Availability, accessibility, and functionality of public restrooms in the City of St. Louis, Missouri.

The interactions between road networks and ecological corridors: A novel dual-network method

Identifying critical areas in urban road networks: A grid-based approach considering route redundancy

New possibilities for fog-based vehicle monitoring have emerged with the expansion of fog computing, but present privacy concerns provide a significant barrier that limits the extent to which vehicles can participate. Because of its potential to improve road network security and vehicle productivity, the field of Vehicle-based Ad hoc Networks (VANETs) is gaining prominence. The security problems with VANETs, such as data confidentiality and message access control still need better solutions to provide better Quality of Service (QoS). The effectiveness of VANET networks is diminished due to their instability problem. Vehicles continually add requests to the Road Side Unit (RSU) queue whenever they want specific information. For ever-evolving networks like VANETs, better routing is a continual process. Fundamental problems arise in large-scale systems when centralized procedures are used to assign jobs to the nodes along a route. The present centralized system for computing and safety has many needs, including the protection of data storage, user authentication, access control, system availability across multiple network connections, and the provision of a real-time data flow overview. Distributed problem solving and work sharing between multiple agents can improve the system's scalability. This paper provides a brief survey on the secured outsourcing and privacy preservation-based traffic monitoring model with routing and task scheduling models in Fog-enabled VANETS. This survey presents the limitations of the traditional models that help the researchers design new solutions for secure outsourcing in VANETs.

Adaptive continuity-preserving simplification of street networks

Adaptive traffic signal control is a critical component of intelligent transportation systems, and multi-agent deep reinforcement learning (MARL) has attracted increasing interest due to its scalability and control efficiency. However, existing methods have two major drawbacks: (i) they are largely driven by current and historical traffic states, without explicit forecasting of upcoming traffic conditions, and (ii) their coordination mechanisms are often weak, making it difficult to model complex spatial dependencies in large-scale road networks and thereby limiting the benefits of coordinated control. To address these issues, we propose TG-MADDPG, which integrates short-term traffic prediction with a graph attention network (GAT) for regional signal control. A WT-GWO-CNN-LSTM traffic forecasting module predicts near-future states and injects them into the MARL framework to support anticipatory decision-making. Meanwhile, the GAT dynamically encodes road-network topology and adaptively captures inter-intersection spatial correlations. In addition, we design a reward based on normalized pressure difference to guide cooperative optimization of signal timing. Experiments on the SUMO simulator across synthetic and real-world networks under both off-peak and peak demands show that TG-MADDPG consistently achieves lower average waiting times, shorter queue lengths, and higher cumulative rewards than IQL, MADDPG, and GMADDPG, demonstrating strong effectiveness and generalization.

Heritage and Transformation: Evolution of Street Networks in the Xiangmen Area, China

The article presents a method for assessing the traffic demand on city streets and roads based on open data using a gravity model. The relevance of the research topic is due to the limited availability of reliable and comprehensive data on actual traffic congestion in Russian cities. The aim of the study is to develop an approach that can serve as an al-ternative or supplement to assessing and forecasting road congestion in conditions of limited data. The initial data used are the parameters of urban neighborhoods – population, density and diversity of services, as well as land use types – which allow calculating indicators of demand and attractiveness for travel between neighborhoods. Based on this data, a correspondence matrix is formed, reflecting the relative volume of traffic flows between nodes of the street and road network, presented in the form of a weighted directed graph. The method allows assessing the demand and potential congestion of city roads without using closed or expensive road traffic data, which is especially relevant when designing new sections of the street and road network and planning urban infrastructure. An experimental test was carried out on the example of Vasilyevsky Island in St. Petersburg using open data from OpenStreetMap. The proposed approach can be useful for improving the efficiency of urban planning and transport management.

ABSTRACT In corridor-led urbanization, historic small towns risk a mismatch between spatial structure and local memory. Using Guoyang, China, as a diachronic case, this study reconstructs 1923/1966/2023 street networks and integrates Muratorian School, space syntax, and structured free recall into a framework centered on the Memory – Morphology Index (MMI) with LISA. Structural advantage shifts from the historical matrix route to a highway belt; system-wide, the median MMI is negative, indicating mild structure-leading, memory-lagging. Generational slices show young cohorts form an “old core and new axis” dual core, while overall migration is limited. These findings refine Cataldi’s “double urban life cycle” and motivate a dual-track strategy: structural optimization and memory gain.

The maintenance and rehabilitation of road networks remain among the most critical challenges in the global road construction sector, as increasing traffic volumes and pavement deterioration demand efficient solutions. Road repair and maintenance are essential not only for ensuring traffic safety but also for optimizing economic expenditures. Full-Depth Reclamation (FDR) is a pavement rehabilitation method in which the existing pavement—comprising the asphalt concrete surface, the base layer, and in some cases, additional base and subbase layers—is uniformly pulverized and blended to a predetermined depth, producing an improved, homogeneous base material. FDR is carried out entirely on-site without the application of heat. The treatment depth depends on the structure of the existing pavement and typically ranges from 100 to 300 mm. Compared to other technologies for rehabilitating flexible pavements, FDR significantly reduces the need for importing new materials, lowers energy consumption, and decreases harmful atmospheric emissions. The implementation of FDR in road construction began several decades ago, initially involving various mechanisms for pavement treatment, including pulverizers, scarifiers, mixers, and a range of additives. However, the adoption of high-powered self-propelled reclaimers provided significant momentum to the use of FDR, enabling deeper processing, higher productivity, and more reliable control of the stabilization process when additives are introduced. This article examines the impact of applying Full-Depth Reclamation (FDR) technology with cement and basalt fibers as additives on the strength and crack resistance of road pavements. The km 93+880 section of the M2 Yerevan–Goris–Meghri highway in the Republic of Armenia was selected as the experimental test site.

Context. Intersections are the most critical areas of a road network, where the largest number of collisions and the longest waiting times are observed. The development of optimal methods for traffic light control at signalized intersections is necessary for improving the flow of traffic at existing urban intersections, reducing the chance of traffic collisions, the time it takes to cross the intersection, and increasing the safety for drivers and pedestrians. Developing such an algorithm requires simulating and comparing the work of different approaches in a simulated environment.Objective. The aim of this study is to develop an effective deep reinforcement-learning model aimed at optimizing traffic lightcontrol at intersections.Method. A custom simulation environment is designed, which is compatible with the OpenAI Gym framework, and two types of algorithms are compared: Deep Q-Networks and Proximal Policy Optimization. The algorithms are tested on a range of scenarios, involving ones with continuous and discrete action spaces, where the set of actions the agent may take are represented either by different states of the traffic lights, or by the length of traffic light signal phases. During training, various hyperparameters were also tuned, and different reward metrics were considered for the models: average wait time and average queue length. The developed environment rewards the agent during training according to one of the metrics chosen, while also penalizing it for any traffic rule violations.Results. A detailed analysis of the test results of deep Q network and Proximal Policy Optimization algorithms was provided. In general, the Proximal Policy Optimization algorithms show more consistent improvement during training, while deep Q network algorithms suffer more from the problem of catastrophic forgetting. Changing the reward function allows the algorithms to minimize different metrics during training. The developed simulation environment can be used in the future for testing other types of algorithms on the same task, and it is much less computationally expensive compared to existing solutions. The results underline the need to study other methods of traffic light control that may be integrated with real-life traffic light systems for a more optimal and safer traffic flow.Conclusions. The study has provided a valuable comparison of different methods of traffic light control in a signalized urban intersection, tested different ways of rewarding models during training and reviewed the effects this has on the traffic flow. The developed environment was sufficiently simple for the purposes of the research, which is valuable due to the large computational requirements of the models themselves, but can be improved in the future by expanding it with more complex simulation features, such as various types of intersections that aren’t urban, creating a road network of intersections that would all be connected to each other, adding pedestrian crossings, etc. Future work may be done to refine the simulation environment, expand the range of considered algorithms, consider the use of models for vehicle control in addition to traffic light control.

We present the first comprehensive geolocated multi-modal transport database for the whole continent of Africa, the African Transport Systems Database (AfTS-Db), including road, rail, aviation, maritime and inland waterway networks. To do so, we created and standardized asset and network data across all transport modes, including inter-modal connections, attributes of road and rail corridors and estimated annual statistics for airports and ports. The African Transport Systems Database includes 234 airports including their airline routes, 179 maritime ports and their connections with each other, 132 inland ports and docking sites with river and lake connections, 4,412 railway stations connected across 99,373 kilometers of rail lines, and 1,004,512 kilometers of roads mainly comprised of all motorways, trunk roads, primary and secondary routes across Africa and some local roads that connect to other transport modes. The AfTS-Db provides key information for transport planning, resilience assessments, asset management and development of transport models and applications. Furthermore, we expect the data will also be of relevance for environmental, health, social and economic studies.

The book:The role of the national road network in supporting interprovincial accessibility in Kalimantan is published in Technium Books.

Nepal’s Strategic Road Network (SRN) forms the backbone of national connectivity and economic integration. By February 2024, Nepal’s national road network come to 34,257 km, including 18,421 km of blacktopped roads, 7,697 km of gravel roads, and 8,139 km of earthen roads. The maintenance of roads continues to suffer from chronic underfunding and weak institutional management. This study aims to evaluate the allocation and sufficiency of funds for SRN maintenance, identify shortcomings, and suggest improvements focusing on the Mugling–Narayanghat–Lothar corridor under the Bharatpur Division Road Office. A mixed-method approach was employed, integrating secondary data from the Roads Board Nepal (RBN), Department of Roads (DoR), and Division Road Offices with Key Informant Interviews (KII) and Focus Group Discussions (FGD) involving engineers, financial officers, and experts. The findings reveal a significant fund deficit, with a shortfall of NRs. 77.73 billion (70.53%) in Strategic Road Network maintenance funding by FY 18/19, and a 51.59% deficit in the Mugling-Narayanghat-Lothar section between FY 13/14 and 17/18. The major challenges include insufficient budget allocation by the Ministry of Finance, improper distribution of revenue collected from road users, and the lack of a performance-based fund disbursement mechanism by the Road Board Nepal. The study recommends revising ARMP based on proper assessments and allocating funds equitably based on user charge contributions. Despite significant revenue generation from fuel levies and vehicle registration fees, only a small portion was allocated to RBN. Key challenges include limited budget allocation, incomplete transfer of road user revenues, manpower shortages, and reliance on traditional maintenance practices. The study recommends ensuring all revenues transfer to RBN, adopting the PBMC approach, integrating maintenance into life cycle cost planning, and establishing a research and development.

ABSTRACT Interdependencies among infrastructure systems may amplify the impact of disruptive events. This paper presents a network-based repair sequencing and resilience assessment model to examine the influence of two-way interdependency between road transportation networks and power distribution networks. We use a modified IEEE 33-bus power network and the Sioux Falls road network (both standard testbed instances) to simulate the effects of a natural disaster, with a single repair crew repairing damaged nodes. Power failures cause delays in the transportation system due to signal outages, and delays in the transportation system affect the times when the repair crew can reach damaged nodes. We use a simulated annealing algorithm heuristic to find good repair sequences that account for both types of interactions between power and road systems. We compare the solutions obtained from the heuristic algorithm to alternative strategies and results indicate that the interdependency-aware strategy achieves faster restoration times and enhanced overall resilience, compared to random, priority-based, and interdependency-naive strategies.

Tourism development increasingly depends on the availability of reliable and sustainable infrastructure, particularly road networks and drainage systems, to support accessibility, environmental protection, and visitor comfort. In many integrated tourism areas, inadequate coordination between transportation and drainage planning has led to congestion, flooding, and environmental degradation. This study aims to analyze and formulate integrated road and drainage infrastructure planning strategies to support the development of sustainable integrated tourism areas. The research adopts a qualitative literature-based approach by reviewing peer-reviewed journal articles, academic books, and institutional reports related to infrastructure planning, sustainable tourism, and environmental management. Data were collected through systematic literature searches and analyzed using content and thematic analysis to identify key planning principles and best practices. The findings indicate that integrating land use, road design, and drainage systems within a unified spatial framework enhances infrastructure efficiency, reduces flood risk, and improves environmental quality. The study also highlights the importance of nature-based drainage solutions, context-sensitive road design, climate-resilient infrastructure, and institutional coordination in achieving long-term sustainability. These results suggest that integrated and sustainability-oriented infrastructure planning is a critical foundation for resilient, attractive, and competitive tourism destinations.

Spatiotemporal traffic flow prediction is a fundamental task in intelligent transportation systems and is crucial for promoting efficient and sustainable urban mobility, especially under increasingly complex and rapidly evolving traffic conditions. To overcome the challenges of modeling high-order spatial dependencies and heterogeneous temporal patterns, this study develops a novel Hierarchical Spatiotemporal Graph–Hypergraph Network (HSTGHN). For spatial representation learning, a hypergraph neural module is employed to capture high-order interactions across the road network, while a hypernode mechanism is designed to characterize complex correlations among multiple road segments. Furthermore, an adaptive adjacency matrix is constructed in a data-driven manner and enriched with prior knowledge of bidirectional traffic flows, thereby enhancing the robustness and accuracy of graph structural representations. For temporal modeling, HSTGHN integrates the complementary strengths of Gated Recurrent Units (GRUs) and Transformers: GRUs effectively capture local sequential dependencies, whereas Transformers excel at modeling global dynamic patterns. This joint mechanism enables comprehensive learning of both short-term and long-term temporal dependencies. Extensive experiments on multiple benchmark datasets demonstrate that HSTGHN consistently outperforms state-of-the-art baselines in terms of prediction accuracy and stability, with particularly significant improvements in long-term forecasting and highly dynamic traffic scenarios. These improvements provide more reliable decision support for intelligent transportation systems, contributing to enhanced traffic efficiency, reduced congestion, and ultimately more sustainable urban mobility.

One of the key challenges in the development of modern cities is maintaining the functional stability of the transport system under conditions of increasing travel demand. The long-standing dominance of the car-oriented model of urban planning has led to a significant growth in the number of private vehicles, which, in turn, results in the overloading of the street and road network, the formation of traffic congestion, and a reduction in travel speed. Consequently, travel times increase, road safety deteriorates, and both noise pollution and the concentration of harmful emissions in the atmosphere rise. In addition, a substantial portion of urban space is allocated to car-oriented infrastructure, limiting opportunities for the development of public, recreational, and pedestrian areas, and thereby reducing the overall quality of the urban environment. In light of the challenges posed by car-centered urban development, there is a need to reconsider urban transport policy in favor of sustainability-oriented models. One of the key directions of this transformation is strengthening the role of public transport, which, provided high service quality, can ensure population mobility and accessibility without the need for private car use. The development of competitive public transport contributes to reducing traffic loads on the network, mitigating negative environmental impacts, and promoting the rational use of urban space - all of which are essential prerequisites for establishing a balanced urban transport system. A justified transition toward such a system requires the application of a multi-criteria approach to the formation of the urban transport system, which accounts for the interests of all stakeholders. These include private vehicle users (costs associated with car ownership and operation), transport operators (operational expenditures), and public transport users (travel time and comfort characteristics), as well as society as a whole through the environmental impacts of transport activities. Considering these aspects makes it possible to determine rational parameters for the operation of the urban transport system and to ensure its sustainable and balanced development. The study proposes a multi-criteria approach to the formation of a sustainable urban transport system with a focus on the priority development of route-based public transport. The developed approach comprehensively considers economic, social, and environmental efficiency criteria of transport subsystems, harmonizing the interests of private car users, public transport operators, and passengers while minimizing the negative impact on the environment.

As urban traffic congestion becomes increasingly severe, supply chain transportation faces the dual challenges of declining efficiency and mounting environmental burdens. To address this, this study constructs a Green Vehicle Routing Problem (GVRP) framework that integrates real-time traffic information. First, K-means clustering technology is employed to classify urban road networks by congestion levels, establishing a three-tier classification system of low, medium, and high congestion zones. Subsequently, polynomial regression methods are utilized to establish a quantitative relationship model between vehicle speed and carbon emission intensity. Based on this theoretical foundation, a multi-objective optimization framework is designed that comprehensively considers environmental costs and traffic impedance factors, with performance comparison tests conducted between genetic algorithms and classical shortest path algorithms. Experimental results demonstrate that genetic algorithms perform excellently when handling high-congestion road segments, significantly reducing carbon footprint while shortening transportation cycles. This research provides scientific basis and operational paradigms for enterprises to construct sustainable supply chain networks based on dynamic traffic information.

Accessibility to public facilities is a crucial factor in regional development, includingat the village level as the smallest administrative unit. The Central Bureau of Statistics (BPS)currently collects data on public facilities and their distances to village offices throughinterviews, making the results dependent on respondents’ perceptions. This research aims tomeasure the nearest distance from village offices to public schools by utilizing the BallTreealgorithm and the Google Maps API. The dataset consists of 128 village offices and a list ofpublic schools classified into four categories. BallTree was used to filter the nearest schoolcandidates within a given radius, after which the route distance of the ten nearest candidates wascalculated using the Google Maps Distance Matrix API to identify the school with the nearestroute distance based on the road network. The findings show that straight-line distance oftenaligns with route distance, although not at all, highlighting the importance of Google Maps routecalculation. This research concludes that combining BallTree and the Google Maps APIimproves computational efficiency while providing objective and reliable information.