Alcir das Neves Gomes
Instituto Federal de Educação, Ciência e tecnologia de
São Paulo, Campus Suzano; FATEC Zona Sul, Brazil
E-mail: alcir.gomes@ifsp.edu.br
Elson Araújo
Instituto Federal de São Paulo, Campus- Suzano, Brazil
E-mail: earaujo775@gmail.com
Osmar Martins Souza
Instituto Federal de São Paulo, Campus- Suzano, Brazil
E-mail: osmoz800@hotmail.com
Wagner Roberto Garo Júnior
Instituto Federal de São Paulo, Campus- Suzano, Brazil
E-mail: wagner.garo@ifsp.edu.br
Submission: 01/31/2019
Accept: 02/10/2019
ABSTRACT
The theme urban mobility has been gaining prominence in recent
times due to the impact it causes on the quality of life of people living in
large centers, this article aims to study and evaluate the Capacity and Level
of Service in a specific route in the city of São Paulo based on the concepts
and methods established in Highway Capacity Manual 2000 (HCM 2000), in addition
to using linear regression to estimate the forecast of short-term traffic
demand in a biennial scenario, to propose alternatives to provide a
satisfactory Service Level compatible with the forecast demand, to analyze the
efficiency of the method as a tool in the decision-making process in the
measures for the improvement of circulation and retardation in the municipal
road system. In this exploratory, quantitative and descriptive study, the
calculations were performed using concepts and methods contained in HCM 2000
evaluating the efficiency of the method as a means of obtaining information to
support decision-making regarding the improvement of urban mobility. The
results showed a tendency to reduce the volume of vehicle flow in the studied
road. The results obtained demonstrate that the tools applied in the present
work can be of great value for decision making or proposition measures for
improvements in the attendance of demand in the capacity of the roads to
provide a Service Level that allows to improve the satisfaction of the users of
the road system of the municipality of São Paulo.
Keywords: urban
mobility, Service Level, demand forecasting, decision making
1. INTRODUCTION
Urban
mobility is fundamental in characterizing the quality of life of a society as
well as in the degree of economic and social development, disordered urban
growth due to occupation and land use, excessive increase in car use, lack of
infrastructure, pollution of the environment, among other factors, directly
interfere with the quality of life of the population. These factors have
influenced researchers, managers and decision makers to seek new ways of
discussing and finding solutions to these urban issues (MAGAGNIN; SILVA, 2008).
Thus,
Ferronatto (2002) explains:
The
demand for travel is derived from the activities of people: activities of
production and consumption of goods. The greater the development of society,
the greater the economic activity and, consequently, the need for
displacements. The current pattern of urban land use in large cities, where
disorderly horizontal growth and specialization of the different residential,
commercial and industrial zones takes place, leads to the need for motorized
transportation to cover the great distances that separate people from most
their destinations (FERRONATTO, 2002, p.1).
However,
the imbalance between the pace of initiatives to improve urban mobility and the
growing problems in large cities (increased travel times, air pollution and
traffic accidents) has contributed to the deterioration of urban living
conditions regardless of income improvement of work and greater access to
durable goods by the poorest part of the population (GOMIDE; GALINDO, 2013).
The
management of the demand for transportation aims to manage times of greater
concentration of vehicles (peak times). These measures are frequently applied
in large urban centers and include traffic management, such as restrictions on
the access of cars to certain areas and the collection of fees for the use of
roads, in some cases differentiated by time of day. In addition to the temporal
and spatial redistribution of traffic, these measures aim to transfer part of
the demand for public transportation (FERRONATTO, 2002).
The
objective of this work is to measure and evaluate the road capacity and the
Service Level in a specific route in the city of São Paulo using the concepts
and methods established in Highway Capacity Manual 2000 (HCM2000) (SETTI,
2002), which can be translated as a Road Capacity Manual, the mathematical
model of linear regression was also used to calculate the traffic demand
forecast to propose alternatives to provide a Service Level compatible with
expected demand, between levels C and D, also analyze its efficiency as an
alternative method in the decision-making process in the measures of
improvement of circulation and retardation in the municipal road system.
The
specific objectives are to propose solutions for the current situation and the
projected demand.
For
the development of this article, a theoretical review is presented containing
some relevant elements for the proposed application including the discussion of
the concept of urban mobility, the presentation of HCM methodology for
calculation and analysis of the capacity and level of service of highways, the
forecasting tool of demand by means of mathematical models for the circulation
of vehicles, and basic aspects of the evaluation techniques employed. The third
part deals with the methodology and development of the research followed by the
analysis of results and finalizing the final discussions about the results of
the study.
2. THEORETICAL REVIEW
Cities
are considered dynamic organisms, with remarkable transformations over time,
including their spatial structure, and transport becomes a key element in these
transformations. Transport systems are strongly linked to the growth and
development of cities with a role in the organization and structuring of urban
space (KNEIB; DA SILVA; PORTUGAL, 2004).
Mobility
is by definition a feature related to the movements carried out by individuals
in their various activities, such as study, work, leisure and others. In this
context, cities play an important role in the innumerable relations of exchange
of goods and services, culture and knowledge among its inhabitants, but this
possibility is only achieved if there are adequate conditions of mobility for
the people (MINISTÉRIO DAS CIDADES, 2006
apud MAGAGNIN;
SILVA, 2008).
A
great example of this is the development and growth of the metropolis of São
Paulo and the problems generated by urban mobility and its transportation
system.
According
to Scaringella (2001), "the best understanding
of the São Paulo urban mobility crisis is a more detailed analysis of the
various relationships between: urban land use and occupation, transportation
systems and road infrastructure, and the interaction between human factor,
vehicle, public road and environment ".
One
of the aggravating factors that most impacted on the issue of urban mobility is
related to the vertiginous growth of individual vehicles in circulation in most
of the Brazilian metropolises, especially in São Paulo, with the largest
vehicle fleet in Brazil.
Thus,
Gomide and Galindo (2013) show data for that growth:
Based
on data from Denatran and IBGE, there is a growth of
the fleet of cars and motorcycles from 1998 to 2012 at a rate ten times higher
than the population growth. As a result, the motorization rate more than
doubled in that period (slightly more than twice for cars and 2.5 times for
total cars and motorcycles), moving to a ratio of 0.2 bikes for every 10
inhabitants for 1 bike / inhabitant and 1.2 car / 10 inhabitants to 2.6 (GOMIDE
and GALINDO, 2013, p.37).
For
this reason, the Traffic Engineering Company (CET) of the city of São Paulo
conducts a systematic study to monitor fluidity by means of classified
volumetric counting and delayed travel time, in the main roads of the city,
since 1977, providing data for society and using them to take measures to
improve urban mobility (CET, 2018).
The
purpose of monitoring the volume of vehicles that use a given route is to
obtain data to obtain the true notion of the demand of the users of the road
system of the city of São Paulo and that the demand is related to the behavior
of the people.
According
to the Highway Capacity Manual (HCM) there is a distinction between demand and
volume. Demand is the amount of vehicles you want to use for a stretch of track
while volume is the discharge rate of a track stretch. If there is no
congestion (or row), demand equals volume (TRB, 2000, apud
SETTI, 2009).
Quality
of service is the mode used by traffic engineers to evaluate the
"quality" of the trip perceived by road users. This concept of
quality of service, initially proposed in the USA, is an essential measure to
evaluate the performance of road segments from the point of view of traffic
flow. The HCM is the basic reference for the evaluation of the quality of service
that defined the parameters used to measure the quality of service and a set of
established procedures to systematize and standardize this measurement (SETTI,
2009).
The publication of the
first edition of the Highway Capacity Manual (HCM) occurred in 1950 as a result
of a joint work of the Highway Research Board Road Capacity Committee, which
became the basic reference for the study of the capacity and the Level of
Service of system components of road transport with wide acceptance worldwide
(SETTI, 2002).
The analysis of the
capacity of a road component consists of elaborating a set of models or
analytical equations that relate the flow levels, the geometry, the
environmental conditions and the strategies of control as well as the measures
of the quality of service. Thus, these models and equations make it possible to
determine the maximum capacity of transport-transport of an infrastructure and
the Service Level in different degrees of flow (HOEL; GARBER; SADEK, 2011).
According
to Setti (2002), the main purpose of a capacity
analysis is to measure the maximum flow rate of vehicles that a stretch of
highway can withstand under pre-established operating conditions through the
application of clearly defined methods.
The
concept of Service Level is closely associated with the concept of capacity
because it is a direct function of the level of utilization of the
infrastructure. As the flow level increases, the quality of service clearly
deteriorates (HOEL; GARBER; SADEK,
2011).
The
Service Level is a quality measure that requires the application of
quantitative measures to determine the characteristics of the operating
conditions in the traffic flow and can be evaluated by means of measures of
performance that cover the speed and time of travel, the ease of maneuvering,
traffic interruptions as well as comfort and convenience (SETTI, 2002).
The
density depicts the proximity between vehicles in the flow stream and reveals
the ease of performing maneuvers within the flow as well as the level of
psychological comfort of the users. It is also the measure of performance
adopted for basic segments of freeways and dual lane highways and the lower the
density, the better the quality of service; the higher the density, the worse
the quality of service (SETTI, 2002).
According
to Setti (2002), the HCM 2000 establishes six levels
of service, identified by letters that vary from A to F, with A being the best
Service Level and F the worst. Table 1 presents the criteria established by the
HCM 2000 for the definition of service levels:
Table 1: Criteria for establishing
service levels
Service level |
Density [vehicle / (km.lane)] |
A |
0 < k
≤ 7 |
B |
7 < k
≤11 |
C |
11 < k
≤ 16 |
D |
16 < k
≤ 22 |
E |
22 < k
≤ 28 |
F |
28
< k |
Source: SETTI, (2002)
The
HCM 2000 stipulates four types of application for the analysis of capacity and
Level of Service of highways denominating type I to type IV analyzes, for the
present study will be used the type I and IV.
The
type I analyzes answer questions such as What
is the Service Level of the highway?. Using the input measurements the
hourly flow rate - Vp and the free - flow velocity -
FFS, resulting in the D - density that allows determining the Service level
(SETTI, 2002).
Type
IV analyzes generate the number of N ranges that provide a desired Service
Level, from the Vp flow rate and the free flow rate
FFS. They are used for roadway design to define the number of lanes required to
support the annual average daily flow obtained by traffic estimates (SETTI,
2002).
The
concept of demand forecasting may be associated with projection, or even
extrapolation of past trends (BOLAND, 1985 apud
VERRUCK; BAMPI; MILAN, 2009). According Makridakis
(1988 apud VERRUCK; BAMPI; MILAN, 2009) the demand
forecast helps in the strategic decisions of the company, its planning or any
attitude that considers future events.
With
regard to the methodology of demand forecasting, it can be qualitative or
quantitative. The quantitative methods are based on mathematical models, based
on statistics, as a way to carry out the forecast (MOREIRA, 2004). In general,
they are more used in short-term scenarios, since it is not possible to predict
by these techniques changes in the scenario (FLEURY; WANKE; FIGUEIREDO, 2003).
Thus,
Ferronatto (2002) states that the demand for
transport is not only determined by the price, as well as the demand for any
goods or services. The quantity demanded is also affected by the
characteristics of the service, and those of competing modes (car, train,
etc.), as well as other factors.
The
public policy interest in a more homogeneous distribution of travel demand
results from the losses caused by congestion (in energy consumption, time spent
on transport and environmental quality) and the waste of resources that
corresponds to the idle capacity of collective transportation (in terms of
equipment and work) and of the urban road system in times of low demand
(FERRONATTO, 2002).
Modeling
in transport tries to predict future demands through mathematical,
computational, behavioral and other resources. In this way, the analysis of
transport problems began to be based on a theoretical basis, through the use of
models that aim to represent the characteristics of a new reality. Demand
modeling strategies and transport system offerings have become essential in the
decision-making process and planning of this system (LEMES, 2005, p.14).
According
to Lopes (2005) to determine the forecast of the transport demand, it is
necessary to carry out a detailed survey of the current conditions. The city is
divided into traffic zones and from there the traffic between each pair of
zones is determined. The result is a table of origins and destinations (O-D
Matrix), with an intimate relationship with the attraction and production of
trips.
Already
Ferreira (1999 cited MENDONCA, 2008) thus states: "the analysis of this
demand is a process that seeks to identify the determinants of demand and how
they interact and affect the evolution of the traffic volume (or travel)".
The development of the transport demand analysis
process is carried out using statistical and mathematical models. According to
Novaes (1986 apud MENDONÇA, 2008):
These
transport demand analysis models can be used for short-term forecasts with
current, medium- and long-term forecasts with detailed projections of
socioeconomic variables and, in the long term, involving regional planning and
land use planning (NOVAES, 1986 apud MENDONÇA, 2008,
p.18).
By
means of demand forecasting models, a future scenario of land use and
occupation and the demand for transport can be reproduced in which it is
possible to predict the socio-economic growth of the city, the future demand
behavior in the road system, to locate routes with a saturated capacity, to
propose modifications in the route of the vehicles and / or in the physical
road network, and even to verify the effectiveness of the planning by means of
dynamic simulation of the future flows allocated in the road system, using
specific programs (LEMES, 2005).
According
to Molinero and Arellano (1998 apud
MENDONÇA, 2008), there is the possibility of developing models for forecasting
urban passenger transport demand using two methods:
·
plan demand in proportion to the evolution of
population growth or to the increase in individual mobility.
·
plan demand by comparing with other cities where
living standards and mobility are similar or slightly larger
3. METHODOLOGY
The
present article takes the form of an exploratory research, according to Gil
(2002), the exploratory research is flexible and can take the form of bibliographic
research. In order to study the Service Level of a specific route in the city
of São Paulo, the volume monitoring data from the Mobility Monitoring Survey of
the CET (2018) was used to evaluate capacity and Type of Service Level I and
IV, according to the methodology proposed by HCM 2000, of a specific point of
the route chosen for the study.
The
route chosen is the “ROTA 01G” which includes the following routes: Eusébio Matoso Avenue, Rebouças Avenue and Consolação
Street, based on data provided by the CET database of the said city and that
route was established point 3 as object of study in the afternoon towards the
center to the neighborhood.
The
objective of the volumetric counting survey conducted by CET is to determine
the quantity, composition and direction of the flow of vehicles in a section of
the road system per unit time. The number of vehicles is raised by researchers
using manual counters. The researcher is positioned in the counting section, in
a location with good visibility of the flow to be observed. The number of
researchers is scaled for each point, depending on the number of bands, volume
and composition of traffic.
The
number of vehicles is counted in three or four points of the route. One of the
counting points is defined as the main one. At this point, counting is
performed for two days. Traffic flow counting is directional, always done in
both directions of the road, when it is two-way. The senses are named
predominantly as neighborhood to the center and center to the neighborhood.
With
the result of the Type I Service Level obtained from the counts, the study was
performed using the Service Level type IV method, as stipulated by HCM 2000, to
define the current need to conduct the flow satisfactorily with a Service Level
C or D and then a demand forecast was made in the vehicle circulation volume
using mathematical models such as the Least Squares Method (LSM) and again an
analysis to define a project proposal to meet future needs with Level of
Service C or D to demonstrate how the method can meet the needs in decision
making or measures for improvements in the flow of vehicles.
4. ANALYSIS OF RESULTS AND DISCUSSION
As
mentioned in the methodology, a specific route was used as object of study as
illustrated in figure 1:
Mobilidade
no Sistema Viário Principal - MSVP – 2017
Figure 1: Route used as object of study
Source: CET
(2018)
According
to HCM 2000 (SETTI, 2002), determination of the Type I Service Level of a track
segment involves three parameters: flow rate, free-flow velocity and density.
For
the definition of the flow rate the following formulas are used:
Equation
1: Flow Rate Formula
·
: passenger-car
equivalent flow rate (pce/h.lane);
·
:
demand volume for full peak hour (veh/h);
·
: peak-hour factor;
·
: number of
traffic lanes;
·
: adjustment
factor for heavy vehicles; and
·
: adjustment
factor for driver type.
Equation
2: Adjustment factor formula for
heavy vehicles
·
: adjustment
factor for heavy vehicles;
·
: percentage of
trucks and buses in the traffic flow;
·
: equivalence
factor for trucks and buses;
·
: percentage of
recreational vehicles; and
·
: equivalence
factor for recreational vehicles.
To
define the free flow speed the following formula:
Equation
3: Free-flow speed formula
·
: free-flow
speed [km/h];
·
: free-flow
speed in the analysis direction [Km/h];
·
: adjustment for
lane width [km/h];
·
: adjustment for
lateral width [km/h];
·
: adjustment for number of lanes [km/h]; and
·
: adjustment for interchange density [km/h].
And
finally, the density:
Therefore,
based on the data provided by the CET database in which:
·
Q
equals 5156 vehicles;
·
PHF
equal to 0.98;
·
N equal to 3 lanes of 3 meters wide each;
·
Pt equal to 4% and Pr equal
to 0%.
Using
the adjustment and equivalence factors established by the HCM, and applying
them in the formulas previously demonstrated, the results obtained were:
For
the purpose of calculation, FFS was defined according to figure 2 of the SETTI
(2009) study in which the regulated speed of the route is used as free flow velocity.
Therefore, the maximum permitted speed of the stretch of track under study is
regulated at 50 km / h. Applying the data in the density formula gives the
following result:
From
the result of the density it was verified that the route is operating at
Service Level F, that is, the flow rate is greater than the capacity that the
route entails. In order to perform a Type IV Service Level analysis, Table 2
was elaborated with the various possibilities for reaching Service Levels C and
D by applying the formulas already presented in previous calculations:
Table 2: Type IV Service Level
Analysis
Number of lanes |
Width of lanes (m) |
Density [pce/(Km.lane)] |
Service level |
4 |
3,0 |
27 |
E |
5 |
3,0 |
22 |
D |
6 |
3,0 |
18 |
D |
7 |
3,0 |
16 |
C |
Source:
Prepared by Authors
It is
noteworthy, analyzing the data obtained in Table 2, that in order to provide
Service Level D in which, according to the HCM, the density varies from 16 to
22 vehicles per Km of track, there is a need to extend the track for at least 5
bearing ranges of 3.0m wide each, but in this case the solution is much of the
maximum flow of this Service Level, which suggests the need to work with 6
bearing ranges Already to provide a Service Level C in which, according to the
HCM, the density varies from 11 to 16 vehicles per km of track would only be
possible with a widening of the track to 7 lanes of 3.0m wide each bearing. The
urban characteristics of the studied section do not allow the process of change
to occur in the short term, making feasible the study of demand forecast by the
proposed method, as seen in the literature review.
Thus
historical data from previous years of vehicle traffic counting at the peak
time of the route shown in figure 1 were used as the basis of the calculations
made for the prediction of vehicle traffic demand at point 3 of the given route
using the mathematical model (LSM), as shown in Table 3:
Table 3: Base data and the forecast
of demand for the next period
|
2009 |
2011 |
2013 |
2015 |
2017 |
2019 |
Peak Hour Volume |
6204 |
6391 |
6004 |
4521 |
5156 |
4465 |
Source: CET (2018)
The
linear regression calculations carried out resulted in a tendency to reduce
vehicle traffic volume at the peak time at point 3 of the route used as the
object of study, as can be seen by means of the negative angular coefficient,
that is, b = - 198.3 and a correlation considered strong and negative since it
presents a coefficient r = - 0.793 which reinforces the reliability of the
data.
For
the demand foreseen in table 3, the Type IV Service Level analysis is presented
in Table 4 below:
Table 4: Type IV Service Level
Analysis for expected demand
Number of lanes |
Width of lanes (m) |
Density [pce/(Km.lane)] |
Service level |
3 |
3,0 |
31 |
F |
4 |
3,0 |
23 |
E |
5 |
3,0 |
19 |
D |
6 |
3,0 |
16 |
C |
Source:
Prepared by Authors
Analyzing
the results presented in Table 4, it can be observed that with the trend of
reduction in the flow volume of the track in the peak time, as obtained by the
forecast of demand for 2019, with 4 lanes of 3.0m wide bearing, almost reached
the density value for the provision of Service Level D that would be 22
vehicles per km of track; which is already possible with the widening of the
track to 5 lanes of 3.0m wide each bearing a density of 19 vehicles per km of
track being considered an acceptable Service Level and of good quality in the
perception of the users mainly if the volume of vehicle circulation in the
studied road maintain its downward trend. The possibility of the provision of a
Service Level C becomes possible if the track is enlarged to 6 lanes of 3.0m
wide, which is twice the number of lanes that are currently available and what
has not occurred in the previous analysis with the demand verified in the year
2017.
The
suggested increase in capacity is difficult to achieve due to the type of land
occupation in the region that would require a long-term project and high
investments. However, traffic engineering solutions, such as Reversible Bands
at a certain time / track direction, adding one or two bearing ranges in the
opposite direction, or diverting part of the flow to parallel paths, thereby
improving the Service Level would be options for solve the problem, remembering
that the opposite flow must be considered so that there is no overhead of the
system.
5. CONCLUSIONS
The
city of São Paulo suffers from the growth of the flow of private motor
vehicles. Not to mention the fact that its main means of public transport (the
bus, despite its equivalence factor, can transit through exclusive lanes, not
always shared with private motor vehicles) also uses the main road system to
operate.
The
study carried out using the methods established by HCM (2000) has shown to be
efficient for the understanding of the current situation regarding urban
mobility in a certain stretch of road or route of the São Paulo city road system
and in relation to the capacity and the Level of Service of the same. Applying
linear regression using the LSM mathematical model, short-term future
requirements can be projected with respect to the capacity of the road to
support the demand and quality of service to be made available to users, as
observed by Fleury, Wanke and Figueiredo
(2003) , forecasting models cannot support long-term decisions because they cannot
predict scenario changes.
However,
for the short-term analysis, there was a tendency to reduce the flow volume of
vehicles on the road, which suggests changes in the behavior of the users who
use the road to make their mobility in the municipality. The development of the
work has shown that the applied tools can be of great value for decision making
or measures for improvements in the service demand in the capacity of the roads
to enable a Service Level with satisfactory quality in the perception of the
users of the road system of the São Paulo in the short term.
In
the last few years, many technologies have been used to obtain data, with
city-wide equipment and detection sensors, not to mention mobile phones, which
enable real-time information of all kinds through applications.
For
this reason, it is possible to suggest future studies on how the use of shared
car applications, shared bicycles, collective transportation (bus, train and
subway) can contribute to the optimization of the route traveled (time /
distance). The dynamics of traffic demand growth is complex, creating the possibility
of associating traffic engineering with scientific research and technological
innovations, to create tools to improve the fluidity of the road system to meet
the displacement needs of society.
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