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...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.展开更多
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 opportunit...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.展开更多
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 cha...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 re<span style="font-family:Verdana;">mains being able to optimally match rider demand with driver supply, reducing congestion and emissions associated with Vehicle clustering, dead</span><span style="font-family:Verdana;">heading, 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 </span><span style="font-family:Verdana;">drop-off location data to predict pick-up points for effective driver</span><span style="font-family:Verdana;"> deployment </span><span style="font-family:Verdana;">under multiple scenarios of privacy and information. Results show neural</span><span style="font-family:Verdana;"> network algorithms perform best in generalizing pick-up and drop-off points </span><span style="font-family:Verdana;">when given only starting point information. Ensemble learning methods,</span><span style="font-family:Verdana;"> 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 </span><span style="font-family:Verdana;">point and Census Tract information only and in reverse predict potential </span><span style="font-family:Verdana;">pick-up points using the Drop-off point as the new starting point.</span>展开更多
The rapid technological developments in the 21</span><sup><span style="font-family:Verdana;">st</span></sup><span style="font-family:Verdana;"> century created n...The rapid technological developments in the 21</span><sup><span style="font-family:Verdana;">st</span></sup><span style="font-family:Verdana;"> century created new opportunities for shared-use economy applications around the globe. Among other </span><span style="font-family:Verdana;">services, Transportation Network Companies (TNCs) like Uber and Lyft</span><span style="font-family:Verdana;"> emer</span><span style="font-family:Verdana;">ged in the US as a transportation alternative that offered a higher level of</span> <span style="font-family:Verdana;">availability, reliability, and convenience than traditional modes. However,</span> <span style="font-family:Verdana;">TNCs deployment was also blamed for increases in vehicle miles traveled</span><span style="font-family:Verdana;"> (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 t</span><span style="font-family:Verdana;">he only significant predictors for the Birmingham region whereas vehicl</span><span style="font-family:Verdana;">e 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.展开更多
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 aspe...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.展开更多
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 thei...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.展开更多
Ride-hailing electric vehicles are mobile resources with dispatch potential to improve resilience.However,they have not been well investigated because their charging and order-serving are affected or managed by the po...Ride-hailing electric vehicles are mobile resources with dispatch potential to improve resilience.However,they have not been well investigated because their charging and order-serving are affected or managed by the power grid dispatching center and the ride-hailing platform.Effective pre-strategies can improve the prevention ability for high-impact and low-probability(HILP)events and provide the foundation for measures in the response and restoration stages.First,this paper proposes a resilience reserve to expand the existing research on power system resilience.Secondly,this paper puts forward an interactive method of deep reinforcement learning,which considers the interests of both the power grid dispatching center and the ride-hailing platform.It improves the resilience reserve by achieving the order dispatch,orderly charging management of ride-hailing electric vehicles,and the pricing strategy of charging stations.Finally,this paper uses a practical example covering about 107.32 km2 in the center of Chengdu to verify that the proposed method improves the resilience reserve of the power system without obviously damaging the interests of the ride-hailing platform.展开更多
Operational strategies and matching algorithms are used to ensure the availability and efficient assignment of ride-hailing services.Such operational strategies may result in services that,rather than complement tradi...Operational strategies and matching algorithms are used to ensure the availability and efficient assignment of ride-hailing services.Such operational strategies may result in services that,rather than complement traditional public transport(PT)systems,compete with them in both market(demand)and road-space use(congestion).This paper introduces and evaluates real-time vehicle dispatching strategies that focus on the prioritization of PT use and the complementarity between PT and ride-hailing in multimodal trips.Utilizing a novel two-step ride-matching algorithm,these strategies aim at decreasing travelers'wait times and motivating the use of PT.The agent-based travel demand forecasting model MATSim is used to implement and test the proposed matching strategies in the study area of Metropolitan Melbourne,Australia.The proposed strategies outperform the original MATSim strategies(which follow a first-come,first-serve approach)regarding average vehicle kilometers traveled(VKT)per ride,number of multimodal trips that use ride-hailing and PT,and the overall PT mode share.The results indicate substantial improvements across all proposed strategies,with PT mode shares increasing by 3.3%–19.8%.A fleet size of 200 was identified as the optimal fleet size.Multimodal trips increased by 7%–13%for all proposed strategies at this fleet size,illustrating a substantial shift towards integrated transport modes.Additionally,VKT per ride decreased by approximately 30%–33%with the proposed strategies at this fleet size.We conclude that serving all ride-hailing requests on a first-come/first-serve basis creates system-level inefficiencies that can be overcome by prioritizing requests that cannot be served by PT and/or have an emergency nature.展开更多
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 locat...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.展开更多
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 beh...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.展开更多
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 ...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.展开更多
This paper investigates the impact of ride-hailing services,particularly the integration of autonomous vehicles(AVs),on urban transportation systems.The paper discusses the challenges faced by ride-hailing platforms i...This paper investigates the impact of ride-hailing services,particularly the integration of autonomous vehicles(AVs),on urban transportation systems.The paper discusses the challenges faced by ride-hailing platforms in managing a fleet of both AVs and conventional vehicles(CVs)within the spatial network of a city.It examines the approaches and methods used to manage demand allocation for AVs and CVs,considering the strategic behavior of human drivers and considerations for possible regulations.Using mean-field game theory,this paper proposes efficient strategies for managing fleet operations along with those of traffic optimization and service efficiency.The analysis highlights the complexities of integrating AVs into existing transportation systems and advocates for the development of robust theoretical traffic models for regulatory decisions and improved urban mobility.展开更多
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, t...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.展开更多
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 foc...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.展开更多
This study investigates e-shopping behavior change through ride-hailing applications(RHAs)for grocery and food as an alternative way to minimize out-of-home activities during the pandemic.Exploratory factor analysis a...This study investigates e-shopping behavior change through ride-hailing applications(RHAs)for grocery and food as an alternative way to minimize out-of-home activities during the pandemic.Exploratory factor analysis and structural equation modeling were applied,which utilized data collected from a web-based questionnaire survey during the implementation of social activity restrictions in August 2021.The modeling results show a complementary effect between food and grocery delivery services,where an increase in food delivery is followed by an increase in grocery delivery,but not vice versa.Meanwhile,grocery delivery could substitute in-store grocery shopping.The frequency of food delivery before the pandemic also significantly affects food and grocery deliveries during the pandemic.The more individuals avail food delivery services before the pandemic,the more they avail grocery delivery services during the pandemic.In contrast,the less likely people are to avail food delivery services before the pandemic,the more likely they are to avail food delivery services during the pandemic.The study also found that RHA use for food delivery is influenced by the latent variable of e-shopping enjoyment,whereas the latent variable of e-shopping benefits affects RHA use for grocery delivery.Regarding the socio-demographic effect,females and well-educated people tend to increase RHA use for grocery delivery,and millennials are more likely to participate in grocery shopping and dining out.The findings provide valuable insights into the suppression of virus spread in the short term and travel demand management in the medium term.展开更多
Ride-hailing(e.g.,DiDi andUber)has become an important tool formodern urban mobility.To improve the utilization efficiency of ride-hailing vehicles,a novel query method,called Approachable k-nearest neighbor(A-kNN),ha...Ride-hailing(e.g.,DiDi andUber)has become an important tool formodern urban mobility.To improve the utilization efficiency of ride-hailing vehicles,a novel query method,called Approachable k-nearest neighbor(A-kNN),has recently been proposed in the industry.Unlike traditional kNN queries,A-kNN considers not only the road network distance but also the availability status of vehicles.In this context,even vehicles with passengers can still be considered potential candidates for dispatch if their destinations are near the requester’s location.The V-Treebased query method,due to its structural characteristics,is capable of efficiently finding k-nearest moving objects within a road network.It is a currently popular query solution in ride-hailing services.However,when vertices to be queried are close in the graph but distant in the index,the V-Tree-based method necessitates the traversal of numerous irrelevant subgraphs,which makes its processing of A-kNN queries less efficient.To address this issue,we optimize the V-Tree-based method and propose a novel index structure,the Path-Accelerated V-Tree(PAV-Tree),to improve query performance by introducing shortcuts.Leveraging this index,we introduce a novel query optimization algorithm,PAVA-kNN,specifically designed to processA-kNNqueries efficiently.Experimental results showthat PAV-A-kNNachieves query times up to 2.2–15 times faster than baseline methods,with microsecond-level latency.展开更多
基金This research was financially supported by the Ministry of Small and Mediumsized Enterprises(SMEs)and Startups(MSS),Korea,under the“Regional Specialized Industry Development Program(R&D,S3091627)”supervised by Korea Institute for Advancement of Technology(KIAT).
文摘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.
文摘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.
文摘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 re<span style="font-family:Verdana;">mains being able to optimally match rider demand with driver supply, reducing congestion and emissions associated with Vehicle clustering, dead</span><span style="font-family:Verdana;">heading, 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 </span><span style="font-family:Verdana;">drop-off location data to predict pick-up points for effective driver</span><span style="font-family:Verdana;"> deployment </span><span style="font-family:Verdana;">under multiple scenarios of privacy and information. Results show neural</span><span style="font-family:Verdana;"> network algorithms perform best in generalizing pick-up and drop-off points </span><span style="font-family:Verdana;">when given only starting point information. Ensemble learning methods,</span><span style="font-family:Verdana;"> 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 </span><span style="font-family:Verdana;">point and Census Tract information only and in reverse predict potential </span><span style="font-family:Verdana;">pick-up points using the Drop-off point as the new starting point.</span>
文摘The rapid technological developments in the 21</span><sup><span style="font-family:Verdana;">st</span></sup><span style="font-family:Verdana;"> century created new opportunities for shared-use economy applications around the globe. Among other </span><span style="font-family:Verdana;">services, Transportation Network Companies (TNCs) like Uber and Lyft</span><span style="font-family:Verdana;"> emer</span><span style="font-family:Verdana;">ged in the US as a transportation alternative that offered a higher level of</span> <span style="font-family:Verdana;">availability, reliability, and convenience than traditional modes. However,</span> <span style="font-family:Verdana;">TNCs deployment was also blamed for increases in vehicle miles traveled</span><span style="font-family:Verdana;"> (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 t</span><span style="font-family:Verdana;">he only significant predictors for the Birmingham region whereas vehicl</span><span style="font-family:Verdana;">e 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.
文摘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.
文摘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.
文摘Ride-hailing electric vehicles are mobile resources with dispatch potential to improve resilience.However,they have not been well investigated because their charging and order-serving are affected or managed by the power grid dispatching center and the ride-hailing platform.Effective pre-strategies can improve the prevention ability for high-impact and low-probability(HILP)events and provide the foundation for measures in the response and restoration stages.First,this paper proposes a resilience reserve to expand the existing research on power system resilience.Secondly,this paper puts forward an interactive method of deep reinforcement learning,which considers the interests of both the power grid dispatching center and the ride-hailing platform.It improves the resilience reserve by achieving the order dispatch,orderly charging management of ride-hailing electric vehicles,and the pricing strategy of charging stations.Finally,this paper uses a practical example covering about 107.32 km2 in the center of Chengdu to verify that the proposed method improves the resilience reserve of the power system without obviously damaging the interests of the ride-hailing platform.
文摘Operational strategies and matching algorithms are used to ensure the availability and efficient assignment of ride-hailing services.Such operational strategies may result in services that,rather than complement traditional public transport(PT)systems,compete with them in both market(demand)and road-space use(congestion).This paper introduces and evaluates real-time vehicle dispatching strategies that focus on the prioritization of PT use and the complementarity between PT and ride-hailing in multimodal trips.Utilizing a novel two-step ride-matching algorithm,these strategies aim at decreasing travelers'wait times and motivating the use of PT.The agent-based travel demand forecasting model MATSim is used to implement and test the proposed matching strategies in the study area of Metropolitan Melbourne,Australia.The proposed strategies outperform the original MATSim strategies(which follow a first-come,first-serve approach)regarding average vehicle kilometers traveled(VKT)per ride,number of multimodal trips that use ride-hailing and PT,and the overall PT mode share.The results indicate substantial improvements across all proposed strategies,with PT mode shares increasing by 3.3%–19.8%.A fleet size of 200 was identified as the optimal fleet size.Multimodal trips increased by 7%–13%for all proposed strategies at this fleet size,illustrating a substantial shift towards integrated transport modes.Additionally,VKT per ride decreased by approximately 30%–33%with the proposed strategies at this fleet size.We conclude that serving all ride-hailing requests on a first-come/first-serve basis creates system-level inefficiencies that can be overcome by prioritizing requests that cannot be served by PT and/or have an emergency nature.
文摘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.
基金the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No.101025896.
文摘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.
基金This research was supported in part by the National Science Foundation of USA under Grant Nos.CNS 1824440,CNS 1828363,CNS 1757533,CNS 1618398,CNS 1651947,and CNS 1564128the National Natural Science Foundation of China under Grant Nos.61872330,61572457,61379132the National Natural Science Foundation of Jiangsu Province of China under Grant Nos.BK20191194 and BK20131174.
文摘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.
基金supported in part by the National Natural Science Foundation of China(Grant No.72271068)in part by the Shenzhen Science and Technology Program(Grant No.KCXST20221021111404010).
文摘This paper investigates the impact of ride-hailing services,particularly the integration of autonomous vehicles(AVs),on urban transportation systems.The paper discusses the challenges faced by ride-hailing platforms in managing a fleet of both AVs and conventional vehicles(CVs)within the spatial network of a city.It examines the approaches and methods used to manage demand allocation for AVs and CVs,considering the strategic behavior of human drivers and considerations for possible regulations.Using mean-field game theory,this paper proposes efficient strategies for managing fleet operations along with those of traffic optimization and service efficiency.The analysis highlights the complexities of integrating AVs into existing transportation systems and advocates for the development of robust theoretical traffic models for regulatory decisions and improved urban mobility.
基金the National Natural Science Foundation of China(NSFC),under Grant No.71871151.The authors thank the anonymous referees and editors for their valuable comments that significantly contributed towards improving the quality of the paper.
文摘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.
基金supported by the National Natural Science Foundation of China(Grant No.72371251)the National Science Foundation for Distinguished Young Scholars of Hunan Province(Grant No.2024JJ2080)+1 种基金the Excellent Youth Foundation of Hunan Education Department(Grant No.21B0015)the State Key Lab-oratory of Rail Traffic Control and Safety of Beijing Jiaotong Uni-v ersity,China(Gr ant No.RCS2022K004).
文摘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.
文摘This study investigates e-shopping behavior change through ride-hailing applications(RHAs)for grocery and food as an alternative way to minimize out-of-home activities during the pandemic.Exploratory factor analysis and structural equation modeling were applied,which utilized data collected from a web-based questionnaire survey during the implementation of social activity restrictions in August 2021.The modeling results show a complementary effect between food and grocery delivery services,where an increase in food delivery is followed by an increase in grocery delivery,but not vice versa.Meanwhile,grocery delivery could substitute in-store grocery shopping.The frequency of food delivery before the pandemic also significantly affects food and grocery deliveries during the pandemic.The more individuals avail food delivery services before the pandemic,the more they avail grocery delivery services during the pandemic.In contrast,the less likely people are to avail food delivery services before the pandemic,the more likely they are to avail food delivery services during the pandemic.The study also found that RHA use for food delivery is influenced by the latent variable of e-shopping enjoyment,whereas the latent variable of e-shopping benefits affects RHA use for grocery delivery.Regarding the socio-demographic effect,females and well-educated people tend to increase RHA use for grocery delivery,and millennials are more likely to participate in grocery shopping and dining out.The findings provide valuable insights into the suppression of virus spread in the short term and travel demand management in the medium term.
基金supported by the Special Project of Henan Provincial Key Research,Development and Promotion(Key Science and Technology Program)under Grant 252102210154in part by the National Natural Science Foundation of China under Grant 62403437.
文摘Ride-hailing(e.g.,DiDi andUber)has become an important tool formodern urban mobility.To improve the utilization efficiency of ride-hailing vehicles,a novel query method,called Approachable k-nearest neighbor(A-kNN),has recently been proposed in the industry.Unlike traditional kNN queries,A-kNN considers not only the road network distance but also the availability status of vehicles.In this context,even vehicles with passengers can still be considered potential candidates for dispatch if their destinations are near the requester’s location.The V-Treebased query method,due to its structural characteristics,is capable of efficiently finding k-nearest moving objects within a road network.It is a currently popular query solution in ride-hailing services.However,when vertices to be queried are close in the graph but distant in the index,the V-Tree-based method necessitates the traversal of numerous irrelevant subgraphs,which makes its processing of A-kNN queries less efficient.To address this issue,we optimize the V-Tree-based method and propose a novel index structure,the Path-Accelerated V-Tree(PAV-Tree),to improve query performance by introducing shortcuts.Leveraging this index,we introduce a novel query optimization algorithm,PAVA-kNN,specifically designed to processA-kNNqueries efficiently.Experimental results showthat PAV-A-kNNachieves query times up to 2.2–15 times faster than baseline methods,with microsecond-level latency.
