Economic Feasibility of Drone-Based Traffic Measurement Concept for Urban Environments

Urban mobility has undergone and continues to undergo a profound transformation driven by the convergence of artificial intelligence (AI), the Internet of Things (IoT), and predictive analytics in recent years. These technologies are redefining adaptive traffic control systems, enabling real-time decision-making and increasing the efficiency and safety of road networks. The main questions addressed in the review explore how the integration of advanced technologies such as IoT, AI in traffic systems, are useful in optimizing traffic flows, vehicle coordination and infrastructure adaptability in increasingly complex traffic environments. The integration of IoT-enabled devices and AI-based algorithms has been essential to enable data-driven approaches to urban traffic control. Predictive analytics improves emergency response mechanisms, improves traffic signal operations, and supports the deployment of autonomous and connected vehicles. Among the various methodologies evaluated, AI-based models combined with IoT sensors demonstrated superior performance, reducing average traffic delays by up to 30% and improving safety metrics in various urban environments. This systematic review underscores the transformative potential of integrating AI, IoT, and predictive analytics into urban traffic management, offering a blueprint for smarter, more sustainable urban transportation solutions.

The rapid expansion of urban drainage pipe networks, driven by economic development, poses significant challenges for efficient monitoring and management. The complexity and scale of these networks make it difficult to effectively monitor and manage the discharge of urban domestic sewage, rainwater, and industrial effluents, leading to illegal discharges, leakage, environmental pollution, and economic losses. Efficient management relies on a rational layout of drainage pipe network monitoring points. However, existing research on optimal monitoring point layout is limited, primarily relying on manual analysis and fuzzy clustering methods, which are prone to human bias and ineffective monitoring data. To address these limitations, this study proposes a coupled model approach for the automatic optimization of monitoring point placement in drainage pipe networks. The proposed model integrates the information entropy index, Bayesian reasoning, the Monte Carlo method, and the stormwater management model (SWMM) to optimize monitoring point placement objectively and measurably. The information entropy algorithm is utilized to quantify the uncertainty and complexity of the drainage pipe network, facilitating the identification of optimal monitoring point locations. Bayesian reasoning is employed to update probabilities based on observed data, while the Monte Carlo method generates probabilistic distributions for uncertain parameters. The SWMM is utilized to simulate stormwater runoff and pollutant transport within the drainage pipe network. Results indicate that (1) the relative mean error of the parameter inversion simulation results of the pollution source tracking model is linearly fitted with the information entropy. The calculation shows that there is a good positive linear correlation between them, which verifies the feasibility of the information entropy algorithm in the field of monitoring node optimization; (2) the information entropy algorithm can be well applied to the optimal layout of a single monitoring node and multiple monitoring nodes, and it can correspond well to the inversion results of the tracking model parameters; (3) the constructed monitoring point optimization model can well realize the optimal layout of monitoring points of a drainage pipe network. Finally, the pollution source tracking model is used to verify the effectiveness of the optimal layout of monitoring points, and the whole process has less human participation and a high degree of automation. The automated monitoring point optimization layout model proposed in this study has been successfully applied in practical cases, significantly improving the efficiency of urban drainage network monitoring and reducing the degree of manual participation, which has important practical significance for improving the level of urban water environment management.

On-demand taxi services have rapidly become a key component of urban mobility systems worldwide, particularly in developing cities where traditional public transport often struggles to meet the demand for flexible and reliable travel options. In Sri Lanka, the introduction of app-based taxi services such as PickMe and Uber in 2015 has significantly altered urban travel behavior, especially in dense zones like the Colombo District. While these platforms offer convenience through real-time ride matching and app based booking, there is limited understanding of how accessible these services truly are across different parts of the city. This study aims to fill that gap by developing a spatial model to assess and quantify accessibility to on-demand taxi services in Colombo. Accessibility in this context refers to the ease with which users can obtain a ride, primarily determined by average waiting time and service availability. The study seeks to model the influence of socio-economic, spatial, and infrastructure-related factors on this accessibility. A quantitative modelling framework grounded in spatial analysis was adopted. The Colombo District was divided into thirteen neighborhoods to capture intra-district variation. Data was sourced from a leading on-demand taxi service provider, including waiting times, trip counts, and service coverage for each neighborhood. These data were combined with socio-economic indicators such as population density, household income, unemployment rates, household size, vehicle ownership, and transport infrastructure indicators like bus stop density and road network availability. Spatial regression analysis was carried out using R to model accessibility as a function of socio-economic and infrastructural variables. A single model was developed based on statistical fit, spatial logic, and predictive accuracy. The results show that accessibility is strongly influenced by both urban structure and socio-economic conditions. Shorter waiting times were associated with neighborhoods that had higher vehicle ownership, better road networks, and higher income levels. Conversely, areas with low density, fewer public transport connections, and higher unemployment showed poorer accessibility. Interestingly, population density was not consistently linked to better service availability, suggesting that density alone does not guarantee access if other supportive infrastructure is lacking. The model offers both predictive and diagnostic value—highlighting areas that are underserved and uncovering the key factors behind these gaps. For service providers, the insights can inform operational strategies like fleet allocation, dynamic pricing, and service expansion. For city planners, the model provides a tool to better integrate digital mobility services within broader transport planning frameworks. From a policy perspective, the findings stress the need for inclusive transport strategies that bridge digital services and spatial equity. As app based taxi platforms become embedded within urban mobility systems, ensuring equitable access will require deliberate, data-driven planning. In conclusion, this study presents a location-specific, rigorous methodology to model accessibility to on-demand taxi services. It contributes to the growing research on app-based mobility in the Global South and offers a transferable framework for analyzing accessibility in other urban environments. As cities continue adapting to digital mobility transitions, such models are vital for fostering sustainable, inclusive, and efficient transport systems.

Urban water infrastructure, i.e., water supply and sewer networks, are underground structures, implying that detailed information on their location and features is not directly accessible, frequently erroneous, or missing. For public use, data is also not made available due to security concerns. This lack of quality data, especially for research purposes, requires substantial effort when such data is sought for both statistical and model‐based analyses. An alternative to gathering data from archives and observations is to extract the information from surrogate data sources (e.g., the street network). The key for such an undertaking is to identify the common characteristics of all urban infrastructure network types and to quantify them. In this work, the network correlations of the street, water supply, and sewer networks are systematically analyzed. The results showed a strong correlation between the street networks and urban water infrastructure networks, in general. For the investigated cases, on average, 50% of the street network length correlates with 80%-85% of the total water supply/sewer network. A correlation between street types and water infrastructure properties (e.g., pipe diameter) cannot be found. All analyses are quantified in the form of different geometric‐ and graph‐based indicators. The obtained results improve the understanding of urban network infrastructure from an integrated point of view. Moreover, the method can be fundamental for different research purposes, such as data verification, data completion, or even the entire generation of feasible datasets.

Assessment of vehicular fuel consumption and interaction with pavement characteristics using HDM-4 on Indian urban road network: A case of Pune city

Vehicle travel paths provide basic information for improving traffic forecasting models, tracking epidemics transmission, and road construction. Nevertheless, the challenge of recognition and verification still exists, especially in urban dense road networks. This paper proposes a vehicle path recognition model combined with mobile phone data. In path fitting module, the spatio-temporal density-based clustering algorithm and Gaussian filter were combined to smooth the position fluctuations of mobile phone data; then non-uniform rational B-splines were used to fit travel paths. In path recognition module, the modified probabilistic map matching algorithm was used to match fitting knots to road networks; then matching results were repaired considering the road network topology and the direction angles. The results were verified from trip lengths, urban environments, and road categories. The recognition accuracy was around 90%, 24.22% higher than that of existing methods. The error rate was around 6%, 30.28% lower than that of existing methods.

Flood incidents are becoming a common circumstance all over the world due to rapid urbanization and climate change. The dependence on flood control structures is becoming unsustainable and modern approaches are taking place with improved spatial planning, design strategies, adaptation techniques, and forecasting. All the major cities in developing countries like Bangladesh are facing flooding issue. The immediate impact on the urban environment is found in the street network due to flooding. Most of the previous studies are focused on the topography to understand the flooding or vulnerability of transport networks with traffic issues, but less attention is given to the street network performance during flooding and possible strategies for solving those issues. This research aims to understand the appropriateness of flooding resilience vs flooding prevention from a context-sensitive point of view of Dhaka city by identifying flooding impact on the road network. Spatial network analysis with sDNA and flooding data from World Bank helped to understand the relationship between street networks, and before-after situations during flooding. The circumstantial impact on the street network by flooding was visualized with GIS and sDNA analysis to define its performance and possible alternative solutions. This research will help to dictate future planning policy frameworks of Dhaka city derived from this exploratory study. Also, this research endeavors to help the government and corresponding authorities of flood-prone cities to enhance an inclusive strategic tool for the existing and future planning policy by developing shortterm and long-term strategic vision.

At present, the urban traffic system is faced with the problems of congestion and low efficiency, and the traditional methods have certain limitations when dealing with the complex urban road network. This paper aims to explore a new method of capacity Optimization of Road Networks in a Smart City environment and proposes a method based on a Graph Neural network, named “Smart City Road optimization Graph Neural Network” (SCRO-GNN). SCRO-GNN first collects and preprocesses multi-source data, including road network data, traffic flow, accident records, environmental factors, etc. The key node and edge features, including the number of lanes at the intersection, the traffic flow of the section, and the adjacency matrix of the road network are defined to characterize the structure of the road network. Then, the graph neural network model is constructed and trained to predict the traffic flow of different sections and evaluate the road capacity by using the graph structure of the road network. This paper tests the performance of SCRO-GNN on real road networks in multiple cities. The results show that, compared with the traditional traffic flow prediction model, SCRO-GNN can significantly improve the prediction accuracy, especially when dealing with the highly complex urban road network structure. Based on these predictions, the proposed optimization strategy performed well in reducing traffic congestion and improving the efficiency of road use. The research in this paper not only demonstrates the potential of graph neural networks in smart city road network optimization, but also provides a new direction for future traffic system research. The successful implementation of the SCRO-GNN approach is expected to provide more efficient and intelligent solutions for urban traffic management and planning.

Advanced urban traffic control systems are often based on feed-back algorithms. For instance, current traffic control systems often operate on the basis of adaptive green phases and flexible co-ordination in road (sub) networks based on measured traffic conditions. However, these approaches are still not very efficient during unforeseen situations such as road incidents when changes in traffic are requested in a short time interval. Therefore, we need self-managing systems that can plan and act effectively in order to restore an unexpected road traffic situations into the normal order. A significant step towards this is exploiting Automated Planning techniques which can reason about unforeseen situations in the road network and come up with plans (sequences of actions) achieving a desired traffic situation. In this paper, we introduce the problem of self-management of a road traffic network as a temporal planning problem in order to effectively navigate cars throughout a road network. We demonstrate the feasibility of such a concept and discuss our preliminary evaluation in order to identify strengths and weaknesses of our approach and point to some promising directions of future research.

Road networks have significant impact on mobility and network characteristics of wireless ad hoc networks. Discovering their characteristics and effects on mobility and network performance in urban environments is a fundamental research task. In this paper, we firstly study the graph attributes of road networks by sampling real road networks in main cities of Europe and USA. We propose a new graph metric, called characteristic central length , in order to estimate the average shortest-path length of a large-scale spatial network. We find that real road networks from Europe and USA have different patterns with regard to some graph attributes and a simple grid model is inadequate to describe them. Considering the diverse patterns of urban road networks caused by obstacles and shortcuts, we propose a random road network model, called the GRE model. The model is validated through fitting it to real road network samples using a genetic algorithm and simulation of delay-tolerant networks. The simulation results have shown that by extending the grid model with new probabilistic parameters, the GRE model has better capability on approximating real road networks. The simulation results have also shown that delay-tolerant networks operating on road networks may have better performance than scenarios without road networks.

The visualization and analysis of street and pedestrian networks are important to various domain experts, including urban planners, climate researchers, and health experts. This has led to the development of new techniques for street and pedestrian network visualization, expanding possibilities for effective data presentation and interpretation. Despite their increasing adoption, there is no established design framework to guide the creation of these visualizations while addressing the diverse requirements of various domains. When exploring a feature of interest, domain experts often need to transform, integrate, and visualize a combination of thematic data (e.g., demographic, socioeconomic, pollution) and physical data (e.g., zip codes, street networks), often spanning multiple spatial and temporal scales. This not only complicates the process of visual data exploration and system implementation for developers but also creates significant entry barriers for experts who lack a background in programming. With this in mind, in this paper, we reviewed 45 studies utilizing street-overlaid visualizations to understand how they are applied in practice. Through qualitative coding of these visualizations, we analyzed three key aspects of street and pedestrian network visualization usage: their analytical purposes, the visualization approaches employed, and the data sources used in their creation. Building on this design space, we introduce StreetWeave, a declarative grammar for designing custom visualizations of multivariate spatial network data across multiple resolutions. We demonstrate how StreetWeave can be used to create various street-overlaid visualizations, enabling effective exploration and analysis of spatial data. StreetWeave is available at urbantk.org/streetweave.

Partitioning urban road network based on travel speed correlation

The greatest influence of motor vehicles is manifested in the urbanized environment. The city is an indicator of sustainable development or an unfavourable relationship between motor vehicles and the urban environment. The study is based on an assessment of the impact of the vehicles current state on the street and road network and trunk road adjacent areas to substantiate the adopted planning protective measures and to determine the functional purpose of the trunk road adjacent areas proceeding from environmental impact on street and road networks. Anthropogenic air pollution sources are primarily represented by industrial enterprises and vehicles emissions. The main task in determining the assessment of the effectiveness of the protective solutions of trunk road adjacent areas is the correct choice of assessment criteria, according to which the efficiency of solutions will differ. Since the street and road network with all its traffic flows is an integral structural element of the city, its impact on the environmental performance of the urban environment can undoubtedly be called the prevailing one. It is necessary to highlight noise, airborne emissions and air (atmosphere) pollution among the main environmental impacts, the source of which is the functioning of the street and road network. Since the street and road system is the main tool in wastewater collection and disposal, it also has a direct impact on the ecological condition of hydrosphere objects, i.e. groundwater, springs, water bodies. Its environmental impacts on the urban setting's lithosphere are also evident: road surface contamination, lubricant residues and gasoline pollute the soil during the removal of rain and melt wastewater. It is impossible to rule out the harmful effects of electromagnetic loads from rail vehicle operation. According to the State Statistics Service of Ukraine, the quality of atmospheric air in a modern developed city is primarily dependent on the volume of pollutant emissions, the two main sources of pollution being stationary 15…30% and mobile 70…85% (using Kyiv's example).

Human expansion into wildlife habitats has increased the need to understand human–wildlife interactions, necessitating interdisciplinary approaches to assess zoonotic disease transmission risks and public health impacts. This study integrated fine-grained human foot traffic data with hourly GPS data from 38 white-tailed deer (Odocoileus virginianus), a species linked to SARS-CoV-2, brucella, and chronic wasting disease, in Howard County, Maryland. We explored spatial and temporal overlap between human and deer activity over 24 months (2018–2019) across a hexagonal tessellation with metrics like hourly popularity and visit counts. Negative binomial models were fitted to the visit counts of each deer and humans per tessellation area, using landscape features as predictors. A separate deer-only model included commercial human activity as another predictor. Spatial analysis showed deer and humans sharing spaces in the study area, with results indicating deer using more populated residential areas and areas with commercial activity. Temporal analysis showed deer avoiding commercial spaces during daytime but using them in late evening and early morning. These findings highlight the complex space use between species and the importance of integrating detailed human mobility and animal movement data when managing wildlife–human conflict and zoonotic disease transmission, particularly in urban areas with a high probability of deer–human interactions.

In modern society, intelligent spatial analysis services have been playing an important role in building smart cities. Especially, people’s spatial information needs have changed with the technological progress of times, from traditional two-dimensional appearance to three-dimensional interior. In this case, the construction of street networks is a key part of spatial information analysis research. Establishing an integrated indoor-outdoor street network data model with accurate information and complete attributes can provide strong technical support for indoor-outdoor navigation, emergency evacuation, and other application services. However, most of the current studies on internal and external road networks are independent of each other. This means that indoor road networks are mainly based on floor plans, which contain little information and do not represent the spatial relationships between floors. Indoor and outdoor links often suffer from poor connectivity and data loss. Heterogeneous robotics is a high degree of integration of intelligent control and platform control technologies. In a sense, it is a technology platform for connecting heterogeneous robotic populations. By developing effective collaboration and control strategies, tasks can be performed with the highest success rate and the lowest risk while respecting spatial and temporal constraints. As a result, the study of the coordination and management of heterogeneous robotic systems for road network construction, management, and maintenance is a topic of a great theoretical value and significance. Therefore, this paper proposes an intelligent road network construction method that incorporates BIM and GIS technologies. This study uses the building information model to enrich the information of indoor building components and combines the strong spatial analysis capability of outdoor areas with GIS road network technology to build an indoor road network and design a connection scheme for indoor and outdoor areas. After that, the construction of an integrated indoor-outdoor street network is completed, and the interaction between indoor as well as outdoor spatial data is very smooth.