Road Network Topology Research Articles

Map-Matching Using Hidden Markov Model and Path Choice Preferences under Sparse Trajectory

In the field of map matching, algorithms using topological relationships of road networks along with other data are normally suitable for high frequency trajectory data. However, for low frequency trajectory data, the above methods may cause problems of low matching accuracy. In addition, most past studies only use information from the road network and trajectory, without considering the traveler’s path choice preferences. In order to address the above-mentioned issue, we propose a new map matching method that combines the widely used Hidden Markov Model (HMM) with the path choice preference of decision makers. When calculating transition probability in the HMM, in addition to shortest paths and road network topology relationships, the choice preferences of travelers are also taken into account. The proposed algorithm is tested using sparse and noisy trajectory data with four different sampling intervals, while compared the results with the two underlying algorithms. The results show that our algorithm can improve the matching accuracy, especially for higher frequency locating trajectory. Importantly, the method takes into account the route choice preferences while correcting deviating trajectory points to the corresponding road segments, making the assumptions more reasonable. The case-study is in the city of Beijing, China.

VRPDiv: A Divide and Conquer Framework for Large Vehicle Routing Problems

The Vehicle Routing Problem (VRP) is an NP hard problem where we need to optimize itineraries for agents to visit multiple targets. When considering real-world travel (road-network topology, speed limits and traffic), modern VRP solvers can only process small instances with a few hundred targets. We propose a framework (VRPDiv) that can scale any solver to support larger VRP instances with up to ten thousand targets (10k) by dividing them into smaller clusters. VRPDiv supports the multiple VRP scenarios and contains a pool of clustering algorithms from which it chooses the ideal one depending on properties of the instance. VRPDiv assigns agents based on cluster demand and targets compatibility (i.e. realizable time-windows and capacity limitations). We incorporate the framework into the Bing Maps Multi-Itinerary Optimization (MIO) 1 online service. This architecture allows MIO to scale up from solving instances with a few hundred to over 10k targets in under 10 minutes. We evaluate our framework on public datasets and publish a new dataset ourselves, as large enough instances supporting real-world travel were impossible to find. We investigate multiple clustering methods and show that choosing the correct one is critical with differences of up to 60% in quality. We compare with relevant baselines and report a 40% improvement in target allocation and a 9.8% improvement in itinerary durations. We compare with existing scores and report an average delta of 10%, with lower values (<5%) in instances with low workload (few targets per agent), which are acceptable for an online service.

Blockage tolerance in roadside millimeter-wave backhaul networks

With the proliferation of mobile devices with bandwidth-hungry applications, mmWave communication is considered a key enabling technology for 5G and beyond cellular networks. One very promising use of mmWave communication is in wireless backhaul, which has also been investigated by 3GPP as part of an integrated access and backhaul study. Unfortunately, the network performance is hard to predict due to the sensitivity of mmWave signals to blockages, which can result in frequent communication outages. In this paper, we consider wireless backhaul links deployed along the side of a road, which have been widely proposed both in urban environments and on highways. We first investigate the inherent robustness of an interference-free topology for roadside mmWave backhaul. Based on a novel four-type blockage model, we apply stochastic geometry to derive blockage probabilities as a function of the topology parameters and obstacle density, which yields insights that lead to a more robust topology. We then present mechanisms to tolerate any blockages that do occur through reconfiguration. Through extensive simulations, the performance of our proposed algorithms are evaluated for different road scenarios and network topologies. Results show that our algorithms not only achieve high throughputs close to the no-blockage case for mmWave backhaul, but also provide high blockage tolerance rates even in the presence of multiple obstacles along the road.

Vehicle travel path recognition in urban dense road network environments by using mobile phone data

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

Assessment of transportation system disruption and accessibility to critical amenities during flooding: Iowa case study

Natural disasters, such as flooding, can cause severe social, environmental, and economic damage to a community. Transportation infrastructure plays an essential role in flood response and recovery efforts. However, flooding may disturb road functionality and generate direct and indirect adverse impacts, including the loss of access to essential services. This paper presents a comprehensive analysis of flood impacts on road network topology and accessibility to amenities for major communities in the State of Iowa using graph-theoretic methods, including single-source shortest path analyses. We assessed the disruption of transportation networks on the accessibility to critical amenities (e.g., hospitals) under 100 and 500-year flood scenarios. Our analysis methodology leads toward the development of an integrated real-time decision support system that will allow decision-makers to explore “what if” flood scenarios to identify vulnerable areas and population in their authority. These analyses could promote possible improvements (e.g., temporary relocation of critical services) to mitigate the consequences of road system failure during flooding. Due to varying environmental conditions at specific locations and effects on road topology under flood events, the results show differential impacts in edge and node losses as well as access to critical services. Results indicate that floods can lead to edge losses of up to 18%, and not only large cities but also some small cities can experience significant vulnerability to flooding. Some new or reconstructed bridges have failed to operate during analyzed flood events. During the 100 and 500-year flood return periods, the total number of inaccessible bridges within the selected cities is 184 and 294, respectively. Our work found that the shortest path length to the closest critical amenity under baseline condition can flip to the second or higher-orders during flooding. Many critical amenities have been found at risk of flooding in the studied cities.

Multi-Objective Optimal Allocation Of DG-EV Charging Station Considering Space-Time Characteristics Model

With the promotion and development of new energy in the country, Distributed Generation (DG) and electric vehicle (EV) are welcoming development opportunities. The paper proposes a DG-EV charging station that considers space-time characteristics. Multi-objective coordination optimization configuration method, by extracting the road network topology in the city, monitoring the road network traffic, using the traffic planning software TransCAD to carry out the OD matrix back-pull, establishing the travel probability matrix to describe the user travel characteristics, considering the timing characteristics of EV, DG and conventional load, taking into account the grid side and the user side, and establishing a multi-objective joint planning model for the DG-EV charging station with the sub-goal of comprehensive benefit, system load fluctuation and charging time-consuming cost. Finally, the simulation analysis of the IEEE-33 node distribution network and the main road in a certain urban area is carried out to verify the validity and feasibility of the model.

Mining Topological Dependencies of Recurrent Congestion in Road Networks

The discovery of spatio-temporal dependencies within urban road networks that cause Recurrent Congestion (RC) patterns is crucial for numerous real-world applications, including urban planning and the scheduling of public transportation services. While most existing studies investigate temporal patterns of RC phenomena, the influence of the road network topology on RC is often overlooked. This article proposes the ST-Discovery algorithm, a novel unsupervised spatio-temporal data mining algorithm that facilitates effective data-driven discovery of RC dependencies induced by the road network topology using real-world traffic data. We factor out regularly reoccurring traffic phenomena, such as rush hours, mainly induced by the daytime, by modelling and systematically exploiting temporal traffic load outliers. We present an algorithm that first constructs connected subgraphs of the road network based on the traffic speed outliers. Second, the algorithm identifies pairs of subgraphs that indicate spatio-temporal correlations in their traffic load behaviour to identify topological dependencies within the road network. Finally, we rank the identified subgraph pairs based on the dependency score determined by our algorithm. Our experimental results demonstrate that ST-Discovery can effectively reveal topological dependencies in urban road networks.

Vehicular crowd-sensing: a parametric routing algorithm to increase spatio-temporal road network coverage

ABSTRACT Current vehicles are equipped with a number of environmental sensors to improve safety and quality of life for passengers. Many researchers have shown that these sensors can also be exploited for opportunistic crowd-sensing. Useful new services can be developed on top of these data, like urban surveillance of Smart Cities. The spatio-temporal sensing coverage achievable with Vehicular Crowd-Sensing (VCS), however, is an open issue, since vehicles are not uniformly distributed over the road network, undermining the quality of potential services based on VCS data. In this paper, we present an evolution of the standard A routing algorithm, meant to increase VCS coverage by selecting a route in a random way among all those satisfying a parametric constraint on the total cost of the path. The proposed solution is based on an edge-computing paradigm, not requiring a central coordination but rather leveraging the computational resources available on-board, significantly reducing the back-end infrastructure costs. The proposed solution has been empirically evaluated on two public datasets of 450,000 real taxi trajectories from two cities, San Francisco and Porto, characterized by a very different road network topology. Results show sensible improvements in terms of achievable spatio-temporal sensing coverage of probe vehicles.

Online Estimation and Prediction of Large-Scale Network Traffic From Sparse Probe Vehicle Data

Network traffic prediction based on probe vehicle data is important for traffic management and route recommendation and has been intensively studied. Previous traffic prediction methods mainly focused on recurring traffic congestion. Predicting non-recurring traffic congestion, caused by events and accidents, is significantly more important; however, it has not been intensively studied. To predict non-recurring traffic congestion using probe data, we need to estimate the current traffic conditions based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sparse</i> observations for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">large</i> traffic networks to track traffic changes <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">online</i> . Conventional traffic forecasting methods have not been able to solve all of these problems. To address these problems, we propose a data assimilation method using a state space neural network (SSNN) with an incorporated topology of road networks. The SSNN model can easily model network traffic and can easily estimate its states and parameters by data assimilation using Bayesian filtering. In this study, we adopted a decoupled extended Kalman filter (DEKF) based data assimilation, which is scalable and applicable to large-scale network traffic, to estimate the states and parameters online. We evaluate the proposed method using an open dataset that includes a road network comprising over 30000 road segments. The results show that our method achieves higher prediction accuracy for predicting unknown traffic congestion and is more robust against data sparsity than conventional state estimation methods.

The Research on Traffic Assignment Method Based on the Urban Road Network Topology

As our country urbanization and economic development accelerating, traffic problems become increasingly serious. In urban road network, the intersection delay has a major influence on road impedance even the choice of the path. In order to reflect the actual situation of traffic flow distribution more accurately, the transportation network assignment model was established based on the intersection flow of delays and traffic control.By comparing delay models of signal control and analyzing the intersection impedance, the intersection optimization model was built based on delay, queuing and traffic capacity. Then, we analyzed the structure of the road network topology and traffic assignment models, researched the advantages and disadvantages of the allocation algorithm in detail. Finally, the balanced allocation algorithm for FW algorithm convergence slowly was improved. The feasibility of the algorithm was verified by examples.

Impact of Road Network Topology on Public Transportation Development

Planning of road networks is fundamental for public transportation. The impact of road network density on public transportation has been extensively studied, but few studies in this regard involved evaluation indicators for connectivity and layout of road networks. With 29 cities in China as the study cases, this paper quantifies the layout structure of the road network based on the network’s betweenness centralization and establishes a multivariate linear regression model to perform regression of the logarithm of the frequency of per capita public transportation on betweenness centralization. It is found in the present work that there is a significant correlation between the layout structure of an urban road network and the residents’ utilization degree of public transportation. A greater betweenness centralization of the urban road network, namely a more centralized road network, means a higher frequency of per capita public transportation of urban residents and a higher degree of the residents’ utilization of public transportation. In the development of public transportation, centralized and axial‐shaped layout structures of road networks can be promoted to improve the utilization of public transportation.

A vulnerability-based vehicle routing approach for solving capacitated arc routing problem in urban snow plowing operations.

Vehicle drivers usually perceive a higher risk when driving on snow covered roads. The city cleaning efficiency would directly influence the risk and mitigation of wintertime events, especially for snow covered roads. Under the risk-informed approach background, more attention is paid to the capacitated arc routing problem (CARP) of urban snow plowing operations. Current algorithms mainly relies on the topology of road network without considering snow covered pavement's negative effect on road capacity and traffic flow. This paper proposes a vulnerability-based parallel heuristic algorithms applied for the CARP by implementing risk-informed approach. First, a method is proposed to set service priorities based on the vulnerability evaluation by considering the added cost of travel demands. Second, a sub-process path-scanning approach is developed to avoid redundant path scans. Then verification and comparison between this newly proposed constructive heuristic and existing algorithms of whole-process path-scanning and sequential processing are conducted. Results show that the sub-process path-scanning approach obviously costs less service completion time than the existing algorithms for solving the CARP. However, this improved algorithm would also cause an increase of deadhead time upon dispatch. The balance between service completion time and deadhead time for more routing problems would be discussed in the near future.

A trajectory restoration algorithm for low-sampling-rate floating car data and complex urban road networks

ABSTRACT Low-sampling-rate floating car data (FCD) are more challenging than those with high-sampling-rate FCD for map matching (MM) algorithms. Some MM algorithms for low-sampling-rate FCD lack sufficient efficiency nor accuracy, especially related to complex urban road networks. This paper proposes a new method named the trajectory restoration algorithm, which is based on geometry MM algorithms to ensure efficiency and accuracy. The proposed algorithm adopts the modified A* shortest path algorithm to reduce the number of function calls and fully considers road network topology and historical matched points to improve its accuracy. We test the efficiency and accuracy of the trajectory restoration algorithm with FCD data for the complex urban road networks in Beijing. The results have strong continuity which greatly improves the utilization of FCD. We show that the proposed algorithm outperforms related MM methods in efficiency and accuracy and its robustness to restore trajectories of both high and low sampling rates in complex urban road networks.

SHERPA-city: A web application to assess the impact of traffic measures on NO2 pollution in cities

This paper presents SHERPA-City, a web application to assess the potential of traffic measures to abate NO2 air pollution in cities. The application is developed by the Joint Research Centre. It is freely available (https://integrated-assessment.jrc.ec.europa.eu) and allows the user to perform a fast screening of possible NO2 abatement measures addressing traffic in European cities. SHERPA-City results depend on the quality of the default input data. It is therefore important to stress that the SHERPA-City default traffic flows, emission factors, fleet composition, road network topology, NO2 pollution from other sources and meteorological data are based on EU-wide datasets that may not always represent perfectly a particular local situation. This is why the SHERPA-City allows the default data to be substituted by local data, to better reflect local features. This tool must be considered as a first step in exploring options to abate NO2 air pollution through transport measures. The final decisions should be based, wherever possible, on full-scale modelling studies incorporating local knowledge.

CREATION OF HIGH DEFINITION MAP FOR AUTONOMOUS DRIVING

Abstract. High Definition Map (HD Map) is one of the essential components for autonomous driving systems. The HD Map helps localization, detection, prediction and planning of autonomous driving cars in combination with GNSS and IMU sensors. A lot of map providers and major car manufactures have been trying the development of efficient ways to create and update HD Maps with centimeter-level precision. However, there are no open standard ways well-established to produce HD Map at the present time. Since 2013, we have been being engaged in the research projects and actual production projects on the creation of HD Maps. This paper introduces practical knowledge on five key steps of our large-scale production lines for HD Map creation, which are 1) Mobile Mapping System (MMS) survey, 2) 3D plotting, 3) Feature attributes editing and road network topology building, 4) Format conversion and 5) Quality assurance.

Urban Traffic Travel Time Short-Term Prediction Model Based on Spatio-Temporal Feature Extraction

In order to improve the accuracy of short-term travel time prediction in an urban road network, a hybrid model for spatio-temporal feature extraction and prediction of urban road network travel time is proposed in this research, which combines empirical dynamic modeling (EDM) and complex networks (CN) with an XGBoost prediction model. Due to the highly nonlinear and dynamic nature of travel time series, it is necessary to consider time dependence and the spatial reliance of travel time series for predicting the travel time of road networks. The dynamic feature of the travel time series can be revealed by the EDM method, a nonlinear approach based on Chaos theory. Further, the spatial characteristic of urban traffic topology can be reflected from the perspective of complex networks. To fully guarantee the reasonability and validity of spatio-temporal features, which are dug by empirical dynamic modeling and complex networks (EDMCN), for urban traffic travel time prediction, an XGBoost prediction model is established for those characteristics. Through the in-depth exploration of the travel time and topology of a particular road network in Guiyang, the EDMCN-XGBoost prediction model’s performance is verified. The results show that, compared with the single XGBoost, autoregressive moving average, artificial neural network, support vector machine, and other models, the proposed EDMCN-XGBoost prediction model presents a better performance in forecasting.

Dynamic Origin–Destination Matrix Prediction with Line Graph Neural Networks and Kalman Filter

Modern intelligent transportation systems provide data that allow real-time dynamic demand prediction, which is essential for planning and operations. The main challenge of prediction of dynamic origin–destination (O-D) demand matrices is that demand cannot be directly measured by traffic sensors; instead, it has to be inferred from aggregate traffic flow data on traffic links. Specifically, spatial correlation, congestion and time dependent factors need to be considered in general transportation networks. This paper proposes a novel O-D prediction framework combining heterogeneous prediction in graph neural networks and Kalman filter to recognize spatial and temporal patterns simultaneously. The underlying road network topology is converted into a corresponding line graph in the newly designed fusion line graph convolutional networks (FL-GCNs), which provide a general framework of predicting spatial-temporal O-D flows from link information. Data from the New Jersey Turnpike network are used to evaluate the proposed model. The results show that the proposed approach yields the best performance under various prediction scenarios. In addition, the advantage of combining deep neural networks and Kalman filter is demonstrated.

Context-Aware Path Ranking in Road Networks

Ranking paths becomes an increasingly important functionality in many transportation services, where multiple paths connecting a source-destination pair are offered to drivers. We study ranking such paths under specific contexts. More specifically, we model ranking as a regression problem where we assign a ranking score to each path with the help of historical trajectories. To solve the regression problem, we first propose an effective training data enriching method to obtain a compact and diversified set of training paths using historical trajectories. Next, we propose a multi-task learning framework that considers features representing both candidate paths and contexts. Specifically, a road network embedding is proposed to embed paths into feature vectors by considering both road network topology and spatial properties. By modeling different departure times as a temporal graph, graph embedding is used to embed departure times. The objective function not only considers the discrepancies on ranking scores but also the reconstruction errors of the spatial properties of the paths, which in turn improves the final ranking estimation. Empirical studies on a substantial trajectory data set offer insight into the designed properties of the proposed framework and indicating that it is effective and practical in real world settings.

Attention-Based Sequence Learning Model for Travel Time Estimation

Travel time estimation (TTE) on a specific route is a challenging task since the complex road network structure and hard-captured temporal patterns. Many excellent methods have been proposed to address the aforementioned problems. Some approaches well designed heuristically in a non-learning based way have the advantage of a quick response to the query for travel time estimation. However, these methods are largely affected by the noise of traffic data since they are limited to a single feature. Existing road segment based methods are generally considered intuitive but are not accurate enough for they fail to model complex factors, like delay and direction of intersections. In this paper, we propose a novel attention based sequence learning model for travel time estimation of a path (ASTTE), that not only considers the real-world road network topology as multi-relational data but also refine the problem to the road segment and intersection direction aspects. Besides, we integrate the traffic information as local and neighbor dependency, which helps to monitor dynamic traffic conditions during the trip. The use of the attention mechanism allows the model to focus on significant elements among the path comprises road segments and intersections. High-quality experiments on two real-world datasets have demonstrated the effectiveness and robustness of our framework.

A roadside unit deployment framework for enhancing transportation services in Maghrebian cities

SummaryRoadside units (RSUs) have a crucial role in maintaining vehicular ad hoc networks (VANETs) connectivity and coverage, especially, for applications gathering or disseminating nonsafety information. In big cities with complex road network topology, a huge number of costly RSUs must be deployed to collect data gathered by all moving vehicles. In this respect, several research works focusing on RSUs deployment have been proposed. The thriving challenge would be to (1) reduce the deployment cost by minimizing as far as possible the number of used RSUs and (2) to maximize the coverage ratio. In this paper, we introduce a spatiotemporal RSU deployment framework including three methods, namely, SPaCov, SPaCov+, and HeSPic. SPaCov starts by mining frequent mobility patterns of moving vehicles from their trajectories; then, it computes the best RSU locations that cover the extracted patterns. Nonetheless, SPaCov+ extracts the frequent mobility patterns as well as the rare ones to enhance the coverage ratio. HeSPiC is a budget‐constrained spatiotemporal coverage method that aims to maximize the coverage ratio subject to a budget constraint, which is defined in terms of RSU number. Performed simulations highlight the efficiency and the effectiveness of the proposed RSU deployment framework in terms of coverage ratio, deployment cost, network latency and overhead.