Optimal Demand-Aware Ride-Sharing Routing

Lin, Qiulin; Dengt, Lei; Sun, Jingzhou; Chen, Minghua

doi:10.1109/infocom.2018.8486278

Cited by 27 publications

(13 citation statements)

References 15 publications

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“…Beirigo, Schulte, and Negenborn (2018) use OSMnx to model service levels, operational and infrastructure costs, and fleet utilization in hybrid street networks with both autonomous‐ready and not autonomous‐ready zones. Lin, Deng, Sun, and Chen (2018) model Manhattan’s street network alongside travel demand data to optimize ride‐share routing. Luo et al (2020) model Shanghai’s street network to predict demand for electric vehicle sharing systems, while Zhang, Lin, and Mi (2019) model Shanghai’s bicycle network to propose a framework for planning dockless bike‐sharing services’ geofences.…”

Section: Empirical Street Network Science With Osmnxmentioning

confidence: 99%

The right tools for the job: The case for spatial science tool‐building

Boeing

2020

Transactions in GIS

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Do I need to know the precise polygonal geometries of Los Angeles and the University of Southern California (USC) to assert that the latter is within the former? No. My mind contains no such precise geometric model of points and lines, yet I know that USC is in Los Angeles. When humans reason with the real world, they focus on its objects, relations, and processes-rather than starting with geometry-because these are the keys to

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Section: Empirical Street Network Science With Osmnxmentioning

confidence: 99%

The right tools for the job: The case for spatial science tool‐building

Boeing

2020

Transactions in GIS

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“…Proposition 3. The problem MP is NP-hard as it covers the NP-hard single-vehicle demand-aware routing problem in [22] as a special case.…”

Section: Problem Formulationmentioning

confidence: 99%

“…4. • Independent routing: our previous demand-aware routing algorithm for single vehicle only [22] and an intuitive uniform request-vehicle assignment scheme for assigning multiple appearing requests to multiple vehicles in the same region. • Fastest routing: a demand-oblivious fastest routing algorithm and a uniform request-vehicle assignment scheme.…”

Section: Schemes For Comparison and Performance Metricmentioning

confidence: 99%

“…Thanks to the advance in machine learning and data analytics, statistical knowledge of future travel requests can be efficiently learned by leveraging their regular hourly/daily/weekly patterns [31]. This approach opens up new design space for optimizing ride-sharing systems, and the initial success of developing demand-aware ride-sharing routing solution [22] is encouraging.…”

Section: Introductionmentioning

confidence: 99%

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A Probabilistic Approach for Demand-Aware Ride-Sharing Optimization

Lin

Chen

et al. 2019

Proceedings of the Twentieth ACM International Symposium on Mobile Ad Hoc Networking and Computing

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Ride-sharing is a modern urban-mobility paradigm with tremendous potential in reducing congestion and pollution. Demand-aware design is a promising avenue for addressing a critical challenge in ride-sharing systems, namely joint optimization of request-vehicle assignment and routing for a fleet of vehicles. In this paper, we develop a probabilistic demand-aware framework to tackle the challenge. We focus on maximizing the expected number of passenger pickups, given the probability distributions of future demands. The key idea of our approach is to assign requests to vehicles in a probabilistic manner. It differentiates our work from existing ones and allows us to explore a richer design space to tackle the requestvehicle assignment puzzle with a performance guarantee but still keeping the final solution practically implementable. The optimization problem is non-convex, combinatorial, and NP-hard in nature. As a key contribution, we explore the problem structure and propose an elegant approximation of the objective function to develop a dual-subgradient heuristic. We characterize a condition under which the heuristic generates a (1 − 1/e) approximation solution. Our solution is simple and scalable, amendable for practical implementation. Results of numerical experiments based on real-world traces in Manhattan show that, as compared to a conventional demand-oblivious scheme, our demand-aware solution improves the passenger pickups by up to 46%. The results also show that joint optimization at the fleet level leads to 19% more pickups than that by separate optimizations at individual vehicles.

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“…Q1: How to predict the city-wide demands of passenger orders? The passenger travel demands usually show weekly and daily pattern [11], [12]. Most demands prediction work focuses on estimating the order number for a given location and time [13], [14], while few of them study the passenger flows [12].…”

Section: Introductionmentioning

confidence: 99%

PPtaxi: Non-Stop Package Delivery via Multi-Hop Ridesharing

Chen

Guo

Tang

et al. 2020

IEEE Trans. on Mobile Comput.

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City-wide package delivery becomes popular due to the dramatic rise of online shopping. It places a tremendous burden on the traditional logistics industry, which relies on dedicated couriers and is labor-intensive. Leveraging the ridesharing systems is a promising alternative, yet existing solutions are limited to one-hop ridesharing or need consignment warehouses as relays. In this paper, we propose a new package delivery scheme which takes advantage of multi-hop ridesharing and is entirely consignment free. Specifically, a package is assigned to a taxi which is guided to deliver the package all along to its destination while transporting successive passengers. We tackle it with a two-phase solution, named PPtaxi. In the first phase, we use the Multivariate Gauss distribution and Bayesian inference to predict the passenger orders. In the second phase, both the computation efficiency and solution effectiveness are considered to plan package delivery routes. We evaluate PPtaxi with a real-world dataset from an online taxi-taking platform and compare it with multiple benchmarks. The results show that the successful delivery rate of packages with our solution can reach 95% on average during the daytime, and is at most 46.9% higher than those of the benchmarks.

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Optimal Demand-Aware Ride-Sharing Routing

Cited by 27 publications

References 15 publications

The right tools for the job: The case for spatial science tool‐building

The right tools for the job: The case for spatial science tool‐building

A Probabilistic Approach for Demand-Aware Ride-Sharing Optimization

PPtaxi: Non-Stop Package Delivery via Multi-Hop Ridesharing

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