Assessing the quality of road networks

Last week I’ve outlined the basic idea behind the indicator-based assessment model for road networks. I hope it became clear how this spatial modelling approach opens up new and more efficient ways for the global assessment of road-networks – not only in the context of bicycle safety.
A valid objection in this context could be, that the effort for setting up such a model is disproportionally high. This might be true at a first glance. But once the model is established, there’s no more efficient way to assess the quality of road networks independently from their size. Besides, taking a closer look to commonly employed assessment methods shows their respective practical and conceptual shortcommings. Let me show this by briefly discussing three assessment approaches which are utilized in connection with bicycle safety.

1) Analysis of accident locations

To my current knowledge this approach is most often used for determining the safety risk for bicyclist on the level of road segments. The concept is quite simple: many bicycle accidents within a certain distance interval indicate dangerous road segments. … sounds striking, but is actually based on a conceptual fallacity. Only the number of accidents doesn’t tell me anything, except the fact that accidents happened. One cannot conclude that road segments with many accidents are unsafe and vice versa, that segments with few accidents are safe. The following example tries to make this clear:

On a cycle way with a high load of bicycles a certain amount of accidents within a given timeframe is reported. A primary road with a high motorized traffic load and without any bicycle facility runs parallel to this cycle way. This road is avoided by most bicyclists (= low bicycle load). On this road only a few bicycle accidents are reported (= low number of accidents). A quality assessment which is exclusively based on reported accident locations, would falsely rank the primary road higher than the cycle way.

The locations (red stars) of reported accidents are derived from an accident data base comprising 10 years of observation.

The locations (red stars) of reported accidents are derived from an accident data base comprising 10 years of observation. Parallel to the river runs a highly frequented cycle way. The close main road with three car lanes is normally avoided by bicyclists. Due to the lack of safe infrastructure and the high traffic load the risk for bicyclists is much higher on the main road. But this fact cannot be deduced if only accident locations are considered for quality assessment!

Any assessment result which is exclusively based on reported accident locations is biased for at least two reasons: First, without a sound exposure variable (number of bicyclists per segment, distance travelled etc.) basically nothing can be said about the risk. Second, only a very small fraction of bicycle accidents are reported. This reporting-biase directly affects the quality of any analysis based on official accident data.

The analysis of collected accident reports is valuable (if not necessary) for many reasons. But building any assessment approach on the location (or density) of accidents cannot lead to valid results.

2) Expert assessment

If you have ever talked to a cycling advocat, a road engineer or a police officer you might have learned that their experience and practical knowledge is of enormous value when it comes to the quality assessment of road networks. Sometimes such experts are responsible for a status-quo analyis of road networks in terms of bicycle safety. If the size of the road network under consideration exceeds a few roads this approach turns out to be a real sisyphean task! Everytime a road is physically modified (which happens non-stop in a city!) the expert needs to re-assess the respective road. Apart from this tremendous effort the expert assessment has two additional weak points:

  • Most of the time experts use ordinal rankings or even “worse” qualitative, verbal descriptions for their assessment. Such an approach inevitable leads to fuzzy classifications and consequently only vague results. These results are hardly useable in further spatial analysis.
  • An assessment system which exclusively relies on expert’s judgements is hard to be standardized. This means, that the results are hardly comparable for different time intervals and geographical regions.

As already mentioned in my last post, experts play a central role in any road network assessment approach. Incorporating their extensive knowledge in adaptable models which can be globally run as often as necessary is by far more efficient than asssessing every road individually.

3) User feedback

Using collective user feedbacks which can be explicit (e.g. feedback app) or implicit (e.g. Twitter messages) for quality assessment seems to be a quite promising approach. Nevertheless there are still several concerns which makes this approach unfeasible for a global road network assessment:

  • Feedbacks are generally not equally distributed over space. For central areas in cities the number of feedback messages might be sufficient, but for a global application the sample size is too small.
  • Voluntary user feedbacks are biased in several ways. The sample is not representative for the whole population (“tech-savvy young male”) and thus can hardly be used for general assessment purposes.
  • In order to transfer verbal, qualitative feedbacks into reliable conclusions, sophisticated (semantic) algorithms are required.

User feedbacks are very helpful for the validation and calibration of assessment models. Furthermore they can help to improve infrastructure very efficiently (open administration). But a global quality assessment that relies entirely on user feedbacks is not reliable and thus hardly applicable.

I hope the benefit of the indicator-based assessment model which I’ve explained in my last post are clear(er) now. None of the methods mentioned above are wrong as such. In fact, they are useful for many purposes – for example as initial model parameters or for validation and calibration of the model. But for a global assessment of road networks with a focus on bicycle safety they are simply not viable.
Have I missed anything? Or do you have experiences with one of the outlined methods? Please feel free to leave a comment, I’d be happy to read your opinion!

 

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