Search Results (1,315)

Trajectory data is valuable for applications such as urban planning, but its public availability is often limited by privacy concerns and data collection costs. While recent diffusion models have shown promise in generative tasks, existing methods rarely integrate personalized conditioning with road network constraints. As a result, they struggle to simultaneously achieve personalized mobility modeling and high road-network spatial validity, resulting in limited trajectory quality. In this paper, we propose Sem-RoadDiff, a symmetry-aware dual-guided diffusion model for personalized and road network-constrained trajectory generation. Specifically, our model incorporates two key components. First, we design a semantic preference guidance mechanism to encode user history into a preference-weighted user embedding using a temperature-scaled softmax. This enables the model to capture user-level mobility patterns without directly using raw trip-level records as generation conditions. Second, we introduce a road-aware mechanism to improve overall spatial validity, employing a spatial validity loss derived from the User Mobility Transition Graph. From a symmetry perspective, Sem-RoadDiff aims to preserve distributional symmetry between real and generated trajectories while respecting the inherent asymmetry of directed road-network transitions. Extensive experiments on the Geolife and Porto datasets demonstrate that our approach improves trajectory distributional fidelity compared with the evaluated baselines and improves road-segment connectivity over the diffusion-based baseline. Full article

Urban mobility modeling plays a critical role in understanding transport infrastructure and improving its efficiency and sustainability. While existing tools are effective for modeling, they typically require extensive data acquisition, such as surveys, questionnaires, or tracking, as well as domain knowledge for calibration. We propose the Mobility Oracle, a framework that can algorithmically approximate urban mobility by incorporating human preferences in the routing process. The framework relies on open-source data and generates synthetic datasets for further analysis. It can be adapted to different contexts as it is reproducible, modular, and flexible. Both the theoretical components and the practical implementation are presented, along with a case study that illustrates the framework’s potential applications. Validation is carried out for Vienna (Austria) and Munich (Germany), comparing our approach against the official city-wide modal splits and a smaller tracked dataset within one of the cities. The resulting mode shares show an average difference of 4.7% at the city scale and a maximum of 1.9% for the tracked sample. These results demonstrate that the Mobility Oracle can be a useful tool to approximate human mobility. City planners and decision-makers can use it to systematically test and evaluate alternative planning scenarios across different urban contexts. Full article

Road transport is one of the most significant sources of environmental pollution and greenhouse gas emissions; therefore, the development of sustainable mobility is becoming an important direction of urban transport policy. The objectives of the European Union’s transport policy encourage cities to plan and implement measures that reduce the environmental impact of transport, improve transport conditions, and increase the availability of mobility alternatives. The aim of this study is to evaluate the planning of sustainable mobility development in Lithuanian cities by analysing sustainable urban mobility plans, the measures proposed in them, and their links to the needs of urban transport systems. The study applied descriptive statistics, comparative analysis, and document content analysis methods. The urban plans of Lithuanian cities were evaluated according to the following criteria: the time scope and relevance of the plan, the completeness of the analysis of the existing transport system, the assessment of the environment and quality of life in cities, and the compliance of the planned sustainable mobility measures with the needs of the city. The results of the study show that only a portion of Lithuanian cities have prepared sustainable urban mobility plans, and their contents and analytical bases differ. Some of the plans do not provide a sufficiently detailed and relevant analysis of the current situation; therefore, the need for the selected measures is not always clearly justified. The cities analysed generally envisage or apply measures to improve public transport, develop pedestrian and bicycle infrastructure, regulate traffic, create electric vehicle infrastructure, and promote multimodality. It was concluded that sustainable mobility planning in Lithuanian cities is uneven, and its assessment depends not only on the diversity of the envisaged measures but also on the analytical quality of planning documents, the justification of measures, and the consistency of envisaged implementation measures. The study highlights the need to strengthen data-based sustainable mobility planning and to more clearly link the measures envisaged in the plans with the specific challenges of urban transport systems. Full article

Efficient data processing and signal acquisition are becoming increasingly critical. Pipeline networks present unique topological constraints that complicate the balance between signal sampling efficiency and data-transmission reliability. In this paper, we propose a bi-objective optimization model for the urban pipeline network (UPN). The model optimizes autonomous mobile sensor (AMS) path planning using an Euler path scheme and communication node (CN) deployment using a deterministic deployment scheme. The model aims to minimize both monitoring time (MMT) and data delay (MDD). These two indicators are used as quality of service (QoS) metrics for communication and sensing. By representing the UPN as a graph structure, we establish two mathematical models for the MMT and MDD problems. Then, we introduce a topology-guided heuristic virtual-edge strategy to construct an Euler traversal for the MMT problem. An adaptive simulated annealing (ASA) algorithm is designed to solve the MMT problem. On this basis, the MDD problem is solved using an enhanced ant colony optimization (EACO) algorithm. Simulation results show that the proposed scheme achieves shorter monitoring times and lower data delays. Specifically, the Euler path scheme for the AMS reduces MMT by more than 43.26%, and the deterministic CN-deployment scheme reduces MDD by more than 44.10%. Full article

Urban Unmanned Aerial Vehicle (UAV) logistics operations are frequently constrained by the intersection of limited battery endurance and dynamic ground traffic. When mobile depots are delayed by congestion, onboard drone fleets experience extended idling periods, leading to constrained sortie generation and reduced asset utilization. To address this bottleneck, this paper introduces a traffic-predictive multi-UAV dispatch framework for deterministic day-ahead planning under modeled urban operating conditions. By coupling a count-derived macroscopic speed surrogate learned using XGBoost with a Particle Swarm Optimization (PSO)–Mixed-Integer Linear Programming (MILP) optimization architecture, the framework synchronizes mobile depot trajectories with forecasted low-congestion windows and pre-allocates endurance-feasible parallel aerial sorties. Controlled computational experiments across 30 synthetic routing instances demonstrate the potential value of this approach within the stated modeling assumptions. Compared to baseline clustered deployments, the traffic-aware framework raises mean fleet utilization from 0.43 to 0.63—a 46.2% relative improvement driven by temporal compression of the mission window rather than an absolute increase in flight hours. Furthermore, the proposed framework reduces total mission completion time by 69.87% relative to the conventional truck-only baseline, while achieving a 29.58% incremental gain over static speed drone deployments. These findings suggest that incorporating predictive ground traffic information into day-ahead UAV scheduling can improve modeled fleet efficiency; however, field validation with measured route-level speeds, real delivery demand, and operational constraints remains necessary before deployment-level claims can be made. Full article

While public sanitation is a fundamental component of urban infrastructure, it is often treated as a discretionary amenity rather than a core public service subject to legal standards of equality and dignity. This article challenges gender-blind approaches to urban planning by examining how inadequate public toilet provision constrains women’s everyday mobility and presence in public space, raising questions of indirect gender discrimination and regulatory responsibility. Drawing on an exploratory mixed-methods study (N = 97), the analysis combines quantitative assessment of access barriers, qualitative user narratives, and time-based measurement of total restroom use duration to examine patterns of use and waiting with particular attention to gender differences. The findings indicate that hygiene-related concerns are reported across both men and women, without clear evidence of a consistent gender-specific pattern, while women are disproportionately affected by throughput failures, long waiting times, and the absence of care-integrated facilities. At the same time, variation in support for gender-neutral toilet solutions suggests that user acceptance may not align with model-based proposals in the literature. These inequalities reflect an institutional accountability gap with legal implications in the governance of everyday public services. By shifting the focus from numerical potty parity to temporal inequality and responsibility, this article contributes to feminist legal scholarship by situating sanitation within questions of temporal inequality and institutional responsibility. While exploratory in nature, the findings offer empirically grounded insights into inequalities in everyday sanitation governance. Full article

Shopping mobility accounts for a significant share of total travel, while the growth of e-commerce is reshaping consumer purchasing behaviour and retail dynamics. Comprehending how territorial and sociodemographic factors shape the choice between physical and digital retail channels is therefore a key issue for transport planning and sustainable urban mobility. In this context, it is important to understand how accessibility to different classes of retailers is configured and how it can impact purchasing choices. Through a discrete choice analysis, this study examines the sociodemographic and territorial determinants of purchasing behaviour, focusing on the clothing market. Four purchase alternatives are considered: medium-sized and small urban retail stores, shopping malls, online purchasing, and no purchase. This multi-alternative framework enables the direct estimation of substitution patterns not only between physical and digital retail, but also between distinct forms of physical retail. Data were collected through a survey conducted in Southern Italy, providing empirical evidence from a territorial setting that is structurally underrepresented in the existing literature. A multinomial logit model and a two-level hierarchical logit model incorporating pedestrian accessibility—measured as walking time from residence to the nearest clothing store—alongside sociodemographic and territorial attributes were calibrated to analyse alternative choice behaviour. The calibrated models show interesting results, highlighting the role of pedestrian accessibility in the choice of clothing stores in city centres. Age, income, and territorial variables further differentiate channel preferences across population segments. The findings offer relevant implications for policymakers, governance managers, urban planners, and researchers concerned with retail location, sustainable accessibility, and consumer behaviour. These insights are highly valuable for developing planning that addresses the United Nations 2030 Agenda, particularly Sustainable Development Goal 11. Full article

Although fine-grained spatial knowledge of the urban population distribution is fundamental for effective urban management, traditional census data lack sufficient resolution. Current disaggregation methods often struggle to probabilistically fuse heterogeneous data, such as noisy mobile signaling and building attributes, while ensuring hierarchical consistency between micro-level predictions and macro-level ground truth. To address these gaps, this study proposes a Bayesian-informed hierarchical learning (BIHL) model framework for building-level population estimation. The methodology integrates three distinct layers: (1) a data-driven prior model using a LightGBM ensemble to generate initial probabilistic estimates and uncertainty weights; (2) an enhanced neural network posterior estimator featuring a multi-branch architecture—incorporating Zone Bias Embedding and Zone Interaction networks—to capture non-linear urban dynamics and spatial heterogeneity; and (3) a constrained optimization layer utilizing a hierarchical loss function that enforces strict consistency between aggregated building estimates and official census data through dynamic curriculum learning. Through empirical validation in Haidian District, Beijing, it is demonstrated that the BIHL framework significantly outperforms baseline models (MLR, Random Forest, and LightGBM), achieving a Mean Absolute Percentage Error (MAPE) of 11.36%. This study confirms that incorporating building-level spatial locations and residential categories is vital for mitigating “spatial smoothing” and systematic under-prediction in high-density areas. This framework provides a robust, high-fidelity solution for generating residential population layers, which are essential for city planning. Full article

Cities preparing Sustainable Urban Mobility Plans (SUMPs) increasingly require practical tools capable of merging diverse mobility datasets and transforming them into planning-relevant indicators. This article introduces PRIORITY (Platform for the tRansition to sustaInable zerO-caRbon mobilITY), a prototype platform designed to support mobility analysis and decision-making in urban contexts. The platform integrates Floating Car Data, GTFS feeds describing public transport supply, and detailed land-use and zoning information. By relying on these heterogeneous data streams, PRIORITY generates indicators such as travel and stop times, trip distances, trip volumes, energy consumption, pollutant emissions, external costs, and electric-vehicle charging behavior. The platform is organized into two main components: a back end and a front end. The back end, which constitutes the operational core, manages all collected data and ensures their structured storage in a shared database capable of handling large volumes of information on urban form, individual mobility patterns, public transport services, and modeling outcomes. The front end provides an intuitive and versatile interface that dynamically presents the outputs generated by the platform’s analytical and modeling processes. A case application for the Metropolitan City of Rome (Italy) illustrates the operational use of the prototype and shows how PRIORITY can support transparent and reproducible evaluations during the preparation and monitoring of SUMPs. The demonstrated workflow highlights the prototype’s value for public authorities and planners seeking data-informed approaches to urban mobility assessment and decarbonization strategies. Full article

Urban air-quality monitoring networks are often sparse, leaving coverage gaps where particulate matter (PM) concentrations cannot be directly observed. This paper extends the CityAirQ pollution tracking platform and its mobile air-quality device prototype by introducing an AI-based benchmark for two Bucharest station networks across three deployment-oriented tasks: multi-station temporal forecasting (Task A), leave-one-station-out same-day spatial estimation (Task B), and a preliminary mobile-site prediction pilot at an uncalibrated location (Task C). The benchmark compares machine-learning models, including ensemble tree methods, recurrent neural networks, and lightweight graph-inspired architectures, evaluated under a unified time-aware rolling protocol. In Task A, the proposed Advanced Stage 0–3 pipeline achieves the best overall MAE (7.12 $\mathsf{\mu }$g/m³), a 4.7% reduction relative to Random Forest (7.47 $\mathsf{\mu }$g/m³), while the Seasonal naïve (10.41 $\mathsf{\mu }$g/m³), Persistence (11.51 $\mathsf{\mu }$g/m³), neural, and graph-inspired references perform worse under recursive forecasting. In Task B, the neighbour-only Random Forest reaches a mean $R^2$ of 0.873 on the classic four-station network and a median $R^2$ of 0.734 on the ten-station city-scale extension. Task C is reported as an exploratory six-day prediction pilot, not as deployment-grade validation: no co-located EPA FRM/FEM or equivalent reference monitor was available at the mobile location ℓ. The historical-transfer Random Forest retained a sample-limited positive PM_2.5 association with the raw mobile readings ($r=0.432$, $n=6$), while a strict one-day-ahead online persistence predictor reduced PM_2.5 MAE from 40.58 to 20.00 $\mathsf{\mu }$g/m³ on the five forecastable mobile days. Ultimately, accurate PM monitoring empowers sustainable urban planning, helping to mitigate exposure risks and supporting long-term public health and environmental sustainability initiatives. Full article

Public transport reliability largely determines the performance of smart urban mobility systems, as it directly affects passenger satisfaction and network efficiency. However, the strategic planning of public transport infrastructure is often carried out without dynamic, data-driven insights into operational performance, instead relying solely on static historical records of network operations. This study develops a data-driven framework based on the XGBoost machine learning algorithm to support the prioritization of infrastructure interventions by predicting delay severity and identifying reliability hotspots along an urban bus route. Delay severity is categorized into three classes (minor, moderate, and severe), using a model that incorporates spatial, temporal, operational, and meteorological variables. The XGBoost framework achieves a high predictive performance, with classification accuracies of 91.5% and 89.7% for the outbound and inbound bus route directions, respectively. Feature importance analysis indicates that seasonal and meteorological variables are critical factors influencing delay severity, highlighting the role of broader external environmental conditions on corridor performance. Furthermore, spatial analysis identifies specific bus stops with high delay probabilities, indicating hotspots where infrastructure upgrades should be prioritized at the stop and corridor levels. This study proposes a decision-support tool that enables targeted infrastructure investments at locations where they are most needed, contributing to more efficient and resilient public transport systems in smart cities. Full article

This study reframes street-level sustainable urban renewal as a coordination problem between street vitality and relative low-carbon performance, rather than treating vibrant activity and carbon-pressure reduction as separate planning objectives. Its main contribution is an integrated street-level diagnostic framework that combines multidimensional vitality measurement, township-constrained carbon-emission reference estimation, vitality–carbon mismatch identification, and interpretable nonlinear mechanism analysis within unified street analytical units. Although previous studies have substantially advanced the measurement of street vitality and urban carbon emissions, these two strands of research have often developed separately. As a result, limited evidence is available on whether high-vitality streets also perform well in low-carbon terms, where vitality–carbon mismatches emerge, and which built-environment conditions are associated with more coordinated outcomes. Taking the five central districts of Chengdu, China, as a case, this study integrates multi-source activity, mobility, built-environment, and emission-related data. Street vitality is measured through activity agglomeration, temporal continuity, functional support, and external connectivity, while relative low-carbon performance is derived from the reverse normalization of length-normalized carbon-emission intensity based on a township-constrained street-level emission reference estimate. The results show that street vitality and low-carbon performance are spatially uneven and frequently mismatched, as high activity does not automatically translate into stronger low-carbon performance, and lower-carbon pressure does not necessarily indicate a vibrant urban environment. More coordinated streets are associated with context-specific combinations of functional organization, transport operation, built form, street-interface quality, and ecological background. Nonlinear diagnostic results further suggest that coordination is favored by moderate, balanced, and locally adapted built-environment conditions rather than by the simple maximization of individual indicators. These findings shift the discussion from whether vitality and low-carbon performance are desirable in isolation to how they can be jointly diagnosed and improved in street-level urban renewal. Full article

Existing unmanned aerial vehicle (UAV) urban logistics planning follows a sequential paradigm—depot siting first, routing second—that embeds a structural information loss. Straight-line distance screening systematically overestimates the feasible service radius of candidate depots, creating a blindzone of depot–demand pairs that appear reachable but prove operationally infeasible under road network distances. We term this range-feasibility blindness and derive its analytical radius $\Delta =R_{max}(\alpha -1)/\left(2\alpha \right)$, where $\alpha$ is the road-to-straight-line distance ratio. Empirical measurement across three Chinese urban districts confirms $\alpha \in [1.40,1.52]$ and blindzone radii exceeding 2.8 km, establishing the phenomenon as a systemic property of high-density urban road geometry. To eliminate this failure by construction, we formulate a feasibility-embedded location–routing mixed-integer linear programme (MILP) that enforces road network range constraints simultaneously with depot opening decisions, making blindzone configurations implicitly inadmissible. A structure-aware Adaptive Large Neighbourhood Search (ALNS) solves the model at practical scales. Benchmark experiments on Dongli District (Tianjin) show cost reductions of 20.6–28.2% over greedy sequential baselines across three demand scenarios, with gains increasing monotonically with instance scale; cross-city experiments in Beijing and Shanghai confirm consistent improvement averaging 11.4% (Chaoyang, Beijing) and 10.2% (Pudong, Shanghai) over greedy initialisation across diverse urban morphologies. These results position joint optimisation as a necessary methodological shift for city-scale UAV infrastructure planning. Full article

Public spaces in informal settlements are often viewed as congested, unregulated, or residual areas, yet they play a central role in everyday urban life. This paper examines how public spaces are socially produced through everyday appropriation, interaction, and routine use in two informal settlements in Lahore, Pakistan. Using a qualitative comparative case-study design, the study draws on field observations, semi-structured interviews, questionnaires, activity mapping, photographic documentation, and spatial interpretation. The findings show that streets function as multifunctional public spaces rather than simple movement corridors. They support livelihood activities, children’s play, domestic extension, informal mobility, social gathering, and community visibility. The results also show that public space use varies by gender, age, time of day, and settlement morphology, with everyday practices shaped by the interaction between street layouts, housing forms, public–private thresholds, and local socio-cultural routines. The paper concludes that informal public spaces should not be understood only as signs of disorder or planning failure. They are adaptive socio-spatial systems that support livelihood, belonging, and everyday resilience. Recognizing these resident-led spatial practices can inform more sensitive upgrading approaches that improve physical conditions without erasing the social relations and everyday uses through which public space is produced. Full article

Post-war Aleppo is often framed through destruction, legal constraints, and the technical demands of reconstruction. This article challenges that assumption by re-reading Aleppo’s pre-2011 municipal vision as an analytical resource for post-war recovery. The study adopts a qualitative interpretive methodology based on municipal archival material, including the City Council work programme, strategic planning presentations, project documents, and materials related to the City Development Strategy, Madinatuna initiative, the old city, Bab Antakiya, and major public-space and service initiatives. The analysis followed three steps: identifying repeated municipal priorities and planning concepts; organizing them into thematic axes; and interpreting flagship projects as spatial expressions of a broader municipal vision. To assess post-war relevance, the archive is also read against evidence of damage, displacement, urban functionality, and heritage loss. The results show that Aleppo’s pre-2011 municipal vision can be reconstructed through six interrelated axes: strategic urban development and managed growth; the old city as a living urban fabric; urban repair in the city centre; mobility and accessibility; culture and social development; and development partnerships and international cooperation. The findings reveal that these axes formed a partially integrated municipal urbanism rather than isolated projects, while flagship interventions such as Bab Antakiya, the Green Path, the river corridor, and the Citadel surroundings materialized this logic. The study also finds that this vision remained institutionally vulnerable because of political centralization and limited municipal autonomy. It concludes that post-war recovery should build on critical continuity rather than reconstruction from scratch. Full article