Transition of car-based human-mobility in the pandemic era: Data insight from a cross-border region in Europe

• URL: http://arxiv.org/abs/2509.05166v2
• Date: Mon, 08 Sep 2025 11:09:04 GMT
• Title: Transition of car-based human-mobility in the pandemic era: Data insight from a cross-border region in Europe
• Authors: Sujit Kumar Sikder, Jyotirmaya Ijaradar, Hao Li, Hichem Omrani,
• Abstract summary: This study reports on individual car-based travel behaviour detected between Germany and neighbouring countries.<n>The dataset contains an hourly traffic count for different vehicle types, daily for representative observation points, followed by a major road network.
• Score: 4.6168779998066745
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Many transport authorities are collecting and publishing almost real-time road traffic data to meet the growing trend of massive open data, a vital resource for foresight decision support systems considering deep data insights. We explored the spatio-temporal transitions in the cross-country road traffic volumes in the context of modelling behavioural transitions in car-based human mobility. This study reports on individual car-based daily travel behaviour detected, before (2018) and during the COVID pandemic (2020), between Germany and neighbouring countries. In the case of Luxembourg, the Bridges and Roads Authority has installed a large digital traffic observatory infrastructure through the adoption of sensor-based IoT technologies, like other European member states. Since 2016, they have provided high-performance data processing and published open data on the country's road traffic. The dataset contains an hourly traffic count for different vehicle types, daily for representative observation points, followed by a major road network. The original dataset contains significant missing entries, so comprehensive data harmonization was performed. We observed the decrease in traffic volumes during pandemic factors (e.g. lockdowns and remote work) period by following global trend of reduced personal mobility. The understanding the dynamic adaptive travel behaviours provide a potential opportunity to generate the actionable insight including temporal and spatial implications. This study demonstrates that the national open traffic data products can have adoption potential to address cross-border insights. In relevance to the net-zero carbon transition, further study should shed light on the interpolation and downscaling approaches at the comprehensive road-network level for identifying pollution hot spots, causal link to functional landuse patterns and calculation of spatial influence area.

Related papers

• Learning Universal Human Mobility Patterns with a Foundation Model for Cross-domain Data Fusion [11.332722237426987]
  We present a foundation model framework for universal human mobility.<n>We leverage cross-domain data fusion and large language models to address limitations.<n>Our framework demonstrates adaptability through domain transfer techniques.
  arXiv  Detail & Related papers  (2025-03-20T01:41:28Z)
• Network-Wide Traffic Flow Estimation Across Multiple Cities with Global Open Multi-Source Data: A Large-Scale Case Study in Europe and North America [10.605083464975749]
  Network-wide traffic flow, which captures dynamic traffic volume on each link of a general network, is fundamental to smart mobility applications.<n>Existing research uses various supplementary data sources to compensate for insufficient sensor coverage and estimate the unobserved traffic flow.<n>We develop an attention-based graph neural network that effectively extracts and synthesizes information from GOMS maps.
  arXiv  Detail & Related papers  (2025-02-06T05:59:18Z)
• Learning Traffic Anomalies from Generative Models on Real-Time Observations [49.1574468325115]
  We use the Spatiotemporal Generative Adversarial Network (STGAN) framework to capture complex spatial and temporal dependencies in traffic data.<n>We apply STGAN to real-time, minute-by-minute observations from 42 traffic cameras across Gothenburg, Sweden, collected over several months in 2020.<n>Our results demonstrate that the model effectively detects traffic anomalies with high precision and low false positive rates.
  arXiv  Detail & Related papers  (2025-02-03T14:23:23Z)
• Multi-Source Urban Traffic Flow Forecasting with Drone and Loop Detector Data [61.9426776237409]
  Drone-captured data can create an accurate multi-sensor mobility observatory for large-scale urban networks.<n>A simple yet effective graph-based model HiMSNet is proposed to integrate multiple data modalities and learn-temporal correlations.
  arXiv  Detail & Related papers  (2025-01-07T03:23:28Z)
• Improving Traffic Flow Predictions with SGCN-LSTM: A Hybrid Model for Spatial and Temporal Dependencies [55.2480439325792]
  This paper introduces the Signal-Enhanced Graph Convolutional Network Long Short Term Memory (SGCN-LSTM) model for predicting traffic speeds across road networks. Experiments on the PEMS-BAY road network traffic dataset demonstrate the SGCN-LSTM model's effectiveness.
  arXiv  Detail & Related papers  (2024-11-01T00:37:00Z)
• IBB Traffic Graph Data: Benchmarking and Road Traffic Prediction Model [0.24999074238880487]
  Road traffic congestion prediction is a crucial component of intelligent transportation systems. IBB Traffic graph dataset covers the sensor data collected at 2451 distinct locations. We propose a novel Road Traffic Prediction Model that strengthens temporal links through feature engineering.
  arXiv  Detail & Related papers  (2024-08-02T05:23:19Z)
• ICST-DNET: An Interpretable Causal Spatio-Temporal Diffusion Network for Traffic Speed Prediction [47.17205142864036]
  ICST-DENT consists of three parts, namely the Spatio-Temporal Causality Learning (STCL), Causal Graph Generation (CGG), and Speed Fluctuation Pattern Recognition (SFPR) modules. ICST-DENT can outperform all existing baselines, as evidenced by the higher prediction accuracy, ability to explain causality, and adaptability to different scenarios.
  arXiv  Detail & Related papers  (2024-04-22T03:35:19Z)
• Enhancing Spatiotemporal Traffic Prediction through Urban Human Activity Analysis [6.8775337739726226]
  We propose an improved traffic prediction method based on graph convolution deep learning algorithms. We leverage human activity frequency data from National Household Travel Survey to enhance the inference capability of a causal relationship between activity and traffic patterns.
  arXiv  Detail & Related papers  (2023-08-20T14:31:55Z)
• Traffic Prediction with Transfer Learning: A Mutual Information-based Approach [11.444576186559487]
  We propose TrafficTL, a cross-city traffic prediction approach that uses big data from other cities to aid data-scarce cities in traffic prediction. TrafficTL is evaluated by comprehensive case studies on three real-world datasets and outperforms the state-of-the-art baseline by around 8 to 25 percent.
  arXiv  Detail & Related papers  (2023-03-13T15:27:07Z)
• Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand Prediction [52.0977259978343]
  This paper proposes a Continuous-time and Multi-level dynamic graph representation learning method for Origin-Destination demand prediction (CMOD) The state vectors keep historical transaction information and are continuously updated according to the most recently happened transactions. Experiments are conducted on two real-world datasets from Beijing Subway and New York Taxi, and the results demonstrate the superiority of our model against the state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-06-30T03:37:50Z)
• Traffic-Twitter Transformer: A Nature Language Processing-joined Framework For Network-wide Traffic Forecasting [14.71745498591372]
  We propose a flexible and comprehensive framework to predict physical-aware long-term traffic conditions for public users and transportation agencies. A correlation study and a linear regression model were first implemented to evaluate the significance of the correlation between two time-series data, traffic intensity and Twitter data intensity. Two time-series data were then fed into our proposed social-aware framework, Traffic-Twitter Transformer, which integrated Nature Language representations into time-series records for long-term traffic prediction.
  arXiv  Detail & Related papers  (2022-06-19T20:17:15Z)
• Road Network Guided Fine-Grained Urban Traffic Flow Inference [108.64631590347352]
  Accurate inference of fine-grained traffic flow from coarse-grained one is an emerging yet crucial problem. We propose a novel Road-Aware Traffic Flow Magnifier (RATFM) that exploits the prior knowledge of road networks. Our method can generate high-quality fine-grained traffic flow maps.
  arXiv  Detail & Related papers  (2021-09-29T07:51:49Z)
• 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)
• Deep traffic light detection by overlaying synthetic context on arbitrary natural images [49.592798832978296]
  We propose a method to generate artificial traffic-related training data for deep traffic light detectors. This data is generated using basic non-realistic computer graphics to blend fake traffic scenes on top of arbitrary image backgrounds. It also tackles the intrinsic data imbalance problem in traffic light datasets, caused mainly by the low amount of samples of the yellow state.
  arXiv  Detail & Related papers  (2020-11-07T19:57:22Z)
• Modeling Traffic Congestion in Developing Countries using Google Maps Data [4.3117073200815375]
  This paper proposes a novel approach to collect traffic data from Google Map's traffic layer with minimal cost. We show that even with these simple models, we could predict the traffic congestion ahead of time.
  arXiv  Detail & Related papers  (2020-10-29T13:46:34Z)

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.