Travel Demand Forecasting Using UK TRICS Database

Transcription

1 Travel Demand Forecasting Using UK TRICS Database Firas H. A. Asad * Department of Civil Engineering, University of Kufa, Kufa City, P.O. Box (21), Najaf Governorate, Iraq * firas.alwan@uokufa.edu.iq Abstract-Travel demand forecasting has become the target of increasing interest from both transport planners and engineers. It not only contributes primarily to sustainable city planning strategies, but is also an effective tool for quantifying transport impacts of new developments. This paper considers an alternative methodology to forecast residential trip demand using the UK TRICS database as the primary resource. The traditional category analysis approach seeks to classify historic travel surveys into categories determined by land use and other influential characteristics. By analysing trip rate variation for residential sites with different characteristics, this paper proposes a new classification system, which clearly represents differences in trip rates and optimises the value of the data. This new classification system (compared to that accomplished via TRICS groupings) simply partitions residential zones into those which are predominantly comprised of flats and houses. Travel models have been developed based on these two groups. These models allow forecasting of proposed residential sites. This paper confirms the importance of observed trip generation data, such as that provided by TRICS, as a valuable resource upon which travel forecasts can be based. In addition, this paper encourages the use of alternative methodologies including reclassification of sites and the adoption of regression techniques in order to improve validity of results. Keywords- ANOVA; Land Use; Trip Rates; Trip Generation; Regression Models I. INTRODUCTION Many years of experimentation and development have led to the use of the classic four-stage model as the most commonly used traditional transport demand forecasting process. Trip generation, trip distribution, mode choice and route assignment are the four primary sub-models of the entire modelling structure [1]. Being the first step in the process, an erratic estimation of the total number of trips generated by a particular development, would likely leads to unreliable outputs. Cortus et al. [2] determined that decisions made based on such unreliable outputs may result in impulsive investments in infrastructure in case of overestimation, or a low level of service and pollution in case of due to underestimation. Thus, it is necessary to conduct research into the standard models used for trip rate prediction. According to previous literature, a home-based trip is defined as a trip in which the traveller s home is either the origin or the destination of the trip. Trip generation models are typically categorized into trip production models and trip attraction models. Trip production models quantify the total number of home-based trips to and from home locations zones. In contrast, trip attraction models estimate the number of home-based trips to and from each zone at the non-home end of the trip. TRICS (Trip Rate Information Computer Software) is a comprehensive industry-standard travel database for the United Kingdom. Its methodology is generally based on simplification of a well-known trip generation model known as the category analysis model first developed by Wootton and Pick [3]. In the UK, TRICS is a recognized trip generation database, and is the system of choice utilized in this study. Category analysis is simple and elegant to use; it attempts to take into account the variability in the number of trips made by households with different characteristics [4, 5]. Accordingly, the basic assumption of TRICS notes the presence of variables which can be quantified to contribute to the explanation of trip rate variation between sites with different characteristics. Variation in the sites according to their land use classification is a central discriminate variable. Two objectives have been investigated in this paper. The first aim is to determine to what extend the residential land use subcategories listed in TRICS (version 2009) are mutually exclusive in terms of production trip rates, and thus to explore more independent alternative groupings. This will illustrate the importance of careful classification of land uses, if the category analysis approach is to be rigorously applied. The second objective is to develop regression models to predict the total number of home-based trips produced from the new grouped residential development subcategories. This will allow exploration of the benefits of the regression modelling approach. II. TRICS AND DATA PROCESSING TRICS is the national trip generation database and analysis system for the UK and Ireland [6]. TRICS is widely recommended in UK planning policy documents, particularly in the traffic impact assessment process whereby TRICS is utilised for predicting traffic generated from proposed developments. TRICS is therefore the source of both the descriptive and traffic data used in this study. For residential land uses, descriptive site data includes type of dwelling unit, possession type

2 (tenure), site area, site location type, the number of dwelling units and bedrooms, and the number of sites in each specific land use category. Alternatively, traffic data includes the number of inbound (arrivals) and outbound (departures) trips for each specific development. Trips can be disaggregated according to the method by which they have been counted into vehicle trips and multi-modal trips. For multi-modals, the number of trips is usually given in terms of pedestrians, cyclists, public transport users, vehicle occupants, vehicles, public service vehicles and other-goods vehicles. During counting surveys, the travel mode is classified as the mode used to enter and exit the site at any access point. In TRICS, both the descriptive and traffic site data are stratified into hypothetically non-overlapping (mutually exclusive) categories to produce representative standard trip rates. This is the cornerstone underlying the TRICS category analysis approach. According to TRICS Good Practice Guide [7], users should not combine weekday and weekend surveys. The Guidance on Transport Assessment [8] states that for residential developments, the peak travel periods occur on weekdays. In addition, a prediction of the person trips (for all modes) should be established when quantifying the possible traffic impacts of a proposed development. Hence, only weekday multi-modal surveys were employed for analysis in this study. These surveys were based on a 12-hour counting period (7:00 am-7:00 pm) for both directions of travel (departures and arrivals). In TRICS (2009a), there are 13 subcategories of residential land use; seven were chosen for investigation by the current study. The seven studied subcategories are: Houses Privately Owned, Houses for Rent, Flats Privately Owned, Flats for Rent, Mixed Private Housing, Mixed Non-Private Housing, and Mixed Private/Non Private Housing (Table 1). A full definition of these residential developments is available in the TRICS Online Help File [4]. Other subcategories were excluded due to lack of an adequate sample size (this study utilized a threshold minimum of three sites), or due to inconsistency in the trip generation calculation parameter (unit of analysis). For all seven selected subcategories, both the number of dwellings and total bedrooms were adopted as units of analysis for calculation of mean person trip rates. In TRICS, the mean trip rate is calculated by dividing the total number of12-hour person trips (departures and arrivals) generated from a specific residential site by the number of dwelling units (or bedrooms) in the site. As stated above, this is the trip production rate for home-based trips. Furthermore, in order to maintain eligibility of subcategory datasets for statistical analysis, no further filtering was applied by TRICS optional parameters or locational disaggregation. However, one would expect supplementary factors such as prevalence of car ownership and location type to demonstrate significance in explaining trip rate variation, which is identified as an area for further study. TABLE 1 RESIDENTIAL LAND USE STATISTICS SUMMARY Land use subcategory No. of sites Mean total trip rate (total 12- hour trips/total dwelling unit) A. Houses Privately Owned B. Houses for Rent C. Flats Privately Owned D. Flats for Rent K. Mixed Private Housing L. Mixed Non-Private Housing M. Mixed Private / Non Private Housing III. LITERATURE REVIEW Worldwide, TRICS generally has a good reputation for its wide range of land uses and travel modes included [9, 10]. However, few studies have been reported concerning the non-overlapping assumption among TRICS residential subcategories. TRICS considers the land use categories and subcategories as the primary and essential discriminate factors in classifying developments according to their travel characteristics. Maclaine [11] concluded that dividing a sample into separate TRICS land-use subtypes (subcategories) may not be the best approach, particularly if the total number of multi-modal surveys available for residential land-use is relatively small. Maclaine adopted the Chi-Squared statistical test to examine the null hypothesis (H o ) that there is no significant difference between observed and predicted trip rates for each site. Four combinations of residential subcategories were tested. Finally, the study determined one significant combination (AB, CD, and KLM, as shown in Table 1) at a 95% confidence interval. Broadstock [12] researched the possible effects of land-use on travel behaviour in the U.K. using TRICS (version 2004b). Using the Bootstrap semi-parametric technique and based on the developed regression-based models, it was determined that traffic characteristics generally do not differ significantly among residential development land uses. All of the developed trip generation models are for the passenger car mode only, hence Broadstock has not explicitly demonstrated that housing types may be a significant determinant in trip-making patterns. Alternatively, O Cinneide and Grealy [13] stated that TRICS is the typical database in Ireland for calculating trip generation of new development at the planning permission stage. However, the study concluded that TRICS results are not

3 accurate for housing developments. In contrast, several researchers such as Melia et.al [14] and Oni [15] have used TRICS as a tool to predict trip rates for developments with specific site and socioeconomic characteristics. IV. METHODOLOGY, RESULTS AND DISCUSSION TRICS implicitly relies on the category analysis technique to calculate trip rates generated by different land uses. Thus, each land use must have different trip characteristics depending on both traffic and descriptive data. A. TRICS Residential Land Use Subcategories The statistical analysis technique ANOVA using PASW Statistics software (version 18, 2009) was employed to examine whether the seven residential subcategories listed in Table 1 are significantly mutually exclusive. Generally, in the statistical methodology known as one-way ANOVA, the objective is to compare the means of several samples to determine if there is sufficient evidence to infer that their population means are also unequal. Moreover, ANOVA makes it possible to determine which sample means are significantly different, and by how much [16]. In this study, the samples are the seven land use subcategories, while the comparing factor is their mean trip rates. Due to restrictions on data availability, the 12-hour person production trip rate (total home-based trips per total dwelling unit) was chosen as the comparing factor. According to Hayter [16], a parametric quantitative ANOVA approach can provide better results than a non-parametric approach. This is true if the assumptions of normality and variance homogeneity are not substantially violated. Thus, the parametric ANOVA was adopted for the current study. A pilot exploratory data analysis was conducted to determine the normality and variance homogeneity of analytical variables. Graphical indicators such as histograms and P-P plots demonstrated evident violations in the normality of the trip rate variable based on dwelling unites (Dtrate) for several subcategories. Alternatively, the box plot lengths indicate no clear evidence of variance homogeneity. For a more objective assessment, the Shapiro-Wilk Test was used as an inferential test of normality. Table 2 demonstrates that the null hypothesis (H o : the sample distribution does not differ from normal distibution) was rejected at a 5% level of significance (α = 0.05) for both the Houses Privately Owned and Mixed Private Housing sub-categories. As a consequence, (Ln(x)) logarithmic data transformation was applied to the trip rate variable. There are two specific reasons for employing the log transformation. First, Osborne [17] revealed that log transformation is efficient when the data distribution is positively skewed. Secondly, only a log transformation is able to provide interpretable results after transformation [18]. Afterward, the Shapiro-Wilk test was conducted on the transformed data (ln Dtrate). As shown in Table 2, the null hypothesis was accepted at a 5% level of significance for all land use subcategories. TABLE 2 SHAPIRO-WILK NORMALITY TEST STATISTICS Shapiro-Wilk Normality Test Land use type (LUtype) Dtrate a Ln(Dtrate) Statistic Sig. (P-value) Statistic Sig. (P-value) A-Houses privately owned B- Houses for rent C- Flats privately owned D- Flats for rent K- Mixed private housing L- Mixed non-private housing M- Mixed priv/non-priv housing a Trip rates based on dwelling units Additionally, Levene s inferential test of homoskedasticity (equal variances assumption) was conducted on the log transformed data to evaluate between-groups variances. Table 3 shows that the second assumption of ANOVA is not rejected at (α =1%). ANOVA yields fairly accurate results unless the variances in levels (subcategories) of the factor being analysed (trip rate) differ greatly [16]. TABLE 3 HOMOGENEITY TEST OF VARIANCES FOR LN(DTRATEA) PARAMETER Levene Statistic df1 df2 Sig. (P-value) a Trip rate based on dwelling units

4 Having determined that the two parametric ANOVA assumptions of normality and homoskedasticity are satisfied, ANOVA was applied. According to Table 4, the null hypothesis that there is no significant difference among all the compared group means is rejected (p-value = 0.000, α = 0.05), i.e., there is at least one trip rate mean which significantly differs from others. TABLE 4 ANOVA STATISTIC TABLE FOR LN(DTRATEA) DEPENDENT VARIABLE Sum of Squares Df Mean Square F-statistic Sig.(P-value) Between Groups Within Groups a Trip rate based on dwelling unit. Next, data was analysed by the post-hoc multiple comparison test (Table 5). Each land use subcategory trip rate mean was compared to the other subcategories means using the Least Significant Difference (LSD) technique. The aim of these tests is to evaluate the amount by which each subcategory differs significantly. It must be noted in Table 5 that there is no significant difference between the mean trip rates of both house subcategories (A and B), regardless of their tenure type (privately owned or for rent). The p-value is while the adopted level of significance (α) is 5%. Similarly, no significant difference was observed (p-value = 0.08) between the mean trip rates of both subcategories of flats (C and D) in spite of their tenure type. Thus, we conclude dwelling unit possession does not significantly affect the trip behaviour of its residents. Thus, it is justified to combine the house residential subcategories as well as the flat subcategories separately, since there is no statistical evidence of their mutual exclusivity according to the TRICS database. Table 5 also demonstrates that there is no noteworthy difference in the trip rate means of the Mixed Non-Private Housing (L) subcategory and the Mixed Private/Non-Private Housing (M); p- value = 0.88 at a 5% level of significance. Consequently, these two land-use subcategories can also be combined. TABLE 5 MULTIPLE COMPARISON POST-HOC TESTS USING LSD METHOD (I) LUtype (J) LUtype Mean Differ. (I-J) Stand. Error Sig. (P-value) 95% Confidence Interval Lower Bound Upper Bound B-Houses for rent C-Flats privately owned * A-Houses privately owned D-Flats for rent * K-Mixed private housing * L-Mixed non-private housing M-Mixed priv./non priv. housing A-Houses privately owned C-Flats privately owned * B-Houses for rent D-Flats for rent * K-Mixed private housing * L-Mixed non-private housing M-Mixed priv./non priv. housing A-Houses privately owned * B-Houses for rent * C-Flats privately owned D-Flats for rent K-Mixed private housing * L-Mixed non-private housing * M-Mixed priv./non priv. housing * D-Flats for rent A-Houses privately owned *

5 B-Houses for rent * C-Flats privately owned K-Mixed private housing L-Mixed non-private housing * M-Mixed priv./non priv. housing * A-Houses privately owned * B-Houses for rent * K-Mixed private housing C-Flats privately owned * D-Flats for rent L-Mixed non-private housing * M-Mixed priv./non priv. housing * A-Houses privately owned B-Houses for rent L-Mixed nonprivate housing C-Flats privately owned * D-Flats for rent * K-Mixed private housing * M-Mixed priv./non priv. housing A-Houses privately owned B-Houses for rent M-Mixed private/nonprivate housing C-Flats privately owned * D-Flats for rent * K-Mixed private housing * L-Mixed non-private housing *The mean difference is significant at a level of PASW software provides an additional optional ANOVA routine: Contrasts (for further information, see the PASW manual). As a complementary step, contrast tests were conducted for two purposes. The first was to investigate whether or not the new groupings (AB, CD, K, and LM) are mutually exclusive. Secondly, in order to identify with which grouping (AB, CD, or LM) the original subcategory of Mixed Private Housing (K) can be combined. Based on the analysis output of the five planned contrasts listed in Table 6 and Table 7, the following conclusions can be drawn: TABLE 6 CONTRAST COEFFICIENTS OF ORIGINAL LAND USES Land use type (LUtype) Contrast No. Houses privately owned Houses for rent Flats privately owned Flats for rent Mixed private housing Mixed non-private housing Mixed Priv/Non-Priv Housing

6 TABLE 7 MULTIPLE COMPARISON CONTRAST TESTS Contrast Value of Contrast Std. Error t-statistic Df Sig. (2-tailed) Ln Dtrate Assume equal variances Contrast No. 1 demonstrates that the house group AB (Houses Privately Owned and Houses for Rent) trip-making behaviour is significantly different from that of flat group CD (Flats Privately Owned and Flats for Rent). Statistically, the null hypothesis suggesting no significant difference between them was rejected with a p-value equal to at a 5% level of significance. - Contrasts No. 2 and No. 3 demonstrate that the trip-making characteristics of the mixed housing group LM (Mixed Non- Private Housing and Mixed Private/Non-Private Housing original subcategories) are statistically eligible to be grouped with the house group AB. Nevertheless, the trip behaviour of the mixed housing group LM significantly deviates (p-value = 0.000) from that of flat group CD. - Contrasts No. 4 and No. 5 demonstrate that the original Mixed Private Housing subcategory (K) does not significantly differ in its trip rate mean from the flat group CD (p-value = 0.095, α = 0.05). Accordingly, seven original TRICS (2009a) residential subcategories can be regrouped according to their trip-making behaviour into two primary combination groups: a- Housing: House-Like Trip Behaviour (includes groups A, B, L and M) b- Housing: Flat-Like Trip Behaviour (includes groups C, D, and K) Fig. 1 illustrates the variation in the person trip rate means (y-axis) of the original seven subcategories (x-axis). An indication of the final regrouping is confirmed by the plot. Fig. 1 Mean trip rate variation among the original seven residential land uses subcategories

7 Finally, regrouping offers three advantages. First, it reinforces the concept of non-overlapping categories required by the category analysis approach used in TRICS. Secondly, it reduces the data collection process time, effort and cost. Finally, it makes the TRICS database more eligible for statistical analysis by increasing the number of sites in each subcategory. B. Regression Analysis The second and more specific aim of this paper is to determine functional regression equations for each of the new combined residential subcategories. The linear regression option in PASW (18) was employed to perform this task. George and Mallery [19] reported that a sample size of at least 50 is necessary to the development of meaningful correlations. It is also highly recommended that both the dependent variable (12-hour total person home-based trips) and the independent variable (total number of dwellings or bedrooms) should be approximately normally distributed. Four regression models were developed. Regression models No. 1 and No. 2 represent the new subcategory named House- Like Travel Behaviour Housing. For these models, the predictor variables are dwelling units and bedrooms, respectively. In contrast, regression models No. 3 and No. 4 represent the new subcategory named Flat-Like Travel Behaviour Housing. The sample size condition is met for all models. A logarithmic transformation was applied to both the dependent and independent raw variables to maintain their normality. In the PASW regression analysis outputs, Table 8 depicts the regression model coefficients for all the four models. The predicted variable is the total 12-hour weekday home-based person trips (ln TPtrps) generated by residents of a housing site using all modes of travel; the explanatory variable is either the total of the site dwelling units (ln Du) or bedrooms (ln Brm). Both the constant and regression model coefficients are included with their t-statistic, indicating the probability of these values having occurred by chance. It can be seen in Table 8 that the corresponding significance values for both coefficients and constants were for each model. In other words, all regression equations are statistically significant. TABLE 8 REGRESSION ANALYSIS COEFFICIENTS Unstandardised Coefficients Model B Std. Error t-statistic Sig.(P-value) Constant LnDu Constant LnBrm Constant LnDu Constant LnBrm * The predicted variable is the total 12-hour weekday home-based person trips (ln TPtrps). The above outputs can be reformed, for convenience, into the following four regression equations: Housing: Houses-Like TB ln (TPtrps) = ln (Du) ln (TPtrps) = ln (Brm) Housing: Flats-Like TB ln (TPtrps) = ln (Du) ln (TPtrps) = ln (Brm) Eq. (1a) Eq. (1b) Eq. (2a) Eq. (2b) The model is summarized in Table 9, which shows the coefficients of correlation R (with the same bivariate Pearson coefficient r, since there are just two variables) and the coefficients of determination R 2 for all four regression models. The coefficient R reveals whether the two variables are related or not, while the R 2 coefficient reflects how much variance in the dependent variable can be explained by the independent variable (the predictor). Hence, both the independent variables (the total dwelling units LnDU, and the total bedrooms LnBrm) for the two new land use subcategories are good predictors. In other words, the high variance in the total 12-hour weekday person trips can be efficiently accounted for by the variance in the

8 total number of dwelling units or bedrooms. However, the dwelling unit model is preferred, because bedroom surveys are not available for all residential sites listed in TRICS. TABLE 9 REGRESSION ANALYSIS MODEL SUMMARY FOR THE HOUSE-LIKE TRIP BEHAVIOUR NEW SUBCATEGORY Model R R-square Adjusted R square Std. error of the estimation a a Predictors: (Constant), LnDu. The ANOVA table (Table 10) represents an inferential judgment, based on the F-statistic and significant value (sig.), regarding the statistical significance of the entire regression model at (α = 5%). Table 10 demonstrates that all four models are statistically significant (p-values equal to zero). TABLE 10 REGRESSION ANALYSIS ANOVA MODEL SUMMARY Model Sum of squares Df Mean square F-statistic Sig. (P-value) a a a a a Predictors: (Constant), LnDu It is worth noting that the previous four regression equations were built depending on ranges of the traffic and descriptive survey data listed in TRICS (Table 11). These equations must be used cautiously for sites with trip parameter data outside these ranges. Thus extrapolation is not advised. TABLE 11 SUMMARY OF THE TRIP RATE CALCULATION PARAMETER DATA RANGES LISTED IN TRICS 2009A Survey parameter Houses-like TP Housing Flats- like TP Housing 1. Site area (hect.) Housing density (Du/areax a ) Dwelling units Bedrooms a Site area excluding open spaces. V. CASE STUDY To show the applicability of the developed regression-based trip production models, two hypothetical examples were chosen: Example 1: Using both TRICS 2009a and the current regression models, predict the 12-hour (7:00 am 7:00 pm) weekday total person home-based trips for a residential development site with 100 privately-owned houses. - Using regression models: The direct substitution of the number of dwelling units (100) in Eq. (1a), using House-Like TB with dwelling units as a trip rate parameter, yields 916 total person home-based trips. - Using TRICS 2009a: The Houses Privately Owned residential subcategory should be initially selected. After selecting dwelling units as the trip rate parameter, their range (maximum and minimum) should be adjusted to produce a representative sample of sites. A range of sites with 50 minimum and 150 maximum dwelling units was adopted to attempt to obtain a sample with an average number of dwelling units as close to 100 as possible. This approach is recommended by the TRICS Good Practice Guide to avoid extrapolation challenges. Moving on without any further fine-tuning by location or optional parameters (to keep the sample size eligible for statistical analysis) yields a mean trip rate of and a total estimate of home-based production trips of 987. Aside from the simplicity of calculation, it is recommended to use the regression-based trips figure (916 trips), since it has resulted from an approach with statistically sound procedures and measurable goodness of fit statistics

9 Example2: Using both TRICS 2009a and the current regression models, predict the total 12-hour (7:00 am 7:00 pm) weekday person home-based trips for a Mixed Non-Private Housings development site with 250 total bedrooms. - Using regression models: According to Eq. (1b), the total estimated number of trips generated by a 250 bedroom development is Using TRICS 2009a: Unfortunately, no representative sample of sites can be formed due to lack of data concerning the number of bedrooms in the sites listed under the Mixed Non- Private Housing subcategory. This example illustrates an extra point in favour of adopting regression models against TRICS. VI. CONCLUSIONS Based on this paper, the following conclusions may be drawn from the statistical analyses: A. ANOVA Analysis 1. Disaggregated residential land use subcategories such as those used in TRICS (2009a) may not be significantly mutually exclusive in terms of their trip-making pattern. 2. The type of possession (tenure) of a dwelling unit does not significantly affect the trip behaviour of its residents. Thus, both house subcategories (A&B) and flat categories (C&D) can be separately combined in two additional independent nonoverlapping groups. 3. Using the trip rate means for each residential land use subcategory as its travel characteristic indicator, all seven original TRICS (2009a) subcategories selected in this study can be safely regrouped as follows: a- Housing: House-Like Trip Behaviour, b- Housing: Flat-Like Trip Behaviour. 4. With this improved categorisation, three advantages arise: a- The conceptual framework of the category analysis approach is strengthened. b- Survey time (and hence effort and cost) is saved. c- The sample size in each subcategory is increased, providing greater confidence in the reliability of results. B. Regression Analysis The conclusions arising from the regression analyses are as follows: 5. For both of the new combined subcategories (House-Like TB and Flat Like TB), the total number of dwelling units and bedrooms have significant and strong affects association with the total number of person generated trips. 6. For both of the new combined subcategories (House-Like TB and Flat Like TB), both the total number of dwelling units and bedrooms as independent variables are good predictors for estimation of the total number of home-based generated trips. All four regression equations are significant and demonstrate a high coefficient of determination. However, extrapolation is not advised. 7. Finally, having demonstrated that the current disaggregation of residential subcategories in TRICS 2009a is not statistically justified, this indicates a need for further research into the value of other trip parameter disaggregations such as car ownership and site location. ACKNOWLEDGMENT I would like to thank the technical and administrative staff at the Computing, Science and Engineering School in the University of Salford (UK) whereby I was granted the access to use the TRICS database as a PhD student. I would also like to express my sincere gratitude to the anonymous reviewers and the proofreader. REFERENCES [1] J. D. Ortuzar and L. G. Willumsen, Modelling Transport, 4th ed., West Sussex: John Wiley & Sons Ltd., [2] A. V. Cortus, J. N. Prashkir, and Y. Shiftan, Spatial and Temporal Transferability of Trip Generation Demand Models in Israel, Journal of Transportation and Statistics, vol. 8, iss. 1, pp , [3] H. J. Wootton and G. W. Pick, A model for trips generated by households, Journal of Transport Economics and Policy, pp ,

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