Hello everyone, my name is Zijian Cao from BUPT, Beijing University of Posts and
Telecommunications and it’s my great honor to present our work D2Park.
It’s a joint work of BUPT and Rutgers university. Here is the outline of my presentation, and I will
give a brief introduction of the background first.
With the continual growth of population and vehicles in many cities, people are faced with
increasingly serious parking pain. Recent surveys show that massive amounts of time, fuel, emissions
are wasted in searching for parking spots. In recent years, many innovative parking Apps
have emerged to help drivers to find a convenient off-street parking spot with useful information
such as parking availability, price and distance. By contrast, on-street parking has some unique
characteristics, such as less spots for temporary use, more immediate influences on the traffic,
which make it’s necessary to design mobile Apps for on-street parking guidance services.
Fortunately, the recent advances of sensing technologies have enabled large-scale,
low-cost and continuous monitoring of on-street parking occupancy.
Parking occupancy prediction can increase the probability of finding free parking spots,
but in turn it changes the driver’s behavior from searching to competing for parking,
resulting in the phenomenon of “multiple cars chasing the same spot”. The other way
to provide parking service is to provide parking reservation services. However, it is impractical
to treat all spots as reservable resources. So, what is the realistic on-street parking
scenario and how to provide a proper parking guidance service?
To solve the above problem, we then conduct a qualitative investigation
based on the online survey data. There are 11 questions and 203 participants
on the survey. And the results indicate the necessity of providing both recommendation
services and reservation services for diversified requirements.
To satisfy diversified requirements, we divide the users into different types involving reserved
users, who requests a guaranteed spot at the cost of a reservation fee, normal users, who requests
a non-guaranteed spot with a “best-effort” recommendation service and external users who
does not send any request and directly drives to a street for parking near her/his destination.
It is challenging to simultaneously consider the three classes of users for
achieving high-efficient dynamic resource allocation due to the following reasons.
As mentioned above, the diversified requirements should be comprehensively considered and should
be well balanced. The allocation mechanisms should be adjusted for various users, such
as the cooperation-based mechanism is suitable for reserved users and for normal users, the
competition-based mechanism is more applicable. Different allocation mechanisms together with
uncertain external demands have complex mutual influences on the resource availability.
And finally, to improve the resource allocation performance, it is needed to consider not only
current parking requests and occupancy status but also uncertain demands and occupancy dynamics at
fine grained temporal levels in the future. Thus, we design a parking guidance system. The
sensing component monitors the real-time occupancy status and stores it in the
database. The computing component first predicts the future occupancy status and then outputs
the guidance results. For user end, the service component is encapsulated as a parking APP.
We first utilize the prediction module to predict the parking inflow and POR,
which combines the Graph Convolutional Neural Network (GCN) and Long Short-Term Memory
(LSTM) to predict a sequence of parking demands and occupancy status
on each street block based on the current parking events data and other external factors
With the prediction results, we design the diversified parking guidance module,
which consists of three parts: DG-MNC, competition-based mechanism for normal users,
DG-MRC, cooperation-base mechanism for reserved users, and Joint Parking Guidance, a three-step
joint guidance algorithm that coordinate the mutual influences of DG-MNC and DG-MRC.
Then, we design 5 different evaluation metrics and 2 baselines.
We also evaluate our system under sufficient comparison experiments.
The average parking delay time has been reduced by at least 33%
through the verification of more than 62 million real world parking data in Shenzhen, China