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TEN DRONES

Hovering simultaneously over different areas

5 DAYS

Monday to Friday

MORNING PEAK-HOUR

Five flight sessions for 2.5 hours per day

100+ INTERSECTIONS

Signalised or not

MASSIVE URBAN TRAJECTORY DATASET

More than 0.5 million trajectories

OPEN ACCESS

Free distribution. No barriers.

GLOBAL IMPACT

Made for researchers around the world

PERFECT FOR PHD

Stop searching for data and start your analyses!

A first of its kind experiment

pNEUMA is an open large-scale dataset of naturalistic trajectories of half a million vehicles that have been collected by a one-of-a-kind experiment by a swarm of drones in the congested downtown area of Athens, Greece. A unique observatory of traffic congestion, a scale an-order-of-magnitude higher than what was not available until now, that researchers from different disciplines around the globe can use to develop and test their own models.

Ideal for multimodal
research

CARS

POWERED TWO WHEELERS

TAXIS

BUSES

MEDIUM AND HEAVY VEHICLES

  Illustration of the experiment

The swarm would take-off at the start of the experiment and each drone would go to its unique hovering point. Then, when all drones were at position, the recording of the traffic stream would start simultaneously and when the battery would run low, they would return to their landing point. Considering that drones could hover up to 25 minutes including take-off, routing and landing times, it was decided that each session would take place every 30 minutes for better coordination and standardization of the experiment.

  Set-up of the experiment

A swarm of 10 drones hovering over the central business district of Athens over five days to record traffic streams in a congested area of a 1.3km2 area with more than 100 km-lanes of road network, around 100 busy intersections (signalized or not), more than 50 bus stops and close to half a million trajectories.

Extracted Trajectories from the whole Network

This is a speed heat map, produced from all the extracted vehicles from the first flight session (8:00-8:30) on Thursday, October 30. The trajectories are plotted on the map to visualize the road network that is covered during the pNEUMA experiment. It can be seen that all major arterials are monitored while there are some parts, mostly in minor roads, that are not fully covered due to visibility issues.

Fundamental Diagrams

High resolution flow density diagrams are plotted based on Eddie’s definitions for virtual loops. We see that the parameters of FDs vary across space and trapeziums with various parameters approximate the upper envelope of FDs.

Multilane Time-Space Diagrams

The complete picture for a 3-lane road arterial (Alexandras Avenue) with a x-t diagram for each lane. The width of the road in the last 80 meters of the specific arterial includes an extra fourth lane and the x-t diagram of the extra lane can be seen in the bottom part of the figure. All red circles represent lane changes that remained in the study are while the black circles represent the vehicles that exited the study area from the right lane to adjacent roads of the network.

Multimodal Interactions

Local disturbances in a multimodal environment and how the way special vehicles (e.g. taxis, buses, delivery vehicles) affect the traffic flow characteristics. The service-related stops of all relevant modes create static and moving bottlenecks of different magnitude. The trajectories of two buses are shown with thicker lines in the right lane. The problem is quite complex because there is a traffic light at the downstream end that interacts with the bus stop.

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Our vision

“pNEUMA is a unique observatory for traffic congestion with data that did not exist before at this resolution or scale.

Our vision is to promote unique open data for tackling diverse transportation related phenomena and also to place drones as an alternative for traffic monitoring of high accuracy.”

Nikolas Geroliminis

“The preliminary analysis of the dataset exceeded our expectations in terms of quality of extraction and information available and provides unique research opportunities.

We expect πNEUMA to become a benchmark dataset for a new era of traffic models that will be utilized for understanding how people behave and what really causes traffic congestion.”

Manos Barmpounakis

EPFL

Recent Posts

Eco-driving strategies using reinforcement learning for mixed traffic in the vicinity of signalized intersections

A paper by Zhiwei Yang, Zuduo Zheng, Jiwon Kim and Hesham Rakha was published inTransportation Research Part C: Emerging Technologies entitled “Eco-driving strategies using reinforcement learning for mixed traffic in the vicinity of signalized intersections”. The authors utilise the pNEUMA dataset instead of simulated trajectories toused to train and test the reinforcement learning methods. Highlights Abstract This study proposes autonomous…

Inferring vehicle spacing in urban traffic from trajectory data

A paper by Yiru Jiao, Simeon Calvert, Sander van Cranenburgh and Hans van Lint was published inTransportation Research Part C: Emerging Technologies entitled “Inferring vehicle spacing in urban traffic from trajectory data”. The authors utilise the pNEUMA dataset to infer the average two-dimensional (2D) spacing between interacting vehicles in urban traffic. Highlights Abstract This study presents a new method to…

The relation of traffic congestion and noise emissions using the pNEUMA dataset

A paper by Jasso Espadaler Clapés,Manos Barmpounakis and Nikolas Geroliminis was published inTransportation Research Part D: Transport and Environment entitled “Traffic congestion and noise emissions with detailed vehicle trajectories from UAVs”. The authors utilise the pNEUMA dataset to investigate the relation between noise emissions and traffic congestion. Abstract Excessive noise in cities due to road traffic negatively affects human health.…