Car-hailing Platforms Research Articles

Analysis and Solution of the Governance Dilemma of Infringement of Car-Hailing Platforms under Sharing Economy

In the current era of rapid development of the Internet, the sharing economy based on the Internet, which combines individual idle resources through the "P2P" mode with the demand side, has emerged at the historic moment and caused various commercial activities. As the leading industry in the sharing economy, online ride-hailing not only mobilizes the social transport capacity and maximizes the economic dividend, but also causes many legal problems due to the lag of laws and regulations. This paper takes the basic legal relationship of online ride-hailing as the starting point, analyzes the infringement disputes arising in the supervision, and puts forward some suggestions for improving online ride-hailing from the relief system and the distribution of legal responsibilities.

Reinforcement Learning for Solving Multiple Vehicle Routing Problem with Time Window

Vehicle routing problem with time window (VRPTW) is of great importance for a wide spectrum of services and real-life applications, such as online take-out and car-hailing platforms. A promising method should generate high-qualified solutions within limited inference time, and there are three major challenges: (a) directly optimizing the goal with several practical constraints; (b) efficiently handling individual time-window limits; and (c) modeling the cooperation among the vehicle fleet. In this article, we present an end-to-end reinforcement learning framework to solve VRPTW. First, we propose an agent model that encodes constraints into features as the input and conducts harsh policy on the output when generating deterministic results. Second, we design a time penalty augmented reward to model the time-window limits during gradient propagation. Third, we design a task handler to enable the cooperation among different vehicles. We perform extensive experiments on two real-world datasets and one public benchmark dataset. Results demonstrate that our solution improves the performance by up to 11.7% compared to other RL baselines and could generate solutions for instances within seconds, while existing heuristic baselines take for minutes as well as maintain the quality of solutions. Moreover, our solution is thoroughly analyzed with meaningful implications due to the real-time response ability.

Retracted: Operation and Management Strategy of Online Car-Hailing Platforms Based on Big Data Diagnosis and Game Perspective

Retracted: Operation and Management Strategy of Online Car-Hailing Platforms Based on Big Data Diagnosis and Game Perspective

Industry development analysis of shared travel platform online car-hailing platform

Catering to the development wave of Internet +, The ride-hailing service, which combines public transport with the Internet's positioning system, has attracted many users. After that, many online car-hailing platforms represented by "Didi Dache" and "Cao Cao Chauffeur" emerged, which achieved great success through online publicity. Its appearance greatly facilitates people's life, and it can also meet the needs of different passengers through personalized services. Nowadays, the ride-hailing industry has entered a stage of steady development, but many problems have been exposed along with the development, and most people focus on safety issues. The development of ride-hailing has changed people's concept of life, so it is also caught in the conflict and contradiction of classic cars, like many new things. However, with the launch of "Guiding Opinions on Deepening Reform and Promoting the Healthy Development of Taxi Industry" and "Interim Measures for the Management of Online Booking Taxi Service", the state has also shown its support and affirmation for the business model of the online booking car industry. This study will explore the market development structure of ride-hailing and will focus on the current problems and solutions of the ride-hailing industry.

Order allocation strategy for online car-hailing platform in the context of multi-party interests

As an emerging internet service, online car-hailing allows users to do the car-hailing via their mobile phone instead of calling cars on the road. Though the convenient service provided by online car-hailing platforms, the following problems encountered in the process of real-time order-matching still cannot be well addressed: (1) the imbalance of the accessible cars in different areas, and (2) the conflict of interests among the drivers, platforms, and passengers. To address those problems, this paper proposes a set of order allocation strategy that fully consider the changing regional characteristics and the interests of multiple parties. Firstly, a dynamic region division method is proposed to identify the corresponding area types for real-time car-hailing. Then, a novel order allocation strategy with the regional multi-party subsidy mechanism is developed for different types of areas. Lastly, to help obtain a better performance for multiple parties, a parameter optimization method for the multi-party subsidy mechanism is introduced. The proposed order allocation strategy is evaluated on the real-world dataset sourced from an online car-hailing company. Our experiments verify the feasibility and effectiveness of the proposed method, without reducing drivers’ benefits, the order response time and order response rate are greatly improved, and the cost in all is acceptable for passengers and the platform. It is illustrated that a suitable regional multi-party subsidy mechanism could help online car-hailing platforms obtain higher passenger satisfaction than that without a regional multi-party subsidy mechanism.

Work engagement of online car-hailing drivers: the effects of platforms' algorithmic management

PurposeAlgorithms are widely used to manage various activities in the gig economy. Online car-hailing platforms, such as Uber and Lyft, are exemplary embodiments of such algorithmic management, where drivers are managed by algorithms for task allocation, work monitoring and performance evaluation. Despite employing substantially, the platforms face the challenge of maintaining and fostering drivers' work engagement. Thus, this study aims to examine how the algorithmic management of online car-hailing platforms affects drivers' work engagement.Design/methodology/approachDrawing on the transactional theory of stress, the authors examined the effects of algorithmic monitoring and fairness on online car-hailing drivers' work engagement and revealed the mediation effects of challenge-hindrance appraisals. Based on survey data collected from 364 drivers, the authors' hypotheses were examined using partial least squares structural equation modeling (PLS-SEM). The authors also applied path comparison analyses to further compare the effects of algorithmic monitoring and fairness on the two types of appraisals.FindingsThis study finds that online car-hailing drivers' challenge-hindrance appraisals mediate the relationship between algorithmic management characteristics and work engagement. Algorithmic monitoring positively affects both challenge and hindrance appraisals in online car-hailing drivers. However, algorithmic fairness promotes challenge appraisal and reduces hindrance appraisal. Consequently, challenge and hindrance appraisals lead to higher and lower work engagement, respectively. Further, the additional path comparison analysis showed that the hindering effect of algorithmic monitoring exceeds its challenging effect, and the challenge-promoting effect of algorithmic fairness is greater than the algorithm's hindrance-reducing effect.Originality/valueThis paper reveals the underlying mechanisms concerning how algorithmic monitoring and fairness affect online car-hailing drivers' work engagement and fills the gap in the research on algorithmic management in the context of online car-hailing platforms. The authors' findings also provide practical guidance for online car-hailing platforms on how to improve the platforms' algorithmic management systems.

Bureaucratic control across enterprise boundaries: labor organization and the control of the online car-hailing platforms

This article shows that the online car-hailing platforms, supported by the digital technology of information matching, are more than some “flat” market organizations but are essentially bureaucratic organizations that are market-oriented and rely on business rules, digital technology, and third-party management institutions. By taking advantage of its monopolistic position, the car-hailing platform has built a bureaucratic control system with multilayer hierarchies in which various market players outside the enterprise participate. The platform first organizes production in a cooperative way by setting up external jobs, then guarantees that both drivers and leasing companies will follow the business rules and improve predictability. Finally, the platform uses digital technology and leasing companies to realize driver management and rule implementation.

Operation and Management Strategy of Online Car-Hailing Platforms Based on Big Data Diagnosis and Game Perspective

With the implementation of a series of preferential strategies by online car-hailing companies, the contradiction between online car-hailing and traditional taxis and passengers has become more and more intense. Coordinating the interests of the three parties has become increasingly important. In order to coordinate the contradiction between online car-hailing and traditional taxis and passengers and to manage the online car-hailing and traditional taxis reasonably, this paper conducts research on the operation and management strategy of online car-hailing platform based on big data diagnosis and game perspective. In order to solve the problem of online car-hailing platform operation and management strategy, this paper adopts a research method combining qualitative judgment and quantitative analysis and conducts research by combining specific logic deduction, field investigation, empirical research, mathematical analysis, and computer simulation. The results found that while the platform rate was reduced to 0.085, the daily income of online motorists increased from 170 yuan to 236 yuan, by 38.6%. In the event of a reduction in taxi fares to -3500, one hire the daily income of motorists increased from 134 yuan to 212 yuan, an increase of 57.8%. This shows that reducing the percentage of the platform has the greatest impact on the revenue of online car-hailing companies, and the recharge rebate strategy has the least impact on the revenue of online car-hailing companies. The strategy of reducing elementary money concessions can greatly increase the income of taxi drivers, but it also reduces nearly one-third of the income of taxi companies.

The GHG Emissions Assessment of Online Car-Hailing Development under the Intervention of Evaluation Policies in China

To assess the impact of the online car-hailing services on the environment in China, in this paper, we develop a model to evaluate greenhouse gas (GHG) emissions and emission reduction during the production and use of car-hailing, as a result of increased online bookings. Since energy use differs according to type of vehicle, this paper also contains different scenarios that depict degrees in the regulation stringency according to the Chinese Preliminary Rules on the Management of Online Car-Hailing, regional differences, and development of electric vehicles. The results show that, under the three different scenarios, the online car-hailing service would have the highest GHG emissions of 325, 413, or 160 MtCO2 and the highest reduction in emissions of 19.46, 25.1, or 9.48 MtCO2 by 2030. The government needs to encourage the development of online car-hailing to reduce the purchase of private cars, avoid compulsory actions to shut down online car-hailing platforms, and mandate that only electric vehicles (EVs) can be used for online car-hailing.

A multi-sided market of personal data resource allocation: An empirical study of China’s car-hailing platform

The current rapid development of online car-hailing services creates a serious challenge to the existing paradigm of market governance and antitrust policy. However, the debate on the market structure of the car-hailing platform requires more empirical evidence to uncover its functions. This research adopts an interdisciplinary methodology based on computer science and economics, and including software reverse engineering tools applied to the interoperability of the terminal application and resource allocation model, to demonstrate the topological market structure of personal data resources allocation in China’s car-hailing industry. Within the discussion of the hybrid nature of technology and economy, the analysis results clearly show that China’s car-hailing platform services present a multi-sided market structure when seen from the perspective of personal data resource allocation. Personal data resource (PDR), that is considered an essential market resource, is applied as an asset transferred unhindered between platforms via the application programming interface, and thus, creating a new market allocation mechanism. The connection between the car-hailing platforms and social media platforms is an essential aspect of the market competition in the domain. As applications of online platforms increase in the global context, this research offers a new perspective in personal data resource allocation with implications for the governance of the platform economy.

A queueing-theoretic framework for vehicle dispatching in dynamic car-hailing

With the rapid development of smart mobile devices, the car-hailing platforms (e.g., Uber or Lyft) have attracted much attention from the academia and the industry. In this paper, we consider a dynamic car-hailing problem, namely maximum revenue vehicle dispatching (MRVD), in which rider requests dynamically arrive and drivers need to serve riders such that the entire revenue of the platform is maximized. We prove that the MRVD problem is NP-hard and intractable. To handle the MRVD problem, we propose a queueing-based vehicle dispatching framework, which first uses existing machine learning models to predict the future vehicle demand of each region, then estimates the idle time periods of drivers through a double-sided queueing model for each region. With the information of the predicted vehicle demands and estimated idle time periods of drivers, we propose two batch-based vehicle dispatching algorithms to efficiently assign suitable drivers to riders such that the expected overall revenue of the platform is maximized during each batch processing. Through extensive experiments, we demonstrate the efficiency and effectiveness of our proposed approaches over both real and synthetic datasets. In summary, our methods can achieve 3% ~ 10% increase on overall revenue without sacrificing on running speed compared with the state-of-the-art solutions.

Study on the minimum fleet size based on deadheading time threshold

By integrating travel demand and car-hailing supply information, the car-hailing platforms have realized a more targeted non-cruise mobile order receiving mode. That reduces the deadheading (DH) time and the number of online car-hailing vehicles. This result will lead to a significant change in the number of online car-hailing vehicles. This paper studies the minimum fleet size based on deadheading time threshold. Then Then we use Suzhou taxi data to solve the minimum number of vehicles. Firstly, the space-time description of online car-hailing operation was established to study the influence of the DH time threshold on the total number of vehicles. The results show that the total number of vehicles decreases with the increase of the DH time threshold, and the trend tends to be gentle. Secondly, this paper discusses the relationship between the time cost of passenger and driver and the DH time threshold. Thirdly, this paper discussed the influence of the number of car-hailing platforms on the total number of vehicles, and it proves that the total number of vehicles in the multi-platform market is larger than that monopolized by a single platform. Finally, the feasibility of the research is verified by using Suzhou taxi data. Meanwhile, it proves that the minimum total number of vehicles has a certain robustness.

Exploring Temporal Intra-Urban Travel Patterns: An Online Car-Hailing Trajectory Data Perspective

Understanding intra-urban travel patterns is beneficial for urban planning and transportation management, among other fields. As an emerging travel mode, online car-hailing platforms provide massive and high-precision trajectory data, thus offering new opportunities for gaining insights into human mobility. This paper aims to explore temporal intra-urban travel patterns by fitting the distributions of mobility metrics and leveraging the boxplot. The statistical characteristics of daily and hourly travel distance are relatively stable, while those of travel time and speed have some fluctuations. More specifically, most residents travel between 2 and 10 km, with travel times ranging from 6.6 to 30 min, which is fairly consistent with our daily experience. Mainly attributed to travel cost, individuals seldom use online car-hailing for too short or long trips. It is worth mentioning that a weekly pattern can be found in all mobility metrics, in which the patterns of travel time and speed are more obvious than that of travel distance. In addition, since October has more rainy days than November, travel distances and travel times in October are higher than that in November, while the opposite is true for travel speed. This paper can provide a beneficial reference for understanding temporal human mobility patterns, and lays a solid foundation for future research.

Driving style recognition and comparisons among driving tasks based on driver behavior in the online car-hailing industry

As a product of the shared economy, online car-hailing platforms can be used effectively to help maximize resources and alleviate traffic congestion. The driver’s behavior is characterized by his or her driving style and plays an important role in traffic safety. This paper proposes a novel framework to classify driving styles (defined as aggressive, normal, and cautious) based on online car-hailing data to investigate the distinct characteristics of drivers when performing various driving tasks (defined as cruising, ride requests, and drop-off) and undergoing certain maneuvers (defined as turning, acceleration, and deceleration). The proposed model is constructed based on the detection and classification of driving maneuvers using a threshold-based endpoint detection approach, principal component analysis, and k-means clustering. The driving styles that the driver exhibits for the different driving tasks are compared and analyzed based on the classified maneuvers. The empirical results for Nanjing, China demonstrate that the proposed framework can detect driving maneuvers and classify driving styles accurately. Moreover, according to this framework, driving tasks lead to variations in driving style, and the variations in driving style during the different driving tasks differ significantly for turning, acceleration, and deceleration maneuvers.

Multi-dimensional spatiotemporal demand forecasting and service vehicle dispatching for online car-hailing platforms

ABSTRACT Forecasting transportation demands can aid online car-hailing platforms to dispatch their service vehicles in advance to areas with more potential orders. This results in a reduction in passengers’ waiting time and better utilisation of transportation resources. However, the complexity and dynamics of multi-dimensional influential factors make the forecasting and dispatching procedures challenging. This paper addresses these issues by using machine learning techniques and an effective probabilistic dispatching strategy. Multiple influential factors were identified in spatial, temporal, and meteorological dimensions, and effective machine learning algorithms were applied to predict the number of passenger orders. The fusion of the multi-dimensional features enables the proposed algorithms to better reveal the spatiotemporal characteristics and their correlations. A sensing-area-based strategy was introduced to dispatch available service vehicles to high demand-intensity regions efficiently with respect to the global demand-supply-balance and the individual probability of receiving orders. Finally, extensive experiments with large-scale real-world datasets were conducted to evaluate the performance of the machine learning algorithms and the effectiveness of the dispatching strategy. Overall, this paper extensively studies the forecasting of the spatiotemporal demand in multiple cities using point-of-interest data and the dispatching of available service vehicles based on such information for online car-hailing platforms.

Data access as a big competitive advantage: evidence from China's car-hailing platforms

PurposeThe online platform is one of the essential components of the platform economy that is constructed by a large scale of the personal data resource. However, accurate empirical test of the competition structure of the data-driven online platform is still less. This research is trying to reveal market allocation structure of the personal data resource of China's car-hailing platforms competition by the empirical data analysis.Design/methodology/approachThis research is applying the social network analysis by R packages, which include k-core decomposition and multilevel community detection from the data connectedness via the decompilation and the examination of the application programming interface of terminal applications.FindingsThis research has found that the car-hailing platforms, which establish more constant personal data connectedness and connectivity with social media platforms, are taking the competitive market advantage within the sample network. Data access discrimination is a complementary method of market power in China's car-hailing industry.Research limitations/implicationsThis research offers a new perspective on the analysis of the multi-sided market from the personal data resource allocation mechanism of the car-hailing platform. However, the measurement of the data connectedness requires more empirical industry data.Practical implicationsThis research reveals the competition structure that relies on personal data resource allocation mechanism. It offers empirical evidence for governance, which is considered as the critical issue of big data research, by reviewing the nature of the data network.Social implicationsIt also reveals the data convergence process of the social system and the technological system.Originality/valueThis research offers a new research method for the real-time regulation of the car-hailing platform.

Coopetition between Taxi Passenger Transport and Passenger Car Rental Industries in Taiwan

At present, taxi passenger transport and passenger car rental industries offer passenger transport services with small-sized vehicles in Taiwan. These two industries overlap but have significant differences in their management and business models. Recently, the introduction of online car-hailing platforms (sharing-economy industries like Uber) into Taiwan has intensified the competition and conflicts between these two industries. Thus, the Taiwan government has amended the law to distinguish them from each other more clearly. This study adopted observation and case study methods for the analysis of competition and cooperation between these two industries in Taiwan. Lastly, it offered suggestions on the future development of the two industries, in the hope of providing reference for the fields across academia, industry and government.

Research on Online Car-Hailing Scheduling Problem Based on Resource Allocation

In recent years, with the rapid development of Internet technology, various online car-hailing platforms (Uber, DiDi, CaoCao, etc.) have swept the world. The convenient and fast information exchange of "Internet +" greatly alleviates the information obstruction caused by the space-time deviation between passengers and drivers in the traditional taxi system, and becomes a powerful tool to improve the efficiency of the taxi market. In order to improve the utilization of resource allocation, this paper designs a model based on the vehicle's empty driving distance, and uses the Hungarian solution to obtain the shortest vehicle's empty driving distance to optimize the resource allocation problem. Then, according to the position coordinates and the travel route of the vehicle, the vehicle information of eleven times a day is calculated, and it is found that the result obtained by the algorithm is ideal；At the same time, we design the subsidy pricing model and its scheme. Finally, we make a systematic summary of the research content of this paper and look forward to the possible research directions in the future.

Factors affecting users' stickiness in online car-hailing platforms: an empirical study

This study investigated factors that affected users' stickiness and interrelationships among confirmation, perceived usefulness, satisfaction, continuance intention and users' stickiness in online car-hailing platforms. An empirical approach was adopted through online questionnaire survey to find out the formation mechanism of users' stickiness. The results indicated that perceived usefulness and satisfaction were positively associated with continuance intention, thereby leading to the increase of users' stickiness. And perceived usefulness also affected the satisfaction positively. What's more, confirmation was found to be the strongest predictor of perceived usefulness. The findings of this study may not only verify the extended model of IT continuance, but provide managerial guidelines for online car-hailing platform, thereby enhancing users' stickiness.

Factors affecting users' stickiness in online car-hailing platforms: an empirical study

Factors affecting users' stickiness in online car-hailing platforms: an empirical study