LAS VOCES QUE ALIGERAN SU PESO

Activity-based models predict activity-travel behaviour as a ‘full day pattern’ that involves an entire chain of trips made between the first time of leaving home in the day and finally arriving back at home (Cambridge Systematics, 2002; Liu et al., 2014). The full day pattern is composed of primary activities and intermediate stops, and the tours and trips that link them up. The definitions of these components, which will be used throughout this chapter, are explained below:

- Primary activities are defined as those with the longest duration among all activities conducted in a tour;

- Intermediate stops refer to the rest of activities with shorter durations; - Trips are one-way movements from one location of staying to the next;

- Tours are chains of linked trips that start from and end at home, the use of which is considered to be a key feature of activity-based model systems in contrast to the trip-based four-step models (Cambridge Systematics, 2002).

As mentioned in Section 5.1.1, various choice facets of the full day pattern are organised in a decision sequence, which is derived from both the results of the small questionnaire survey and operational considerations. The survey results show that 44%

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people chose ‘what shall I do today’ as the first consideration in making activity-travel plans on weekdays, 24% chose ‘where shall I go’ or ‘how far shall I go’, 16% chose ‘shall I go by car/metro/bus’ and 15% chose ‘when shall I go’. The results on weekends are similar with those on weekdays, except that the proportion of people choosing ‘Where shall I go?’ or ‘How far shall I go?’ is higher. This makes sense since people are usually faced with less time constraints on weekends, so that they have more freedom to choose activity locations that best suits their needs. Regarding to the planning of primary destinations and intermediate stops, 60% people chose that they ‘first decide long-stay/primary destinations and then short-stay/intermediate stops’ and 40% chose ‘decide all destinations together’. For weekends, the former option was more chosen (69%). The survey results suggest that: (1) the priorities that people give to these choice facets can be ranked as: the number and type of activities > the location of activities > the time of activities/the mode of travel, and (2) the location choice of primary activities and intermediate stops tend to be separate decisions, the latter dependent on the former.

Table 5-2 Results of the small questionnaire survey

What is your consideration when you make plans about your activities (except work) on weekdays?

First consideration Second consideration

‘What shall I do today?’ 44% 14%

‘When shall I go?’ 15% 22%

‘Shall I go by car/metro/bus/walk..?’ 16% 24% ‘Where shall I go?’ or ‘How far shall I go?’ 24% 40%

What is your consideration when you make plans about your activities (except work) on weekends?

First consideration Second consideration

‘What shall I do today?’ 45% 13%

‘When shall I go?’ 12% 20%

‘Shall I go by car/metro/bus/walk..?’ 14% 17% ‘Where shall I go?’ or ‘How far shall I go?’ 29% 50%

When deciding about the activity destinations on weekdays, which do you prefer?

‘Decide all destinations together’ 60% ‘First decide long-stay/primary destinations and then short-stay/intermediate stops’

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When deciding about the activity destinations on weekends, which do you prefer?

‘Decide all destinations together’ 69% ‘First decide long-stay/primary destinations and then short-stay/intermediate stops’

31%

Based on the survey results, the general structure of the model is designed as follows (Figure 5-1):

- First, the number and type of activities to conduct in the simulated day are decided and organised into primary destinations and intermediate stops;

- Second, the locations of primary destinations are selected;

- Third, the times of activities (in terms of time periods) and modes of tours are decided;

- Last, the locations of intermediate stops are selected.

These four decision components are simulated in the four sub-models described in Section 5.2 to Section 5.5. It should be noted that although a survey was conducted to inform the sequencing of decisions, this consequent model structure should not be taken as the only way of model construction. Actually, most activity-based models use more or less different sequences of decision making and there is no clear evidence that one sequence outperforms another (see for instance, the SACSIM ‘family’ of models mentioned in Section 2.4).

113 Figure 5-1 Model flow diagram

Note: The detailed flow diagram of each sub-model will be zoomed in in subsequent sections.

A total of 300,000 individual entities are simulated in the model, which is approximately 2% of the population in Beijing. The distributions of gender, age, employment status, household type and social status are kept in consistency with the travel survey. Other elements of the model include (see Table 5-3):

- Types of activities: two types of commute activities, which are work and go to school; six types of non-commute activities, which are shopping, entertainment, dining out, personal business, escorting/picking up/dropping off other people and others. The first five types of non-commute activities are modelled separately since they took up more than 2% of all activities recorded in the travel diary survey. The other activity types in the travel survey are categorised as ‘others’ (see Table C-1 in Appendix C).

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travel survey, 95% people conducted no more than three activities in a day. - Activity plans: eleven most common plans from the travel survey.

- Time of activity: six time slots.

- Locations: 652 TAZs in the study area.

- Modes of travel: four most popular travel modes identified from the survey, which together accounted for 96% of all trips.

Table 5-3 Elements of the model

Choice facets Alternatives

Types of activities Commute: work, go to school

Non-commute: shopping, entertainment, dining out, personal business, escorting/picking up/dropping off other people and others

Numbers of activities 0-3

Activity plans a h-d-h, h-d-h-d-h, h-s-d-h, h-d-s-h, h-d-h-d-h-d-h, h-d-s-s-h, h-s-d-s-h, h-s-s-d-h, h-s-d-h-d-h, h-d-s-h-d-h, h-d-h-d-s-h Time of activity early (3-7am), am peak (7-9 am), before noon (9 am-12

pm), afternoon (12-17 pm), pm peak (17-19 pm), evening (19 pm-3 am)

Locations 652 TAZs (for each activity, a distance-weighted subset of 10 TAZs)

Modes of travel driving, public transit, cycling, walking

a ‘h’ denotes home, ‘d’ denotes a primary destination, ‘s’ denotes an intermediate stop. ‘h-d- h’, for instance, means travelling from home to a primary destination and then back to home.

Table 5-4 Information delivery among sub-models

Sub-model 1 Sub-model 2 Sub-model 3 Sub-model 4

Numbers of activities O I I Types of activities O I Activity plans O I I I Locations of primary destinations O I I Time of activity O Modes of travel O I Locations of intermediate stops O

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There are basically two types of parameters in the model. The first type are parameters that reflect the impacts or weights of socioeconomic, built environment and travel- related factors, which are estimated from statistical regressions (i.e. ordered regression, multinomial logit model) or derived directly from the statistical distribution of the observed data. The second type are constants or threshold values which are calibrated in the model through parameter sweep (Castle & Crooks, 2006). The parameters are listed in Table 5-5. Eighty percent of the samples in the travel diary survey are used as the training set and the rest twenty percent are used as the test set.

Table 5-5 Parameters in the model

Sub-models Parameters estimated from

regressions or statistical distributions

Parameters calibrated in the model

Sub-model 1 Impacts of socioeconomic and built environment variables on the number of commute and non-commute activities in the day;

Probabilities of choosing different types of non-commute activities; Probabilities of choosing an activity plan given the total number of activities

Cut-off values for the ordered regression models

Sub-model 2 Weights given to different distance bands when drawing a random sample of candidate activity locations; Impacts of socioeconomic, built environment and distance variables on the attractiveness of a location

Extra weights of different distance bands on the attractiveness of a location

Sub-model 3 Probability of choosing a certain time slot for an activity given the purpose and the day activity plan;

Impacts of socioeconomic, built environment and travel variables on the attractiveness of a travel mode

Constants for different travel modes

Sub-model 4 Weights given to different detour distance bands when drawing a random sample of candidate stop locations; Impacts of socioeconomic, built environment and travel variables on the attractiveness of a location

Extra weights of different detour distance bands on the

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