Traffic Network Design Research Articles

Two-dimensional network scene design for traffic simulation: An approach using computational Delaunay triangulation

Traffic network design is a pivotal aspect of urban planning, necessitating a harmonious blend of theoretical and practical approaches. In this paper, we propose a novel network design methodology utilizing Delaunay triangulation. We address the limitations of raw Delaunay networks, particularly their triangular configurations, by introducing edge-reduction techniques guided by a newly developed metric, detour redundancy. This metric is instrumental in assessing edge importance, facilitating the transformation of the network to better mirror real-world traffic scenarios. The efficacy and practical application of the designed networks are then evaluated through traffic simulations using the principles of cellular automata. Our findings underscore the potential of this approach in enriching traffic network design and pave the way for future investigations into diverse network characteristics and their implications in urban environments.

Security and Privacy for Smart Transportation Management using Big Data Analytics

Security and privacy are vital aspects of smart transportation management with big data analytics because they assure the security of sensitive information, prevent unwanted access to essential systems, and retain public trust in the safety and dependability of the transportation infrastructure. Protecting data from cyber threats, ensuring secure communication and data transmission, protecting passengers' personal information and addressing privacy concerns related to data collection and usage to maintain transparency and accountability in data handling practices are all obstacles to smart transportation management using big data analytics. This paper proposes a Secure Data Encryption Control based Big Data Framework (SDEC-BDF) to strike a middle ground between data analytics and privacy protection, establishing the way for more private and secure transportation systems that benefit everyone involved. The intention of this approach is to offer strong security while simultaneously safeguarding people's privacy. The method has many potential uses in the Intelligent transportation sector (ITS), including traffic control, passenger security, fleet management, preventative maintenance, and road network design. It ensures privacy and security while facilitating effective data analysis. Furthermore, it protects the public confidence in the security and dependability of the transportation system, protects sensitive passenger data, and stops hackers from breaking into vital systems. The simulation analysis is conducted on the assumption that the system can maximize its security, privacy, and efficiency to create a more trustworthy transportation network.

Metamodel-Based Optimization Method for Traffic Network Signal Design under Stochastic Demand

Traffic network design problems (NDPs) play an important role in urban planning. Since there exist uncertainties in the real urban traffic network, neglecting the uncertainty factors may lead to unreasonable decisions. This paper considers the transportation network signal design problem under stochastic origin-destination (OD) demand. In general, solving this stochastic problem requires a large amount of computational budget to calculate the equilibrium flow corresponding to a certain demand distribution, which limits its real applications. To reduce the computational time in calculating the equilibrium flow under stochastic demand, this paper proposes a metamodel-based optimization method. First, a combined metamodel that integrates a physical modeling part and a model bias generic part is developed. The metamodel is used to approximate the time-consuming average equilibrium flow solution process, hence to improve the computational efficiency. To further improve the convergence and the solution optimality performance of the metamodel-based optimization, the gradient information of traffic flow with respect to the signal plan is incorporated in the optimization model. A gradient-based metamodel algorithm is then proposed. In the numerical example, a six-node test network is used to examine the proposed metamodel-based optimization method. The proposed combined metamodel is compared with the benchmark method to investigate the importance of incorporating a model bias generic part and the traffic flow gradient information in the combined metamodel. Although there is a reduction in solution optimality since the metamodel is an approximation of the original model, the metamodel methods greatly improve the computational efficiency (the computational time is reduced by 4.84 to 13.47 times in the cases of different initial points). By incorporating the model bias, the combined metamodel can better approximate the original optimal solution. Moreover, incorporating the gradient information of the traffic flow in the optimization search algorithm can further improve the solution performance. Numerical results show that the gradient-based metamodel method can effectively improve the computation efficiency while slightly reducing the solution optimality (with an increase of 0.09% in the expected total travel cost).

Replacement of signalized traffic network design with Hamiltonian roads: delay? Nevermind

Signal optimisation is essential in traffic engineering. The traffic light control timings should be set as optimum. However, it is challenging because the traffic network is a non-polynomial problem. In this study, the problem is evaluated from a unique perspective. The primary idea is to remove the crossings of the intersections. A Hamiltonian cycle algorithm has been used to design the network. So, vehicles are only able to join or split. Thus, no control mechanism is needed that delays or interrupts the flow. The suggested algorithm and intersection design were tested on Allsop and Charlesworth’s widely used sample network. Findings were compared with the literature in the form of delay calculated using the Highway Capacity Manual 2010 formula. The suggested network’s delay is calculated to be 98.17% and 95.45% less than the original network and recently published study-based delay, respectively. As a result, Hamiltonian roads seem sustainable in time and fuel consumption and could be used for future designs.

Fast L2 Calibration for Inexact Highway Traffic Flow Systems

Transportation systems need more accurate predictions to further optimize traffic network design with the development and application of autonomous driving technology. In this article, we focus on highway traffic flow systems that are often simulated by the modified Greenshields model. However, this model can not perfectly match the true traffic flow due to its underlying simplifications and assumptions, implying that it is inexact. Specifically, some parameters affect the simulation accuracy of the modified Greenshields model, while tuning these parameters to improve the model’s accuracy is called model calibration. The parameters obtained using the L2 calibration have the advantages of high accuracy and small variance for an inexact model. However, the method is calculation intensive, requiring optimization of the integral loss function. Since traffic flow data are often massive, this paper proposes a fast L2 calibration framework to calibrate the modified Greenshields model. Specifically, the suggested method selects a sub-design containing more information on the calibration parameters, and then the empirical loss function obtained from the optimal sub-design is utilized to approximate the integral loss function. A case study highlights that the proposed method preserves the advantages of L2 calibration and significantly reduces the running time.

Impact of Participatory Community Planning on Publicity in Public Space Renewal Based on Machine Learning Algorithm Based on Child-friendly Concept

In the existing community planning and implementation, the design concept often does not take into account the needs of children, but only blindly pursues standardization and modernization. This would make the entire community structure unfriendly to children. Factors such as insufficient public play areas, messy traffic network design, and lack of recreational facilities suitable for children would limit children’s fun in the community’s public spaces. Children’s happy growth would be greatly hindered. In order to solve the above problems, this paper has put forward the child-friendly concept and has applied it to community planning, so as to carry out scientific research on the original participatory community planning model based on the support vector machine technology under the machine learning algorithm. And the improved gray wolf optimization algorithm is combined with it for the shortcomings of the technology’s algorithm accuracy and other performance, which is not good enough. The final optimization algorithm has played an extremely important role in testing the impact of the participatory community planning model on publicity in the renewal of public spaces. The experimental results have shown that the highest accuracy of the improved gray wolf optimization support vector machine technology can reach 97.88% and the highest Kappa coefficient can reach 96.83%, which greatly improves the feasibility of the research on the impact of public space publicity.

Behavior analysis of travel route selection with consideration of risk aversion

In order to study the effect of travelers' attitudes on route selection behavior when risk aversion is considered in intelligent transportation system, this study provides a methodology about multi-attribute route selection for travelers with different characteristics. A multi-objective decision-making model of route selection with consideration of risk aversion is established by integrating the cumulative prospect theory to describe the traveler's risk aversion attitude and the solution algorithm based on the assignment model. A case in practice verifies the rationality of the model. The result shows that traveler's risk aversion has a significant impact on the route selection result. The result of path selection varies with the traveler's different composite impedance and comprehensive influence weight. The target sensitivity with consideration of risk aversion is stronger than that without consideration of risk aversion, and the consideration of risk aversion is closer to the actual situation of route selection. The proposed travel route selection decision-making model which considers risk avoidance can be used to study the problem of multi-type travelers and multi-attribute route selection in intelligent transportation system, also can be used as the basis for the study of traffic problems such as traffic flow allocation and network design.

The influence of average speed ratio on multilayer traffic network

In traffic network design, operation and management, operation speed is one of the key indicators. However, only a few studies are based on traffic dynamics on networks with the consideration of different operation speeds, and less reflected the influence of multilayer networks, which illustrate and represent different appropriate urban traffic modes. In this paper, we constructed a multilayer traffic network model with time weighted adjacency matrix. Then with the study of the influence of different network operation speed ratios on the corresponding characteristics of the comprehensive traffic network, we can clearly know that the change of network operation speed ratio will have very obvious impact on the travel time between nodes, the average travel times, the cooperation strength, the accessibility, and the network community numbers. This research has connected the rencent related study of multilayer traffic network and network operation speeds, the findings of the study afford to improve the current design and operation of traffic integrated models, and can provide some suggestions for urban and urban agglomeration development.

Collision Risk Modeling and Analysis for Lateral Separation to Support Unmanned Traffic Management.

This article investigates the utility and extension of well-established collision risk modeling approaches for lateral separation standard development for unmanned traffic management and network design. The applicability of standard assumptions and simplifications made in the manned environment to a scaled-down unmanned aerial systems (UAS) environment is first investigated. The results are used to derive an iso-risk surface that shows the tradeoff between separation distance and navigation performance for a given risk level or target level of safety. The model is then extended to consider collision risk from one to tracks, and finally to determine the total collision risk and the related iso-risk surfaces for systems of parallel tracks with additions to account for the effects of more than two parallel tracks. The extended model is applied to a case study where airspace design for an urban area is conducted via maximization of the number of same-direction parallel tracks while meeting the target level of safety. The results suggest that lateral separation distances less than 100 m are achievable for small UAS, and that the separation standards are mostly affected by the proportion of poorly navigatingaircraft.

Jeddah City Ride-hailing Company Bus Routes Planning by Using Traffic Neural Network Design

This study aims to discover the relationship between neural systems and their use and nature in optimizing public transportation in real life. As neural networking system in the human body represents a smooth delivery and reception of data between the neural joints, in similarity to public transportation delivering public to multiple points/zones. Today's simulation systems depend on simulating real life to help us find an optimized solution but never the ultimate optimization. Applying an intelligent design like the human neural system into real life will deliver an ultimate solution to such a problem. Applying a neural networking system to Jeddah's traffic platform allowed us to reach an optimized map/network for the public transportation services with clear stations/stops numbers and real-life path/lane to be followed.

Mining Route Set Distribution Range and Affecting Factor Threshold Based on Global Positioning System Data

Traditional route generation algorithms may result in many routes that few drivers choose in reality, whereas other route generation algorithms need to determine thresholds for route set generation but lack data support. To avoid invalid route generation, reduce computation time, and provide a scientific basis for the generation of navigation routes and traffic assignment, this paper confines the route set size by mining the spatial distribution range of route sets and the threshold of factors affecting route set generation. Global positioning system data are used to determine hotspots based on a hotspot origin–destination identification method. The route set spatial distribution range is mined by the standard deviational ellipse. Finally, the factors affecting the generation of route sets are selected, and the classification and regression trees algorithm is used to mine their thresholds. The results show that the spatial distribution range of route sets is elliptical, and the threshold values of the number of turns per kilometer for medium and long travel distances are 1.794 and 2.508, respectively. The maximum travel time per kilometer for long travel distance is 4.773 min. The maximum numbers of road intersections for short, medium, and long travel distance per kilometer are 1.648, 0.984, and 0.592, respectively. The implications of results on reducing the search range and time of the route set and their applications to traffic network design and route navigation are also discussed.

How realistic is static traffic assignment? Analyzing automatic number-plate recognition data and image processing of real-time traffic maps for investigation

Travel demand information in the form of the origin–destination (OD) matrix plays an essential role in studying urban traffic management and network design. The present study takes a novel step toward urban traffic analysis using data mining of processed images of real-time traffic maps as a location-based data model, in which the data were analyzed by software programs such as KNIME and Python workspaces and comparing the results with the conventional traffic assignment results. Thus, we investigated a real-time OD matrix based on the trip-per-vehicle by automatic number-plate recognition (ANPR) cameras for the congestion charge zone (CCZ) of Tehran, Iran. The obtained matrix was assigned to the CCZ transportation network by the convex combination method concerning user equilibrium (UE) condition. The traffic pattern and assignment results were compared to the real traffic data gathered by ANPR, big data analysis and image processing of real-time traffic maps. Considering that the OD based on the trip-vehicle matrix was estimated for vehicle entrances-exits and found to be acceptably accurate compared to real-life conditions, it could be concluded that the UE could not find the practical assignment in 27% of cases in comparison with reality.

Sky Highway Design for Dense Traffic

The number of Unmanned Aerial Vehicles (UAVs) continues to explode. Within the total spectrum of Unmanned Aircraft System (UAS) operations, Urban Air Mobility (UAM) is also on the way. Dense air traffic is getting ever closer to us. Current research either focuses on traffic network design and route design for safety purpose or swarm control in open airspace to contain large volume of UAVs. In order to achieve a tradeoff between safety and volumes of UAVs, a sky highway with its basic operation for Vertical Take-Off and Landing (VTOL) UAV is proposed, where traffic network, route and swarm control design are all considered. In the sky highway, each UAV will have its route, and an airway like a highway road can allow many UAVs to perform free flight simutaneously. The geometrical structure of the proposed sky highway with corresponding flight modes to support dense traffic is studied briefly one by one. The effectiveness of the proposed sky highway is shown by the given demonstration.

Research on Road Traffic Network Design Model and Algorithm Considering Traffic Fairness

Urban transportation is an important part of urban economic structure, which plays an important role in promoting the orderly development of society, residents' work and life. This paper analyzes the fairness in traffic network design problem, constructs the average network improvement index to measure fairness, and establishes a bi-level programming model for road traffic network design considering traffic fairness. The upper model includes the optimal total travel time and traffic fairness, and the lower model is the traffic flow allocation model. Then, according to the characteristics of the model, a genetic algorithm is designed to solve the model. Finally, a simple example is given to illustrate the effectiveness of the mathematical model and algorithm. The model proposed in this paper can get the final road design scheme satisfying the total travel time and traffic fairness of the road network under certain road network conditions, and can provide decision support for traffic planners.

A New Model for Locating Plate Recognition Devices to Minimize the Impact of the Uncertain Knowledge of the Routes on Traffic Estimation Results

A number of research papers have recently shown that the use of techniques based on the installation of vehicle identification devices allows us to address the observability problem of a traffic network in a much more efficient way than if it were done with traditional techniques. The use of such devices can lead to a better data set in terms of flows and therefore to a better definition of traffic flows, which is essential for traffic management in cities and regions. However, the current methodologies aimed at network modeling and data processing which are not fully adapted to the use of these devices in obtaining the necessary data for analyzing traffic and making network forecasts. This is because the essential variable in models which used data from plate scanning (as a particular case of AVI sensors) is composed of the route flows, while traditional methods are based on the observation of link and/or origin-destination flows. In this context, this paper proposes several practical contributions, in particular: (1) a traffic network design method aimed to use the plate scanning data to estimate traffic flows and (2) an algorithm for locating plate reader devices to reduce the effect of the uncertain knowledge of route enumeration. Next, using the well-known Nguyen-Dupuis network, a sensitivity analysis has been carried out to evaluate the influence of different parameters of the model on the final solution. These parameters are the considered routes, the degree of network simplification, and the available budget to install devices. Finally, the method has been applied to a real network.

Deployment Optimization of Connected and Automated Vehicle Lanes with the Safety Benefits on Roadway Networks

Reasonable deployment of connected and automated vehicle (CAV) lanes which separating the heterogeneous traffic flow consisting of both CAVs and human-driven vehicles (HVs) can not only improve traffic safety but also greatly improve the overall roadway efficiency. This paper simplified CAV lane deployment plan into the problem of traffic network design and proposed a comprehensive decision-making method for CAV lane deployment plan. Based on the traffic equilibrium theory, this method aims to reduce the travel cost of the traffic network and the management cost of CAV lanes using a bilevel primary-secondary programming model. In addition, the upper level is the decision-making scheme of the lane deployment, while the lower level is the traffic assignment model including CAV and HV modes based on the decision-making scheme of the upper level. After that, a genetic algorithm was designed to solve the model. Finally, a medium-scaled traffic network was selected to verify the effectiveness of the proposed model and algorithm. The case study shows that the proposed method obtained a feasible scheme for lane deployment considering from both the system travel cost and management cost of CAV lanes. In addition, a sensitivity analysis of the market penetration rate of CAVs, traffic demand, and the capacity of CAVLs further proves the applicability of this model, which can achieve better allocation of system resources and also improve the traffic efficiency.

Data traffic capability of double-layer network based on coupling strength

The two-layer network model offers us a new viewpoint to observe the traffic dynamics of multilayer network systems. An efficient coupling mechanism is of great importance for alleviating the traffic congestion on two-layer networks. In order to reduce the network congestion and improve network transmission performance, the coupling mechanism between two layers of network and three coupling methods, which are random coupling, disassortative coupling and assortative coupling, are studied based on degree correlation. The packet transmission process is analyzed with both the shortest path routing strategy and degree-based weight routing strategy. The influences of the coupling mode and its corresponding routing strategy on the traffic capacity of the two-layer network are studied. In this paper, two scale-free networks are used to construct the two-layer network for simulation experiments. The network scale is in a range from 200 to 2400 with the value of average degree being 8. We focus on the traffic dynamics of two-layer network, and analyze the relationship between the traffic capacity and the three coupling modes, which are random coupling, disassortative coupling and assortative coupling, under the constraints of the shortest path routing strategy and the weight-based routing strategy. According to the characteristics of the coupling connection between the two layers of network, the best coupling method which is suitable for a certain routing strategy should be investigated. The suitable coupling connection between the two layers can effectively increase the traffic capacity. Both numerical result and analytical result show that the packet generation rate, average transmission time, and average throughput can be obviously improved under the shortest path routing strategy with the disassortative coupling method. When the degree-based static weight routing strategy is used, the traffic performance parameters such as packet generation rate, average transmission time, and average throughput can reach the optimal values with the assortative coupling method. It makes the traffic flow uniform that the routing strategy is chosen with the most suitable coupling method on the two-layer network, and the network traffic capacity may be effectively enhanced. More generally, the results indicate that the coupling modes can give rise to traffic behavior that relies subtly on the routing strategy on the two-layer network. Our work may shed some light on the design and optimization of some real traffic or communication networks.

Real-virtual consistent traffic flow interaction

Traffic simulation has become an efficient tool, with the assistance of computer visualizing techniques, to solve traffic issues such as traffic congestion, network design, and similar problems. Properly controlling simulated traffic flow and modeling each vehicle’s irregular behaviors are key issues in the traffic simulation field. In this paper, we introduce real vehicle trajectories as a data-driven factor in simulated traffic situations to drive behaviors of other simulated vehicles. First, we train a driving model for each simulated vehicle using real traffic data that have a unique control strategy. Then, we fuse real trajectories driven vehicles with simulated trajectories driven vehicles to interact, guided by our learned traffic model, to accurately depict the reality of traffic flows. Compared with existing methods, traffic flows simulated using this method are more realistic and can preserve irregular characteristics of the real traffic flows.

Fuzzy System and Time Window Applied to Traffic Service Network Problems under a Multi-Demand Random Network

The transportation network promotes key human development links such as social production, population movement and resource exchange. As cities continue to expand, transportation networks become increasingly complex. A bad traffic network design will affect the quality of urban development and cause regional economic losses. How to plan transportation routes and allocate transportation resources is an important issue in today’s society. This study uses the network reliability method to solve traffic network problems. Network reliability refers to the probability of a successful connection between the source and sink nodes in the network. There are many systems in the world that use network architecture; therefore, network reliability is widely used in various practical problems and cases. In the past, some scholars have used network reliability to solve traffic service network problems. However, the processing of time is not detailed enough to fully express the real user’s time requirements and does not consider that the route traffic will affect the reliability of the entire network. This study improves on past network reliability methods by using a fuzzy system and a time window to construct a network model. Using the concept of fuzzy systems, according to past experience, data or expert predictions to define the degree of flow, time and reliability, can also determine the relationship between these factors. The time window can be adjusted according to the time limit in reality, reaching the limit of the complete expression time. In addition, the network reliability algorithm used in this study is a direct algorithm. Compared with the past indirect algorithms, the computation time is greatly reduced and complex problems can be solved more efficiently.

Traffic Road Network Design Using Traffic Simulator

Traffic Road Network Design Using Traffic Simulator