Online Ride-hailing Research Articles

Active Supervision Strategies of Online Ride-Hailing Based on the Tripartite Evolutionary Game Model

As a very important passenger transportation model in the era of sharing economy, the online ride-hailing (ORH) has also caused new traffic management issues while improving resource allocation. Although regulations and policies have imposed macro-level supervision on the ORH market, they have not prevented some drivers from cheating on platforms' subsidies and jeopardizing passengers' safeties at the source. In order to realize the voluntary and sustainable ORH supervision, and enable relevant participants to actively supervise, report and comply with rules, this paper constructs an evolutionary game model among the platform, passengers and drivers. Based on the bounded rationality and expected benefits of the participants, the main factors determining the optimal strategies are analyzed. At the same time, the evolution path and the equilibrium state of the three game groups are studied by numerical simulation. The results show that important factors of realizing the benign supervision of ORH include minimizing the reporting costs of passengers, making penalties for drivers who violate the rules far greater than the illicit incomes, realizing the platform supervision costs less than the sum of penalty incomes and positive social effects. In addition, improving rewards for reporting can promote the continuity of passengers' participation but increase the possibility of false reports. Therefore, the platform needs to consider the cost of identifying false information when designing the reward amount.

LpRide: Lightweight and Privacy-Preserving Ride Matching Over Road Networks in Online Ride Hailing Systems

An Online Ride Hailing (ORH) system enables a rider to request a taxi through a smartphone app on a short notice. To enjoy ORH services, users have to submit their location information to ORH servers. There are serious privacy concerns for users to reveal location information to ORH servers. In this paper, we propose a lightweight and privacy-preserving ride matching scheme for ORH systems, named lpRide, which can find the taxi with the minimum road distance to serve an incoming rider, while protecting the location privacy of both riders and taxis against the ORH server. In lpRide, we propose an efficient shortest road distance computation approach over encrypted data, which computes approximate road distance by using road network embedding and the modified Paillier cryptosystem. Moreover, we design a secure comparison method to compare two distances over the corresponding blinded ciphertexts, without revealing the actual distances. Theoretical analysis and experimental evaluations show that lpRide is private, accurate and efficient.

<italic>pRide</italic>: Privacy-Preserving Ride Matching Over Road Networks for Online Ride-Hailing Service

An online ride-hailing (ORH) service, such as Uber and Didi Chuxing, can provide on-demand transportation service to users via mobile phones, which brings great convenience to people's daily lives. Along with the convenience, high privacy concerns are also raised when using an ORH service since users and drivers must share their real-time locations with the ORH server, which results in the leakage of the mobility patterns and additional privacy of users and drivers. In this paper, we propose a privacy-preserving ride-matching scheme, called pRide, for ORH service. pRide allows an ORH server to efficiently match rider and drivers based on their distances in the road network without revealing the location privacy of riders and drivers. Specifically, we make use of the road network embedding technique together with cryptographic primitives and design a scheme to securely and efficiently estimate the shortest distances between riders and drivers in road networks approximately. Moreover, by incorporating garbled circuits, the proposed scheme is able to output the nearest driver around a rider. We implement the scheme and evaluate it on the representative real-world datasets. The theoretical analysis and experimental results demonstrate that pRide achieves an efficient, secure, and yet accurate ride matching for ORH service.

GPS data in urban online ride-hailing: A comparative analysis on fuel consumption and emissions

Abstract: The transportation sector has become the leading and most-rapidly growing contributor to greenhouse gas emissions. Promoting low-carbon travel mode is critical to alleviate this issue. As a new travel mode, online ride-hailing (such as Didi Chuxing and Uber), is becoming increasingly popular in cities around the world. However, there is still no comparative analysis on fuel consumption and emissions: does online ride-hailing have a distinct fuel consumption and emissions pattern with traditional taxis? In this study, we use one month global positioning system dataset and orders dataset, averagely covering around 7 thousand taxis with 0.3 million trips and 23 thousand Didi Chuxing Express vehicles with 0.1 million trips per day in Chengdu, China to answer this question. Empirical results show that taxi trips associate with longer idle distance and shorter delivery distance than Didi trips. Didi trips’ average idle velocity is apparently smaller than their delivery velocity. Online ride-hailing mode is concluded to contribute to these difference: after dropping off previous passengers, Didi drivers usually park their cars until being dispatched new orders and then drive directly to pick up passengers rather than search circuitously. Fuel consumption and carbon monoxide, nitrogen oxides, hydrocarbon emissions per passenger-on kilometer of taxi trips are found to be about 1.36, 1.45, 1.36 and 1.44 times that of Didi trips, respectively. Additionally, only taxi drivers with good performance have the ability to reduce fuel consumption and emissions; while most Didi drivers can perform well on fuel consumption saving and emissions reduction. Finally, several feasible policies are suggested for improving and upgrading the traditional taxi business. Our study provides convincing evidence for understanding the advantage of online ride-hailing mode in reduction fuel consumption and emissions sourced from empty cruising, so as to support better traffic policy making and the promotion on low-carbon travel mode.

Dynamic pricing model for cruising taxicab based on system dynamics

The cruising taxi industry has been greatly impacted by the development of the ride-hailing taxi services like the Didi and Uber. The service orders of cruising taxis fell 35% or more. To intervene the conflicts between the cruising and ride-hailing taxis, system dynamics model of cruising taxicab price was proposed by considering the competitive relationships between online ride-hailing and cruising taxis. The proposed model was calibrated and validated by the taxi operational data in Ningbo. Simulation experiments were designed to explore the impact of the cruising taxi price changes on the balance of market supply and demand. Simulations were divided into two scenarios: one was under the different levels of supply and demand at peak and low peak; the other was under the different proportions of ride-hailing taxi. The results indicate that (1) the market supply and demand are close to equilibrium during peak hours when the price increases by ¥0.6 per kilometer. The increased price can reduce the peak demand level effectively and also increase the total earning of cruising taxis; (2) when the service order ratio of cruising taxis to ride-hailing taxis accounts for about 10:4.5 and 10:8, the price increases by ¥0.6 per kilometer and decreases by ¥0.4 per kilometer to achieve the equilibrium of demand and supply, respectively. That is, when the cruising taxis have an advantage over the ride-hailing taxis, the price of cruising taxis should be raised to gain more benefits. Instead, the cruising taxis price should be decreased to improve their competitiveness.

PSRide: Privacy-Preserving Shared Ride Matching for Online Ride Hailing Systems

Online Ride Hailing (ORH) has extensively made our trip more convenient. With mobile devices, riders can request taxis through ORH systems in a short time. However, to enjoy ORH services, users need to submit their location information to ORH systems, which raises serious privacy concerns. In this paper, we study the privacy leakage of online ridesharing matching, a more complex and economy ORH service that allows riders to share rides with others, and propose a privacy-preserving shared ride matching scheme, called PSRide. PSRide can find the taxi with the minimum additional travel time to serve a new rider based on its existing schedule, while protecting the location privacy of both riders and taxis. In PSRide, we propose a zone-based minimum road travel time estimation approach and a secure comparison protocol to efficiently optimize the schedules of taxis for a new rider over encrypted data. We implement PSRide and analyze it thoroughly. Theoretical analysis and experimental evaluations show that PSRide is secure and efficient for ORH systems.

Charging Demand Forecasting Model for Electric Vehicles Based on Online Ride-Hailing Trip Data

Electric vehicle (EV) has been popularized and promoted on a large scale because of its clean and efficient features. Charging this increasing number of EVs is expected to have an impact on the electricity grid and traffic network. Therefore, it is necessary to model and forecast the EV charging demand. Most of the existing researches have not utilized real-world traffic data to analyze the EV charging demand. Few researches have considered and analyzed the characteristics of space-time transfer of charging load in urban functional areas. As an emerging mode of transportation, however, online ride-hailing trip data provide an ideal source for analyzing traffic planning and operation. On the basis of this, a charging demand forecasting model of EVs based on a data-driven approach was presented in this paper. In this methodology, it is firstly assumed that residents’ transportation trip demand is not restricted by vehicle categories(electric or fuel vehicles). The original trip trajectory data of Didi online ride-hailing were conducted to model via data mining and fusion. And the process of data analysis included region-scope selection, spatial grid modeling, trajectory data mapping, retrieval data identification and urban functional area clustering as well as traffic network modeling. Through modeling and processing, the following regenerative feature data were obtained: functional regional division (i.e., residential areas, industrial areas, commercial areas, and public service areas), trip rule distribution (i.e., temporal distribution and spatial distribution on weekdays, weekends and holidays) and actual driving path (i.e., driving path with the shortest distance or with the minimum time-consuming). And then, considering the movable load feature of EVs, vehicles were subdivided into three kinds such as private vehicles, taxis and other public vehicles, and the single EV model with driving and charging characteristic parameters was established. Furthermore, the regeneration data obtained from modeling and analysis along with the determined single EV model were supported as data sources and model for the charging demand forecast architecture. At last, the actual urban traffic network in Nanjing, China was selected as an example to design the path planning experiments and charging demand load experiments in different scenarios. The results demonstrate that this proposed model is able to realistically simulate the actual dynamic driving process of EVs, and effectively predict the spatial-temporal distribution characteristics and load transfer trends of charging demands in different date type as well as different functional areas. The model also lays a theoretical foundation for the subsequent research on investment and construction of charging facilities, as well as charging control and charging guidance of EVs.

基于大学生视角的网约车平台服务现状分析

基于大学生视角的网约车平台服务现状分析

Efficient and Privacy-Preserving Dynamic Spatial Query Scheme for Ride-Hailing Services

With the prosperity of mobile internet and the pervasiveness of location-aware mobile terminals, online ride-hailing, a high-level location-based service (LBS) which relies on dynamic spatial query, has made our life more convenient. However, the flourish of ride-hailing service still faces many severe challenges since users’ location privacy and service provider's data security. In this paper, we present an efficien t and p r ivacy-preserving dyn a mi c spatial qu e ry scheme (TRACE) for ride-hailing service. With TRACE, users (i.e., consumers and vehicles) can access ride-hailing service without divulging their sensitive location information, meanwhile, the ride-hailing server can achieve the necessary commercial operating information while keeping its sensitive data (i.e., the space division information) confidential. Specifically, with two proposed efficient and secure spatial query algorithms, named FSSQ and ESVQ, all location-related data are encrypted by its owner before being sent out, and are calculated without decryption during the spatial query process. Therefore, consumers, vehicles, and service provider cannot obtain each other's sensitive information. Detailed security analysis shows that TRACE can resist various known security threats. Furthermore, TRACE is implemented in the real environment, and extensive simulation results over smart phones demonstrate that the scheme is highly efficient and can be implemented effectively.