Posted Content•
The Optimal Route and Stops for a Group of Users in a Road Network
TL;DR: This paper introduces several optimization problems to recommend a suitable route and stops of a vehicle, in a road network, for a group of users intending to travel collectively, and proposes a novel near-optimal polynomial-time-and-space heuristic algorithm for the ORIS query.
Abstract: Recently, with the advancement of the GPS-enabled cellular technologies, the location-based services (LBS) have gained in popularity. Nowadays, an increasingly larger number of map-based applications enable users to ask a wider variety of queries. Researchers have studied the ride-sharing, the carpooling, the vehicle routing, and the collective travel planning problems extensively in recent years. Collective traveling has the benefit of being environment-friendly by reducing the global travel cost, the greenhouse gas emission, and the energy consumption. In this paper, we introduce several optimization problems to recommend a suitable route and stops of a vehicle, in a road network, for a group of users intending to travel collectively. The goal of each problem is to minimize the aggregate cost of the individual travelers' paths and the shared route under various constraints. First, we formulate the problem of determining the optimal pair of end-stops, given a set of queries that originate and terminate near the two prospective end regions. We outline a baseline polynomial-time algorithm and propose a new faster solution - both calculating an exact answer. In our approach, we utilize the path-coherence property of road networks to develop an efficient algorithm. Second, we define the problem of calculating the optimal route and intermediate stops of a vehicle that picks up and drops off passengers en-route, given its start and end stoppages, and a set of path queries from users. We outline an exact solution of both time and space complexities exponential in the number of queries. Then, we propose a novel polynomial-time-and-space heuristic algorithm that performs reasonably well in practice. We also analyze several variants of this problem under different constraints. Last, we perform extensive experiments that demonstrate the efficiency and accuracy of our algorithms.
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24 May 2018
TL;DR: This paper introduces a generic similarity measure between a query object and a data object that helps to define the nearest neighbors according to user requirements, and proposes some pruning strategies that can quickly compute k-NNs (or top-k) facility trajectories for a given user trajectory.
Abstract: In this paper, we address a popular query involving trajectories, namely, the Maximizing Reverse k-Nearest Neighbors for Trajectories (MaxRkNNT) query. Given a set of existing facility trajectories (e.g., bus routes), a set of user trajectories (e.g., daily commuting routes of users) and a set of query facility trajectories (e.g., proposed new bus routes), the MaxRkNNT query finds the proposed facility trajectory that maximizes the cardinality of reverse k-Nearest Neighbors (NNs) set for the query trajectories. A major challenge in solving this problem is to deal with complex computation of nearest neighbors (or similarities) with respect to multi-point queries and data objects. To address this problem, we first introduce a generic similarity measure between a query object and a data object that helps us to define the nearest neighbors according to user requirements. Then, we propose some pruning strategies that can quickly compute k-NNs (or top-k) facility trajectories for a given user trajectory. Finally, we propose a filter and refinement technique to compute the MaxRkNNT. Our experimental results show that our proposed approach significantly outperforms the baseline for both real and synthetic datasets.
5 citations
TL;DR: The proposed GMP algorithm uses group computation to avoid the redundant computation of network distances between the query and data points and the proposed solution’s effectiveness and efficiency is demonstrated.
Abstract: Advances in mobile technologies and map-based applications enables users to utilize sophisticated spatial queries, including k-nearest neighbor and shortest path queries. Often, location-based servers are used to handle multiple simultaneous queries because of the popularity of map-based applications. This study focuses on the efficient processing of multiple concurrent k-farthest neighbor (kFN) queries in road networks. For a positive integer k, query point q, and set of data points P, a kFN query returns k data points farthest from the query point q. For addressing multiple concurrent spatial queries, traditional location-based servers based on one-query-at-a-time processing are unsuitable owing to high redundant computation costs. Therefore, we propose a group processing of multiple kFN (GMP) algorithm to process multiple kFN queries in road networks. The proposed GMP algorithm uses group computation to avoid the redundant computation of network distances between the query and data points. The experiments using real-world roadmaps demonstrate the proposed solution's effectiveness and efficiency.
4 citations
Cites background from "The Optimal Route and Stops for a G..."
...of spatial queries has become an important LBS research topic [7], [8], [27], [36], [37], [52], [53]....
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...Although the group computation of spatial queries has received considerable attention [7], [8], [27], [36], [37], [52], [53], group computation has not been...
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...have proven to be effective in multiple applications involving high-load conditions [7], [8], [15], [19]–[21], [27]–[31], [34]–[37], [52], [53]....
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01 Feb 2020
TL;DR: This work proposes an effective query processing method for the CTP query using G-tree index structure on a road network, and shows that the proposed method can be obtained the optimal query result without being affected the limitations or constraints of the previous studies.
Abstract: Since the sharing economy is defined as economic model based on combination of social relationships, many related services of sharing economy have emerged. Among these services, the location based ride sharing services have been the most common. We discuss a collective trip planning (CTP) query that minimizes the overall cost, one of the problems addressed in ride sharing services. The CTP query finds a meeting point that minimizes the overall cost to pick up passengers. Although many researches related to the CTP query have been conducted, there is a problem that the query performance is effective only in a specific situation. Therefore, we propose an effective query processing method for the CTP query using G-tree index structure on a road network. Furthermore, We analyze the limitations of the previous researches, and show that the proposed method can be obtained the optimal query result without being affected the limitations or constraints of the previous studies.
1 citations
Cites background from "The Optimal Route and Stops for a G..."
...[4] proposed a optimal end stop (OES) query to find the optimal pair of st and en such that st is a gathering point and en is a scattering point for each user get to their desired destinations....
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Posted Content•
TL;DR: In this article, the authors propose LikeMind, a POI recommendation system which tackles the challenges of cold start, customizability, contextuality, and explainability by exploiting look-alike groups mined in public POI datasets.
Abstract: Recommending Points-of-Interest (POIs) is surfacing in many location-based applications. The literature contains personalized and socialized POI recommendation approaches which employ historical check-ins and social links to make recommendations. However these systems still lack customizability (incorporating session-based user interactions with the system) and contextuality (incorporating the situational context of the user), particularly in cold start situations, where nearly no user information is available. In this paper, we propose LikeMind, a POI recommendation system which tackles the challenges of cold start, customizability, contextuality, and explainability by exploiting look-alike groups mined in public POI datasets. LikeMind reformulates the problem of POI recommendation, as recommending explainable look-alike groups (and their POIs) which are in line with user's interests. LikeMind frames the task of POI recommendation as an exploratory process where users interact with the system by expressing their favorite POIs, and their interactions impact the way look-alike groups are selected out. Moreover, LikeMind employs "mindsets", which capture actual situation and intent of the user, and enforce the semantics of POI interestingness. In an extensive set of experiments, we show the quality of our approach in recommending relevant look-alike groups and their POIs, in terms of efficiency and effectiveness.
References
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01 Jan 2014
TL;DR: This chapter provides an overview of the fundamentals of algorithms and their links to self-organization, exploration, and exploitation.
Abstract: Algorithms are important tools for solving problems computationally. All computation involves algorithms, and the efficiency of an algorithm largely determines its usefulness. This chapter provides an overview of the fundamentals of algorithms and their links to self-organization, exploration, and exploitation. A brief history of recent nature-inspired algorithms for optimization is outlined in this chapter.
8,285 citations
Book•
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TL;DR: Pseudo-code explanation of the algorithms coupled with proof of their accuracy makes this book a great resource on the basic tools used to analyze the performance of algorithms.
Abstract: If you had to buy just one text on algorithms, Introduction to Algorithms is a magnificent choice. The book begins by considering the mathematical foundations of the analysis of algorithms and maintains this mathematical rigor throughout the work. The tools developed in these opening sections are then applied to sorting, data structures, graphs, and a variety of selected algorithms including computational geometry, string algorithms, parallel models of computation, fast Fourier transforms (FFTs), and more. This book's strength lies in its encyclopedic range, clear exposition, and powerful analysis. Pseudo-code explanation of the algorithms coupled with proof of their accuracy makes this book is a great resource on the basic tools used to analyze the performance of algorithms.
2,972 citations
TL;DR: This paper reviews the exact algorithms based on the branch and bound approach proposed in the last years for the solution of the basic version of the vehicle routing problem (VRP), where only the vehicle capacity constraints are considered, and concludes by examining possible future directions of research in this field.
1,019 citations
"The Optimal Route and Stops for a G..." refers background in this paper
...ning (TP)[24], [20], the optimal sequenced route (OSR)[38], [11], [36], [7], [39], the keyword-aware optimal route (KOR)[6], the carpooling [17], [49], [48], the vehicle routing (VRP)[31], [4], [29], [43], the collective travel planning (CTP)[37], and the Steiner diagram [3], [8], [14] problems. Detour ride-sharing is another related, yet different, problem [13], [18]. Be that as it may, none of these...
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...ingle vehicle. Again, unlike the VRP and the CTP problems, our queries do not require that the vehicle passes through any node(s) specified in the input. Therefore, the techniques in [31], [4], [29], [43], [37], [3], [8], and [14] do not help in solving our problems. TheVRP and the CTP queries fare better in addressing the first challenge of the ride-sharing system than in dealing with the second one....
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TL;DR: A more general mathematical model for real-time high-capacity ride-sharing that scales to large numbers of passengers and trips and dynamically generates optimal routes with respect to online demand and vehicle locations is presented.
Abstract: Ride-sharing services are transforming urban mobility by providing timely and convenient transportation to anybody, anywhere, and anytime. These services present enormous potential for positive societal impacts with respect to pollution, energy consumption, congestion, etc. Current mathematical models, however, do not fully address the potential of ride-sharing. Recently, a large-scale study highlighted some of the benefits of car pooling but was limited to static routes with two riders per vehicle (optimally) or three (with heuristics). We present a more general mathematical model for real-time high-capacity ride-sharing that (i) scales to large numbers of passengers and trips and (ii) dynamically generates optimal routes with respect to online demand and vehicle locations. The algorithm starts from a greedy assignment and improves it through a constrained optimization, quickly returning solutions of good quality and converging to the optimal assignment over time. We quantify experimentally the tradeoff between fleet size, capacity, waiting time, travel delay, and operational costs for low- to medium-capacity vehicles, such as taxis and van shuttles. The algorithm is validated with ∼3 million rides extracted from the New York City taxicab public dataset. Our experimental study considers ride-sharing with rider capacity of up to 10 simultaneous passengers per vehicle. The algorithm applies to fleets of autonomous vehicles and also incorporates rebalancing of idling vehicles to areas of high demand. This framework is general and can be used for many real-time multivehicle, multitask assignment problems.
920 citations
"The Optimal Route and Stops for a G..." refers background in this paper
...In recent years, several studies, [11], [19], [2], have demonstrated the benefits of ride-sharing in reducing the traffic congestion [11], the number of DWI fatalities [19], and the greenhouse gas emission [2]....
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TL;DR: In this paper, the most important properties of network-based moving objects are presented and discussed and a framework is proposed where the user can control the behavior of the generator by re-defining the functionality of selected object classes.
Abstract: Benchmarking spatiotemporal database systems requires the definition of suitable datasets simulating the typical behavior of moving objects. Previous approaches for generating spatiotemporal data do not consider that moving objects often follow a given network. Therefore, benchmarks require datasets consisting of such “network-based” moving objects. In this paper, the most important properties of network-based moving objects are presented and discussed. Essential aspects are the maximum speed and the maximum capacity of connections, the influence of other moving objects on the speed and the route of an object, the adequate determination of the start and destination of an object, the influence of external events, and time-scheduled traffic. These characteristics are the basis for the specification and development of a new generator for spatiotemporal data. This generator combines real data (the network) with user-defined properties of the resulting dataset. A framework is proposed where the user can control the behavior of the generator by re-defining the functionality of selected object classes. An experimental performance investigation demonstrates that the chosen approach is suitable for generating large data sets.
889 citations
"The Optimal Route and Stops for a G..." refers background or methods in this paper
...ACM, New York, NY, USA, 10 pages. https://doi.org/10.1145/3139958.3140061 Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page....
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...SIGSPATIAL’17, November 7–10, 2017, Los Angeles Area, CA, USA © 2017 Association for Computing Machinery....
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..., the road network graph of the San Francisco city [3]....
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...In the experiments, we have used a road network graph of San Francisco, CA, USA [3], with 174956 nodes and 223001 bidirectional edges....
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