Demand–supply equilibrium of taxi services in a network under competition and regulation

Yang, Hai; Wong, Stephen C. P.; Wong, Ka-Io

doi:10.1016/s0191-2615(01)00031-5

Cited by 196 publications

(117 citation statements)

References 11 publications

Supporting

Mentioning

106

Contrasting

Unclassified

Order By: Relevance

“…On the other hand, based on the expected customer waiting time distribution, the approximate distribution of the customer waiting time formula can be validated and modified in different kinds of land use type, which will be more useful in describing the principal characteristics in the taxi market and affect customers' decision and taxi driver's cruising time [37,38].…”

Section: Conclusion and Recommendationsmentioning

confidence: 99%

Taxi Driver’s Operation Behavior and Passengers’ Demand Analysis Based on GPS Data

Jian

2018

Journal of Advanced Transportation

View full text Add to dashboard Cite

The existing research outputs paid less attention to the relationship between land use and passenger demand, while the taxi drivers' searching behavior for different lengths of observation period has not been explored. This paper is based on taxi GPS trajectories data from Shenzhen to explore taxi driver's operation behavior and passengers' demand. The taxi GPS trajectories data covers 204 hours in Shenzhen, China, which includes the taxi license number, time, longitude, latitude, speed, and whether passengers are in the taxi vehicle, to track the passenger's pick-up and drop-off information. This paper focuses on these important topics: exploring the taxi driver operation behavior by the measurements of activity space and the connection between different activity spaces for different time duration; mainly focusing on eight traffic analysis zones (TAZs) of Shenzhen and exploring the customer's real-time origin and destination demands on a spatial-temporal distribution on weekdays and weekends; taxi station optimization based on the passenger demand and expected customer waiting time distribution. This research can be helpful for taxi drivers to search for a new passenger and passengers to more easily find a taxi's location.

show abstract

Section: Conclusion and Recommendationsmentioning

confidence: 99%

Taxi Driver’s Operation Behavior and Passengers’ Demand Analysis Based on GPS Data

Jian

2018

Journal of Advanced Transportation

View full text Add to dashboard Cite

show abstract

“…Researchers linked competitiveness of taxi industry with profitability and survival [24]. For taxi companies, the social surplus, firm profit, and customer demand are influenced primarily by the demand-supply equilibrium in regulated, competitive, or monopoly markets [25].…”

Section: Literature Reviewmentioning

confidence: 99%

A data-driven quantitative assessment model for taxi industry: the scope of business ecosystem’s health

Zhang

Zhong

Jiang

2017

Eur. Transp. Res. Rev.

View full text Add to dashboard Cite

Introduction The taxi industry has boomed over the years, both in street-hail and dispatch market. However, few studies focus on comprehensive perspectives, which make decisions such as implementing regulative or incentive policies difficult. As an economic community working in the value-oriented process, the taxi industry requires a holistic performance evaluation to determine how to adapt strategies to survive and grow. Methods This paper proposed the concept BTaxi Industry Health degree^based on the theory of Business Ecosystem and the evaluation of its health. A four-layer criteria set is developed from aspects of robustness, productivity and sustainability, and weights are determined through Analytic Hierarchy Process. Results A synthetic evaluating model combining Fuzzy Comprehensive Evaluation and Artificial Neural Network is used to maintain the goal of multi-criteria decision-making. With the GPS and Taximeter dataset of taxicabs in the whole taxi industry in Wuxi, the model is applied to empirical studies. Conclusions This paper provides sensibility analysis on not only the company's order volatility, revenue growth and utilization of resources, but also influence on citizen's welfare, energy consumption and environmental pollution, which enables practical regulations and policies within taxi industry.

show abstract

“…Similar service fairness metric has been applied in mobility on demand systems [32], and supply demand ratio considered as an indication of utilization ratio of taxis is also one regulating objective in taxi service market [29]. For the situation that taxi i will not pick up passengers in its original region but will be dispatched to another region, the dispatch decision results from the fact that global customers' satisfaction level will be increased.…”

Section: B Optimal Dispatch Under Operational Constraintsmentioning

confidence: 99%

Taxi dispatch with real-time sensing data in metropolitan areas

Miao¹,

Han²,

Lin³

et al. 2015

Proceedings of the ACM/IEEE Sixth International Conference on Cyber-Physical Systems

View full text Add to dashboard Cite

Abstract-Traditional taxi systems in metropolitan areas often suffer from inefficiencies due to uncoordinated actions as system capacity and customer demand change. With the pervasive deployment of networked sensors in modern vehicles, large amounts of information regarding customer demand and system status can be collected in real time. This information provides opportunities to perform various types of control and coordination for large-scale intelligent transportation systems. In this paper, we present a receding horizon control (RHC) framework to dispatch taxis, which incorporates highly spatiotemporally correlated demand/supply models and real-time GPS location and occupancy information. The objectives include matching spatiotemporal ratio between demand and supply for service quality with minimum current and anticipated future taxi idle driving distance. Extensive trace-driven analysis with a data set containing taxi operational records in San Francisco shows that our solution reduces the average total idle distance by 52%, and reduces the supply demand ratio error across the city during one experimental time slot by 45%. Moreover, our RHC framework is compatible with a wide variety of predictive models and optimization problem formulations. This compatibility property allows us to solve robust optimization problems with corresponding demand uncertainty models that provide disruptive event information.Note to Practitioners-With the development of mobile sensor and data processing technology, the competition between traditional "hailed on street" taxi service and "on demand" taxi service has emerged in the US and elsewhere. In addition, large amounts of data sets for taxi operational records provide potential demand information that is valuable for better taxi dispatch systems. Existing taxi dispatch approaches are usually greedy algorithms focus on reducing customer waiting time instead of total idle driving distance of taxis. Our research is motivated by the increasing need for more efficient, real-time taxi dispatch methods that utilize both historical records and real-time sensing information to match the dynamic customer demand. This paper suggests a new receding horizon control (RHC) framework aiming to utilize the predicted demand information when making taxi dispatch decisions, so that passengers at different areas of a city are fairly served and the total idle distance of vacant taxis are reduced. We formulate a multi-objective optimization problem based on the dispatch requirements and practical constraints. The dispatch center updates GPS and occupancy status information of each taxi periodically and solves the computationally tractable optimization problem at each iteration step of the RHC framework. Experiments for a data set of taxi operational records in San Francisco show that the RHC framework in our work can redistribute taxi supply across the whole city while reducing total idle driving distance of vacant taxis. In future research, we plan to design control algorithms for various types of...

show abstract

Demand–supply equilibrium of taxi services in a network under competition and regulation

Cited by 196 publications

References 11 publications

Taxi Driver’s Operation Behavior and Passengers’ Demand Analysis Based on GPS Data

Taxi Driver’s Operation Behavior and Passengers’ Demand Analysis Based on GPS Data

A data-driven quantitative assessment model for taxi industry: the scope of business ecosystem’s health

Taxi dispatch with real-time sensing data in metropolitan areas

Contact Info

Product

Resources

About