Peak Hour Trip Distribution Accuracy with Central Statistical Office Data in Trinidad and Tobago

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1 R.J Furlonge and M. Cudjoe: Peak Hour Trip Distribution Accuracy with Central Statistical Office Data in Trinidad and Tobago 28 ISSN The Journal of the Association of Professional Engineers of Trinidad and Tobago Vol.44, No.1, April 2016, pp Peak Hour Trip Distribution Accuracy with Central Statistical Office Data in Trinidad and Tobago Rae J. Furlonge a,ψ and Miguel Cudjoe b L F Systems Ltd, Traffic and Transportation Engineering, Brook Street, Lange Park, Trinidad and Tobago, West Indies; a info@ccost.org b miguelcudjoe@hotmail.com Ψ - Corresponding Author (Received 21 July 2015; Revised 12 February 2016; Accepted 29 February 2016) Abstract: The future of land-use planning and transportation development is sustainable transport. The emphasis will be on engineering sustainable, low-cost, easily implementable transportation. Trip distribution models are used to understand and hence forecast the pattern of travel behaviour in the urban environment. The usual needs of data for this model are to provide the basis for accurate predictions, and in this case, the key data elements are trips between origins and destinations, and the data collection process is called Origin-Destination (O-D) Survey. Trinidad and Tobago is not known for collecting transportation data, and O-D data are virtually non-existent, and any suggestion of this need is usually frowned upon. This paper seeks an efficient, cost-effective process to compute adequate O-D data from secondary sources. The morning journey from home to work and school has historically been the highest peak hour travel, and often forms about 10 percent of the average daily trips on any route. The Central Statistical Office (CSO) of Trinidad and Tobago collects important national household data every 10 years during its Census Programme, such as population and employment information at the enumeration district level. These are often aggregated to community and regional corporation levels. Information on the distribution of primary and secondary schools and their population are also available from the Ministry of Education. A study area has been selected in Trinidad for which O-D Survey data were available, and a dataset was built using CSO zonal secondary data as well as school data. The trip distribution model was run with these two datasets, and the results analysed. The findings and recommendations were then made. Keywords: Sustainable Transportation; Trip Distribution Modelling; Origin-Destination Surveys 1. Introduction Transport is known to affect and be affected by the urban environment, and spatial interaction models are used to understand and hence forecast the pattern of travel behaviour in the urban environment; that is, they are used to give a better idea from where to where trips take place. This ability to estimate travel behaviour serves as an aid to the developing and testing of urban planning policy. The usual needs of data for this model are to provide the basis for accurate predictions, and in this case, the key data elements are trips between origins and destinations, and the data collection process is called Origin- Destination (O-D) Survey. The O-D Survey is a very expensive effort, and demands enormous quantities of time and money in collection and analysis, and in many cases little time and money remain for the critical tasks of preparing and evaluating transportation plans. Trinidad and Tobago is not known for collecting transportation data, and O-D data are virtually nonexistent, and any suggestion of this need is usually frowned upon. This paper seeks an efficient, cost-effective process to compute adequate O-D data from secondary sources. 2. Datasets The morning journey from home to work and school has historically been the highest peak hour travel, and often forms about 10 percent of the average daily trips on any route. The Central Statistical Office (CSO) of Trinidad and Tobago collects important national household data every 10 years during its Census Programme, such as, population and employment information at the enumeration district level. These are often aggregated to community and regional corporation levels. Information on the distribution of primary and secondary schools and their population are also available from the Ministry of Education. A study area has been selected in Trinidad for which O-D Survey data was available, and a secondary dataset was built using CSO zonal secondary data as well as school data. The trip distribution model was run with these two datasets, and the results analysed. The CSO origin and destination data for the work trip model were the employed population addresses and the employed work addresses, respectively. The employed population consists of all persons in the population who had a job and worked, or had a job and did not work during the period of the census count.

2 R.J Furlonge and M. Cudjoe: Peak Hour Trip Distribution Accuracy with Central Statistical Office Data in Trinidad and Tobago 29 The origin and destination data for the school trip model were the student population addresses and the school locations respectively. The student population comprises students in both government-assisted and private primary and secondary schools. The student population dataset used to develop the school trip matrix was gathered from the CSO 2000 census, while the school location dataset was extracted from data provided from the Ministry of Education. Juan/Laventille, Tunapuna/Piarco, Arima, Chaguanas, Couva/Tabaquite/Talparo and San Fernando regional corporations. The external zone to the east comprises the Sangre Grande, Rio Claro/Mayaro and the Princes Town regional corporations. The southern zone comprises the regional corporation Siparia. 3. Study Area Zonal System The internal zones are defined by its socio-economic, demographic and land use characteristics. These characteristics serves as the basis of all analysis conducted in this study. The Penal-Debe-Siparia-Fyzabad study area in south Trinidad has been divided into nineteen (19) aggregated districts based on the CSO communities. These 19 districts were used as the internal traffic analysis zones (TAZ) for this study. Both internal and external zones are listed in Table 1, and Figure 1 gives a map of the study area. Table 1. Penal-Debe-Siparia-Fyzabad Zoning System Zones Community 1 Avocat Village, Delhi Settlement, Dow, Village, Fyzabad, Harris Village, Mon Desir, Oropouche, Pepper Village, St. John and St. Mary's Village. 2 Duncan Village, La Romain, Rambert Village 3 Apex Oil Field, Gheerahoo, Sudama Village 4 Quarry Village, Waddle Village. 5 Mendez Village, Penal Quinam Beach Road, Scott Road Village 6 Siparia 7 Morne Diablo, Penal Rock Road, Rochard Road 8 La Fortune/Pluck, Robert Hill/Siparia, Thick Village, San Francique, Tulsa Village 9 Penal 10 Batchyia Village 11 Charlo Village 12 Syne Village 13 De Gannes Village 14 Hermitage Village, La Fortune 15 Debe Proper 16 Canaan Village Palmiste, Palmiste, Phillipine 17 Diamond, Monkey Town, Wellington 18 Barrackpore 19 Lengua Village, Friendship, Esperance Village, Picton, Borde Narve, Golconda, St. Croix Village, St. John's Village 20 North 21 East-North-East 22 South All zones that facilitate traffic interactions outside of the study area are identified as external zones. These zones function as the focal point for trips to and from the study area. The zone outside of the study area zones was divided into three external zones, that is, North, East- North-East and South. The north external zone is comprised of the Diego Martin, Port-of-Spain, San Figure 1. Map showing Penal-Debe-Siparia-Fyzabad Study Area 4. Trip Production and Attraction In this study, different CSO data were used to generate both origin and destination trips. This practice often leads to inconsistencies between the estimated trips that originate in particular zones (production) and the estimated trips that are attracted/destined to particular zones (attraction). In the case of this study, both work and school trips datasets for the destination/attraction were understated. In practice, production models are considered to be more accurate predictors of reality, so productions were held constant while attractions were adjusted so that their sum equals the sum of the productions. 5. Trip Distribution In this stage, trip productions are distributed on the transportation network to match the trips attracted in each traffic analysis zone. The trip distribution process was employed through the use of the gravity model. The Gravity-type trip distribution model was first derived theoretically by Wilson (1970) from information-theoretic statistical mechanics. The gravity model used in this paper is the doubly-constrained version as given in Equations (1) to (3): T ij = A i B j O i D j ƒ(c ij ) (1) and A i = 1 j n B j D j ƒ(c ij ) (2) B j = 1 i n A i O i ƒ(c ij ) (3) where, T ij is the estimated number of trips from zone i to zone j; n is the number of zones;

3 R.J Furlonge and M. Cudjoe: Peak Hour Trip Distribution Accuracy with Central Statistical Office Data in Trinidad and Tobago 30 O i is the number of trips originating in zone i; D j is the number of trips ending in zone j; c ij is the time or distance of travel between zone i and zone j; Equations (2) and (3) are commonly called the balancing factors. For this study, ƒ(c ij ) = exp(-βc ij ) where β is the deterrence parameter to be calibrated, and a travel time matrix of the shortest routes was used. The value of the deterrence parameter was selected as 0.005, as this was calibrated from an earlier trip distribution study for the nearby southern city of San Fernando, Trinidad. 6. Results Figure 2 gives a map showing Peak Morning Trip Desire Lines for the O-D Travel Survey Figure 3 shows Peak Morning Trip Desire Lines for CSO Data Figure 2. Peak Morning Trip Desire Lines for O-D Travel Survey 2012 Figure 3. Peak Morning Trip Desire Lines for CSO Data 2012

4 R.J Furlonge and M. Cudjoe: Peak Hour Trip Distribution Accuracy with Central Statistical Office Data in Trinidad and Tobago 31 External-External trip linkages, or travel interactions from external to external zones usually display a complex structure of these types of trips which are exclusively regional in nature, and so external-external trip linkages are often excluded, in order to be handled separately. Comparison of the actual traffic survey data O-D matrix and the estimated CSO data O-D matrix, also called Goodness-of-Fit, is the zonal cell-to-cell fit of the observed trip matrix with the estimated trip matrix, and is traditionally judged by summary statistics. The goodnessof-fit summary statistics used in this paper are as follows: 1) Maximum likelihood method of estimation, or the matching of mean trip lengths, that is the minimising of the difference between the modelled (M) and observed (O) mean trip lengths, as given in Equation (4) (Hyman, 1969 and Openshaw, 1976). ( i j (T M ij c ij ) i j T M ij ) ( i j (T O ij c ij ) i j T O ij ) (4) 2) Minimise the Mean Absolute Error, given by equation (5) (Openshaw, 1976). min (1 h) x i j T M ij T O ij (5) where h is the number of trip linkages 3) Minimise the Standard Deviation of the Residuals, as given by equation (6) (Openshaw, 1976). min [(1 h) x i j (T M ij T O ij ) 2 ] (6) Table 2 gives the goodness-of-fit summary statistical results. These do not however provide a suitable assessment of the zone cell residuals. This is the subject of the rest of this paper. Table 1. Goodness-of-Fit Summary Statistical Results Statistic Computed Ideal Value Value Maximum likelihood method Mean Absolute Error Standard Deviation of the Residuals In order to further investigate these zone cell residuals, the residuals versus trip length were computed by the Moving Average Method. Moving average is a device for smoothing away some of the fluctuations that occur in data. The trip residuals are plotted against travel time. The term moving average is coined because this method is derived from trend analysis of time-series data range (Kendall, 1973; Ehrenberg, 1975, page 17). In this study case, trip length ranges are not used, but the actual trip length for each zone cell residual. The difference between the Actual Travel Surveys and the CSO Data was computed for each origin-destination pair (zone pair), and each resulting zone pair residual plotted against the Travel Time in minutes for the particular zone pair. Figure 4 gives the Residuals of CSO Data and Actual Travel Surveys Versus Travel Time (min). With a total number of peak morning person trips of the order of 90,000 (not including external-external linkages), then tolerable over- and under-estimation of zonal trips can be observed caused by perhaps (a) data errors, especially with the production and attraction data collected or estimated; (b) spatial biases due to locational distribution of activities and trips ends; and, (c) model misspecification. Figure 4. OD Zonal Work Trips: Residuals of CSO Data and Actual Travel Surveys versus Travel Time (min) 7. Conclusion Comparison of the actual traffic survey data O-D matrix and the estimated CSO data O-D matrix, also called Goodness-of-Fit, gives traditional summary statistics of Maximum likelihood method, 3.78; Mean Absolute Error, ; and, Standard Deviation of the Residuals, , which appear to be reasonably small. The Moving Average Method was used to plot zone cell residuals versus zonal trip lengths, in order further investigate the difference between the observed and estimated O-D data at the cell level.

5 R.J Furlonge and M. Cudjoe: Peak Hour Trip Distribution Accuracy with Central Statistical Office Data in Trinidad and Tobago 32 Tolerable over- and under-estimation of zonal trips can be observed caused likely by (a) data errors, especially with the production and attraction data collected or estimated; (b) spatial biases due to locational distribution of activities and trips ends; and, (c) model misspecification. Scenario-building or sensitivity testing is suggested for applying this approach with secondary CSO data. The trip distribution model (in this case the gravity model) can be run with varying values of the zonal trip ends, and the O- D trip matrices computed. These matrices would then be used to produce the road traffic assignments as the final stage of transportation planning modelling, and validity testing undertaken through comparison of some key intersections and / or roadways for actual data with modelled data. References: Ehrenberg, A.S.C. (1975), Data Reduction: Analysis and Interpreting Statistical Data, Wiley, London Hyman, G.M. (1969) The calibration of trip distribution models, Journal of Environment and Planning, Vol.1, pp Kendall, M.G. (1973), Time Series, Griffin, London. Openshaw, S. (1976) An empirical study of some spatial interaction models, Journal of Environment and Planning A, Vol.8, pp Wilson, A.G. (1970), Entropy in Urban and Regional Modelling, Pion, London. Authors Biographical Notes: Rae J. Furlonge is a Traffic and Transportation Engineer, and Managing Director of L F Systems Ltd. He is a weekly columnist in the Trinidad and Tobago Newsday, Business Supplement. He holds a PhD in Civil (Transportation) Engineering, from the Indian Institute of Technology (IIT) in Roorkee, India; and, a BSc Civil Engineering from The University of the West Indies in St. Augustine, Trinidad and Tobago. Dr. Furlonge is a Fellow of the American Society of Civil Engineers (ASCE), a Fellow of the Institute of Transportation Engineers (ITE) in the USA, and a Fellow and Past-President of the Association of Professional Engineers of Trinidad and Tobago (APETT). Miguel Cudjoe has been an Associate Transportation Planner with L F Systems Ltd, since June He holds an MSc in Urban and Region and Planning in 2014; and, a BSc in Management Studies, with Minor in Psychology in 2010, from The University of the West Indies (UWI) in St. Augustine, Trinidad and Tobago. He is an Associate Member of the Trinidad and Tobago Society of Planners (TTSP).

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