The Skeletal Trap: Mapping Spatial Inequality and Ghost Stops in Ankara's Transit Network

• URL: http://arxiv.org/abs/2602.15470v1
• Date: Tue, 17 Feb 2026 10:19:05 GMT
• Title: The Skeletal Trap: Mapping Spatial Inequality and Ghost Stops in Ankara's Transit Network
• Abstract summary: leapfrog urban expansion has produced fragmented peripheral clusters disconnected from a rigid, center-oriented bus network.<n>The deficiency is rooted in the misalignment between urban macroform and network architecture.<n>Findings reveal persistent center-periphery asymmetries, structural bottlenecks, and spatially embedded accessibility inequalities.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Ankara's public transport crisis is commonly framed as a shortage of buses or operational inefficiency. This study argues that the problem is fundamentally morphological and structural. The city's leapfrog urban expansion has produced fragmented peripheral clusters disconnected from a rigid, center-oriented bus network. As a result, demand remains intensely concentrated along the Kizilay-Ulus axis and western corridors, while peripheral districts experience either chronic under-service or enforced transfer dependency. The deficiency is therefore not merely quantitative but rooted in the misalignment between urban macroform and network architecture. The empirical analysis draws on a 173-day operational dataset derived from route-level passenger and trip reports published by EGO under the former "Transparent Ankara" initiative. To overcome the absence of stop-level geospatial data, a Connectivity-Based Weighted Distribution Model reallocates passenger volumes to 1 km x 1 km grid cells using network centrality. The findings reveal persistent center-periphery asymmetries, structural bottlenecks, and spatially embedded accessibility inequalities.

Related papers

• Inferring Urban Mobility Interactions from Aggregated Dynamics [45.73534370352232]
  Real-time urban governance depends not only on knowing where people are, but on how they move between places.<n>Here we show that this directional structure need not be observed to be known.<n>We infer future OD flows from area-level counts alone across twelve mobility datasets from cities in the United States and China.
  arXiv  Detail & Related papers  (2026-09-07T11:13:51Z)
• GUIDED Network-Agnostic Feature Initialization for Spatial Transferability in GNN-based Models [46.28228608289378]
  The Traffic Assignment Problem is a fundamental but computationally expensive component of transportation planning.<n>This research proposes a network-agnostic initialization layer, termed Geometrically Unconstrained Inductive Demand EmbeDding.<n>This framework standardizes the input space regardless of network scale.<n>It provides a versatile blueprint for generalized origin-destination spatial problems, such as freight logistics and multimodal network optimization.
  arXiv  Detail & Related papers  (2026-07-21T16:41:59Z)
• Quantum percolation based dynamic propagation connectivity for critical-area identification in transport networks [14.492101931546765]
  Link-centred and binary percolation measures identify important facilities or connectivity failures.<n>Time-varying link travel times are converted into continuous propagation strengths.<n>Candidate regions are then evaluated by a regional degradation experiment.
  arXiv  Detail & Related papers  (2026-06-29T12:52:47Z)
• Urban Power Grid Topology and Hierarchy Identification from Open Data [53.468443050795344]
  This paper develops an open-data-driven framework for identification of urban power grid topology.<n>We fuse public infrastructure data (power-lines, substations, transformers, poles) to map the high and medium-voltage skeleton.<n>We then leverage geospatial machine learning on OpenStreetMap to group power demand clusters.<n>We reconstruct the complete grid topology that connects 7,330 buildings and all major electricity infrastructure assets.
  arXiv  Detail & Related papers  (2026-06-19T11:51:38Z)
• Beyond Resilience -- A Conceptual Framework for Civic Ascent [45.88028371034407]
  Civic ascent is defined as the condition in which a city emerges from shock with higher functional capacity than before.<n>Post-shock ascent is measured by a normalised improvement index A(T) applied to a composite civic performance signal P(t)
  arXiv  Detail & Related papers  (2026-06-10T23:38:43Z)
• SCOT: Multi-Source Cross-City Transfer with Optimal-Transport Soft-Correspondence Objective [29.70837520752111]
  Cross-city transfer improves prediction in label-scarce cities by leveraging labeled data from other cities.<n>We propose SCOT, a cross-city representation learning framework that learns explicit soft correspondences between unequal region sets.
  arXiv  Detail & Related papers  (2026-04-08T02:42:55Z)
• CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge [50.42067935605982]
  We propose a novel Compression and Combination-based Federated learning framework that unifies local memory efficiency and global communication reduction.<n>CoCo-Fed significantly outperforms state-of-the-art baselines in both memory and communication efficiency while maintaining robust convergence under non-IID settings.
  arXiv  Detail & Related papers  (2026-01-02T03:39:50Z)
• Exploring Dissatisfaction in Bus Route Reduction through LLM-Calibrated Agent-Based Modeling [0.0]
  This study employs an agent-based modelling (ABM) approach calibrated through a large language model (LLM)<n>Using IC-card data from Beijing's Huairou District, the LLM-calibrated ABM estimated passenger sensitivity parameters related to travel time, waiting, transfers, and crowding.<n>Results show that the structural configuration of the bus network exerts a stronger influence on system stability than capacity or operational factors.
  arXiv  Detail & Related papers  (2025-10-30T05:59:48Z)
• Neighbor-aware informal settlement mapping with graph convolutional networks [1.226598527858578]
  We propose a graph-based framework that incorporates local geographical context into the classification process.<n>Experiments are conducted on a case study in Rio de Janeiro using spatial cross-validation.<n>Our method outperforms standard baselines, improving Kappa coefficient by 17 points over individual cell classification.
  arXiv  Detail & Related papers  (2025-09-30T12:25:25Z)
• Recover and Match: Open-Vocabulary Multi-Label Recognition through Knowledge-Constrained Optimal Transport [45.866011150937425]
  We present RAM (Recover And Match), a novel framework that effectively addresses the above issues.<n> RAM achieves state-of-the-art performance on various datasets from three distinct domains.
  arXiv  Detail & Related papers  (2025-03-19T15:33:44Z)
• Understanding the Structure and Resilience of the Brazilian Federal Road Network Through Network Science [44.99833362998488]
  This paper models the federal road network in Brazil as weighted networks.<n>We aim to unveil its topological characteristics and identify key locations (cities) that play important roles for the country through 75,000 kilometres of roads.<n>Our findings aim to bring clarity to the overall structure of federal roads in Brazil, thus providing actionable insights for improving infrastructure planning and prioritising resources to enhance network resilience.
  arXiv  Detail & Related papers  (2024-12-20T13:02:50Z)
• Cross-City Matters: A Multimodal Remote Sensing Benchmark Dataset for Cross-City Semantic Segmentation using High-Resolution Domain Adaptation Networks [82.82866901799565]
  We build a new set of multimodal remote sensing benchmark datasets (including hyperspectral, multispectral, SAR) for the study purpose of the cross-city semantic segmentation task. Beyond the single city, we propose a high-resolution domain adaptation network, HighDAN, to promote the AI model's generalization ability from the multi-city environments. HighDAN is capable of retaining the spatially topological structure of the studied urban scene well in a parallel high-to-low resolution fusion fashion.
  arXiv  Detail & Related papers  (2023-09-26T23:55:39Z)
• Adaptive Hierarchical SpatioTemporal Network for Traffic Forecasting [70.66710698485745]
  We propose an Adaptive Hierarchical SpatioTemporal Network (AHSTN) to promote traffic forecasting. AHSTN exploits the spatial hierarchy and modeling multi-scale spatial correlations. Experiments on two real-world datasets show that AHSTN achieves better performance over several strong baselines.
  arXiv  Detail & Related papers  (2023-06-15T14:50:27Z)
• Complexity-aware Large Scale Origin-Destination Network Generation via Diffusion Model [24.582615553841396]
  Origin-Destination(OD) networks provide an estimation of the flow of people from every region to others in the city. We propose to generate the OD network, and design a graph denoising diffusion method to learn the conditional joint probability distribution of the nodes and edges.
  arXiv  Detail & Related papers  (2023-06-08T02:02:55Z)
• Correlating sparse sensing for large-scale traffic speed estimation: A Laplacian-enhanced low-rank tensor kriging approach [76.45949280328838]
  We propose a Laplacian enhanced low-rank tensor (LETC) framework featuring both lowrankness and multi-temporal correlations for large-scale traffic speed kriging. We then design an efficient solution algorithm via several effective numeric techniques to scale up the proposed model to network-wide kriging.
  arXiv  Detail & Related papers  (2022-10-21T07:25:57Z)
• Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation [67.19443246236048]
  Airway segmentation is crucial for the examination, diagnosis, and prognosis of lung diseases. Some small-sized airway branches (e.g., bronchus and terminaloles) significantly aggravate the difficulty of automatic segmentation. This paper presents an efficient method for airway segmentation, comprising a novel fuzzy attention neural network and a comprehensive loss function.
  arXiv  Detail & Related papers  (2022-09-05T16:38:13Z)
• A Spatial-Temporal Attention Multi-Graph Convolution Network for Ride-Hailing Demand Prediction Based on Periodicity with Offset [9.897431292540393]
  Ride-hailing service is becoming a leading part in urban transportation. To improve the efficiency of ride-hailing service, accurate prediction of transportation demand is a fundamental challenge. In this paper, we tackle this problem from both aspects of network structure and data-set formulation.
  arXiv  Detail & Related papers  (2022-03-23T16:03:55Z)
• Coupled Layer-wise Graph Convolution for Transportation Demand Prediction [43.23120462553671]
  Graph Convolutional Network (GCN) has been widely applied in transportation demand prediction. This paper provides a novel graph convolutional network for transportation demand prediction. Experiments have been conducted on two real-world datasets, NYC Citi Bike and NYC Taxi.
  arXiv  Detail & Related papers  (2020-12-15T04:10:09Z)
• Multiple abrupt phase transitions in urban transport congestion [0.0]
  We show that the location of the onset of congestion changes according to the considered urban area. We introduce a family of planar road network models composed of a dense urban center connected to an arboreal periphery.
  arXiv  Detail & Related papers  (2020-05-26T17:58:44Z)

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.