Supply and demand management in ride-sourcing markets / Jintao Ke [and three others].

Format:

Book

Author/Creator:

Ke, Jintao, HKUST., author.

Language:

English

Subjects (All):

Ridesharing.

Ridesharing--Mathematical models.

Supply and demand--Mathematical models.

Supply and demand.

Physical Description:

1 online resource (408 pages)

Place of Publication:

Amsterdam : Elsevier Inc., [2023]

Summary:

Supply and Demand Management in Ride-Sourcing Markets offers a fundamental modeling framework for characterizing ride-sourcing markets by spelling out the complex relationships among key endogenous and exogenous variables in the markets. This book establishes several economic models that can approximate matching frictions between drivers and passengers, describes the equilibrium state of ride-sourcing markets, and more. Based on these models, the book develops an optimum strategy (in terms of trip fare, wage and/or matching) that maximizes platform profit. While the best social optimum solution (for maximizing the social welfare) is generally unsustainable, this book provides options governments can use to encourage second-best solutions. In addition, the book's authors establish models to analyze ride-pooling services, with traffic congestion externalities incorporated into models to see how both new platforms and government designs can optimize operating strategies in response to the level of traffic congestion.

Contents:

Front Cover

Supply and Demand Management in Ride-Sourcing Markets

Copyright

Contents

Contributors

About the authors

Preface

1 - Introduction of ride-sourcing markets

1.1 Background

1.2 Theoretical developments

1.2.1 Stationary equilibrium state

1.2.2 Monopoly optimum, social optimum, and Pareto-efficient solutions

1.2.3 Regulations

1.2.4 Ride-pooling services

1.2.5 Congestion externalities

1.2.6 Platform competition and platform integration

1.2.7 Ride sourcing and public transit

1.2.8 On-demand matching and its key decision variables

1.3 Outline of this book

References

2 - Fundamentals of ride-sourcing market equilibrium analyses

2.1 Introduction

2.1.1 Passenger demand

2.1.2 Driver supply

2.2 Matching frictions (inductive approaches)

2.2.1 Perfect matching function

2.2.2 Production functions

2.3 Matching frictions (deductive approaches)

2.3.1 Queuing models

2.3.2 First-come-first-served (FCFS)

2.3.3 Batch-matching process

2.4 Market measures

2.4.1 Monopoly optimum

2.4.1.1 Production-function-based model

2.4.1.2 Queuing model

2.4.1.3 FCFS-based model

2.4.2 Social optimum

2.4.2.1 Production-function-based model

2.4.2.2 Queuing model

2.4.2.3 FCFS-based model

2.4.3 Pareto-efficient solutions

2.4.3.1 Production function-based model

2.4.3.2 Queuing model

2.4.3.3 FCFS-based model

2.5 Discussion

Glossary of notation

3 - Calibration and validation of matching functions for ride-sourcing markets

3.1 Introduction

3.2 Matching functions and market metrics

3.2.1 Base model

3.2.2 Matching functions

3.2.2.1 Perfect matching

3.2.2.2 Cobb-Douglas production function

3.2.2.3 M/M/1 queuing model

3.2.2.4 M/M/1/k queuing model.

3.2.2.5 M/M/N queuing model

3.2.2.6 First-come-first-served (FCFS) model

3.2.2.7 Batch-matching model

3.2.2.8 Summary of matching functions

3.2.3 Key market metrics

3.3 Experimental settings

3.3.1 Simulator

3.3.2 Experiment

3.4 Analysis of experimental results

3.4.1 Market segmentation

3.4.2 Best-fit models for estimation of matching rate

3.4.3 Best-fit models for the estimation of matching time

3.4.4 Best-fit models for the estimation of passenger pick-up time

3.4.5 Best-fit models for the estimation of passengers' total waiting time

3.5 Summary

3.6 Discussion and conclusion

Appendix 3.A

4 - Government regulations for ride-sourcing services

4.1 Properties of the pareto-efficient solutions

4.2 An alternative method to obtain and analyse pareto-efficient solutions

4.3 Regulations

4.3.1 Price-cap regulation

4.3.2 Fleet size regulation

4.3.3 Wage regulation

4.3.4 Income regulation

4.3.5 Commission regulation

4.3.6 Commission ratio regulation

4.3.7 Minimum utilisation rate regulation

4.3.8 Demand regulation

4.3.9 Summary

4.3.10 Numerical illustrations

4.4 Discussion and conclusion

5 - Equilibrium analysis for ride-pooling services

5.1 Introduction

5.2 Pool-matching schemes

5.2.1 En-route pool-matching scheme

5.2.1.1 General model

5.2.1.2 Probabilistic model

5.2.2 Pre-assigned pool-matching with meeting points

5.2.3 Comparisons

5.3 Equilibrium analyses

5.3.1 Supply and demand function

5.3.2 Market equilibrium

5.3.3 Comparative static effects of regulatory variables

5.4 Market measures

5.4.1 Monopoly optimum

5.4.2 Social optimum

5.4.3 Pareto-efficient solutions

5.5 Numerical illustrations

5.5.1 Experimental settings.

5.5.2 Detour-unconstrained scenario

5.5.3 Detour-constrained scenario

5.6 Conclusion

6 - Ride-pooling services and traffic congestion

6.1 Introduction

6.2 Equilibrium analyses

6.2.1 Demand function

6.2.2 Speed function

6.2.3 Supply function

6.2.4 Equilibrium solution

6.3 Market measures

6.3.1 Monopoly optimum

6.3.2 Social optimum

6.3.3 Pareto-efficient solutions

6.4 Conclusion

7 - Equilibrium analysis for ride-pooling services in the presence of traffic congestion

7.1 Introduction

7.2 Equilibrium analyses

7.2.1 Vehicle conservation

7.2.2 Demand function

7.2.3 Supply function

7.2.4 Equilibrium solution

7.3 Market measures

7.3.1 Monopoly optimum (MO)

7.3.2 Social optimum (SO)

7.3.3 Pareto-efficient solutions

7.4 Numerical studies

7.4.1 Equilibrium outcomes

7.4.2 Optimal operating strategies (non-pooling market)

7.4.3 Optimal operating strategies (ride-pooling market)

7.4.4 Effects of matching window

7.5 Conclusion and remarks

8 - Revisiting government regulations for ride-sourcing services under traffic congestion

8.1 Introduction

8.2 Theoretical analyses

8.2.1 Monopoly optimum

8.2.2 Social optimum

8.2.3 Pareto-efficient solutions

8.3 Numerical studies

8.3.1 Settings

8.3.2 Market with drivers with heterogeneous reservation rates and no traffic congestion

8.3.3 Market with drivers with heterogeneous reservation rates and traffic congestion

8.3.4 Effects of driver rationing

8.3.5 Summary and discussion

8.4 Conclusion

9 - Third-party platform integration in ride-sourcing markets

9.1 Background

9.2 Market equilibrium and optimal strategies.

9.2.1 Market without platform integration

9.2.2 Market with platform integration

9.3 Evaluation of the performance of platform integration

9.3.1 Effect of vehicle fleet size at the Nash equilibrium/social optimum

9.3.2 Effect of platform integration at the Nash equilibrium

9.3.3 Effect of platform integration at the Social optimum

9.4 Numerical studies

9.4.1 Effect of market fragmentation

9.4.2 Effect of vehicle fleet size

9.4.3 Effect of commission fee

9.5 Conclusion

Appendix 9.A. Proof of Lemma 9-1

Appendix 9.B. Proof of theorem 9-1

Appendix 9.C. Proof of Lemma 9-2

Appendix 9.D. Proof of theorem 9-2

Appendix 9.E. Proof of Lemma 9-3

Appendix 9.F. Proof of Lemma 9-4

Appendix 9.G. Proof of theorem 9-3

Appendix 9.H. Proof of Lemma 9-5

Appendix 9.I. Proof of theorem 9-4

Appendix 9.J. General matching function

10 - Ride-sourcing services and public transit

10.1 Background

10.2 Model description

10.3 Optimal strategy design

10.3.1 Monopoly optimum

10.3.2 Social optimum

10.3.3 Second-best solution

10.4 Numerical case study

10.4.1 Analysis of equilibrium states

10.4.2 Analysis of profit- and/or social welfare-maximising strategies

10.5 Conclusion

Appendix 10.A

11 - Optimization of matching-time interval and matching radius in ride-sourcing markets

11.1 Research problem

11.2 Modelling and optimising the matching process

11.2.1 Online matching process

11.2.2 Matched passenger-driver pairs

11.2.3 Expected pick-up distance

11.2.4 System performance measure

11.2.5 General model properties

11.2.5.1 Effect of matching radius

11.2.5.2 Effect of matching-time interval

11.3 Model properties in imbalanced scenarios

11.3.1 Effects of matching-time interval.

11.3.2 Properties of optimal matching-time interval

11.3.3 Further discussion

11.4 Numerical studies

11.4.1 Balanced scenario

11.4.2 Imbalanced scenarios

11.4.3 Model performance in a dynamic simulation environment

11.5 Conclusion

12 - Labour supply analysis of ride-sourcing services

12.1 Background

12.1.1 Motivation

12.1.2 Research questions

12.1.3 Methodology

12.1.4 Results

12.1.5 Main contributions

12.2 Related literature

12.3 Labour supply model

12.3.1 Optimal decisions on hours worked based on income targets

12.3.2 Importance of the extensive margin in the labour supply model

12.4 Modelling endogeneity of income rates and self-selected participation in the labour force

12.4.1 Methodological implications of self-selection and endogeneity

12.4.1.1 Modelling self-selected participation in the labour force

12.4.1.2 Modelling the endogeneity of the hourly income rate

12.4.2 Model of labour supply elasticity on a ride-sourcing platform

12.5 Research design

12.5.1 Research context

12.5.2 Large-scale natural experiment

12.5.2.1 Income multiplier

12.5.2.2 Exogenous shocks in natural experiments

12.5.3 Data description

12.5.3.1 Variable description

12.5.4 Driver classification along the extensive and intensive margins

12.5.5 Empirical analysis

12.5.5.1 Basic model

12.5.5.2 Consideration of potential sample selection bias

12.5.5.3 Identification of the outcome equation

12.6 Results and discussion

12.6.1 Model estimation

12.6.1.1 Evidence of sample selection and endogeneity bias

12.6.1.2 Validity of IVs

12.6.2 Estimates of labour supply elasticity in the presence of driver heterogeneity

12.6.3 Labour supply in subgroups

12.7 Conclusion

References.

13 - Some empirical laws of ride-pooling services.

Notes:

Includes bibliographical references and index.

Description based on print version record.

Other Format:

Print version: Ke, Jintao Supply and Demand Management in Ride-Sourcing Markets

ISBN:

9780443189388

OCLC:

1378391464