WO2013054310A1 - System and method of calculation of routes - Google Patents

Abstract

System and method to calculate routes comprising the steps of obtaining polygons with attributes of air and noise pollution from hourly maps (pollution map) , wherein each polygon corresponds to a class of air and noise pollution from a predefined number of classes of air and noise pollution; splitting each segment of the road network into subsegments by the intersection with those polygons (distance contamination model) ; determining the corrected distance of the sub-segments, by applying to the actual distance of each sub-segment noise (K) and air pollution (W) multipliers obtained through the known average value of the respective class of pollution (contaminated distances map) ; calculating the route, considering the sub-segments with the corrected distances, which minimizes the total corrected distance (navigation) to obtain the desired routes (healthy route, less noisy route, less air polluted route) .

Description

 DESCRIPTION

 ROUTE CALCULATION SYSTEM AND METHOD

Technical field of the invention

The present invention is a method and navigation system for determining routes. More specifically, it includes a method that determines less noisy and polluted routes in particular that pedestrians or cyclists should follow.

Background of the Invention

Navigation and route planning systems are usually geared towards the circulation of motor vehicles, namely automobiles. For soft or active modes (pedestrians and cyclists) the market offers a scarcer and less diverse set of solutions.

 Within the transport system active modes are considered to be the most economically, socially and environmentally sustainable modes, since mobility is guaranteed by its own means (without engines) and the emission of pollutants into the atmosphere and noise is considered null. . On the other hand, the active modes do not have any protection against external agents derived from pollution, as in the case of automobiles that have comfortable cabin and fitted with filters of various species.

Active and motorized modes in most cases share the same traffic infrastructure, and active modes always end up being damaged, because they do not emit pollutants and are the most exposed and vulnerable to the atmospheric conditions of the places.

In order to improve and promote walking and cycling, a model for route calculation has been developed. allowing pedestrians and cyclists to travel through less polluted circuits (air pollution and environmental noise).

Of the existing methods, we highlight the patent application US 2011/0161002 which is a method for a navigation system to determine pedestrian routes between a given origin and destination. It consists of displaying a set of messages specifically for the pedestrian mode that will be used in the navigation process. The described invention presents in detail the overall process of a pedestrian navigation system, having regard to its design in terms of hardware / software relationships. It presents a data collection structure on a geographical basis at the level of traffic infrastructure and pedestrian points of interest. Data are collected for the creation of databases on a geographical basis for two geometric elements of these bases, the segments and the orientation nodes, which correspond to the ends of a segment. Data that may be related to pedestrian circulation will be collected for geographical areas referred to as organized and disorganized, and in the case of segments raised their geographical position and other attributes related to circulation, such as speed limit, directions traffic, road class, among others. At the level of the nodes is raised its georeferenced positioning (latitude, longitude) and the necessary characteristics to allow the orientation of pedestrians during their travels. In addition, some data that may be collected may include the following attributes at both node and segment levels: the accessibility level of wheelchairs, if they are child and pet-friendly, if the areas are well or poorly lit, very or poorly moved areas, noisy / quiet, poorly smelled / polluted, protected from rain or not, bicycles are prohibited, outlined by trees, paved / unpaved areas, grassy area, dirty area, and other information. It should be noted that attributes related to pollution and noise are mentioned, however they are presented in a slight way and only as attributes of characterization of streets and intersections. No method of evaluation and measurement is mentioned and therefore gives the idea that the collection is purely qualitative on a descriptive basis (eg yes / no) and may at most be nominal in character (eg weak, medium, good).

 In calculating pedestrian routes the origin and destinations can be identified by geographical coordinates. Route calculation may present a tool where users may enter other data or attributes such as personal preferences and has been referred to as an example of "avoiding polluted areas". The route calculation is based on the data collected and existing in the geographic databases, and various optimization criteria can be used such as the minimization of the time traveled, the distance, or any other criteria, and any calculation algorithm can be employed to route determination. In short, it is unclear how all collected attributes are used in route calculation. Measurement of environmental attributes related to noise and pollution is not based on long-term modeling of pollutant concentration, and there is no reference to whether these values are evaluated throughout the day. In terms of pollution, pollutants are not even mentioned, referring to a more qualitative analysis of spaces at the sensory level, namely the sense of smell.

The way attributes enter the process of determining pedestrian routes is not addressed, so it can be concluded that although it refers to the survey and hypothetical Using noise and pollution in route calculation, this invention has no overlap and similarity with the method developed in the determination of healthy routes, where the heart is in the quantitative assessment of noise and air pollutants and in the choice of more or less polluted paths between a given origin and destination by pedestrians.

EP 2372305 is a method for a navigation system to determine pedestrian routes between a particular origin and destination. The method assesses a wide range of possible paths by assessing the safety cost to pedestrians, this cost represents the safety risk to the pedestrian. At the end, it indicates a path where it is possible to move the pedestrian safely between origin and destination, provided that continuity is ensured between all elements of the route, since the pedestrian infrastructure integrates elements such as a network. walks and pedestrian crossings, such as crosswalks, tunnels and overpasses. Finally, the method provides a route guidance that minimizes the cost associated with pedestrian safety.

 The structure of the navigation system consists of a geographical database and a route calculation program which will be processed on a computer.

 The navigation system is considered a set of hardware / software components, which go through the positioning definition until the information passes between the server where the route calculations are performed and the navigation equipment.

The navigation system is initially based on the creation of a geographical database based on the characterization of the infrastructures that make up a road network. and particularly the sidewalk network adjacent to the pathways of that network. Data are collected for the creation of geographic databases for two geometric elements, the lines (segments) and the points designated by us, which correspond to the ends of a segment. Line segments may also assume the role of pedestrian links that include exclusively pedestrian paths such as squares, pedestrian underpasses, overpasses, stairs, squares, pedestrian streets, etc. For these geometric elements were presented some attributes that would be raised to form the database, such as the speed limit, traffic volumes, traffic directions, road class, among other duly georeferenced physical characteristics. In addition, some data that may be collected may include the following attributes at the node, segment, and pedestrian link level, such as the type of pavement of the sidewalks, the accessibility level of the wheelchair, the suitability for circulation of children and pets, whether the areas are well or poorly lit, areas that are very or not busy, noisy / quiet, smelly / polluted, protected from rain or not, bicycles are prohibited, outlined by trees, paved areas / unpaved, grassy area, dirty area, and other information.

It should be noted that attributes related to pollution and noise are mentioned, however they are presented in a slight way and only as attributes of characterization of streets and intersections. No method of evaluation and measurement is mentioned and therefore gives the idea that the collection is purely qualitative on a descriptive basis (eg yes / no) and may at most be nominal in character (eg weak, medium, good). Measurement of environmental attributes related to noise and pollution is not based on long-term modeling of pollutant concentration. There is no reference to whether these values are evaluated throughout the day. In terms of pollution, pollutants are not even mentioned, referring to a more qualitative analysis of spaces at the sensory level, namely the sense of smell.

In calculating pedestrian routes, origin and destinations can be identified by geographical coordinates. Route calculation can present a tool where users can enter other data, or attributes, such as personal preferences.

 The route calculation is based on the data collected and existing in the geographic databases that give rise to a pedestrian maneuver table that will be used in the calculation of the safest pedestrian routes, and this table includes in addition to the information given previously more pedestrian safety-related attributes, such as whether pedestrian crossing is signaled.

Route calculation may use various optimization criteria such as minimizing time traveled, distance, or any other type of criteria, and any route calculation algorithm may be employed. When route calculation identifies more than one possible solution, the route that optimizes safety and / or efficiency and / or minimizes the complexity of the routes, particularly at intersections, is selected. For this purpose, a cost function is created that evaluates the cost of the trip, and for this purpose weights are assigned to the network segments and nodes according to some criteria, such as: existence of black spots (accidents), volumes and speed. traffic, identification of the occurrence of works, weather, crime levels, passing through some Points of Interest, among other factors.

In short, the method has as its main objective to ensure the safest route calculation and therefore environmental aspects are not relevant in the route calculation process, It is found that the process of processing the databases is very different from that presented in the present invention.

US patent application 2008/0312819 aims to produce a pedestrian planning and navigation system and method that can integrate driving into motor vehicles

(BUS) The invention is more oriented to use on a University Campus and aims to customize pedestrian routes by enhancing campus and community locations while ensuring navigation options that address the length of pedestrian paths and safety factors. Each user can choose a safer or faster path.

Route calculation includes the following steps: generate a source and a destination; generate a safety factor; assign each segment a length and safety factor; calculate at least one route and ensure its representation.

The route generation system is based on a network of lines and points based on a geographic information system. For each segment its length is calculated, a value that translates into safety through the objective assessment of that section's risk factor (safety rating) and a safety factor.

(safety factor) set by the user. These elements will allow you to calculate the desired route.

 Routes are measured by minimizing the weights resulting from the combination of the risk factor with the user-defined safety factor, for this purpose the optimization algorithm uses the criterion of minimizing the distance multiplied by that weight.

Objective risk factor assessment is found to depend on several parameters that influence pedestrian safety, such as: lighting, incident occurrence, proximity to service points, nighttime areas, proximity to roads, crime records, existence of tours, among others. In this context environmental factors such as noise and pollution are not considered. Apparently this method bears some similarities to that developed in the present invention, however apart from not integrating the environmental components the process of calculation and definition of weight is very different. Comparing the multiplier used to define the contaminated distances of the present invention with the weight used in this method, the differences are significant. In US 2008/0312819 the weight derives from two independent factors, one that objectively assesses the risk to which the pedestrian is exposed and the other that defines the level of safety the user wants for his trip (safer or faster), therefore these two variables are taken as independent. In turn, in the present invention, the multiplier contaminating the distances is only one, which simultaneously contemplates the existing pollution situation and the user's preference, ie the multiplier always depends on the pollutant concentration (noise and pollution). . It is further added that one of the aims of the present invention is to minimize the impact of pollution rather than to find the shortest way. In the present invention, the length of the route and the route may vary because the relative magnitude of the multiplier values for the different pollutant concentration classes and noise levels may vary greatly depending on the aggravation the user wishes to attribute to the maximum allowable concentration value. of pollutant.

In the case of US 2008/0312819, the safest route may, at the limit, have segments with different levels of pedestrian safety, as the pedestrian may choose exclusively to favor speed over safety, ie to assign zero weight. to the safety factor. On the other hand, in the case of the calculation of the present invention the multiplier variation in relation to the pollutant concentration depends on a fuzzy function. In the case of routes safe, presented in US 2008/0312819, the way in which pedestrian exposure risk is assessed is not defined and will be quantified on an ordinal scale from 0 to 10 which can be corrected by a user, which guarantees some lack of objectivity .

 Another aspect that distinguishes the two methods is that in the new invention the maximum value of the multiplier associated with the maximum pollutant concentration defined by the legislation is that it will be possible to define the level of aggravation imposed to the different values of the pollutant concentration, obtaining various types of routes, healthier, healthier, moderately healthier, among others. In the case of safe routes different levels of safety are not defined, ie the user only chooses between having a shorter route or a safer route.

General Description of the Invention

The present invention is a method of generating environmentally sound routes in an original and innovative urban environment since it minimizes the impact of noise and air pollution on walking and cycling in the route planning process. or any other active mode of transport.

 The originality and innovation lies in the contamination process of the pedestrian and cyclable network axes by noise pollutants, environmental noise, and various atmospheric pollutants, namely PM10 particles.

Concentration values for various pollutant species and environmental noise levels are extracted from long-term hourly maps. These maps result from the application of pollutant dispersion simulation models and the propagation of environmental noise, and specific programs such as CadnaA are used for this purpose. The results of the simulations are maps in vector format or raster type. This way they can be used in a Geographic Information System, which will allow the use of these values in the phase contamination phase and consequently in the route calculation.

The characterization of the influence and affect of pollutants on the networks presupposes the average hourly assessment of noise levels and pollutant concentration for long-term scenarios, which are estimated and obtained for annual periods. The values of these levels and concentrations will be transformed into multipliers that derive from the use of fuzzy functions for each pollutant, intended to translate the evolution of the effect of these pollutants on human health. In this way, the so-called "contaminated distances" that result from the product of the actual distances by the selected multiplier are calculated, called the contamination of distances and is expressed by:

. Dc = D x K

Where: Dc - contaminated distance; D - actual distance and K - multiplication factor

The distance multiplication factor (k) is a function of the pollutant concentration level (r) according to the following formula (see Fig. 14): r <F ^ k = 1 /

 1-d / K. )

 ≤r <F¾: k = 1 - - (F¾ - r)

 F \ ≤r <F¾: k = l

R ₃ ≤r <R ₄ : k = 1 +, -1 ^• (r - F¾)

 F¾≤r k = K,

The values of R1, R2, R3 and R4 are arbitrary and depend on the type of pollutant. The values of K1 and K2 (K2 in this specific example correspond to the value of 1 / Ki, but this is not required) are arbitrary and depend on the intended distance adjustment intensity.

The healthy route generation model for active modes of transport will be used in route calculation programs whose impedance values (optimization variable) will correspond to a contaminated distance. This model will enable innovative planning and navigation solutions to be created in route planners available on digital platforms as well as in mobile navigation systems (portable smartphones or GPS).

One of the main advantages of the present invention is related to the fuzzy preprocessing phase, in which the road map vector map is subsegmented into subsegments established by noise and air pollution classes. This gives a new road network map that accurately reflects air and noise pollution indicators, which has the advantage that it can be used transparently and without any adaptation to the usual routing optimization algorithms. That is, there is no need for any change in the route planning engine, while a much more accurate simulation is obtained than currently available. Another advantage is that these benefits are achieved without excessive impact on the computational load involved. Yet another advantage is linked to the fact that the computational load involved is easily adjusted by manipulating the parameters of the present invention, namely the number of classes chosen for the basis of subsegmentation.

Description of the Figures

 For an easier understanding of the invention, attached are figures which represent preferred embodiments of the invention which, however, are not intended to limit the scope of the present invention.

Figure 1: Schematic representation of healthy route generation flowchart for active (pedestrian and cycling) modes of transport.

Figure 2: Schematic representation of fuzzy functions of multipliers KH and KL.

Figure 3: Schematic representation of fuzzy functions of the WH and WL multipliers.

Figure 4: Schematic representation of the contamination of distances at axle level.

Figure 5: Schematic representation of Braga's road network produced by Navteq in 2011).

Figure 6: Example schematic representation of multiplier assignment for noise levels for the period from 11:00 to 12:00. Figure 7: Example schematic representation of polygons aggregation with noise level between 75 and 80 dB (A)).

Figure 8: Schematic representation of the multipliers' allocation to the road network.

Figure 9: Example schematic representation of the transformation of a section of a street after overlapping with pollution maps.

Figure 10: Example schematic representation of the shortest route calculation between two points using the ESRI network analyst.

Figure 11: Example schematic representation of the calculation of different routes between two points: a) less / moderately noisy; b) less / moderately polluted; c) healthier and healthier.

Figure 12: Schematic representation of examples of the variation of the various routes between points 1 and 2 at different times of the day.

Figure 13: Schematic representation of the calculated routes between two points (1 and 2) between 8 and 9 o'clock.

Figure 14: Schematic representation of the distance multiplication factor (k) is a function of the pollutant concentration level (r).

Figure 15: Simplified schematic representation of the elements of an embodiment of the invention. Detailed Description of the Invention

The model for determining a healthy route is the construction of an information infrastructure based on a Geographic Information System, through which data from simulations of environmental noise levels and atmospheric pollutant concentration will be integrated into a given pedestrian network. cycling.

From this infrastructure, information from the relevant pollution maps for each section of the network will be extracted, namely the decibel level and PM10 particle concentration as well as the concentration of nitrogen oxides (Ox), carbon monoxide (CO), sulfur dioxide (S02) benzene (C6H6) and other types of pollutants that are considered harmful to health that will give rise to multipliers defined by fuzzy functions for each type of pollutant, thus defining the impedances. Consequently, these impedances will be affected to the axles of the road network giving rise to the so-called contaminated road network. In turn, this contaminated network will be used as a base element, with the attributes needed to run any path optimization algorithm for the planning and navigation of soft (active) modes. In general terms, according to the scheme of Figure 1, the model can be structured in three distinct phases, which are:

I) the generation of long-term atmospheric and noise pollution hourly maps;

II) the determination of the impedances and their affectation to their axes of the road network, giving rise to the contamination of the pedestrian and cycling network; III) the generation of various types of routes, according to the optimization criteria, including the healthiest routes.

In addition to the innovative aspect of incorporating environmental factors into a road-walking and cycling route-generating model, which allows for less noisy and less polluted routes, the combination of which creates the healthiest, the core of innovation. lies in the second point of the model that corresponds to the multipliers generation process and consequent determination of the contaminated distances. Appropriate tools for the production of pollution maps are used upstream of this process, such as hourly pollutant concentration estimation and environmental noise level programs such as CadnaA, as well as the geographical platform that may use various types of pollution. such as ESRI's ArcGIS case. The route generation phase involves the use of geographically based programs that incorporate route optimization programs and allow the cost variable to be assigned the values of the contaminated distances. These contaminated distances, particularly aggravated, may be referred to as impedances.

The phase of distance contamination concerns the creation of impedances that will be attributed to the various network segments of the active modes of transport, based on long-term environmental noise maps, various air pollutants and Global pollution map for all hours of the day.

Impedance calculation is associated with the definition and characterization of a distance multiplicative factor whose purpose is to translate the effects on human health. noise levels and pollutant concentration, in particular PM10 particles.

One of the main problems in the definition of the multiplicative factor is the knowledge of the true impact that different pollutants have on human and animal health, ie in accounting for the pollutant load limit values from which exposure is harmful. the human being. On the other hand, there is a combined effect of the various pollutants, even if they are analyzed separately. Thus, the validation and definition of limit values for the various pollutants is difficult to account for. This complexity is even more significant when it comes to accounting for how different individuals assimilate and react to different types of pollutants.

The characterization of environmental noise allows us to define the level of annoyance of people facing different noise values. However, extrapolation of this relationship has to be carried out with many restrictions due to the enormous variability of factors that influence human noise sensitivity. This process is even more complex when it comes to finding the law between the dose and the effect that a given air pollutant has on man.

 Therefore, the definition of the impedances to be used for contamination of road network axle distances (pedestrian and cyclable) should allow for the introduction of variability of various factors that influence the sensitivity of different humans, notably for very similar exposure levels of the concentration. of a particular pollutant.

Therefore, it is necessary to define a law that simultaneously translates the limits within which health is impaired, as well as the level of tolerance to certain levels of exposure that a person is willing to accept to move along a certain path. Thus, two fuzzy linear functions are proposed to define a multiplicative factor (K) for noise (equivalent noise level - Leq) and a multiplicative factor (W) for particle concentration - PM10.

The yy axis of the fuzzy functions represents the evolution of the multiplicative factor, while the xx axis shows the equivalent noise level - Leq (A) and the particle concentration - PM10. The function is defined by the linear variation between the anchor values, called fuzzy control points, which are the minimum and maximum values of noise and particle concentration - PM10, defined according to the limits imposed by the legislation or, taking into account basis scientific studies for this purpose.

 In order for the user to be able to define the level of contamination aggravation to the network, the limit values of the multiplicative factors K (noise) and W (PM10 particles) for the control points are set by the same at the beginning of the planning and navigation process. Healthy routes.

To illustrate this process, the user can make a strong or weak increase of distances by defining contaminated distances that will be used as optimization variables in the shortest path calculation process available in GIS software.

For this purpose the user only chooses the level of aggravation according to a numerical scale of integer values, where for example 3.0 corresponds to the maximum value of the coefficient KH and WH which will give rise to a strong contamination of the distances and in turn the minimum value will automatically be in a preferred embodiment defined by the algorithm as 1/3. In turn, for a weak contamination maximum multiplier value will correspond to half of the maximum value defined for a strong contamination, resulting in a KL and WL = 3/2 and therefore the minimum value 1 / (3/2) = 2/3, or arbitrarily chosen as for example KL and WL equal to 0.75.

In practical terms, the option for a strong aggravation implies that the algorithm assigns three times the actual distance to distances contaminated by pollution levels above the upper anchor value and to values below the lower anchor value the distances will be converted into a distance. third of the actual distance. Among the anchor values, the multiplicative factor variation is linear.

The use of weak aggravations is geared towards obtaining routes that are intended for regular use, such as commuting, as pedestrians or cyclists will be willing to extend their route to achieve healthier route, but without requiring them to significantly increase travel times and length of the route.

On the other hand, if pedestrians or cyclists have some kind of disease, especially respiratory, or even healthy people whose purpose of travel is leisure or sport, they may choose even less polluted routes, ie healthier than those obtained by a slight worsening of distances by selecting a strong worsening, even if this means a considerable increase in the overall length of the route.

Thus, the routes generated from the attribution of a strong aggravation involve the circulation by less polluted paths than those determined using a weak aggravation and, of course, those without any aggravation of the distances (shortest path).

Having defined the upper limit values of the multiplication factors to be attributed to the axles of the road network (pedestrian and cycling), it is important to define and characterize the fuzzy multiplier function related to a strong aggravation derived from environmental noise (KH) and weak (KL). To do so, it is necessary to define the shape of the curve by fixing the anchor values, ie the equivalent sound level values, Leq (dB (A)) for which the values of KH and KL are known.

In the case of environmental noise, anchor values are defined on the basis of the maximum permissible noise (Lden) values associated with different zones as set out in the General Noise Regulation. Since the temporal coverage of the production of healthy routes covers all hours of the day, limit values for night time (Ln) will not be used, as the objective is to minimize the effects on people's health when walking. on foot or by bike. Similarly, exposure limits for mixed zones at night are equal to the limit for sensitive zones during the day and correspond to 55 dB (A), as shown in Table 1. Thus, only the use of the limits imposed is permissible. to Lden in defining the fuzzy function for all hours of the day.

Table 1 - Limits of exposure to environmental noise

 Lden

 Zones Ln (dB (A))

 (dB (A))

 Mixed 65 55

 Sensitive 55 45

In the specific case of the definition of the fuzzy function for the multiplier K associated with noise contamination of distances, it is assumed that the contaminated distance equals the actual distance for values between 55 and 65 dB (A), which include the values between boundaries associated with the sensitive and mixed zones for Lden. For anchor values corresponding to the upper and lower limits of K, whether in the case of a strong or weak impedance, a margin of 10 dB (A) (by analogy with the values defined for the same zones as Ln and Lden. On the other hand, values below 35 dBA do not correspond to reality) above and below the limits of Lden, as shown in Tables 1 and 2 and the graph in Figure 2.

Table 2 - Fuzzy anchor values for the multiplier K associated with ambient noise

 Leq (dB (A))

 45 0.33 (3) 0.750

 75 3,000 1,500

 [55 65] 1,000 1,000

For the definition of the fuzzy function for the multiplier W associated with the contamination of distances due to PM10 particle concentration in air, it is proposed for higher anchor value the use of the PM10 annual average concentration limit for human protection defined by the national legislation of PM10. 40 μg / m3. On the other hand, the value recommended by the World Health Organization in 2005 for mean hourly concentrations of PM10 measured throughout the year is 20 μg / m3 in urban environments. Given that, for small and medium-sized cities, these values are far from being achieved for most urban streets, a lower anchor value of 10 μg / m3 was then set as can be seen. Table 3 and Figure 3. Between the values of 10 and 40 μg / m3, a linear variation of the factor W was considered.

Table 3 - Fuzzy anchor values for W multiplier associated with PM10 mean particle concentration PM10 (g / m ³ ) WH WL

 10 0.33 (3) 0.750

 40 3,000 1,500

Having defined the fuzzy functions for the two types of pollutants used to generate the least polluted, least noisy and healthiest routes, the process of contamination of road distances (pedestrian and cyclable) results from the application of the following expression:

Contaminated Distance = Distance x Multiplier [1]

In order to integrate the information generated in the pollution maps into the road network, and thus build the matrix of axes contaminated by the various pollutants, or a combination of them, it is indispensable that the work be performed in a GIS environment. This process involves the conversion of long-term noise and average annual PM10 particle concentration maps from raster to vector formats. This is followed by the overlapping of the maps to the road network, which will allow their attribution to the attributes axes of these maps. At the level of the road network attribute table, the multipliers and their contaminated distances are calculated. In Figure 4 is presented schematically the network contamination process described above.

It should be noted that, having generated maps of environmental noise pollution and particle concentration (PM10) for different hours of the day, the contamination process gives rise to a georeferenced road network, which corresponds to a geometric entity (lines) to which a set of attributes are associated, which will correspond fields with pollutant quantities, associated multiplicative factors, and other elements such as actual and contaminated distances.

The production of long-term hourly maps of pollution associated with ambient noise and the average PM10 concentration for the case study area, simultaneously with the road network, represent the key factors of the process phase designated by distance contamination.

By analyzing the long-term pollution maps it is possible to see that pollution levels vary over time and space. On the other hand, in the genesis of the concept of healthier routes is inherent the attribution of the impact of pollution levels on the choice of route by pedestrians and cyclists.

In summary, the contamination phase consists of:

- in the treatment of the road network in vector format; transforming map information into a vector format;

- applying the multipliers determined to pollution maps; contamination of distances according to the pollutant and the multiplier.

The treatment of the road network consists of checking the continuity of the links between axles, particularly at the intersection level. It is necessary to ensure that the axes of the study area are all represented, both for pedestrian and cycling traffic, as this will be the basic structure for route generation. The definition of the directions of the road axes is another aspect to be taken into account, as it is a necessary condition for bicycle circulation and can be done at this stage or at the beginning of the route generation phase. However, for walking, this question does not arise because it is not restrictive as pedestrians can move both ways in any square, sidewalk or verge.

Issues associated with access restrictions also need to be defined at this stage, notably as regards exclusive access to pedestrians in pedestrian areas and streets. Similarly, access restrictions have been defined, in particular on highways or in areas where the risk of accident for pedestrians and cyclists is high, as is the case in the branches of the road junctions where there are no formal pedestrian paths. Restrictions may be imposed by creating attribute fields in the database associated with the road network where access is defined according to certain criteria, such as: Tours: Y / N, if yes (S) the pedestrian may circle and the segment enters the pedestrian route calculation, otherwise (N) the segment does not enter this process.

 Figure 5 shows the road network for the study area in vector format with the database treatment described above.

The attribute table associated with the road network must have a vast set of information that can be used in navigation algorithms for soft modes and other modes of transport, including:

- the name and type of street;

- the number of directions; - the number of lanes;

- the postal code;

- speed limits; a wide range of restrictions associated with different types of use (pedestrians, cyclists, cars, trucks, emergency vehicles, taxis, buses, car pooling, among others);

- the existence of tolls; information on street paving, among many other elements.

Cartography producers' databases such as Navteq already contain this vast array of information, but their use must be preceded by field validation.

In addition to the traditional attribute fields presented above, it is possible to increase the number of fields according to the work to be carried out, such as data on air pollution and environmental noise.

With the consolidated road network, it is necessary to process the information present in the pollution maps in order to determine the contamination of each axis. For this purpose, it is desirable that the pollution mapping software allows the export of calculation grids in various formats, such as raster files and vector files of lines and polygons of the same level of pollution. Software such as CadnaA presents these export possibilities in their menus, which facilitates the use of pollution map data in other types of GIS programs, such as ESRI's ArcGIS.

Exporting pollution maps to polygons is most appropriate, as the definition of interval classes can be defined at the time of export, and each class may be have a unit amplitude. However, given the configuration of the noise and PM10 maps, it was concluded that too low an amplitude would give rise to a large set of polygons. This could result in a large segmentation of road axes, which could result, for example, that a 100-meter axle could be divided into dozens of segments, with clear consequences for the enlargement of the navigation database. Thus, according to the average, minimum and maximum values observed in the various modeled maps, it was decided to define in a preferred embodiment an amplitude of 5 units for each class, ie 5 dB (A) and 5 μg / m3, for ambient noise and PM10 concentration, respectively. Although this value should be taken as an indicative value.

Once the amplitude of the class interval was defined, the multiplier value was calculated for each of these classes according to the fuzzy functions defined in Table 4, considering a heavy (K Heavy) to obtain a noisy route. , or a faint (K Light) for a moderately noisy route.

Table 4 - Distance Multipliers for Noise Levels

Noise (dB (A)) Multipliers

 [Max - Min] Average K Heavy K Light

 [DBH - DBL] DBA KH KL

 <45 42.50 333 0.7500

 45 - 50 47.5 0.500 0.8125

 50 - 55 52, 50 0, 833 0, 9375

 55 - 60 57, 5 1,000 1,0000

 60 -65 62, 5 1,000 1,0000

 65 -70 67.5 1,500 1, 1250

 70 - 75 72, 5 2,500 1, 3750

 > 75 77.5 3,000 1.5000

Figure 6 shows an example of the noise multipliers (K) affecting the period from 11:00 to 12:00. It should be noted that at this stage each class corresponds to a set of polygons represented in each row of the attribute table, as shown in Figure 7.

The same process was applied in the treatment of PM10 particle concentration map data, as can be seen in Table 5. Similarly, a W Heavy was considered to obtain a poorly polluted route, or an aggravation. W Light) for a moderately polluted route.

Table 5 - Distance Multipliers for PM10 Mean Concentration Levels PM10 (g / m ³ ) Multipliers

 [Max - Min] Average W Heavy W Light

 [PMH - PML] PMA WH WL

 <20 <17.50, 33333 0.75000

20 - 25 22, 50, 66667, 0, 84375

25 - 30 27.5 1, 33333 1, 03125

30 - 35 32, 5 2.00000 1.21875

35 - 40 37.562.66667 1, 40625

> 40> 42, 53.00000 1.50000

Having calculated the values of the distance multipliers K and W for the value classes presented in Tables 4 and 5, and the pollution maps were assigned to the polygons, these maps (schedules) were overlapped with the road network of the as shown in Figure 8.

It is noteworthy that the entire process of handling the network and maps in vector format is done using GIS tools available in ESRI's ArcGis program.

Overlaying the network with maps and capturing multiplier values through the line-on-polygon operator implies segmentation of the intersected axes by the polygons. The base road network of the study area, ie without any overlap, consists of 1206 segments. In the specific case of the example presented, for the period from 11 am to 12 noon, after overlapping the noise and concentration map of PM10, the road network was divided into 6618 segments.

By way of example, Figure 9 shows a 280-meter long stretch of Rua D. Pedro V of Braga, divided into 67 segments, reflecting a large variation in the combination of noise values. and PM10 concentration in this section, between 11 and 12 hours. The contamination process ends with the calculation of the contaminated distances resulting from the multiplier product by the associated segment length. Thus, four contaminated distances can be directly determined: dKH, dKL, dWH and dWL, which will be used as cost variables in the generation of the least noisy, moderately noisy, least polluted and moderately polluted route. It should be clarified here that these qualifying route expressions are relative in character, reporting a level (less and moderate) than the shortest route.

The healthiest route results from the average of the multipliers KH and WH, giving rise to the distance dKWH. The healthy route results from the average of the multipliers KL and WL, giving rise to the distance dKWL.

Following the example of the section of Rua D. Pedro V, the calculation of contaminated distances gave rise to the values given in Table 6.

Table 6 - Example of final result of the contamination process of the distances of an axis

 Distance Contaminated distances (m)

 (m) dKH dKL dWH dWL dKWH dKWL

 281.0 476.8 330.0 153.8 233, 0 315.3 281.0

In order to show the integration of the distance contamination model in the calculation of the healthiest routes, we present as an example the calculation of the different routes between points 1 (residential neighborhood) and 2 (educational establishment) located in the city center of Braga. The shortest route calculation result is shown in Figure 10. Using the same procedure that was presented for the determination of the shortest route of Figure 10, and considering the impedances related to the six contaminated distances using the GIS software calculation tool called network analyst, the following set of routes is obtained: healthy healthier, less and moderately polluted (in terms of PM10 concentration) and more and moderately noisy for the 8 to 9 h period, as can be seen in Figure 11.

From the analysis of the obtained routes, it is possible to verify that the contamination of the distances led to distinct routes between points 1 and 2, especially between the routes affected by noise and PM10 concentration. On the other hand, it was found that all routes depart from the shortest route, which will be a priori the noisiest and most polluted route, the least healthy of the seven calculated.

Once 24 24-hour network datasets have been produced, the route calculation results will be presented for randomly chosen time intervals in order to demonstrate and analyze the variability of the routes obtained over the period. time.

From the analysis of Figure 12 it can be concluded that the route associated with each route varies, especially when considering the strongest impedance values, that is when we consider the "Heavy" values of the multipliers, which consequently give rise to the routes. healthier, with less polluted air and less noisy.

However, when considering “Light” multipliers, the results are more homogeneous, with similar routes to moderate levels of noise or air pollution, giving rise to a healthy route with them. features. While this result may seem redundant, it is indeed very important because it highlights the issue of the low level of complexity and the alternatives that network configuration can generate. Of course, in networks with higher axis density, the results would certainly be slightly different.

In addition, it should be noted that in the exemplary embodiment of the preferred embodiment, all routes that derive from contaminated distances depart from the shortest path, namely on the axis of Avenida João XXI.

Throughout this case study the main form of presentation of the results is the map, which underlies a certain set of geographic features that translate into a datum. The definition of the datum corresponding to each map is crucial for some operations, such as the measurement of areas and distances, among many others.

The process of validating the healthy routes model consists of calculating two environmental indices, relating to noise and the average PM10 concentration for a given route and comparing them with the indices associated with the shorter route.

 Figure 13 shows the shortest route and the six routes with the least health impact due to environmental aspects, between points 1 and 2, for the period from 8:00 to 9:00.

Table 7 provides a set of data for each route, such as extension, contaminated extent and noise and particle exposure rates, complemented by data comparing said route attributes with the shortest route. . Table 7 - Summary of calculation of indices for different routes and comparison with shortest route (í 3-9h)

 Difference indices for the

Extension

 Exten shortest route exposure

 account¬

ROUTES are PM10 PM10 mined Noise Extens Noise

(m) (g / m ³ (g / m ³

 (dBA) No (dBA)

 ))

 Shorter 1785, 9 1785, 9 75, 5 30, 8

Less Noisy

 2459, 5,476,865, 6,673.5

 - "Heavy"

Less Noisy

 1816, 1 2471, 4 72, 7 30.2

 - "Light"

Less polluted

 2285, 7 1981, 9 24.0 499, 8-6,

- "Heavy"

Less polluted

 1816.1 1875.0 27.5 30.2 -3.2

- "Light"

Healthier

 2459.5 3214.8 65.6 24.1 673.5 -9.9 -6.7

- "Heavy"

Healthier

 1816.1 2173.2 72.7 27.5 30.2 -2.7 -3.2

- "Light"

From the analysis of the indices it is evident the reduction of noise levels and particle concentration for all less noisy, polluted and healthier routes, regardless of the multiplier used. However, for routes with heavily contaminated distances ("Heavy" multiplier), the gains are considerable, around 10 dB (A) and 7 μg / m3, respectively. For the remaining routes, the shortest route gains are around 3.0 dB (A) and 3.0 μg / m3. These figures clearly show the gains that opting for less contaminated routes brings to public health.

However, by comparing the length of the routes for the different routes, it can be concluded that at this time to decrease the exposure index by 10 dB (A) and 7 μg / m3 it is necessary to travel between 500 and 600 meters longer. lengths, which can result in an increase of about 10 minutes (considering a circulation speed of 1 m / s) over a journey that could take at least about 30 minutes. To decrease the exposure rate by about 3 dB (A) and 3 μg / m3 of PM10, it will only be necessary to walk at a further 30 meters, ie about 30 seconds.

The results shown in the previous example demonstrate the ability for pedestrians or cyclists to customize or adapt their travel by defining the level of aggravation they wish to assign to each pollutant.

Moreover, it reinforces the idea that healthier, less noisy and less polluted routes involve longer routes that can be associated with sports, leisure circuits or simply a more sensitive disposition towards a particular pollutant.

Cases of healthy, moderately noisy and polluted routes may be good options for the shortest route, as they have very close extensions, making it only a matter of choosing which streets to drive. These routes clearly and clearly represent a viable alternative for commuting or high-frequency commuting, such as home-school and home-work travel. Hence the interest in the consideration of weak "Light" factors.

The preferred embodiments described above are obviously combinable with each other. The following claims further define preferred embodiments of the present invention.

Claims

Method for determining a route on a road network in a navigation system comprising the steps of: a. obtain polygons with air and / or noise attributes from previously generated time maps that include the road network, where each polygon corresponds to a air and / or noise class from a predefined number of air pollution classes and / or sonorous;

 B. segmenting each segment of the road network by intersecting said polygons in sub-segments; ç. determine corrected distance from subsegments by applying the actual distance of each subsegment of noise pollution multiplication (K) and atmospheric pollution multiplication (W) factors obtained by a fuzzy function of the mean value of the respective pollution class;

 d. calculate the route with the subsegments and their corrected distances that minimizes the total corrected distance.

Method according to the preceding claim, characterized in that the noise pollution multiplication factor (K) is a continuous fuzzy function comprising a central unit threshold between two predefined noise pollution level limits (R ₂ , R 3), and comprises positive slope ramps at both ends of the center landing, ramps bounded between two predefined noise pollution level threshold values (Ri, R4) such that the function is limited between two minimum predefined threshold values (K ₂ ) and maximum (Ki).

Method according to claim 2, characterized in that in the fuzzy function of the noise pollution multiplication factor (K), the minimum default value (K ₂ ) is the inverse value of the maximum default value (Ki)).

Method according to Claims 2 - 3, characterized in that in the fuzzy function of the noise pollution multiplication factor (K), the two predefined noise pollution limit values for the ramp ends (Ri, R ₄ ) defined by a 10dbA offset from the two predefined noise pollution limit values for the extremes of the central level threshold (R ₂ , R ₃ ).

Method according to the preceding claims, characterized in that the air pollution multiplication factor (W) is a continuous fuzzy function comprising a positive slope central ramp between two predefined air pollution level limits (R ₂ , R ₃ ), ramp containing the unit value, and the function is limited between two pre-defined minimum (W ₂ ) and maximum (Wi) limit values.

Method according to claim 5, characterized in that in the fuzzy function of the air pollution multiplication factor (W) the minimum predefined value (W ₂ ) is the inverse value of the maximum predefined value (Wi).

7. Method according to claims 5 - 6 , characterized in that the pollution level measured prior to understand the measured concentration of pollutant the concentration of PM10, the concentration of oxides of nitrogen (N0 _X), carbon monoxide (CO ) of benzene sulfur dioxide (SO ₂ ) (C ₆ H ₆ ).

Method according to claims 2 - 7, characterized in that the maximum value (Wi) of the fuzzy multiplying factor (W) function and the maximum value (Ki) of the function noise pollution multiplication factor (K) be previously defined by the user of the navigation system.

Method according to the preceding claims, characterized in that the predefined number of air and noise pollution classes is defined by a variable class amplitude, in particular 5 μg / m3, and 5 dB (A) of air and noise pollution.

Method according to the preceding claims, characterized in that it comprises the step of calculating four corrected distances for each subsegment, two of the atmospheric pollution multiplication factor (W) and two of the noise pollution multiplication factor (K), each of the following. two obtained for a smaller value (dWL, dKL) and a larger value (dWH, DKH) of the respective upper limits (Wi, Ki).

Method according to the preceding claims, characterized in that the route is calculated by averaging the multiplication factors (K, W), giving rise to two distances (dKWH, dKWL) corresponding to the healthiest route and the healthy route.

A system configured to perform the method referred to in any preceding claim.

Computer program comprising the appropriate program code for performing each of the steps of the method referred to in any one of claims 1 - 11 when said program is executed in a data processing system.

Computer readable medium according to the preceding claim, characterized in that it incorporates said computer program.