The analyses of the loss times function module provide initial answers to a wide range of typical traffic-related questions in municipalities:

• Where is the river affected by traffic jams or roadworks? At what times?
• Is traffic diverting from the motorway into the city?
• Is there through traffic in the residential area?
• How does traffic from major events affect overall traffic?
• Where does the targeted installation of counting points for traffic control make sense (and save costs)?

These questions can often only be answered by complex measurements and traffic studies, which - in order not to overlook anything - are often defined more comprehensively than necessary. However, many questions do not require precise answers. Experience has shown that simple, almost qualitative but well-visualised analysis results are often sufficient to make the right decisions about measures.

The contribution of the function blocks and in particular the loss time analyses with its visualisations therefore lies in its ability to determine important facts within a short time without complex measurements, thereby saving time and considerable costs in many cases.

• Typical application examples are the analysis of traffic quality at traffic lights, on important entry or exit roads, the impact of high traffic volumes in residential areas or also impairments to local public transport on line sections where vehicles share the routes with motorised private transport.
  
  
• Analysing the loss times provides the first objective indications of where the most severe impairments occur, at what times this typically happens and - in particularly pronounced cases - how a regular disturbance propagates upstream in the grid.
  
  
• Here, initial visual presentations of the analyses can often already provide orientation as to where the focus of measures should be placed - without the need for time-consuming, months-long studies, but also without having to rely on subjective anecdotal observations.
  
  

Our tip for more insights - traffic analysis in a double pack:

The interactive visualisations of the traffic volume and lost time function blocks represent a powerful combination for the first overall assessment of the traffic situation on a road network. In many cases, they already serve as a basis for decisions on new traffic management measures.

As the lost time function module is always useful when traffic jams, congestion or frequent disruptions present a challenge on the current road network, it is ideally complemented by the information from the holistic analysis of the traffic volume.

In [ui!] DATALAB, the results of this analysis are made available to the customer as an interactive map ("Kepler visualisation") or as a CSV export or GeoPackage (for one-off analyses). The Kepler analyses can be easily accessed via a modern web browser without the customer having to fulfil any additional technical requirements.

The calculations of the function module loss times are also based on the analysis of FCD data. By analysing the speeds and their progression over the day and week, we can determine the impairment of traffic quality and then display it clearly on a map. As with the first function module, there is no need for the customer to provide data for this.

For internal municipal use, the results of this analysis are made available to the customer in [ui!] DATALAB as an interactive map ("Kepler visualisation") or as a CSV export or GeoPackage (for one-off analyses). Kepler visualisation is a web-based service (open source software library) for the interactive display of geo-related data.

Access is very easy via a modern web browser, without the customer having to fulfil any additional technical requirements. Various filter and configuration options are provided to optimise the display.

• For example, a satellite image can be activated for the background map; various map layers allow specific customisation of the map.
  
  
• The colouring of the value scale can be adjusted. The time slices can be animated via a playback mode.
  
  
• You can switch between a two- and three-dimensional display, zoom in and out as required and adjust the perspective.
  
  

If the Kepler visualisations are used within the [ui!] DATALAB of an [ui!] UrbanPulse instance as described here, the displayed evaluations are updated weekly to show the previous 15 weeks.

The source-destination matrix function module provides you with a basic overview of transport demand, which is the determining factor for the volume of traffic in a municipality. If you know where some of the vehicles in a municipality come from, where they leave the municipality, or even what proportion of the vehicles only move within the municipality, you can tackle traffic management challenges in a much more appropriate and targeted manner.

This allows you to easily create basic contributions to the assessment of traffic situations within a very short time, for example by answering these and similar questions:

• Where do the tourists who park in the car parks in our recreational areas at the weekend come from?
• For which groups of travellers can digital visitor guidance measures be set up on suitable sections of the route?
  
  
• What proportion of traffic in the city centre only has short journeys and could possibly switch to other modes of transport?
  
  
• How high is the commuter share of traffic in the city centre? Where do the main commuter flows come from?

Creating a common, data-based basis of information regarding the need for mobility should therefore always be one of the first steps when specialist departments or politicians are looking for solutions in the field of road transport, as this makes all subsequent decision-making processes much simpler and more consensus-based.

A concrete added value compared to the classic source-destination matrices of transport planning is the ability of the function module to determine the demand for the typical time periods of a day, for example the morning peak or the evening peak. As demand models are usually calculated for 24 hours in total, this information is not available there due to the principle.

But even if initial analyses using the source-destination analysis function module are followed by a transport planning study based on a complex demand model, this can be commissioned in a much more qualified manner and thus lead to the desired results more cheaply and quickly. The functional module therefore has the potential to qualitatively improve the results of planning and decision-making processes in the road transport sector - at unbeatably low costs and in the shortest possible time.

Report from the field:

We have now used these analyses to advise a large number of tourist regions and are currently supporting local authorities in analysing vehicle traffic in shopping streets or residential areas.

The source-destination analyses can often be ideally supplemented with analyses from Smart Parking, namely via the parking distribution and parking search traffic function modules (see Smart Parking solution sheet).

The specialised application is provided in the form of a web-based interactive map. The results of the data analysis on parking search traffic are displayed geo-localised on the map. The interactive map allows users to customise the presentation of the data by setting filters.

Parking search traffic is determined on the basis of floating car data (vehicle movements measured by navigation devices and / or mobile devices) and is made available for motorised private transport. A detailed description of the data fields included can be found below.

The evaluation looks at the period of the last 15 weeks before the order. Spatial cells are differentiated with an edge length depending on the size of the area under consideration (100m-10km), as well as a distinction between weekday / weekend and four different traffic-relevant day phases as time slices.

In a first step, the actual parking processes are analysed using a machine learning model. The previous telemetry points of parked vehicles in a 300 metre area are then aggregated into the individual cells and time slices.

The following data fields are provided as results of the analysis:

• lat / lon: The latitude and longitude of the centre of a spatial cell in the EPSG:4326 (GPS / WGS84) coordinate system.
  
  
• parking_search_pct: The percentage of vehicles in a spatial cell that are looking for a parking space.
  
  
• relative_parking: A measure of the relative parking volume in the spatial cell as a whole. Standardised for the duration of the time slice in order to establish comparability between time slices.

No further data sources are required for this function block, i.e. no connection of connectors. Floating Car Data (FCD), which is used by [ui!] to carry out the analyses, is used as the data source.