Problem: LMICs road administrations struggle to successfully adopt efficient ways to manage their assets and to utilize their capabilities due to limited resources.

Solution: Data is a key factor to evaluate investments. This is especially important with limited budgets, as effectiveness of road safety measures should be demonstrated and inefficient use of limited funding or an increase of crash risk should be known.

Recommendation: Improved access to data on the effectiveness and costs involved in establishing interventions can lead to ongoing improvements and support for their broader application.

Read More:

• Road Safety Manual - Road Safety Management - Safety Data

Problem: Data quality is important, as misleading data-points or other pieces of information can lead to wrong conclusions and ineffective decisions being made.

Solution: Strengthened data collection methods locally and nationally contributes to improved evidence-based policies, especially when specific subgroups require more attention such as vulnerable road users, professional drivers or new/inexperienced drivers.

Recommendation: Data collection and analysis can facilitate the maintenance and management of the road network and provide stress-free journey to road users; it is thus important to improve data quality. The more comprehensive the data set, the higher the reliability. Training of data entry staff and improving data collection tools can also contribute to the amelioration of data quality.

Read More:

• Road related data and how to use it

Problem: While some road crashes are predictable and thus preventable, efforts to reduce crashes in developing countries are further hampered by lack of accurate crash and casualty data.

Solution: Road condition data can be collected using in-vehicle sensors, drones, smart phones, recorded signals. For LMICs, crash data may not be reliable or available. Surveys and other data sources to measure and monitor road safety are needed. When crash data is analysed, an understanding of the most crucial aspects of crash occurrence needs to be collected in a standardized manner. When using crash data for causation analysis, it is critical to examine multiple angles and aspects to define the cause-and-effect chain as appropriately as possible. Also, it is important to strictly define and in-depth analyse the crash locations and the time period to be investigated.

Recommendation: The main steps to improving data quality include: a review of variable definitions, ensuring they are simple to understand and apply; reinforcing the need to report crashes, e.g. by making it a legal requirement; improving data collection tools (e.g. crash report documents and apparatus, coding procedures); collecting accurate location information; improving training of police and data entry staff; ensuring the data collected is accurate and reliable through quality assurance measures.

Read More:

• Road related data and how to use it

Problem: Traffic Congestion is a principal challenge due to increase in vehicle numbers and population and a changing balance of transport modes.

Solution: Collecting traffic related data aims for better support of traffic engineering actions for traffic to flow more smoothly and to keep travelers better informed. The data collection methods include traditional sensors, smart sensors that provide real-time traffic information, cameras Closed-Circuit Television (CCTV) systems and GPS signals, as well as Unmanned Aerial Vehicles (UAVs).

Recommendation: Smart Transportation is cost-effective and significant component of Smart Cities, new and emerging transport modes have to be taken into account and have a seat reserved for them in the design of the future transport ecosystems, which should include dedicated data collection schemes for them.

Read More:

• Utilizing data to optimize road network operations. A PIARC collection of case studies