transferring link data between networks · the integrated transport network (itn) and traffic...

Transferring Link Data between NetworksAn Algorithmic Approach

Steven O’Hare

Senior Transport Modeller & Practice Champion for Modelling

Mott MacDonald

Transferring Link Data between Networks

Typical project

• Inherit an existing model of an area with the intention of using it to model a transport intervention

• This requires significant updates to both the supply and demand representation, including the networks

• Once updates applied, model needs to be validated to show that it provides a reasonably accurate representation of reality, which includes journey times/speeds

The Challenge

• Doing all of the above requires data and several important data sources come in network form e.g. the Integrated Transport Network (ITN) and Traffic Master Journey Time Data

• However, the network representation for the data seldom overlaps with the network representation in the model and does not come with a correspondence list to transfer data across

• To make use of this data we have to find a way to make this transfer

15 June 2018Mott MacDonald | Presentation 2

Context

Typically Current Practice

Approach

• One or two unlucky graduates are tasked with manually transferring data between the networks

• This is:

• Extremely time consuming

• Boring and monotonous for the graduates involved

• Prone to human error, which is often randomly distributed and difficult to detect

Implications

• Process is so time consuming that transfer is limited to a select number of important routes

• This does not extract the full value that is available in the data

• Need a method to get data for all of the links transferred across en masse using a technique that is reliable and not resource intensive


Manual Transfer

The GIS Option

Approach

• Use of geoprocessing techniques in GIS packages to create relationships between links

• Relationships based on their spatial characteristics

• E.g.

• Search from the midpoint of each link in the model network to find the closest link in the data network

• Search out from each link in the model network to find all links in the data network within a certain radius and then accept relationships where link orientations are similar

Problems

• Parallel links

• Elevated carriageways and tunnels

The GIS Option

15 June 2018Mott MacDonald | Presentation

A challenging example

7

An Algorithmic Approach using Path Searching Algorithms


Node Match

• Develop a node based correspondence list between model network and data network

• Match nodes that are within a distance threshold of each other

Link to Link

Link to Path

Path to Path

Apply Hierarchy

• Identify links in model network where a) start and end nodes have matches and b) where the

matched start and end nodes in the data network form a link in that network

• Data transfers directly to model network link

• Identify links in model network where a) start and end nodes have matches and b) where the

matched start and end nodes in the data network do not form a link (but a path) in that network

• Aggregate data from path in data network and transfer to model network link

• Identify links in model network where only start or end node is matched with a node in the data

network. Apply breadth first search to find a second matched node

• Find shortest paths between node pairs and use distance weighted data transfer process

• Where multiple allocations for a model network link prioritise in following order: 1) Link to Link,

2) Link to Path, 3) Path to Path

Step 1: Node Match

Method

• Use spatial characteristics to create a correspondence list between nodes in model network and nodes in data network

• Accept matches within a defined distance threshold

Potential Criticism

• Relies on accuracy of spatial characteristics and GIS techniques

• However:

• Node relationships not as complex as links

• Distance cut-off to limit incorrect matches

Step 2: Link to Link

Method

• Links in model network where a) start and end nodes have matches and b) where the matched start and end nodes in the data network form a link in that network

Model Network

Data NetworkMatch Match

Step 3: Link to Path

Method

• Identify links in model network where a) start and end nodes have matches and b) where the matched start and end nodes in the data network do not form a link (but a path) in that network


Model Network


Step 3: Link to Path

Method

• Identify links in model network where a) start and end nodes have matches and b) where the matched start and end nodes in the data network do not form a link (but a path) in that network


Model Network


++

Step 4: Path to Path

Method

• Identify links in model network where only start or end node is matched with a node in the data network. Apply breadth first search to find a second matched node


Model Network


A1

B1

A2 A3

B2 B3 B4


Method



Model Network


A1

B1

A2 A3

B2 B3 B4

dA


Method



Model Network


A1

B1

A2 A3

B2 B3 B4

dA

dB


Method



Model Network


A1

B1

A2 A3

B2 B3 B4

dA

dB

LA1A2 LA2A3

LB1B2 LB2B3 LB3B4


Model Network


A1

B1

A2 A3

B2 B3 B4

dA

dB

LA1A2 LA2A3

LB1B2 LB2B3 LB3B4

Value on Link A1A2 = X + Y * ( (LA1A2 – LB1B2) / LB2B3 )

X Y Z


Model Network


A1

B1

A2 A3

B2 B3 B4

dA

dB

LA1A2 LA2A3

LB1B2 LB2B3 LB3B4

Value on Link A2A3 = Y * ( (LB2B3 + LB1B2 – LA1A2) / LB2B3 ) + Z

X Y Z

Step 5: Apply Hierarchy

Method

• Where multiple allocations for a model network link prioritise in following order:

1. Link to Link

2. Link to Path

3. Path to Path

Example Application

Greater Manchester SATURN Model

• Used to investigate impacts of a motorway scheme

• Need to transfer Traffic Master Journey Time data from an ITN layer base onto the SATURN model network for the purposes of:

• Calibrating speed/flow curves

• Validating modelled journey times against observed data


Issues

• Identify how to measure success of the method

• Additional step required to exclude links in data and/or model network that do not have matched pair. This was a late addition to the example application shown here

Future Work

• Fine tuning of parameters/thresholds and also sensitivity testing to better understand behaviour

• Applications to other data sources with other network based representations


Future Work


Current Environment

• Increasing processing challenges posed by large and complex datasets

• Opportunity cost: the more time we spend processing data to make it usable, the less time we get to spend adding value elsewhere, e.g. building “better” models, doing more sensitivity tests

Conclusion

• Big datasets are good and are potentially really useful but…

• … we can only extract their full value if they come with good translation tools to get them into a base that matches up with other data sources


Broader Thoughts

Thank you

[email protected]

transferring link data between networks · the integrated transport network (itn) and traffic...

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