バイクシェアの出発地と目的地を接続する

Question

<\/HEAD>こんにちは！<\/P> バイクシェアの各出発地と目的地を接続するのに最適なツールがどれか知りたいです。大きなcsvファイルを月ごとに分割しましたが、それでも各ファイルには少なくとも20万行以上含まれています。csvファイルにはObject ID、出発駅と目的地駅の地理的位置、そして関連する時間情報が含まれています。OD cost matrixを使うと、まず数時間かかり、その後すべてのポイントを接続しますよね？<\/P> <\/P> おそらく私は考えすぎているのかもしれません。私の目的は、バイクシェアシステムで最も利用されているエリアを示す通り沿いのフローマップを作成することです。<\/P> <\/P> 事前にありがとうございます！<\/P><\/BODY><\/HTML>

DanielLapczuk · Answer

Thank you for the fast request.

Indeed, it is not possible to process all 200k routes in one step. I received an error. Thus, I have to figure the way via python out.

Thanks so far.

Melinda Morang · Answer

If you need to know which streets were traversed, you can't use OD Cost Matrix.  OD Cost Matrix calculates only the travel time or distance but does not preserve the actual paths taken along the network.  This allows OD Cost Matrix to operate efficiently, but it sounds like this isn't the right tool for the job for you.

Instead, you need to use the Network Analyst Route solver to calculate the route taken between each pair of points.  Once you have done that, you can combine the results to find the most frequently traversed road segments.

When setting up the Route analysis, you should use the RouteName input field in the Route Stops table to make it so the origin and destination get directly connected to one another.  If you just load in all the points without using RouteName, they will all connect sequentially, which isn't what you want.  Each OD pair should have a unique RouteName value.  Configured that way, the results will contain one individual route from the origin to the destination for each unique RouteName.

It's unlikely you will be able to solve all 200 thousand routes in one go.  You will probably have to break up the problem into multiple chunks and combine the outputs afterwards.  You might be able to do something in Model Builder, but an easier and more reliable way is with python.  This DevSummit presentation talks about how to solve large Network Analyst problems in python.  It focuses on the OD Cost Matrix solver, but many of the lessons apply equally to Route.

Once you have calculated the Routes, you will need to combine the results to identify the most frequently traversed road segments.  If you're doing an analysis with Route layers (as opposed to the python solver objects discussed in the video), the correct way to do this is with the Copy Traversed Source Features tool.  If you're using the python solver objects, you'll probably have to do a spatial join or something, or even make a raster and count the number of times each cell is touched by a route.