Path Matching Algorithm Research Articles

Understanding the route choice behavior of metro passenger using the smartphone applications

Understanding the distribution of passenger flow is one of the crucial prerequisites for improving operational efficiency and implementing travel demand management strategies. This paper proposes an improved Random Regret Minimization (i-RRM) model based on the regret theory, where the passenger’s regret on selecting routes is minimized. Considering the growing trend of passengers relying on smartphone apps for route planning and navigation, three factors are investigated, namely, travel time, the number of transfer stations, and the total number of stations traveled. Automatic Fare Collection (AFC) is used to get the proportion of each route, and mobile phone signaling data is used to obtain the ground truth of passengers’ routes proportions for parameter calibration and model validation. The results show that, compared to the ground truth data, the average errors of the proposed i-RRM model, the classic RRM model, and the direct path matching algorithm are 6.03%, 8.96%, and 10.45%, respectively.

Research on the Integration of Curriculum Civics and Politics Construction into the High-Quality Talent Cultivation System for Rural Revitalization in the Internet Era

Abstract In promoting rural revitalization strategy, the construction of curriculum Civics and politics is closely related to cultivating high-quality talents. Facing complex and changing social demands, the education system urgently needs to integrate civic and political education with the teaching of professional knowledge to optimize the talent cultivation process. This study analyzes the degree of matching between students’ course selection behavior and rural revitalization practice through data mining techniques. It proposes a curriculum optimization plan to strengthen the connection between the curriculum Civics and politics content and the actual needs of rural revitalization to improve the quality and efficiency of education. The study uses the particle swarm algorithm and the improved tree path matching algorithm to optimize the curriculum by analyzing the student course selection data and the rural revitalization practice data and mining the association rules between them. Through the improved data mining method, the match between students’ course selection preferences and rural revitalization needs can be effectively identified, in which the degree of integration of course civics and politics is increased by 23%, and students’ participation in rural revitalization practice activities is increased by 15%. The hierarchical clustering algorithm-based talent cultivation model is more suitable for rural revitalization. This study shows that optimizing curriculum ideology and politics in the Internet era, combined with data mining technology, can effectively promote the high-quality development of rural revitalization talent cultivation systems.

An Improved DV-Hop Scheme Based on Path Matching and Particle Swarm Optimization Algorithm

Distance vector hop (DV-Hop) is a frequently-used localization technology for wireless sensor networks. The traditional DV-Hop scheme estimates the node–anchor distance depending on the hop-count between the network nodes. It is the advantage of the scheme because no costive direct range finding is needed, but it still is the disadvantage of the scheme because the heterogeneity of network topology will make the node–anchor distance estimation precision poor and the localization precision unstable. Since the heterogeneity of network topology is very common due to random node deployment in real applications, the effectiveness of DV-Hop scheme in these applications becomes difficult to confirm and the algorithm needs applicability improvement. Focusing on above problem of the traditional DV-Hop, improved strategies are provided. A path matching algorithm is presented to find out the optimal anchor-anchor shortest path, which is used to determine the average hop distance between an unknown node and its target anchor independently, aiming at making the estimated node-anchor distance as close as possible to the real distance; furtherly, a modified particle swarm optimization algorithm is presented to optimize the initial position of each unknown node, aiming at improving the whole node localization accuracy of the network. Simulations are carried out on different network topologies both in square area and in C-shaped area, and comparisons are made for our scheme with the traditional DV-Hop and the other three existed representative improved schemes. Results show that our scheme has better performance both on distance estimation accuracy and on average node localization accuracy.

Location Privacy Violation via GPS-agnostic Smart Phone Car Tracking

Smart phones nowadays are equipped with global positioning systems (GPS) chips to enable navigation and location-based services. A malicious app with the access to GPS data can easily track the person who carries the smart phone. People may disable the GPS module and turn it on only when necessary to protect their location privacy. However, in this paper, we demonstrate that an attacker is still able to track a person by using the embedded magnetometer sensor in victim's smart phone, even when the GPS module is disabled all the time. Moreover, this attack neither requests user permissions related to locations for installation, nor does its operation rely on wireless signals like WiFi positioning or suffer from signal propagation loss. Only the angles of a car's turning measured by the magnetometer sensor of a driver's smart phone are utilized. Without loss of generality, we focus on car tracking, since cars are popular transportation tools in developed countries, where smart phones are commonly used. Inspired by the intuition that a car may exhibit different turning angles at different road intersections, we find that an attacker can match car turning angles to a map to infer the actual path that the driver takes. We address technical challenges about car turn angle extraction, map database construction, and path matching algorithm design to make this attack practical and efficient. We also perform an evaluation using real-world driving paths to verify the relationship between the numbers of turns and the time cost of the matching algorithm. The results show that it is possible for attacker to precisely pinpoint the actual path when the driving path includes 11 turns or more. More simulations are performed to demonstrate the attack with lager selected local areas.

Carpool Algorithm Based on Similarity Measure of Partition Trajectory Line

Rapid increase of private cars leading to serious road congestion and air pollution problems, carpool has become an important choice for people to travel. In order to improve the service quality of carpool, trajectory matching is becoming a hot topic. However,traditional path matching algorithm based on Hausdorff distance has exposed two problems: (2) Ignoring waiting time of general users; (2) The direct calculation of the Hausdorff distance of the entire path cannot reflect the impact of special sections on the matching metric. To solve the above mentioned problems, this paper proposed an improved algorithm: (1) Hausdorff Distance is combined with turning point; (2) Turning point is taken as an effect way to segment trajectories. Compared to the classical algorithm MCT (Minimum completion time online mode scheduling algorithm), proposed algorithm get better performance.