Multi layered data is the key to enabling data driven urban planning. Urban traffic is one of the main challenges of cities and city environments, where rapidly increasing traffic congestion is threatening quality of life. Longer journey times, increased fuel consumption, and environmental pollution are a daily reality for urban drivers. Ongoing roadworks further aggravate driving conditions. This, and urban gridlock and traffic jams have a profound effect on drivers, businesses, vehicles, infrastructure and the environment:
- Drivers are struggling to be on time and suffer from increased anxiety, stress and road rage
- Businesses experience financial and productivity losses due to long hours spent on the road
- Vehicles undergo excessive wear and tear
- Road infrastructure is eroded, compromising safety
- Air pollution and carbon dioxide levels are skyrocketing
The magnifying factors of urban traffic
Rapid urbanization is leading to quick spatial expansion of cities, which in turn face intensified urban traffic issues. With more people living and working in cities, the number of cars on the roads has dramatically increased, while infrastructure is lagging with inadequate and worn roads. The only way to tackle these challenges is through data driven urban planning that can also take into account additional contributing factors:
- Construction sites that are a vital part of every urbanization process, have a significant effect on traffic, leading to increased congestion and delays during the construction period. In the city of Hamburg for example, more than 3,000 construction projects affected the roads during 2019, bringing great misery to drivers and commuters.
- Poor synchronization of traffic lights. Traffic signal control is considered as one of the most effective ways to reduce traffic congestion at intersections, however most existing traffic signal synchronization strategies do not perform well in the saturated, high-density urban environment.
- Unforeseen events on the road such as a car breaking down or accidents may cause ripple effects. Drivers that slow down out of curiosity or as a precautionary measure, cause a ripple effect that spreads out to create a sustained traffic jam.
- Severe weather conditions that may include heavy rain, fog, ice, snow, and dust, have a significant impact on traffic operations, traffic flow and road safety.
- Cruising for parking creates a moving queue of cars that are waiting for parking vacancy and slows down the entire traffic lane.
What is multi-layered vehicle data?
Modern vehicles typically use 60 – 100 sensors to keep running the way they should. These sensors are a vital part of any modern vehicle design, and help car manufacturers bring safer, more fuel efficient and more comfortable vehicles.
Multiple vehicle sensors generate real-time, actionable information about the vehicle and the dynamics of the road. The data is then sent by connected vehicles and can be made available, pending on drivers consent, for services that benefit drivers, communities, and the environment.
Vehicle-related information, also known as traffic data or Floating Car Data (FCD) is the basic layer of vehicle data, with information that typically includes vehicle, heading and speed. But vehicle sensors generate additional, road-related information, such as road signs data, weather data, hazard data, parking data, EV data, construction data and road friction data thus enabling data driven urban planning.
Multi-layered vehicle data alleviates many of the contributing factors to traffic congestion and improves driving experience by boosting predictions accuracy and yielding more targeted and effective actions that
- Improves traffic routing
- Eases parking problems
- Boosts safety and prevents accidents
- Expedites first medical response
- Enables more effective infrastructure planning
How Can Multi-layered vehicle data can make a huge difference?
Each of the vehicle data layers is valuable by itself and provides a host of benefits to multiple use cases. Real-time traffic data powers algorithms used by municipalities to manage traffic congestion and improve traffic flows by optimizing road logistics and routes. Mapping and planning solutions employ traffic data to maintain accurate maps and measure traffic load, while location intelligence and research applications rely on real-time and historic vehicle data to evaluate location utilization and potential.
Adding additional layers of data, on top of traffic data, can take insights and actions to the next level:
- Real-time road sign data can be applied to test the impact of new or updated road signs and identify potentially obstructed road signs to increase safety
- Real-time weather data can be used to warn drivers of upcoming road conditions to avoid accidents and more accurately predict weather-related accident locations. Based on accident prediction, medical care and accident scene cleanup can be expedited to save lives, reduce consequent congestion and avoid secondary incidents
- Real-time hazard data can be employed to warn drivers on upcoming breakdown events and speed up response to clear the road. A quick response will ease subsequent congestion, prevent resulting incidents and increase drivers’ safety
- Real-time parking data is utilized to map parking spots and generate parking statistics that improves parking availability, which in turn will reduce cruising for parking and the resultant congestion and emissions
- Historical & real-time EV data can support EV charging site planning and optimization to reduce range anxiety and make EV ownership more convenient. More accurate prediction of crowded charging periods can facilitate load management and flexible charging rates planning and improve charging experience.
- Real-time construction equipment data can ease congestion and increase safety by informing mapping services and drivers on closed or construction affected roads. Traffic can be rerouted to alternative roads
- Real-time road friction data can provide insights into the conditions of the road surface to warn drivers on the status of the road ahead and alert on required maintenance. With friction data, accident locations can be more accurately predicted to accelerate medical care and save lives, expedite accident cleanup and reduce the subsequent congestion and potential incidents
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