Road Network Topology Research Articles

Electric Vehicle Charging Load Prediction Model Considering Traffic Conditions and Temperature

The paper presents a novel charging load prediction model for electric vehicles that takes into account traffic conditions and ambient temperature, which are often overlooked in conventional EV load prediction models. Additionally, the paper investigates the impact of disordered charging on distribution networks. Firstly, the paper creates a traffic road network topology and speed-flow model to accurately simulate the driving status of EVs on real road networks. Next, we calculate the electric vehicle power consumption per unit kilometer by considering the effects of temperature and vehicle speed on electricity consumption. Then, we combine the vehicle’s main parameters to create a single electric vehicle charging model, use the Monte Carlo method to simulate electric vehicle travel behavior and charging, and obtain the spatial and temporal distribution of total charging load. Finally, the actual traffic road network and typical distribution network in northern China are used to analyze charging load forecast estimates for each typical functional area under real vehicle–road circumstances. The results show that the charging load demand in different areas has obvious spatial and temporal distribution characteristics and differences, and traffic conditions and temperature factors have a significant impact on electric vehicle charging load.

Lanczos method for spatio‐temporal graph convolutional networks to forecast expressway flow

AbstractTraffic forecasting has made pronounced progress with the development of graph convolution networks and the use of the topology of road networks. However, existing works face some limitations when it comes to modelling spatial dependencies. For example, pre‐defined graphs rely on global information to establish spatial relationships, and the spatial receptive field is limited by the polynomial convolutional method. To address these limitations, the authors propose the Lanczos method for Spatio‐Temporal Graph Convolutional Networks (LSTGCN); this approach uses the low‐rank approximation theory to drive pre‐defined graphs to collect important information and eliminate spatial redundancy. Additionally, a learnable dynamic graph feature (LDGF) module generates adaptive graphs and perceives the latent invariance‐variability between graph nodes. To further improve the model's ability to capture spatial dependencies and temporal correlations, multi‐span spatial learning is employed for enlarged receptive fields, which can be well integrated into gated recurrent units. The authors conducted baselines comparison and ablation experiments on real‐world datasets, and the findings show that the LSTGCN model outperforms the baselines and improves prediction accuracy. Notably, this work is the first attempt to use graph low‐rank theory for traffic prediction.

A Constraint-Based Routing and Charging Methodology for Battery Electric Vehicles With Deep Reinforcement Learning

Electric vehicle route planning (EVRP) is generated from the collaborative operation of smart grids and intelligent transportation systems. It has become an essential issue for the widespread use of battery electric vehicles. However, the existed EVRP solutions are of either high computational complexity or low efficiency for large-scale problems. Towards such issues, we propose an efficient Deep Reinforcement Learning based methodology for constraint-based routing while simultaneously considering electric vehicles’ charging policies. We design a two-layer model to find near-optimal solutions and handle the broad range of problem instances according to the rewards and the preset feasibility rules. The first layer is to approximate a sequence of consecutive actions in reality and correspondingly produce a minimum time-consuming feasible route without re-training for each new problem instance. The second layer is to generate a charging scheme along the previously generated feasible path. The proposed methodology is independent of the road network topology and the electric vehicles’ types. Besides, the convergence of value function in the presented model for EVRP is studied. The experiment shows that our methodology outperforms the traditional ones in computation time with comparable solution quality. Moreover, the obtained model can be directly applied to treat other problem instances on various road networks without re-training procedures.

Optimisation of collaborative supply transportation based on traffic road network topology

AbstractWith the rapid development of China's economy, enterprises need to plan their logistics transportation routes reasonably in advance. This will make the transportation service more efficient. For the supplier's transportation service problem, an analysis method of critical path nodes is provided and a multi‐supplier collaborative transportation strategy is designed in this article. First, a model for minimising the transportation cost was established, then a path diagram was simulated and the optimal and alternative transportation paths of suppliers based on the k‐shortest path algorithm were calculated. In addition, path node availability during COVID‐19 is used as a research context in this article. A multi‐stage path analysis method was provided by discussing different cases of critical path nodes, which can make a reasonable selection of paths in a timely and effective manner. Finally, simulations of collaborative transportation for suppliers were performed in three scenarios and the results verified the effectiveness of the collaborative transportation strategy. The proposed collaborative transportation strategy of suppliers not only strengthened the synergistic cooperation among suppliers, but also cultivated the potential customer for suppliers in this article. Furthermore, the strategy could improve the flexibility of the supply chain, maximise the overall efficiency and also provide a new solution for the development of logistics and transportation services.

The Influence of Road Network Topology on Street Flooding in New York City—A Social Media Data Approach

The Influence of Road Network Topology on Street Flooding in New York City—A Social Media Data Approach

End-to-end learning of user equilibrium with implicit neural networks

This paper intends to transform the transportation network equilibrium modeling paradigm via an “end-to-end” framework that directly learns travel choice preferences and the equilibrium state from multi-day link flow observations. The centerpiece of the proposed framework is to use deep neural networks to represent travelers’ route choice preferences and then encapsulate the neural networks in a variational inequality that prescribes the user equilibrium flow distribution. The proposed neural network architecture ensures the existence of equilibrium and accommodates future changes in road network topology. The variational inequality is then embedded as an implicit layer in a learning framework, which takes the context features (e.g., road network and traveler characteristics) as input and outputs the user equilibrium flow distribution. By comparing computed equilibrium flows with observed flows, the neural networks can be trained. The proposed end-to-end framework is demonstrated and validated using synthesized data for the Sioux Falls network.

Multi-view dynamic graph convolution neural network for traffic flow prediction

The rapid urbanization and continuous improvement of road traffic equipment result in massive daily production of traffic data. These data contain the long-term evolution of traffic flow and dynamic changes in the traffic road network. Due to the complex topology of the traffic road network, traffic flow prediction is challenging as it contains complex, multi-periodic patterns, and is often affected by sudden events. In this paper, we propose a Multi-View Dynamic Graph Convolution Network (MVDGCN) that captures different levels of spatial–temporal dependencies to predict traffic flow. Firstly, we use the coupling graph convolution network to learn the relationship matrix among stations dynamically, capturing the spatial dependencies at different levels in the traffic network. Secondly, we establish three encoder–decoders, representing hourly, daily, and weekly views, to extract the evolution law of traffic flow from three different time periods. Finally, we use the dynamic fusion module to merge the spatial–temporal dependencies extracted from the multi-view encoder–decoders. We conducted experiments on two real datasets, NYCTaxi and NYCBike, and found that our proposed MVDGCN model outperformed the best baseline, improving the RMSE, MAE, PCC, and MAPE by 12.9%, 6.2%, 0.8%, and 6.5% respectively on the NYCBike dataset and 9.2%, 4.2%, 4.6%, and 3.0% respectively on the NYCTaxi dataset. These results show that the proposed MVDGCN model performs better than state-of-the-art algorithms.

Urban Congestion Avoidance Methodology Based on Vehicular Traffic Thresholding

Vehicular traffic in urban areas faces congestion challenges that negatively impact our lives. The infrastructure associated with intelligent transportation systems provides means for addressing the associated challenges in urban areas. This study proposes an effective and scalable vehicular traffic congestion avoidance methodology. It introduces a traffic thresholding mechanism to predict and avoid vehicular traffic congestion during route computation. Our methodology was evaluated and validated by employing four road network topologies, three vehicular traffic density levels and various traffic light configurations, resulting in 26 urban traffic scenarios. Using our approach, the number of vehicles that can run in free flow can be increased by up to 200%, whereas for traffic congestion scenarios, the time spent in traffic may be reduced by up to 69% and CO2 emissions by up to 61%. To the best of our knowledge, in the vehicular traffic flow prediction domain, this is the first approach that covers a set of road network topologies and a large and representative set of scenarios for simulated urban traffic congestion testing. Moreover, the comparative analysis with different other solutions in the domain, showed that we obtained the best driving time and CO2 emission reduction.

Spatio‐temporal adaptive graph convolutional networks for traffic flow forecasting

AbstractAccurate forecasting of traffic flow is crucial for intelligent traffic control and guidance. It is very challenging to forecast the traffic flow due to the high non‐linearity, complexity and dynamicity of the data. Most existing forecasting methods focus on designing complicated graph neural network architectures to capture the spatio‐temporal features of traffic data with the help of predefined graphs. However, traffic data exhibit a strong spatial dependency, which means that there are often complex correlations between nodes in a road network topology graph. Moreover, the spatial correlations of the road network change over time. To capture the properties of the road network topology graph, a novel spatio‐temporal adaptive graph convolutional networks model (STAGCN) based on deep learning is proposed. Utilize adaptive graph generation block to capture the static and dynamic structures of the traffic road network respectively, and they are integrated to construct an adaptive road network topology graph. Then the spatio‐temporal features of the traffic data are captured using spatio‐temporal convolution blocks. Experiments were conducted on publicly available traffic datasets for freeway traffic in California, USA, and the results showed that the prediction accuracy of the STAGCN model outperformed multiple baseline methods.

High-Frequency Trajectory Map Matching Algorithm Based on Road Network Topology

Accurately mapping the raw global position system (GPS) trajectories to the road network is the basis for studying the application of trajectory data. This study proposes a novel off-line map matching algorithm based on road network topology, to address the problems of low execution efficiency and poor matching accuracy of selective look-ahead map matching (SLAMM) algorithm. First, the noise points of the trajectory data are removed by data preprocessing. Second, the algorithm searches for critical samples in the trajectory data and segments the data accordingly. Then, the adjacent road segments around the transition node corresponding to the critical sample are selected as candidate arcs. Finally, the segmented trajectory data are matched to the road network by constructing an error ellipse. The algorithm fully considers the topology of the road network and the characteristics of high-frequency trajectory data. The experimental results, using Beijing trajectory data to perform matching on an actual road network environment, show that the proposed algorithm is more efficient and robust than other map matching algorithms for high-frequency trajectories.

Extracting interrelated information from road-related social media data

The social media data (SMD) have been viewed as a potential and promising information source of road conditions. However, most existing SMD-based sensing approaches (SMDSAs) either ignore interrelations among information items (e.g., name, direction, and status of the road) or rely on rigid grammar rules to establish entities’ interrelations. Additionally, current SMDSAs in the transportation domain are unable to link the extracted text-formatted information with domain-specific models (e.g., virtual road model, VRM). In order to fill such gaps, this work proposes an improved SMDSA of road conditions, which involves a three-stage (i.e., SMD classification, relation inference, and entity pair recognition) interrelated information extraction model, as well as a semantic converter to feed the SMD-provided text-formatted information into VRMs. The proposed SMDSA is demonstrated by the newly annotated datasets of tweets in Lexington, USA. The three-stage interrelated information extraction model outperforms conventional rule-based methods and deep-learning algorithms (e.g., Text CNN, Bi-LSTM, Piecewise CNN, and Capsule Net). The SMD-enabled VRM also preliminarily shows its capacity to optimize signal timings during incidents that change the road network topology. This work contributes to circumventing the reliance on human-made rules during SMDSAs’ development, bridging user-generated SMD with operable VRMs for potential real-world road management, and providing a standard tweet dataset annotated with interrelation triplets to help promote SMDSA studies.

A pagerank self-attention network for traffic flow prediction

Traffic information is collected from sensors in the urban road network, and traffic information can be said to be a mapping of people’s activities, which are difficult to model as a linear function, so this makes traffic information difficult to be predicted. In other words, traffic information is difficult to build effective models to predict traffic information because of its non-linear characteristics that are difficult to capture. As researchers go deeper, researchers have been able to extract good spatio-temporal features for modern urban road networks. However, it is worth mentioning that most researchers have neglected the importance of models for global potential features under the topology map of urban road networks, yet this global potential feature is very important for traffic prediction. In this paper, we propose a new spatio-temporal graph convolutional network model A Pagerank Self-attention Network (hereafter we abbreviate as PSN) in order to solve this problem based on a full consideration of the urban road network topology features, in which we employ a global spatio-temporal self-attention module to capture the global spatio-temporal features well. and the graph wandering module is used to propagate the spatio-temporal feature information effectively and widely. It is worth mentioning that experiments on two well-known datasets show that our proposed method achieves better prediction results compared to existing baseline methods.

Uncovering the Relationship between Urban Road Network Topology and Taxi Drivers’ Income: A Perspective from Spatial Design Network Analysis

Over the past few decades, taxi drivers’ income has received extensive attention from scholars. Previous studies have investigated the factors affecting taxi drivers’ income from multiple perspectives. However, less attention has been paid to road network topology, which has a direct impact on taxis’ operation efficiency and drivers’ income. To fill this gap, this paper examines the relationship between taxi drivers’ income and urban road network topology; we employed various methods, namely, spatial design network analysis (sDNA), bivariate Moran’s I, and geographically weighted regression (GWR). The results show the following. (1) The total order income (TOI) of taxi drivers has a certain degree of positive spatial correlation with closeness and betweenness. (2) The impact of urban road network topology on the average order income (AOI) of taxi drivers is stable. Specifically, closeness and betweenness have significant impacts on the AOI of taxi drivers at the medium and larger scales. (3) Closeness has a negative impact on the AOI of taxi drivers, and betweenness has a positive impact on the AOI of taxi drivers. (4) Compared with betweenness, the impact of closeness on the AOI of taxi drivers is greater and more stable. These findings can provide useful reference values for the development of policies aimed at improving both taxi drivers’ income and urban road network efficiency.

Bayesian Path Inference Using Sparse GPS Samples With Spatio-Temporal Constraints

Path inference aims to reveal missing paths given a few number of GPS samples associated with a moving object by exploiting the topology of road network and statistical information of historical GPS trajectories, and plays a vital role in data preprocessing of location based information services. But, in practice path inference severely suffers from the data sparsity as well as the randomness of drivers path selection behaviors. In this paper, we propose a novel Bayesian path inference model subject to spatiotemporal constraints by taking into account the drivers path selection behaviors. To be specific, the problem of path inference is cast as the problem of searching K most probable candidate paths according to the joint posterior selection probabilities of candidate paths. When estimating model parameters, we use the frequency of each road segment in the historical GPS trajectories instead of that of road segment transfers to mitigate the influence of data sparsity. In addition, both spatiotemporal constraints and probability thresholds are introduced to narrow the search space, which significantly improves the time efficiency. The experiments are conducted using practical data and show that the proposed model is significantly superior to three existing popular models. When the GPS sampling interval varies from 1 minute to 5 minutes, the accuracy of the proposed method is 0.94, 0.91, 0.86, 0.80 and 0.74, and the Jaccard similarity 0.89, 0.85, 0.83, 0.80 and 0.75 respectively, the average improvement in accuracy rises from 3.68% to 18.69% and that in the Jaccard similarity from 4.56% to 18.42%.

Computing traffic accident high-risk locations using graph analytics

Analysis of the dynamic relationship between traffic accident events and road network topology based on connectivity and graph analytics offers a new approach to identifying, ranking and profiling traffic accident high risk-locations at different levels of space and time granularities. Previous studies on traffic accident hot spots have mostly adopted spatial statistics and Geographic Information Systems (GIS) where spatial point patterns are discovered based only on spatial dependence with no recognition of the temporal dependence of the events. A limitation arises from the fact that the results are either under or over-estimated because of the temporal aggregation of the events to an absolute time point. Furthermore, the existing methods apart from the Network Kernel Density Estimation (NETKDE), consider traffic accident events as events randomly on a 2-D geographic space. However, traffic accident events are network constrained events that happens majorly on the road network space. Therefore, in this paper, we adopt the connectivity of graph on a network space approach that identifies accident high risk-locations based on space-time-varying connectivity between traffic accident events and the road network geometry. A simple but extensible traffic accident space time-varying graph (STVG) model is developed and implemented for this study. Traffic accident high risk-locations are identified and ranked in space and time using time-dependent degree centrality and PageRank centrality graph metrics respectively through time-incremental graph queries. This study offers urban traffic accident analysts with a new and efficient approach to identify, rank and profile accident-prone areas in space and time at different scales.

Deep Fusion for Travel Time Estimation Based on Road Network Topology

With the wide application of vehicular location-based services, precise estimation of the travel time plays a crucial role in intelligent transportation systems, such as driving navigation, traffic monitoring, and route planning. Recent methods have made significant progress on public datasets, but are not satisfied for current ride-hailing platforms with complex road network topology and dynamic traffic fluctuation. In this article, we propose an end-to-end Deep Fusion framework for Travel Time Estimation, which exploits multisource heterogeneous traffic information within an encoder–decoder architecture. Specifically, we explore a relational fusion network to learn the relationship of road link segments, and employ an attention mechanism to capture efficient correlations among spatial and temporal features. Extensive experiments have been conducted on two large-scale real-world traffic datasets collected by DiDi Corporation (DiDi) platform, and the results have demonstrated our effectiveness compared with the state of the art.

A trajectory map matching algorithm via exploring the average directional features of continuous windows

Many location-based services require a pre-processing step of map matching. Due to the error of the original position data and the complexity of the road network, the matching algorithm will have matching errors when the complex road network is implemented, which is therefore challenging. Aiming at the problems of low matching accuracy and low efficiency of existing algorithms at Y-shaped intersections and roundabouts, this paper proposes a space-time-based continuous window average direction feature trajectory map matching algorithm (STDA-matching). Specifically, the algorithm not only adaptively generates road network topology data, but also obtains more accurate road network relationships. Based on this, the transition probability is calculated by using the average direction feature of the continuous window of the track points to improve the matching accuracy of the algorithm. Secondly, the algorithm simplifies the trajectory by clustering the GPS trajectory data aggregation points to improve the matching efficiency of the algorithm. Finally, we use a real and effective data set to compare the algorithm with the two existing algorithms. Experimental results show that our algorithm is effective.

Enhanced Map-Aided GPS/3D RISS Combined Positioning Strategy in Urban Canyons

To realize the effective positioning in urban canyons, an enhanced map-aided Global Positioning System (GPS)/three-dimensional (3D) reduced inertial sensor system (RISS) tightly combined positioning strategy is proposed. First, the 3D RISS is only based on the built-in controller area network (CAN). CAN bus sensor without additional sensors is first constructed to lower the cost. Then, a simple but effective enhanced map is created to assist positioning. Based on the map, a Kalman filtering (KF) tightly coupled method is proposed to fuse the 3D RISS with GPS information and to achieve the preliminary positioning. In KF-based preliminary positioning method, a simply observation noise variance optimization algorithm based on 2D enhanced map is proposed to improve KF method. In this algorithm, the value of the observation noise variance matrix is determined only according to the building plane information which is contained in the enhanced map. Further, a multiweight map matching algorithm is proposed for optimizing the initial positioning results. In this algorithm, factors such as distance, direction, road network topology, and lane change are considered and applied to map matching to further increase the positioning performance and form the final positioning results. Finally, the effectiveness of the strategy is proved by field test. The results show that this method has better accuracy and reliability than the conventional method.

Recent Advancements in Techniques used to Solve the RSU Deployment Problem in VANETs: A Comprehensive Survey

Abstract: Vehicular ad hoc network makes use of Roadside Unit to boost the communication ability and provide internet access to vehicles. Unfortunately, in big cities with dense road network topology, wireless propagation is disrupted due to several obstacles. Hence, a crowded RSU deployment is required for full coverage of the urban environment. However, the installation cost of RSU is immense; researchers thought of solving this issue as an optimization problem whose objective is to cover all parts of the roads with a minimum number of RSUs, in return reducing the cost of the entire VANET deployment. In this article, a brief account has been given on VANET and its challenges, followed by a survey of different methods introduced to solve the RSU deployment problem. It will help the reader to get a quick glance at the progress made in the field over the years.

A Grade Identification Method of Critical Node in Urban Road Network Based on Multi-Attribute Evaluation Correction

Accurately identifying the key nodes of the road network and focusing on its management and control is an important means to improve the robustness and invulnerability of the road network. In this paper, a classification and identification method of key nodes in urban road networks based on multi-attribute evaluation and modification was proposed. Firstly, the emergency function guarantee grade of road network nodes was divided by comprehensively considering the importance of road network nodes, the consequences of failure, and the degree of difficulty of recovery. The evaluation indexes were selected according to the local attributes, global attributes, and functional attributes of the road network topology. The spatial distribution patterns of the evaluation indexes of the nodes were analyzed. The dynamic classification method was used to cluster the attributes of the road network nodes, and the TOPSIS method was used to comprehensively evaluate the importance ranking of the road network nodes. Attribute clustering of road network nodes by dynamic classification method (DT) and the TOPSIS method was used to comprehensively evaluate the ranking of the importance of road network nodes. Then, combined with the modification of the comprehensive evaluation and ranking of the importance of the road network nodes, the emergency function support classification results of the road network nodes were obtained. Finally, the method was applied to the road network within the second Ring Road of Beijing. It was compared with the clustering method of self-organizing competitive neural networks. The results show that this method can identify the key nodes of the road network more accurately. The first-grade key nodes are all located at the more important intersections on expressways and trunk roads. The spatial distribution pattern shows a “center-edge” pattern, and the important traffic corridors of the road network show a “five vertical and five horizontal” pattern.