Assessing speeds to improve school safety

Goal

Understand driver behaviour at school crossings and around schools by using the Path Analysis tool. We’ll learn this by completing the City of Whittlesea use case.

Context

After community outreach, the City of Whittlesea wanted to understand key road safety challenges around six school locations in Mill Park. Specifically, they wanted to understand speeds, congestion, braking, swerving and near-misses at pedestrian crossings and nearby streets during school pick-up and drop-off times.

Do

Let’s re-create this use case, and look at two types of crossings outside Mill Park Primary School:

• School crossing at Mill Park Drive
• Raised crossing at Higgs Avenue

Download this file and upload it to the Road Intelligence platform to complete this module. If you don't remember how to upload a ciot file, see the instructions here.

Further Links

• Improving school safety using connected vehicle data use case
• How to use the Nearmap Imagery Integration

00:00

Let's use the Road Intelligence platform to compare driver behaviour around school crossings. We'll pretend we're part of the City of Whittlesea council in Australia. We want to understand how different types of school crossings affect driver behaviour during school pick-up and drop-off times.

00:18

‍To start this study, first we'll open the Path Analysis tool and then search for Mill Park Drive Victoria. To make sure we're looking at a school, we'll turn on the map features for school zones. First we'll click on the three dots in the bottom right of the screen and click on schools. Mill Park Primary School should immediately pop up. We'll use this school as part of our study.

00:49

‍Next, we'll put in the date range for the 1st of March, 2024 to the 30th of November, 2024. We also want to look at only weekdays and school pick-up and drop-off times. So we’ll click on the Date Options and click on Saturday and Sunday to deselect those days from our selection. We'll also add in our Time Options - 7 am to 9 am, click add, and then 2 pm to 4 pm and click add.

01:43

For this case study, we recommend you turn on Satellite map view to see the crossings as you draw. You can also use the Nearmap integration for better satellite imagery. Now we'll draw our paths. First select Mill Park Drive at this crossing, we'll select both north and south directions. Then select along Higgs Avenue at this raised crossing, going north and south as well.

02:24

Now we can hit the Analyse button.You can also upload the CIOT file, which you can download in the lesson notes below.

02:38

Now that the results are loaded, we'll go through the platform to see how driver behavior changed at school crossings.The questions we need to answer are what were the 85th percentile speeds at Mill Park Drive? What were the 85th percentile speeds at Higgs Avenue? And how did average braking g-forces compare at the two crossings?

02:57

So what were the speeds at the Mill Park Drive school crossing? First we'll open the Results Panel and choose the two paths on Mill Park Drive, where the non-raised crossing is. Then we'll pick car in vehicle type. Looking at the Speed Analysis section, we'll select the chainage graph and the 85th percentile option.

03:21

Hovering over the graph, we can see that the 85th percentile speeds drops to roughly 44km/h on approach to the crossing. Although we can see vehicles are slowing down, they are still travelling above the safe threshold of 40km/h.

03:38

Scrolling through the Speed Bins chart, we can also see 50% of vehicles traveling northbound while traveling in the 40 to 50km/h category.

03:48

Now let's compare this with Higgs Avenue where there is a raised crossing. How were the 85th percentile speeds there?

We'll scroll back up to the paths, keep all our settings the same, but instead of the Mill Park Drive roads, we’ll deselect those and select both Higgs Avenue paths. Scroll back to the Speed Analysis section.

04:08

Here we can see on approach the 85th percentile speeds drop to 37km/h northbound and 36km/h southbound. This shows that the raised crossing type is more effective at maintaining speeds below 40km/h.

04:25

But what about braking behaviour at the crossings? Did having different crossings have an effect? This time we'll have all paths selected and we'll scroll down to the G-forces graph.We'll select the Braking graph and make sure we change the setting from All to Average. Just to make it easier to use, we'll turn off all the Mill Park Drive selections by clicking on the tags of the graph.

04:52

We can see on Higgs Avenue cars will brake as they approach the crossing. Next we’ll turn Mill Park Drive back on.

04:59

We can immediately see on the graph that there's not a big change in braking compared to Higgs Avenue. When we scroll across the graph and look at the map, we can see this crossing doesn't make a big difference to driver behaviour.

05:12

And that's it. Through this analysis, we identified which crossing type was most effective for road safety. And we did this by comparing the 85th percentile speeds between both roads and looking at where braking occurred on those roads.

05:26

‍Just like the City of Whittlesea, we can see how different types of school crossings impact driver behaviour. Now, you can apply these skills to your own studies or try following along with this video.

05:37

Just know that the platform is constantly updating its dataset, so results may vary, but the steps are generally the same. We'll see you in the next in practice lesson.