Cyclers Analytics

Visualize, analyze & optimize
your micromobility transport system

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Powerful and Flexible Analytics Pipeline

Cyclers will get you from micromobilty data to actionable insights in no time--while being
customizable to accommodate your specific needs.

Easily Import Your Cycling and Micromobility Data

Cyclers will import your cycling/micromobility data in a variety of formats, including GPX trajectories or GeoJSON data on trips. Your data gets automatically integrated with Cyclers built-in data, such as maps, to create a rich basis for subsequent analysis.

Run Powerful Analytics Algorithms

Run one or more of the Cyclers Analytics engine's algorithms to calculate the desired analytics report. Leveraging the Cycling Navigate engine, the analytics engine can compute a wide range of advanced reports.

Explore and Integrate Analytics Results

Use built-in map-based visualization tools to interactively explore analytics results in your browser---no special tools needed. Or use Cyclers analytics export to get the results as structured files and feeds suitable for integration into your own analytics pipeline.

Trip and Route Analytics

Visualize and understand the movement of your bike and scooter
riders in space and time.

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GPS track processing
GeoJSON Region boundaries
Interactive network-centric data visualization

Trip Patterns

Know from where to where and when your riders go.

For the trip pattern analysis, the Cyclers engine processes GPX tracks or bike lock/unlock data and calculates the number of riders moving between different zones at different times.

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OpenStreetMap import and cycling network modelling
GPS track to transport network map-matching
Non-uniform counting data extrapolation
Interactive network-centric data visualization

Traffic Intensities

Know the intensity and directionality of micromobilty traffic on the street level.

For traffic intensity analytics, the Cyclers engine processes GPX tracks and optional stationary counters data, in order to calculate the volume of riders on each link of the transport network (i.e. road, cyclepath and pavement segments). The engine cleans noisy input GPX tracks and match them on the underlying model of the transport network, so that it can count the number of riders that cross each link of the network in both directions.

When the optional data form stationary cycling counters are available, the engine can also estimate the total number of riders on each link. All results include statistical estimates of analysis errors.

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OpenStreetMap import and cycling network modelling
GPS track to transport network map-matching
Outlier data points extraction
Interactive network-centric data visualization

Traffic Speed and Fluency

Know how fast your riders go and where they need to wait.

For the traffic speed and fluency analytics, the Cycler engine processes GPX tracks, in order to calculate at which speed riders travel through the transport network. The engine cleans noisy input GPX tracks and matches them on the underlying model of the transport network to accurately determine the time it takes the rider to cross links of the networks or to cross individual junctions.

The analysis allows to pinpoint locations that pose a bottleneck to the flow of riders and which might require optimization.

System Analytics

Understand how well the organization of your micromobility service and/or infrastructure serves
your riders and how you could improve it.

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OpenStreetMap import and cycling network modelling
GPS track to transport network map-matching
Cyclers Navigate Engine
Interactive network-centric data visualization

Link Attractivity

Understand which links of your transport network attract riders and which links the riders prefer to avoid.

For the link attractivity analysis, the Cyclers engine combines GPX data on the real-world movement of riders with sophisticated transport network analysis algorithms, in order to calculate link attractivity index for each link of the transport network.

Positive link attractivity index indicates that riders choose the ride along the link even if it means taking a longer path; negative link attractivity index means riders tend avoid a given link even if it lies on the fastest route to their destination. Link attractivity analysis helps you determine, in an objective, data-driven way, which parts of your network work well and which should be improved.

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OpenStreetMap import and cycling network modelling
Intermodal Routing Engine
Interactive network-centric data visualization

Ridership Potential

Understand where cycling/micromobility has the highest potential to attract riders.

For ridership potential analysis, the Cyclers engine combines the data on mobility patterns with sophisticated intermodal network analysis algorithms. It determines the areas where using bikes/scooters would command the highest competetive advatage compared to other means of transport, and where cities and mobility providers should thus focus their attention.

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OpenStreetMap import and cycling network modelling
GPS track to transport network map-matching
Cyclers Navigate engine

Travel Times

Understand how long it takes to reach a given area by bike or scooter.

For travel time analysis, the Cyclers engine combines GPX tracks with micromobility routing algorithms, in order to calculate accurate estimates of how long it takes to reach target area on bike or scooters.