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Patrick Jaillet - One of the best experts on this subject based on the ideXlab platform.

  • Zone Path Construction (ZAC) based approaches for effective real-time ridesharing
    Journal of Artificial Intelligence Research, 2021
    Co-Authors: Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet
    Abstract:

    Real-time ridesharing systems such as UberPool, Lyft Line and GrabShare have become hugely popular as they reduce the costs for customers, improve per trip revenue for drivers and reduce traffic on the roads by grouping customers with similar itineraries. The key challenge in these systems is to group the “right” requests to travel together in the “right” available vehicles in real-time, so that the objective (e.g., requests served, revenue or delay) is optimized. This challenge has been addressed in existing work by: (i) generating as many relevant feasible combinations of requests (with respect to the available delay for customers) as possible in real-time; and then (ii) optimizing assignment of the feasible request combinations to vehicles. Since the number of request combinations increases exponentially with the increase in vehicle capacity and number of requests, unfortunately, such approaches have to employ ad hoc heuristics to identify a subset of request combinations for assignment. Our key contribution is in developing approaches that employ zone (abstraction of individual locations) Paths instead of request combinations. Zone Paths allow for generation of significantly more “relevant” combinations (in comparison to ad hoc heuristics) in real-time than competing approaches due to two reasons: (i) Each zone Path can typically represent multiple request combinations; (ii) Zone Paths are generated using a combination of offline and online methods. Specifically, we contribute both myopic (ridesharing assignment focussed on current requests only) and non-myopic (ridesharing assignment considers impact on expected future requests) approaches that employ zone Paths. In our experimental results, we demonstrate that our myopic approach outperforms the current best myopic approach for ridesharing on both real-world and synthetic datasets (with respect to both objective and runtime). We also show that our non-myopic approach obtains 14.7% improvement over existing myopic approach. Our non-myopic approach gets improvements of up to 12.48% over a recent non-myopic approach, NeurADP. Even when NeurADP is allowed to optimize learning over test settings, results largely remain comparable except in a couple of cases, where NeurADP performs better.

  • zone Path Construction zac based approaches for effective real time ridesharing
    arXiv: Artificial Intelligence, 2020
    Co-Authors: Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet
    Abstract:

    Real-time ridesharing systems such as UberPool, Lyft Line, GrabShare have become hugely popular as they reduce the costs for customers, improve per trip revenue for drivers and reduce traffic on the roads by grouping customers with similar itineraries. The key challenge in these systems is to group the "right" requests to travel together in the "right" available vehicles in real-time, so that the objective (e.g., requests served, revenue or delay) is optimized. This challenge has been addressed in existing work by: (i) generating as many relevant feasible (with respect to the available delay for customers) combinations of requests as possible in real-time; and then (ii) optimizing assignment of the feasible request combinations to vehicles. Since the number of request combinations increases exponentially with the increase in vehicle capacity and number of requests, unfortunately, such approaches have to employ ad hoc heuristics to identify a subset of request combinations for assignment. Our key contribution is in developing approaches that employ zone (abstraction of individual locations) Paths instead of request combinations. Zone Paths allow for generation of significantly more "relevant" combinations (in comparison to ad hoc heuristics) in real-time than competing approaches due to two reasons: (i) Each zone Path can typically represent multiple request combinations; (ii) Zone Paths are generated using a combination of offline and online methods. Specifically, we contribute both myopic (ridesharing assignment focussed on current requests only) and non-myopic (ridesharing assignment considers impact on expected future requests) approaches that employ zone Paths. In our experimental results, we demonstrate that our myopic approach outperforms (with respect to both objective and runtime) the current best myopic approach for ridesharing on both real-world and synthetic datasets.

  • zac a zone Path Construction approach for effective real time ridesharing
    International Conference on Automated Planning and Scheduling, 2019
    Co-Authors: Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet
    Abstract:

    Real-time ridesharing systems such as UberPool, Lyft Line, GrabShare have become hugely popular as they reduce the costs for customers, improve per trip revenue for drivers and reduce traffic on the roads by grouping customers with similar itineraries. The key challenge in these systems is to group the right requests to travel in available vehicles in real-time, so that the objective (e.g., requests served, revenue or delay) is optimized. The most relevant existing work has focussed on generating as many relevant feasible (with respect to available delay for customers) combinations of requests (referred to as trips) as possible in real-time. Since the number of trips increases exponentially with the increase in vehicle capacity and number of requests, unfortunately, such an approach has to employ ad hoc heuristics to identify relevant trips.To that end, we propose an approach that generates many zone (abstraction of individual locations) Paths – where each zone Path can represent multiple trips (combinations of requests) – and assigns available vehicles to these zone Paths to optimize the objective. The key advantage of our approach is that these zone Paths are generated using a combination of offline and online methods, consequently allowing for the generation of many more relevant combinations in real-time than competing approaches. We demonstrate that our approach outperforms (with respect to both objective and runtime) the current best approach for ridesharing on both real world and synthetic datasets.

  • ICAPS - ZAC: A Zone Path Construction Approach for Effective Real-Time Ridesharing
    2019
    Co-Authors: Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet
    Abstract:

    Real-time ridesharing systems such as UberPool, Lyft Line, GrabShare have become hugely popular as they reduce the costs for customers, improve per trip revenue for drivers and reduce traffic on the roads by grouping customers with similar itineraries. The key challenge in these systems is to group the right requests to travel in available vehicles in real-time, so that the objective (e.g., requests served, revenue or delay) is optimized. The most relevant existing work has focussed on generating as many relevant feasible (with respect to available delay for customers) combinations of requests (referred to as trips) as possible in real-time. Since the number of trips increases exponentially with the increase in vehicle capacity and number of requests, unfortunately, such an approach has to employ ad hoc heuristics to identify relevant trips.To that end, we propose an approach that generates many zone (abstraction of individual locations) Paths – where each zone Path can represent multiple trips (combinations of requests) – and assigns available vehicles to these zone Paths to optimize the objective. The key advantage of our approach is that these zone Paths are generated using a combination of offline and online methods, consequently allowing for the generation of many more relevant combinations in real-time than competing approaches. We demonstrate that our approach outperforms (with respect to both objective and runtime) the current best approach for ridesharing on both real world and synthetic datasets.

Meghna Lowalekar - One of the best experts on this subject based on the ideXlab platform.

  • Zone Path Construction (ZAC) based approaches for effective real-time ridesharing
    Journal of Artificial Intelligence Research, 2021
    Co-Authors: Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet
    Abstract:

    Real-time ridesharing systems such as UberPool, Lyft Line and GrabShare have become hugely popular as they reduce the costs for customers, improve per trip revenue for drivers and reduce traffic on the roads by grouping customers with similar itineraries. The key challenge in these systems is to group the “right” requests to travel together in the “right” available vehicles in real-time, so that the objective (e.g., requests served, revenue or delay) is optimized. This challenge has been addressed in existing work by: (i) generating as many relevant feasible combinations of requests (with respect to the available delay for customers) as possible in real-time; and then (ii) optimizing assignment of the feasible request combinations to vehicles. Since the number of request combinations increases exponentially with the increase in vehicle capacity and number of requests, unfortunately, such approaches have to employ ad hoc heuristics to identify a subset of request combinations for assignment. Our key contribution is in developing approaches that employ zone (abstraction of individual locations) Paths instead of request combinations. Zone Paths allow for generation of significantly more “relevant” combinations (in comparison to ad hoc heuristics) in real-time than competing approaches due to two reasons: (i) Each zone Path can typically represent multiple request combinations; (ii) Zone Paths are generated using a combination of offline and online methods. Specifically, we contribute both myopic (ridesharing assignment focussed on current requests only) and non-myopic (ridesharing assignment considers impact on expected future requests) approaches that employ zone Paths. In our experimental results, we demonstrate that our myopic approach outperforms the current best myopic approach for ridesharing on both real-world and synthetic datasets (with respect to both objective and runtime). We also show that our non-myopic approach obtains 14.7% improvement over existing myopic approach. Our non-myopic approach gets improvements of up to 12.48% over a recent non-myopic approach, NeurADP. Even when NeurADP is allowed to optimize learning over test settings, results largely remain comparable except in a couple of cases, where NeurADP performs better.

  • zone Path Construction zac based approaches for effective real time ridesharing
    arXiv: Artificial Intelligence, 2020
    Co-Authors: Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet
    Abstract:

    Real-time ridesharing systems such as UberPool, Lyft Line, GrabShare have become hugely popular as they reduce the costs for customers, improve per trip revenue for drivers and reduce traffic on the roads by grouping customers with similar itineraries. The key challenge in these systems is to group the "right" requests to travel together in the "right" available vehicles in real-time, so that the objective (e.g., requests served, revenue or delay) is optimized. This challenge has been addressed in existing work by: (i) generating as many relevant feasible (with respect to the available delay for customers) combinations of requests as possible in real-time; and then (ii) optimizing assignment of the feasible request combinations to vehicles. Since the number of request combinations increases exponentially with the increase in vehicle capacity and number of requests, unfortunately, such approaches have to employ ad hoc heuristics to identify a subset of request combinations for assignment. Our key contribution is in developing approaches that employ zone (abstraction of individual locations) Paths instead of request combinations. Zone Paths allow for generation of significantly more "relevant" combinations (in comparison to ad hoc heuristics) in real-time than competing approaches due to two reasons: (i) Each zone Path can typically represent multiple request combinations; (ii) Zone Paths are generated using a combination of offline and online methods. Specifically, we contribute both myopic (ridesharing assignment focussed on current requests only) and non-myopic (ridesharing assignment considers impact on expected future requests) approaches that employ zone Paths. In our experimental results, we demonstrate that our myopic approach outperforms (with respect to both objective and runtime) the current best myopic approach for ridesharing on both real-world and synthetic datasets.

  • zac a zone Path Construction approach for effective real time ridesharing
    International Conference on Automated Planning and Scheduling, 2019
    Co-Authors: Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet
    Abstract:

    Real-time ridesharing systems such as UberPool, Lyft Line, GrabShare have become hugely popular as they reduce the costs for customers, improve per trip revenue for drivers and reduce traffic on the roads by grouping customers with similar itineraries. The key challenge in these systems is to group the right requests to travel in available vehicles in real-time, so that the objective (e.g., requests served, revenue or delay) is optimized. The most relevant existing work has focussed on generating as many relevant feasible (with respect to available delay for customers) combinations of requests (referred to as trips) as possible in real-time. Since the number of trips increases exponentially with the increase in vehicle capacity and number of requests, unfortunately, such an approach has to employ ad hoc heuristics to identify relevant trips.To that end, we propose an approach that generates many zone (abstraction of individual locations) Paths – where each zone Path can represent multiple trips (combinations of requests) – and assigns available vehicles to these zone Paths to optimize the objective. The key advantage of our approach is that these zone Paths are generated using a combination of offline and online methods, consequently allowing for the generation of many more relevant combinations in real-time than competing approaches. We demonstrate that our approach outperforms (with respect to both objective and runtime) the current best approach for ridesharing on both real world and synthetic datasets.

  • ICAPS - ZAC: A Zone Path Construction Approach for Effective Real-Time Ridesharing
    2019
    Co-Authors: Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet
    Abstract:

    Real-time ridesharing systems such as UberPool, Lyft Line, GrabShare have become hugely popular as they reduce the costs for customers, improve per trip revenue for drivers and reduce traffic on the roads by grouping customers with similar itineraries. The key challenge in these systems is to group the right requests to travel in available vehicles in real-time, so that the objective (e.g., requests served, revenue or delay) is optimized. The most relevant existing work has focussed on generating as many relevant feasible (with respect to available delay for customers) combinations of requests (referred to as trips) as possible in real-time. Since the number of trips increases exponentially with the increase in vehicle capacity and number of requests, unfortunately, such an approach has to employ ad hoc heuristics to identify relevant trips.To that end, we propose an approach that generates many zone (abstraction of individual locations) Paths – where each zone Path can represent multiple trips (combinations of requests) – and assigns available vehicles to these zone Paths to optimize the objective. The key advantage of our approach is that these zone Paths are generated using a combination of offline and online methods, consequently allowing for the generation of many more relevant combinations in real-time than competing approaches. We demonstrate that our approach outperforms (with respect to both objective and runtime) the current best approach for ridesharing on both real world and synthetic datasets.

Pradeep Varakantham - One of the best experts on this subject based on the ideXlab platform.

  • Zone Path Construction (ZAC) based approaches for effective real-time ridesharing
    Journal of Artificial Intelligence Research, 2021
    Co-Authors: Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet
    Abstract:

    Real-time ridesharing systems such as UberPool, Lyft Line and GrabShare have become hugely popular as they reduce the costs for customers, improve per trip revenue for drivers and reduce traffic on the roads by grouping customers with similar itineraries. The key challenge in these systems is to group the “right” requests to travel together in the “right” available vehicles in real-time, so that the objective (e.g., requests served, revenue or delay) is optimized. This challenge has been addressed in existing work by: (i) generating as many relevant feasible combinations of requests (with respect to the available delay for customers) as possible in real-time; and then (ii) optimizing assignment of the feasible request combinations to vehicles. Since the number of request combinations increases exponentially with the increase in vehicle capacity and number of requests, unfortunately, such approaches have to employ ad hoc heuristics to identify a subset of request combinations for assignment. Our key contribution is in developing approaches that employ zone (abstraction of individual locations) Paths instead of request combinations. Zone Paths allow for generation of significantly more “relevant” combinations (in comparison to ad hoc heuristics) in real-time than competing approaches due to two reasons: (i) Each zone Path can typically represent multiple request combinations; (ii) Zone Paths are generated using a combination of offline and online methods. Specifically, we contribute both myopic (ridesharing assignment focussed on current requests only) and non-myopic (ridesharing assignment considers impact on expected future requests) approaches that employ zone Paths. In our experimental results, we demonstrate that our myopic approach outperforms the current best myopic approach for ridesharing on both real-world and synthetic datasets (with respect to both objective and runtime). We also show that our non-myopic approach obtains 14.7% improvement over existing myopic approach. Our non-myopic approach gets improvements of up to 12.48% over a recent non-myopic approach, NeurADP. Even when NeurADP is allowed to optimize learning over test settings, results largely remain comparable except in a couple of cases, where NeurADP performs better.

  • zone Path Construction zac based approaches for effective real time ridesharing
    arXiv: Artificial Intelligence, 2020
    Co-Authors: Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet
    Abstract:

    Real-time ridesharing systems such as UberPool, Lyft Line, GrabShare have become hugely popular as they reduce the costs for customers, improve per trip revenue for drivers and reduce traffic on the roads by grouping customers with similar itineraries. The key challenge in these systems is to group the "right" requests to travel together in the "right" available vehicles in real-time, so that the objective (e.g., requests served, revenue or delay) is optimized. This challenge has been addressed in existing work by: (i) generating as many relevant feasible (with respect to the available delay for customers) combinations of requests as possible in real-time; and then (ii) optimizing assignment of the feasible request combinations to vehicles. Since the number of request combinations increases exponentially with the increase in vehicle capacity and number of requests, unfortunately, such approaches have to employ ad hoc heuristics to identify a subset of request combinations for assignment. Our key contribution is in developing approaches that employ zone (abstraction of individual locations) Paths instead of request combinations. Zone Paths allow for generation of significantly more "relevant" combinations (in comparison to ad hoc heuristics) in real-time than competing approaches due to two reasons: (i) Each zone Path can typically represent multiple request combinations; (ii) Zone Paths are generated using a combination of offline and online methods. Specifically, we contribute both myopic (ridesharing assignment focussed on current requests only) and non-myopic (ridesharing assignment considers impact on expected future requests) approaches that employ zone Paths. In our experimental results, we demonstrate that our myopic approach outperforms (with respect to both objective and runtime) the current best myopic approach for ridesharing on both real-world and synthetic datasets.

  • zac a zone Path Construction approach for effective real time ridesharing
    International Conference on Automated Planning and Scheduling, 2019
    Co-Authors: Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet
    Abstract:

    Real-time ridesharing systems such as UberPool, Lyft Line, GrabShare have become hugely popular as they reduce the costs for customers, improve per trip revenue for drivers and reduce traffic on the roads by grouping customers with similar itineraries. The key challenge in these systems is to group the right requests to travel in available vehicles in real-time, so that the objective (e.g., requests served, revenue or delay) is optimized. The most relevant existing work has focussed on generating as many relevant feasible (with respect to available delay for customers) combinations of requests (referred to as trips) as possible in real-time. Since the number of trips increases exponentially with the increase in vehicle capacity and number of requests, unfortunately, such an approach has to employ ad hoc heuristics to identify relevant trips.To that end, we propose an approach that generates many zone (abstraction of individual locations) Paths – where each zone Path can represent multiple trips (combinations of requests) – and assigns available vehicles to these zone Paths to optimize the objective. The key advantage of our approach is that these zone Paths are generated using a combination of offline and online methods, consequently allowing for the generation of many more relevant combinations in real-time than competing approaches. We demonstrate that our approach outperforms (with respect to both objective and runtime) the current best approach for ridesharing on both real world and synthetic datasets.

  • ICAPS - ZAC: A Zone Path Construction Approach for Effective Real-Time Ridesharing
    2019
    Co-Authors: Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet
    Abstract:

    Real-time ridesharing systems such as UberPool, Lyft Line, GrabShare have become hugely popular as they reduce the costs for customers, improve per trip revenue for drivers and reduce traffic on the roads by grouping customers with similar itineraries. The key challenge in these systems is to group the right requests to travel in available vehicles in real-time, so that the objective (e.g., requests served, revenue or delay) is optimized. The most relevant existing work has focussed on generating as many relevant feasible (with respect to available delay for customers) combinations of requests (referred to as trips) as possible in real-time. Since the number of trips increases exponentially with the increase in vehicle capacity and number of requests, unfortunately, such an approach has to employ ad hoc heuristics to identify relevant trips.To that end, we propose an approach that generates many zone (abstraction of individual locations) Paths – where each zone Path can represent multiple trips (combinations of requests) – and assigns available vehicles to these zone Paths to optimize the objective. The key advantage of our approach is that these zone Paths are generated using a combination of offline and online methods, consequently allowing for the generation of many more relevant combinations in real-time than competing approaches. We demonstrate that our approach outperforms (with respect to both objective and runtime) the current best approach for ridesharing on both real world and synthetic datasets.

Christian Freksa - One of the best experts on this subject based on the ideXlab platform.

  • WSC - Translation of string-and-pin-based shortest Path Construction into data-scalable agent-based computational models
    2018 Winter Simulation Conference (WSC), 2018
    Co-Authors: Yun-ming Shih, Munehiro Fukuda, Collin Gordon, Jasper Van De Ven, Christian Freksa
    Abstract:

    From the viewpoint of strong spatial cognition in graph problems, the shortest Path can be identified in one physical action using strings and pins that respectively represent graph edges and vertices. By pulling a start and an end pin, we can construct a series of stretched strings as the shortest Path. We use agent-based models (ABMs) to translate this action into computational representations. Assuming that a set of strings and pins are hung on a wall with a start pin, agents are disseminated downward to a destination as gravity forces. We implemented three models: a discrete-event, an asynchronous, and an aggregated agent dissemination on top of the MASS (multi-agent spatial simulation) library. To address large-scale network environments, we blended HDFS into MASS so that a graph data set is read over a cluster system in parallel. This paper presents these ABM implementations and performance measurements over a cluster system.

  • TRANSLATION OF STRING-AND-PIN-BASED SHORTEST Path Construction INTO DATA-SCALABLE AGENT-BASED COMPUTATIONAL MODELS
    2018 Winter Simulation Conference (WSC), 2018
    Co-Authors: Yun-ming Shih, Munehiro Fukuda, Collin Gordon, Jasper Van De Ven, Christian Freksa
    Abstract:

    From the viewpoint of strong spatial cognition in graph problems, the shortest Path can be identified in one physical action using strings and pins that respectively represent graph edges and vertices. By pulling a start and an end pin, we can construct a series of stretched strings as the shortest Path. We use agent-based models (ABMs) to translate this action into computational representations. Assuming that a set of strings and pins are hung on a wall with a start pin, agents are disseminated downward to a destination as gravity forces. We implemented three models: a discrete-event, an asynchronous, and an aggregated agent dissemination on top of the MASS (multi-agent spatial simulation) library. To address large-scale network environments, we blended HDFS into MASS so that a graph data set is read over a cluster system in parallel. This paper presents these ABM implementations and performance measurements over a cluster system.

Yun-ming Shih - One of the best experts on this subject based on the ideXlab platform.

  • WSC - Translation of string-and-pin-based shortest Path Construction into data-scalable agent-based computational models
    2018 Winter Simulation Conference (WSC), 2018
    Co-Authors: Yun-ming Shih, Munehiro Fukuda, Collin Gordon, Jasper Van De Ven, Christian Freksa
    Abstract:

    From the viewpoint of strong spatial cognition in graph problems, the shortest Path can be identified in one physical action using strings and pins that respectively represent graph edges and vertices. By pulling a start and an end pin, we can construct a series of stretched strings as the shortest Path. We use agent-based models (ABMs) to translate this action into computational representations. Assuming that a set of strings and pins are hung on a wall with a start pin, agents are disseminated downward to a destination as gravity forces. We implemented three models: a discrete-event, an asynchronous, and an aggregated agent dissemination on top of the MASS (multi-agent spatial simulation) library. To address large-scale network environments, we blended HDFS into MASS so that a graph data set is read over a cluster system in parallel. This paper presents these ABM implementations and performance measurements over a cluster system.

  • TRANSLATION OF STRING-AND-PIN-BASED SHORTEST Path Construction INTO DATA-SCALABLE AGENT-BASED COMPUTATIONAL MODELS
    2018 Winter Simulation Conference (WSC), 2018
    Co-Authors: Yun-ming Shih, Munehiro Fukuda, Collin Gordon, Jasper Van De Ven, Christian Freksa
    Abstract:

    From the viewpoint of strong spatial cognition in graph problems, the shortest Path can be identified in one physical action using strings and pins that respectively represent graph edges and vertices. By pulling a start and an end pin, we can construct a series of stretched strings as the shortest Path. We use agent-based models (ABMs) to translate this action into computational representations. Assuming that a set of strings and pins are hung on a wall with a start pin, agents are disseminated downward to a destination as gravity forces. We implemented three models: a discrete-event, an asynchronous, and an aggregated agent dissemination on top of the MASS (multi-agent spatial simulation) library. To address large-scale network environments, we blended HDFS into MASS so that a graph data set is read over a cluster system in parallel. This paper presents these ABM implementations and performance measurements over a cluster system.