The power of context:
Mooven integrates with various customer systems and real-world datasets, combining 1st, 2nd and 3rd party data into an ecosystem powered by AI to inform how infrastructure gets delivered across cities and countries.
We live in a complex and unpredictable world, at Mooven we are helping our customers to control this unpredictability by bringing modern datasets that can be used to control, predict and manage work.
There are four main categories of data that are important:
User contributed data: Gaining a clear understanding of when and where your teams are working, the work delivered and associated updates is an essential input into understanding your impact on the road network.
Traditional traffic and ITS data sources: Traffic management plans are typically based on count information collected from manual traffic surveys, tube counts and traffic signals. This information is often translated into Annual Average Daily Traffic Counts (AADT), the average volume of vehicles on a road per day, as a mechanism to estimate how busy an area is, and therefore when work can occur.
Live traffic and environmental data: Modern technology like mobile phones, connected vehicles, and next-generation Internet of Things (IoT) devices now makes it possible to collect live, real-time information across entire countries, providing insights into current conditions. Examples of data sets in this category include live traffic data from TomTom, Google and HERE, along with telematics devices in commercial, heavy haulage vehicles and buses, environmental sensors and machine vision cameras.
Contextual and Situational Data: Finally, to understand why variability in traffic and public transport occurs, it is important to capture information on the underlying factors that influence travel demand. Examples of data sets used include weather feeds, information on when businesses are open and data on events.
Additional information on the datasets used in each section is contained below.
User Contributed Data
Record of when work occurred
Mooven can be used to track when and where work occurred directly within our project management module, or customers can build an integration with common job management systems or work planning tools.
With a clear picture of when and where work occurred, it is possible to accurately record if disruption was caused or if there were opportunities to work more efficiently.
Information on the work completed
Mooven's project management module also enables you to track all of your project's key details like costs, work completed, treatment types and task information. This enables you to associate work activity with actual traffic impacts.
User-created events:
In addition to planned and scheduled information, any user in Mooven can also record onsite or relevant events that can be used to share knowledge and better manage delivery. Common examples include delays setting up, incidents, and heavy rain.
Traditional traffic and ITS data sources:
There is a wide range of data sources used in the delivery of horizontal infrastructure, such as road maintenance. The section below contains additional details on frequently used sources and their strengths and weaknesses.
With a clear understanding of how data is collected and how to use a source most effectively, all data sources provide useful and important context.
Tube Counters (Pneumatic Traffic Counting Tubes)
Pneumatic traffic counting tubes (sometimes called “road tube counters” or simply "tubs") are a widely used method for collecting traffic data such as vehicle counts, speed, and classification.
Tube counters from companies like Metro Count are some of the most widely used traffic collection devices.
Here’s how they work:
Installation: One or two rubber tubes are stretched across a roadway and secured to the pavement. These tubes are connected at one end to a roadside counter unit, and the other end is sealed.
Vehicle Interaction When a vehicle’s tires pass over the tube, they compress it, creating a burst of air pressure inside the tube.
Air Pulse Detection The burst of air travels through the tube to the counter unit, where a pressure sensor detects the pulse.
Data Interpretation
Single-Tube Setup: Can detect each axle strike. Used primarily for simple vehicle counts and estimating speed (with less accuracy).
Dual-Tube Setup: Two tubes are spaced a known distance apart (usually a few feet). This setup allows:
Speed Measurement: The time between pulses on each tube allows calculation of vehicle speed.
Axle Count and Spacing: Helps classify vehicles by type (e.g., passenger car, two-axle truck, multi-axle truck) based on axle configuration.
Direction of Travel: Determined by the order in which the tubes are compressed.
Data Storage and Analysis
The counter unit records the time and characteristics of each pressure event. Data is later downloaded and analysed to produce traffic volume, speed distributions, and vehicle classifications.
Advantages
Cost-effective and portable, typically costing between $500 and $ 2,500 per deployment.
Quick to install and remove.
Provide an easy way to collect accurate vehicle volume data for short-term studies in a fixed location.
Limitations
Not suitable for permanent installation
Accuracy can be affected by weather, debris, or improper installation
Cannot distinguish between types of vehicles without axle-based classification.
Typically have a lead time of 1-2 weeks to deploy and data is only captured for a week - it can be hard to predict where tubes should be deployed and if traffic in the survey week is 'unusual', there is no way to identify that the data may not be representative of 'normal' patterns.
Tubes can be damaged by coming loose, causing data loss.
The data quickly becomes out of date and may no longer be a fair representation of traffic patterns in the area.
Manual Traffic Surveys
Manual traffic surveys are used to collect data about the movement of vehicles and pedestrians on roads. They are frequently used to collect data for traffic engineering, road design, congestion management, and infrastructure planning.
Here’s how they work:
Defining the purpose. Before any data collection, it’s crucial to understand why the traffic survey is being conducted. Common objectives include: Measuring traffic volume, assessing congestion or delays, evaluating parking demand, classifying vehicle types, supporting infrastructure design or upgrades, and analysing travel behaviour and patterns.
Manual traffic counts: Use trained surveyors who are stationed at observation points. Surveyors will likely count vehicles/pedestrians using tally sheets or clickers. However, some suppliers will record video footage to be processed by people later.
Choose the Location and Timing: Next, the person commissioning the survey needs to determine the location that needs to be monitored, when (typically peak hours like e.g., 7–9 AM, 4–6 PM) and the duration (typically one to three survey days)
Collect and validate data: Surveyors are deployed to collect data from the monitoring site. Data is reviewed for consistency and cleaned to remove anomalies or errors (e.g., double-counts, manual errors).
Analyse and Interpret Results: Typical analysis includes: traffic volumes (vehicles per hour), vehicle classification (cars, buses, trucks, etc.), Intersection performance (e.g., delays, queue lengths) and Trip patterns (from O-D surveys).
Advantages:
Generally, people find it comforting to understand that someone was on site and counted vehicles or queue lengths. There is a high degree of trust that people were involved in data collection.
Manual surveys can collect complex information like turning movement patterns, as surveyors can observe and categorise behaviour.
Cheaper than installing dedicated hardware, where a sample of data is sufficient for planning or design.
Limitations:
While people are trusted, it is not uncommon for human error to have a material impact on the insights gained from manual surveys. Most manual surveys are completed by students, accuracy can vary due to people missing counts, missing movements, or incorrectly estimating queue lengths.
While manual surveys provide a rich snapshot, it can be hard to ensure that the day the survey is completed is representative of normal conditions. It is cost-prohibitive to collect long time periods, making so teams can tell how traffic different times of the day change.
Manual Drive Throughs
Drive-through surveys (also known as test runs or floating car surveys) are a practical method used to measure traffic delays and assess the operational impact of a worksite, such as road construction or maintenance. They involve driving through the affected area multiple times and recording travel times and traffic conditions.
Here’s how they work:
Define the objectives: Determine what exactly needs to be measured: Total travel time through the work zone, Delay compared to normal (baseline) conditions, Queue lengths and stop durations, Impact area (how far upstream traffic backs up)
Plan the Survey: Choose a representative route that passes through the worksite and includes any detours or diversions. Conduct surveys during relevant periods - typically peak hours, or when the worksite is most active.
Baseline comparison: If possible, collect data before construction begins for comparison.
Equipment and Setup: Use a car equipped with a GPS device, stopwatch, or mobile app capable of recording time at start and end points, speed variations, stop durations, and location data. If running with an observer, one person may drive while another logs data manually or electronically.
Conduct the Drive-Throughs: Drive through the work zone multiple times in both directions (if applicable), recording all metrics that need to be captured.
Analyse the Data: Calculate the average travel time through the worksite,
Average delay compared to pre-construction data or control route, Maximum and minimum delays, Queue length trends
Advantages
Many customers like to complete manual drive throughs as it provides them with hard evidence of conditions on site.
Provides real-world insight into actual driver experience
Low-cost and easy to conduct
Can capture qualitative details (e.g., confusion due to signage, aggressive merging)
Limitations
While data is captured for each drive-through, this does not necessarily translate into an accurate picture of the average experience. See the section on ANPR data collection with the Data Collection Logic and Reporting article, which demonstrates how different the driver experience can be between individual vehicles.
Labor-intensive
Limited sample size; may not reflect the full variability of conditions
Can be subjective if not standardised
Not continuous—only shows conditions at specific times
Mooven typically does not use this information as the sample size is too lower and data from floating vehicle providers provides a better representation of actual conditions.
Bluetooth
Bluetooth traffic monitoring is a method of measuring travel times, speeds, and origin-destination patterns by detecting Bluetooth signals emitted by devices inside vehicles (like smartphones, infotainment systems, or GPS units). It is non-intrusive, cost-effective, and useful for analysing real-time traffic flow over large areas.
Here’s how they work:
Detectors (scanners or readers) are installed at multiple fixed points like poles, gantries, roadside cabinets, or traffic signals to capture traffic at key locations like intersections, freeway ramps, major corridors. These detectors continuously listen for Bluetooth signals from passing vehicles.
Detection and Matching When a Bluetooth-enabled device passes a sensor, its MAC address and a timestamp are recorded. When the same MAC address is picked up at a downstream sensor, the system calculates Travel time, Average speed, Route or origin-destination pair. MAC addresses are usually hashed or anonymised to protect privacy, which means the detectors need to be spaced closely enough to prevent data loss.
Data Aggregation and Analysis The system aggregates data to determine real-time traffic conditions, identify congestion points and delays, analyze travel time variability and map origin-destination patterns across the network.
Advantages
Non-intrusive (no need to dig into pavement or disrupt traffic)
More cost-effective than ANPR cameras but more expensive than floating vehicle data
Real-time, continuous monitoring; however they don't detect incidents until vehicles complete the next journey and can lose data in these instances if the mac address has already updated.
Effective for origin-destination analysis across wide networks
Limitations
Only detects vehicles with active Bluetooth devices (typically 25–50% of traffic)
May pick up non-vehicle devices (e.g., pedestrians, cyclists)
MAC address randomisation (a feature in newer devices) can reduce accuracy
Detection zones are limited to locations with installed sensors.
No good for large distance.
Radar
Radar traffic counting devices use radio waves to detect and monitor the movement of vehicles on a roadway. They are widely used for measuring traffic volume, speed, and sometimes vehicle classification—all without any physical contact with the road surface.
Here’s how they work:
Device installation: Radar sensors are mounted on poles, gantries, or roadside fixtures. They can be aimed at one or multiple lanes, depending on the design.
Devices are usually positioned above or beside the roadway at an angle to capture vehicle movement across lanes.
Emission of Radio Waves: The radar unit continuously emits microwave signals (typically in the 24 GHz or 77 GHz range). These signals travel through the air and reflect off moving vehicles.
Detection via the Doppler Effect: When a vehicle moves toward or away from the radar, the reflected signal changes frequency - a phenomenon called the Doppler shift. The radar unit measures this shift to determine the Speed of the vehicle and the Direction of movement. Some radar systems also estimate vehicle length based on how long the signal is disturbed.
Data Processing: The radar system processes the reflected signal and logs data values like Vehicle count, Speed, Direction, (In advanced models) Lane position and vehicle classification (based on size or profile)
Advantages
Non-intrusive (no need to cut into the road or stop traffic flow)
Works in all weather conditions
Portable models available for temporary studies
Can monitor multiple lanes with a single unit
24/7 operation with low maintenance
Limitations
Accuracy may decrease in heavy congestion
Can struggle with vehicle classification
Requires clear line of sight to the roadway
May need calibration to distinguish between closely spaced vehicles
Automated Number Plate Recognition
Automatic Number Plate Recognition (ANPR) traffic counters are systems that use optical character recognition to identify and record vehicle license plates as vehicles pass through a checkpoint. They are powerful tools for traffic monitoring, journey analysis, enforcement, and security.
Here’s how they work:
Camera Setup: High-resolution ANPR cameras are installed at fixed locations (e.g., roadside poles, gantries, overpasses). They can be single-lane or multi-lane and typically operate in infrared (IR) to work day and night. Cameras are angled and focused to capture clear images of front or rear license plates.
Image Capture: As a vehicle passes the camera, a trigger is activated (e.g., loop detector, radar, motion detection). The system takes one or more high-speed images of the vehicle, focusing on the license plate area.
Optical Character Recognition (OCR): Software processes the image to detect the license plate and extract and convert characters into machine-readable text. Advanced systems account for blurry plates, dirt, angle, and different fonts or country formats.
Data Logging: Each vehicle entry generates a data record containing License plate number, timestamp, camera/location ID, vehicle direction; optionally, speed, lane, vehicle image, and classification
Optional Cross-matching: If multiple ANPR sites are networked vehicles can be tracked between points to determine journey times, average speed, and origin-destination patterns. Data can also support congestion monitoring, tolling, or low-emission zone enforcement.
Data Use and Storage: Data is stored locally or transmitted to a central database.
It may be anonymised for traffic analysis or retained for enforcement/legal use, depending on the jurisdiction.
Advantages
Highly accurate in identifying vehicles
Operates 24/7 in all weather conditions
Enables complex data analysis (O-D patterns, travel time)
No physical impact on road surface.
Limitations
Privacy concerns; must comply with data protection laws
No visibility of why a journey may have been longer than expected. For example, if a driver stops at a McDonald's drive-through, this will likely show as a delay.
Limitations for real-time incident detections as journey times are only captured on the completion of a journey. During extreme congestion events, this can lead to significant delays before delays are seen and vehicles need to start completing the journey again before the data will be available.
Performance affected by dirty, obscured, or altered plates
Requires reliable power and connectivity
Higher cost compared to basic counters
Weigh in Motion (WIM) Systems
Weigh-in-Motion (WIM) traffic counting devices are advanced systems used to measure the weight of vehicles as they drive over a sensor-equipped roadway—without requiring the vehicles to stop. These systems are commonly used for vehicle classification, load enforcement, infrastructure planning, and road maintenance forecasting.
Here’s how they work:
Sensor Installation: WIM systems are embedded in or mounted on the roadway surface. Types of sensors include:
Load cells: Measure the direct force of the axle on a scale.
Bending Plattes: Measure strain in a steel plate caused by a vehicle.
Piezoelectric sensors: Measure voltage generated by pressure.
Fiber optic sensors: Detect light changes under strain.
Vehicle Passage and Data Capture: As a vehicle drives over the WIM sensors, each axle’s weight is recorded, the speed, vehicle length, number of axles, and axle spacing are measured using additional sensors (e.g., inductive loops or piezoelectric strips). All measurements are made in real-time while the vehicle is in motion.
Data Processing: The system’s computer calculates Axle weight, Gross vehicle weight (GVW), Axle load distribution, Vehicle classification (based on axle count and spacing), Speed and direction. Environmental factors like temperature or vehicle dynamics are accounted for through calibration and filtering algorithms.
Output and Use: WIM data is stored locally or transmitted to a central system.
Advantages
No need for vehicles to stop (non-intrusive to traffic flow)
High data throughput (thousands of vehicles per day)
Captures both traffic and load data simultaneously
Limitations
Accuracy depends on calibration and pavement conditions
Generally less precise than static weigh stations
Initial installation is costly
Modern crowdsourced and IoT hardware
Floating vehicle data
Floating vehicle data (also known as probe data) is generated by traffic information shared with suppliers like Google, TomTom and HERE. Instead of relying solely on fixed roadside infrastructure, they capture anonymised location and speed data from moving vehicles and mobile devices:
Google analyses the GPS, cellular and wifi determined locations transmitted to it by mobile devices across Android and iOS platforms, in addition to connected vehicle data (where Google is used within car navigation) and third-party sources to provide accurate current live travel times. Google processes the incoming raw data about mobile phone device locations, then excludes anomalies and removes all personally identifiable information.
TomTom gathers data from connected in-vehicle navigation units, fleet telematics, and mobile apps. Millions of vehicles and devices transmit real-time GPS traces that are fused with historical travel patterns to build highly accurate travel time and congestion models.
Advantages
Wide Coverage: Provides data across entire road networks, including arterial routes and local streets not covered by ITS sensors.
Scalability: Expands naturally as more users and vehicles contribute, without the need for new roadside infrastructure.
Cost-Effective: Lower capital and maintenance costs compared to installing and maintaining ITS hardware (e.g., loop detectors, CCTV, radar).
Real-Time & Historical Insights: Supports both live traffic monitoring and long-term analytics.
Flexibility: Easily integrates with other data sources (weather, incidents, events) for richer traffic intelligence.
Useful for identifying unusual traffic: Provide a good source of information for quickly identifying congestion
Limitations
Data Quality Variability: Dependent on penetration rates (how many vehicles or users contribute data). Low penetration can reduce accuracy on rural or low-volume roads.
Latency: May be slower to detect sudden incidents (e.g., a crash causing an immediate blockage) compared to roadside sensors that trigger instantly. This is partly due to algorithms that are designed to remove individual results that shouldn't be included within the journey time, i.e. a driver stopping at a McDonald's drive-through resulting in a longer point-to-point journey time.
Limited Modal Coverage: Focused on vehicles; less visibility of pedestrians, cyclists, and some forms of public transport unless explicitly integrated.
Data Privacy & Reliance on Third Parties: Data comes from global commercial providers; agencies have limited control over how it is collected, processed, or shared.
Calibration Needs: Often requires validation or calibration against physical ITS assets to ensure reliability in local contexts.
Telematics Data
Fleet telematics providers such as EROAD and Geotab install in-vehicle hardware or use OEM-integrated systems that continuously capture and transmit data. Typical data includes:
GPS Location & Speed: Captured in real-time to track vehicle position and movement.
Vehicle Diagnostics: Data from the engine control unit (ECU), including fuel use, braking, acceleration, idling, and emissions.
Operational Context: Driver behaviour (e.g., harsh braking, speeding), routing, delivery/pickup timestamps, and geofencing events.
Connectivity: Data is transmitted via cellular or satellite networks to secure cloud platforms for aggregation, analysis, and sharing.
This data is highly structured, tied to specific fleets, and often used for logistics optimisation, compliance, and safety monitoring. Some providers like Compass IOT are also purchasing licenses from multiple vehicle manufacturers to build aggregated datasets.
Advantages
High Accuracy: Vehicle-mounted units capture precise, validated GPS and diagnostic data directly from vehicles.
Rich Contextual Data: Provides not just location/speed, but also engine, load, and driver behaviour information.
Consistency: Data streams are continuous and standardised across the fleet.
Granular Coverage of Freight & Commercial Traffic: Particularly valuable for monitoring heavy vehicles, freight corridors, and high-impact logistics routes.
Compliance Integration: Often aligned with regulatory frameworks (e.g., road user charges, emissions, driver hours), which improves reliability and trustworthiness.
Limitations
Limited Penetration: Only represents vehicles from subscribing fleets/logistics operators — generally freight-heavy, with limited coverage of private vehicles. Each service often only provides 1-4% of all vehicles
Bias in Vehicle Mix: Over-represents heavy and commercial vehicles, under-represents passenger cars, active modes, and public transport.
Coverage Gaps: May miss parts of the network with low freight/logistics activity (e.g., residential or minor urban streets).
Privacy & Data Sharing Constraints: Data is commercially sensitive, requiring aggregation and anonymisation before being shared externally.
Cost & Access: Typically requires commercial agreements/licensing with fleet operators or telematics providers.
Public Transport Data
Mooven uses access to real-time and scheduled public transport data to capture information on public transport performance, covering modes like Trains, Trams, Ferries, Buses and Light Rail.
The live positioning of vehicles is compared to their schedules to determine variables like delays versus schedules and journey times.
Advantages
Provides an accurate record of public transport performance with the ability to isolate construction-related delays.
Provide individual vehicle-level performance.
It can be used to identify general traffic issues and measure journey time performance, particularly for buses that use general traffic lanes.
Limitations
Only captures public transport modes.
Requires significant process resources to constantly monitor all vehicles and their performance vs the schedule.'
Granularity: While strong for timing and location, it does not inherently capture passenger loads, dwell times, or detailed operational metrics (though these can be integrated from other sources).