LA TORSION DE LAS VOCES

As mentioned in the introduction, the activity-based modelling approach can be developed into a helpful tool for the analysis of built environment-travel relationship, with special strength in simulating the detailed behavioural processes. Activity-based models have been put forward as a superior alternative to the widely-used four-step models, which was the dominant method in the field of transport modelling (McNally, 2007; Rasouli & Timmermans, 2014a; Yasmin et al., 2015; Yasmin, Morency, & Roorda, 2017). The four-step model is a kind of spatial interaction model that predicts the aggregate trip productions and trip attractions of traffic zones based on the propensity to travel and the travel impedance (time and/or cost), which finds its theoretical roots in social physics (Batty, 2009; McNally, 2007). The main critique of four-step models lies in that it is aggregate in nature and does not involve any behavioural mechanism—the unit of measurement is not an individual, but rather the number of trips emanating from any particular zone (Rasouli & Timmermans, 2014a). As a consequence, the lack of behavioural mechanism also makes four-step models patently inadequate when it comes to accounting for the effects of the built environment on person travel (Ewing et al., 2015).

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The major advantage of activity-based model is claimed to be the behavioural realism and the integrity, which allows for a comprehensive prediction of the sequence of activities and the associated travel, where, when, for how long, subject to a set of spatial, temporal and institutional constraints (Acheampong & Silva, 2015; Rasouli & Timmermans, 2014a). This modelling approach provides a means of forecasting the impacts of a given policy at the disaggregate level, so that a wider set of more detailed policies can be tested in ways that are generally infeasible with the conventional four- step approach (Bhat et al., 2004; Goulias, 2002).

Although practical models in this strand started to increase since the 1990s, particularly after 2000, the theoretical underpins can be traced back to the 1970s. Chapin (1968) first put forward the idea to relate human activity systems to the spatial structure of the city as a critique to the disposition to rely wholly on land rent theory and the market mechanism in the study of urban structure and processes. Hägerstrand (1970) introduced the time-space concept which also emphasised the importance of understanding the micro-situation of human activities in studying the large scale aggregate outcomes such as traffic generation.

Activity-based models typically fall into one of two categories: utility-maximising econometric models and computational process models. The former involves using systems of equations to capture the relationships among activity and travel attributes, and to predict the probability of decision outcomes (Bhat et al., 2004). The latter approach is, on the other hand, a computer program implementation of a production system model, which is a set of rules in the form of condition-action (if-then) pairs that specify how a task is solved (Gärling, Kwan, & Golledge, 1994; Shabanpour, Javanmardi, Fasihozaman, Miralinaghi, & Mohammadian, 2017). However, it is important to note that the above two approaches have been neither exclusive nor exhaustive. Several other approaches, including: (a) time-space prisms and constraints, (b) operations research/mathematical programming approaches, and (c) agent-based

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approaches have been employed, either in combination with the above approaches or separately, to develop activity-based model systems (Pinjari & Bhat, 2011).

This review is not going into the details of the many paradigms, frameworks and techniques in activity-based modelling, which are well reviewed by Henson, Goulias, & Golledge (2009), Pinjari & Bhat (2011) and Rasouli & Timmermans (2014), etc. Instead, I will particularly focus on the treatment of built environment features in the existing model systems, e.g. what built environment features are included and how they are accounted for in the behavioural process. This issue is reviewed and summarised based on the models that emerged or are actively updated after 2000, which may not be exhaustive, but it is not very possible that highly influential and referenced models could be left out (see Table 2-5).

It turns out that most existing model systems include only zonal ‘size’ variables in the aspect of built environment, such as population, employment by sectors or number of commercial establishments. Size variables are usually used to weight the likelihood for a zone to be selected as the location of an activity, either through simple statistical distribution, or econometric models, or some other functions. This is conceptually similar to four-step models which estimate the attractions of traffic zones and distribute travel demands based on zonal ‘size’ characteristics (McNally, 2007). When a model incorporates a dynamic module of route choice and traffic flow estimation, the road network can also be considered as an included built environment feature (e.g. in RAMBLAS, MATSIM). The SACSIM model (and probably other models in the same ‘family’) takes most account of built environment features. It is related to the fact that the model system is composed of a series of discrete choice models, which is particularly convenient and straightforward for the inclusion of an extended set of explanatory variables. Nonetheless, to the author’s knowledge, the ‘D-variables’ in the built environment-travel research as mentioned before are never fully accounted for in existing model systems.

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A reason for this gap could be that there are basically two groups of people working in the two fields: those interested in the impacts of nuanced built environment features are more urban planning and design oriented, while those building models tend to have a stronger background in transport planning and civil engineering. Therefore, my research will build on this gap and link the activity-based modelling approach with the analysis of the built environment-travel relationship. The development of an activity- based model that takes full account of the built environment will, on one hand, improve the comprehensiveness and realism of travel modelling, and on the other hand, enable the analysis of the detailed and decomposed influence of the built environment on the behavioural processes of daily travel.

Table 2-5 Summary of the inclusion of built environment features in existing activity-

based models

Model names and key references

What built environment features are included and how

RAMBLAS

(Veldhuisen, Timmermans, & Kapoen, 2000)

Features: Land use per zone, population per zone, dwellings

by type

How: The destinations for shopping and services are drawn

based on the distribution of employment in the relevant services. The destinations for social participation and social contacts are drawn based on the distribution of households. SIMAP

(Kulkarni & McNally, 2000)

Features: density by land use

How: to assign a selection likelihood to candidate activity

locations. CEMDAP

(Bhat et al., 2004)

Features: zone-level land use, zonal basic/service/retail

employment levels

How: as independent variables in the econometric model for

household activity-generation and activity location choice. PCATS & FAMOS (which

incorporates PCATS as the activity-travel module) (Kitamura, 1996; Pendyala, Kitamura, Kikuchi,

Yamamoto, & Fujji, 2005)

Features: zone size, population density, commercial

employment

How: as explanatory variables in the nested logit model of

destination-mode choice.

TASHA & ILUTE (which incorporates TASHA)

Features: population per zone, employment per zone,

54 (Miller & Roorda, 2003;

Roorda, Miller, & Habib, 2008; Salvini & Miller, 2005)

How: to estimate the probability of choosing a zone as the

location of an activity.

SACSIM

(Bowman & Bradley, 2005)b

Features: mixed use density, intersection density, purpose-

specific size in parcels, parking and employment mix, accessibility from home, accessibility to nearest transit stop

How: as explanatory variables in the econometric models

for car ownership, activity generation, mode choice and destination choice.

AURORA & PUMA (which incorporates an updated version of AURORA) (Ettema, de Jong,

Timmermans, & Bakema, 2007)

Features: NA

How: to calculate the attractiveness of a location, which is

then used to model the probability of location choice.

ALBATROSS & FEATHERS

(Arentze & Timmermans, 2004; Bellemans et al., 2010)

Features: total amount of floor space and number of

employees per sector per zone

How: input to the decision tree.

MATSIM

(Balmer et al., 2009)

Features: land-use information about the capacities of

different activity types like ‘work’, ‘shopping’, ‘education’, etc.

How: to indicate potential activity locations.

ADAPTS

(Auld & Mohammadian, 2009; Auld &

Mohammadian, 2012)

Features: zonal size variables, including the land-use area

and employment by various categories.

How: as explanatory variables in the multinomial logit

models of destination choices.

a Note that many published articles do not describe every detail of the model, therefore the information in the table may not be absolutely complete.

b There are several other models in the same ‘family’, which include models for Portland Metro I/II, San Francisco SFCTA, New York NYMTC, Columbus MORPC, Atlanta ARC, etc. (Bradley & Bowman, 2006). The inclusion of built environment features can be more or less different from in SACSIM. However, it is difficult to find detailed technical documents of these models as the technical memos for SACSIM (http://jbowman.net/#Implementation). c Full names of the models are:

RAMBLAS - Regional Planning Model based on the Micro-Simulation of Daily Activity Patterns

SIMAP - Microsimulation of Daily Activity Patterns

CEMDAP - A Comprehensive Econometric Micro-Simulator for Daily Activity-travel Patterns

55 FAMOS - Florida Activity Mobility Simulator

TASHA - Toronto Area Scheduling Model for Household Agents ILUTE - Integrated Land Use, Transportation, Environment SACSIM – Sacramento Activity-based Travel Demand Model PUMA - Predicting Urbanisation with Multi-Agents

ALBATROSS - A Learning Based Transportation Oriented Simulation System FEATHERS - Forecasting Evolutionary Activity-Travel of Households and their Environmental RepercussionS

MATSIM – Multi-Agent Transport Simulation

ADAPTS – Agent-based Dynamic Activity Planning and Travel Scheduling