Simulation approach for investigating dynamics of passenger matching problem in smart ridesharing system

Thaithatkul, Phathinan; Seo, Toru; Kusakabe, Takahiko; Asakura, Yasuo

doi:10.1016/j.trpro.2017.03.075

Cited by 9 publications

(5 citation statements)

References 6 publications

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“…Some other approaches for matching algorithms for dynamic ridesharing can be found in [86], [87], [88] and [89]. The authors in [86] proposed a match making algorithm based on partitions.…”

Section: Matching Algorithms and Techniquesmentioning

confidence: 99%

“…Optimization approaches for matching drivers and passengers in dynamic ridesharing scenarios [84] Real-time approach for peer-to-peer matching for flexible ride-sharing system (ESTAM) [85] Matching algorithms for dynamic ridesharing [86], [87], [88] and [89] Clustering Algorithms and Techniques Clustering techniques to identify high potential opportunities for ridesharing in pick-up and drop-off (PUDO) locations [92] Cluster-first-route-second approach for large-scale ridesharing scenarios containing thousands of participants [93] Clustering vehicle trajectories for ridesharing (TOPOSCAN) [94] Formal connection between clustering and set partitioning for large-scale ridesharing [95] Hierarchical clustering for ridesharing application [96] Pathfinding, Routing & Scheduling Algorithms Ant colony optimization (ACO) heuristics and geosocial networks for solving ridesharing routing paths [98] Theoretical basis and efficient implementation for empty-car routing problem in ridesharing [100] Task Scheduling, Assignment and Management Heuristic greedy approach and algorithm termed as Saving Most First (SMF) to for task assignment [103] Two-stage task assignment coopetition model by utilizing three-way decision classification (TWD) [104] Machine and Deep Learning Techniques Intelligent Complementary Ride-Sharing System termed as Plus Go [105] Machine learning approach for package delivery framework through multi-hop ridesharing [106] Distributed optimized deep learning framework for dispatching vehicles in largescale ridesharing (DeepPool) [107] Deep reinforcement learning approach for joint passengers and goods transportation (FlexPool) [108] Transfer learning for joint passengers and goods transportation (CoTrans) [109] Heuristics and Evolutionary Computing Algorithms…”

Section: Focus Area Work Contributions References Matching Algorithms...mentioning

confidence: 99%

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Ridesharing and Crowdsourcing for Smart Cities: Technologies, Paradigms and Use Cases

et al. 2023

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Recent technology developments and the numerous availabilities of mobile users, devices and Internet technologies together with the growing focus on reducing traffic congestion and emissions in urban areas have led to the emergence of new paradigms for ridesharing and crowdsourcing for smart cities. Compared to carpooling approaches where the driver and participant passengers or riders are usually prearranged and the journey details known beforehand, the paradigm for ridesharing requires the participants to be selected at short notice and the rider trips are often dynamically formed. Crowdsourcing techniques and approaches are well suited to match drivers and riders for these dynamic scenarios, although there are many challenges to be addressed. This paper aims to survey this new paradigm of ridesharing and crowdsourcing for smart city transportation environments from several technological and social perspectives including: (1) Ridesharing and architecture in transportation; (2) Techniques for ridesharing; (3) Artificial intelligence for ridesharing; (4) Autonomous vehicles and systems ridesharing; and (5) Security, policy and pricing strategies. The paper concludes with some use cases and lessons learned for the practical deployment of ridesharing and crowdsourcing platforms for smart cities.

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“…Some other approaches for matching algorithms for dynamic ridesharing can be found in [86], [87], [88] and [89]. The authors in [86] proposed a match making algorithm based on partitions.…”

Section: Matching Algorithms and Techniquesmentioning

confidence: 99%

Section: Focus Area Work Contributions References Matching Algorithms...mentioning

confidence: 99%

Ridesharing and Crowdsourcing for Smart Cities: Technologies, Paradigms and Use Cases

et al. 2023

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“…Markov et al designed a simulation of an on-demand mobility service based on the City of Chicago, Illinois, to find a socially optimal service design [18]. Thaithatkul et al investigated the existence of day-to-day homogeneity by simulating the dynamics of a smart ridesharing system (SRS) [19]. Inturri et al constructed an agent-based simulation model, compared Demand Responsive Shared Transport (DRST) with taxi services and applied it to a real case in Italy [20].…”

Section: Literature Reviewmentioning

confidence: 99%

Simulation-Based Modelling of the Impact of Ridesharing on Urban System

Sun¹,

Liu²,

Zhang³

et al. 2022

Int. j. simul. model.

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This paper follows the basic principles of taxi dynamics and uses an agent-based modelling approach (ABM) to simulate the underlying road network, vehicles and passenger situations in an urban environment and to carry out evaluations. First, based on two evaluation metrics: passenger travel time and total number of passengers transported, a comparison is made between the ride-hailing system (RH) and the system with ridesharing under different supply and demand rates, revealing the impact of ridesharing on system efficiency at urban scale. Then, in the ridesharing system (RS), four levels of supply are taken as examples, and passenger travel time is disaggregated to quantify how the definition of vicinity in the matching process affects the passenger travel time, passenger waiting time and vehicle detour time in a given city. Finally, a summary of the level of supply and the range of vicinity where ridesharing can be effective is presented in terms of the total distance travelled by the system. From a city management perspective, these results can help in the analysis and planning of efficient transport services.

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“…Until the mid-1990s, the time value was considered to be linear (equal to some percentage of wage rate [5,26,28]) or considered as one of possible passenger activities [12,14,21,27]. Work in the mid-1990s confirmed and quantified non-linearities in time value and revealed a group of nonlinearities.…”

Section: Related Workmentioning

confidence: 99%

Empty Vehicle Redistribution with Time Windows in Autonomous Taxi Systems

Babicheva

Cebecauer

Barth

et al. 2021

ACM/IMS Trans. Data Sci.

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In this article, we investigate empty vehicle redistribution algorithms with time windows for personal rapid transit or autonomous station-based taxi services, from a passenger service perspective. We present an Index Based Redistribution Time Limited algorithm that improves upon existing algorithms by incorporating expected passenger arrivals and predicted waiting times limitations. We evaluate 17 variations of algorithms on a test case in Stockholm, Sweden. The results show that the combination of Send The Nearest and Index Based Redistribution Time Limited algorithms provides promising results for both Poisson arrivals and real demand, outperforming the other tested methods, in terms of passenger waiting time and number of passengers not served within their time windows.

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Simulation approach for investigating dynamics of passenger matching problem in smart ridesharing system

Cited by 9 publications

References 6 publications

Ridesharing and Crowdsourcing for Smart Cities: Technologies, Paradigms and Use Cases

Ridesharing and Crowdsourcing for Smart Cities: Technologies, Paradigms and Use Cases

Simulation-Based Modelling of the Impact of Ridesharing on Urban System

Empty Vehicle Redistribution with Time Windows in Autonomous Taxi Systems

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