Blockchain and Machine Learning for Intelligent Multiple Factor-Based Ride-Hailing Services

One of the common transportation systems in Korea is calling taxis through online applications,which is more convenient for passengers and drivers in the modern area.However,the driver’s passenger taxi request can be rejected based on the driver’s location and distance.Therefore,there is a need to specify driver’s acceptance and rejection of the received request.The security of this systemis anothermain core to save the transaction information and safety of passengers and drivers.In this study,the origin and destination of the Jeju island SouthKorea were captured from T-map and processed based on machine learning decision tree and XGBoost techniques.The blockchain framework is implemented in the Hyperledger Fabric platform.The experimental results represent the features of socio-economic.The cross-validation was accomplished.Distance is another factor for the taxi trip,which in total trip in midnight is quite shorter.This process presents the successful matching of ride-hailing taxi services with the specialty of distance,the trip request,and safety based on the total city measurement.

Operational Characteristics of Paratransit Services with Ride-Hailing Apps in Asian Developing Cities: The Phnom Penh Case

Rapid adoption of ride-hailing apps (RHAs) has greatly influenced the way people travel—there is no exception for paratransit users. However, it remains unclear whether RHAs would be regarded as threats or opportunities among paratransit operators in Asian developing cities. While RHAs have been viewed as disruptive transportation, several studies explored the threats of RHAs on taxi industry—but only a few examined such threats on other paratransit services (e.g., auto-rickshaws). This study assessed the changes in the operational services among paratransit operators who have adopted RHAs. The changes were examined by statistical comparisons using data collected from questionnaire survey with 182 Bajaj drivers in Phnom Penh, January 23-27, 2018, as a case study. Results showed that majority of the interviewed drivers started new services with RHAs less than a year ago—they were younger (88%) satisfied with RHAs and acknowledged improvements on their operational services. The results suggested that RHAs would be opportunities for those paratransit drivers who have adopted them, while they would be threats for those who have not. The collected data serve as useful inputs for future public transport planning in Asian developing cities.

Demand Prediction of Ride-Hailing Pick-Up Location Using Ensemble Learning Methods

Ride-hailing and carpooling platforms have become a popular way to move around in urban cities. Based on the principle of matching riders with drivers, with Uber, Lyft and Didi having the largest market share. The challenge remains being able to optimally match rider demand with driver supply, reducing congestion and emissions associated with Vehicle clustering, deadheading, ultimately leading to surge pricing where providers raise the price of the trip in order to attract drivers into such zones. This sudden spike in rates is seen by many riders as disincentive on the service provided. In this paper, data mining techniques are applied to ultimately develop an ensemble learning model based on historical data from City of Chicago Transport provider’s dataset. The objective is to develop a dynamic model capable of predicting rider drop-off location using pick-up location data then subsequently using drop-off location data to predict pick-up points for effective driver deployment under multiple scenarios of privacy and information. Results show neural network algorithms perform best in generalizing pick-up and drop-off points when given only starting point information. Ensemble learning methods, Adaboost and Random forest algorithm are able to predict both drop-off and pick-up points with a MAE of one (1) community area knowing rider pick-up point and Census Tract information only and in reverse predict potential pick-up points using the Drop-off point as the new starting point.

Transportation Users’ Attitudes and Choices of Ride-Hailing Services in Two Cities with Different Attributes

The rapid technological developments in the 21st century created new opportunities for shared-use economy applications around the globe. Among other services, Transportation Network Companies (TNCs) like Uber and Lyft emerged in the US as a transportation alternative that offered a higher level of availability, reliability, and convenience than traditional modes. However, TNCs deployment was also blamed for increases in vehicle miles traveled (VMT) in large cities that embraced TNC services early on. Concerns about TNC adoption are also magnified by the current controversy in policy and legislation as to the regulation of TNCs. These new realizations create a need to examine the transportation users’ attitudes and perceptions regarding ride-hailing service, after nearly a decade of service in the Unites States market. In doing so, this paper compares and contrasts results from two recently completed studies aiming at creating links between socio-demographic factors and TNC use. The paper describes the methods employed to collect the data and presents findings from the analysis of 790 users’ responses in the Birmingham, AL and Miami Beach, FL markets. The study documents preferences and attitudes toward TNCs and highlights similarities and differences in travel behaviors related to local considerations. Moreover, the study uses the Least Absolute Shrinkage and Selection Operator (Lasso) method to identify predictors for TNC use based on the users’ responses in Birmingham and Miami Beach case studies. Vehicle availability and waiting time emerged as the only significant predictors for the Birmingham region whereas vehicle ownership, vehicle use, residency, and prior use of transit and TNC where some of the predictors identified for the Miami Beach area. Understanding the characteristics of TNC users and the leading reasons that drive people towards the use of TNCs services is expected to help transportation agencies and TNC providers in their efforts to plan for transportation services that meet customer needs in the future.

Business Analysis of Didi Chuxing in the Chinese Ride-hailing Market

This essay presents my analysis of Didi Chuxing’s ride-hailing business in the Chinese market considering economic concepts and tools such as market structure,economies of scale and price anchoring to understand aspects of Didi Chuxing such as its cost structure,pricing strategies and marketing methods.As the largest ride-hailing platform in China,the success of Didi Chuxing can offer important guidelines for the healthy development and expansion of this industry to other countries.

The social impact of ride-hailing services

Nowadays,sharing economy-based companies are gradually increasing their influence on our daily lives:we hire drivers on apps like Didi and Uber to pick us up instead of waiting for taxis.As P2P companies increase their influence in the public services sector,the government rationally diverts more of its attention towards regulating the quality and service of these companies.

The Sequential Pricing of Ride-Hailing System with Rental Service in the Context of Fleet Electrification

Surging demand and reduced capacity in the ride-hailing industry have prompted numerous ride-hailing platforms to build their own car-rental services catering to drivers who do not possess private vehicles. However, the trade-off between the ride-hailing service and the car-rental service is an important issue that is still unclear in theory. Moreover, ride-hailing platforms are transitioning towards all-electric fleets in the context of Carbon Neutrality goals and government regulations. This paper considers a ride-hailing system comprising a monopolist ride-hailing platform, an electric vehicle (EV) rental firm, and a gasoline vehicle (GV) rental firm. Furthermore, we build a stylized model to study the sequential pricing of the system. The equilibrium outcomes show the significant impact of the ride-hailing platform’s decision to continue or withdraw offering EV rental services on EV and GV drivers’ net earnings, rental prices, and wages. The ride-hailing platform providing EV rental services increases EV drivers’ net earnings but decreases the GV driver wages and rental prices. However, the EV rental service offered by the ride-hailing platform does not necessarily lead to an increased total profit for the system. The improved profitability of the system by the ride-hailing platform providing EV rental services is contingent upon lower rider prices and higher fuel costs. The ride-hailing platform’s EV rental services provision also effectively fosters the ride-hailing fleet’s electrification. Furthermore, as the number of riders increases, the ride-hailing platform should reduce the commission rate for EV drivers to maintain competitiveness and profitability.

Preserving Personalized Location Privacy in Ride-Hailing Service

Ride-hailing service has become a popular means of transportation due to its convenience and low cost.However,it also raises privacy concerns.Since riders’mobility information including the pick-up and drop-off location is tracked,the service provider can infer sensitive information about the riders such as where they live and work.To address these concerns,we propose location privacy preserving techniques that efficiently match riders and drivers while preserving riders’location privacy.We first propose a baseline solution that allows a rider to select the driver who is the closest to his pick-up location.However,with some side information,the service provider can launch location inference attacks.To overcome these attacks,we propose an enhanced scheme that allows a rider to specify his privacy preference.Novel techniques are designed to preserve rider’s personalized privacy with limited loss of matching accuracy.Through trace-driven simulations,we compare our enhanced privacy preserving solution to existing work.Evaluation results show that our solution provides much better ride matching results that are close to the optimal solution,while preserving personalized location privacy for riders.

Cloaking Region Based Passenger Privacy Protection in Ride-Hailing Systems

With the quick development of the sharing economy,ride-hailing services have been increasingly popular worldwide.Although the service provides convenience for users,one concern from the public is whether the location privacy of passengers would be protected.Service providers(SPs)such as Didi and Uber need to acquire passenger and driver locations before they could successfully dispatch passenger orders.To protect passengers’privacy based on their requirements,we propose a cloaking region based order dispatch scheme.In our scheme,a passenger sends the SP a cloaking region in which his/her actual location is not distinguishable.The trade-off of the enhanced privacy is the loss of social welfare,i.e.,the increase in the overall pick-up distance.To optimize our scheme,we propose to maximize the social welfare under passengers’privacy requirements.We investigate a bipartite matching based approach.A theoretical bound on the matching performance under specific privacy requirements is shown.Besides passengers’privacy,we allow drivers to set up their maximum pick-up distance in our extended scheme.The extended scheme could be applied when the number of drivers exceeds the number of passengers.Nevertheless,the global matching based scheme does not consider the interest of each individual passenger.The passengers with low privacy requirements may be matched with drivers far from them.To this end,a pricing scheme including three strategies is proposed to make up for the individual loss by allocating discounts on their riding fares.Extensive experiments on both real-world and synthetic datasets show the efficiency of our scheme.

How machine learning informs ride-hailing services:A survey

In recent years,online ride-hailing services have emerged as an important component of urban transportation system,which not only provide significant ease for residents’travel activities,but also shape new travel behavior and diversify urban mobility patterns.This study provides a thorough review of machine-learning-based methodologies for on-demand ride-hailing services.The importance of on-demand ride-hailing services in the spatiotemporal dynamics of urban traffic is first highlighted,with machine-learning-based macro-level ride-hailing research demonstrating its value in guiding the design,planning,operation,and control of urban intelligent transportation systems.Then,the research on travel behavior from the perspective of individual mobility patterns,including carpooling behavior and modal choice behavior,is summarized.In addition,existing studies on order matching and vehicle dispatching strategies,which are among the most important components of on-line ridehailing systems,are collected and summarized.Finally,some of the critical challenges and opportunities in ridehailing services are discussed.

Ride-hailing origin-destination demand prediction with spatiotemporal information fusion

Accurate demand forecasting for online ride-hailing contributes to balancing traffic supply and demand,and improving the service level of ride-hailing platforms.In contrast to previous studies,which have primarily focused on the inflow or outflow demands of each zone,this study proposes a conditional generative adversarial network with a Wasserstein divergence objective(CWGAN-div)to predict ride-hailing origin-destination(OD)demand matrices.Residual blocks and refined loss functions help to enhance the stability of model training.Interpretable conditional information is employed to capture external spatiotemporal dependencies and guide the model towards generating more precise results.Empirical analysis using ride-hailing data from Manhattan,New York City,demon-strates that our proposed CWGAN-div model can effectively predict the network-wide OD matrix and exhibits strong convergence performance.Comparative experiments also show that the CWGAN-div outperforms other benchmarking methods.Consequently,the proposed model displays potential for network-wide ride-hailing OD demand prediction.