City Road Network Research Articles

Evaluating the connectivity of the road network in Ramadi city using sustainable transport indicators

Evaluating the connectivity of the road network in Ramadi city using sustainable transport indicators

Public perception of autonomous vehicles acceptance in Hungary

AbstractThe integration of Autonomous Vehicles (AVs) into our modern society hinges on gaining widespread acceptance from potential road users. To indicate the preparedness of these road users and elucidate their perspectives regarding the use of AVs in future, it is imperative to conduct surveys gauging public acceptance and satisfaction with this emerging mode of transportation. This paper reports the results of a comprehensive questionnaire study involving 1,000 individuals in Gyor City. The survey's primary objective was to assess participants' attitudes and willingness to embrace autonomous vehicles within the city's road networks. The study delved into various socio-demographic factors, such as age, gender, and employment status, while also exploring participants' prior knowledge and opinions regarding the advantages and limitations of AVs. The findings reveal a generally favorable disposition among the public toward the inclusion of AVs in urban traffic, paving the way for the acceptance of mixed traffic patterns. Notably, respondents in younger age groups exhibit greater enthusiasm for incorporating AVs into their daily transportation, whereas individuals aged 65 and above express more reservations, displaying a conservative outlook. Furthermore, participants with prior knowledge and a deeper understanding of AVs exhibit a markedly more positive inclination toward this emerging technology compared to those lacking such familiarity.

Research on Smart City Road Network Capacity Optimization Configuration Based on Deep Learning Algorithms

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.

Обгрунтування доцільності влаштування саморегульованих кільцевих розв’язок на вулично-дорожній мережі міста

Introduction. In the modern world, the transport infrastructure of cities faces significant challenges related to the growth of traffic intensity, congestion and the need to ensure road safety. Among the wide range of engineering solutions and technologies aimed at solving these problems, it is worth highlighting the issue of justifying the choice of planning solutions in places of the highest concentration of traffic flows - transport hubs. International experience shows that the construction of roundabouts is one of the ways to optimize traffic, reduce congestion and improve road safety. Therefore, it is important to consider this type of planning solutions for transport hubs in terms of the feasibility of their use on the city's street and road network.

A multiple asset-type, collaborative vehicle routing problem with proximal servicing of demands

This research examines the problem of routing multiple assets of different types over a network to service demands in a collaborative manner. The servicing is collaborative in that, when servicing a demand, the different types of assets must do so nearly simultaneously. Moreover, whereas some asset types must service demands by visiting them, other asset types may provide service proximally. This study sets forth a mixed-integer linear program to model this variant of a vehicle routing problem. In addition to directly solving problem instances via a commercial solver, this research proposes two permutations of a model decomposition heuristic, as well as two preprocessing techniques to impose instance-specific bounds on selected decision variables. Comparative testing on mesh networks evaluates nine combinations of solution methods and preprocessing options to solve a set of 216 instances that vary significant parameters. Results manifest trade-offs between the likelihood of finding a feasible solution with bounded computational effort and the relative quality of solutions identified. For larger networks, the preprocessing technique leveraging a nearest neighbor heuristic in combination with any solution method most frequently identified feasible solutions for the set of test instances (i.e., ∼90% of instances), with lesser solution quality (i.e., within 15% of the best solutions identified, on average). Worst performing for larger networks was a model decomposition technique that first routes assets providing service proximally, and omitting either preprocessing technique; although this combination yielded the best solutions when it identified a feasible solution, it only did so for ∼55% of instances. Other solution method performances exhibit noteworthy nuance, as detailed herein. Further testing of the solution procedures on 216 additional instances for a scenario motivated by a disaster relief using a city road network yielded relatively consistent results; the superlative method leveraged model decomposition and a nearest neighbor preprocessing heuristic, albeit when routing proximally-serving assets first.

Expanding Ground Vehicle Autonomy into Unstructured, Off-Road Environments: Dataset Challenges

As with the broad field of deep learning, autonomy is a research topic that has experienced a heavy explosion in attention from both the scientific and commercial industries due to its potential for the advancement of humanity in many cross-cutting disciplines. Recent advancements in computer vision-based autonomy has highlighted the potential for the realization of increasingly sophisticated autonomous ground vehicles for both commercial and non-traditional applications, such as grocery delivery. Part of the success of these technologies has been a boon in the abundance of training data that is available for training the autonomous behaviors associated with their autonomy software. These data abundance advantage is quickly diminished when an application moves from structured environments, i.e., well-defined city road networks, highways, street signage, etc., into unstructured environments, i.e., cross-country, off-road, non-traditional terrains. Herein, we aim to present insights, from a dataset perspective, into how the scientific community can begin to expand autonomy into unstructured environments, while highlighting some of the key challenges that are presented with such a dynamic and ever-changing environment. Finally, a foundation is laid for the creation of a robust off-road dataset being developed by the Engineer Research and Development Center and Mississippi State University’s Center for Advanced Vehicular Systems.

Assessing vehicle interdiction strategies on a complex transportation network: A simulation-based study

The escape interdiction problem within the context of attacker activities on a transportation network is addressed in this study. In the absence of traffic within the network, the attacker attempts to flee the city by choosing one of the shortest paths from the crime scene to a randomly selected exit point. However, in the presence of traffic, the attacker strategically selects the optimal route that minimizes his time to reach a randomly selected exit point. On the other side, defenders try to interdict the attacker on his escape route. Defenders face the daunting challenge of interdicting the attacker’s escape route while operating under limited resources. Dealing with a real city road network further adds complexity to the scenario. A simulation-based model is proposed for the optimal allocation of resources to tackle this issue. The focus then shifts to the development of an advanced search strategy that involves routing with optimal resource allocation. This paper presents the first comparative study for escape interdiction problems within a simulation environment, explicitly focusing on solution methodologies. An optimal resource allocation approach is proposed in the presence of traffic, constituting a novel contribution that has not been previously implemented in escape interdiction problems. In addition, the paper introduces a Genetic Algorithm (GA)-based meta-heuristic approach within a simulation environment. This approach generates optimal paths for defenders, wherein each node is associated with a fixed time window, representing the defender’s waiting time. In this proposed methodology, defenders undertake a tour of the network rather than remaining stationary at a single location. This approach expands the network search capabilities, thereby requiring optimization to ascertain the optimal routes and schedules for the defender vehicles. A case study is conducted using the map of IIT Kharagpur, India, to evaluate the effectiveness of this approach. By employing this approach and conducting in-depth analyses, the aim is to provide valuable insights into the efficiency and practicality of the developed methods on real-world transportation networks.

Evaluating the traffic characteristics of the road network in Ramadi city using sustainable transportation indicators (Hazard Index)

Evaluating the traffic characteristics of the road network in Ramadi city using sustainable transportation indicators (Hazard Index)

Шляхи оптимізації кількості індивідуального транспорту в обмежених можливостях вулично-дорожньої мережі

An analysis of scientific research was conducted in the direction of reducing the negative impact of the use of individual vehicles on the street and road network of cities. In order to solve the problem of transport oversaturation of public transportation systems, the foreign experience of limiting the use of individual vehicles on the street and road network of cities was analyzed. The main directions of restrictions on the use of individual vehicles on the street and road network of cities have been established, including restrictions on the operation of individual vehicles depending on the number of passengers in the cabin, the digital configuration of the state license plate, and the presence of vehicle certification. Methods of limiting the operation of individual vehicles in the busy part of the city were also considered. These measures are aimed at reducing the intensity of traffic in cities and increasing the filling factor of the vehicle interior. Considering that a large share of individual vehicles used in Ukraine belongs to Euro-2 and Euro-4 environmental standards, their use significantly worsens the environmental situation in large cities. Approaches aimed at increasing the use of public transport and other alternative modes of transport for transporting passengers in cities are summarized, which reduces the load on the street and road network and improves the environmental situation. To solve the described problems, it is necessary to use a comprehensive approach, in particular, to apply measures of both a regulatory and an informational nature, primarily to popularize the use of public transport. The proposed measures to solve the described transport problems will improve the comfort of transport connections for all road users and the environmental situation, especially in large cities of Ukraine.

Using traffic assignment models to assist Bayesian inference for origin–destination matrices

Estimation of traffic volumes between each origin and destination of travel is a standard practice in transport engineering. Commonly the available data constitute traffic counts at various locations on the network, supplemented by a survey-based prior estimate of mean origin–destination traffic volumes. Statistical inference in this type of network tomography problem is known to be challenging. Moreover, the difficulties are increased in practice by the presence of a large number of nuisance parameters corresponding to route choice probabilities, for which we have no direct prior information. Working in a Bayesian framework, we determine these parameters using a stochastic user equilibrium route choice model. We develop an MCMC algorithm for model fitting. This requires repeated computation of stochastic user equilibrium flows, and so we develop a computationally cheap emulator. Our methods are tested on numerical examples based on a section of the road network in the English city of Leicester.

MDTGAN: Multi domain generative adversarial transfer learning network for traffic data imputation

Accurate, real-time, and efficient traffic data, crucial for intelligent transportation systems, which is often disrupted in the real world due to the influence of weather, interference, and equipment failures. Generative Adversarial Networks (GANs), have achieved significant results in image restoration, providing insights for traffic data imputation. Recent research has focused on developing more effective and reasonable model by learning transferable common knowledge from different cities or different scenarios, which is also an excellent idea for improving the data imputation performance. In light of, we contrive to design a GANs based transferable traffic data imputation model, namely Multi Domain Generative Adversarial Transfer Learning Network (MDTGAN). Firstly, the model consists of two stages. In pre-training stage, the model utilizes the source domain datasets (Similar cities road network datasets) to update parameters and transferred them. Then, the model parameters are optimized using the target domain dataset (Target city road network dataset) in fine-tuning stage to avoid the issue of insufficient samples caused by data missing. Secondly, to adapt the model to different dataset topologies, MDTGAN models the node-level data by taking node time series as input, enabling the model to autonomously learn the prevalent spatiotemporal correlations inherent in nodes. Additionally, we introduce a domain discriminator module that guides the spatial encoder in learning domain-invariant features of network node spatial information, enhancing the model generalization ability. The experiments conducted on three publicly available datasets, in which one dataset is regarded as the target dataset, while the others serve as source datasets. The experimental results demonstrate that the MDTGAN model consistently outperforms the baseline models. Specifically, the MDTGAN model can transfer valuable knowledge from the source domain datasets to improve data imputation performance on the target domain dataset, providing practical significance for cities lacking historical traffic data.

Study of the correspondences distribution of vehicle traffic on the road network of cities

The problems of traffic management in large cities are complicated as the increase in traffic volume far exceeds the road network capacity. It leads to saturation of the road network, which negatively affects its functioning. This article analyzes the state of the art of improving the quality of road traffic by predicting the size of traffic after the implementation of traffic management measures. While the issue of modeling traffic flow parameters based on technological factors has been sufficiently studied, the problems of considering the human factor need to be clarified. The object of study is traffic flow on the city's road network. The criteria used by drivers to compare the characteristics of alternative traffic routes were identified based on the field study results. Based on the data obtained, the parameters of alternative routes that drivers choose when driving on the road network are formed. According to the survey results, the most significant factor is the minimum mileage along the route. Deviations from the shortest route were determined to determine the patterns of distribution of correspondence of non-route vehicles along alternative routes. It made it possible to define the distribution of transport correspondence along alternative routes. Since the process under consideration is probabilistic, the law of distribution of a random variable was determined. After having determined the law of distribution of a random variable for the data obtained, the calculations showed that the change in the random variable is well described by the gamma distribution. It was determined that the share of transport correspondence that will be carried on them decreases as the length of the route deviates from the shortest one. The obtained results make it possible to predict road network congestion by modeling the distribution of traffic flows. In further research, it would be advisable to analyze the distribution of transport correspondence by other criteria.

Organization of parking space in the central part of Volgograd

An increase in the level of motorization of the population, changes in the density of residential and commercial and office buildings, the functional use of buildings and structures in areas adjacent to urban roads contribute to an increase in the intensity of motor transport and require measures to increase the capacity of the city's road network. Unauthorized parking of personal vehicles on the carriageway of streets and roads leads to a decrease in capacity, and during rush hour to an increase in accidents, which largely necessitates the organization of paid parking space, especially in the central areas of large cities. The authors analyzed the methods of calculating the cost of using paid parking in the cities of the Russian Federation, and justified the feasibility of using the methodology to calculate the cost of using paid parking in the central part of the city. Volgograd based on a comparison with an alternative tariff calculation method based on calculating the cost of creating and maintaining paid parking lots and existing tariffs for parking in large cities of the Russian Federation. The justification of the required number of parking spaces and the minimum fee for parking vehicles, depending on the size of the cars, the axial load of vehicles, the intensity of vehicle traffic in parking areas, has been carried out, the need for parking spaces has been determined. The proposed methodology is quite simple and understandable to users of highways.

METHODS OF TRANSPORT MODELLING AS RESEARCH TOOLS IN CREATING TRAFFIC SAFETY MODELS IN URBAN AREAS

In a modern city with heavy traffic and increasing complexity of transportation systems, the issue of traffic planning to minimize the number of road accidents becomes particularly acute. Transport modelling is one of the tools used in the research and development of traffic models in urban areas. By using transport modelling, it is possible to identify potential hazards and evaluate the effectiveness of proposed safety measures that reduce accident risks and improve traffic safety in urban planning. Existing transport modelling methods, including microscopic, mesoscopic, macroscopic, dynamic, and static, have been examined. An analysis of the main characteristics of each method was conducted, along with the software that supports their implementation, evaluating the advantages and limitations of each method as research tools in creating traffic safety models in urban areas. As a result of the study, the simulation micromodelling method was proposed for assessing safety levels when analyzing existing solutions or during the design phase of new elements of the street and road network in cities. Key factors have been identified that justify the use of micromodelling when forming a traffic safety model. Based on the research, it was found that for assessing the safety level of existing solutions for street and road network elements and during the development of a traffic safety model in urban areas, the simulation micromodelling method is the most appropriate. At the same time, applying a set of measures to adapt the chosen implementation of this method to a specific type of street and road network element significantly improves the accuracy of traffic safety assessment results in cities.

Road Network Traffic Flow Prediction Method Based on Graph Attention Networks

With the acceleration of urbanization and the continuous growth of transportation demand, the traffic management of smart city road networks has become increasingly complex and critical. Traffic flow prediction, as an important component of smart transportation systems, is of great significance for optimizing traffic planning and improving traffic efficiency. The study collected and preprocessed traffic data in the smart city road network, including multi-dimensional information such as traffic flow, road conditions, and meteorological data. Then, based on the idea of graph neural networks, we constructed the topological structure of the urban road network and abstracted elements such as roads and intersections into nodes, using edges to represent their connection relationships, thus forming a graph dataset. Next, we introduced an attention mechanism to extract more representative node features through the weighted aggregation of node features, thereby achieving effective modeling of urban road network traffic flow. During the model training phase, we used real traffic datasets for experimental verification and integrated various information such as time, space, and road features into the model. The experimental results show that compared to traditional methods, this research prediction method has achieved better performance in traffic flow prediction tasks, with higher prediction accuracy and robustness. It has stronger applicability and effectiveness in different traffic scenarios. By integrating multi-dimensional information and introducing attention mechanisms, this method has significant advantages in improving the accuracy and robustness of traffic flow prediction, and has important practical significance and application prospects for the construction of smart transportation systems and the development of smart cities.

Resilience assessment and optimization method of city road network in the post-earthquake emergency period

Resilience assessment and optimization method of city road network in the post-earthquake emergency period

Co-evolutionary traffic signal control using reinforcement learning for road networks under stochastic capacity

A co-evolutionary traffic signal control using reinforcement learning approach (CORLA) is proposed for time-varying road networks under stochastic capacity. Classic reinforcement learning based traffic signal control cannot effectively reduce traffic congestion for large-scale road networks while standard evolutionary metaheuristics often suffer from significantly high computational cost. A co-evolutionary decomposition algorithm (CODA) is proposed to improve traffic mobility for urban road networks with time-varying traffic flow. To capture time-varying spatial evolution of traffic flow inside road links, a stochastic traffic model is presented. To fully consider road users’ response, a stochastic bi-level optimization problem (SBOP) is given where road users’ route choice can be fully taken into account. To efficiently implement CORLA in a large-scale road network against high-consequence realization for stochastic capacity, a coordinated co-evolutionary two phase control (CCTPC) is proposed. Numerical experiments are performed at a real-data city road network and various sizable traffic grids. As compared to state-of-the-art traffic signal control for various traffic conditions, obtained results showed that CCTPC exhibits sufficient gain of achieving road network performance and suffers from the least computational cost in all cases.

Predicting the Aggregate Mobility of a Vehicle Fleet within a City Graph

Predicting vehicle mobility is crucial in domains such as ride-hailing, where the balance between offer and demand is paramount. Since city road networks can be easily represented as graphs, recent works have exploited graph neural networks (GNNs) to produce more accurate predictions on real traffic data. However, a better understanding of the characteristics and limitations of this approach is needed. In this work, we compare several GNN aggregated mobility prediction schemes to a selection of other approaches in a very restricted and controlled simulation scenario. The city graph employed represents roads as directed edges and road intersections as nodes. Individual vehicle mobility is modeled as transitions between nodes in the graph. A time series of aggregated mobility is computed by counting vehicles in each node at any given time. Three main approaches are employed to construct the aggregated mobility predictors. First, the behavior of the moving individuals is assumed to follow a Markov chain (MC) model whose transition matrix is inferred via a least squares estimation procedure; the recurrent application of this MC provides the aggregated mobility prediction values. Second, a multilayer perceptron (MLP) is trained so that—given the node occupation at a given time—it can recursively provide predictions for the next values of the time series. Third, we train a GNN (according to the city graph) with the time series data via a supervised learning formulation that computes—through an embedding construction for each node in the graph—the aggregated mobility predictions. Some mobility patterns are simulated in the city to generate different time series for testing purposes. The proposed schemes are comparatively assessed compared to different baseline prediction procedures. The comparison illustrates several limitations of the GNN approaches in the selected scenario and uncovers future lines of investigation.

Multi-scenario location-allocation in decision-making for improving educational facility services in Kupang City

Regional development is inseparable from the support of education facilities. Spatial disparities in education facilities' service scales occur unevenly in the Kupang city administration. Overlaying several thematic maps with Location-Allocation analysis provides a development scenario for placing education facilities. Secondary data in the form of the distribution of existing educational facilities, consisting of elementary schools, junior high schools , and senior high schools derived from Kupang City's Regional Spatial Plan (RTRW) data, road network and building distribution obtained based on Open Street Map (OSM) publications. Comparing the overall parameters, it can be recommended that the most efficient scenario that has the least positive impact and the least negative impact is the scenario with ¼ of the optimal number of new facilities for elementary and junior high schools, while for senior high schools there needs to be further consideration between the scenario with the optimal number and the scenario with ¼ of the optimal number of new facilities in Kupang City.

CoTEV: Trustworthy and Cooperative Task Execution in Internet of Vehicles

Due to the increasing number of service requests from the vehicles, the load at the road side units (RSUs) increases, which affects the delay-sensitive vehicle services. In Internet of Vehicles (IoV), the vehicles can communicate directly with other vehicles and take help from the vehicles to cooperatively accomplish a task. However, it is very challenging to cooperatively execute a task in an IoV environment with high traffic and dynamic vehicle movements. Furthermore, it is difficult for a task vehicle to choose trustworthy and cooperative vehicles. In this paper, we propose algorithms for cooperative task execution by taking the help of trusted vehicles, when it is not possible to complete a deadline-specified task through the RSUs. We propose a hedonic coalition formation game-based approach to form distributed coalitions of cooperative vehicles. We consider the trust score of the vehicles along with their computational capabilities and journey routes. After each task execution, the service feedback is reflected in the trust score of each cooperative vehicle in the coalition. Our proposed algorithms allow the cooperative vehicles to autonomously choose the coalitions and select a vehicle task to maximize their payoffs. To satisfy the task deadlines in multiple coalitions, we design the merging of vehicle coalitions. We consider the simulation of urban mobility (SUMO) tool to generate the mobility traces of the vehicles in a real road network of Berlin city, which considers the traffic junctions and vehicle density on the roads. Through extensive simulations, we show that the proposed algorithms significantly increase the service rate of delay-sensitive task requests by at least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$30.5 \%$</tex-math></inline-formula> and the trust score by at least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$20.61 \%$</tex-math></inline-formula> , compared to the benchmark schemes.