Can Machine Learning Uncover Insights into Vehicle Travel Demand from Our Built Environment?

• URL: http://arxiv.org/abs/2311.06321v1
• Date: Fri, 10 Nov 2023 06:52:17 GMT
• Title: Can Machine Learning Uncover Insights into Vehicle Travel Demand from Our Built Environment?
• Authors: Zixun Huang, Hao Zheng
• Abstract summary: We propose a machine learning-based approach to address the lack of ability for designers to optimize urban land use planning from the perspective of vehicle travel demand. Research shows that our computational model can help designers quickly obtain feedback on the vehicle travel demand.
• Score: 6.878774457703503
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: In this paper, we propose a machine learning-based approach to address the lack of ability for designers to optimize urban land use planning from the perspective of vehicle travel demand. Research shows that our computational model can help designers quickly obtain feedback on the vehicle travel demand, which includes its total amount and temporal distribution based on the urban function distribution designed by the designers. It also assists in design optimization and evaluation of the urban function distribution from the perspective of vehicle travel. We obtain the city function distribution information and vehicle hours traveled (VHT) information by collecting the city point-of-interest (POI) data and online vehicle data. The artificial neural networks (ANNs) with the best performance in prediction are selected. By using data sets collected in different regions for mutual prediction and remapping the predictions onto a map for visualization, we evaluate the extent to which the computational model sees use across regions in an attempt to reduce the workload of future urban researchers. Finally, we demonstrate the application of the computational model to help designers obtain feedback on vehicle travel demand in the built environment and combine it with genetic algorithms to optimize the current state of the urban environment to provide recommendations to designers.

Related papers

• Advancing Transportation Mode Share Analysis with Built Environment: Deep Hybrid Models with Urban Road Network [12.349403667141559]
  We propose deep hybrid models (DHM) which directly combine road networks and sociodemographic features as inputs for travel mode share analysis. In experiments of mode share prediction in Chicago, results demonstrate that DHM can provide valuable spatial insights into the sociodemographic structure.
  arXiv  Detail & Related papers  (2024-05-23T00:59:00Z)
• Unified Data Management and Comprehensive Performance Evaluation for Urban Spatial-Temporal Prediction [Experiment, Analysis & Benchmark] [78.05103666987655]
  This work addresses challenges in accessing and utilizing diverse urban spatial-temporal datasets. We introduceatomic files, a unified storage format designed for urban spatial-temporal big data, and validate its effectiveness on 40 diverse datasets. We conduct extensive experiments using diverse models and datasets, establishing a performance leaderboard and identifying promising research directions.
  arXiv  Detail & Related papers  (2023-08-24T16:20:00Z)
• Implicit Occupancy Flow Fields for Perception and Prediction in Self-Driving [68.95178518732965]
  A self-driving vehicle (SDV) must be able to perceive its surroundings and predict the future behavior of other traffic participants. Existing works either perform object detection followed by trajectory of the detected objects, or predict dense occupancy and flow grids for the whole scene. This motivates our unified approach to perception and future prediction that implicitly represents occupancy and flow over time with a single neural network.
  arXiv  Detail & Related papers  (2023-08-02T23:39:24Z)
• Deep Occupancy-Predictive Representations for Autonomous Driving [6.591194329459251]
  We show that our proposed architecture encodes the probabilistic occupancy map as a proxy for obtaining pre-trained state representations. By leveraging a map-aware graph formulation of the environment, our agent-centric encoder generalizes to arbitrary road networks and traffic situations.
  arXiv  Detail & Related papers  (2023-03-07T20:21:49Z)
• TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction [149.5716746789134]
  We show data-driven traffic simulation can be formulated as a world model. We present TrafficBots, a multi-agent policy built upon motion prediction and end-to-end driving. Experiments on the open motion dataset show TrafficBots can simulate realistic multi-agent behaviors.
  arXiv  Detail & Related papers  (2023-03-07T18:28:41Z)
• Street-View Image Generation from a Bird's-Eye View Layout [95.36869800896335]
  Bird's-Eye View (BEV) Perception has received increasing attention in recent years. Data-driven simulation for autonomous driving has been a focal point of recent research. We propose BEVGen, a conditional generative model that synthesizes realistic and spatially consistent surrounding images.
  arXiv  Detail & Related papers  (2023-01-11T18:39:34Z)
• Generative methods for Urban design and rapid solution space exploration [13.222198221605701]
  This research introduces an implementation of a tensor-field-based generative urban modeling toolkit. Our method encodes contextual constraints such as waterfront edges, terrain, view-axis, existing streets, landmarks, and non-geometric design inputs. This allows users to generate many, diverse urban fabric configurations that resemble real-world cities with very few model inputs.
  arXiv  Detail & Related papers  (2022-12-13T17:58:02Z)
• Predicting Future Occupancy Grids in Dynamic Environment with Spatio-Temporal Learning [63.25627328308978]
  We propose a-temporal prediction network pipeline to generate future occupancy predictions. Compared to current SOTA, our approach predicts occupancy for a longer horizon of 3 seconds. We publicly release our grid occupancy dataset based on nulis to support further research.
  arXiv  Detail & Related papers  (2022-05-06T13:45:32Z)
• Deep Learning based Urban Vehicle Trajectory Analytics [1.3706331473063877]
  This dissertation focuses on the urban vehicle trajectory,' which refers to vehicles in urban traffic networks. The objective of this dissertation is to develop deep-learning based models for urban vehicle trajectory analytics.
  arXiv  Detail & Related papers  (2021-11-15T01:44:18Z)
• 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)
• TLab: Traffic Map Movie Forecasting Based on HR-NET [23.40323690536007]
  In our solution, hand-crafted features are input into the model in a form of channels. In terms of prediction accuracy, our solution has won 2nd place in NeurIPS 2020, Traffic4cast Challenge.
  arXiv  Detail & Related papers  (2020-11-13T18:48:13Z)

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.