810 research outputs found
Optimization of vehicular networks in smart cities: from agile optimization to learnheuristics and simheuristics
Vehicular ad hoc networks (VANETs) are a fundamental component of intelligent transportation systems in smart cities. With the support of open and real-time data, these networks of inter-connected vehicles constitute an ‘Internet of vehicles’ with the potential to significantly enhance citizens’ mobility and last-mile delivery in urban, peri-urban, and metropolitan areas. However, the proper coordination and logistics of VANETs raise a number of optimization challenges that need to be solved. After reviewing the state of the art on the concepts of VANET optimization and open data in smart cities, this paper discusses some of the most relevant optimization challenges in this area. Since most of the optimization problems are related to the need for real-time solutions or to the consideration of uncertainty and dynamic environments, the paper also discusses how some VANET challenges can be addressed with the use of agile optimization algorithms and the combination of metaheuristics with simulation and machine learning methods. The paper also offers a numerical analysis that measures the impact of using these optimization techniques in some related problems. Our numerical analysis, based on real data from Open Data Barcelona, demonstrates that the constructive heuristic outperforms the random scenario in the CDP combined with vehicular networks, resulting in maximizing the minimum distance between facilities while meeting capacity requirements with the fewest facilities.Peer ReviewedPostprint (published version
Online Ridesharing with Meeting Points [Technical Report]
Nowadays, ridesharing becomes a popular commuting mode. Dynamically arriving
riders post their origins and destinations, then the platform assigns drivers
to serve them. In ridesharing, different groups of riders can be served by one
driver if their trips can share common routes. Recently, many ridesharing
companies (e.g., Didi and Uber) further propose a new mode, namely "ridesharing
with meeting points". Specifically, with a short walking distance but less
payment, riders can be picked up and dropped off around their origins and
destinations, respectively. In addition, meeting points enables more flexible
routing for drivers, which can potentially improve the global profit of the
system. In this paper, we first formally define the Meeting-Point-based Online
Ridesharing Problem (MORP). We prove that MORP is NP-hard and there is no
polynomial-time deterministic algorithm with a constant competitive ratio for
it. We notice that a structure of vertex set, -skip cover, fits well to the
MORP. -skip cover tends to find the vertices (meeting points) that are
convenient for riders and drivers to come and go. With meeting points, MORP
tends to serve more riders with these convenient vertices. Based on the idea,
we introduce a convenience-based meeting point candidates selection algorithm.
We further propose a hierarchical meeting-point oriented graph (HMPO graph),
which ranks vertices for assignment effectiveness and constructs -skip cover
to accelerate the whole assignment process. Finally, we utilize the merits of
-skip cover points for ridesharing and propose a novel algorithm, namely
SMDB, to solve MORP. Extensive experiments on real and synthetic datasets
validate the effectiveness and efficiency of our algorithms.Comment: 18 page
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