The Harsh Truth: Segment-Level Analysis of Harsh Driving Events in Milan Using Large-Scale Telematics, Street Networks, and Google Street View

• URL: http://arxiv.org/abs/2606.00261v1
• Date: Fri, 29 May 2026 18:46:55 GMT
• Title: The Harsh Truth: Segment-Level Analysis of Harsh Driving Events in Milan Using Large-Scale Telematics, Street Networks, and Google Street View
• Abstract summary: This study analyses harsh events across the urban road network of Milan.<n>Harsh acceleration and braking events are widely used as surrogate safety indicators, but have so far been studied only in comparatively small urban samples.
• Score: 3.6759575438610184
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Police-reported crash statistics remain the standard input for urban road-safety assessment, but their incompleteness and reporting lag limit their usefulness for timely, fine-grained intervention design. Harsh acceleration and braking events are widely used as surrogate safety indicators, but have so far been studied only in comparatively small urban samples. This study analyses harsh events across the urban road network of Milan, combining high-resolution telematics from more than 4.2 million vehicles equipped with On-Board Units, segment-level traffic metrics from TomTom, street-network and infrastructure attributes from OpenStreetMap, and visual streetscape features extracted from Google Street View via semantic segmentation using a OneFormer model. We employ an analytical framework combining non-parametric Mann--Whitney U tests of segment-feature distributions between high- and low-harshness groups with supervised machine-learning regressors. We find that, once exposure is controlled for, wider carriageways, crossings and transit stops, and more open visual fields (higher sky- and road-pixel proportions) are associated with higher harsh-event intensity, while denser built frontage is associated with lower intensity. Finally, the cycling-infrastructure case study identifies a gradient in harsh-event intensity across facility types: markings-only cycle lanes are associated with a 19.5% higher harshness score, and mixed-traffic configurations with an 11.5% higher score, relative to physically separated cycle paths, conditional on the included controls. These results support context-specific rather than uniform urban-safety interventions and illustrate how large-scale telematics combined with open geospatial and visual data can inform Vision Zero decision-making at the metropolitan scale.

Related papers

• Measuring Braking Behavior Using Vehicle Tracking and Camera-to-Satellite Homography Rectification [4.61803711540329]
  This paper presents an open-source software application for analyzing traffic camera footage, focusing on vehicle behavior and braking events at signalized urban highways.<n>The core innovation is a robust ground-plane homography estimation that links fixed traffic camera views to satellite orthoimagery.<n>All detection and trajectory data are stored in a ClickHouse database for subsequent analysis.
  arXiv  Detail & Related papers  (2026-01-24T18:59:32Z)
• Semantic4Safety: Causal Insights from Zero-shot Street View Imagery Segmentation for Urban Road Safety [14.300819977717708]
  We propose a framework that applies zero-shot semantic segmentation to street-view imagery to derive 11 interpretable streetscape indicators.<n>We analyze approximately 30,000 accident records in Austin to uncover accident-type-specific causal patterns.<n>By bridging predictive modeling with causal inference, Semantic4Safety supports targeted interventions and high-risk corridor diagnosis.
  arXiv  Detail & Related papers  (2025-10-17T08:45:28Z)
• Spatio-Temporal Graph Neural Network for Urban Spaces: Interpolating Citywide Traffic Volume [4.188237759092441]
  We introduce the Graph Neural Network for Urban Interpolation (GNNUI), a novel urban traffic volume estimation approach.<n>GNNUI employs a masking algorithm to learn, integrates node features to capture functional roles, and uses a loss function tailored to zero-inflated traffic distributions.<n>In addition to the model, we introduce two new open-scale urban traffic volume benchmarks, covering different transportation modes.
  arXiv  Detail & Related papers  (2025-05-07T13:34:00Z)
• The Impact of Building-Induced Visibility Restrictions on Intersection Accidents [32.698248334786854]
  We develop a novel approach to estimate accident risk at intersections.<n>Method factors in the area visible to drivers, accounting for views blocked by buildings.<n>Findings reveal a notable correlation between the road visible percentage and accident frequency.
  arXiv  Detail & Related papers  (2025-02-13T17:45:51Z)
• 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)
• Homography Guided Temporal Fusion for Road Line and Marking Segmentation [73.47092021519245]
  Road lines and markings are frequently occluded in the presence of moving vehicles, shadow, and glare. We propose a Homography Guided Fusion (HomoFusion) module to exploit temporally-adjacent video frames for complementary cues. We show that exploiting available camera intrinsic data and ground plane assumption for cross-frame correspondence can lead to a light-weight network with significantly improved performances in speed and accuracy.
  arXiv  Detail & Related papers  (2024-04-11T10:26:40Z)
• Leveraging Driver Field-of-View for Multimodal Ego-Trajectory Prediction [69.29802752614677]
  RouteFormer is a novel ego-trajectory prediction network combining GPS data, environmental context, and the driver's field-of-view.<n>To tackle data scarcity and enhance diversity, we introduce GEM, a dataset of urban driving scenarios enriched with synchronized driver field-of-view and gaze data.
  arXiv  Detail & Related papers  (2023-12-13T23:06:30Z)
• 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)
• Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Centers [126.81938540470847]
  We propose Euro-PVI, a dataset of pedestrian and bicyclist trajectories. In this work, we develop a joint inference model that learns an expressive multi-modal shared latent space across agents in the urban scene. We achieve state of the art results on the nuScenes and Euro-PVI datasets demonstrating the importance of capturing interactions between ego-vehicle and pedestrians (bicyclists) for accurate predictions.
  arXiv  Detail & Related papers  (2021-06-22T15:40:21Z)
• Detecting 32 Pedestrian Attributes for Autonomous Vehicles [103.87351701138554]
  In this paper, we address the problem of jointly detecting pedestrians and recognizing 32 pedestrian attributes. We introduce a Multi-Task Learning (MTL) model relying on a composite field framework, which achieves both goals in an efficient way. We show competitive detection and attribute recognition results, as well as a more stable MTL training.
  arXiv  Detail & Related papers  (2020-12-04T15:10:12Z)
• Identifying safe intersection design through unsupervised feature extraction from satellite imagery [9.306618776574025]
  The World Health Organization has listed the design of safer intersections as a key intervention to reduce global road trauma. Approximately 900,000 satellite images were downloaded for all intersections in Australia and customized computer vision techniques emphasized the road infrastructure. A deep autoencoder extracted high-level features, including the intersection's type, size, shape, lane markings, and complexity.
  arXiv  Detail & Related papers  (2020-10-29T03:42:09Z)

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.