A Data-Driven Travel Mode Share Estimation Framework based on Mobile Device Location Data

• URL: http://arxiv.org/abs/2006.10036v4
• Date: Wed, 17 Feb 2021 16:04:48 GMT
• Title: A Data-Driven Travel Mode Share Estimation Framework based on Mobile Device Location Data
• Authors: Mofeng Yang, Yixuan Pan, Aref Darzi, Sepehr Ghader, Chenfeng Xiong and Lei Zhang
• Abstract summary: This paper studies the capability of MDLD on estimating travel mode share at aggregated levels. A data-driven framework is proposed to extract travel behavior information from the MDLD. The proposed framework is applied to two large-scale MDLD datasets.
• Score: 5.767204062337505
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mobile device location data (MDLD) contains abundant travel behavior information to support travel demand analysis. Compared to traditional travel surveys, MDLD has larger spatiotemporal coverage of population and its mobility. However, ground truth information such as trip origins and destinations, travel modes, and trip purposes are not included by default. Such important attributes must be imputed to maximize the usefulness of the data. This paper tends to study the capability of MDLD on estimating travel mode share at aggregated levels. A data-driven framework is proposed to extract travel behavior information from the MDLD. The proposed framework first identifies trip ends with a modified Spatiotemporal Density-based Spatial Clustering of Applications with Noise (ST-DBSCAN) algorithm. Then three types of features are extracted for each trip to impute travel modes using machine learning models. A labeled MDLD dataset with ground truth information is used to train the proposed models, resulting in 95% accuracy in identifying trip ends and 93% accuracy in imputing five travel modes (drive, rail, bus, bike and walk) with a Random Forest (RF) classifier. The proposed framework is then applied to two large-scale MDLD datasets, covering the Baltimore-Washington metropolitan area and the United States, respectively. The estimated trip distance, trip time, trip rate distribution, and travel mode share are compared against travel surveys at different geographies. The results suggest that the proposed framework can be readily applied in different states and metropolitan regions with low cost in order to study multimodal travel demand, understand mobility trends, and support decision making.

Related papers

• Urban Mobility Assessment Using LLMs [19.591156495742922]
  This work proposes an innovative AI-based approach for synthesizing travel surveys by prompting large language models (LLMs) Our study evaluates the effectiveness of this approach across various U.S. metropolitan areas by comparing the results against existing survey data at different levels.
  arXiv  Detail & Related papers  (2024-08-22T19:17:33Z)
• Link Representation Learning for Probabilistic Travel Time Estimation [21.092166159353702]
  In this paper, we propose to model trip-level link travel time using a Gaussian hierarchical model. The results demonstrate its superior performance compared to state-of-the-art deterministic and probabilistic baselines. We find that the learned link representations align well with the physical geometry of the network, making them suitable as input for other applications.
  arXiv  Detail & Related papers  (2024-07-08T13:01:53Z)
• Combining data from multiple sources for urban travel mode choice modelling [0.8437187555622164]
  There is a growing need to predict when people will decide to use different travel modes with an emphasis on environmentally friendly modes. In a growing number of cases machine learning methods are used to predict travel mode choices given respondent and journey features. We propose an architecture of a software platform performing the data fusion combining data documenting journeys with the features calculated to summarise transport options.
  arXiv  Detail & Related papers  (2024-05-15T16:41:53Z)
• Hybrid Ensemble-Based Travel Mode Prediction [0.0]
  Travel mode choice (TMC) prediction helps in understanding what makes citizens choose different modes of transport for individual trips. As behaviour may evolve over time, we also face the question of detecting concept drift in the data. This necessitates using appropriate methods to address potential concept drift.
  arXiv  Detail & Related papers  (2024-04-22T09:32:38Z)
• IDD-3D: Indian Driving Dataset for 3D Unstructured Road Scenes [79.18349050238413]
  Preparation and training of deploy-able deep learning architectures require the models to be suited to different traffic scenarios. An unstructured and complex driving layout found in several developing countries such as India poses a challenge to these models. We build a new dataset, IDD-3D, which consists of multi-modal data from multiple cameras and LiDAR sensors with 12k annotated driving LiDAR frames.
  arXiv  Detail & Related papers  (2022-10-23T23:03:17Z)
• Inertial Hallucinations -- When Wearable Inertial Devices Start Seeing Things [82.15959827765325]
  We propose a novel approach to multimodal sensor fusion for Ambient Assisted Living (AAL) We address two major shortcomings of standard multimodal approaches, limited area coverage and reduced reliability. Our new framework fuses the concept of modality hallucination with triplet learning to train a model with different modalities to handle missing sensors at inference time.
  arXiv  Detail & Related papers  (2022-07-14T10:04:18Z)
• An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation [65.28133251370055]
  We organize an experimental campaign with video measurement in an area within the urban network of Zurich, Switzerland. We focus on capturing the traffic state in terms of traffic flow and travel times by ensuring measurements from established thermal cameras. We propose a simple yet efficient Multiple Linear Regression (MLR) model to estimate travel times with fusion of various data sources.
  arXiv  Detail & Related papers  (2021-08-02T08:13:57Z)
• 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)
• A Data-Driven Analytical Framework of Estimating Multimodal Travel Demand Patterns using Mobile Device Location Data [5.902556437760098]
  This paper presents a data-driven analytical framework to extract multimodal travel demand patterns from smartphone location data. A jointly trained single-layer model and deep neural network for travel mode imputation is developed. The framework also incorporates the multimodal transportation network in order to evaluate the closeness of trip routes to the nearby rail, metro, highway and bus lines.
  arXiv  Detail & Related papers  (2020-12-08T22:49:44Z)
• DMD: A Large-Scale Multi-Modal Driver Monitoring Dataset for Attention and Alertness Analysis [54.198237164152786]
  Vision is the richest and most cost-effective technology for Driver Monitoring Systems (DMS) The lack of sufficiently large and comprehensive datasets is currently a bottleneck for the progress of DMS development. In this paper, we introduce the Driver Monitoring dataset (DMD), an extensive dataset which includes real and simulated driving scenarios.
  arXiv  Detail & Related papers  (2020-08-27T12:33:54Z)
• Stance Detection Benchmark: How Robust Is Your Stance Detection? [65.91772010586605]
  Stance Detection (StD) aims to detect an author's stance towards a certain topic or claim. We introduce a StD benchmark that learns from ten StD datasets of various domains in a multi-dataset learning setting. Within this benchmark setup, we are able to present new state-of-the-art results on five of the datasets.
  arXiv  Detail & Related papers  (2020-01-06T13:37:51Z)

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.