Road Network Environment Research Articles

RNDLP: A Distributed Framework for Supporting Continuous k-Similarity Trajectories Search over Road Network

Continuous k-similarity trajectories search over a data stream is an important problem in the domain of spatio-temporal databases. Given a set of trajectories T and a query trajectory Tq over road network G, the system monitors trajectories within T, reporting k trajectories that are the most similar to Tq whenever one time unit is passed. Some existing works study k-similarity trajectories search over trajectory data, but they cannot work in a road network environment, especially when the trajectory set scale is large. In this paper, we propose a novel framework named RNDLP (Road Network-based Distance Lower-bound-based Prediction) to support CKTRN over trajectory data. It is a distributed framework based on the following observation. That is, given a trajectory Ti and the query trajectory Tq, when we have knowledge of D(Ti), we can compute the lower-bound and upper-bound distances between Tq and Ti, which enables us to predict the scores of trajectories in T and employ these predictions to assess the significance of trajectories within T. Accordingly, we can form a mathematical model to evaluate the excepted running cost of each trajectory we should spend. Based on the model, we propose a partition algorithm to partition trajectories into a group of servers so as to guarantee that the workload of each server is as the same as possible. In each server, we propose a pair-based algorithm to predict the earliest time Ti could become a query result, and use the predicted result to organize these trajectories. Our proposed algorithm helps us support query processing via accessing a few points of a small number of trajectories whenever trajectories are updated. Finally, we conduct extensive performance studies on large, real, and synthetic datasets, which demonstrate that our new framework could efficiently support CKST over a data stream.

Economic Impact of Plantain Ripening: A Case Study Among Plantain Traders in Accra

ABSTRACT Over 70% of plantain produced in Ghana is traded along the fresh market which is a hotspot for postharvest loss. This study sought to assess the economic impact of plantain ripening among traders in three satellite markets of Accra. Using the interview technique, primary data was obtained from 320 respondents within 3 satellite markets. Ripening was found to increase the market value of plantain whereas over-ripening reduced the market value of plantain. Money to the tune of over two million cedis (one hundred and seventy-four thousand dollars) was lost by weekly plantain retailers and suppliers in the three satellite markets. Poor road network and market environment were the key causes of postharvest of plantain amongst traders in the 3 satellite markets. Two identified solution gaps that can curb the accrued loss along the plantain supply chain include the provision of tailored business development services to plantain traders and the development of marketable food applications of over-ripe plantain that advances the circular economy agenda.

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.

Low-Frequency Trajectory Map Matching Method Based on Vehicle Heading Segmentation

Numerous Global Positioning System connected vehicles are collecting extensive data remotely in cities, enabling data-driven infrastructure planning. To truly benefit from this emerging technology, it is important to combine telematics and map data to make it easier to extract and mine useful information from the data. By performing map matching, data points that cannot be accurately located on the road network can be projected onto the correct road segment. As an important means of remote data processing, it has become an important pre-processing step in the field of data mining. However, due to the various errors of location devices and the complexity of road networks, map matching technology also faces great challenges. In order to improve the efficiency and accuracy of the map matching algorithm, this study proposes an offline method for low-frequency trajectory data map matching based on vehicle trajectory segmentation. First, the trajectory is segmented based on the vehicle’s travel direction. Then, the comprehensive probability of the corresponding road segment is calculated based on the spatial probability and the directional probability of each road segment around the location. Third, the k candidate matching paths under consideration are selected based on the comprehensive probability evaluation. Finally, the shortest path planning and the probability calculation of the different candidate paths are combined to find the optimal matching path. The experimental results on the real trajectory dataset in Shanghai and the road network environment show that the proposed algorithm has better accuracy, efficiency, and robustness than other algorithms.

PCP: A Pseudonym Change Scheme for Location Privacy Preserving in VANETs

In vehicular ad hoc networks (VANETs), pseudonym change is considered as the vital mechanism to support vehicles’ anonymity. Due to the complicated road conditions and network environment, it is a challenge to design an efficient and adaptive pseudonym change protocol. In this paper, a pseudonym change protocol for location privacy preserving (PCP) is proposed. We first present the requirements of pseudonym change in different scenarios. According to variable network states and road conditions, vehicles are able to take different pseudonym change strategies to resist the tracking by global passive adversaries. Furthermore, the registration protocol, authentication protocol, pseudonym issuance protocol, and pseudonym revocation protocol are introduced for the pseudonym management mechanism. As a consequence, it is not feasible for global passive adversaries to track a vehicle for a long time and obtain the trajectory of the vehicle. The analysis results show that the security and performance of PCP are improved compared with the traditional ones.

Trajectory Privacy Protection Mechanism based on Salp-Like Swarm Algorithm

Location-based services have been widely used in daily life, providing diversified services for users. However, users may face the risk of trajectory privacy disclosure while enjoying the convenience of location-based services. Most of the existing trajectory protection schemes cannot match the road network and are vulnerable to attacks based on background information. In this paper, the concept of salp swarm algorithm is introduced to construct salp-like swarm algorithm, which can generate K−1 false trajectories that are highly similar to real trajectories. It is difficult for attackers to distinguish them. Besides, a road network matching model is designed in order to match the proposed trajectory privacy protection algorithm with the real road network environment, so that the effect of trajectory privacy protection is improved. Morever, a false location selection mechanism is proposed to find false location points, which not only considers the location and speed of users, but also ensures that the selection of false location points is more in line with the road network environment. The experimental results show that, under the condition of satisfying the same service quality, the trajectory privacy leakage probability of this scheme is reduced by 33% compared with the existing schemes, and it has better privacy protection effect.

Integrated Sensing and Communication in UAV Swarms for Cooperative Multiple Targets Tracking

Various interconnected Internet of Things (IoT) devices have emerged, led by the intelligence of the IoT, to realize exceptional interaction with the physical world. In this context, UAV swarm-enabled Multiple Targets Tracking (UAV-MTT), which can sense and track mobile targets for many applications such as hit-and-run, is an appealing topic. Unfortunately, UAVs cannot implement real-time MTT based on the traditional centralized pattern due to the complicated road network environment. It is also challenging to realize low-overhead UAV swarm cooperation in a distributed architecture for the real-time MTT. To address the problem, we propose a cyber-twin-based distributed tracking algorithm to update and optimize a trained digital model for real-time MTT. We then design a distributed cooperative tracking framework to promote MTT performance. In the design, both short-distance and long-distance distributed tracking cooperation manners are firstly realized with low energy consumption in communication by integrating resources of sensing and communication. Resource integration promotes target sensing efficiency with a highly successful tracking ratio as well. Theoretical derivation proves our algorithmic convergence. Hardware-in-the-loop simulation results demonstrate that our proposed algorithm can remarkably save 65.7% energy consumption in communication compared to other benchmarks while efficiently promoting 20.0% sensing performance.

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.

A Stochastic User Equilibrium Formulation for the Cumulative Prospect Theory-Based Cross-Nested Logit

The cumulative prospect theory provides a better description for route choice behavior of the travelers in an uncertain road network environment. In this study, we proposed a multiclass cumulative prospect value- (CPV-) based cross-nested logit (CNL) stochastic user equilibrium (SUE) model. For this model, an equivalent variational inequality (VI) model is provided, and the existence and equivalence of the model solutions are also proved. The method of successive averages (MSA), method of successive weighted averages (MSWA), and self-regulated averaging (SRA) method are designed and compared. In addition, the proposed multiclass CPV-based CNL SUE model is also compared with the multiclass utility value- (UV-) based CNL SUE model. The results show that the path flow assigned by the multiclass CPV-based CNL SUE model is more consistent with the actual situation. The impact of different model parameters on the cumulative prospect value (CPV) is investigated.

Optimal design of urban cloud transportation planning based on information retrieval and social IoT

Optimal design of urban cloud transportation planning based on information retrieval and social IoT is proposed in this manuscript. Transportation is a necessary part of the human society's production, life and economic development. With the development of the times and advancement of science and technology, people are strongly dependent on transportation and at the same time, they have put forward more demands on transportation. In this paper, based on the omnidirectional and complex road conditions of vehicle nodes in the urban road network environment, and the road environment has blocking and superimposed interference effects on wireless signals, combined with the intelligent driving model, this paper analyzes and studies the propagation of network worms and builds a stable and reliable system. The experimental results have proven the performance, and the future research directions have been pointed out.

CONTINUOUS MULTIQUERIES K-DOMINANT SKYLINE ON ROAD NETWORK

The increasing use of mobile devices makes spatial data worthy of consideration. To get maximum results, users often look for the best from a collection of objects. Among the algorithms that can be used is the skyline query. The algorithm looks for all objects that are not dominated by other objects in all of its attributes. However, data that has many attributes makes the query output a lot of objects so it is less useful for the user. k-dominant skyline queries can be a solution to reduce the output. Among the challenges is the use of skyline queries with spatial data and the many user preferences in finding the best object. This study proposes IKSR: the k-dominant skyline query algorithm that works in a road network environment and can process many queries that have the same subspace in one processing. This algorithm combines queries that operate on the same subspace and set of objects with different k values by computing from the smallest to the largest k. Optimization occurs when some data for larger k are precomputed when calculating the result for the smallest k so the Voronoi cell computing is not repeated. Testing is done by comparing with the naive algorithm without precomputation. IKSR algorithm can speed up computing time two to three times compared to naive algorithm.

Simulation study on the effect of automated driving in a road network environment

Automated driving, which is considered to be able to reduce driving workload, enhance driving safety and improve traffic efficiency, has become a research hotspot in recent years. It is believed that traffic flow will consist of manual vehicles and automated vehicles at different automation levels in the near future. Researchers have carried out many studies on mixed traffic; most of them focus on the highway scenario. However, the majority of traffic occurs in urban/suburban road networks, which contains many different scenarios, such as expressways, merging/diverging areas, signalised intersections, trunk roads, branch roads, etc. To evaluate the effect of automated driving in a more realistic way, the authors first take adaptive cruise control (ACC) and cooperative ACC as representatives of automated driving. Then, the authors make simulations at the road network level and investigate macroscopic fundamental diagram in different scenarios. Results show that compared to a highway, the improvement of traffic flow is significantly limited in the road network.

Using location semantics to realize personalized road network location privacy protection

With the rapid development of location-based services in the field of mobile network applications, users enjoy the convenience of location-based services on one side, and they are exposed to the risk of privacy disclosure on the other side. Attackers may attack based on the semantic of the user’s location and user’s query location. A few of existing works on location privacy protection consider to protect the user’s location and his query location simultaneously, while the query location may reflect his requirement. In this paper, based on the existing location privacy protection framework, we first generate sensitive weight documents based on the user’s sensitivity to different location semantics automatically, then obtain the best collaborative segment for k-anonymity of the user’s location by using the reinforcement learning algorithm, and finally, the bidirectional k-disturbance of the user’s location and query location is performed based on the location semantics in real road network environment. The experiment verifies the effectiveness of the proposed method.

Service node selection optimization for mobile crowd sensing in a road network environment

Mobile crowd sensing (MCS) can be an effective method for urban traffic sensing applications by collecting data in urban road networks through ubiquitous sensor-mounted vehicles. However, due to the limited network resources and the randomness of automobiles, the quality of service (QoS) of MCS cannot be effectively guaranteed. Some related works noted that optimizing the selection of service nodes can effectively improve the QoS of MCS. However, existing node selection methods are unsuitable for MCS in an urban road network (MCS-URN). An MCS-URN is a unique MCS environment in which the service nodes are vehicles, and the sensing area is road segments. In this paper, we focus on improving the QoS in an MCS-URN by optimizing the selection of service nodes with limited network resources. First, the utility function of the QoS in MCS-URN is proposed based on the coverage and the data score. Then the service node optimization model in the MCS-URN is presented, by selecting an appropriate set of service nodes within the maximum proportion of the total network resources to maximize the utility value of the QoS. Also, an innovative service node selection method which considering the mobility of automobiles and the topological structure of urban road networks is introduced. In the end, a simulation study is carried out to evaluate the service node optimization model in the MCS-URN. The simulation results show that our service node optimization model can effectively improve the QoS of the MCS-URN.

Template-Based 3D Road Modeling for Generating Large-Scale Virtual Road Network Environment

The 3D road network scene helps to simulate the distribution of road infrastructure and the corresponding traffic conditions. However, the existing road modeling methods have limitations such as inflexibility in different types of road construction, inferior quality in visual effects and poor efficiency for large-scale model rendering. To tackle these challenges, a template-based 3D road modeling method is proposed in this paper. In this method, the road GIS data is first pre-processed before modeling. The road centerlines are analyzed to extract topology information and resampled to improve path accuracy and match the terrain. Meanwhile, the road network is segmented and organized using a hierarchical block data structure. Road elements, including roadbeds, road facilities and moving vehicles are then designed based on templates. These templates define the geometric and semantic information of elements along both the cross-section and road centerline. Finally, the road network scene is built by the construction algorithms, where roads, at-grade intersections, grade separated areas and moving vehicles are modeled and simulated separately. The proposed method is tested by generating large-scale virtual road network scenes in the World Wind, an open source software package. The experimental results demonstrate that the method is flexible and can be used to develop different types of road models and efficiently simulate large-scale road network environments.

T-SR: A Location Privacy Protection Algorithm Based on POI Query

With the popularity of location-based services in the field of mobile network applications, users enjoy the convenience on one side, they may face the risk of privacy disclosure on the other side. Attackers can easily dig out the user's home address, occupation, and other personal privacy from the data of location-based services. For the problem of user location privacy disclosure based on semantic query of point of interest (POI) in the road network environment, the previous research seldom paid attention to the temporal association relationship between the query semantic and the user's location, as well, seldom considered whether the constructed anonymity sets of fake-locations can fulfill the property of reciprocity. Therefore, in this paper, we propose a location privacy protection algorithm T-SR based on POI query. The algorithm can be divided into three steps. First, the whole road network is divided into multiple areas based on the Voronoi structure, so that the areas are independent and non-overlapping, and the road grid that contains user's real location will be located according to the level of privacy protection H. Second, the temporal association between the POI semantics are mined from the real check-in data based on Gaussian distribution σ rule, and the POIs which have weak association with the POI of user's location will be filtered to resist the temporal association attack. Third, the POI semantics within the grid will be split into several buckets according to query times record, anonymity degree k, and semantic degree l, then the dummy locations will be selected from various kinds of POIs of the bucket where the user's location is. The resulted anonymity set can defend against replay attack, inference attack, and temporal association attack. The theoretical analysis and experimental evaluation prove that the proposed solution can protect user's location privacy efficiently and effectively.

Parallel computing of KNN Query in road network based on MapReduce

K-nearest neighbor (KNN) query algorithm based on road network plays an important role in location based service, which had been widely used in intelligent transportation, roadside assistance and other fields. However, as road network density increases and the number of points of interest increases, query efficiency decreases sharply.In order to improve the query efficiency, we adopted the MapReduce parallel computing framework to complete the query of K neighbor moving objects by designing Map, Reduce, Combiner and other functions. Before the start of the query, the road network was divided into pieces, and each fragment was calculated. The final K-nearest neighbor moving objects were obtained by aggregating the calculated results of each slice to realize the parallel optimization of KNN algorithm based on road network. The experimental results showed that the performance of parallel KNN algorithm based on MapReduce was better than that of serial KNN query algorithm in large-scale road network environment and the larger K value of query request.

Intelligent route planning system based on interval computing

We investigate the problem of vehicle route planning in a dynamic environment. In order to better reflect real-life situations, we assume that travel times are not known exactly, but bounded from below and from above, i.e., they are given as interval quantities. Accordingly, we develop algorithms for effective route replanning in a highly dynamic road network environment that combines traffic image processing with interval data for dynamic path optimisation. The developed algorithms are integrated into a larger system for traffic management. The efficiency of the proposed algorithms and their ability to support the dynamics of road traffic is verified using real data. The experimental research was also conducted using the microscopic, time-discrete, space-continuous traffic simulator.

On Efficiently Answering Why-Not Range-Based Skyline Queries in Road Networks

The range-based skyline (r-skyline) query on road networks retrieves the skyline objects for each of the query points that are within a road region, considering the objects’ spatial and non-spatial attributes. However, reasoning about missing query results, specified by why-not questions , has not till recently received the attention it is worth of. In this paper, we systematically carry out the study of why-not questions on the r-skyline query in the road network environment (abbrev. as the why-not RSQ problem ). We present three modification strategies, including modifying the query range, modifying the why-not point, and modifying both of them, for supporting the why-not RSQ problem. We also propose three efficient algorithms to tackle the why-not RSQ problem, where several newly presented effective concepts/techniques are leveraged, such as the concepts of skyline scope and skyline dominance region , non-spatial attribute modification pruning , and G-tree index . Extensive experimental evaluation using both real and synthetic data sets demonstrates the performance of our proposed algorithms.

A Method of Location Privacy Protection in Road Network Environment

With the widespread application of location-based services, users 'privacy concerns have become the focus of users' attention. Based on the k-anonymity method and the SpaceTwist algorithm, this paper proposes a method of incremental inquiry user privacy protection. The method preliminarily anonymizes the user's location information and points of interest on the client side, On the anonymous server side, combining the road network environment with the latitude and longitude grid generates the minimum anonymous area of random loop, instead of the user initiating incremental inquiry to the location service provider, Anonymous zones ensure k-anonymity for mobile users and road information to protect user privacy. Security and experimental analysis show that this scheme can improve the effectiveness of user query service while meeting the privacy requirements of users.