Road Network Model Research Articles

A perimeter control model of urban road network based on cooperative-noncooperative two-stage game

A perimeter control model of urban road network based on cooperative-noncooperative two-stage game

A new approach to estimating link performance on Indonesian urban roads: deriving the BPR 1964 function

Predictions of traffic volumes on road networks must be precise for transportation planning, and equilibrium trip assignment models based on the Volume Delay Function (VDF) are typically employed. In 1964, the U.S. Bureau of Public Roads (BPR) created a VDF widely used in many countries, including Indonesia. However, using capacity manuals from Western nations in Indonesia did not produce the desired results due to differences in traffic composition and driver behaviour. Using the Indonesian Highway Capacity Manual’s macroscopic fundamental diagram, this paper aims to derive a more accurate VDF for Indonesia. This method enables the development of a VDF tailored to the unique traffic conditions in Indonesia. It can enhance the precision of traffic volume forecasts in the region. The BPR function parameters tends to overestimate travel time delay, making the road network model flow misestimated. This is presumably caused by traffic count survey inability to record the build-up of traffic flow in the urban network during peak traffic periods.

A similarity calculation model of road network and its application in map generalization quality evaluation

ABSTRACT As an important part of maps, road networks have always been the focus of research in map generalization. However, the fully automated quality evaluation of road network generalization has not been implemented. This paper proposes a framework of Sample-Map database to evaluate road network generalization quality using spatial similarity measures. The similarity calculation model considers spatial distribution, topological relations, and distance relations of road networks. A Sample-Maps database is created as an evaluation set for different road network types. The method is tested on multi-scale maps from Shenzhen, China, and validated by psychological cognition experiments and professional cartographers’ judgment.

Recovery Model and Maintenance Optimization for Urban Road Networks with Congestion

Urban road networks have promoted high-quality travel for residents by increasing connectivity and intelligence. But road congestion has not been effectively alleviated, causing a loss of time and energy. At present, the recovery of urban road networks mainly considers removing the failed edges. Considering the recovery cost and time, it is important to take active maintenance behavior to restore these networks. One of the key problems is dispatching traffic workers reasonably to achieve timely maintenance. In this paper, a flow-distribution-based process and execution (FD-PE) model is established for solving congestion. The maintenance centers (MC) study the reasons for and spread of congestion by edge flow. Based on the genetic algorithm (GA), two models of maintenance for urban road networks are developed, which include a single MC-centered dispatching plan and the co-scheduling of MCs. Both models aim at minimizing recovery time and allocating maintenance resources. The road network in Zhengzhou is borrowed as a case to explain the feasibility of the proposed models. The results show that on the premise of dividing network regions, it is reasonable to take a single MC to recover congestion. Compared with a single MC, the co-scheduling of MCs may save more time.

Optimal dispatch of hydrogen/electric vehicle charging station based on charging decision prediction

The hydrogen/electric vehicle charging station (HEVCS) is widely regarded as a highly attractive system for facilitating the popularity of hydrogen and electric vehicles in the future. However, conventional optimal dispatch of HEVCS could lead to poor performance due to the lack of adequate consideration of vehicle charging decision behaviours and neglection of the impacts of different information sources on it. This paper investigates a charging demand prediction method that considers multi-source information and proposes a multi-objective optimal dispatching strategy of HEVCS. First, an information interaction framework of integrated road network, vehicles and HEVCS is introduced. Road network model and HEVCS model are established based on the proposed framework. To improve the flexibility of dispatch, two charging modes are designed, which are intended to guide drivers to adjust their consumption behaviour by electricity price incentives. Furthermore, psychologically based hybrid utility-regret decision model and Weber-Fechner (W–F) stimulus model are developed to reasonably predict drivers' choice of charging stations and charging modes. The daily revenue of HEVCS and the total queuing time of drivers are the objective functions considered in this paper simultaneously. The above multi-objective optimization results that the proposed strategy can effectively improve the benefits of HEVCS and reduce energy waste. Additionally, this paper discusses the results of a sensitivity analysis conducted by varying incentive discount, which reveals the combined benefits of the HEVCS and the vehicles are effectively increased by setting reasonable incentive discounts.

Accelerated and Refined Lane-Level Route-Planning Method Based on a New Road Network Model for Autonomous Vehicle Navigation

Lane-level route planning is a critical issue for a lane-level navigation system for autonomous vehicles. Current route-planning methods mainly focus on the road level and applying them directly to search for lane-level routes results in a reduction in search efficiency. In addition, previously developed lane-level methods lack consideration for vehicle characteristics and adaptability to multiple road network structures. To solve this issue, this study proposes an accelerated and refined lane-level route-planning algorithm based on a new lane-level road network model. First, five sub-layers are designed to refine the internal structure of the divided road and intersection areas so that the model can express multiple variations in road network structures. Then, a multi-level route-planning algorithm is designed for sequential planning at the road level, lane group level, lane section level, and lane level to reduce the search space and significantly improve routing efficiency. Last, an optimal lane determination algorithm considering traffic rules, vehicle characteristics, and optimization objectives is developed at the lane level to find the optimal lanes on roads with different configurations, including those with a constant or variable number of lanes while satisfying traffic rules and vehicle characteristics. Tests were performed on simulated road networks and a real road network. The results demonstrate the algorithm’s better adaptability to changing road network structures and vehicle characteristics compared with past hierarchical route planning, and its higher efficiency compared with direct route planning, past hierarchical route planning, and the Apollo route-planning method, which can better support autonomous vehicle navigation.

Collaborated eco-routing optimization for continuous traffic flow based on energy consumption difference of multiple vehicles

Transportation accounts for a large proportion of energy consumption and environmental pollution, and eco-routing is recognized as a potential solution to green mobility. In this context, this study investigates the co-optimization problem of eco-routing on a road network for heterogeneous continuous vehicle flow. Firstly, the energy consumption estimation models for 33 types of vehicles are constructed by artificial neural networks with a large amount of historical driving data. In this case, the Bureau of Public Roads function and traffic light models are imported to establish the road network model, accurately reflecting the impact of congestion and traffic lights change on vehicle speeds. Finally, based on the energy consumption difference of different vehicles, a collaborative heterogeneous multi-vehicle eco-routing optimization strategy is proposed to improve the overall economy in the road network. Simulation experiments are conducted under different traffic flow conditions and multiple road networks. The results verify that an energy-saving improvement up to 11.50% is obtained compared with the conventional path planning approach, providing efficient promotions to the energy consumption reduction of connected and automated vehicles.

Electric vehicle navigation and cluster dispatch for reliable low-carbon traffic–Power systems

Large-scale integration of electric vehicles (EVs) into the city system for charging will affect the operation of both traffic and distribution networks. An electric vehicle navigation and cluster dispatch model is proposed for improving the overall charging efficiency of EVs on the transportation network and increasing the voltage level of the distribution network. First, a simplified model of vehicles and traffic road network is established, and a cell transmission model (CTM) is used to simulate the real traffic network. The traffic system takes into account charging EVs, discharging EVs, and other vehicles, and traffic congestion is considered. Then, a coupled model of the traffic–power system is built for the orderly charging of electric vehicles upon arrival at a charging station. The model considers the coupling of the two systems on a time scale, and the charging/discharging power at each charging station node is controlled. The validity of the model is verified in a coupled system of 357 cell traffic network and modified IEEE33 nodes. The results show that the proposed model can ensure good guarantee of the distribution network voltage reliability and reveal the scheduling process of the traffic network. The proposed model also provides a reference for planning of charging stations in the distribution network.

Dynamic Weighted Road Network Based Multi-Vehicles Navigation and Evacuation

Many events such as large-scale activities and traffic accidents could cause an increase in vehicle density in an area, which makes the evacuation of vehicles important. However, the existing evacuation methods are not efficient limit to multi-vehicles sequences or destinations. In this paper, we introduce a novel dynamic weighted road network model for route planning. Based on the model, the route planning algorithm can obtain higher search efficiency while avoiding congested roads. For multi-vehicles evacuation, we propose a spatial diversity theory to evaluate the overlaps of routes between vehicles to be evacuated and those already evacuated. To verify the efficiency and effectiveness of our model, we conducted experiments on real road network. The results showed that our methods and algorithms can provide more reasonable paths and manage the process more efficiently.

Optimization of Safety Facilities for Collision Analysis between Motor and Non-Motor Vehicles at Intersections

With the increasing number of the motor and non-motor vehicles, conflicts and safety risks between motor vehicles and non-motor vehicles are increasing. Among them, the collision between right-turning vehicles and non-motor vehicles going straight leads to traffic accidents accounting for up to 50% of the non-accident cases at intersections. The intersection of Erdos East Street and Xing an’ South Road in Hohhot City is selected as the research object. Analyze the conflict between right-turning motor vehicles and non-motor vehicles. The optimized design was carried out from three perspectives: right-turn channelization island design, road deceleration facilities layout, and signal control, and the road network model was built with VISSIM survey data to demonstrate and evaluate the scheme. The results show that when the traffic volume is unchanged, the number of non-collision decreases and the passage time of non-motor vehicles decreases by 4%. The travel time for left-turn vehicles also dropped 16 percent. It shows that the collision between motor vehicles and non-motor vehicles at right-turn intersections is significantly improved through the above optimal design scheme.

A data-based framework for automatic road network generation of multi-modal transport micro-simulation

<abstract> <p>In microscopic traffic simulation, the fidelity of the road network model has a significant impact on the difference between the simulation and the actual urban traffic state. Accurately matching data on the simulated road network and the surroundings has become a central concern in traffic simulation research. This study provides a multi-source data-based framework for automatic road network generation (ARNG) to address the issue of manual procedures in the creation of the simulated road network and surroundings. First, the proposed method of fusion and matching of diverse road network data is used to acquire the basic road network information, and the combining of the features of different road network data can enhance the authenticity of the basic road network. Second, a multi-modal simulation road network is developed based on multi-modal traffic operation data to serve as the simulation operation's foundational environment. To address the requirements of the dynamic evolution of the simulated road network, an editor for the dynamic road network is built based on spatial closest neighbor matching. The case study illustrates the process of building the simulated road network and environment in the old city zone of Suzhou. Real-world examples demonstrate that the data-based ARNG approach provided in this study is highly automatic and scalable.</p> </abstract>

A Three Stage Optimal Scheduling Algorithm for AGV Route Planning Considering Collision Avoidance under Speed Control Strategy

With the trend of terminal automation and the requirement for port operation efficiency to be greatly improved, it is very necessary to optimize the traveling route of automatic guided vehicles (AGV) with reference to the connection of loading and unloading equipment. As a complex multi-equipment system, it is inevitable that AGV will collide when traveling due to various accidents in actual operation, which will lead to AGV locking and reduce the efficiency of terminal operation. Considering the locking problem of AGV, we propose a three-stage integrated scheduling algorithm for AGV route planning. Through joint optimization with quay cranes (QC) and yard blocks, a road network model is established in the front area of the container port to optimize the path of AGV in the road network, and a speed control strategy is proposed to solve the problem of AGV collision avoidance. In the first stage, we establish the AGV optimal route model with the goal of minimizing the AGV path according to the AGV road network situation. In the second stage, on the basis of the determination of AGV route planning, and when the container task is known, the AGV task assignment model is established with the goal of minimizing the maximum completion time, and the model is solved by genetic algorithm (GA). In the third stage, on the basis of AGV task assignment and route determination, the AGV route and AGV task assignment scheme are input into the simulation model by establishing the AGV collision avoidance control model for speed control, and establishing the AGV route network simulation model for automated terminals considering collision avoidance in plant simulation software. The maximum completion time obtained from the simulation model is compared with the completion time obtained from the genetic algorithm. The proposed three-stage joint scheduling algorithm can improve the loading and unloading efficiency of the port, reduce the AGV locking situation, and has a certain contribution to the formulation of the actual operation planning of the port.

Risk-Aware Travel Path Planning Algorithm Based on Reinforcement Learning during COVID-19

The outbreak of COVID-19 brought great inconvenience to people’s daily travel. In order to provide people with a path planning scheme that takes into account both safety and travel distance, a risk aversion path planning model in urban traffic scenarios was established through reinforcement learning. We have designed a state and action space of agents in a “point-to-point” way. Moreover, we have extracted the road network model and impedance matrix through SUMO simulation, and have designed a Restricted Reinforcement Learning-Artificial Potential Field (RRL-APF) algorithm, which can optimize the Q-table initialization operation before the agent learning and the action selection strategy during learning. The greedy coefficient is dynamically adjusted through the improved greedy strategy. Finally, according to different scenarios, our algorithm is verified by the road network model and epidemic historical data in the surrounding areas of Xinfadi, Beijing, China, and comparisons are made with common Q-Learning, the Sarsa algorithm and the artificial potential field-based reinforcement learning (RLAFP) algorithm. The results indicate that our algorithm improves convergence speed by 35% on average and the travel distance is reduced by 4.3% on average, while avoiding risk areas, compared with the other three algorithms.

Development of a cyber-physical-system perspective based simulation platform for optimizing connected automated vehicles dedicated lanes

Recently, many studies have shown that during the transition period of the coexistence of autonomous vehicles (AVs)/connected automated vehicles (CAVs) and human-driven vehicles (HDVs), a dedicated lane (DL) for AVs/CAVs can improve traffic throughput and lower collision risk, which has become a hot topic in the field of intelligent transportation systems (ITS). This study presents an integrated simulation platform for DL optimization from cyber-physical-system (CPS) perspective. The developed simulation platform can be used to qualitatively and quantitatively evaluate the design parameters, operation modes, contingency plan, etc. of DL in a “What You See Is What You Get (Wysiwyg)” style. First, we introduce the architecture and features of the proposed platform, which comprises of SUMO, OMNeT++, and Unity3D. We implemented all experimental scripts for DL optimization in SUMO, including the configuration of the DLs, car-following models, lane-changing models, vehicle departure models, origin–destination demands, and 2D visualization of traffic flow simulation. We used OMNeT++ for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. Unity3D is a 3D visualization platform for simulations synchronized with SUMO. Connected with the driving simulator and paired with a virtual reality (VR) helmet, Unity3D can offer a high-fidelity immersive environment for all types of users. We developed a specific communication protocol to achieve data sharing and synchronization of all components of the proposed simulation platform. Second, this paper depicts the functions of the proposed simulation platform in detail, including the modeling of road networks, vehicles, traffic flow, optimal design, and evaluation indicators of DL. Finally, by using the developed simulation platform, we demonstrated and analyzed three cases: (1) without DL vs with DL, (2) inner lane vs outer lane, and (3) without V2I vs with V2I. The experimental results confirm the feasibility of the developed simulation platform, and yield some interesting findings, such as (1) market penetration rates (MPRs) are an important factor in deciding whether to deploy DLs, (2) the number of lane changes when the vehicle enters/leaves the designated lane is the key criterion for determining the placement of the DLs, and (3) the cooperative vehicle-infrastructure system can offer AVs/CAVs with better over-the-horizon perception capabilities, helping to improve the smoothness of traffic flow in the case of abnormal events. The developed simulation platform is expected to offer a new tool for optimal design and evaluation of DLs for AVs/CAvs.

A novel approach for semantic segmentation of automatic road network extractions from remote sensing images by modified UNet

Accurate and up-to-date road maps are crucial for numerous applications such as urban planning, automatic vehicle navigation systems, and traffic monitoring systems. However, even in the high resolutions remote sensing images, the background and roads look similar due to the occlusion of trees and buildings, and it is difficult to accurately segment the road network from complex background images. In this research paper, an algorithm based on deep learning was proposed to segment road networks from remote sensing images. This semantic segmentation algorithm was developed with a modified UNet. Because of the lower availability of remote sensing images for semantic segmentation, the data augmentation method was used. Initially, the semantic segmentation network was trained by a large number of training samples using traditional UNet architecture. After then, the number of training samples is reduced gradually, and measures the performance of a traditional UNet model. This basic UNet model gives better results in the form of accuracy, IOU, DICE score, and visualization of the image for the 362 training samples. The idea here is to simply extract road data from remote sensing images. As a result, unlike traditional UNet, there is no need for a deeper neural network encoder-decoder structure. Hence, the number of convolutional layers in the modified UNet is lower than that in the standard UNet. Therefore, the complexity of the deep learning architecture and the training time required by the road network model was reduced. The model performance measured by the intersection over union (IOU) was 93.71% and the average segmentation time of a single image was 0.28 sec. The results showed that the modified UNet could efficiently segment road networks from remote sensing images with identical backgrounds. It can be used under various situations.

An Attention and Wavelet Based Spatial-Temporal Graph Neural Network for Traffic Flow and Speed Prediction

Traffic flow prediction is essential to the intelligent transportation system (ITS). However, due to the complex spatial-temporal dependence of traffic flow data, it is insufficient in the extraction of local and global spatial-temporal correlations for the previous process on road network and traffic flow modeling. This paper proposes an attention and wavelet-based spatial-temporal graph neural network for traffic flow and speed prediction (STAGWNN). It integrated attention and graph wavelet neural networks to capture local and global spatial information. Meanwhile, we stacked a gated temporal convolutional network (gated TCN) with a temporal attention mechanism to extract the time series information. The experiment was carried out on real public transportation datasets: PEMS-BAY and PEMSD7(M). The comparison results showed that our proposed model outperformed baseline networks on these datasets, which indicated that STAGWNN could better capture the spatial-temporal correlation information.

A Dynamic Road Network Model for Coupling Simulation of Highway Infrastructure Performance and Traffic State

The state of the road network contains both the infrastructure performance and the traffic operation state of the road network. There is a strong coupling between the decay of the infrastructure performance and the redistribution of the traffic flow on the road network. In this paper, a dynamic road network description model is proposed to apply to the couple simulation of highway network infrastructure performance and traffic state. First, a road network description model is constructed by associating the highway network topology with state attributes. The topology contains traffic information and is dynamically editable. Then, a dynamic road network model is proposed that can dynamically represent the changes in local connectivity relationships caused by traffic control, such as lane/ramp closures and turning restrictions in actual roads due to construction operations and access to the state of multi-scale spatio-temporal road networks. It overcomes the defects of the existing road network model, which is difficult to apply to the analysis of service performance and traffic state of the road network in different periods. Finally, the application of the dynamic road network model in the highway network coupled simulation system (HNCS) is completed, which provides a method for improving the efficiency and accuracy of large-scale highway network traffic simulation and highway infrastructure performance prediction.

Land Vehicle Navigation Using Low-Cost Integrated Smartphone GNSS Mems and Map Matching Technique

Abstract The demand for smartphone positioning has grown rapidly due to increased positioning accuracy applications, such as land vehicle navigation systems used for vehicle tracking, emergency assistance, and intelligent transportation systems. The integration between navigation systems is necessary to maintain a reliable solution. High-end inertial sensors are not preferred due to their high cost. Smartphone microelectromechanical systems (MEMS) are attractive due to their small size and low cost; however, they suffer from long-term drift, which highlights the need for additional aiding solutions using road network that can perform efficiently for longer periods. In this research, the performance of the Xiaomi MI 8 smartphone’s single-frequency precise point positioning was tested in kinematic mode using the between-satellite single-difference (BSSD) technique. A Kalman filter algorithm was used to integrate BSSD and inertial navigation system (INS)-based smartphone MEMS. Map matching technique was proposed to assist navigation systems in global navigation satellite system (GNSS)-denied environments, based on the integration of BSSD–INS and road network models applying hidden Marcov model and Viterbi algorithm. The results showed that BSSD–INS–map performed consistently better than BSSD solution and BSSD–INS integration, irrespective of whether simulated outages were added or not. The root mean square error (RMSE) values for 2D horizontal position accuracy when applying BSSD–INS–map integration improved by 29% and 22%, compared to BSSD and BSSD–INS navigation solutions, respectively, with no simulated outages added. The overall average improvement of proposed BSSD–INS–map integration was 91%, 96%, and 98% in 2D horizontal positioning accuracy, compared to BSSD–INS algorithm for six GNSS simulated signal outages with duration of 10, 20, and 30 s, respectively.

Deep reinforcement learning for dynamic incident-responsive traffic information dissemination

This study is concerned with the optimal dynamical information dissemination (DID) problem in a transportation network interrupted by traffic incidents. Optimizing system performance with DID after road incidents is challenging because of the uncertainty in traffic flow variation and travelers’ heterogeneous responses to information. To address the problem, we consider a traffic manager who aims to improve the system performance by dynamically generating and disseminating information to road users in a time period after an incident happens. We develop a decision tool for obtaining DID strategy based on double deep Q-learning (DDQL) for the traffic manager, aiming at finding an optimal DID strategy. The decision tool is integrated with traffic sensors which collect traffic data in real time. With advanced traveler information systems, the DID system dynamically sends out various types of information to users according to the current and anticipated traffic states so as to minimize congestion and enhance road network capacity. In particular, the proposed DDQL method utilizes a double deep Q-network (DQN) structure to learn the state–action values. To test and evaluate the performance of the decision tool, we develop a microscopic simulation model of a real road network in the Serangoon area of Singapore in PTV VISSIM and calibrate the model with real historical traffic data. We train and compare the DDQL controller model with different reward signals, including the weighted sum of the average speed and queue delay, total traffic flow, and average travel time. Numerical experiments demonstrate the good performance of the proposed DDQL-based DID approach in improving the congestion and other performance metrics of the expressway. The robustness and generalizability of the DDQL agent are also verified by evaluating the algorithm performance in environments with different traffic demand patterns and driving behavior profiles.

Microsimulation Modeling of Traffic Congestion at Signal Intersection of Thirumangalam Chennai Metrocity

Background: Generally, Chennai is a hub of traffic congestion, where the major roads of the city are filled with vehicle. It gives the challenging situation to the road and traffic management officials to control and regulate the existing traffic with the existing facilities. Objectives: This paper deals with the microscopic simulation of existing road network model which helps in evaluating the performance of the Thirumangalam road intersection such as delay, queue length and also to evaluate and understand the pedestrian vehicle interaction at the intersection and to propose the improvements of signalized intersection in Thirumangalam at Chennai which is without basic facilities like pedestrian crossing time and it causes heavy congestion during peak hour which makes the management to operate the signals manually. Methods/Findings: PTV VISSIM software is used for evaluation and validation of the road network in order to choose the best alternative and optimization measures before implementation. This study helps to improve the existing junction by suggesting the improvement proposals by referring Indian road congress codes (IRC 106, 86, 103) and it also gives the new optimized signal design which is automated to regulate the traffic. Keywords: Signalized Intersections; Signal Design; Microscopic Simulation; Junction Improvements