to “see” the city and its travel behavior in very new and different ways, opening up new avenues for research and for the development of new model formulations. These new datasets, however, are not without their challenges. Most notably, they are inevitably anonymized, so that we know nothing about the trip-makers being observed. Further, the data generally provides space-time traces of trip-maker movements, but does not directly observe the mode of travel nor the purpose of the trip. Depending on the sensor technology being used (e.g., cellphone traces, GPS tracking, etc.), the spatial-temporal precision of these traces can also vary considerably. Thus, considerable manipulation of such data, typically involving data cleaning and fusion methods, among others, is required to make the data useable for travel demand modeling purposes. Nevertheless, it is clear that the future development of next generation travel demand models will be tied closely to the availability and quality of these new big datasets. The third major challenge for next generation travel demand modeling is to deal much better with the analysis and design of global mega-cities. The history of human civilization has been tied directly to the development and growth of cities (Mumford, 1961). But throughout much of history cities necessarily remained generally small in size due to technology limitations. The emergence of the Industrial Revolution in the late 18th and early 19th Centuries, however, unleashed an expansion of urban living that has been continuing ever since, and which will continue throughout the current century. More than 50% of the world’s population now lives in urban regions (Urbanet, 2018), and our social, economic and political futures depend fundamentally on how well these urban regions function in terms of efficiency, equity, energy use and productivity. Transportation is not the only factor affecting urban wellbeing, but it is a very critical and fundamental one. Evidence-based planning and design will be critical to the addressing the major challenges facing urban transportation systems worldwide. Travel demand models obviously have their part of play in this process. And, again, current methods must improve and adapt considerably to meet this analysis challenge, especially in fast-growing urban regions in developing countries.
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Inducon (Descripon/Exploraon)
DATA
THEORY Deducon (Hypothesis Tesng)
METHODS
COMPUTING FIG. 3.1 The 4 Pillars of modeling.
3. The four pillars of modeling All models (travel demand or otherwise) are operational implementation of theory, built within the limitations of current data, methods and computational capabilities. As illustrated in Fig. 3.1, all useful models are based upon at least a loose balance among these four “pillars” of modeling. Great theory in the absence of methods (software, estimation techniques, simulation algorithms, etc.) with which to implement the theory cannot be operationalized. Similarly, without empirical data to test one’s theories they cannot be verified or improved. In particular, theory can only emerge and evolve over time through the “ying and yang” of both inductive and deductive reasoning/analysis, both of which depend upon the availability of appropriate, good quality data. And, certainly in the case of large-scale regional travel demand models, computing power is essential to the practical development and application of these models. If a next generation of travel demand models is to be developed, this will require advances in all four pillars. This chapter briefly explores a few issues facing travel demand modeling with respect to each of these pillars. It does so through a set of four “provocations” concerning current weaknesses and potential opportunities with respect to each pillar.
4. Provocation 1: travel behavior theory We know a tremendous amount about how the world works, but not nearly enough. Our knowledge is amazing; our ignorance even more so. Donella H. Meadows (2008)
34 PART | I Retrospective and prospective survey of travel behavior research
On the one hand, it can be argued that travel demand modelers have a relatively rich theoretical base upon which to draw. Random utility theory (Domencich and McFadden, 1975; Ben-Akiva and Lerman, 1985; Hensher and Johnson, 1981; Train, 2009; Ortuzar and Willumsen, 2011), in particular, provides a very powerful and (generally) operationally tractable basis for modeling most aspects of travel behavior. Much of the strength of current operational models derive in large part from their random utility foundations. Recent extensions/alternatives to random utility theory emerging out of behavioral economics (Kahneman, 2011; Thayler and Sunstein, 2008), such as regret minimization (Chorus, 2014a,b), also represent potentially promising avenues for improving our models, although these have not yet seen widespread operational implementation. In addition Information Theory also provides a very sound theoretical basis for statistical modeling (Anas, 1983; Shannon, 1948; Webber, 1977; Wilson, 1967), although its explicit application is not nearly so common in most current models.1 At the same time, it is also arguable that random utility theory is largely a methodological framework that provides a calculus for computing “how” we make decisions (maximize utility) within which we still need to “pour” our understanding of the “what” and the “why” and the “when” underlying these decisions (e.g., “What” defines utility? “Why” do we travel?). That is, there is lot of “behavioral theory” about which random utility theory is silent. To illustrate this point, consider the classic multinomial logit model (Ben-Akiva and Lerman, 1985): ebXit Pit ¼ P bX 0 t e i
(3.1)
i0 εCt
where: Pit ¼ Probability that person t chooses alternative i Ct ¼ Choice set for person t Xit ¼ Vector of explanatory variables defining the systematic utility of alternative i for person t; potentially includes attributes of both alternative i and person t b ¼ Row vector of parameters Random utility theory provides the theoretical derivation of Eq. (3.1), but it provides no insights into the specification of Xit, nor the determination of the elements of Ct. These must come “from elsewhere”. While we have both considerable empirical experience in the specification of such models in a variety of contexts (notably mode and destination choice), as well as a plethora of travel behavior research papers on a multitude of topics, it is arguable that 1. Although every time a “gravity model” of trip distribution is employed, its theoretical roots (whether the modeller knows it or not) lie in information theory.
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we still have very little consensus on many issues of practical importance within our models, including: l l l l l l
Generalizable values of time. Standardized specifications of socio-economic effects. How to model destination choice effectively. Mode-route choice interactions. Elastic trip/activity generation. Learning, adaptation and dynamics within travel decision-making.
The complexity (heterogeneity) of travel behavior makes testable generalized theory difficult to construct. As a result, we usually frame our “theory” in terms of a specific model for empirical testing. But these models are themselves extremely complicated constructs of many hypotheses and assumptions, many of which are very difficult to independently test. How you frame the problem, however, constraints the solutions found. We need a much more open process to encourage experimentation, new ideas, the testing of multiple specifications and hypotheses, and the opportunity to fail. It is not feasible within this chapter to have a detailed discussion of specific areas for travel demand model improvement. A few, representative research areas include: l
l
l l
l
l
l l
l
Moving beyond static models to incorporate dynamics and information flows (memory, inertia, state dependencies, adaptation, etc.). Better handling of heterogeneity in both trip-makers and choice contexts, particularly with respect to practical model implementation. Much better modeling of spatial choice (activity episode locations). Better, more comprehensive modeling of auto passenger models and mobility services. Dealing with the multi-dimensionality and sequencing of activity/travel decisions. Representing inter-agent interactions (within and between households; individual- vs. household-based models). Better specification of activity episode utilities (why do we travel?). Mobility auto ownership and other “mobility tools” (driver’s licenses, transit passes, car/bike-sharing service membership, etc.), interconnected with daily activity/travel modeling. Moving beyond daily out-of-home travel: l Multi-day (week-long) models. l Modeling in-home activity. l Modeling intercity (long-distance) travel.
A few personal propositions for approaching the development of next generation models include: l
People are “rational” but not global optimizers. Therefore, myopic, boundedly-rational decision-making should be assumed.
36 PART | I Retrospective and prospective survey of travel behavior research l
l
l
l
l
l
Maslow’s Hierarchy of Needs provide a useful starting point for thinking about activity participation utility. Take the activity-based approach seriously: build activity-scheduling, not merely tour-based, models. Take human agency seriously: l Use an explicit agent-based approach to model formulation. l Get the context and structure of decision-making “right”. l Decompose processes to manage complexity (object-orientation). Model implementations can then follow: l Model structures should be both behaviorally sound and feasible to implement. l Build a flexible/extensible framework. Computing efficiency is critical (run times matter): l Keep it simple, stupid; incorporate detail where needed, not for detail’s sake. We must respect data (and computing) constraints, but design for what is needed, not what is currently feasible.
5. Provocation 2: microsimulation Virtually all current best-practice operational activity/tour-based travel demand models use some form of microsimulation as their computational framework. This involves synthesizing a disaggregate population for the forecast year using one of many available methods (Muller and Axhausen, 2010) and then applying disaggregate choice models to determine the daily activity/travel behavior for each person in the synthesized population. Total road link and transit line flows, congested travel times, etc. emerge out of the interactions of this population of trip-makers within the road and transit network assignment models (Castiglione et al., 2015). This use of microsimulation is a natural (indeed, inevitable) outcome of the consistent trend in travel demand modeling since the 1960s toward increased disaggregation to deal with trip-maker heterogeneity combined with non-linear decision functions (such as Eq. 3.1, above), as well as to account for inter-agent and intertrip interactions and other contextual factors (Miller, 2018b). The microsimulation approach has enabled practical implementations of more behaviorally-based and more policy-sensitive travel demand models than are possible with “classic” four-step models (Spear, 1994). This has represented a very significant advancement in the modeling state of practice. The question arises, however, as to whether more parsimonious, holistic approaches might be conceivable in next generation models. Specifically, as computing power and increasing detailed datasets become increasingly available, there is a tendency for models to become ever more detailed and “micro” in design. Up to a point, this may continue to be welcome
Travel demand models, the next generation Chapter | 3
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in order to deal even more effectively with issues of heterogeneity, etc. But, inevitably there comes a point where increasing disaggregation is difficult or even impossible to support with available data (and possibly theory as well), computation burden increases exponentially, and the “signal to noise ratio” declines precipitously. The great land use modeler and pioneer microsimulator, Michael Wegener, warns of the “Spitfire syndrome”, in which over-zealous microsimulators strive to achieve a level of micro representation that is well beyond want is supportable or needed.2 This represents taking to the extreme the reductionist approach to modeling (in which the system whole is modeled as the emergent outcome of the actions of its individual parts/agents), which has been the standard approach to travel demand modeling for the past 60 years. But the question arises as to whether a more holistic approach (in which system behavior is modeled directly) might, in some cases at least, be feasible and a better approach. As Hofstadter (1979) observes in his Pulitzer Prize-winning book, Go¨del, Escher, Bach: an Eternal Golden Braid, the question is do we model the anthill or the ants? The following two sub-sections provide several examples of more macro/holistic approaches to travel demand modeling.
5.1 A holistic approach to modeling cities: urban scale laws It is now clear that urban regions worldwide exhibit very strong, statistically significant scaling laws, similar to those observed in nature. These take the general form: logðYi Þ ¼ a þ b logðPOPi Þ
(3.2)
where Yi is some output/input/activity measure for urban region i (GDP, total road lane-miles, total energy consumed, etc.) and POPi is the population of the region. Eq. (3.2) is called a scaling law, since it states that an urban region’s inputs and output scale with its size (as measured by its population). Further, as illustrated in Fig. 3.2, the values of the scale parameter b fall into three distinct categories, depending on the measure Y under consideration (Bettencourt et al., 2007; Bettencourt and West, 2010): l
b < 1.0: All infrastructure-related variables, such as total road lane-miles, display sub-linear scaling. This implies economies of scale: larger urban regions can more efficiently meet their infrastructure needs than smaller cities; i.e., the amount of infrastructure required per person declines as the city grows. This is the standard relationship that is observed in the
2. The name comes from the analogy of someone wanting to build a model of a Spitfiredperhaps the most beautiful plane ever built. The modeller keeps adding detail: movable ailerons, elevators and rudders, cockpit details, etc., etc., until eventually he no longer has a model of a Spitfire but an actual, full-scale airplane.
38 PART | I Retrospective and prospective survey of travel behavior research
log(Y)
β>1 β=1 β<1
log(Pop)
FIG. 3.2 Urban scale Relations.
l
l
biological world: all biological activity scales sub-linearly with animal body mass. Thus, an elephant is more efficient than a mouse. But, this also means that an elephant is in many respects a very large mousedit obeys the same biological scale law. b z 1.0: All “consumption” variables, such as energy consumption, scale approximately linearly with population size. b > 1.0: All “economic”/“social”/“innovation” variables, such as GDP, patents generated per year, crime, disease, etc. scale super-linearly. This means that larger cities are not just more efficient than smaller ones, but that they also display agglomeration economies: they are more productive (for both good and ill) than smaller ones. This super-linear behavior is a purely human invention: it is unknown in the biological, “natural” world. It represents a pervasive “positive feedback” process in cities, in which growth begets more growth at accelerated rates over time (just as interest in a bank account compounds exponentially over time).3 It is what explains the growth in knowledge, wealth and other key attributes of our economies, societies and cultures.
The question of relevance to this discussion is what the implications of scaling are for models/theories of travel behavior. Bettencourt (2013) argues that this super-linear behavior is the result of non-linear increases in social network interactions as cities grow larger. If this is the case, then the connections between super-linear scaling and travel and, in particular the transportationdland usedeconomic system of systems may be very direct. In 3. Of course, this “positive” feedback loop can run in the other direction. Population decline will generate super-linear declines in economic activity, etc. as well.
Travel demand models, the next generation Chapter | 3
Initial location advantage (e.g., a natural port)
39
Activity 1 attracted to this location
“Customers” of Activity 1 attracted to this location
Infrastructure Improvements
More of activity 1 attracted
Activity 2 attracted
More activities attracted
FIG. 3.3 Accessibility & agglomeration.
particular, the rather surprisingly illusive concept of accessibility (which is so central to all of transportation) and yet is arguable still poorly defined and measured (Miller, 2019) surely is central to the agglomeration processes driving the super-linear behavior. Fig. 3.3 attempts a quick sketch of accessibility-driven agglomeration/positive-feedback effects.
5.2 Bridging the gap between micro & macro If holistic, macro relationships such as the scaling laws discussed in the previous sub-section are to be of practical use in most transportation policy analyses then some linkage to the more micro processes which are generating them may well be essential.4 For example, truly holistic models of transportation system behavior are probably not feasible at a level that would permit decisions concerning individual major infrastructure investments (e.g., build this subway line here vs. that freeway there). One approach (among possibly others) for addressing this problem might be to find ways to analytically “bridge the gap” between a “behaviorally sound” micro representation and much more computationally efficient macro representation which is of direct policy relevance. Three examples of such an approach are briefly presented below to illustrate this concept. The first is drawn from traffic flow theory. Consider the classic carfollowing model shown in Eq. (3.3), in which the acceleration of car n þ 1 4. Establishment of this linkage is also probably essential to establish these relationships as actual “laws” rather than, in the extreme, just interesting empirical “quirks”.
40 PART | I Retrospective and prospective survey of travel behavior research
(which is following car n) in the next time step, x€nþ1 ðt þ DtÞ, is a function of _ and their current locations within the the current speeds of the two cars (x) road (x). . x€nþ1 ðt þ DtÞ ¼ l0 x_nþ1 ðtÞM ½x_n ðtÞ x_nþ1 ðtÞ ½xn ðtÞ xn ðtÞL (3.3) It is well known that, under appropriate assumptions, this dynamic, micro car-following model can be integrated to yield the steady-state fundamental equation of traffic flow theory that describes the relationship between average flow (q), density (k) and speed (v) for a roadway section (Eq. 3.4): q ¼ kv
(3.4)
This ability to move analytically from the micro to macro provides strong theoretical support for both the micro and macro models and deeper insights into traffic flow behavior at both levels. Might something similar exist in travel behavior? A second, simple example of analytical aggregation is the tour-based mode choice (TBMC) model in the TASHA (Travel/Activity Scheduler for Household Agents) agent-based microsimulation model. In this model, a random utility (U) is defined for each trip mode m in the tth trip in tour k for a person i, with a normally distributed error term in the usual way, consisting of a systematic utility component (V) and a random term (ε) (Miller and Roorda, 2003; Miller et al., 2005): UimðtjkÞt ¼ VimðtjkÞt þ εimðtjkÞt
(3.5)
The total utility of a person’s tour is then simply defined as the sum of the tour’s trip utilities (Eq. 3.6). Since the sum of normal random variables remains normal, the tour utility is also normally distributed and the person’s choice of a tour is a multinomial probit model. Ueik ¼
T X t¼1
UimðtjkÞt ¼
T X t¼1
VimðtjkÞt þ
T X
εimðtjkÞt
(3.6)
t¼1
This formulation permits arbitrarily complex tours to be modeled without needing to pre-specify the possible set of tours that can be modeled or any “nesting” structure for the trips within the tour (as would be the case in a typical nested model formulation). Complex multinomial probit models are rarely used in practice (or even research) due to their computational requirements. But with modern computing power, they are, in fact, computationally practical. Given their ability to directly integrate a micro process to a macro one (including the possibility of estimating the micro parameter using macro data) (Daganzo, 1979), might we not usefully explore the use of probit models in appropriate applications?
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The third, and much more elaborate example of establishing micro support for an emergent macro phenomenon is provided by Martinez (2016, 2018), in which he demonstrates that it is possible to derive a scale law relationship (such as discussed in the previous section) between housing rents (prices) and city population from his disaggregate bid-choice urban housing market models: Under plausible conditions of the economy, such as nonnegative economies of scale in production, the main result . is the emergence of rent and welfare superlinear power laws with population from standard microeconomic assumptions. Compared with Bettencourt’s (2013) macroscale model, . [this] approach allows simulation of the urban system’s microeconomic interactions to test the simultaneous emergence of all market prices and the scale law of the growth of cities. This . allows the estimation of bids’ parameters from observed data of households’ and firms’ locations and the associated rents using standard maximum likelihood estimators. Martinez (2016)
6. Provocation 3: social Heisenberg uncertainty principle There is no longer much of an excuse to ignore many of the measurable properties of cities. Cities across the globe and through time are now knowable like never before, across many of their dimensions: social, economic, infrastructural and spatial. Luis Bettencourt (2013)
We are a very empirically driven field. But we have always faced significant limitations on what we can and cannot observe, due to a variety of factors. These include latent variables (utility being a prime example), which are inherently unobservable, and dependency on limited, expensive, relatively small-sample surveys for much of our data. Further, such survey data is more often than not static and cross-sectional in nature. This long-standing dependence on small-sample, cross-sectional survey data, however, is potentially changing. New, “big” datasets are providing the opportunity to observe massive amounts of passive, revealed preference data concerning trip-making within urban regions. Such data includes cellphone traces, GPS location tracking from a variety of sources, including customdesigned smartphone apps, public transit smartcard transaction data, credit card transaction data, etc. These data are: l
Potentially relatively consistent across urban regions, since they are often collected using common methods. A cellphone trace, for example, is, by and large, a cellphone trace whether it is collected in Santa Barbara or Toronto or Montevideo.
42 PART | I Retrospective and prospective survey of travel behavior research l
l
Continuously collected, day after day, week after week, providing timeseries observations of the same trip-makers over extended periods of time, thereby facilitating much more dynamic models of travel behavior than previously possible to construct. VERY large-sample, thereby potentially providing much more comprehensive characterization of trip-making than is possible from small-sample surveys. Such data, of course, are also not without their challenges. These include:
l
l
l
l
Spatial precision of the location traces is often problematic, most notably for cellphone traces. The data are inevitably anonymized to preserve the privacy of the people being tracked, and so are lacking trip-maker attributes. Similarly, trip purpose (and often mode) generally are not collected and need to be inferred. The data usually track an individual’s movements and information concerning the household within which the person resides is usually lacking.
Thus considerable processing of the raw data and/or fusing of the data with other data sources (census data, GIS place of interest (POI) data, etc.) is typically required to make the tracking data fully useable for travel demand modeling purposes. Despite these challenges, there is very likely that future models will make much more use of such data. Is cellphone trace data (for example), fused with census and POI data, collected across very large segments of the population over extended periods of time “any worse” than the small-sample, cross-sectional survey data that w have been working with all these years? Might it not be much better?
7. Provocation 4: computing The history of travel demand modeling is tied directly to the history of digital computing (Meyer and Miller, 2013). Travel demand modelers are as much in the software business as they are the travel behavior and econometrics business. Even in today’s computing world, big urban travel demand models are computationally big, with network modeling (road and transit assignment) being the primary culprit. But to be of practical use, travel demand model systems need to run within reasonable time limits. Arguably many/most current activity-based travel demand models run much too slowly to be truly useful to planning agencies. And so, computational efficiency remains a very important design criterion for operational models. Despite this dependency upon the computer as our “lab”, as a field we arguably have not exploited advances in High Performance Computing (HPC), especially cluster-based and other parallelized/distributed computing capabilities, as we might have to deal more efficiently with the large, complex, computationally-intensive problems which we are studying.
Travel demand models, the next generation Chapter | 3
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Further, it is arguable that we could often do much better in both our theoretical conceptualizations and our software implementations to design for efficient computation. Parsimonious model formulations, in which the model formulation is as simple as possible to adequately model the behavior of interest, can go a long way toward improving model system run times. It is also arguable that, as a field, we suffer from a proliferation of bespoke software that often: l l
l
l
Represents expensive and inefficient reinvention of wheels. Presents barriers to the sharing of data and ideas (due to lack of common data standards, semantic, etc.). Is overly “hard-wired” to deal with a particular model specification, with little flexibility to incorporate new ideas, specifications, etc. Is either expensive proprietary commercial software or else poorly supported open source software.
In other words, we do not share and collaborate enough with each other in terms of software development and usage. We often discuss the “My Model” syndrome in our field, in which each modeler views his/her model as “the best” and we see relatively little convergence around best practice and similarly little rejection of poor practice. But there is arguably also a “My Software” problem, in which little convergence in software is occurring. Can/should we be standardizing within a common, open source software “environment” which everyone can use and to which everyone can contribute? This is not a new idea, but it has not yet come to pass in our fielddalthough it does exist in many other fields. This proposal might be viewed as creating “a big (virtual) lab” within which testing and experimentation by many researchers can proceed in a systematic, controlled useful way. It would encourage comparison of alternative methods and theories within a common environment that would lead to rejection of weaker ideas and convergence on stronger ones. It would reduce “barriers to entry” (especially for younger researchers) since less time would be spent on developing one’s own software and more time would be spent on using the existing, common software to actually build models and test hypotheses. And it would also reduce barriers to the testing of new approaches to modeling, since a highly modularized software “lab” would facilitate rapid prototyping of new procedures, etc., rather than “locking” researchers into hard-wired computational structures.
8. Toward the next generation Our mission: to go boldly where no-one has gone before. James T. Kirk.
Model complicatedness, computaonal burden, …
44 PART | I Retrospective and prospective survey of travel behavior research
Acvity-scheduling? What’s next?
Tour-based 4-Step
Behavioural representaon, policy sensivity, … FIG. 3.4 Technological growth curves.
Virtually all technologies follow growth curves in which: l
l
l
l
Performance increases within a given technology come at exponentially increasing costs. At some point additional growth in performance is restricted by the highly non-linear growth in cost (energy, etc.). For further improvements to occur, innovation must occur to replace the old technology with a more efficient new technology. The process of growth can then continue.
As illustrated in Fig. 3.4, travel demand modeling is subject to this same general principle. Although arguably the “efficiency gains” in shifts in modeling paradigms to date have been modest at best. This chapter has argued that there is a critical need for travel demand modeling to undertake such a “technology shift”, to a much more powerful, much more efficient next generation of models. These models will exploit as best as possible the new, large sources of data that are increasingly available from a variety of sources. They will be much more computationally efficient that the typical current generation model, exploiting much more fully advanced hardware and software capabilities. And they will have a much more fundamental theoretical foundation that will enable them to robustly and insightfully address the many technological, operational and societal challenges facing 21st Century urban transportation systems worldwide.
References Anas, A., 1983. Discrete choice theory, information theory, and the multinomial logit and gravity models. Transportation Research B 17, 13e23.
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Ben-Akiva, M., Lerman, S.R., 1985. Discrete Choice Analysis: Theory and Application to Predict Travel Demand. MIT Press, Cambridge MA. Bettencourt, L.M.A., 2013. The origins of scaling in cities. Science 340, 1438e1441. Bettencourt, L.M.A., West, G.B., 2010. A unified theory of urban living”. Nature 467, 912e913. Bettencourt, L.M.A., Lobo, J., Helbing, D., Kuhnert, C., West, G.B., 2007. Growth, innovation, scaling, and the pace of life in cities”. Proceedings of the National Academy of Sciences 104 (17), 7301e7306. Castiglione, J., Bradley, M., Gleibe, J., 2015. Activity-based Travel Demand Models: A Primer, Report S2-C46-RR-1. Transportation Research Board, Washington, DC. Chorus, C.G., 2014a. A generalized random regret minimization model. Transportation Research Part B 68, 224e238. Chorus, C.G., 2014b. Risk aversion, regret aversion and travel choice inertia: an experimental study. Transportation Planning and Technology 37 (4), 321e332. Daganzo, C.F., 1979. Multinomiql Probit. Academic Press, New York. Domencich, T., McFadden, D., 1975. Urban Travel Demand: A Behavioral Analysis. North Holland, Amsterdam. Hagerstrand, T., 1970. What about people in regional science? Regional Science Association Papers 24 (1), 6e21. Hensher, D.A., Johnson, L.W., 1981. Applied Discrete-Choice Modelling. Routledge, London. Hofstadter, D.R., 1979. Go¨del, Escher, Back: An Eternal Golden Braid. Vintage Books, New York. Jones, P.M., 1979. New approaches to understanding travel behaviour: the human activity approach. In: Hensher, D., Stopher, P. (Eds.), Behavioral Travel Modeling. Redwood Burn Ltd, London, pp. 55e80. Kahneman, D., 2011. Thinking, Fast and Slow. Doubleday Canada, Toronto. Manheim, M.L., 1978. Fundamentals of Transportation Systems Analysis Volume 1: Basic Concepts. MIT Press, Cambridge, MA. Martinez, F.J., 2016. Cities’ power laws: the stochastic scaling factor. Environment and Planning B 43 (2), 257e275. Martinez, F.J., 2018. Microeconomic Modeling in Urban Science. Academic Press, New York. McFadden, D., 1978. Modeling the choice of residential location. In: Karlquist, et al. (Eds.), Spatial Interaction Theory and Residential Location. North Holland, Amsterdam, pp. 75e96. Meadows, D.H., 2008. In: Wright, D. (Ed.), Thinking in Systems: A Primer. Chelsea Green Publishers, White River Junction, VT. Meyer, M.D., Miller, E.J., 2013. Urban Transportation Planning, A Decision-Oriented Approach, third ed. Available at: http://mtsplan.com/services.html. Miller, E.J., 2003. Microsimulation. In: Goulias, K.G. (Ed.), Chapter 12 in Transportation Systems Planning Methods and Applications. CRC Press, Boca Raton, FL, pp. 12-21e12-22. Miller, E.J., 2018a. The case for microsimulation frameworks for integrated urban models. Journal of Transport and Land Use 11 (1), 1025e1037. Miller, E.J., 2018b. Agent-based activity/travel microsimulation: what’s next? In: Briassouli, et al. (Eds.), Spatial Analysis: Tools and Land Use, Transport and Environmental Applications. Springer, pp. 119e150. Miller, E.J., 2019. Accessibility: measurement and application in transportation planning (editorial, forthcoming in). Transport Reviews. Miller, E.J., Roorda, M.J., 2003. A prototype model of household activity/travel scheduling. Transportation Research Record, Journal of the Transportation Research Board 1831, 114e121.
46 PART | I Retrospective and prospective survey of travel behavior research Miller, E.J., Roorda, M.J., Carrasco, J.A., 2005. A tour-based model of travel mode choice. Transportation 32 (4), 399e422. Muller, K., Axhausen, K.W., 2010. Population Synthesis for Microsimulation: State of the Art. ETH, Zurich. http://www.strc.ch/2010/Mueller.pdf. Mumford, L., 1961. The City in History. Harcourt, New York. Ondaatje, M., 1970. The Collected Works of Billy the Kid. Vintage Books, New York. Ortuzar, J., de, D., Willumsen, L.G., 2011. Modeling transport, 4th edition. John Wiley and Sons, New York. Shannon, C.E., 1948. A mathematical theory of information”. Bell System Technical Journal 27, 379e423, 623e656. Spear, B.D., 1994. New Approaches to Travel Demand Forecasting Models, A Synthesis of Four Research Reports, DOT-T-94-15. U.S. Department of Transportation, Washington, D.C. Thayler, R.H., Sunstein, C.R., 2008. Nudge: Improving Decisions About Health, Wealth and Happiness. Yale University Press, New Haven. Train, K., 2009. Discrete Choice Methods with Simulation, second ed. Cambridge University Press, Cambridge. Urbanet, 2018. https://www.urbanet.info/world-urban-population. Webber, M.J., 1977. Pedagogy again: what is entropy? Annals of the Association of American Geographers 67 (2), 254e266. Wilson, A.G., 1967. A statistical theory of spatial distribution models (Chapter 3). In: Quandt, R. (Ed.), The Demand for Travel: Theory and Measurement. Lexington Books, Lexington, MA, pp. 55e82.