Optimizing ride-sharing operations in smart sustainable cities: Challenges and the need for agile algorithms

Martins, Leandro do C.; Torre, Rocío de la; Corlu, Canan G.; Juan, Ángel A.; Masmoudi, Mohamed Amine

doi:10.1016/j.cie.2020.107080

Cited by 49 publications

(24 citation statements)

References 77 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another example refers to connecting vehicles in motion to roadside units, where the dynamic movement of vehicles requires their re-assignment to these units in real-time [46]. Similar requirements can be also found in ride-sharing operations in smart cities, where requests, demands, and traffic conditions alter the expected natural operation of this environment [2].…”

Section: Agile Optimization Algorithms In Itsmentioning

confidence: 95%

“…The static version of the ride-sharing problem [2] consists of a finite set of capacitated heterogeneous vehicles, each one driven by an individual owner, who offers empty seats to users with similar itineraries. Each user requests a service, providing their current location, and drivers pick them up in these locations.…”

Section: An Illustrative Case Studymentioning

confidence: 99%

“…People are increasingly consuming more information through their mobile devices, vehicles are equipped with different intelligent systems, devices are distributed around the cities for gathering and generating information, and urban areas are continuously taking advantage of these information technologies and big data. Consequently, so-called smart cities have emerged, whose scope combines sustainable development with the intelligent management of gathered data in order to enhance the operation of different services within urban areas, such as waste collection management [1], car-sharing/ride-sharing activities [2], the optimal location of recharging stations for electric vehicles (EVs), among others. In this matter, during the past few years, the Internet of things (IoT) has become a popular term that plays a significant role to expand and produce a lot of data through sensors and allows citizens and things to be connected in any situation or with anyone [3].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Edge Computing and IoT Analytics for Agile Optimization in Intelligent Transportation Systems

et al. 2021

Self Cite

View full text Add to dashboard Cite

With the emergence of fog and edge computing, new possibilities arise regarding the data-driven management of citizens’ mobility in smart cities. Internet of Things (IoT) analytics refers to the use of these technologies, data, and analytical models to describe the current status of the city traffic, to predict its evolution over the coming hours, and to make decisions that increase the efficiency of the transportation system. It involves many challenges such as how to deal and manage real and huge amounts of data, and improving security, privacy, scalability, reliability, and quality of services in the cloud and vehicular network. In this paper, we review the state of the art of IoT in intelligent transportation systems (ITS), identify challenges posed by cloud, fog, and edge computing in ITS, and develop a methodology based on agile optimization algorithms for solving a dynamic ride-sharing problem (DRSP) in the context of edge/fog computing.These algorithms allow us to process, in real time, the data gathered from IoT systems in order to optimize automatic decisions in the city transportation system, including: optimizing the vehicle routing, recommending customized transportation modes to the citizens, generating efficient ride-sharing and car-sharing strategies, create optimal charging station for electric vehicles and different services within urban and interurban areas. A numerical example considering a DRSP is provided, in which the potential of employing edge/fog computing, open data, and agile algorithms is illustrated.

show abstract

Section: Agile Optimization Algorithms In Itsmentioning

confidence: 95%

Section: An Illustrative Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Edge Computing and IoT Analytics for Agile Optimization in Intelligent Transportation Systems

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Similarly, ref. [8] conducted a review of optimisation problems related to ridesharing, carsharing, and carpooling, together with the employed methodologies used to solve these problems. The methodologies include exact, approximate, and simulation methods.…”

Section: Previous Review Papermentioning

confidence: 99%

“…A total of 86 papers are selected, from 2008 to 2019, sourced from the Web of Science (WoS) database. The researchers in [8] make use of the term "carsharing" to represent both "ridesharing" and "carpooling" in conducting the review. However, "carsharing" is one part of the shared mobility that is mentioned in [9], which reduced the number of vehicle ownership by sharing the vehicles among people, such as in neighborhood carsharing.…”

Section: Previous Review Papermentioning

confidence: 99%

Shared Mobility Problems: A Systematic Review on Types, Variants, Characteristics, and Solution Approaches

et al. 2021

View full text Add to dashboard Cite

The Shared Mobility Problems (SMP) with the rideshare concept based on sharing a vehicle are fast becoming a trend in many urban cities around the world. Examples of these problems are like ridesharing, carpooling, taxisharing, buspooling, vanpooling, and multi-modal ridesharing. This is the new way to access transportation services by those who are propelling the sharing economy, where access rather than ownership is the new norm. This paper provides a systematic review of SMP using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) method. A total of 110 papers from the last decade are selected from 12 abstracts and citation databases to be reviewed and classified. This is done based on the problem types, variants, characteristics, and solution approaches. The current trends and analysis of the survey findings are also summarised. From this systematic review, it is observed that both the time window and multi-objective problems are popular among the researchers, while the minimisation of the total cost is the main concern in the literature of the SMP. Both static and dynamic cases of the SMP are the most researched where heuristic and metaheuristic approaches are widely adopted by the researchers in the literature. Finally, challenges and suggestions for future work are discussed and highlighted.

show abstract

Two‐stage stochastic one‐to‐many driver matching for ridesharing

2023

View full text Add to dashboard Cite

We introduce a modeling framework for stochastic rider‐driver matching in many‐to‐one ridesharing systems, in which drivers have to be selected before the exact rider demand is known. The modeling framework allows for the use of driver booking fees and penalties for unmatched drivers, therefore supporting different system operating modes. We model this problem as a two‐stage stochastic set packing problem. To tackle the intractability of the stochastic problem, we introduce three model approximations and evaluate them on a large set of benchmark instances for three different system operating modes. Our computational experiments show the superiority of some model approximations over others and provide valuable insights on the impact of penalties and booking fees on the system's profitability and user satisfaction.

show abstract

Optimizing ride-sharing operations in smart sustainable cities: Challenges and the need for agile algorithms

Cited by 49 publications

References 77 publications

Edge Computing and IoT Analytics for Agile Optimization in Intelligent Transportation Systems

Edge Computing and IoT Analytics for Agile Optimization in Intelligent Transportation Systems

Shared Mobility Problems: A Systematic Review on Types, Variants, Characteristics, and Solution Approaches

Two‐stage stochastic one‐to‐many driver matching for ridesharing

Contact Info

Product

Resources

About