Mobility analytics is the collection and analysis of mobility-related data—such as travel demand, trip patterns, vehicle movements, modal split, parking behavior, and charging usage—to improve how transport systems and services are planned, operated, and optimized. In e-mobility, mobility analytics helps stakeholders make better decisions about EV charging deployment, fleet operations, and energy management.
What mobility analytics includes
Mobility analytics typically combines multiple data sources to build a usable picture of movement and demand:
– Origin–destination (OD) flows and trip volumes by time of day
– Traffic and congestion indicators (speed, travel time reliability)
– Parking dwell time and turnover by location type
– Fleet telematics (routes, stops, utilization, idle time)
– EV charging data (sessions, kWh, power, occupancy, faults)
– Public transport usage and interchange patterns
– Geospatial data (land use, POIs, demographics, zoning, road network)
Why mobility analytics matters for EV charging
EV charging success depends on matching infrastructure to real-world behavior:
– Identifies where drivers actually park long enough for AC destination charging
– Predicts charger utilization to avoid underused or overcrowded sites
– Supports corridor planning for public networks and reliability targets
– Improves load management by forecasting time-based charging demand
– Helps prioritize sites for mixed-use developments, depots, or workplace parking
– Strengthens business cases with evidence-based demand and revenue projections
Common use cases
Mobility analytics is used by cities, CPOs, fleets, developers, and utilities for:
– Site selection and charger placement optimization
– Capacity planning (how many charge points, what power levels, phased rollout)
– Fleet electrification planning (route suitability, charging windows, depot sizing)
– Pricing and tariff design (time-based pricing, idle fees, congestion pricing)
– Reliability and operations (predictive maintenance, fault hotspots, SLA tracking)
– Policy and planning (EV-ready requirements, curbside allocation, equity analysis)
Key metrics and KPIs
Typical mobility analytics outputs include:
– Trip counts, OD pairs, peak-hour demand, dwell time distributions
– Charger utilization, occupancy, session success rate, average kWh per session
– Fleet utilization, miles/km per day, stop duration, energy per km, route feasibility
– Demand forecasts, peak demand risk, diversity factor, maximum site demand estimates
– Service quality: uptime, mean time to repair (MTTR), customer wait time
Tools and methods
Mobility analytics often uses:
– Geospatial analysis (GIS) for mapping demand and coverage gaps
– Time-series forecasting and clustering for recurring behavior patterns
– Simulation models for parking, traffic, and charging queuing
– Optimization models for charger placement and depot scheduling
– Data integration with CPMS, OCPP logs, and telematics platforms
Limitations and considerations
– Data quality varies; biased samples can distort conclusions (e.g., app-only data)
– Privacy requirements may require aggregation, anonymization, and minimization
– Mobility patterns change with policy, seasons, fuel prices, and new infrastructure
– Charging demand is shaped by tariffs, access rules, and reliability—not only traffic
Related glossary terms
Charging demand forecasting
Load management
Load profiling
Fleet telematics
Depot charging
Charger utilization
CPMS
OCPP
Site selection
Dwell time