A Strategic Tour Generation Modeling within a Dynamic Land-Use and Transport Framework: A Case Study of Bogota, Colombia

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1 Available online at ScienceDirect Transportation Research Procedia 25C (2017) World Conference on Transport Research - WCTR 2016 Shanghai July 2016 A Strategic Tour Generation Modeling within a Dynamic Land-Use and Transport Framework: A Case Study of Bogota, Colombia Luis A. Guzman a*, Ana M. Gomez a, Carlos Rivera a a Grupo de Sostenibilidad Urbana y Regional, Universidad de los Andes, Bogota, Colombia Abstract The development of integrated techniques to evaluate long-term urban trends is a top priority in terms of creating a more sustainable society. In order to take a step forward from traditional peak-hour models, the purpose of this paper is to develop a travel demand (generation and attraction) strategic model of a typical day. The methodology integrates a commuting and noncommuting-related tour generation/attraction model and a Land-Use and Transport Interaction (LUTI) model to capture the feedback mechanisms that may affect tour generation in the long term. The travel demand model is developed from a cross-sectional household mobility survey carried out in Bogota in Multiple linear regression analysis is used to investigate and model the effect of income, household size and structure, car ownership, travel time and mixed land-use on the number of trips generated by a household on an average weekday. The trip attraction models are at best estimated using zonal data. The Bogota LUTI model that is adopted in this methodology has been benchmarked against other published models to compare its features and capabilities. The integration of the travel demand model and Bogota LUTI model will allow for a discussion on the suitability of the proposed modeling approach in order to test several scenarios with high motorization growth rates, and the possible advantages (or disadvantages) associated with them; thus, providing useful knowledge that will inspire future research on the evaluation of complex transportation policies in developing cities The Authors. Published by Elsevier B.V. Peer-review under responsibility of WORLD CONFERENCE ON TRANSPORT RESEARCH SOCIETY. Keywords: tour generation; daily tours; Bogota; LUTI models * Corresponding author. Tel.: address: la.guzman@uniandes.edu.co The Authors. Published by Elsevier B.V. Peer-review under responsibility of WORLD CONFERENCE ON TRANSPORT RESEARCH SOCIETY /j.trpro

2 Luis A. Guzman et al. / Transportation Research Procedia 25C (2017) Introduction The Bogota Urban Planning Office is funding a research project which is about a development of a land-use and transport interaction model (LUTI) for the Bogota region. This paper presents a component of this project: a travel demand model which is incorporated under a unified modelling framework. This research proposes an evaluation framework using one long-term system dynamic (SD) that takes into account how travel demand rates are affected by socioeconomic and spatial factors. At the same time, the travel demand rates affect transport use, and therefore, distance-related impacts such as traffic congestion, travel time and accessibility, among others. In this case, the SD approach was used to take into account the dynamic interaction between variables such as household income, population, mix land-uses and car ownership in the tour generation and attraction rates. The SD approach assumes that land-use is not a constant but is rather part of a dynamic system that is influenced by transport infrastructure (Guzman, Gómez 2015, Pfaffenbichler, Emberger & Shepherd 2008). The Bogota LUTI model includes a transport model, which simulates the travel behavior of the population depending on their housing and workplace location; a housing development model, a household location choice model, a workplace development model and a workplace location choice model, make this possible. This model is broken down by commuting and non-commuting tours. Car speed in the transport model is volume and capacity dependent and hence not constant. The travel demand model (tour generation/attraction models in Fig. 1) belongs to the transport model and is the core of this research. Changes in the transport system cause time-lagged changes in the land-use system and changes in the land-use system cause immediate reactions in the transport system. The evaluation framework and the links between the sub-models are shown in Fig. 1. Fig. 1. Dynamic transport and land-use framework The transport model developed for the Bogota LUTI model is a strategic and dynamic model. Such models use a high level of simplification to represent the road and public transport network; i.e., the network is aggregated to one link per origin-destination pair. This means that there is no route choice as traditionally known. We built aggregate

3 2542 Luis A. Guzman et al. / Transportation Research Procedia 25C (2017) speed-flow curves using the network model (VISUM ) to form the Bogota LUTI speed-flow relationships for private cars, as a useful interface to simulate a private car travel time matrix for future years incorporating responses to transport factors (such as increased travel demand) and land-use changes (Shepherd et al. 2010). This paper presents a daily transport demand estimation model, which will then be incorporated to a unified modelling framework. The land-use model was purposely left aside. We did this so as not to stray from the main focus of the paper, which is the development of different models to estimate the travel demand in the study zone. In short, the land-use module is mentioned, but not emphasized this issue in this article. Different research studies vary in their approach to forecasting travel demand, but many of them have used static models ignoring the possible feedback mechanisms among several other factors over time. One of those factors is the access: areas with low accessibility will be less attractive to people who are looking for a place to live. The main feedback mechanism that is evaluated occurs through accessibility. As travel demand increases in a particular zone, accessibility tends to decrease due to congestion. As accessibility decreases, land development and population increases are less likely to occur in the future, which restraining future travel demand growth. This is a balancing feedback mechanism that tends to distribute travel demand more evenly within an urban area (Guzman, Gómez 2015). To deal with this issue, the present methodology uses a tour-based concept similar to the Copenhagen s Orestad traffic model (Jovicic, Overgaard Hansen 2003). In this concept, a tour is defined as a sequence of a simple trip to a destination and a simple return trip from a destination and back home. Two different types of tours are considered: Home Work/Study Home (HWH) and; Home Others Home (HOH) The two tour types considered cover a high percentage of daily mobility in Bogota region (according to data from the 2011 mobility survey in Bogota, home-based tours HWH and HOH cover about 91.2% of the daily mobility). Given the strategic nature of the presented model they are seen as representing urban mobility precisely enough. Bogota has always developed peak-hour transport models, mainly for analysis of capacity and levels of service. We want to move forward and develop a daily travel demand model that has never been done. Thus, the methodology involves the estimation of a tour generation model using multiple linear regression analysis at the household level. The tour attraction models are at best estimated using zonal data. The feedback processes are modeled with stocks and flows over time, in order to understand the impacts of mobility conditions and land-use characteristics and to allow an analysis of the impact on the transport system. The proposed methodology is applied to Bogota, Colombia, with two main purposes: first, to develop a model sensitive to planning and policy variables in the whole Bogota region; and, second, to address the implication of tour generation/attraction in urban demand mobility at a strategic level. 2. The travel demand in Bogota region Bogota is a dense and compact city with a rapidly growing population. It had 7.6 million inhabitants in 2012 (1.3 million more than in 2000), spread over an area of 365 km 2. This results in a population density of almost 20,800 inhabitants per km 2, which makes it one of the most densely populated cities in the world. Nowadays, Bogota city is divided into urban planning zonal units (UPZ, Unidades de Planeamiento Zonal), which are similar in terms of urban characteristics such as land-use or activities, height of buildings, state of the roads, sidewalks and public spaces. On the other hand, the urban agglomeration outside Bogota has no institutional organization and each of the 12 contiguous municipalities still makes autonomous decisions. These other areas extend across 1,194 km 2, with a population of 1.22 million inhabitants (see Fig. 2), and they are part of the LUTI model proposed.

4 Luis A. Guzman et al. / Transportation Research Procedia 25C (2017) The spatial distribution of population and activities shows important differences throughout the city. The densest sectors are located in the low-income zones, settled on the southern and western border of the urban area which can reach densities of up to 75,000 inh/km 2. These sectors are composed of both consolidated areas and new settlements, generally those of informal origin. On the other hand, the city s northern and central sectors are characterized by high-rise developments, lower population densities (13,000-30,000 inh/km 2 ) and a high concentration of formal employment. Fig. 2. Study area: the Bogota region The Bogota LUTI model represents the complete Bogota region, with 127 zones (UPZ and municipalities) covering approximately 1,550 km 2, 112 of which correspond to the Bogota city. The remaining 15 zones correspond to neighboring municipalities. According to the last mobility survey in 2011, walking is the most commonly used mode of transport, accounting for 46% of total daily trips. Second, public transport approaches 30% (20% transit buses, 9% Transmilenio (BRT) and 1% inter-municipal transport). External variable projections such as economic and population growth are estimated based on data for official statistics (SDP 2011). The travel demand modelling base-year is 2011 when 17.5 million of trips were estimated in the study zone on a typical work day. Of those 17.5 million of trips, 7.9 million entailed travelling back home so that the home-based tours produced were around 9.6 million of trips. Of these trips, 49% were commuting trips (HWH) and the rest, HOH. Fig. 3 shows the total number of trips produced and attracted in the study area according to the aggregate

5 2544 Luis A. Guzman et al. / Transportation Research Procedia 25C (2017) transport mode. This result highlights the large proportion of non-motorized trips (walking and cycling): 39% are HWH trips, while in HOH trips, the proportion rises to almost 58%. Fig. 3. Travel demand in a typical work day Additionally, the mobility survey reports eight predefined ranges for household monthly income. The eight ranges were (1 USD = 1,900 COP approx., in 2011): Range 1: $0 - $282 Range 2: $282 - $632 Range 3: $632 - $1,052 Range 4: $1,052 - $1,475 Range 5: $1,475 - $2,105 Range 6: $2,105 - $2,895 Range 7: $2,895 - $4,210 Range 8: > $4,210 According to these income ranges, categorical rates for home-based HWH and home-based HOH purposes are presented in the Table 1. As shown, although low-income households are larger, they have lower trip rates than those of higher-income households. In some cases, these differences can be more than double. Table 1. Trip rates per household, travel purpose and income ranges Income range Households Average household size Total HWH trips Total HOH trips HWH trip rate HOH trip rate ,023,878 1,248, ,780,610 1,688, , , , , , , , , , , , , , ,225 84, , ,245 61,

6 Luis A. Guzman et al. / Transportation Research Procedia 25C (2017) In addition, for the low-income ranges, the use of non-motorized transport is much more important than motorized transport. In many cases, these people simply have no choice but to walk (or cycle), because they cannot afford public transport fares or because the supply is insufficient (low accessibility levels). In a comparison of average values, a person belonging to the highest income range makes around 20% more trips per day than a person from the lowest income range. If motorized trips are compared, the difference can be up to 150%. Thus, the income ranges is a very important variable in terms of trip generation. 3. Models formulation In the last decades, transport studies and planning agencies have begun to implement activity or tour-based modeling techniques instead of trip-based techniques (Bath, Koppelman 2003). These frameworks are expected to better represent travel demand since they consider it as being derived from people s desires to take part in different activities, and they use tours instead of trips as the base unit of analysis, hence considering the inter-relationship that exists between several trips. As stated earlier, a tour is defined as a journey that starts and ends at the same location and comprises two trips. Travel demand models are based on information from the 2011 Bogota Household Travel Survey (SDM 2011). Linear regression methods were used to estimate the number of productions and attractions associated with each of the two home-based trip purposes. This approach generates the number of tours to estimate full-day activity patterns. The number of generated tours of each type is estimated at household level. This reduces the intra-zonal variation and adds variability to the model. In this household-based application, each household is taken as an input data vector in order to include the range of the household s observed variability characteristics and its travel behavior into the model. Then, we expanded these values using the number of households by zone, explaining the variability of tour generation by zone. For HOH tours, the number is estimated according to the HWH travel time. In contrast, the attraction models are calculated per zone. This is undertaken because of the need to aggregate urban and economic characteristics per zone that attract trips to those particular destinations, e.g. number of jobs or land-use area ratios Generation The generation model works with household characteristics by zone in the region based on the sociodemographic information. The linear models are generic and the generation ones are formulated as follows: kk y ii = αα + jj=1 ββ jj xx iiii + ee ii, ii [1, NN] (1) Where i is an index for each household, y ii is the number of HWH or HOH tours made by household members on a typical day, αα is a constant term, j is an index for the explanatory variables, ββ jj is a coefficient associated to each explanatory variable, xx iiii is the value of explanatory variable j for household i, ee ii is an error term and N is the total number of households. As it is necessary to estimate the number of tours by zone, the model using rates would be: kk Y ii = αα + jj=1 ββ jj XX iiii + EE ii (2) With Y i = y i*h i, x i = X i/h i, e i = E i/h i and Hi is the number of households in zone i. Equations 1 and 2 explain the variability between zones and in both cases the parameters have the same meaning. The fundamental difference relates to the error term distribution in each case; the constant variance condition of the model could be hold in both cases if H, was itself constant for all zones i (Ortuzar, Willumsen 2011). The Bogota mobility survey provides disaggregate information respect to household characteristics, the number of vehicles available to each household, characteristics of household members, and the trips made by each member the day before the survey. This information per household units in Bogota is exhaustive. After removing households with incomplete information, a total of 13,797 household observations were used for the HWH tour generation analysis and 10,587 for the HOH tours.

7 2546 Luis A. Guzman et al. / Transportation Research Procedia 25C (2017) The calculations of travel demand start with the HWH tours variable (Fig. 4). A multiple linear regression including the population, household composition, car ownership and household income ranges (exogenously defined 1 ), gives the total number of HWH tours. Then, multiplying the total number of HWH tours with the travel time per HWH tour and by zone, gives the total HWH travel time. The link between the total number of HWH tours and travel time per HWH tour indicates that the Bogota LUTI model considers a speed-flow relationship. This link is part of the tour distribution and mode choice sub-models. Travel time per HWH tour is also influenced by the destination and mode choices resulting from this sub-model. The distribution and mode choice models for the HWH tours is run before the tour generation sub-model for HOH tours can work. The total travel time spent for HWH tours is calculated and then, introducing a variation of the principle of constant travel time budget (Mokhtarian, Chen 2004), the HOH tours are modeled. Thus, this HWH travel time is an input for the non-commuting tours (HOH) model. The tour generation model evaluation framework and the links between the sub-models are shown in Fig. 4. Fig. 4. Tour generation system diagram Examining the information available in the Bogota mobility survey and considering the findings of previous research with respect to trip and tour-generation modeling (Mwakalonge, Waller & Perkins 2012, Nowrouzian, Srinivasan 2012, Ortuzar, Willumsen 2011, Bath, Koppelman 2003), the following set of explanatory variables was defined for the generation model. Household income: in general, it is expected that richer households generate a higher number of tours, as they demand participation in a higher number of activities. However, the difference may be less in HWH tours than in HOH ones. According to the income ranges obtained from the Bogota mobility survey (see Section 2), income was initially included into the linear regression models as seven dummy variables identifying the income range of the household (the first income range is set as the base). However, since the coefficients among some income ranges were not significantly different, the income ranges were classified into different groups. In the case of HWH tours, four groups were defined: income range 1 as first and the base, income ranges 2 and 3, income ranges 4 and 5 and 1 As Pfaffenbichler P. (2003) says, exogenous means exogenous to the considered sub-model. From the perspective of the whole Bogota LUTI model those variables are endogenous.

8 Luis A. Guzman et al. / Transportation Research Procedia 25C (2017) finally income ranges 6, 7 and 8. In contrast, three income range groups were defined for the HOH tour model: incomes ranges 1, 2, 3 and 4 (as a base), ranges 5 and 6 and ranges 7 and 8. Number of household members: to explain tour generation, household size was included as three explanatory variables: number of students, number of workers, and others (not workers or students). The higher the number of workers in a household, the higher the number of tours this is expected to generate. The same behavior is expected for the number of students. The variable others explains the household members who do not work or study as a main activity. The sum of workers, students and other members of the households correspond to the household size. Number of cars: it has generally been found that the number of cars in the household affects the number of tours that the household generates. Car ownership makes mobility easier, especially in terms of HOH tours. Because of this, the number of cars per household was included as an explanatory variable for HWH and HOH tour generation Attraction The attraction models works with aggregate characteristics by zone. The linear models are generic and the attraction models are formulated as follows: kk Y ii = αα + jj=1 ββ jj XX iiii + ee ii, ii [1, NN] (3) Where i is an index for each zone, Y ii is the number of HWH or HOH tours attracted to the destination by zone, αα is a constant term, j is an index for the explanatory variables, ββ jj is a coefficient associated to each explanatory variable, XX iiii is the value of explanatory variables j for zone i, ee ii is an error term and N is the total number of zones, where 112 zones were used. The constant term and the coefficients associated to each explanatory variable are estimated by ordinal least squares. To complete the travel demand model set, another set of models were developed to identify the trip attraction at the end of each trip. The attractiveness of each area depends on the activities carried out at the destination. The number of these trips is influenced by the characteristics of the destinations instead of the socioeconomic characteristics of the household. Because of this, tour attraction models correlate these trips to inputs that describe the location such as the number and types of jobs, population or land-uses types at a destination. Thus, these models are based on the zonal characteristics of potential destination zones. The system diagram of the attraction model assumed in this study is shown in Fig. 5. Fig. 5. Tour attraction system diagram

9 2548 Luis A. Guzman et al. / Transportation Research Procedia 25C (2017) The dependent variable for the tour attraction model is given by the observed number of trips attracted by zone and purpose. It considers HWH and HOH tours separately. The following set of explanatory variables was defined. Residents: this explains the total population per zone and it is expected that a higher population increases the tour attraction. Jobs and study positions: the HWH tour attraction is given mainly by the offer of jobs and study positions. It is expected that an area with a higher number of jobs and study positions attracts more HWH tours. Retail and services jobs are specific explanatory variables for HOH tour attraction. Land-uses: the land-use entropy index explains the residential and commercial use of a specific zone. The index ranges between 0 (land-use is concentrated in one type) and 1 (land-use is evenly distributed among types, completely mixed land-use). Figures 4 and 5 present the general methodological SD approach, taking into account the complexity and multifaceted nature of the transport system and its interactions with other subsystems (land-use or travel patterns). Tour generation and attraction models are influenced by many variables of different character; however, the models presented are restricted by the available variables of the Bogota LUTI model and the data sources. 4. Analysis of results The variables used in the models were selected to explain the dependent variable and to minimize the correlation among them to avoid problems of heteroscedasticity and autocorrelation. Additionally, the analysis was made taking into account the assumptions of the lineal regressions; therefore, the variance was estimated as a first-order Taylorseries linearization method. The model was estimated in the statistical software STATA. The specific formulations and results for the travel demand model are described below Tour generation models Table 2 shows the results of the regression model to explain HWH tours. The model s independent variables are the number of students, workers and cars per household, considering its income level. The results show the coefficient of the multiple linear regressions for each variable, its coefficient, standard error and t-statistic. All the variables are significant at a 5% level of confidence and the resulting R-squared of the model is Table 2. Estimation results for generation model for HWH tours Variable Description Coefficient Standard error t-statistic C Constant i1 Income range 1 na na na i23 Income ranges i45 Income ranges i678 Income ranges Car-O Car ownership (cars per household) Students Students per household Workers Workers per household na= not applicable because it is used as base variable R-Squared: 0.27 F-test:

10 Luis A. Guzman et al. / Transportation Research Procedia 25C (2017) Number of observations: 13,797 households As expected, the number of workers and students per household increase the number of HWH tours, as does car ownership. A household with income ranges 2 or 3 undertakes 0.09 more tours than the base group per day; and a household with income level 6, 7 or 8 undertakes 0.16 more tours than the base group. Additionally, a household with a student and a worker will make 0.52 more tours than a household without one of them. The correlation between independent variables was checked to review possible effects of multicollinearity. For the variables in the HWH model, the biggest correlation was between the dummy variables income range 2-3 and 4-5. Additionally, it was checked the correlation between the different income ranges and the car ownership, as theory suggest, however their correlation was Values under 0.7 considered as important correlation value between variables. Table 3 shows the results of regression model for the HOH tour generation. For each variable, it shows its coefficient, standard error and t-statistic. The resulting R-squared of the model is Despite this low value, all variables are significant at a 5% level of confidence and the global test was significant, F=140.87, F test > F critic. This significance implies how all together the dependent variables explain the independent variable, in this case HOH tours. It is important to note how, for HOH tours, the income ranges and the presence of other members in the household (other than students or workers) are the variables that most influence the number of HOH tours. Table 3. Estimation results for generation model for HOH tours Variable Description Coefficient Standard error t-statistic C Constant i1234 Income ranges na na na i56 Income ranges i78 Income ranges Car-O Car ownership (cars per household) TravelT Travel time spent on HWH tours Students Students per household Workers Workers per household Other Other member in the household different from student or worker na= not applicable because it is used as base variable R-Squared: F-test: Number of observations: 10,586 households The wealthier households with more members and more cars tend to generate more tours. Ceteris paribus, households in the 7-8 income ranges undertake 0.35 more tours than the base group. Additionally, a household with a student will make 0.15 more tours than a household without one. Furthermore, the presence of other members in the household influences the generation of HOH tours by Finally, each minute spent on HWH travel reduces the number of HOH tours. This last variable has special relevance regarding the travel time budget. Within Bogota, the lower income population tends to commute larger distances; therefore, it is not only due to their socioeconomic situation but also because of the time they spend commuting that they do not engage in many HOH trips. The biggest correlation was between the workers per household and other member in the household. Nevertheless, correction methods such as estimating the variance with first-order Taylor series linearization methods were taken into account to correct the generation model.

11 2550 Luis A. Guzman et al. / Transportation Research Procedia 25C (2017) In order to evaluate the validity of the models, correlation tests between the independent variables were performed. No important correlation was found between them, and multicollinearity and heteroscedasticity were eliminated. The F-test proves the global significance of the model, the F calc > F critic at a 5% level of confidence, which means there is validity to the model. Nevertheless, is necessary taking into account how in traditional econometric models, the error term usually explains multiple exogenous variables or terms that cannot be explained by the model. Depending of the model this error term varies. However, recent research on the topic suggests how the academia is focusing more in the causality of the models rather than its predicting power Tour attraction models To complete the travel demand model, here we present the attraction models. As mentioned above, these tour attraction models are based on the zonal characteristics of destination zones according to the travel purpose and are formulated as linear regressions. The attraction tours of a zone j to be a destination for HWH tours is the number of work and study positions within each zone. The location of jobs is given by the land-use model. Study positions are an exogenous variable. Table 4 shows the estimations results for the HWH tour attraction. It shows the estimated coefficients, their t-statistic and standard error. The R-squared is 0.88 and all the variables are significant at a 5% level of confidence. Table 4. Estimation results for attraction model for HWH tours Variable Description Coefficient Standard error t-statistic C Constant 3, , J Number of workplaces in j S Number of student positions in j R-Squared: 0.8. F-test: Number of observations: 112 The number of jobs and student positions per zone increases the attraction of HWH tours. The offer of one job in a zone increases the number of HWH tours attracted by 0.34, while one study position increases the quantity of tours 1.14 times. Table 5 shows the results of the attraction model for HOH tours. It shows for each variable its coefficient, standard error and t-statistic. The resulting R-squared of the model is Additionally, all variables are significant at a 5% level of confidence and the global test was significant, F= It is important to notice how the constant of the model is negative; nevertheless, it works as an adjustment of the model. Table 5. Estimation results for attraction model for HOH tours Variable Description Coefficient Standard error t-statistic C Constant -4, , P Population per zone R Retailing and service jobs Ind Entropy index (residential and commercial land-uses) 5, , R-Squared: 0.89 F-test: Number of observations: 112

12 Luis A. Guzman et al. / Transportation Research Procedia 25C (2017) The results show how a zone completely mixed zone (between residential and commercial land-uses) attracts more trips that a zone with a single land-use. The numbers of jobs given by retail or service are significant, and each job attracts 1.05 HOH tours. Additionally, areas with more population, denser zones, attract more HOH tours. There is no correlation between the independent variables of the models. In the HWH tours model, the correlation between the number of workplaces and number of student s seats in a zone is For the variables in the HOH tour model, the biggest correlation was 0.21 between the population per zone and the entropy index. Nevertheless, correction methods such as estimating the variance with first-order Taylor series linearization methods were taken into account to correct the attraction model. This hypothesis was validated with a correlation test. The global significance of the model was evaluated with an F-test, supporting that all variables working together were significant in explaining the independent variable of the model, F test > F critic Models validation After completing estimation of all models, the results were applied to the 2011 socioeconomic information to test their ability to replicate existing travel rates by purpose. Results of the travel demand estimation process were compared by trip purpose for the Bogota region. These comparisons were made by the aggregation of zones. Aggregating zones imply considering only typical households per zone. According to Ortuzar and Willumsen (2011) the main difference between the household based regression and the zonal based multiple regression relates to the error term distribution. Nevertheless, in both cases the parameters have the same meaning as they explain. Furthermore, by taking into account the necessities of the Bogota LUTI model, the aggregation had to be done. Moreover, by aggregating zones the heteroscedasticity of the model is reduced. Additionally, different types of aggregation were tested: zones, districts (an aggregation of zones) and the whole Bogota region. The predicting power of the different aggregation of the models was appropriate. As a matter of fact, by testing these different types of aggregations the minimum predictive power between the modeled data and the observed one was 0.94 Nevertheless, the comparison is not exact because of influences of the aggregation by zones, but it can be considered pretty close to reality, except HOH tours in high-income households. However, the number of those tours compared with the total tours in the Bogota region is marginal. Furthermore, given the nature of HOH tours (no regular patterns or schedules); it is much more difficult to adjust these models. Table 6 shows the comparison of models vs. survey by travel purpose and model type. Table 6. Total tours by travel purpose and model type (Model- Survey comparison) Model type Generation Income ranges Travel purpose Model Survey M/S ratio 4,087,444 4,054, HWH 583, , ,302 98, ,401,707 4,404, HOH 450, , , Total - 9,550,138 9,564, Attraction - HWH 3,804,746 3,788, HOH 3,973,568 4,122, Total - 7,778,314 7,910, A second comparison was made to examine the zone distribution of tour generation. Total surveyed tours (expanded) by zone were plotted against total modeled tours by travel purpose (expanded by zone as well by number of households per zone) for the same zones and the correlation coefficient between the parallel data points was

13 2552 Luis A. Guzman et al. / Transportation Research Procedia 25C (2017) calculated. Fig. 6 shows that the gradients of the regressions are very satisfying (0.98 and 0.95 for HWH and HOH models, respectively). However, the HOH tour generation modes underestimate the number of tours by origin zone. To correct this, modelled tours were adjusted accordingly from observed data, minimizing the squared deviations to fit the results. Fig. 6. Comparison tour generation results for the year 2011 Regarding attraction models, Fig. 7 compares the tours modeled and observed (from mobility survey) according to activity types in 2011 and shows a significant correlation. The conclusions reached from the analysis of validation of attraction models are similar to those indicated in the analysis of generation models. However, it s important to stand out how even though the comparison between attraction models had lower correlation the model did not had any kind of aggregation. Fig. 7. Comparison tour attraction results for the year 2011 The results show that the travel demand model developed for the Bogota LUTI model correlate well on an aggregate basis to observed data from the household survey and data observed. The second step to validate these models and their results over time will be available when a new survey is carried out. Another performance evaluation approach uses measures the overall statistical performance of the models by comparing all available observed data to corresponding model results. To measure the quality of modelling process

14 Luis A. Guzman et al. / Transportation Research Procedia 25C (2017) results and to compare the closeness of modeled tours to the observed tours, we used the Percent Root Mean Square Error (%RMSE) which is perhaps the single best overall error statistic for comparing data modelled to data observed. The %RMSE values found were: Produced tours: 12.5% for HWH tours, 17.6% for HOH tours and 12.0% for total tours. Attraction tours: 27.1% for HWH tours, 50.9% for HOH tours and 68.0% for total tours. As expected, the HWH modeling results presented a better fit than HOH tours, given the characteristics of the activities and regularity of travel patterns. As a complement, larger differences in attraction models were found. This may be due to zonal aggregation used to estimate these models and land-use data used. Practical results suggest that average daily forecasts come with %RMSE between 30-60%, and zones with low values tend to have higher %RMSE than those with high values (Zhao, Kockelman 2002). Finally, Fig. 8 shows a comparison between HWH generated tours modeled vs. those observed. The figure also shows the basic statistics of the model results compared with the observed data in each modeled zone. Average tour generation is also shown by zone, median and standard deviation. Again, these results show the good fit of the models. Model HWH tours Survey HWH tours Fig. 8. Comparison generation HWH tour modelled vs. observed by zone Although the simulations are consistent with each other and with the observed data, the results should be viewed with special care in order to be projected over time. Pending issues to resolve include the intra-zonal tours, which we expect to address in the next stages of the research (trip distribution and mode choice models).

15 2554 Luis A. Guzman et al. / Transportation Research Procedia 25C (2017) Conclusions A new strategic travel demand model has been developed and validated for the Bogota region. The methodology includes the development of a tour-generation and attraction regression models (based on a cross-sectional survey and land-use data) and its (future) integration within a dynamic model that uses causal loop diagrams to model the complex relations between transport and land-use in the long-term. The travel demand model presents some unique and innovative components and features, in particular, in terms of their sensitivity to policy variables. This sensitivity to different planning scenarios and policy alternatives (population growth by zone, car ownership, income levels, and number of workers and/or students) provided motivation for this new development and is a key element of the model s added value over its traditional peak-hour trip generation models. However, the simple framework adopted for this Bogota LUTI travel demand model still offers significantly improved sensitivity over traditional models, because, a) it represents the mobility on an average day; and b) it could take into account spatial and socio-economic changes over time. The new model structure is significantly more robust and sophisticated than the travel model previously used in Bogota (which is beyond the traditional static and peak hour models, so common in our local context). This is the first model that estimates the average daily mobility in the whole Bogota region (city and municipalities), according to the type of activity. Beyond the traditional peak hour models used in our context, these models incorporate dynamic relationships and sensitivity to land-use development and household composition. They also allow other types of analyses due to their sensitivity to policy variables. Another innovative component that this development incorporates is the inclusion of HWH travel time in the estimation of HOH tours. This new travel demand model incorporates the differences in travel patterns by income ranges. The variations in the housing locations and the availability of urban land or built areas allow more accurate estimation of changes in generation/attraction over time. Finally, the Bogota LUTI model processes begin with the generation of population growth in the study zone as an exogenous variable. The new population was distributed into zones by residential location choice modeling, using a wide range of spatial attributes, valued differently according to household characteristics by zone. The technique applied uses different model types to develop these distributions as inputs for the travel demand model. This transport model has several feedback loops; for example, one that takes into account the congestion effects and the number of HWH trips according to the transport mode, travel time and costs variables by car and public transport. Travel time by car is influenced by the speed/flow relationship and additional costs (parking, fuel). 6. References Bath, C.R. and Koppelman, F.S. (2003) 'Activity-based modeling of travel demand', in Hal, R.W. (ed) Handbook of Transportation Science, Kluwer Academic Publishers, Dordrecht, Netherlands. Guzman, L.A. and Gómez, J. (2015) 'Projection of Travel Demand: Dynamic Model Approach Considering Feedback Mechanisms of Accessibility', TRB 94th Annual Meeting Compendium of Papers, Jovicic, G. and Overgaard Hansen, C. (2003) 'A passenger travel demand model for Copenhagen', Transportation Research Part A: Policy and Practice, 37(4), pp Mokhtarian, P.L. and Chen, C. (2004) 'TTB or not TTB, that is the question: a review and analysis of the empirical literature on travel time (and money) budgets', Transportation Research Part A, 38(9-10), pp Mwakalonge, J.L., Waller, J.C. and Perkins, J.A. (2012) 'Temporal Stability and Transferability of Non-Motorized and Total Trip Generation Models', Journal of Transportation Technologies, 2, pp

16 Luis A. Guzman et al. / Transportation Research Procedia 25C (2017) Nowrouzian, R. and Srinivasan, S. (2012) 'Empirical Analysis of Spatial Transferability of Tour-Generation Models', Transportation Research Record, 2302, pp Ortuzar, J.d.D. and Willumsen, L.G. (2011) Modelling Transport, Wiley, United Kingdom. Pfaffenbichler, P.C. (2003) 'The strategic, dynamic and integrated urban land use and transport model MARS (Metropolitan Activity Relocation Simulator)',. Pfaffenbichler, P.C., Emberger, G. and Shepherd, S. (2008) 'The Integrated Dynamic Land Use and Transport Model MARS', Networks and Spatial Economics, 8(2-3), pp SDM (2011) 'Encuesta de Movilidad de Bogotá 2011', Secretaría Distrital de Movilidad. Bogotá. SDP (2011) 'Inventario estadístico. Información geográfica', Secretaría Distrital de Planeación. Bogotá. Shepherd, S., Balijepalli, C., Koh, A. and Pfaffenbichler, P.C. (2010) 'MARS - DISTILLATE Future developments',. Zhao, Y. and Kockelman, K.M. (2002) 'The propagation of uncertainty through travel demand models: An exploratory analysis', The Annals of Regional Science, 36(1), pp