Modeling the bilateral micro-searching behavior for urban taxi services using the absorbing markov chain approach

Wong, K. C.; Wong, Stephen C. P.; Bell, M.G.; Yang, Hai

doi:10.1002/atr.5670390107

Cited by 62 publications

(37 citation statements)

References 11 publications

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“…In response to this lack, Wong, Wong, Bell, and Yang (2005) developed a mathematical model to consider the bilateralsearching behaviour of vacant taxi drivers and customers using the absorbing Markov chain approach. Yang et al (2010) developed another mathematical model to characterize the same bilateral-searching behaviour on road networks by incorporating a macroscopic searching-and-meeting model into the network model of Wong et al (2003).…”

Section: Introductionmentioning

confidence: 99%

A time-dependent logit-based taxi customer-search model

Szeto

Wong

et al. 2013

International Journal of Urban Sciences

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Section: Introductionmentioning

confidence: 99%

A time-dependent logit-based taxi customer-search model

Szeto

Wong

et al. 2013

International Journal of Urban Sciences

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“…Taxi customer bilateral searching and meeting behavior in a network was proposed in [15], which considered stochastic micro-searching behavior of both taxis and customers when they are searching for each other based on customer origin-destination (OD). The model featured location variation in the level of taxi services and stochastic microscopic searching behavior, such that the taxi searched for passenger locally in the network that incorporated Markov chain approach as a route for which transition probability or the link choice probability was specified by the customer pick-up rate within the network.…”

Section: Literature Reviewmentioning

confidence: 99%

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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“…By comparing the operation pattern of the Olympic period and pre-Olympic period, the authors showed the efficiency improvement in taxi operation during the Olympic period. Wong et al [22] developed a mathematical model based on the absorbing Markov chain approach to describe taxi movement, and they provided a novel and potentially useful measure of the urban taxi operation efficiency in a network. Recently, taxi service demand is analyzed by data mining and machine learning approaches.…”

Section: Related Workmentioning

confidence: 99%

“…This article aims to fill this gap by developing an approach to evaluating taxi driver performance comprehensively, and accurately rank taxi drivers by performance. Starting from previous studies [20][21][22], we take a step further to evaluate taxi driver performance by measuring taxi operation efficiency, which considers both the income generated by taxis in an occupied status and the cost when vacant.…”

Section: Introductionmentioning

confidence: 99%

Uncovering Distribution Patterns of High Performance Taxis from Big Trace Data

Tang

Sun

Kan

et al. 2017

IJGI

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Abstract:The unbalanced distribution of taxi passengers in space and time affects taxi driver performance. Existing research has studied taxi driver performance by analyzing taxi driver strategies when the taxi is occupied. However, searching for passengers when vacant is costly for drivers, and it limits operational efficiency and income. Few researchers have taken the costs during vacant status into consideration when evaluating taxi driver performance. In this paper, we quantify taxi driver performance using the taxi's average efficiency. We propose the concept of a high-efficiency single taxi trip and then develop a quantification and evaluation model for taxi driver performance based on single trip efficiency. In a case study, we first divide taxi drivers into top drivers and ordinary drivers, according to their performance as calculated from their GPS traces over a week, and analyze the space-time distribution and operating patterns of the top drivers. Then, we compare the space-time distribution of top drivers to ordinary drivers. The results show that top drivers usually operate far away from downtown areas, and the distribution of top driver operations is highly correlated with traffic conditions. We compare the proposed performance-based method with three other approaches to taxi operation evaluation. The results demonstrate the accuracy and feasibility of the proposed method in evaluating taxi driver performance and ranking taxi drivers. This paper could provide empirical insights for improving taxi driver performance.

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Modeling the bilateral micro-searching behavior for urban taxi services using the absorbing markov chain approach

Cited by 62 publications

References 11 publications

A time-dependent logit-based taxi customer-search model

A time-dependent logit-based taxi customer-search model

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

Uncovering Distribution Patterns of High Performance Taxis from Big Trace Data

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