IJERT-Finding the Shortest Paths in Road Networks with Minimum Pair (original) (raw)
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Finding the Shortest Paths in Road Networks with Minimum Pair
International journal of engineering research and technology, 2019
Finding the shortest path in road networks becomes one of important issues in location based services (LBS). The problem of finding the optimal meeting point for a group of users has also been well studied in existing works. This paper investigates a new problem for two users. Each user has his / her own source and destination. However, whether to meet before going to their destinations is with some uncertainty. The paper models it as minimum path pair (MPP) query, which consists of two pairs of source and destination and a user-specified weight α to balance the two different needs. The result is a pair of paths connecting the two sources and destinations respectively, with minimal overall cost of the two paths and the shortest route between them. To solve MPP queries, it devises an algorithm by enumerating node pairs. An efficient algorithm based on point-to-point shortest path calculation is proposed to further improve query efficiency. It also presents a smart driving direction system IS proposed to model the dynamic traffic pattern so as to provide a user with the fastest route to a destination with edge failure situation.
Transactions on Emerging Telecommunications Technologies, 2019
Commuting in big cities with heavy traffic is a real-world task faced by many on a daily basis. Finding a suitable path for commuting in real-life complex traffic networks is an important research problem with many applications. The existing work in this domain is based on the travel time and distance from source to destination. However, other than these two factors, there are many additional features that impact the overall travel time and its quality. Some of these additional features include environmental factors, road condition, and the traffic flow. The driving time can be minimized by selecting the most suitable path where there is less congestion and other travel related conditions are favorable. Commuting duration can increase even on the shortest path if there is congestion or the route is blocked. This work presents a mobile crowdsourcing-based model to find suitable commuting path(s) by considering the factors that directly or indirectly influence the overall travel time. Experiment in this work refers the naturalistic driving study to select the travel related features. An algorithm is proposed to find the suitable path from the user provided source to the destination using crowdsourced data generated using mobile application. Unlike other algorithms, the proposed approach can address the network peculiarities where travel cost is not only based on the distance between the nodes but other indirect factors are also involved. This work extracts all possible paths from a source to the destination and then computes the travel cost in terms of distance and satellite factors across the paths. This proposal is evaluated on eight large real-world road network data sets. A comparison is performed with four state-of-the-art pathfinding methods. These include, Floyd-Warshall algorithm, Bellman-Ford algorithm, open shortest path first algorithm, and Dijkstra algorithm. Empirical analysis shows that the additional factors incorporated in the proposed mobile crowdsourcing model while finding a suitable path have a significant impact on the travel time. The results show better performance of the proposed model than its counterparts.
A SURVEY ON OPTIMAL ROUTE QUERIES FOR ROAD NETWORKS
In daily life the need to find optimal routes between two points is critical, for example finding the shortest distance to the nearest hospital. Internet based maps are now widely used for this purpose. Route search and optimal route queries are two important classes of queries based on road network concept. Route search queries find the route according to the given constraints. The optimal route queries find the optimum route from a set of specifications by a user. In road map queries, users have to give the specification of starting point and ending point of their travelling with or without constraints. Some spatial features about the categories and the different locations should be specified along with this. If the travelling constraints are given then it should be unique. These constraints may be either total order or partial order. In this specification order there should be information about both starting point and destination point of the travelling. The optimal route queries optimize the possible routes and give the optimal route that satisfies all the constraints. This paper describes the survey on optimal route query processing, two categories namely optimal route query processing and spatial search with categorical information have been considered, a discussion on technique for optimal route query with constraints and without constraint is also included. The total order needs a specification of list of points and in the same order that they should be visited but that is not required for partial order constraints. Finally this paper concludes with pros and cons of different techniques under optimal route queries.
Feasible Route Search on RoadNetworks by Using Clues
The booming industry of location based services is accumulated many collection of users location trajectories of driving, cycling, hiking. We find the problem of discovering the Most Popular Route (MPR) between two locations by taking the traveling behaviors of many backend users. To determining the waiting time every parking vertex to achieve the minimal on-road time becomes a big challenge which further breaks FIFO property. We propose two efficient algorithms using minimum on-road travel cost function to answer the query. This paper focuses on the highly developed solution is using ACO algorithm. It also applied the method considering flow, distance, cost, and emergency. Given a query location and a set of candidate objects in a road network the kNN search finds the k nearest objects to the query location. We propose balanced search tree index, called G tree. The G tree is road network andconstructed by recursively partitioning the road network into sub-networks and each G-tree node corresponds to a sub-network. Propose a class of routing schemes is finding the nodes of highest utility for routing improving the delay and delivery ratio. Additionally proposed an analytical framework based on fluid models is used to analyze the performance of many opportunistic routing strategies, in heterogeneous settings.
Leveraging Route Saver Based On Location Service in Carpooling System Using K-NN Algorithm
Travel time forecasting is considered as theme of Intelligent Transportation System(ITS) particularly in the topics of advanced traffic management systems (ATMS) and advanced traveler information systems(ATIS). Interests of travel time forecasting model is revived due to LBS(location based service) which is rapidly increasing. Carpooling is also an environment friendly app based on LBS also known as car-sharing in which one can travel to their destination while sharing the vehicle with other passengers. The logic behind this carpooling is, vehicle owner deploys the vehicles sharing application and any passengers like to share can consume it .When more people travel together in one vehicle it give the impact that fuel costs, tolls and the stress of driving will be reduced .Additionally, it frees from earthly gases in the air and traffic congestion.. During high fuel prices and high pollution periods, making use of the car pooling system is an intelligent decision. This proposed system has three modules like user module, LBS module and route saver module. First module allows users to register with destination point they want to reach, number of seat needed along with their payment option. LBS module cluster the user details based on destination point, and matching payment option. The last module frames the intermediate route for the destination point and display the reservation chart of the vehicle allotted to the user using k-NN algorithm .Thus, pre-registration ensures security as only identified people get into the vehicle so that trust can be established. Thus the proposed carpooling system will be effective in reducing environment pollution and save a lot of space in the Parking lot.
Finding shortest paths on real road networks: the case for A*
International Journal of Geographical Information Science, 2009
The problem of identifying the shortest path along a road network is a fundamental problem in network analysis, ranging from route guidance in a navigation system to solving spatial allocation problems. Since this type of problem is solved so frequently, it is important to craft an approach that is as efficient as possible. Based upon past research it is generally accepted that several efficient implementations of the Dijkstra algorithm are the fastest at optimally solving the 'one-to-one' shortest path problem (Cherkassky, et al. 1996). We show that the most efficient, state-of-the-art implementations of Dijkstra can be improved by taking advantage of network properties associated with GIS-sourced data. The results of this paper, derived from tests of different algorithmic approaches on real road networks, will be extremely valuable for application developers and researchers in the GIS community.
Shortest Path Algorithms: An Evaluation Using Real Road Networks
Transportation Science, 1998
The classic problem of finding the shortest path over a network has been the target of many research efforts over the years. These research efforts have resulted in a number of different algorithms and a considerable amount of empirical findings with respect to performance. Unfortunately, prior research does not provide a clear direction for choosing an algorithm when one faces the problem of computing shortest paths on real road networks. Most of the computational testing on shortest path algorithms has been based on randomly generated networks, which may not have the characteristics of real road networks. In this paper, we provide an objective evaluation of 15 shortest path algorithms using a variety of real road networks. Based on the evaluation, a set of recommended algorithms for computing shortest paths on real road networks is identified. This evaluation should be particularly useful to researchers and practitioners in operations research, management science, transportation, ...
Efficient and Effective Route Planning in Road Networks with Probabilistic Data using Skyline Paths
In this paper, we study the problem of effective route search in road networks. Given a pair of source and destination locations, the aim is to find a path from the source to the destination that visits k different types of sites in a particular order as prescribed by the user. The route planning problem has two objectives to optimize: minimize the total path length and maximize the probability of getting served from the k sites. Since the problem has a multi-objective nature, we utilize the skyline setting and retrieve all skyline paths according to the two aggregated attributes. The naïve way of determining the path lengths can involve a large number of shortest path computations. Although the shortest paths between the sites can be pre-computed, the shortest paths from the source to the first type of site and those from the last type of site to the destination cannot be computed in an offline manner as the source and destination points are arbitrary points that are available only at runtime. Since in a large road network, it is prohibitory to compute many shortest paths, we employ a heuristic to approximately solve the problem. The shortest path computation from the source to a site (and similarly, from a site to the destination) is approximated by introducing reference points. The reference points are chosen by employing a gridbased partitioning method on the space underlying the road network. We show that the above heuristic introduces only an additive error to the distance while reducing the running times by up to orders of magnitude.
IJSRST173792 | Optimal Path Calculation for Route Leveraging APIs Using Location Based Service
© 2017 IJSRST | Volume 3 | Issue 7 | Print ABSTRACT A location based service (LBS) is an application for user's mobile device that requires necessary information about where the mobile device is located. Location-based services shall be query-based and provide the end user with valuable information, for e.g LBS enable mobile users to query points-of-interest (e.g.,ATM, restaurants) on various features (e.g., category, price, quality, and variety). They manage points-of-interest (POIs) specific to their applications, and enable mobile users to query for POIs that match with their preferences and time constraints and also it secures a user identity and locality within basic mobile communication services. For this approach this paper provides the survey about various techniques for providing the accurate and efficient query for the mobile users.
Carpooling : the 2 Synchronization Points Shortest Paths Problem
HAL (Le Centre pour la Communication Scientifique Directe), 2013
Carpooling is an appropriate solution to address traffic congestion and to reduce the ecological footprint of the car use. In this paper, we address an essential problem for providing dynamic carpooling: how to compute the shortest driver's and passenger's paths. Indeed, those two paths are synchronized in the sense that they have a common subpath between two points: the location where the passenger is picked up and the one where he is dropped off the car. The passenger path may include time-dependent public transportation parts before or after the common subpath. This defines the 2 Synchronization Points Shortest Path Problem (2SPSPP). We show that the 2SPSPP has a polynomial worst-case complexity. However, despite this polynomial complexity, one needs efficient algorithms to solve it in realistic transportation networks. We focus on efficient computation of optimal itineraries for solving the 2SPSPP, i.e. determining the (optimal) pickup and drop-off points and the two synchronized paths that minimize the total traveling time. We also define restriction areas for reasonable pickup and drop-off points and use them to guide the algorithms using heuristics based on landmarks. Experiments are conducted on real transportation networks. The results show the efficiency of the proposed algorithms and the interest of restriction areas for pickup or drop-off points in terms of CPU time, in addition to its application interest.