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Integrated connected data for safer more efficient traffic management operations

Driven by Data: a Progress Report

This world-leading research by a multi-party government led team examines untapped data sources with the potential to revolutionise transport management. Examining how to better leverage existing data and technology; and embracing the opportunities offered by connected vehicles; the project will discover how data will deliver safer, faster and more efficient road travel, shaping a future of seamless mobility.


The following Progress Report is based on the research project 'Integrated connected data for safer, more efficient traffic management operations' - an ITS Australia project, in collaboration with the University of Melbourne, Victorian Department of Transport & Planning, Transport for NSW, Transport & Main Roads QLD, Main Roads Western Australia, Transport Accident Commission, and iMOVE Australia.

For more about the background, scope and project methodology please view the accordions at the bottom of this page.

Setting the scene

Australian governments have the shared targets of halving road deaths by 2030, achieving zero road deaths by 2050, and reaching a net-zero emissions target by 2050. A broad approach, including a wide range of technologies and policy interventions, will all be crucial in achieving these goals, as well as close collaboration with industry and our communities.

The advancement of technology and data sources in traffic operations and management has created numerous opportunities to enhance the functionality, safety, and efficiency of road networks. Traffic management systems traditionally rely on basic inputs like in-ground loop detectors installed into and under roadways. The influx of integrated connected data presents an opportunity to enhance traffic control practices, particularly at intersections, reducing congestion and improving safety for all road users.

While the full potential of connected vehicle applications may only be achieved once standardised and completely connected transport ecosystems are deployed, there is an opportunity to start utilising these emerging technologies and data sources to support traffic control and management applications.

What are we trying to achieve?

The project aims to consider current transport management methods and how integrating existing and emerging vehicle data can enhance traffic control systems in the short term. This aligns with long-term safety and mobility goals while promising immediate enhancements in traffic systems and driver support. It's a step towards leveraging available technology to create more efficient and safer roads without relying solely on fully realized connected vehicle ecosystems, offering a practical way to improve traffic operations and safety.

What does the existing evidence tell us?

Findings from the literature review

A review of existing literature focused on better understanding the current use of road user location data in traffic control and management and determining whether such methods would lead to real-time applications. It also explored current evidence on the potential to integrate emerging real-time road user location data into existing adaptive traffic control systems. This analysis focused on four key areas of traffic management:

  1. Intersection management
  2. Network and freeway optimisation
  3. Incident management
  4. Micromobility enablement
Four Key Areas of Traffic Management

Four Key Areas of Traffic Management

According to the existing evidence, intelligent traffic management systems gather road user movement data using manual, infrastructure-based, or user-specific sensors. Data is then transferred to a central/local network for analysis, which can happen in real-time or offline. Most existing traffic management systems are reactive (detecting and reacting to traffic problems), but advancements in machine learning, artificial intelligence and greater access to automated data sources offer great potential to improve their predictive capabilities in coming years.

The analysis of previous research, identifies that real-time road user location data is offers real advantages for improving traffic control and management. The spatial resolution (the smallest unit of space that can be captured/represented), temporal resolution (how frequently data is collected) as well as accuracy and precision should all be considered when planning traffic management applications. How quickly data is transmitted for processing is also critical in supporting real-time decision-making.

Location data is currently used for road user trajectories, i.e., the path travelled by road users, providing insight into the direction, speed, acceleration, travel time, and route. This provides valuable insights into assessing and monitoring congestion and can inform better traffic management decisions.

How are the different areas of traffic management currently using data?

The research looked at the existing use of data across four key traffic management domains including current limitations that could be overcome through data enhancements.

Intersection management
– uses metrics such as queue length, delay, pedestrian trajectory prediction, public transport priority, and bicycle volumes for intersection optimisation. Probe vehicle data is a significant development in this area, with high-frequency and real-time GPS data supporting improvements to adaptive control strategies and improving public transport priority schemes.

Network and freeway optimisation – the use of data focuses on estimating and predicting traffic volume, traffic speed, and vehicle travel time with probe vehicle data a cost-effective and flexible data collection method compared to fixed installed road sensors. Current challenges include oversaturated traffic conditions, noise, and accessing lane-specific information, but emerging solutions, such as high-resolution maps, may overcome these limitations.

Incident management
– data such as video, crowdsourced information from social media, and vehicle speeds extracted from GPS data can be used to understand incident locations in real-time, while video processing methods and subsequent machine learning analysis can identify incident locations and near-miss conflicts. Artificial intelligence is increasingly being used for predictive crash modeling and real-time conflict prediction. Complementary data sources, such as historical incident data and crash risk factors, are still required to support real-time incident and detection.

Micro-mobility enablement
– uses density and volume metrics to determine high-demand areas for corridors and parking locations; however, these methods are still emerging, and the review indicates a need for more research in this domain, especially for real-time application to adaptive control systems.

What did the experts tell us?

Findings from the stakeholder consultation

Stage two of the research interviewed stakeholders across government and industry to understand:

  • Key areas of focus for transport operators
  • Promising data sources and technology that can provide real-time or near-real-time data for proactive network management, and
  • Opportunities and challenges for integrating new datasets into existing systems.

Key areas of focus

Stakeholders identified two main areas of focus: improving safety as we strive to reduce the road toll ‘towards zero’ and improving efficiency in the transport network. These are achieved in use cases of varying complexity across the four areas of traffic management: intersection management, network and freeway optimisation, micromobility enablement, and incident management. In addition, stakeholders identified the potential for emerging data to support longer-term transport infrastructure planning applications.

Intersection management
Intersections currently have the highest coverage for data collection to support traffic management activities. At these locations, stakeholders see the need to improve management further by understanding ‘pain points’ in traffic flow, queue development to calibrate traffic signals, and improving safety for all modes at intersections, including cyclists and pedestrians.

Network and freeway optimisation?

Current network and freeway optimisation includes route guidance, congestion monitoring, lane use management, ramp metering, and freeway-to-freeway coordination. Existing applications could be further supported to better understand and alleviate congestion, estimate traffic volumes, evaluate and leverage origin-destination information, and create automation-ready networks.

Incident management

In this traffic management domain, stakeholders all focus on general safety and sustainability across the network with the overarching goal of reducing the road toll towards zero, a priority in all Australian jurisdictions. Stakeholders identified the extensive road network in regional areas as a priority, where fatalities are proportionally high compared to the urban road network.

Micromobility enablement and other vulnerable road user safety

Management of vulnerable road users, active transport users, and micromobility modes is an emerging concern for transport network operators. Some applications that need to be considered include sourcing accurate micromobility locations and behaviour data to guide safe transport movements and managing crowds from mobility data of vulnerable road users during special events.

Real-time data for proactive network management: what are the data and technology options?

According to stakeholders, numerous existing and emerging datasets and technology could support improving transport network operations management, including data from probe data, camera-based video analytics, vehicle Bluetooth devices, in-vehicle safety sensor data, and approved location app data from a range of vendors. There is a desire for data sources to not only capture probe data for motorised vehicles but also include information on other modes including micromobility, active transport, and pedestrians. Stakeholders identified a range of potential scenarios where integrated connected data could be harnessed more effectively:

  • Congestion performance measurement
  • Historical pattern database development
  • Supporting heavy vehicle movements
  • Demand management and congestion charging
  • Speed compliance enforcement
  • Machine learning operations
  • Vehicle priority and pre-emption system
  • Improved coordinated corridors

Opportunities and challenges for integrating new datasets into existing systems

This research has uncovered numerous datasets that could be integrated with existing systems to support improvements in transport operations management. Stakeholder consultation has also identified challenges that need to be addressed before these benefits can be realised:

  • Privacy and security management is critical to ensure trust and reliability of data.
  • The existing traffic control system needs to be developed to accept the new data.
  • Complex algorithms and analysis is not yet proven on large scale.
  • Prediction and simulation models do not always reflect real-world scenarios.
  • Deployment of use cases is constrained by existing telecommunications infrastructure.
  • Some use cases will be constrained by cost, with particular concern around economic pricing models used by data providers to deliver data in a useable format.
  • Agreement across agencies and across jurisdictions is required.
  • There are inherent biases within alternative data sources.
  • Some datasets are constrained in their ability to provide high accuracy information on location and speed.
  • Datasets come at different levels of granularity.

    What happens next?

    The information collected during the literature review and stakeholder consultation has been synthesised by the University of Melbourne and will inform the next stage of the project and be included in the final report due for publication in 2024.

    In partnership with project partners, the next stages of the project will develop an analysis of connected vehicle data for traffic efficiency at intersections some examples to be investigated are queue length information, midblock speeds and delay measurements, stop-and-go traffic states.

    Note: Findings from the Literature Review and Stakeholder Consultation will be incorporated into the final research report and when published, should be the source for any citations.


    IMOVE Australia
    The University of Melbourne
    Queensland Department of Transport & Main Roads
    Transport for NSW
    Victorian Department of Transport and Planning
    Transport Accident Commission TAC
    Department of Transport Western Australia

    More about this project

    If you are interested in learning more about the research project - click on the accordions below.

    Project Overview



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