Is there a way to restrict road access by vehicle type? Some roads restrict trucks, some roads have height/weight restrictions, and some roads are restricted by the type of license plate (e.g., commercial, personal, etc.).
I am familiar with restricting road access by height and weight of the vehicles, but how would I add attributes in Network Dataset to restrict road access by vehicle type (e.g., trucks) and type of license plate (e.g., commercial)?
Is it possible to achieve with Network Analyst extension?
To do this look at the section on Assigning values to restrictions in the Network Analyst page on Types of evaluators used by a network:
Each attribute defined in the network must have values for each source participating in the network. An evaluator assigns values for the attribute of each source… In ArcGIS, the field evaluator assigns values to a network attribute from a field of a network source. In addition, ArcGIS offers other types of evaluators that can be used, for instance, constant, field expression, function, and script evaluators.
Restriction attributes have a Boolean data type. Since a source element can either use or ignore the associated restriction, it can be assigned a constant value of Use Restriction or Ignore Restriction.
Alternatively, restriction attributes can be derived using the function evaluator to compare another attribute to a parameter value. For example, an attribute that models vehicle height restrictions can use the function evaluator to compare the height limit of a road to the vehicle's actual height stored in an attribute parameter. When the expression evaluates to true, the restriction is used on the road; when the expression evaluates to false, the restriction is ignored. The exception to this rule is that anytime either of the operands (MaxHeight or Vehicle Height) have a value of zero, the expression always evaluates to false.
A simple turning movement between two edges connected at a junction is referred to as a two-edge turn. The ArcGIS Network Analyst extension supports modeling multiedge turns. A multiedge turn is a movement from one network edge element to another through a sequence of connected intermediate edge elements. These intermediate edges are referred to as the interior edges of a turn. In a street network, the interior edges of a turn are typically those edge elements that represent the interior of an intersection of divided roads.
The example shown above depicts a multiedge left turn at an intersection of two divided roads. Edges c and d represent interior edges of the turn, while edges f and i represent the exterior edges.
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This stores the resultant service areas as linear features and covers the streets, or network edges, that can be reached within the given time, distance, or other travel-cost cutoff. Lines are a truer representation of a service area than polygons since service area analyses are based on measurements along the network lines.
This data type supports the following fields:
The ObjectID value of the input facility feature used to generate the service area lines. This field is often used to join information from the input facilities.
Each service area line traverses a feature from a network source feature class—a feature class used to create the network dataset on which the service area analysis is performed. This field contains the name of the edge source feature class from which the line was generated.
The ObjectID of the traversed street feature. Summarizing the values for this field can provide useful information such as the number of times a particular street feature is included from the facilities.
Specifies where along the underlying source feature the service area line begins.
- A value of 0 (zero) indicates that the service area line begins at the from point of the underlying source feature.
- A value of 1 indicates that the service area line begins at the to point of the source feature.
- A value between 0 and 1 indicates that the line begins at a point along the underlying source feature for example, a value of 0.25 means the line begins 25 percent along the digitized direction of the underlying source feature.
Specifies where along the underlying source feature the service area line ends.
- A value of 0 (zero) indicates that the service area line ends at the from point of the underlying source feature.
- A value of 1 indicates that the service area line ends at the to point of the source feature.
- A value between 0 and 1 indicates that the line ends at a point along the underlying source feature for example, a value of 0.25 means the line ends 25 percent along the digitized direction of the underlying source feature.
This field contains the cumulative cost, in minutes, of the path from the facility to the beginning of the line feature. The cost of the adjacent junction at the beginning of the line is included in this value.
An additional field, FromCumul_[TimeUnits] , is included if the timeUnits property of the analysis object is not set to TimeUnits.Minutes . The field values are in the units specified by the timeUnits property.
Additional fields, FromCumul_[AccumulateAttributeName]_[TimeUnits] , are included for each time-based cost attribute that is accumulated during the analysis.
This field contains the cumulative cost, in minutes, of the path from the facility to the end of the line feature. The cost of the adjacent junction at the end of the line is excluded from this value.
An additional field, ToCumul_[TimeUnits] , is included if the timeUnits property of the analysis object is not set to TimeUnits.Minutes . The field values are in the units specified by the timeUnits property.
Additional fields, ToCumul_[AccumulateAttributeName]_[TimeUnits] , are included for each time-based cost attribute that is accumulated during the analysis.
This field contains the cumulative cost, in miles, of the path from the facility to the beginning of the line feature. The cost of the adjacent junction at the beginning of the line is included in this value.
An additional field, FromCumul_[DistanceUnits] , is included if the distanceUnits property of the analysis object is not set to DistanceUnits.Miles or DistanceUnits.Kilometers . The field values are in the units specified by the distanceUnits property.
Additional fields, FromCumul_[AccumulateAttributeName]_[DistanceUnits] , are included for each distance-based cost attribute that is accumulated during the analysis.
This field contains the cumulative cost, in miles, of the path from the facility to the end of the line feature. The cost of the adjacent junction at the end of the line is excluded from this value.
An additional field, ToCumul_[DistanceUnits] , is included if the distanceUnits property of the analysis object is not set to DistanceUnits.Miles or DistanceUnits.Kilometers . The field values are in the units specified by the distanceUnits property.
Additional fields, ToCumul_[AccumulateAttributeName]_[DistanceUnits] , are included for each distance-based cost attribute that is accumulated during the analysis.
This field is analogous to the FromCumul_Miles field, except the field values are in kilometers instead of miles.
This field is analogous to the ToCumul_Miles field, except the field values are in kilometers instead of miles.
This field contains the cumulative cost, in unknown units, of the path from the facility to the beginning of the line feature. The cost of the adjacent junction at the beginning of the line is included in this value.
This field is included only when the travel mode used for the analysis has an impedance attribute that is neither time based nor distance based.
Additional fields, FromCumul_[AccumulateAttributeName]_Other , are included for each cost attribute that is neither time based nor distance based and accumulated during the analysis.
This field contains the cumulative cost, in unknown units, of the path from the facility to the end of the line feature. The cost of the adjacent junction at the end of the line is excluded from this value.
This field is included only when the travel mode used for the analysis has an impedance attribute that is neither time based nor distance based.
Additional fields, ToCumul_[AccumulateAttributeName]_Other , are included for each cost attribute that is neither time based nor distance based and accumulated during the analysis.
Influences of forest roads on the spatial patterns of human- and lightning-caused wildfire ignitions
Understanding the spatial patterns of fire ignitions and fire sizes is essential for understanding fire regimes. Although previous studies have documented associations of human-caused fire ignitions with road corridors, less consideration has been given to understanding the multiple influences of roads on the fire regime at a broader landscape-scale. Therefore, we examined the difference between lightning- and human-caused fire ignitions in relation to forest road corridors and other anthropogenic and biophysical factors in the eastern Cascade Mountains of Washington State. We used geographical information systems and case-control logistic regression models to assess the relative importance of these explanatory variables that influence the locations of lightning versus human-caused ignitions.
We found that human-caused ignitions were concentrated close to roads, in high road density areas, and near the wildland–urban interface (WUI). In contrast, lightning-caused ignitions were concentrated in low road density areas, away from WUI, and in low population density areas. Lightning-caused ignitions were also associated with fuels and climatic and topographic factors. A weak but significant relationship between lightning-caused fire and proximity to gravel roads may be related to fuels near roads or to bias in detection and reporting of lightning-caused fires near roads. Although most small fires occurred in roaded areas, they accounted for only a small proportion of the total burned area. In contrast, the large fires in roadless and wilderness areas accounted for most of the burned area. Thus, from the standpoint of the total area burned, the effect of forest roads on restricting fire size is likely greater than the impact of roads on increasing fire ignitions. The results of our study suggest that roads and their edge effect area should be more widely acknowledged as a unique type of landscape effect in fire research and management.
► We compared human- and lightning-caused fires with the effects of different roads types. ► Human-caused ignitions were concentrated near roads. ► Lightning-caused ignitions were concentrated in low road density areas. ► Fires are smaller in the roaded areas and are larger in roadless and wilderness areas. ► Forest roads and their edge effects influence fire ignition and size.
5 Case study: determining the minimum energy route using EV-specific energy expenditure values
EV-specific energy consumption for different topographical and traffic conditions was determined in Section 4.2. This section will visualise the route choice minimising energy consumption for a given journey on an actual road network. Two steps are needed for this visualisation. First, all the routes on a road network were assigned an EV energy cost value, depending on their topography and for various traffic levels that indicate the EV energy consumption of driving on the roads within the network. Second, Dijkstra's graph search algorithm [ 25 ] was used to find the path with the lowest cost (i.e. the path minimising energy consumption) for a given journey. Table 4 shows the input components for this case study that will be detailed in the following sections.
|ordnance survey integrated transport network (ITN) GIS dataset||digitisation of the UK road network and holds within it information about the road class (a road etc.) and road type (single carriageway etc.)|
|ordnance survey land-form PANORAMA GIS dataset||dataset provides altitude information across the UK|
|department for transport COBA manual||average speeds for the road network under different levels of capacities, i.e. capacity 15 (cap 15) indicating free flow|
|energy cost based on Switch EV data to travel on a road in the network depending on the topography and the capacity level of that road (three capacity levels were used for this work, i.e. cap 15,60,90)||energy cost for travelling 100 m was determined for different slopes and speeds (as a proxy for the road network capacity or congestion level) using the thousands of driving data collected in the trial. These values were then used to define the energy cost of real-world driving on roads with corresponding slope and average speeds (that would match a certain road capacity level)|
|ArcGis network analyst extension based on Dijkstra's graph search algorithm||finds the least cost route between a chosen start and end point|
5.1 Modelling a road network
High accuracy road grade or topographical information is not currently widely available [ 19 ]. This paper develops a simplified road network topography and capacity level model. The base road network in this work was created through manipulation of the Ordnance Survey Integrated Transport Network (ITN) geographic information system (GIS) dataset [ 26 ] for the study area. The ITN dataset is a digitisation of the UK road network and holds within it information about the road class (a road etc.) and road type (single carriageway etc.).
5.1.1 Adding topographical information to the road network
In order to determine the slope of a road segment, the ITN road network was split into start and end points for each road segment. These points were then assigned altitude information from the Ordnance Survey Land-Form PANORAMA dataset. The Open Source dataset provides, if downloaded as a Digital Terrain Model (DTM), a continuous raster surface of heights across the UK. The DTM altitude information assigned to the start and end points of the road segment, combined with the length between the points are used to determine the slope of the road segment using standard trigonometric methods. There is an inherent assumption that the road segment connecting these two points is of a constant gradient over its measured length.
5.1.2 Creating bi-directional links
Originally, the network dataset only exhibits link geometry in the direction they were initially digitised and this is, for all intents and purposes, random. Using the Unique identifier within the dataset, for each original feature, the original digitised geometry direction is termed direction ‘A’. A copy of the dataset is then created and the geometry reversed creating direction ‘B’. This task is essential so that the bidirectional links can display different impedance values during the network analyst and routing algorithms (i.e. different slope depending on the direction of the travel on that road).
5.1.3 Assigning average speed at predefined COBA capacities
In addition to determining the gradient and direction of the road network in order to visualise the EV data, it is also necessary to prepare the road networks for various levels of traffic capacities. In order to simulate varying levels of congestion, average speeds for different road types at different levels of capacity are determined from the COBA manual. The speed of the roads that make up the ITN network are variable in terms of speed limits, but also in terms of how the speeds of these roads vary when reacting to different levels of network capacity. The capacity conditions range from 15% capacity (essentially free flow speed) to 145% capacity (severe congestion). Each road segment in the ITN dataset was assigned to a COBA link type classification creating a lookup between road type descriptions. Capacities of 15, 60 and 90% (Cap15, Cap60, Cap90) are used for this work.
5.2 Route choice minimising energy consumption
The Network Analyst extension in ArcGis based on Dijkstra's graph search algorithm is used to determine the minimum energy route between an origin and destination. Network Analyst is also used to determine the area of the network that an EV with a certain level of charge could cover. Fig. 9 is an example of finding the route between an origin and destination that minimises energy consumption. It shows the route representing the shortest distance between two points and several energy minimising routing decisions.
The analysis of the routing decisions made under different levels of capacity, and thus average traffic speed, shows that in order to minimise the expenditure of energy, the minimum distance route between the two points can change dramatically. For example, when using Newcastle City Centre and Edinburgh as an origin and destination, two distinctly different routes are chosen one using predominantly the A1/A697 and the other the A696. The reason for this is that under different capacity levels, the two A Roads react differently in terms of their average speeds. Combining this information, along with topographical changes, the most efficient route is selected. For example, in free flow conditions the chosen route is of a similar distance (159 and 155 km, respectively) to the 90% capacity route however, there is a noticeable difference in the energy consumption figures (15.95 and 11.75 kWh) which could be related to driving at energy-intensive high speeds. The shortest distance route does not take into consideration the topographical and traffic conditions of the roads it minimises the distance but not necessarily the energy consumption.
Fig. 10 is an example of finding the area that an EV could cover within the specified network energy cost cut-off. In this work, the energy cost cut-off is the amount of charge on the vehicle. In other words, Fig. 10 shows how far the EV could go from a starting point until it runs out of charge for different levels of network capacity. Comparing the covered area of an EV between free flow conditions, congestion and cap 60, it is found that the driving range of an EV is at its minimum under free flow conditions where average speeds are highest with related high energy consumption as showcased in Fig. 6. Cap 60 (i.e. condition in between congestion and free flow) exhibits the largest range and this is because the average speeds for this road network condition are optimal in terms of energy consumption. The roads are not so heavily congested to have speeds dropping under 35 kmph and they have traffic that could indirectly lead the user to drive in the optimal average speeds (35 to 70 kmph).
Community Planning & Mapping
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Analyses and modeling of urban land use and road network interactions using spatial-based disaggregate accessibility to land use
Rapid urbanization in developing countries enforces authorities to assess the sustainability and balanced development of urban cities from multiple perspectives and recommend appropriate solutions for policy and decision-makers. This study proposes spatial-based disaggregate accessibility to land use to figure out the influencing factors and key indicators that enable a comprehensive categorization of cities’ subdivisions from the perspectives of urban land use and road transport network. A general GIS and statistical software programs-based methodology is adopted and validated in the case example of Kandahar City, located in Afghanistan. The methodology involves five major steps 1) limited primary and secondary data collection regarding road network and land uses, 2) calculation of urban land uses weights, 3) identifying critical centers of neighborhood land uses, 4) estimation of origin-destination cost matrix, and 4) formulating and modeling accessibility measures. The statistical and theoretical significances have been illustrated in the result. The final accessibility measures have divided the subdivisions of Kandahar City (case study) into three different categories, namely, a) high-level municipal districts (CBD), b) medium-level municipal districts (semi-urban), and c) peripheral municipal districts. The research has incorporated qualitative and quantitative approaches for sustainable and balanced urban development.
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Esri’s says its core business has remained strong and stable throughout the Coronavirus pandemic. However, like many organizations, the crisis required Esri to change the way it was working, which it says led it to accelerate the digital transformation and streamlining of many of its business processes.
In another move reflecting these unique times, Esri has created a Racial Equity team to assist its users with solutions and resources focused on racial, social, economic, and health inequalities. Esri says this initiative will deliver technology configurations and technical assistance for these communities, and has already launched a new Racial Equity Microsite and Racial Equity GIS Hub.
Restricting road access by vehicle type in ArcGIS Network Analyst - Geographic Information Systems
p-ISSN: 2167-7263 e-ISSN: 2167-7247
Assessing Geographical Inaccessibility to Health Care: Using GIS Network Based Methods
Sudha Yerramilli 1 , Duber Gomez Fonseca 2
1 Trent Lott Center for Geospatial Technology, CSET, Jackson State University, Jackson, MS, 39204, USA
2 College of Science Engineering and Technology, Jackson State University, Jackson, MS, 39204, USA
Correspondence to: Sudha Yerramilli, Trent Lott Center for Geospatial Technology, CSET, Jackson State University, Jackson, MS, 39204, USA.
Copyright © 2014 Scientific & Academic Publishing. All Rights Reserved.
Disparities in the geographic accessibility to health care may be due to the location/distribution of the population and the characteristics of the transportation infrastructure relative to spatial arrangement of the health care delivery system within a region. Access to health care is a complicated concept and is largely dependent on the characteristics of the population in need of services. The most significant features affecting the health status and health outcomes involve distance between the population’s geographic regions and health care facilities and the travel time taken to reach the health care delivery system. Because of Mississippi's rural nature and uneven distribution of physicians, geographic disparities exist in access to primary care services leaving women, children, elderly and general populations in underserved health care regions. The purpose of the research is to identify hot spots of vulnerable population burdened due to geographical accessibility to right kind of health services. This research investigates these features by using network-based GIS methods in ten counties with urban-rural settings. The methodology assesses the geographic accessibility of three types of critical health care facilities: obstetrician/gynaecology (Women in child bearing age) paediatrics (children) and Trauma/Burn Centers (general population). To examine, using network analyst GIS functionalities, these facilities are geocoded, and optimal travel-time based service areas were generated and pertinent vulnerable population data layers were developed. The results identified hot spots of vulnerable populations residing outside the optimal service areas, with rural regions and pregnant women bearing most of the health burden due to geographic inaccessibility. This GIS methodology equip health administrators and policy makers in providing comprehensive view of the health systems from a territorial perspective while assisting them in making conscious policy decisions.
Keywords: Geographic Accessibility, Network Based Methods, Health Care, GIS