Predicting Parking Lot Availability by Graph-to-Sequence Model: A Case Study with SmartSantander

• URL: http://arxiv.org/abs/2206.10160v1
• Date: Tue, 21 Jun 2022 07:36:59 GMT
• Title: Predicting Parking Lot Availability by Graph-to-Sequence Model: A Case Study with SmartSantander
• Authors: Yuya Sasaki, Junya Takayama, Juan Ram\'on Santana, Shohei Yamasaki, Tomoya Okuno, Makoto Onizuka
• Abstract summary: We study the prediction of parking lot availability using historical data from more than 300 outdoor parking sensors with SmartSantander. Our model achieves a high accuracy compared with existing sequence-to-sequence models. We apply our model to a smartphone application to be widely used by citizens and tourists.
• Score: 6.370669405709256
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Nowadays, so as to improve services and urban areas livability, multiple smart city initiatives are being carried out throughout the world. SmartSantander is a smart city project in Santander, Spain, which has relied on wireless sensor network technologies to deploy heterogeneous sensors within the city to measure multiple parameters, including outdoor parking information. In this paper, we study the prediction of parking lot availability using historical data from more than 300 outdoor parking sensors with SmartSantander. We design a graph-to-sequence model to capture the periodical fluctuation and geographical proximity of parking lots. For developing and evaluating our model, we use a 3-year dataset of parking lot availability in the city of Santander. Our model achieves a high accuracy compared with existing sequence-to-sequence models, which is accurate enough to provide a parking information service in the city. We apply our model to a smartphone application to be widely used by citizens and tourists.

Related papers

• Efficient Large-Scale Urban Parking Prediction: Graph Coarsening Based on Real-Time Parking Service Capability [7.5350858327743095]
  This paper proposes an innovative framework for predicting large-scale urban parking graphs leveraging real-time service capabilities. To effectively handle large-scale parking data, this study combines graph coarsening techniques with temporal convolutional autoencoders. Our framework achieves improvements of 46.8% and 30.5% in accuracy and efficiency, respectively.
  arXiv  Detail & Related papers  (2024-10-05T03:54:25Z)
• AutoBencher: Creating Salient, Novel, Difficult Datasets for Language Models [84.65095045762524]
  We present three desiderata for a good benchmark for language models. benchmark reveals new trends in model rankings not shown by previous benchmarks. We use AutoBencher to create datasets for math, multilingual, and knowledge-intensive question answering.
  arXiv  Detail & Related papers  (2024-07-11T10:03:47Z)
• Planning with Adaptive World Models for Autonomous Driving [50.4439896514353]
  Motion planners (MPs) are crucial for safe navigation in complex urban environments. nuPlan, a recently released MP benchmark, addresses this limitation by augmenting real-world driving logs with closed-loop simulation logic. We present AdaptiveDriver, a model-predictive control (MPC) based planner that unrolls different world models conditioned on BehaviorNet's predictions.
  arXiv  Detail & Related papers  (2024-06-15T18:53:45Z)
• Efficient Parking Search using Shared Fleet Data [2.0967973517861003]
  Finding a free parking spot in a smart environment can be modeled and solved as a Markov decision process (MDP) Knowing the parking intention of every vehicle in the environment would eliminate uncertainty. In this paper, we examine the question of how useful sharing data within a vehicle fleet might be for the search times of particular drivers.
  arXiv  Detail & Related papers  (2024-04-16T15:20:28Z)
• RFID-Cloud Integration for Smart Management of Public Car Parking Spaces [0.0]
  This paper presents a successful proof-of-concept implementation of a framework for managing public car parking spaces. Reservation of parking slots is done through a cloud-hosted application, while access to and out of the parking slot is enabled through Radio Frequency Identification (RFID) technology. This framework could bring considerable convenience to City dwellers since motorists only have to drive to a parking space when sure of a vacant parking slot.
  arXiv  Detail & Related papers  (2022-12-25T00:39:42Z)
• IntentNet: Learning to Predict Intention from Raw Sensor Data [86.74403297781039]
  In this paper, we develop a one-stage detector and forecaster that exploits both 3D point clouds produced by a LiDAR sensor as well as dynamic maps of the environment. Our multi-task model achieves better accuracy than the respective separate modules while saving computation, which is critical to reducing reaction time in self-driving applications.
  arXiv  Detail & Related papers  (2021-01-20T00:31:52Z)
• End-to-end Interpretable Neural Motion Planner [78.69295676456085]
  We propose a neural motion planner (NMP) for learning to drive autonomously in complex urban scenarios. We design a holistic model that takes as input raw LIDAR data and a HD map and produces interpretable intermediate representations. We demonstrate the effectiveness of our approach in real-world driving data captured in several cities in North America.
  arXiv  Detail & Related papers  (2021-01-17T14:16:12Z)
• Semantic Segmentation on Swiss3DCities: A Benchmark Study on Aerial Photogrammetric 3D Pointcloud Dataset [67.44497676652173]
  We introduce a new outdoor urban 3D pointcloud dataset, covering a total area of 2.7 $km2$, sampled from three Swiss cities. The dataset is manually annotated for semantic segmentation with per-point labels, and is built using photogrammetry from images acquired by multirotors equipped with high-resolution cameras.
  arXiv  Detail & Related papers  (2020-12-23T21:48:47Z)
• The Smart Parking Management System [0.0]
  This paper proposes the Smart Parking Management System (SPMS) that depends on Arduino parts, Android applications, and based on IoT. IR sensors are utilized to know if a car park space is allowed. Its area data are transmitted using the WI-FI module to the server and are recovered by the mobile application.
  arXiv  Detail & Related papers  (2020-09-28T16:08:10Z)
• FADACS: A Few-shot Adversarial Domain Adaptation Architecture for Context-Aware Parking Availability Sensing [5.160087162892865]
  We design an end-to-end transfer learning framework for parking availability sensing to predict parking occupancy in areas in which the parking data is insufficient to feed into data-hungry models. This framework overcomes two challenges: 1) many real-world cases cannot provide enough data for most existing data-driven models, and 2) it is difficult to merge sensor data and heterogeneous contextual information.
  arXiv  Detail & Related papers  (2020-07-13T08:25:26Z)
• ParkPredict: Motion and Intent Prediction of Vehicles in Parking Lots [65.33650222396078]
  We develop a parking lot environment and collect a dataset of human parking maneuvers. We compare a multi-modal Long Short-Term Memory (LSTM) prediction model and a Convolution Neural Network LSTM (CNN-LSTM) to a physics-based Extended Kalman Filter (EKF) baseline. Our results show that 1) intent can be estimated well (roughly 85% top-1 accuracy and nearly 100% top-3 accuracy with the LSTM and CNN-LSTM model); 2) knowledge of the human driver's intended parking spot has a major impact on predicting parking trajectory; and 3) the semantic representation of the environment
  arXiv  Detail & Related papers  (2020-04-21T20:46:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.