Data-Driven
Mobility Analytics

Understand a optimize the performance of your transport system

Nástroje pro integraci dat a API o mobilitě

Integrujte data různých formátů a zdrojů do jednotného datové modelu mobility

Data Integration Pipeline

Sběr GPS dat o pohybu

Jednoduše sbírejte a analyzujte detailní data o pohybu

Vysoké časoprostorové rozlišení

Pozice je zaznamenána každo vteřinu s vysokou přesnosti lokalizace díky fúzi data z GPS, WiFi a GSM sítě.

Zpracování využívajících podkladových map

GPS trajektorie zpracováváme s využitím síťového modelu dopravní infrastruktury s cílem eliminovat šum, zvýšit přesnost a doplnit dodatečné atributy.

Automatická kategorizace a anotace

Zaznamenané trajektorie dokážeme automaticky klasifikovat do definovaných tříd na základě široké škály atributů.

Analýza

Tvoříme širokou škálu deskriptinvích a prediktivních modelů z dat v čase a prostoru

Route-oriented

Analyze quantities related directly to roads, streets and paths on which people, vehicles or goods travel within the transport system. Determine traffic volumes, speeds nebo directionality, identify staypoints. Detect trends and identify anomalies in route-related quantities.

Trips-oriented

Analyze mobility patterns of trips, tours and trip chains. Understand from where to where, when a how people or goods move. Calculate and visualize fine-grained origin-destination matrices and break them down according to time, population segment or trip purpose.

Network-oriented

Analyze the properties of the transport system as a whole. Evaluate connectivity of your system, identify gaps nebo choke points in your network and suggest extensions that optimize selected network-level KPIs.

Interaktivní vizualizace

Vytváříme přehledné interaktivní vizualizace dat v čase a prostoru

Různé typy dat

Vizualizujeme různé typy dat s časovou a prostorovu dimenzí. Podporujeme veličiny navázané na místa, linie nebo oblasti.

Pokročilé filtry

Umožnujeme definovat pokročilé filtry na základě místa, času, typu vozidla, čísla linek apod.

Proměnlivá úroveň detailu

Míru detailu ve vizualizaci dynamicky přizpůsobujeme úrovni přiblížení tak, abychom umožnili jak rychle porozumět celku, tak snadno získat informace o detailech.

Náš záběr

Naše nástroje pro datovou analytiku se hodí na různé typy dopravy

Cycling-4-3

Cyklistika a aktivní mobilita

Analyzing cycling related data to understand cycling behavior: from where to where people travel by bike, when they travel bike, which routes they cycle, and which issues they face.

Modernizace tramvajové trati v úseku Koh-i-noor – Kubánské náměstí

Veřejná doprava a MaaS

Analyze planning and operational aspects of public transport systems: evaluate public transport coverage, analyze line utilization, nebo service punctuality.

Fleet-4_3

Správa flotil

Analyze strategic and operational KPIs on your fleet ops: monitor vehicle routes, speeds and stop locations, detect deviations a anomalies, identify areas for efficiency improvement.

Ukázky analýz

Vybrané datové analýzy, které jsme zpracovali pro naše zákazníky

Počty cestujících ve veřejné dopravě

Z transakčních dat o nákupu jízden.a jednotlivých linkách a jejich úsecích.

Intenzity cyklistického provozu

Z dat GPS o pohybu cyklistů a statických sčítačů jsem odvozovali počty cyklistů na jednotlivých úsecích sítě.

Kvalita pokrytí veřejnou dopravou

Kvantifikovali jsem kvalitu pokrytí veřejnou dopravou a porovnali s individuální automobilovou dopravou.

Analýza potenciálu pro P+R

Quantifying benefits of intermodal combination of public transport with P+R. XXX XXX XXX XXX XXX XXX XXX XXX XXX XXX​ 

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Analysing Public Transport Ridership

For  a public transport authority (PTA) we have calculated ridership statistics from hundreds of GBs of ticket purchase data. Our analysis provided the PTA with accurate information on how many riders of which category use each public transport service at different times. A challenge here was to reliably map purchase transactions from multiple transport operators to the underlying public transport timetables and to build an interactive map-based data visualization that conveyed the insights the PTA needed to plan optimization of the public transport network.

Key technologies used 

  • GTFS/JDF timetable import and modelling
  • data to timetable matching
  • network-centric data visualization

Intenzity cyklistického provozu

For the city of Prague, we have calculated the number of cyclists on each segment of the city’s street and cycleway transport network. For each network segment, we’re provided the number of cyclists travelling in both directions at different times. The analysis required mapping millions of kms of cycling GPS tracks to the underlying model of the transport network and counting data extrapolation using data from stationary cycling counters. The result is provided in a form of an interactive visualization that allows the city to use the analytical output for a number of cycling-related policy decisions.

Key technologies used 

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

Public Transport Coverage Analysis

For a public transport authority, we have calculated quality of service coverage metrics and compared them against private car. For each pair of destinations, we have evaluated all available door-to-door public transport connections and evaluated their travel times, frequencies and number of transfers. We have then compared travel times with travel times by individual car to identify origin-destination pairs for which public transport is highly competetive. The analysis served the PTA for optimizing the design of the public transport network.

Key technologies used 

  • GTFS/JDF Timetable import and transport network modelling
  • Door-to-door public transport journey planning
  • Car route planning with real-world travel time data
  • Map-centric interactive data visualization

P+R Potential Analysis

Employing our network-centric analytics tools, we have evaluated the potential for P+R for Prague metropolitan area. For each settlement, we have compared average travel time to the city center between a door-to-door trip with public transport with an intermodal door-to-door trip combining driving to a P+R facility and then taking a public transport from there. The regions with the highest relative travel time savings when employing P+R compared to public transport-only journey are marked as green. The analysis helps to quantify the benefits of building a P+R facilities in different locations around/in Prague.

 

Key technologies used 

  • GTFS/JDF timetable import and transport network modelling
  • Intermodal door-to-door public transport with P+R routing
  • Car routing with real-world travel time data
  • Map-centric interactive data visualization