Upload
others
View
0
Download
0
Embed Size (px)
Citation preview
Contemporary Engineering Sciences, Vol. 11, 2018, no. 5, 215 - 227
HIKARI Ltd, www.m-hikari.com
https://doi.org/10.12988/ces.2018.812
New Transportation Infrastructure Impact in
Terms of Global Average Access - Intersection
"La Carola" Manizales (Colombia) Case Study
Diego Julián Perilla Ramírez
Universidad Nacional de Colombia, Departamento de Ingeniería Civil
Manizales, Colombia
Diego Alexander Escobar García
Universidad Nacional de Colombia, Departamento de Ingeniería Civil
Manizales, Colombia
Santiago Cardona Urrea
Universidad Nacional de Colombia, Departamento de Ingeniería Civil
Manizales, Colombia
Copyright © 2018 Diego Julián Perilla Ramírez et al. This article is distributed under the Creative
Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any
medium, provided the original work is properly cited.
Abstract
This study diagnoses the impact of the implementation of the “Carola” Intersection
in global accessibility terms, establishing a comparative into mean travel time
vectors, calculated from vehicle operational speed between the city of Manizales’
existing network and modified network including the new intersection, besides
defining zones and users benefited by the new transport infrastructure.
Keywords: Global Average Accessibility, Geo-Statistics, Road Network, Case
study
1 Introduction
The Manizales municipality (414,906 residents in the metropolitan area), is located
216 Diego Julián Perilla Ramírez et al.
in the department of Caldas at 5°03’58’’N and 75°29’05’’W in the Greenwich
meridian over the Central mountain range at 2150 MASL, near the “Nevado del
Ruiz”, as a part of the Colombian gold triangle (see Figure 1).
Fig. 1: Geographic location of the city of Manizales. Source: Authors.
The administration of the city of Manizales has sought to be consistent with the
national government’s purpose to consolidate transport infrastructure as one of the
most important for economic growth [19] through the execution of high-impact
projects. Manizales has been characterized for promoting projects of great impact
for mobility, such as the intersection of Fuente, inaugurated at the beginning of
2017, strengthening the transit of the Panamericana highway, as well as the transit
of users on their trips to Bogotá or Medellín [20]. Other type of projects with similar
characteristics are being executed in key points for mobility during peak hours, i.e.
the Intersection San Marcel in charge of the National Institute of Roads and the
Carola intersection in charge of the Mayorship of Manizales.
The Carola intersection is established within the current Manizales Development
Plan (2016-2019), proposed in the Physical-Spatial dimension, and framed as a safe,
effective and sustainable road infrastructure, transit and transport project, with the
purpose of making mobility efficient, safe and environmentally compatible [19].
The reason why the research is developed in terms of global average accessibility
is to evaluate the impact that this infrastructure would have on the average travel
times and the new spatial coverage in relation to the current situation.
New transportation infrastructure impact 217
A literary review, a description of the research methodology, discussion on data and
results, as well as a review of the main conclusions, follow this paper’s introduction
2 Literature Review
The concept of accessibility has been frequently used in territory planning and in
transport analysis during the last fifty years. There are records of its use since the
last century in the 1920s. North America was a pioneer in its use associated with
transport networks and travel distribution patterns [2]. However, the most classic
definition of this term was proposed by Hansen in his article "How Accessibility
Shapes Land Uses" published in 1959 where accessibility is defined as "The
potential of opportunities for interaction", also laying the first mathematical bases
of the model. On the other hand, in 1976, Weibull developed an axiomatic approach
for territorial accessibility models, making a meticulous mathematical analysis of
the previously proposed models [7, 16, 18, 26].
Accessibility measures have been classified in different ways over time, according
to study purposes, scope and data availability, which would determine its accuracy
[11]. Generally, the literature defines three types of accessibility: relative, integral
average and global average. Relative accessibility is the measure between two
points of the transport network considering distance or time [18]. Integral average
accessibility refers to different types such as: accumulated opportunities [3, 18],
impedance function [1, 9] and utility as a measure of the economic potential of a
city [5, 11]. Global average accessibility results from the average travel time
between all nodes or zones of an infrastructure network, and is used to determine
network infrastructure quality through average operating speeds and their
directionality [8]. In the literature, global average accessibility was proposed by
Geurs in 2001, who considered accessibility as a measure of infrastructure.
Furthermore, accessibility calculations can take different perspectives and different
factors can be used to enrich the analysis such as: travel mode, purpose of travel,
age, gender, socioeconomic groups, among many others [22].
Accessibility measures have been used for decades by researchers to analyze related
topics such as: urban development planning and land use [12], public transport
analysis [14], equity and social exclusion in transport [24], sustainable transport
development [6], access to health systems [9], economic development [17], social
cohesion [10], provision and location of services [4], re road addressing [8], among
many other studies.
3 Methodology
The methodology of the study is based on the development of four consecutive
stages shown in Figure 2 with their description enunciated below.
218 Diego Julián Perilla Ramírez et al.
Fig. 2: Research Methodology. Source: Authors.
3.1 Verification and adjustment of the transport infrastructure network of
Manizales
Geo-referenced information was taken from the network of Manizales, updated
with recent infrastructure changes. Software using geographic information systems
(GIS) with coordinates data of node location, arc lengths (La) and information on
operating speeds (Vprom), which were taken from official reports such as the
Mobility Plan of Manizales in 2017 [23], were used.
3.2 Existing network modification with the implementation of new transport
infrastructure
Based on the existing road network and the geometric design of the new intersection
(Figure 3), the transport infrastructure network is modified by creating the nodes
and arcs necessary for their representation, also changing address attributes, arc
length and expected operating speed according to the design. Prior to the calculation
of the global average accessibility, the updated network is reviewed and evaluated,
verifying the topology between nodes and graph, including the works executed in
the last year. The new intersection gives continuity to the traffic on the Kevin Angel
Avenue that connects the city from East to West, eliminating the existing traffic
signal and increasing the average operation speed to 50 Km/h on said corridor.
New transportation infrastructure impact 219
Fig. 3: Location of the infrastructure project “Intersección de La Carola”
Source: Author´s from [19]
3.3 Calculation of global average accessibility
The matrix of minimum times between all network nodes is calculated with the
information obtained from the existent and modified networks in previous stages,
based on the travel time of each arc; the minimum travel time (tvi) between each
node is measured with the Dkjistra algorithm; and, finally, the average travel time
(Tvi, Expression 1) is determined. [27]. The vector of travel time obtained is related
to the geographic coordinates of each of the nodes obtaining the matrix from which
the isochronous curves of average travel time are generated.
𝑇𝑣𝑖 = ∑ 𝑡𝑣𝑖𝑛
𝑗=1
𝑛 − 1 𝑖 = 1,2,3, … , 𝑛; 𝑗 = 1,2,3, … , 𝑛
(1)
For the geostatistical calculations, the ordinary Kriging Method with linear
semivariogram was used as a prediction model of the average travel times. The
semivariogram characterizes the properties of spatial dependence between points
belonging to an observed sample, which is calculated by Expression (2) [13], where
Z (x) = value of the variable in a site with x, y coordinates; Z (x + h) = another
sample value separated by a distance h; n = number of couples that are separated
by that distance. It is emphasized that, at a lower distance, the similarity or spatial
correlation between the observations is greater.
𝜸(𝒉)
=∑(𝒁(𝒙+𝒉) − 𝒁(𝒙))𝟐
𝟐𝒏
(2)
The ordinary Kriging method proposes that variable value can be predicted as a
linear combination of the n random variables, as presented in Expression (3) [13],
where λ1 = the weights of the original values. This method has been used for
different studies related to the prediction of supply models in transport networks
[25].
𝒁(𝒙𝟎) = 𝝀𝟏𝒁(𝒙𝟏) + 𝝀𝟐𝒁(𝒙𝟐) + 𝝀𝟑𝒁(𝒙𝟑) + 𝝀𝟒𝒁(𝒙𝟒)+. . +𝝀𝒏𝒁(𝒙𝒏) = ∑ 𝝀𝒊𝒁(𝒙𝒊)
𝒏
𝒊=𝟏
(3)
220 Diego Julián Perilla Ramírez et al.
In order to establish the relationship between the isochronous accessibility curves
and the population of the city, the neighborhood division map of the city of
Manizales, which has information on areas, densities and socioeconomic strata, will
be used. It is possible to establish the percentage of the population that will have
the greatest reduction in travel times given the construction of the new transport
infrastructure.
3.4 Calculation of the variation gradient of travel times
Based on the calculation of the global average accessibility of the existing network
(AMGa) and the modified network (AMGf) of the city of Manizales and the use of
software tools (GIS), the gradient percentage variation of travel time in each point
of the network (see Expression 4) is determined (% Gvar). The result will be a map
evidencing the estimated variation in average trip times with the implementation of
the new intersection La Carola.
%𝐺𝑣𝑎𝑟 = ( 𝐴𝑀𝐺𝑎 − 𝐴𝑀𝐺𝑓
𝐴𝑀𝐺𝑎) ∗ 100
(4)
4 Results and discussion
4.1 Global average accessibility in its current scenario, year 2017
The results of the geostatistical calculations, represented by isochronous curves in
ranges of every 5 minutes (See Figure 4), show that the city is covered with average
travel times of between 20 and 65 minutes. It is worth to highlight that the
isochronous curves found between 20 and 35 minutes of average travel time covers
86% of the total study population (358,269 inhabitants).
Isochronous curves between 20 and 25 minutes cover 15% of the study area and are
located in the downtown area (Central Bussiness District - CBD) and areas adjacent
to the two main arterial roads that connect the city, which are Kevin Angel Avenue
and Santander Avenue.
In contrast, the curves showing the highest average travel times are found in the
peripheries of the city: the 55-minute curves cover the Maltería and Gallinazo
sectors east of the city and within the same curve to the southwest of the city are
the sectors of Bajo Tablazo and Villamaría, as shown in the boxes of Figure 4. The
area with the least favorable average travel times are located northwest of the city
in the sector of La Linda with curves greater than 60 minutes.
New transportation infrastructure impact 221
Figure 4: Isochronous curves current scenario, year 2017. Source: Authors.
4.2 Global average accessibility future scenario, including La Carola
intersection
Once the global average accessibility with the modified network was estimated,
isochronous curves were obtained in the same ranges of accessibility calculation
made in the existing network; between 20 and 65 minutes, but with noticeable
changes mainly in the curve of 20 to 25 minutes, area that increases significantly.
Figure 5 shows the results of the global average accessibility calculation to the
network in the future scenario. The reduction of average travel times is more
noticeable on Kevin Angel Avenue, this corridor communicates the city from East
to West. This change is shown in detail in the red box, which also shows the
modification to the existing network with the start of operation of the new
intersection.
4.3 Global average accessibility gradient
Figure 6 shows the gradient curves of the areas where an increase or savings in the
average travel time will take place, measured as a percentage of change between
the current and future scenario. It is evident that some areas of the periphery, such
as the La Linda sector (northeast), had savings between 5% and 10% in the average
travel times. Sectors close to the intersection are observed with savings of up to
30% (see Fig. 6). Likewise, there is a small increase in the average travel time in
certain sectors of the La Carola neighborhood (next to the intervention).
222 Diego Julián Perilla Ramírez et al.
Fig.5: Isochronous curves future scenario. Source: Authors
Fig.6: Gradient Saving (%). Source: Authors.
This is due to the fact that the average travel time is directly related to the length of
the arches. However, this means that users, despite having more mobility options
in the sector, will have to travel longer lengths, but without representing
disturbances due to traffic or waiting times for turns at intersections.
New transportation infrastructure impact 223
Figure 7 shows the saving percentage and its effect on the population. Up to 8%
perceive savings between 5% and 10% of their mean travel times, which equals to
5000 families favored in the city. Moreover, up to 6% of the population obtain
savings of 15%. Finally, it should be noted that 2% of the population receives
savings in their average travel times of up to 20%, which indicates that this road
infrastructure alternative will benefit a large number of inhabitants.
Fig.7: Covered Population per saving percentage of travel time. Source: Authors.
With information obtained from the accessibility calculations in the average travel
time matrix, it is possible to classify the covered population into socioeconomic
strata. This classification consists of assigning a category from 1 to 6 to the areas
depending on dwellings conditions, access to basic services and income per family,
1 indicating the lowest and 6, the highest. Figures 8 and 9 show the variation
between the current situation and the future one by socioeconomic stratum. As can
be seen, the most benefited stratum 5, located in close proximity to the curve whose
average travel time is close to 30 minutes for about 99% of the population of the
same stratum.
5 Conclusions
The methodology used to calculate global average accessibility can also be used in
developing countries and intermediate cities with similar contexts, allowing to
determine the impacts that new infrastructure works can generate.
The impact generated with the implementation of the new intersection mainly
covers Kevin Angel Avenue. Coverage percentage of the 25-minute curve increases
from 5.15% to 5.71% and the 30-minute curve of 28.10 % to 29.90%, decreasing
the average travel times for Laureles, Los Rosales and La Leonora neighborhoods,
with socioeconomic strata 4-5.
224 Diego Julián Perilla Ramírez et al.
Fig.8: Coverage distribution by socioeconomic strata. Current situation. Source:
Authors.
Fig.9: Coverage distribution by socioeconomic strata. Future situation. Source:
Authors.
According to the analysis, 7.2% of the population save from 5% up to 25% in their
mean travel times, which translates into a benefit to a considerably large group in
the city.
Acknowledgements. The authors thank the research center of sustainable mobility
of the National University of Colombia, campus Manizales.
New transportation infrastructure impact 225
References
[1] T. Adler and M. Ben-Akiva, A theoretical and empirical model of trip chaining
behavior, Transportation Research Part B: Methodological, 13 (1979), no. 3,
243-257. https://doi.org/10.1016/0191-2615(79)90016-X
[2] M. Batty, Accessibility: In search of a unified theory, Environment and
Planning B: Planning and Design, 36 (2009), 191-194.
https://doi.org/10.1068/b3602ed
[3] M. J. Breheny, The measurement of spatial opportunity in strategic planning,
Regional Studies, 12 (1978), no. 4, 463-479.
https://doi.org/10.1080/09595237800185401
[4] Z. Calcuttawala, Landscapes of information and consumption: A location
analysis of public libraries, Chapter in Advances in Library Administration
and Organization, E.D. Garten, D.E. Williams, J.M. Nyce (Editors), Vol. 24,
2006, 319–388. https://doi.org/10.1016/s0732-0671(06)24009-4
[5] E. Cascetta, A. Carteni and M. Montanino, A behavioral model of accessibility
based on the number of available opportunities, Journal of Transport
Geography, 51 (2016), 45-58. https://doi.org/10.1016/j.jtrangeo.2015.11.002
[6] J. Cheng, L. Bertolini and F. le Clerq, Measuring sustainable accessibility,
Transportation Research Record: Journal of the Transportation Research
Board, 2017 (2007), 16-25. https://doi.org/10.3141/2017-03
[7] M. Echenique, D. Crowther and W. Lindsay, A spatial model of urban stock
and activity, Regional Studies, 3 (1969), no. 3, 281-312.
https://doi.org/10.1080/09595236900185291
[8] D.A. Escobar, J. P. Cañas and A. Montoya, Accesibilidad como herramienta
de planeación urbana. Caso de estudio: Re direccionamiento vial en Riosucio
(Caldas-Colombia), Revista Avances: Investigación en Ingeniería, 11 (2014),
no. 2, 9-18.
[9] D.A. Escobar, J.M. Holguin and J.D Zuluaga, Accesibilidad de los centros de
ambulancias y hospitales prestadores del servicio de urgencias y su relación
con la inequidad espacial. Caso de estudio Manizales–Colombia, Revista
Espacios, 37 (2016), no. 20, 20.
[10] J. Farrington and C. Farrington, Rural accessibility, social inclusion and social
justice: towards conceptualization, Journal of Transport Geography, 13
(2005), no. 1, 1-12. https://doi.org/10.1016/j.jtrangeo.2004.10.002
226 Diego Julián Perilla Ramírez et al.
[11] K.T. Geurs and J.R. Ritsema van Eck, Accessibility Measures: Review and
Applications. Evaluation of Accessibility Impacts of Land-use Transport
Scenarios, and Related Social and Economic Impacts, National Institute of
Public Health and the Environment (2001).
http://www.rivm.nl/bibliotheek/rapporten/408505006.pdf
[12] K.T. Geurs and B. van Wee, Accessibility evaluation of land-use and transport
strategies: review and research directions, Journal of Transport Geography,
12 (2004), no. 2, 127–140. https://doi.org/10.1016/j.jtrangeo.2003.10.005
[13] R. Giraldo, Introducción a la Geo Estadística: Teoría y Aplicación,
Universidad Nacional de Colombia, Colombia, Bogotá, Colombia, 2002.
[14] G. Gulhan, H. Ceylan, M. Özuysal and H. Ceylan, Impact of utility-based
accessibility measures on urban public transportation planning: A case study
of Denizli, Turkey, Cities, 32 (2013), 102-112.
https://doi.org/10.1016/j.cities.2013.04.001
[15] S.L. Handy and D.A. Niemeier, Measuring accessibility: an exploration of
issues and alternatives, Environment and planning A, 29 (1997), no. 7, 1175-
1194. https://doi.org/10.1068/a291175
[16] W.G. Hansen, How accessibility shapes land use, Journal of the American
Institute of Planners, 25 (1959), no. 2, 73-76.
https://doi.org/10.1080/01944365908978307
[17] A. Holl, Twenty years of accessibility improvements. The case of the Spanish
motorway building programme, Journal of Transport Geography, 15 (2007),
286-297. https://doi.org/10.1016/j.jtrangeo.2006.09.003
[18] D. Ingram, The concept of accessibility: a search for an operational form,
Regional Studies, 5 (1971), no. 2, 101-107.
https://doi.org/10.1080/09595237100185131
[19] INVAMA, Detalle del proceso: INV-L-001-2017
[Online], (2017) Recuperado de:
https://www.contratos.gov.co/consultas/detalleProceso.do?numConstancia=1
7-1-169254
[20] INVIAS, Presidente Santos inauguró intersección la Fuente en Manizales
[Online], (2017) Recuperado de:
https://www.invias.gov.co/index.php/mas/sala/noticias/2894-presidente-
santos-inauguro-interseccion-la-fuente-en-manizales
New transportation infrastructure impact 227
[21] R.B. Mitchell and C. Rapkin, A Function of Land Use, Urban Traffic-A,
Columbia University Press, 1954.
[22] G.H. Pirie, Measuring accessibility: a review and proposal, Environment and
Planning A, 11 (1979), no. 3, 299-312. https://doi.org/10.1068/a110299
[23] Plan Maestro de Movilidad del Municipio de Maizales, Secretaría de Tránsito
y Transporte, Alcaldía de Manizales, 2017.
[24] J. Preston and F. Raje, Accessibility, mobility and transport-related social
exclusion, Journal of Transport Geography, 15 (2007), no. 3, 151–160.
https://doi.org/10.1016/j.jtrangeo.2006.05.002
[25] S.S. Prasetiyowati, M. Imrona, I. Ummah and Y. Sibaroni, Prediction of Public
Transportation Occupation Based on Several Crowd Spots Using Ordinary
Kriging Method, Journal of Innovative Technology and Education, 3 (2016),
no. 1, 93-104. https://doi.org/10.12988/jite.2016.6723
[26] J.W. Weibull, An axiomatic approach to the measurement of accessibility,
Regional Science and Urban Economics, 6 (1976), no. 4, 357-379.
https://doi.org/10.1016/0166-0462(76)90031-4
[27] J.D. Zuluaga, D.A. Escobar, Geomarketing analysis for shopping malls in
Manizales (Colombia). Accessibility approach methodology, Revista Espacio,
38 (2017), no. 21, 20.
Received: January 20, 2018; Published: February 27, 2018