Papers for

urban transport planners

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Dataset design process improves truck perception in city driving

Towards Safer Cities: A Design Process for Perception Datasets in Urban Driving Scenarios

Abstract: Urban environments present significant challenges for the perception systems of automated driving due to their high complexity, characterized by dense traffic, diverse obstacles, or a wide range of possible scenarios. While numerous automotive datasets exist, few focus on urban settings, and the majority are acquired exclusively on passenger cars and overlook other transport modalities such as heavy-duty trucks, busses and trams. In contrast to passenger vehicles, these platforms operate under different geographical and operational constraints; including traversing pedestrian zones or platform-specific tasks such as movement of trailer or winter service. In this work a method is proposed for a guided dataset creation based on an analysis of existing datasets using user-generated map data. Furthermore, representative truck-specific use cases are identified and exemplified to demonstrate the applicability of the proposed methodology and to support targeted acquisition planning.

Fri 9 OctRobotics
The gist
City streets are busy and complicated, making it hard for self-driving vehicles to see and understand everything around them. Most existing data used to teach these vehicles focuses only on passenger cars, ignoring trucks, buses, and trams which behave differently. The authors present a method to create better datasets using map information and real-world examples, especially for trucks in special urban situations. This helps plan what data to collect so self-driving trucks can operate more safely in cities.
Open → 2610.13077v1

Digital twin simulations get model-focused DevOps system for cloud use

A Model-Centric DevOps Architecture for DEVS-Based Digital Twin Simulation Services

Abstract: Digital twin simulation models are evolved and redeployed like software, yet DEVS-based engines offer a sound formal basis with little support for versioning, automated validation, or continuous delivery in cloud-native environments, leaving model lifecycle management ad hoc in most deployments. This paper proposes a model-centric DevOps architecture for deploying DEVS-based digital twin simulations as managed services. Simulation models are treated as first-class DevOps artefacts defined in a declarative YAML language with a formal mapping to multiPDEVS, supporting structural and semantic validation in a CI/CD pipeline that produces immutable versioned artefacts, so that reverting to an earlier validated version reduces to pinning its identifier. The platform is decomposed into containerised microservices on Kubernetes, with engine adaptations for state externalisation and lifecycle control. An initial case study on the Riga Route 22 public-transit corridor, the first instantiation of a planned city-wide multi-modal transport digital twin for Riga, Latvia, exercises the full lifecycle and reports single-container engine throughput for a scenario with roughly 47,870 DEVS atomic components; pipeline-level catch statistics and cluster-level concurrent multi-scenario execution are the subject of companion empirical studies.

Thu 10 SeptSoftware EngineeringComputational Engineering, Finance, and ScienceDistributed, Parallel, and Cluster Computing
The gist
Digital twin simulations—virtual copies of real-world systems—are tricky to update and manage like software. The authors created a system that treats simulation models like code, using a special format and tools to check and deploy them automatically. This makes it easier to keep track of different versions, fix errors, and run these simulations reliably in the cloud. They tested their approach with a big transit simulation for Riga, showing it can handle many components efficiently.
Open → 2609.11122v1