Online Car-hailing Platforms Research Articles

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

Network Car Hailing Pricing Model Optimization in Edge Computing-Based Intelligent Transportation System

The purpose of this study is to investigate Network Car Hailing (NCH) price or the deficiency in NCH Platform in Edge Computing (EC)-based Intelligent Transportation System. Aiming at the uncertain capacity and unbalanced load in the car-hailing platform, this work innovatively introduces the EC to unload, constructs an EC-based online car-hailing resource allocation and pricing optimization model by combining with factors such as the number of users and reputation in the network, and further analyzes the performance of the resource allocation and pricing optimization model in the constructed car-hailing platform through simulation experiments. The experimental results show that with the increase in the number of vehicles with computing tasks, the amount of resources purchased from various car-hailing vehicles also increases, the cost of paying is showing an increasing trend, and the utility function of NCH platforms and operators has declined. In the task resource analysis, the average unloading utility of the algorithm in this work is the highest, and the average unloading utility is basically stable at about 70% when the number of vehicles is 98. With the increase of the delay weight, the delay is smaller and the energy consumption is lower. Therefore, the model constructed in this work can minimize the average cost and consumes less energy while the delay is small. It can provide a reference for intelligent pricing and resource allocation of the online car-hailing platform in the later period of intelligent transportation.

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.

Short-Term Demand Forecasting of Urban Online Car-Hailing Based on the K-Nearest Neighbor Model

Accurately forecasting the demand of urban online car-hailing is of great significance to improving operation efficiency, reducing traffic congestion and energy consumption. This paper takes 265-day order data from the Hefei urban online car-hailing platform from 2019 to 2021 as an example, and divides each day into 48 time units (30 min per unit) to form a data set. Taking the minimum average absolute error as the optimization objective, the historical data sets are classified, and the values of the state vector T and the parameter K of the K-nearest neighbor model are optimized, which solves the problem of prediction error caused by fixed values of T or K in traditional model. The conclusion shows that the forecasting accuracy of the K-nearest neighbor model can reach 93.62%, which is much higher than the exponential smoothing model (81.65%), KNN1 model (84.02%) and is similar to LSTM model (91.04%), meaning that it can adapt to the urban online car-hailing system and be valuable in terms of its potential application.

The Legitimacy of a Sharing Economy-Enabled Digital Platform for Socioeconomic Development

A sharing economy based on improved ICT is an emerging economic−technological concept. Sharing economy-enabled digital platforms in China have changed patterns of consumption, exploited under-utilized resources, and increased employment. Previous studies on sharing economy-enabled digital platforms mainly focused on the positive and negative effects, users’ perception and behavioral intention, and the business model, but few studies have addressed these platforms for socioeconomic development from the perspective of legitimacy. This study applied legitimacy to analyze a typical sharing economy-enabled digital platform in China for socioeconomic development via a longitudinal interpretive case study. A process model of variation and evolution of an online car-hailing platform for socioeconomic development was inductively derived, allowing elucidation of the complexities and interplay of regulative challenges, normative challenges, and cognitive challenges in each developmental phase, resulting in improving and enriching the way people go out, optimizing resource allocation, increasing employment, and undertaking social responsibility. The findings of this case study provide a comprehensive and supported framework and demonstrate a successful model for managers and other peer organizations for future business efforts in the sharing economy.

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.

Deep multi-view graph-based network for citywide ride-hailing demand prediction

Urban ride-hailing demand prediction is a crucial but challenging task for intelligent transportation system construction. Predictable ride-hailing demand can facilitate more reasonable vehicle scheduling and online car-hailing platform dispatch. Conventional deep learning methods with no external structured data can be accomplished via hybrid models of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) by meshing plentiful pixel-level labeled data, but data sparsity of high grid granularity in spatial perspective and limited learning capabilities of long-term dependencies in temporal perspective are still two striking bottlenecks. To address these problems, we propose a novel virtual graph modeling approach to focus on significant demand regions and a novel Deep Multi-View Spatio-temporal Virtual Graph Neural Network (DMVST-VGNN) to strengthen the learning capabilities of spatial dynamics and long-term temporal dependencies. Specifically, DMVST-VGNN integrates structures of 1D CNN, Multi-Graph Attention Neural Network and Transformer Network, which correspond to short-term temporal dynamics view, spatial dynamics view and long-term temporal dynamics view respectively. In this paper, multiple experiments are conducted on two large-scale New York City datasets in higher granularity prediction scenes. And the experimental results demonstrate the effectiveness of DMVST-VGNN framework in ride-hailing demand prediction, no matter in spatial scale or the temporal scale.

Evolutionary game study on the governance and development of online car-hailing based on blockchain technology

Changes in the online car-hailing industry have brought new challenges to government governance. Effectively enhancing governance efficiency has become the focus of academic research. Based on the technical governance perspective, this paper introduces the consortium blockchain to construct an evolutionary game model between the online car-hailing platform and the government under blockchain technology. By solving the replicated dynamic equations and the Jacobian matrix, the influences of the change in initial conditions and decision parameters on the evolutionary stability results are revealed, and numerical experiments are carried out by using the Python programming language. This paper claims that the system presents three evolutionary stable results and a periodic stochastic state when the key parameters are located in different thresholds. The additional cost of the platform’s negative regulation and the government’s punishment intensity have a positive effect on the evolution of the system to the ideal state (active regulation, active governance). Platform technology R&D cost and government innovation input have a negative effect on the evolution of the system to the ideal state. Therefore, using blockchain to increase the additional cost of the platform’s negative regulation, appropriately increasing the government’s punishment intensity, reasonably controlling the government’s innovation input to the platform, and reducing the technology R&D cost of the platform will help the system evolve into an ideal state. This paper provides useful references to implement effective governance and the innovative and healthy development of the online car-hailing industry.

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.

Data Legal Supervision of Online Car-Hailing Platform Based on Big Data Technology and Edge Computing

There is a lack of fairness in market competition and disorder of order as a result of the lack of supervision. OCH (online car-hailing) driver accidents are also reported frequently, causing a slew of social and traffic safety issues. In this paper, we reconstruct the logical path of OCH platform data legal supervision in China’s big data era. We propose a big data encryption algorithm based on data redundancy technology that combines the characteristics of the ECC (Elliptic Curve Cryptography) and AES (Advanced Encryption Standard) block cipher modes in terms of computing speed, parallelism, and security. The system can process large amounts of network data and detect distributed denial of service (DDOS) attacks in real time. The observed feature change trend charts before and after the attacks show significant differences, demonstrating that the proposed features can better distinguish normal traffic from abnormal traffic.

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.

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.

Supervision of the Default Risk of Online Car-Hailing Platform from an Evolutionary Game Perspective

Online car-hailing services are becoming a key component of the public transportation system, despite there being some certain risks, especially default risk. Turning to the evolutionary game method, this research constructed an evolutionary game model of online car-hailing platform, and then analyzed the equilibrium state of three scenarios (i.e., no supervision, internal supervision of platform enterprises, and external supervision of regulators), followed by carrying out a simulation. The results showed that to realize the evolution stability strategies (ESS) of default risk control, a strong credit constraint or the establishment of a coordinated supervision mode with appropriate intensity are needed. On this basis, this research puts forward the coordinated “platform enterprise + regulator” supervision mode, as well as the following four specific strategies: Promoting the construction of a credit system, strengthening the construction of laws and regulations, establishing a service process control mechanism, and introducing innovative regulatory means.

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.