Part 1: Guide to routing
Several public sector organisations use network data for optimising refuse collection routes, school transport and reorganisation of the locations of key services. Several case studies have been published demonstrating significant cost savings. As a result, more organisations are seeking to reduce costs by better use of network data. This has raised awareness of the benefits of using route analysis, and this document provides a guide to route analysis aimed at helping users understand what it is and how to get started.
Route analysis can be used to answer questions such as
Where is my nearest library?
What is the most efficient route for my deliveries?
Which is the best location to build a new office?
When considering these questions, a simple radius search using a buffer zone can be used to find objects within a set distance of a feature. For example, a school needs to know how many addresses are within a 1 km catchment area. The image on the left shows all the addresses highlighted within a radius buffer however addresses on the opposite side of the river are included.
To improve this analysis, the road network can be used to give a more accurate picture. Generating a polygon based on 1 km driving distance creates a better representation, returning fewer addresses as shown on the right. This demonstrates that route analysis provides a more effective method for defining catchment areas, managing location-based services and answering the questions listed earlier.
Route analysis uses either road or path networks to determine the driving or walking distances, or time, between objects and can be used to solve complex problems. These include, but are not limited to, where is my nearest facility, what is the optimal location for a new business. There are different types of analysis that can be performed on data. These are:
A to B route generation
How do I get from point A to point B? This is the simplest form of routing and is in common use in web mapping applications giving details of how to travel between 2 points, the distance and estimated journey time. Journey times can also take into consideration the type of transport used, e.g. car, bus, cycle, or by foot. Start and end points could be a grid reference, address, post code or a place name. The example shows a journey between Cardiff and Cwmbran using the quickest route and includes directions. However, it is also possible to set options to include certain types of road or go via another place en route, this is often referred to as Travelling Sales Person (TSP)
Travel time
Travel or drive time analysis is used to define how far can be traveled in either a set time or over a set distance. It works by creating a series of irregular or Voronoi polygons for each point on a network, each polygon represents either distance or timed traveled. Although it is often referred to as drive time, it can also be used to model walking or cycling time as well. The results are displayed as a series of isochrones, either as polygons or lines. This type of analysis is used to show what is within certain distances of a specific point. For instance, a hospital may want to know how many patients live within a certain travel time to the facility to help with service planning.
Closest Centre
Closest centre analysis is used to calculate the cost of travelling between several locations from a fixed point (or points) determining which are nearest to each other. The resulting analysis includes a rank of the points and corresponding locations based on the time to travel between them. This can be used for assigning the closest resource to a specific location, such as emergency responders to an incident. This analysis can also be used to help plan the location of resources such as bus stops. The example shows the location of people without cars on a housing estate and the closest bus stop to each.
Cost matrix
An origin-destination cost matrix measures the distance across a network that costs the least to travel (or least cost path) form either a single or multiple origin to multiple destinations. The results are displayed either as a table or a spider diagram linking the origin to destinations with a straight line. This type of analysis is useful when large volumes of data need to be analysed.
Using the bus stop example, it’s important to identify the distances to all stops in the area from a specific location, as we need to understand the impact of changing a route. The table shows the distance to each location ranked from closest to furthest displayed graphically as series of lines.
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