Embedded Files
UAM demand scenario generation and analysis
UAM demand scenario generation and analysis
Summary
Background
In the near future, urban airspace can be congested with drones due to the demand for faster delivery of food and parcels. The extent to which airspace will be utilized is uncertain at this point and will vary from time to time and from region to region. Identifying potential congested areas as well as reducing congestion levels will be one of the critical functions of drone traffic management.
Objective
This research in progress generates a hypothetical demand scenario in which food and parcels being delivered by land vehicle are partially replaced by air services. The ultimate goal is to assess which part of airspace may be subject to potential congestion and evaluate traffic management strategies that can contribute to alleviating such congestion.
Future directions
This study can be developed further to :
identify the portions of airspace that is subject to potential congestion
examine how much cost improvement or environmental benefit the emerging aerial service can bring, compared to the existing mobility
find the optimal location of droneports/charging station, by considering the level of congestion around candidate locations
evaluate the effectiveness of traffic management strategies in reducing airspace congestion and complexity.
Data description
Food delivery data
The data used are extracted from food deliveries from restaurants located in four adjacent districts* of Seoul on June 21, 2020.
There are 3,759 food deliveries** in the four districts on this specific day. The Euclidean distance between origin and destination has the median of 1.14 km (IQR: 0.69 km - 1.69 km)
For preliminary analysis, we selected the cases occured between 18:00 and 18:20 (= 153 cases) and had the Euclidean distance between O and D greater than 2 km, which leaves 85 cases.
* There are 25 districts in Seoul, and the four districts located in the south-western part of Seoul are selected due to limited data availability
** This data do not account for all food deliveries in Seoul but only those processed by a specific company.
Illustration of food deliveries occured in the south-western part of Seoul on June 21, 2020 (red: restaurants, white: destination)
Parcel delivery data
The data used were extracted from the records of parcels delivered by a leading logistics company (with 50% of the national market share) from across the country to the city of Seoul.
There were 625,632 cases of clothing deliveries in the third week of June, 2020. The Euclidean distance between origin and destination has the median of 16.1 km (IQR: 9.59 km - 26.81 km).
For preliminary analysis, I sampled the cases that occured inside the 20 km X 40 km boundary surrounding the city of Seoul (leaving 369,640 cases). Considering twelve working hours and five working days, it is assumed that this logistics company have approximately 2,000 clothing shipments within the 20 km X 40 km boundary every 20 minutes.
Since there is no timestamp when each delivery was started and completed, we randomly selected 200 cases (i.e. 10% of clothing shipments replaced by drone delivery service).
Illustration of parcel deliveries occured in eoul in the third week of June, 2020
(red: sender's location, white: recipient's location)
Results
Food delivery
The figures below show 50 food deliveries being replaced by aerial service (which can be considred as approximately two thirds of the current demand processed by one company that has the Euclidean distance between OD greater than 2km).
The unobstructed airspace with diemension 13 km X 13 km is divided into uniform hexagonal grids having edge length of 100 m
The traffic density threshold is set to 2 per grid (meaning no more than two agents can occupy the same grid at the same time)
By applying a density control algorithm, 8 agents were diverted to lower density areas, and 6 agents delayed their operation at the starting position.
50 drones moving from restaurants to receiver's locations (without density control)
Each grid is colored green and orange if it is occupied by 1 agent and 2 agents, respectively. Black grid indicates 3 or more agents are occupying the grid at the same time.
50 drones moving from restaurants to receiver's locations (with density control)
The maximum traffic density is kept under 3 per grid throughout the time horizon.
Agent #7 waited 2 time steps at the starting location
Agent #8 ,#9, #13, #21, #24 waited 1 time step at the starting location
Agent #4, #6, #12, #14, #16, #17, #25, #41 took alternative paths (“bypass”) with no additional distance
Maximum traffic density map shows the maximum number of agents that will occupy each grid during the time horizon.
By applying a density control algorithm, 8 agents were diverted to lower density areas, and 6 agents delayed their operation at the starting position.
Results
Results
Parcel delivery
The figures below show 200 shipments replaced by aerial service (which can be considred as approximately 10% of the current demand)
The unobstructed airspace with diemension 20 km X 40 km is divided into uniform hexagonal grids having edge length of 400 m.
The traffic density threshold is set to three per grid (meaning no more than two agents can occupy the same grid at the same time)
By applying a density control algorithm, 32 agents were diverted to lower density areas, and 2 agents delayed their operation at the starting position.
200 drones moving from sender's locations to receiver's locations (without density control)
Each grid is colored green and orange if it is occupied by 1 agent and 2 agents, respectively. Black grid indicates 3 or more agents are occupying the grid at the same time.
200 drones moving from sender's locations to receiver's locations (with density control)
The maximum traffic density is kept under 3 per grid throughout the time horizon.
Agent #13, #38 waited 1 time step at the starting location
Total of 32 agents took alternative paths (“bypass”) with no additional distance
Maximum traffic density map shows the maximum number of agents that will occupy each grid during the time horizon.
By applying a density control algorithm, 32 agents were diverted to lower density areas, and 2 agents delayed their operation at the starting position.
Future directions
Future studies can be conducted to :
identify which parts of airspace will be congested/preferred
examine how much cost improvement or environmental benefit the emerging aerial service can bring, compared to the existing service
find the optimal location of droneports/charging station, by considering various demand scenarios
investigate how effective any traffic management strategy will be in reducing the complexity within the airspace
Page updated
Google Sites
Report abuse