Search Results (80)

Rapid urban expansion in Riyadh has intensified spatial disparities in access to higher education services. However, empirical studies that comprehensively evaluate university accessibility using GIS-based network analysis integrating both travel time and distance remain limited, highlighting a critical research gap in spatially equitable higher education planning. This study examines the spatial distribution and accessibility efficiency of universities in Riyadh City using GIS-based network analysis, integrating both travel time and network distance metrics. The method of this research uses service area analysis and Origin-Destination (OD) cost matrix techniques to quantify both ways, travel time and distance from residential areas to the nearest universities in terms of actual road network conditions. The spatial analysis stage was set and performed by combining datasets from the Riyadh Municipality and the Ministry of Education and by processing in ArcGIS Pro using topologically corrected road networks. The results showed that there were considerable spatial disparities in accessibility; the central sectors had very high accessibility (within 0–10 min or <10 km), whereas the outer sectors had very limited accessibility (>20 min or >20 km). The analysis reveals that approximately 46% of Riyadh’s residential population resides in areas characterized by low or very low accessibility to universities, highlighting substantial spatial inequities in higher education provision. The comparison between time-based and distance-based service areas unveiled the great effect that travel time efficiency has on accessibility efficiency. Thus, the combined accessibility evaluation figured out the areas where people were deprived of services and hence, the need for strategic intervention in those areas. The results highlight the importance of spatially equitable educational planning and offer a set of evidence-based recommendations for the optimal locations of future universities to increase accessibility, facilitate balanced urban development, and create equal opportunities for higher education throughout the sprawling metropolitan area of Riyadh. This paper adds to the body of literature that employs GIS to analyze accessibility to educational facilities in rapidly urbanizing contexts. Full article

While Low Emission Zone (LEZ) enforcement cameras provide a constant stream of traffic data, such resources remain significantly underexploited for urban mobility planning, as their current application is restricted to enforcing vehicle access regulations and issuing fines. This paper presents MobiCugat, a framework demonstrating that Automatic Number Plate Recognition (ANPR) camera data from a municipal LEZ network can serve as the calibration backbone for high-fidelity, city-scale traffic simulations for a policy-testing Digital Twin. The case study is Sant Cugat del Vallès (Barcelona), where the local council sought to evaluate new scenarios for the area using an evidence-based, data-driven approach. Vehicle detection records from 102 LEZ ANPR cameras were processed into 15-min traffic intensity time series through a General Data Protection Regulation (GDPR)-compliant pipeline. The Realistic Urban Traffic Generator (RUTGe), a Deep Reinforcement Learning-based tool, was used to generate SUMO-compatible traffic demand whose simulated detector counts reproduce the observed camera-based intensities. The resulting simulations reproduced the observed detector-level traffic intensities with ${\mathrm{MARE}}_{\%}$ values between $2.29\%$ and $2.90\%$ across representative morning peak, midday off-peak, and evening peak traffic conditions. Additionally, camera analysis of over $470,000$ vehicle records revealed that resident traffic ($37.4\%$) dominates over through-traffic ($3.8\%$), significantly refining prior survey-based estimates. Our high-fidelity simulation tool based on SUMO, features realistic traffic patterns calibrated through AI-driven techniques, enabling the evaluation of diverse ’what-if’ scenarios—such as road closures, pedestrianization, changes in traffic direction, or relocation of bus stops. By quantifying the impact of these interventions, our tool facilitates informed decision-making prior to physical implementation. The proposed pipeline is cost-effective, privacy-preserving, and directly replicable for any municipality operating an LEZ camera network, offering a scalable template for evidence-based urban mobility planning, aligned with the European Strategy for Data and the EU Green Deal goals for sustainable mobility. Full article

Origin–destination (OD) matrix forecasting is essential for urban railway operations because it enables simultaneous understanding of the direction and magnitude of passenger flows. However, OD matrices in large-scale subway networks are difficult to predict owing to their high dimensionality and sparsity, and existing approaches often rely on station-level predictions or complex structural designs. This study addresses the offline full-matrix OD forecasting problem, where complete historical OD sequences are available at prediction time, and proposes Metro-GATF, a spatiotemporal forecasting framework that jointly models railway topology and dynamic OD interactions. The model employs a GATv2-based spatial encoder to learn static inter-station relationships and encodes time-varying interactions using sparse OD graphs. A non-autoregressive transformer decoder generates future multi-step node representations in parallel, whereas origin–destination factorization and sparsity-aware gating are used to reconstruct the full OD matrix. Experiments on minute-level AFC-based OD data from a 637-station metropolitan subway network demonstrated that Metro-GATF achieved the lowest sMAPE among the compared full-matrix models. These results indicate that the proposed framework effectively captures complex spatiotemporal OD patterns and offers a practical end-to-end framework for forecasting urban railway demand. Full article

Equitable access to rehabilitation services is essential for individuals living with a disability, yet geographic disparities in outpatient rehabilitation care remain understudied. This study examined spatial accessibility to outpatient physical and occupational therapy services across Texas to identify regional inequities and inform workforce and policy planning. A descriptive cross-sectional geospatial analysis was conducted using outpatient clinic location data from the Texas Health and Human Services database (2022) and population data from the 2020 U.S. Census. Clinic addresses were verified and geocoded. Accessibility was measured using an origin–destination cost matrix to estimate the travel time to the nearest clinic, and the two-step floating catchment area (2SFCA) method to calculate an accessibility index. Spatial clustering of access was assessed using the Getis-Ord Gi* statistic to identify hot and cold spots. The analysis included 2255 outpatient rehabilitation clinics across 6896 census tracts. Travel times varied substantially, with rural areas experiencing the longest travel burdens. The 2SFCA analysis revealed pronounced disparities, with low-accessibility clusters concentrated in rural and border regions and high-accessibility clusters in urban metropolitan areas. These findings demonstrate persistent geographic disparities in outpatient rehabilitation access across Texas, suggesting the need for targeted workforce placement, transportation investment, and policy interventions to improve equitable access. Full article

Following China’s completion of the removal of provincial boundary toll stations and expressway network integration reform, a large number of electronic toll collection (ETC) gantries were deployed along expressway mainlines nationwide, transforming these facilities from dedicated toll terminals into pervasive traffic-sensing infrastructure covering the entire road network. However, the data value and technological potential embedded in this major infrastructure transformation have not yet been systematically reviewed. This paper adopts a narrative review methodology, incorporating 71 publications identified through multi-database systematic searches. The review is organized along the functional upgrade path of ETC gantries, covering the progression from toll terminals to traffic sensing nodes, multi-source fusion hubs, and finally vehicle–road–cloud cooperative control nodes, and synthesizes research progress in expressway traffic sensing, multi-source data fusion, safety operations, and emerging applications. The review reveals that ETC data have enabled a diverse methodological repertoire spanning travel time estimation, traffic flow prediction, origin–destination (OD) matrix inference, toll plaza safety analysis, dynamic pricing strategies, and environmental impact assessment. Nevertheless, a single ETC data source suffers from inherent limitations: spatial–temporal resolution constrained by gantry spacing and real-time capability limited by transmission latency. This fundamental contradiction constitutes the core driving force behind multi-source data fusion and vehicle–road–cloud integration technologies. The paper further argues that establishing a closed-loop pipeline integrating sensing, fusion, decision, and control and anchored on ETC gantry nodes represents the key direction for realizing intelligent expressway transformation. Full article

Traffic variability along heavily congested signalised urban corridors undermines roadway safety, reduces energy efficiency, weakens operational reliability, and can hinder emergency response. Although many simulation-based studies have examined the impacts of Autonomous Vehicles (AVs), relatively few have combined high-resolution congestion observations with link-level microscopic calibration in a real urban network, particularly when evaluating implications for emergency mobility. This study develops and calibrates a microscopic Aimsun traffic simulation model for the Atakum district of Samsun, Türkiye, using a 10 min Google Traffic Index (GTI) observation stream converted into a four-level ordinal congestion scale. The calibration process began with an origin–destination (OD) matrix derived from 2020 traffic counts and was refined through link-level GTI synchronization, iterative OD scaling on mismatched corridors, and signal retiming at key intersections. GTI was validated as an ordinal congestion proxy through both categorical agreement and volumetric consistency, achieving 83% class agreement and GEH values below 5 for more than 90% of links. Five AV penetration scenarios (0%, 25%, 50%, 75%, and 100%) were simulated under peak-hour conditions. Network performance was evaluated using delay, stop time, mean speed, throughput, missed turns, and total journey time, while emergency mobility was assessed along a representative ambulance corridor on Atatürk Boulevard using seconds per kilometre. The results indicate that increasing AV penetration improves flow stability more clearly than nominal capacity. Mean speed increased from 36.2 to 39.2 km/h, delay and stop time declined steadily, and throughput remained nearly constant at 22.2–22.5 thousand vehicles/h. Along the ambulance corridor, travel time improved by 11.5%, from 112.4 to 99.4 s/km, between the baseline and full automation scenarios. These findings provide scenario-based evidence that, within a calibrated signalised urban network, increasing AV penetration can enhance operational stability and emergency response efficiency. More broadly, the study demonstrates the practical value of integrating GTI-based congestion observations with microscopic simulation for AV impact assessment in real urban networks. Full article

Background: Geographic and demographic disparities strongly influence access to community-based healthcare, especially in rural and mountainous areas. In Italy, Ministerial Decree 77/2022 promotes a territorial reorganization based on networked care models, but practical tools for translating policy standards into operational catchment areas remain limited. Methods: We developed a transparent, data-driven allocation framework combining travel-time accessibility and population-based capacity constraints. A case study was conducted in the Province of L’Aquila, within Local Health Authority ASL 1 Avezzano–Sulmona–L’Aquila, a low-density mountainous area including 65 municipalities. Using official ISTAT data, including the 2021 national origin–destination road travel-time matrix, municipalities were allocated to 3 hub nodes and 8 spoke nodes. Population caps of 50,000 residents per hub and 40,000 per spoke were applied. Scenario analyses were performed under 20, 30, and 40 min travel-time thresholds. Results: Under the 30 min scenario, all municipalities were allocated, but the L’Aquila hub exceeded the capacity cap. A cap-compliant 30 min allocation eliminated this violation at the cost of longer upper-tail travel times. Under the 20 min scenario, only 54 municipalities were allocated, leaving 11 mountainous municipalities outside the threshold. Under the 40 min scenario, all municipalities were allocated without capacity violations. Conclusions: The proposed framework provides a reproducible approach for territorial healthcare planning and makes explicit the trade-off between accessibility and capacity compliance in hub-and-spoke network design, particularly in geographically complex mountain settings. Full article

Non-motorized mobility networks increasingly serve as critical infrastructure for sustainable regional development that integrates recreational, environmental, and transportation functions across diverse geographical contexts. To enhance the spatial planning efficiency and support evidence-based policy development, this study develops a Geographic Information Systems (GIS)-based analytical framework to evaluate the connectivity and accessibility of Korea’s integrated non-motorized mobility system. The model systematically maps 606 walking courses, 60 cycling routes, and 66 water activity sites nationwide, and examines their spatial relationships with major transportation hubs, including Korea Train e-Xpress (KTX) stations and airports within 20–30 km buffer zones. Using proximity analysis, connectivity mapping, and origin–destination (OD) cost matrix modeling, the framework identifies intermodal distance structures and spatial integration patterns. The analysis reveals a hybrid network configuration characterized by localized multimodal clustering alongside regional accessibility gaps, with urban–coastal regions demonstrating stronger connectivity than inland–rural areas. This study proposes a data-driven Korean mobility network framework that integrates walking, cycling, and water routes with the existing transportation infrastructure. These findings demonstrate how GIS-based tools can support evidence-based sustainable mobility policies and regional tourism planning on a national scale. Full article

Transportation management within university campuses presents distinct challenges due to highly fluctuating traffic patterns. King Abdulaziz University (KAU), which attracts over 350,000 trips daily, is experiencing substantial congestion-related issues. This study focuses specifically on King Abdulaziz University Hospital (KAUH), a major trip generator on campus characterized by significant temporal variations in travel demand. The objective of this research is to develop a validated and operational traffic demand model using PTV VISUM 2025. A four-step framework was implemented, where campus gates were defined as trip production sources and 13 parking areas were designated as trip attractions. The morning peak-hour, identified as 7:15 AM to 8:15 AM, was selected for analysis due to the highest observed inflow of vehicles. Traffic surveys were conducted at seven bidirectional stations along key links to support Origin–Destination (O–D) matrix estimation and calibration. Both static and dynamic traffic assignment methods were applied to assess model performance. Model validity was evaluated using the R² statistic, percentage deviations, and the GEH measure of fit. The results demonstrate that both the equilibrium static assignment and the dynamic stochastic assignment achieved strong levels of accuracy, with R² = 0.98 and 86% of links exhibiting GEH values below 5, alongside average GEH scores of 3.2 and 2.7, respectively. This dual-model approach provides a robust analytical foundation for KAU, enabling long-term strategic planning through static assignment outputs and supporting short-term, peak-hour operational management through dynamic assignment results. Full article

As typical dynamic loads, electric vehicles (EVs) introduce significant uncertainty into distribution network operations due to the randomness of their travel behavior and charging demand. To achieve precise spatiotemporal forecasting of charging loads, this paper constructs a multi-dimensional transportation network model that accounts for dynamic road impedance factors and introduces a unit-distance energy consumption calculation method based on road impedance. By integrating the division of urban multifunctional zones and differentiated state-of-charge (SOC) threshold distributions across various EV types, a mapping model between travel chains and charging behaviors is established. Subsequently, large-scale travel and charging events are generated using an origin–destination (OD) probability matrix and Monte Carlo sampling to derive the spatiotemporal distribution of regional EV charging loads. Simulation results for a representative city in southwest China show that the predicted charging loads exhibit a dual-peak pattern, with significant differences across regions and vehicle types, and align well with observed load trends, validating the effectiveness and engineering applicability of the proposed method. Full article

This study develops a recent model proposed by Zhang et al. to predict bus short-term origin-destination (OD) demand based on a small-scale dataset (i.e., one week’s data per 30 mins’ collecting interval). We distinctively use sole input sequence by introducing a multi-head attention mechanism while simultaneously ensuring prediction accuracy. Extensive experiments demonstrate that one-layer bidirectional LSTMs (BiLSTMs) perform better than multi-layer ones. A modified deep learning model integrating physics-guided mechanisms, adaptive graph convolution, attention networks, and spatiotemporal encoder–decoder is constructed. We retained the original name, i.e., physics-guided adaptive graph spatio-temporal attention network (PAG-STAN) model. The model uses an encoder–decoder architecture, where the encoder captures spatiotemporal correlations via an adaptive graph convolutional LSTM (AGC-LSTM), enhanced by an attention mechanism that adjusts the importance of different spatiotemporal features. The decoder utilizes bidirectional LSTM to reconstruct the periodic patterns and predict the full OD matrix for the next interval. A masked physics-guided loss function, which embeds the quantitative relationship between boarding passenger volume and OD demand, is adopted for training. The Adam optimizer and early stopping technique are used to enhance training efficiency and avoid overfitting. Experimental results show that PAG-STAN outperforms other deep learning models in prediction accuracy. Compared with the suboptimal model, the proposed model achieved reductions of 6.19% in RMSE, 6.59% in MAE, and 8.20% in WMAPE, alongside a 1.13% improvement in R². Full article

Identifying urban functional areas increasingly relies on data-driven approaches that utilize multimodal spatial information. There is a growing focus on purpose-oriented functional area identification with greater policy relevance. This paper proposes a data-driven methodology to identify functional areas from the perspective of bicycle users. To achieve this, line-based road network units were defined around bicycle stations, and spatial–temporal data such as Origin–Destination flows and Point of Interest information were semantically integrated to delineate functional areas. An experiment was conducted on 2628 public bicycle stations in Seoul, Republic of Korea, for May 2022, and a total of five functional areas were identified via a Co-Matrix Factorization-based fusion approach. Additionally, the proposed method was validated through visual evaluation and comparison with actual bicycle usage data. The results demonstrate that by simultaneously incorporating spatial–temporal information and latent connectivity, this approach identifies bicycle-friendly areas, even with low observed usage, highlighting its potential for policy applications. Full article

Vehicle kilometers traveled (VKT) is a critical metric in transportation and environmental research. However, conventional VKT estimation approaches frequently fail to capture the complexity of route selection and spatiotemporal dynamics of individual road users. This study presents a framework for accurately estimating the total VKT using high-resolution trajectory data obtained from a commercial navigation system. To address the structural limitations of conventional origin destination matrix-based models, such as the modifiable areal unit problem, representative routes were identified based on cumulative travel distance coverage. A novel metric, coverage of estimated travel (CET), was introduced to quantify the explanatory capacity of these routes in approximating total travel distance. Representative routes were selected to maximize CET, and the resulting VKT estimates were validated against national statistical yearbook data. Robustness was further evaluated using mean absolute percentage error, correlation analysis, paired t-tests, and bootstrap-based confidence intervals. The results indicated that as few as five representative routes accounted for over 80% of the total estimated VKT, exhibiting strong agreement with the national statistics after temporal adjustment. These findings demonstrate that trajectory data can serve as a practical alternative to traditional methods, offering higher spatial resolution and enabling dynamic traffic analyses that support transportation policy and environmental planning. Full article

One type of transportation system developed in several cities is the Bus Rapid Transit (BRT) system. BRT systems are influenced by various factors, and route planning is one of the most important ones, which involves aspects such as route design, bus schedules, and passenger load. BRT systems can generate certain service data, which can be useful for calculating passenger load. However, these service data are insufficient to accurately predict future passenger loads. Processes such as origin–destination matrix analysis are required, which are time-consuming and not suitable in most cases. This paper proposes a machine learning (ML) model that allows predicting passenger load at the key stations of a BRT system. An exploration of datasets from several BRT systems was performed for a particular use case. Open data on the Transmilenio BRT system from Bogotá (Colombia) was determined as the source. The obtained results showed that the model using the Long-Short Term Memory (LSTM) algorithm obtained the best results in the metrics using one of the two generated datasets. However, the initial results were not satisfactory enough, so it was necessary to use a hyperparameter-tuning tool and vary the range of dates in the dataset to improve the respective metrics. Full article

This paper presents an initial contribution to a broader research initiative focused on the aggregate calibration of travel demand sub-models using low-cost and widely accessible data. Specifically, this first phase investigates methods and algorithms for the aggregate calibration of destination choice models, with the objective of assessing the possible utilization of an external observed matrix, eventually derived from opportunistic data. It can be hypothesized that such opportunistic data may originate from processed mobile phone data or result from the application of data fusion techniques that produce an estimated observed trip matrix. The calibration problem is formulated as a simulation-based optimization task and its implementation has been tested using a small-scale network, employing an agent-based model with a nested demand structure. A range of optimization algorithms is implemented and tested in a controlled experimental environment, and the effectiveness of various objective functions is also examined as a secondary task. Three optimization techniques are evaluated: Simultaneous Perturbation Stochastic Approximation (SPSA), Particle Swarm Optimization (PSO), and Adaptive Moment Estimation (ADAM). The application of the ADAM optimizer in this context represents a novel contribution. A comparative analysis highlights the strengths and limitations of each algorithm and identifies promising avenues for further investigation. The findings demonstrate the potential of the proposed framework to advance transportation modeling research and offer practical insights for enhancing transport simulation models, particularly in data-constrained settings. Full article