Agent Based Modelling for Simulating Taxi Services

Grau, Josep Maria Salanova; Estrada, Miquel

doi:10.1016/j.procs.2015.05.162

Cited by 27 publications

(18 citation statements)

References 9 publications

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“…The previous study on agent-based modeling for taxi behavior analyses have compared the measured and the modeled travel distance, and travel time with the cost, to validate the model. However, for travel time, results were scattered between the measured and the model data [12], for which two issues regarding road network density and routing were mentioned. In the agent-based modeling presented here, taxi service modeling was categorized based on weekday and weekend.…”

Section: Discussionmentioning

confidence: 99%

“…Agent-based simulation in the field of computational science has proved to become a powerful tool for analyzing complex problems, where random or stochastic behavior, as similar to the taxi behavior, can be presented together with behavioral rules. In this regard, [12] proposed a discrete event simulation model for modeling the behavior of agents operating in a city road network of which agents make their own decisions for making trips. Similarly, [6] further provided a multi-agent-based simulation, which modeled taxi driver's strategies as a decentralized discrete event, focusing on modeling only the taxi driver's behavior, which was designed to make an aggregated pattern of taxi movement as similar to the real world.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Agent-based modeling and simulation describes the dynamic action of an entity i.e., taxi agent governed by behavior rule and properties, similar to the work presented in [6,11,12], to emulate the taxi behavior in Bangkok, Thailand.…”

Section: Introductionmentioning

confidence: 99%

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Agent-Based Modeling of Taxi Behavior Simulation with Probe Vehicle Data

Ranjit

Witayangkurn

Nagai

et al. 2018

IJGI

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Abstract:Taxi behavior is a spatial-temporal dynamic process involving discrete time dependent events, such as customer pick-up, customer drop-off, cruising, and parking. Simulation models, which are a simplification of a real-world system, can help understand the effects of change of such dynamic behavior. In this paper, agent-based modeling and simulation is proposed, that describes the dynamic action of an agent, i.e., taxi, governed by behavior rules and properties, which emulate the taxi behavior. Taxi behavior simulations are fundamentally done for optimizing the service level for both taxi drivers as well as passengers. Moreover, simulation techniques, as such, could be applied to another field of application as well, where obtaining real raw data are somewhat difficult due to privacy issues, such as human mobility data or call detail record data. This paper describes the development of an agent-based simulation model which is based on multiple input parameters (taxi stay point cluster; trip information (origin and destination); taxi demand information; free taxi movement; and network travel time) that were derived from taxi probe GPS data. As such, agent's parameters were mapped into grid network, and the road network, for which the grid network was used as a base for query/search/retrieval of taxi agent's parameters, while the actual movement of taxi agents was on the road network with routing and interpolation. The results obtained from the simulated taxi agent data and real taxi data showed a significant level of similarity of different taxi behavior, such as trip generation; trip time; trip distance as well as trip occupancy, based on its distribution. As for efficient data handling, a distributed computing platform for large-scale data was used for extracting taxi agent parameter from the probe data by utilizing both spatial and non-spatial indexing technique.

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Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Agent-Based Modeling of Taxi Behavior Simulation with Probe Vehicle Data

Ranjit

Witayangkurn

Nagai

et al. 2018

IJGI

View full text Add to dashboard Cite

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“…The demand for taxi services has been estimated by the Metropolitan Taxi Institute (IMT) as more than 200,000 trips per day, which corresponds to 1.52 % of the total number of trips in the wider area (roughly 13,000,000 trips daily). Salanova (2013) estimates the demand for taxi trips at 60,000,000 trips per year based on the GPS trackers collecting origin and destination of trips during the last ten years. On the supply side, the input data has been calculated by Salanova (2013) based on the data provided by the IMT surveys where all the drivers participated.…”

Section: Taxi Supply and Demand In Barcelonamentioning

confidence: 99%

“…Salanova (2013) estimates the demand for taxi trips at 60,000,000 trips per year based on the GPS trackers collecting origin and destination of trips during the last ten years. On the supply side, the input data has been calculated by Salanova (2013) based on the data provided by the IMT surveys where all the drivers participated.…”

Section: Taxi Supply and Demand In Barcelonamentioning

confidence: 99%

Large-scale microscopic simulation of taxi services. Berlin and Barcelona case studies

Maciejewski

Grau

Bischoff

et al. 2016

J Ambient Intell Human Comput

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The paper presents research on large-scale microscopic simulation of taxi services in Berlin and Barcelona based on floating car data collected by local taxi fleets. Firstly, Berlin's and Barcelona's taxi markets are shortly described and the demand and supply data obtained from FCD analysed. Secondly, the online taxi dispatching problem formulation for this specific case is given, followed by the definition of two real-time rule-based heuristics used to dispatch taxis dynamically within the simulation. Finally, the simulation setup in MATSim is described, and the results obtained with both heuristics are analysed and compared in terms of dispatching performance, proving the effectiveness of the second strategy at different demand and supply scales. This paper is an extended version of Maciejewski and Bischoff 2015, where only the Berlin case study was presented.

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Finding time‐robust fuel‐efficient paths for a call‐taxi in a stochastic city road network

Godwin

Sajeev

George

2016

J of Advced Transportation

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Summary Intra‐city commuting is being revolutionized by call‐taxi services in many developing countries such as India. A customer requests a taxi via phone, and it arrives at the right time and at the right location for the pick‐up. This mode of intra‐city travel has become one of the most reliable and convenient modes of transportation for customers traveling for business and non‐business purposes. The increased number of vehicles on city roads and raising fuel costs has prompted a new type of transportation logistics problem of finding a fuel‐efficient and quickest path for a call‐taxi through a city road network, where the travel times are stochastic. The stochastic travel time of the road network is induced by obstacles such as the traffic signals and intersections. The delay and additional fuel consumption at each of these obstacles are calculated that are later imputed to the total travel time and fuel consumption of a path. A Monte‐Carlo simulation‐based approach is proposed to identify unique fuel‐efficient paths between two locations in a city road network where each obstacle has a delay distribution. A multi‐criteria score is then assigned to each unique path based on the probability that the path is fuel efficient, the average travel time of the path and the coefficient of variation of the travel times of the path. Copyright © 2016 John Wiley & Sons, Ltd.

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Agent Based Modelling for Simulating Taxi Services

Cited by 27 publications

References 9 publications

Agent-Based Modeling of Taxi Behavior Simulation with Probe Vehicle Data

Agent-Based Modeling of Taxi Behavior Simulation with Probe Vehicle Data

Large-scale microscopic simulation of taxi services. Berlin and Barcelona case studies

Finding time‐robust fuel‐efficient paths for a call‐taxi in a stochastic city road network

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