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On ride-sourcing services of electric vehicles considering cruising for charging and parking
Abstract
Cruising of electric ride-sourcing vehicles (ERVs) when waiting for trip orders can create additional vehicle miles, which increase congestion and waste electricity. Reducing cruising is an important issue. This study investigates the strategy of allocating a portion of road space as parking for ERVs. Considering ERVs cruising for parking/charging, we analytically examine the trade-off between road capacity reduction due to reserving road space as parking and less cruising. We evaluate the effects of parking provision on reducing congestion and charging demand. We also investigate the optimal fare and fleet size of ERV services to achieve profit or social welfare maximization. Numerical studies indicate that vehicles cruising for charging might be reduced significantly with a mild increase of charging pile supply, where cruising can increase sharply after charging pile occupancy rate is at critical levels. By providing parking to ERVs, ride-sourcing demand increases, charging demand reduces, profit and social welfare increase.
... While most studies focus on battery electric buses (BEBs), several also explore various other electric modes of public transportation. For instance, works by Cai et al. (2023), Guo et al. (2022), investigated the electrification pathways of taxis, Havre et al. (2022) examined battery vessel services, and Wei et al. (2023) studied electric ride-sourcing vehicles (ERVs). ...
... The study revealed that long-term planning can result in significant cost savings and emission reductions. Wei et al. (2023) investigated the operation of ERVs and their cruising behavior for charging and parking. Their research indicated that cruising could be significantly reduced with a mild increase in charging pile supply, and that providing parking for ERVs could increase ride-sourcing demand, reduce charging demand, and enhance profit and social welfare. ...
The problem of learning from imbalanced ride-hailing demand data with spatiotemporal heterogeneity and highly skewed demand distributions is a relatively new challenge. Current prediction methods usually filter out some spatiotemporal partitions with sparse demands by setting a minimum ride-hailing demand threshold, where the dataset is always assumed to be well balanced in terms of its spatiotemporal partitions, with equal misprediction costs. However, this widely used assumption results in large prediction biases. To achieve better prediction performance, we propose a bagging learning approach based on hexagonal convolutional long short-term memory (H-ConvLSTM), which combines three components. 1) By setting multiple minimum ride-hailing demand thresholds, several subdatasets with different majority ride-hailing demand prediction ranges are obtained. The H-ConvLSTM regression model is applied to each undersampled dataset to train multiple submodels with their respective biased ride-hailing demand prediction ranges. 2) The H-ConvLSTM classification model is trained on the total ride-hailing demand dataset to predict the potential demand range for a certain partition at a future time. 3) The submodel with the best performance with respect to the potential demand range is selected to predict the future demand for this partition. Experiments conducted on order data obtained from Didi Chuxing in Chengdu, China, are conducted. The results show that the proposed approach achieves significantly improved prediction performance relative to that of other models.
Charging management has a profound impact on the reliability and safety of electric bus (EB) services. However, the actual charging operation of EB fleets is a critical challenge due to uncertain energy consumption, limited charging resources and other factors. A deterministic model and a robust model with a probability-free uncertainty set are proposed and compared. The power is optimized via rational allocation of charging resources, where the uncertainty of energy consumption is addressed to achieve the dual goals of reducing charging expenses and improving system robustness. A column generation algorithm is designed to solve the optimization issue. The experimental results show that the obtained robust charging strategies can achieve up to 97.88% utilization of charging resources at low electricity prices. Moreover, the robust model can effectively prevent low electric quantity and delayed departure issues for EBs caused by the uncertainty of energy consumption.
The large-scale application of electric buses highlights a series of practical problems, such as high charging costs, unreasonable utilization of charging resources, and chaotic charging schedules. In this study, a mixed integer programming model was first proposed to reduce the total charging cost of electric bus fleets by optimizing the charging power and charging time. Meanwhile, to improve the efficiency of the model under large-scale charging scenarios, a column-generation-based algorithm is developed to decompose the original model into a master problem and several subproblems, where each electric bus’s charging strategy is solved in an independent subproblem to guarantee a stable operational bus fleet schedule and to avoid intermittent charging. An experimental analysis is carried out. The results show that the optimal charging strategy can reduce the charging cost by approximately 36.1% compared with the uncontrolled charging strategy, which carries the potential to be applied in large-scale bus fleet operation.
In a coordinated mobility-on-demand system, a fleet of vehicles is controlled by a central unit and serves transportation requests in an on-demand fashion. An emerging field of research aims at finding the best way to operate these systems given certain targets, e.g., customer service level or the minimization of fleet distance. In this work, we introduce a new element of fleet operation: the assignment of idle vehicles to a limited set of parking spots. We present two different parking operating policies governing this process and then evaluate them individually and together on different parking space distributions. We show that even for a highly restricted number of available parking spaces, the system can perform quite well, even though the total fleet distance is increased by 20% and waiting time by 10%. With only one parking space available per vehicle, the waiting times can be reduced by 30% with 20% increase in total fleet distance. Our findings suggest that increasing the parking capacity beyond one parking space per vehicle does not bring additional benefits. Finally, we also highlight possible directions for future research such as to find the best distribution of parking spaces for a given mobility-on-demand system and city.
Electrified shared mobility services need to handle charging infrastructure planning and manage their daily charging operations to minimize total charging operation time and cost. However, existing studies tend to address these problems separately. A new online vehicle-charging assignment model is proposed and integrated into the fast charging location problem for dynamic ridesharing services using electric vehicles. The latter is formulated as a bi-level optimization problem to minimize the fleet’s daily charging operation time. A surrogate-assisted optimization approach is proposed to solve the combinatorial optimization problem efficiently. The proposed model is tested on a realistic flexible bus service in Luxembourg. The results show that the proposed online charging policy can effectively reduce the charging delays of the fleet compared to the state-of-the-art methods. With 10 additional DC fast chargers installed, charging operation time can be reduced up to 27.8% when applying the online charging policy under the test scenarios.
The advent of shared-economy and smartphones made on-demand transportation services possible, which created additional opportunities, but also more complexity to urban mobility. Companies that offer these services are called Transportation Network Companies (TNCs) due to their internet-based nature. Although ride-sourcing is the most notorious service TNCs provide, little is known about to what degree its operations can interfere in traffic conditions, while replacing other transportation modes, or when a large number of idle vehicles is cruising for passengers. We experimentally analyze the efficiency of TNCs using taxi trip data from a Chinese megacity and an agent-based simulation with a trip-based MFD model for determining the speed. We investigate the effect of expanding fleet sizes for TNCs, passengers’ inclination towards sharing rides, and strategies to alleviate urban congestion. We observe that, although a larger fleet size reduces waiting time, it also intensifies congestion, which, in turn, prolongs the total travel time. Such congestion effect is so significant that it is nearly insensitive to passengers’ willingness to share and flexible supply. Finally, parking management strategies can prevent idle vehicles from cruising without assigned passengers, mitigating the negative impacts of ride-sourcing over congestion, and improving the service quality.
Parking infrastructure is pervasive and occupies large swaths of land in cities. However, on-demand (OD) mobility has started reducing parking needs in urban areas around the world. This trend is expected to grow significantly with the advent of autonomous driving, which might render on-demand mobility predominant. Recent studies have started looking at expected parking reductions with on-demand mobility, but a systematic framework is still lacking. In this paper, we apply a data-driven methodology based on shareability networks to address what we call the “minimum parking” problem: what is the minimum parking infrastructure needed in a city for given on-demand mobility needs? While solving the problem, we also identify a critical tradeoff between two public policy goals: less parking means increased vehicle travel from deadheading between trips. By applying our methodology to the city of Singapore we discover that parking infrastructure reduction of up to 86% is possible, but at the expense of a 24% increase in traffic measured as vehicle kilometers travelled (VKT). However, a more modest 57% reduction in parking is achievable with only a 1.3% increase in VKT. We find that the tradeoff between parking and traffic obeys an inverse exponential law which is invariant with the size of the vehicle fleet. Finally, we analyze parking requirements due to passenger pick-ups and show that increasing convenience produces a substantial increase in parking for passenger pickup/dropoff. The above findings can inform policy-makers, mobility operators, and society at large on the tradeoffs required in the transition towards pervasive on-demand mobility.
The use of plug-in electric vehicles in ride-hailing services is expected to have substantial emission reduction benefits. However, these benefits depend on the energy fuel mix in the grid and vehicle usage. Here we employ high-resolution data from Uber and Lyft in California to provide insights into the use of electric vehicles in ride-hailing. The growth in electric vehicle use has been rapid in the past two years and a proportionally small number of electric vehicles are already using a large share of electricity provided by the public charging infrastructure. Concerns about the ability of electric vehicles to provide the same level of service as gasoline vehicles has been overstated: we found no statistical difference between the two technologies for services provided to ride-hailing companies. Lastly, the potential environmental and emission reduction benefits are approximately three times higher for electric vehicles being used in ride-hailing compared with those of regular vehicle usage in California. Use of electric vehicles in ride-hailing services such as Uber and Lyft is rising but their impact on environment and emissions remains difficult to assess. Using data from more than 100,000 rides, Alan Jenn shows the substantial emission reduction potential of electric vehicle use in ride-hailing in California.
With the availability of the location information of drivers and passengers, ride-sourcing platforms can now provide increasingly efficient online matching compared with physical searching and meeting performed in the traditional taxi market. The matching time interval (the time interval over which waiting passengers and idle drivers are accumulated and then subjected to peer-to-peer matching) and matching radius (or maximum allowable pick-up distance, within which waiting passengers and idle drivers can be matched or paired) are two key control variables that a platform can employ to optimize system performance in an online matching system. By appropriately extending the matching time interval, the platform can accumulate large numbers of waiting (or unserved) passengers and idle drivers and thus match the two pools with a reduced expected pick-up distance. However, if the matching time interval is excessively long, certain passengers may become impatient and even abandon their requests. Meanwhile, a short matching radius can reduce the expected pick-up distance but may decrease the matching rate as well. Therefore, the matching time interval and matching radius should be optimized to enhance system efficiency in terms of passenger waiting time, vehicle utilization, and matching rate. This study proposes a model that delineates the online matching process in ride-sourcing markets. The model is then used to examine the impact of the matching time interval and matching radius on system performance and to jointly optimize the two variables under different levels of supply and demand. Numerical experiments are conducted to demonstrate how the proposed modeling and optimization approaches can improve the real-time matching of ride-sourcing platforms.
We evaluate the impact of three proposed regulations of transportation network companies (TNCs) like Uber, Lyft and Didi: (1) A minimum wage for drivers, (2) a cap on the number of drivers or vehicles, and (3) a per-trip congestion tax. The impact is assessed using a queuing theoretic equilibrium model which incorporates the stochastic dynamics of the app-based ride-hailing matching platform, the ride prices and driver wages established by the platform, and the incentives of passengers and drivers. We show that a floor placed under driver earnings can push the ride-hailing platform to hire more drivers and offer more rides, at the same time that passengers enjoy faster rides and lower total cost, while platform rents are reduced. Contrary to standard competitive labor market theory, enforcing a minimum wage for drivers benefits both drivers and passengers, and promotes the efficiency of the entire system. This surprising outcome holds for almost all model parameters, and it occurs because the wage floor curbs TNC labor market power. In contrast to a wage floor, imposing a cap on the number of vehicles hurts drivers, because the platform reaps all the benefits of limiting supply. The congestion tax has the expected impact: fares increase, wages and platform revenue decrease. We also construct variants of the model to briefly discuss platform subsidy, platform competition, and autonomous vehicles.
Ridesourcing fleets present an opportunity for rapid uptake of battery electric vehicles (BEVs) but adoption has largely been limited to small pilot projects. Lack of charging infrastructure presents a major barrier to scaling up, but little public information exists on the infrastructure needed to support ridesourcing electrification. With data on ridesourcing trips for New York City and San Francisco, and using agent-based simulations of BEV fleets, we show that given a sparse network of three to four 50 kW chargers per square mile, BEVs can provide the same level of service as internal combustion engine vehicles (ICEVs) at lower cost. This suggests that the cost of charging infrastructure is not a significant barrier to ridesourcing electrification. With coordinated use of charging infrastructure across vehicles, we also find that fleet performance becomes robust to variation in battery range and placement of chargers. Our analysis suggests that mandates on ridesourcing such as the California Clean Miles Standard could achieve electrification without significantly increasing the cost of ridesourcing services.
Today massive on-demand ride requests are dispatched every hour on ride-sourcing platforms, but an unneglectable amount of confirmed orders is later cancelled by customers. Customers’ order cancellation not only wastes drivers’ efforts, but also lowers the availability of supplies on the ride-sourcing platform. Based on a two-month hourly-average dataset provided by Didi Chuxing, this paper makes the first attempt to look into customers’ cancellation behaviours of the confirmed-orders. The customers’ confirmed-order cancellation rate (COCR) is observed to be significantly and negatively correlated to the customers’ waiting time for pick-up time. We regard such counter-intuitive phenomena as an outcome of customers’ mode switch upon meeting vacant taxis while in waiting for ride-sourcing vehicles. The cancellation rate is thus characterized as a function of customers’ average waiting time for ride-sourcing vehicles and cruising taxis, the platform’s penalty strategy for cancellation of confirmed-orders, and the customers’ own characteristics (i.e., value of time, psychological cost of order cancellation). As both the customers’ waiting times for ride-sourcing vehicles and cruising taxis are endogenously determined by the demand and supply of ride-sourcing and taxi markets respectively, a system of nonlinear equations is proposed to depict the complex interactions between the two markets at equilibrium while considering customers’ order cancellation behaviour under a given penalty and compensation rule. With the proposed model, we theoretically establish the existence of the equilibrium under mild conditions, and examine the optimal pricing and penalty/compensation strategies that respectively maximize the platform’s profit and social welfare respectively. Numerical examples are presented to demonstrate the distinct optimal pricing and penalty/compensation strategies for different objectives and examine the various impacts of the social credibility.
This paper identifies major aspects of ridesourcing services provided by Transportation Network Companies (TNCs) which influence vehicles miles traveled (VMT) and energy use. Using detailed data on approximately 1.5 million individual rides provided by RideAustin in Austin Texas, we quantify the additional miles TNC drivers travel: before beginning and after ending their shifts, to reach a passenger once a ride has been requested, and between consecutive rides (all of which is referred to as deadheading); and the relative fuel efficiency of the vehicles that RideAustin drivers use compared to the average vehicle registered in Austin. We conservatively estimate that TNC drivers commute to and from their service areas accounts for 19% of the total ridesourcing VMT. In addition, we estimate that TNC drivers drove 55% more miles between ride requests within 60 minutes of each other, accounting for 26% of total ridesourcing VMT. Vehicles used for ridesourcing are on average two miles per gallon more fuel efficient than comparable light-duty vehicles registered in Austin, with twice as many are hybrid-electric vehicles. New generation battery electric vehicles with 200 miles of range would be able to fulfill 90% of full-time drivers’ shifts on a single charge. We estimate that the net effect of ridesourcing on energy use is a 41% to 90% increase compared to baseline, pre-TNC, personal travel.
Battery-powered electric buses are gaining popularity as an energy-efficient and emission-free alternative for bus fleets. However, battery electric buses continue to struggle with concerns related to their limited driving range and time-consuming recharging processes. Fast-charging technology, which utilizes dwelling time at bus stops or terminals to recharge buses in operation employing high power, can raise battery electric buses to the same level of capability as their diesel counterparts in terms of driving range and operating time. To develop an economical and effective battery electric bus system using fast-charging technology, fast-charging stations must be strategically deployed. Moreover, due to the instability of traffic conditions and travel demands, the energy consumption uncertainty of buses should also be considered. This study addresses the planning problem of fast-charging stations that is inherent in a battery electric bus system in light of the energy consumption uncertainty of buses. A robust optimization model that represents a mixed integer linear program is developed with the objective of minimizing the total implementation cost. The model is then demonstrated using a real-world bus system. The performances of deterministic solutions and robust solutions are compared under a worst-case scenario. The results demonstrate that the proposed robust model can provide an optimal plan for a fast-charging battery electric bus system that is robust against the energy consumption uncertainty of buses. The trade-off between system cost and system robustness is also addressed.
For electric taxicabs, the idle time spent on cruising for passengers, seeking chargers, and charging is wasteful. Previous works can only save cruising time through better routing, or charger seeking and charging time through proper charger deployment, but not for both. With the advancement of wireless charging techniques, efficient opportunistic charging of electric vehicles at their parked positions becomes possible. This enables a taxicab to get charged while waiting for the next passenger. In this paper, we present an opportunistic wireless charger deployment scheme in a city, which both maximizes the taxicabs' opportunity of picking up passengers at the chargers and supports the taxicabs' continuous operability on roads, while minimizing the total deployment cost. We studied a metropolitan-scale taxicab dataset on several factors important for deploying wireless chargers and determining the numbers of the chargers in the regions: the number of passengers, the functionalities of buildings, and the frequency of passenger appearance in a region, and taxicab traffic flows in a city. Then, we formulate a multi-objective optimization problem and find the solution. Our trace-driven experiments demonstrate the superior performance of our scheme over other representative methods in terms of reducing idle time and supporting the operability of the taxicabs.
Short-term passenger demand forecasting is of great importance to the on-demand ride service platform, which can incentivize vacant cars moving from over-supply regions to over-demand regions. The spatial dependences, temporal dependences, and exogenous dependences need to be considered simultaneously, however, which makes short-term passenger demand forecasting challenging. We propose a novel deep learning (DL) approach, named the fusion convolutional long short-term memory network (FCL-Net), to address these three dependences within one end-to-end learning architecture. The model is stacked and fused by multiple convolutional long short-term memory (LSTM) layers, standard LSTM layers, and convolutional layers. The fusion of convolutional techniques and the LSTM network enables the proposed DL approach to better capture the spatio-temporal characteristics and correlations of explanatory variables. A tailored spatially aggregated random forest is employed to rank the importance of the explanatory variables. The ranking is then used for feature selection. The proposed DL approach is applied to the short-term forecasting of passenger demand under an on-demand ride service platform in Hangzhou, China. Experimental results, validated on real-world data provided by DiDi Chuxing, show that the FCL-Net achieves better predictive performance than traditional approaches including both classical time-series prediction models and neural network based algorithms (e.g., artificial neural network and LSTM). This paper is one of the first DL studies to forecast the short-term passenger demand of an on-demand ride service platform by examining the spatio-temporal correlations.
This paper aims to examine the impact of ridesourcing on the taxi industry and explore where, when and how taxis can compete more effectively. To this end a large taxi GPS trajectory data set collected in Shenzhen, China is mined and more than 2,700 taxis (or about 18% of all registered in the city) are tracked in a period of three years, from January 2013 to November 2015, when both e-hailing and ridesourcing were rapidly spreading in the city. The long sequence of GPS data points is first broken into separate “trips”, each corresponding to a unique passenger state, an origin/destination zone, and a starting/ending time. By examining the trip statistics, we found that: (1) the taxi industry in Shenzhen has experienced a significant loss in its ridership that can be indisputably credited to the competition from ridesourcing. Yet, the evidence is also strong that the shock was relatively short and that the loss of the taxi industry had begun to stabilize since the second half of 2015; (2) taxis are found to compete more effectively with ridesourcing in peak period (6–10 AM, 5–8 PM) and in areas with high population density. (3) e-hailing helps lift the capacity utilization rate of taxis. Yet, the gains are generally modest except for the off-peak period, and excessive competition can lead to severely under-utilized capacities; and (4) ridesourcing worsens congestion for taxis in the city, but the impact was relatively mild. We conclude that a dedicated service fleet with exclusive street-hailing access will continue to co-exist with ridesourcing and that regulations are needed to ensure this market operate properly.
Ride-sourcing services have become increasingly important in meeting people's mobility needs since their emergence. Compared to traditional street-hailing taxi services, ride-sourcing services significantly reduce the matching frictions in the markets by matching drivers and passengers with relatively distant distances through an online platform. Motivated by this new feature as well as the need for designing operating and regulating strategies, researchers have attempted to describe these innovative ride-sourcing markets through mathematical models, the core of which is the matching functions for characterizing matching frictions. Previous studies have developed a variety of matching functions for ride-sourcing markets, including perfect matching function, Cobb-Douglas type matching function, queuing models, and some physical models. However, less is known about the applicability and performance of these matching functions, that is, under what situations each of these matching functions well characterizes the real market. To address this issue, this paper makes one of the first attempts to calibrate, validate, and compare the prevailing matching functions in the literature, and ascertain the conditions of their applicability. In particular, we establish a simulator to simulate a total of 420 scenarios of the ride-sourcing market under different combinations of supply and demand. The key performance metrics, including the matching rate in the market, passengers' average matching time, passengers' average pick-up time, and passengers' average total waiting time, are utilized to test and compare seven widely used matching functions under various market scenarios.
This study examines regulatory policies for electrifying a ridesourcing system. To do so, we develop an aggregate modeling framework to analyze a ridesourcing market involving electric vehicles and examine the response of the ridesourcing platform to regulatory policies such as annual permit fees (AP), differential trip-based fees (TB) or differential commission caps (CC). Our modeling exercises and numerical analyses suggest that both AP and TB are effective at achieving a high electrification level, while CC may only electrify the system to a low level. However, CC is the most cost-efficient as it simultaneously benefits drivers and customers, and allows finer intervention to the market. TB is the least cost-efficient as the platform prefers to deliver fewer customers to avoid the trip-based fees and surcharge drivers and customers for higher per-trip profits. Under all policies, the platform’s best response is always to adopt fast chargers and gradually expand the charging network to accommodate the increasing charging needs of its electric vehicle fleet.
The continuous improvement of charging technologies makes fast charging applicable to overcoming the limited driving range of electric vehicles by providing en-route opportunity charging. Fast chargers can be deployed at bus stops to top up the battery for an electric bus during its dwelling time when passengers board and alight, which allows the usage of small size battery and avoids deadheading trips to depot or charging station for recharging. This paper examines the joint optimisation problem of locating fast chargers at selected bus stops and developing charging schedule to support electric bus operation considering time-varying electricity price and penalty cost if passengers’ extra waiting time, the delay time of starting a trip or the excessive travel time of completing a trip is caused by charging activities. To handle the uncertainties associated with the travel time and passenger demand, a robust model is proposed and formulated. The proposed methods are applied to multiple bus routes in Sydney in the numerical studies. The numerical results show that frequent charging activities occur at both intermediate and last stops during bus service trips, indicating that it is beneficial for buses to utilise passengers’ boarding and alighting time at intermediate stops and resting time at the last stop between two adjacent trips to top up battery. Sensitivity analysis on a series of system parameters, such as the acquisition cost of chargers, amortized battery price and charging power, is conducted. The numerical results verify the feasibility of deployment of fast chargers and corresponding top-up charging activities at both intermediate and last stops. Electric buses outperform the conventional diesel buses regarding energy consumption cost, especially considering the increasing diesel fuel price and its costly environmental impacts. Moreover, charging activities at intermediate stops will become more common when passenger demand (the volume of boarding and alighting passengers) increases at intermediate stops.
Electrification of transportation is an important option to reduce fossil fuels consumption and carbon emissions. However, electric vehicles (EVs) comprise less than 1.2% of New Zealand’s light vehicle fleet, and there are significant hurdles to limit EV uptake. This study uses spatial negative binomial regression models to estimate spatio-temporal factors that influence consumers’ purchase decisions. Two hypotheses are tested: (1) if charging infrastructure has a “neighbourhood effect” on EV uptake; (2) if EVs adoption by technology enthusiasts during the early-adopter phase affects subsequent adoption once the technology becomes widely spread. Results show that charging infrastructure in neighbouring areas and early adoption positively affect subsequent technology adoption. Robustness analysis suggests that the distance spatial weights matrices outperformed other types of alternatives in this study may represent the boundaries of a convenience threshold that drivers perceive when they get their EVs. Thus, policy initiatives aiming to increase the electrification of transport should consider the likelihood of early adoption and the spatial proximity of charging infrastructure.
Time-dependent order cancellation behaviour affects ridesourcing platform operation and overall system performance. This paper models impacts of time-dependent order cancellation behaviour on the driver–rider matching efficiency and examines ridesourcing platform operation strategies, where taxi is the alternative to the ridesourcing mode. A time-dependent matching model is developed to characterize the complex interaction among participants (drivers and riders) considering ridesourcing order cancellation behaviour, where the impact of the cancelled orders on the numbers of drivers and riders is explicitly modelled. In particular, we formulate the time-dependent order cancellation rates before and after the matching stage (i.e., cancellation of unconfirmed and confirmed orders). Under the proposed model, a platform profit maximization problem is formulated and three pricing strategies are examined. Our numerical studies demonstrate that the dynamic pricing (i.e., customer/rider fare and driver wage) can well accommodate time-dependent system inputs (e.g., demand rates) and thus enable the platform to increase profit via better market segmentation. We also investigate the objective of maximizing the number of completed trips and examine the trade-off between the platform profit and the number of completed trips. In addition, we show that the relaxation of upper bounds of the ridesourcing fare and order cancellation penalty can increase the ridesourcing platform’s profit and indirectly improve the utilization rate of taxis as well as the taxi company’s profit.
Electric vehicles (EVs) are more environmentally friendly than gasoline vehicles (GVs). To reduce environmental pollution caused by ride-hailing gasoline vehicles (RGVs), they have been gradually replaced with ride-hailing electric vehicles (REVs). Like RGVs, REVs can allow passengers to share trips with others. However, REVs are plagued by charging needs in daily operations. This study develops a simulation–optimization framework for the dynamic electric ride-hailing sharing problem. This problem integrates a dynamic electric ride-hailing matching problem (with sharing) and a dynamic REV charging problem, both of which aim to match REVs to passengers willing to share their trips with others and schedule the charging events of REVs on temporal and spatial scales, respectively. The dynamic electric ride-hailing matching problem is divided into a set of electric ride-hailing matching subproblems by a rolling horizon approach without a look-ahead period, while the dynamic REV charging problem is divided into a set of REV charging subproblems by a rolling horizon approach with look-ahead periods. Each REV charging subproblem incorporates a novel charging strategy to determine the charging schedules of REVs and relieve the charging anxiety by considering the information of requests, REVs, and charging stations. Each REV charging subproblem is formulated as a mixed integer linear program (MILP), whereas each electric ride-hailing matching subproblem is formulated as a mixed integer nonlinear program (MINLP). The MINLP and MILP are solved by the artificial bee colony algorithm and CPLEX, respectively. The proposed simulation–optimization framework includes a simulation model which is used to mimic the operations of REVs and update and track the state of passengers and the charging processes at charging stations over time using the outputs of each MILP and MINLP. The results show that the proposed charging strategy outperforms the benchmarks with a shorter waiting time for charging and a higher matching percentage in the dynamic ride-hailing matching problem. The robustness of the proposed charging strategy is tested under different scenarios with changing the initial state of charge (SOC), the number of REVs, the number of charging piles at each charging station, the time to fully charge, and the distribution of charging piles. The results show that REV drivers can charge their vehicles more flexibly without waiting too long and then pick up more passengers under all test scenarios.
There have been various approaches to tackle vehicle emissions from a road congested network in pursuit of environmental sustainability. However, it is unclear how should the government set the trip fares and fleet sizes of a market with traditional street-hail taxis and the ride-sourcing service to maximize social benefit when both congestion effects and emission externalities are considered. Moreover, there have been real-world practices to reduce emissions from taxis and ride-sourcing vehicles by controlling the numbers of gasoline vehicles and electric vehicles. On one hand, some cities (e.g., Shenzhen, London) have launched a taxi replacement project that replaces all fossil-fueled taxis with electric taxis. On the other hand, the regulation preventing gasoline vehicles from being registered as ride-sourcing vehicles can also be observed. It remains a question of how should the social optimal fares and fleet sizes in a market with electric taxis and ride-sourcing vehicles be different from those in the market with gasoline vehicles. To address the above questions, this study investigates the optimal trip fare and fleet size regulation in an aggregate taxi/ride-sourcing market with congestion effects and environmental externalities. Two market scenarios are investigated, in which all taxis/ride-sourcing vehicles are gasoline vehicles or electric vehicles. We develop two social benefit maximization models and duopoly profit-maximization models, and derive the corresponding optimality conditions. Analytical and numerical results are given to gain insights into the regulation of taxi/ride-sourcing markets.
Ride-sourcing platforms create cruising traffic that exacerbates road congestion in large cities. This paper considers shared parking services for ride-sourcing vehicles to reduce the cruising traffic. We propose a novel business model that integrates shared parking services into the ride-sourcing platform. In the proposed business model, the platform (a) provides app-based interfaces to connect passengers, drivers and garage operators (private or commercial garages); (b) matches passengers to ride-sourcing vehicles for ride services; (c) matches idle vehicles to vacant parking spaces to reduce cruising traffic; and (d) determines the ride fare, driver payment, and parking rates to maximize its profit. We model the integrated platform as a three-sided market (i.e., passenger, driver and garage) that involves two matching processes: passenger-driver matching and driver-garage matching. An economic equilibrium model is proposed to characterize the incentives of passengers, drivers, garage operators, and the ride-sourcing platform. Optimal pricing strategies and the corresponding market outcomes are derived by solving the platform’s profit maximization problem. Based on the model, we show that integrated parking and ride-sourcing service can offer a Pareto improvement: in certain regime, it leads to higher passenger surplus, higher driver surplus, higher garage surplus, higher platform profit, and reduced cruising traffic. Further, we investigate the impacts of matching efficiency between drivers and garages on the proposed business model. We show that a higher matching efficiency always leads to increased platform profit and driver surplus, but it may not necessarily benefit passengers or garage operators in all cases. The above insights are validated through realistic numerical studies for San Francisco.
Ride-sourcing services play an increasingly important role in meeting mobility needs in many metropolitan areas. Yet, aside from delivering passengers from their origins to destinations, ride-sourcing vehicles generate a significant number of vacant trips from the end of one customer delivery trip to the start of the next. These vacant trips create additional traffic demand and may worsen traffic conditions in urban networks. Capturing the congestion effect of these vacant trips poses a great challenge to the modeling practice of transportation planning agencies. With ride-sourcing services, vehicular trips are the outcome of the interactions between service providers and passengers, a missing ingredient in the current traffic assignment methodology. In this paper, we enhance the methodology by explicitly modeling those vacant trips, which include cruising for customers and deadheading for picking up them. Because of the similarity between taxi and ride-sourcing services, we first extend previous taxi network models to construct a base model, which assumes intranode matching between customers and idle ride-sourcing vehicles and thus, only considers cruising vacant trips. Considering spatial matching among multiple zones commonly practiced by ride-sourcing platforms, we further enhance the base model by encapsulating internode matching and considering both the cruising and deadheading vacant trips. A large set of empirical data from Didi Chuxing is applied to validate the proposed enhancement for internode matching. The extended model describes the equilibrium state that results from the interactions between background regular traffic and occupied, idle, and deadheading ride-sourcing vehicles. A solution algorithm is further proposed to solve the enhanced model effectively. Numerical examples are presented to demonstrate the model and solution algorithm. Although this study focuses on ride-sourcing services, the proposed modeling framework can be adapted to model other types of shared use mobility services.
Electric Vehicles (EVs) are regarded as a feasible solution to achieving decarbonisation in the transportation sector. However, EVs powered by fossil dominated energy sources may offer a discounted solution. This paper presents a comparative study of Australian and New Zealand’s vehicle markets on Greenhouse Gas (GHG) emissions and energy consumption using well-to-wheel analysis. A vehicle uptake projection model is proposed to predict future uptake of EVs and associated emissions under three scenarios with different mix of EVs. Our empirical results suggest that, with the current electricity mix, in terms of energy consumption, Battery Electric Vehicles (BEVs) perform better than other types in New Zealand and Australia. Emission wise, BEVs emit 90% less GHG than the second-best option Plug-in EV in New Zealand, and 40% less than the second-best, Fuel Cell EVs (FCEVs), in Australia. In the long run, as more “green hydrogen” is produced, FCEVs will play a critical role in minimising emissions. Emissions in the two countries are predicted to reach their peak around 2030, provided that BEVs form the major portion of the EV mix with a higher penetration of renewables and more FCEVs enter the fleet. The empirical outcomes provide important policy insights to support decision making.
In recent years, electric ride-hailing has increased considerably in the taxi industry with the development of battery electric vehicles (BEVs) and implementation of greenhouse gas (GHG) emission regulations. Private BEVs are charged mostly in private garages, while electric ride-hailing services require public charging stations (CSs). This work uses an improved genetic algorithm (GA) to locate public CSs by considering the investment of CS operators and the travel costs of BEV owners. A case study is presented with large-scale order data collected from the ride-hailing fleet of the city of Haikou and charging data from the electric ride-hailing fleet of the city of Shanghai. The elastic demand for electric ride-hailing is also considered by incorporating feedback between congestion at the CS and the geographical area. The proposed methodology uses the multipopulation genetic algorithm (MPGA) to provide more feasible allocations for public CSs and could reduce the total cost by 7.6%.
Ridesourcing services provide alternative mobility options in several cities. Their market share has grown exponentially due to the convenience they provide. The use of such services may be associated with car-light or car-free lifestyles. However, there are growing concerns regarding their impact on urban transportation operations performance due to empty, unproductive miles driven without a passenger (commonly referred to as deadheading). This paper is motivated by the potential to reduce deadhead mileage of ridesourcing trips by providing drivers with information on future ridesourcing trip demand. Future demand information enables the driver to wait in place for the next rider’s request without cruising around and contributing to congestion. A machine learning model is employed to predict hourly and 10-minute future interval travel demand for ridesourcing at a given location. Using future demand information, we propose algorithms to (i) assign drivers to act on received demand information by waiting in place for the next rider, and (ii) match these drivers with riders to minimize deadheading distance. Real-world data from ridesourcing providers in Austin, TX (RideAustin) and Chengdu, China (DiDi Chuxing) are leveraged. Results show that this process achieves 68%–82% and 53%–60% reduction of trip-level deadheading miles for the RideAustin and DiDi Chuxing sample operations respectively, under the assumption of unconstrained availability of short-term parking. Deadheading savings increase slightly as the maximum tolerable waiting time for the driver increases. Further, it is observed that significant deadhead savings per trip are possible, even when a small percent of the ridesourcing driver pool is provided with future ridesourcing demand information.
To improve the appeal of carsharing, we propose an integrated operation scheme of car-sharing and parking-sharing services, where a carsharing operator rents parking spaces from private owners to provide convenient parking options to carsharing users. We examine how the operator's profit and social welfare differ under the existing carsharing-only service scheme and a bundled carsharing and parking-sharing service scheme. In particular , multiple groups of decision makers, i.e., suppliers of shared parking spaces, platform operators, carsharing users and private vehicle users, and interactions among them are modeled in the context of an integrated sharing platform. The properties of carsharing user and private-vehicle traveler choice equilibrium are discussed. The profit-maximizing and social-optimal platform pricing and supply strategies are explored. Numerical examples illustrate that the bundled carsharing and parking-sharing service scheme holds the potential to improve the operator's profit and social welfare.
This paper formulates duopoly competition between two non-cooperative heterogeneous ride-sourcing platforms considering the adoption of electric vehicles (EV) and government subsidies on EVs. One ride-sourcing platform adopts an EV asset-heavy strategy by undertaking EV depreciation costs, while the other ride-sourcing platform adopts an asset-light strategy by hiring drivers who own vehicles. The first-order conditions of each platform's pricingunder competitive equilibrium are derived based on a game-theoretical model that involves the stakeholders (i.e., riders, drivers, and ride-sourcing platforms). Based on the modeling framework ofduopoly competition between two heterogeneous ride-sourcing platforms, this paper proposes an optimization model with social welfare maximization for the determination of governmentsubsidy strategies. We conduct numerical illustrations to demonstrate how governmental subsidies on EV purchase and charging stations impact the endogenous variables in equilibrium (e.g., price for riders, wage for drivers, and market share for each platform) under different formulations of riders' waiting time cost function, which are increasing returns to scale. Also, a specialmodel with a fixed commission ratio is discussed. The results provide suggestions for decision-makers on how to allocate subsidy within the budget constraint.
This study models a multi-modal network with ridesharing services. The developed model reproduces the scenario where travelers with their own cars may choose to be a solo-driver, a ridesharing driver, a ridesharing rider, or a public transit passenger while travelers without their own cars can only choose to be either a ridesharing rider or a public transit passenger. The developed model can capture the (clock) time-dependent choices of travelers and the evolution of traffic conditions, i.e., the within-day traffic dynamics. In particular, the within-day traffic dynamics in a city region is modeled through an aggregate traffic representation, i.e., the Macroscopic Fundamental Diagram (MFD). This paper further develops a doubly dynamical system that examines how the within-day time-dependent travelers’ choices and traffic conditions will evolve from day to day, i.e., the day-to-day dynamics. Based on the doubly dynamical framework, this paper proposes two different congestion pricing schemes that aim to reduce network congestion and improve traffic efficiency. One scheme is to price all vehicles including both solo-driving and ridesharing vehicles (for ridesharing trips, the price is shared by the driver and rider), while the other scheme prices the solo-driving vehicles only in order to encourage ridesharing. The pricing levels (under each scheme) can be determined either through an adaptive adjustment mechanism from period to period driven by observed traffic conditions, or through solving a bi-level optimization problem. Numerical studies are conducted to illustrate the models and effectiveness of the pricing schemes. The results indicate that the emerging ridesharing platform may not necessarily reduce traffic congestion, but the proposed congestion pricing schemes can effectively reduce congestion and improve system performance. While pricing solo-driving vehicles only may encourage ridesharing, it can be less effective in reducing the overall congestion when compared to pricing both solo-driving and ridesharing vehicles.
On-demand ride services reshape urban transportation systems, human mobility, and travelers' mode choice behavior. Compared to the traditional street-hailing taxi, an on-demand ride services platform analyzes ride requests of passengers and coordinates real-time supply and demand with dynamic operational strategies in the ride-sourcing market. To test the impact of dynamic optimization strategies on the ride-sourcing market, this paper proposes a dynamic vacant car-passenger meeting model. In this model, the accumulative arrival rate and departure rate of passengers and vacant cars determine the waiting number of passengers and vacant cars, while the waiting number of passengers and vacant cars in turn influence the meeting rate (which equals to the departure rate of both passengers and vacant cars). The departure rate means the rate at which passengers and vacant cars match up and start a paid trip. Compared with classic equilibrium models, this model can be utilized to characterize the influence of short-term variances and disturbances of current demand and supply (i.e., arrival rates of passengers and vacant cars) on the waiting numbers of passengers and vacant cars. Using the proposed meeting model, we optimize dynamic strategies under two objective functions, i.e., platform revenue maximization, and social welfare maximization, while the driver's profit is guaranteed above a certain level. We also propose an algorithm based on approximate dynamic programming (ADP) to solve the sequential dynamic optimization problem. The results show that our algorithm can effectively improve the objective function of the multi-period problem, compared with the myopic algorithm. A broader range of surge pricing and commission rate and the introduction of incentives are helpful to achieve better optimization results. The dynamic optimization strategies help the on-demand ride services platform efficiently adjust supply and demand resources and achieve specific optimization goals.
Passengers are increasingly using e-hailing as a means to request transportation services. Adoption of these types of services has the potential to impact the travel behavior of individuals as well as increase congestion and vehicle miles driven since extra deadhead miles must be added to the trip (e.g., the extra miles from the driver location to the pick-up location of the customer). The objective of this paper is to develop a basic mathematical model to help transportation planners understand the relationship between the wide-scale use of e-hailing transportation services and deadhead miles and resulting impact on congestion. Specifically, this paper develops a general economic equilibrium model at the macroscopic level to describe the equilibrium state of a transportation system composed of solo drivers and the e-hailing service providers (e-HSPs). The equilibrium model consists of three interacting sub-models: e-HSP choice, customer choice, and network congestion; the model is completed with a “market clearance” condition describing the waiting costs in the customer’s optimization problem in terms of the e-HSPs’ decisions, thereby connecting the supply and demand sides of the equilibrium. We show the existence of an equilibrium of a certain kind under some mild assumptions. In numerical experiments, we illustrate the sensitivity on the usage of these modes to various parameters representing cost, value of time, safety, and comfort level, as well as the resulting relationship between usage of these services and vehicle miles.
Regulating on-demand ride-hailing services (e.g., Uber and DiDi) requires a balance of multiple competing objectives: encouraging innovative business models (e.g., DiDi), sustaining traditional industries (e.g., taxi), creating new jobs, and reducing traffic congestion. This study is motivated by a regulatory policy implemented by the Chinese government in 2017 and a similar policy approved by the New York City Council in 2018 that regulate the “maximum” number of registered Uber/DiDi drivers. We examine the impact of these policies on the welfare of different stakeholders (i.e., consumers, taxi drivers, on-demand ride service company, and independent drivers). By analyzing a two-period dynamic game that involves these stakeholders, we find that, without government intervention, the on-demand ride service platform can drive the traditional taxi industry out of the market under certain conditions. Relative to no regulations and a complete ban policy, a carefully designed regulatory policy can strike a better balance of multiple competing objectives. Finally, if a government can reform the taxi industry by adjusting the taxi fare, then lowering the taxi fare instead of imposing a strict policy toward on-demand ride services can improve the total social welfare.
This paper was accepted by Serguei Netessine, operations management.
With the rapid development and popularization of mobile and wireless communication technologies, ridesourcing companies have been able to leverage internet-based platforms to operate e-hailing services in many cities around the world. These companies connect passengers and drivers in real time and are disruptively changing the transportation industry. As pioneers in a general sharing economy context, ridesourcing shared transportation platforms consist of a typical two-sided market. On the demand side, passengers are sensitive to the price and quality of the service. On the supply side, drivers, as freelancers, make working decisions flexibly based on their income from the platform and many other factors. Diverse variables and factors in the system are strongly endogenous and interactively dependent. How to design and operate ridesourcing systems is vital—and challenging—for all stakeholders: passengers/users, drivers/service providers, platforms, policy makers, and the general public. In this paper, we propose a general framework to describe ridesourcing systems. This framework can aid understanding of the interactions between endogenous and exogenous variables, their changes in response to platforms’ operational strategies and decisions, multiple system objectives, and market equilibria in a dynamic manner. Under the proposed general framework, we summarize important research problems and the corresponding methodologies that have been and are being developed and implemented to address these problems. We conduct a comprehensive review of the literature on these problems in different areas from diverse perspectives, including (1) demand and pricing, (2) supply and incentives, (3) platform operations, and (4) competition, impacts, and regulations. The proposed framework and the review also suggest many avenues requiring future research.
Electric vehicles (EVs) are widely considered to be a solution to the problems of increasing carbon emissions and dependence on fossil fuels. However, the adoption of EVs remains sluggish due to range anxiety, long charging times, and inconvenient and insufficient charging infrastructure. Various problems with EV service operations should be addressed to overcome these challenges. This study reviews the state-of-the-art mathematical modeling-based literature on EV operations management. The literature is classified according to recurring themes, such as EV charging infrastructure planning, EV charging operations, and public policy and business models. In each theme, typical optimization models and algorithms proposed in previous studies are surveyed. The review concludes with a discussion of several possible questions for future research on EV service operations management.
Ride-sourcing is a prominent transportation mode because of its cost-effectiveness and convenience. It provides an on-demand mobility platform that acts as a two-sided market by matching riders with drivers. The conventional models of ride-sourcing systems are equilibrium-based, discrete, and suitable for strategic decisions. This steady-state approach is not suitable for operational decision-making where there is noticeable variation in the state of the system, denying the market enough time to balance back into equilibrium. We introduce a dynamic non-equilibrium ride-sourcing model that tracks the time-varying number of riders, vacant ride-sourcing vehicles, and occupied ride-sourcing vehicles. The drivers are modeled as earning-sensitive, independent contractor, and self-scheduling and the riders are considered price- and quality of service-sensitive such that the supply and demand of the ride-sourcing market are endogenously dependent on (i) the fare requested from the riders and the wage paid to the drivers and (ii) the rider’s waiting time and driver’s cruising time. The model enables investigating how the dynamic wage and fare set by the ride-sourcing service provider affect supply, demand, and states of the market such as average waiting and search time especially when drivers can freely choose their work shifts. Furthermore, we propose a controller based on the model predictive control approach to maximize the service provider’s profit by controlling the fare requested from riders and the wage offered to drivers to satisfy a certain quality of market performance. We assess three pricing strategies where the fare and wage are (i) time-varying and unconstrained, (ii) time-varying and constrained so that the fare is higher than the wage such that the instantaneous profit is positive, and (iii) time-invariant and fixed. The proposed model and controller enable the ride-sourcing service provider to offer a wage to the drivers that is higher than the fare requested from the riders. The result demonstrates that this myopic loss can potentially lead to higher overall profit when customer demand (i.e., riders who may opt to use the ride-sourcing system) increases while the supply of ride-sourcing vehicles decreases simultaneously.
In today's world, massive on-demand mobility requests are dispatched every hour on ride-sourcing platforms, however, customers later cancel quite a number of these confirmed orders. This paper makes the first attempt to look into this customer confirmed-order cancellation behaviour based on a two-month, hourly average dataset of Didi Express in Shanghai provided by Didi Chuxing. The mean ride distance and pick-up distance of cancelled orders are observed to be obviously longer than those of completed orders of the same time period, reflecting an obvious impact of travel cost on customer decisions of order cancellation. However, the correlation between the mean customer confirmed-order cancellation rate (COCR) and the mean customer waiting time for pick-up of cancelled orders is significantly negative. Shanghai, like many other cities around the world, has coupled ride-sourcing and taxi markets, and as such, this counter-intuitive phenomenon can be explained as an outcome of the lower (higher) chance of meeting vacant taxis while waiting for pick-up during peak (non-peak) hours. We formulate COCR as a function of customer waiting time for ride-sourcing vehicles and cruising taxis, the penalty strategy by the platform for cancellation of confirmed orders, and customers’ own characteristics (i.e., ride distance, value of time, perceived psychological cost of order cancellation, additional safety concern over ride-sourcing), and propose a system of nonlinear equations to depict the complex interactions between the ride-sourcing and taxi markets considering the probability of ride-sourcing cancellation after order confirmation. With the proposed model, we replicate the observed lower COCR under a higher demand rate and longer waiting time for pick-up through numerical examples, and highlight the potential improvement of platform profit that can be achieved by appropriately designed penalty/compensation strategies.
Recently, electric vehicles (EVs) have been introduced to the ride-sourcing market, raising interesting issues on the interaction between EVs and the ride-sourcing market. This study proposes an analytical framework for understanding drivers’ behavior in the ride-sourcing market with both EVs and gasoline vehicles (GVs). A time-expanded network is established to sketch out the schedules of working periods of both EV and GV drivers, and recharging schedules of EV drivers under the user equilibrium. The impacts of operating strategies of ride-sourcing platforms and electrical power suppliers on the benefits of different stakeholders are investigated both analytically and numerically.
Managing morning commute traffic through parking provision management has been well studied in the literature. One conventional assumption made in most previous studies is that all commuters require parking spaces at CBD area. However, in recent years, due to technological advancements and low market entry barrier, more and more e-dispatch FHVs (eFHVs) are provided in service. The rapidly growing eFHVs, on one hand, supply substantial trip services and complete the trips requiring no parking demand; on the other hand, imposes congestion effects on all commuters. In this study, we investigate the morning commute problem with bottleneck congestion and parking space constraints in the presence of ride-sourcing service. The within-day dynamic equilibrium and its travel pattern are examined. Meanwhile, we explore the optimal supply of parking spaces and ride-sourcing services to best manage the commute traffic. To minimize system total travel costs, the optimal quantity control of parking spaces and eFHVs is determined, which indeed addresses a critical issue on how to regulate the supply of eFHVs.
We present a novel order dispatch algorithm in large-scale on-demand ride-hailing platforms. While traditional order dispatch approaches usually focus on immediate customer satisfaction, the proposed algorithm is designed to provide a more efficient way to optimize resource utilization and user experience in a global and more farsighted view. In particular, we model order dispatch as a large-scale sequential decision-making problem, where the decision of assigning an order to a driver is determined by a centralized algorithm in a coordinated way. The problem is solved in a learning and planning manner: 1) based on historical data, we first summarize demand and supply patterns into a spatiotemporal quantization, each of which indicates the expected value of a driver being in a particular state; 2) a planning step is conducted in real-time, where each driver-order-pair is valued in consideration of both immediate rewards and future gains, and then dispatch is solved using a combinatorial optimizing algorithm. Through extensive offline experiments and online AB tests, the proposed approach delivers remarkable improvement on the platform's efficiency and has been successfully deployed in the production system of Didi Chuxing.
Smartphone-based taxi-hailing applications (apps) bring about significant changes to the taxi market in recent years. As platforms that connect customers and taxi drivers, taxi-hailing apps charge different rates for completed orders and penalize reservation-cancellation behaviors with different fines. In this paper, an equilibrium framework is proposed to depict the operations of a regulated taxi market on a general network with both street-hailing and e-hailing modes for taxi services, considering the reservation-cancellation behaviors of e-hailing customers. Based on the proposed equilibrium model, an optimal design problem of taxi-hailing platform's pricing and penalty/compensation strategies is formulated and solved by the penalty successive linear programming algorithm. To demonstrate the practicability of the proposed solution algorithms and the optimal pricing and penalty/compensation schemes, large-scale numerical examples are presented based on a realistic taxi network of Beijing.
Ride-sourcing services have become increasingly important in meeting travel needs in metropolitan areas. However, the cruising of vacant ride-sourcing vehicles generates additional traffic demand that may worsen traffic conditions. This paper investigates the allocation of a certain portion of road space to on-street parking for vacant ride-sourcing vehicles. A macroscopic conceptual framework is developed to capture the trade-off between capacity loss and the reduction of cruising. Considering a hypothetical matching mechanism adopted by the platform, we further materialize the framework and then apply it to study the interactions between the ride-sourcing system and parking provision under various market structures.
The world is on the cusp of a new era in mobility given that the enabling technologies for autonomous vehicles (AVs) are almost ready for deployment and testing. Although the technological frontiers for deploying AVs are being crossed, transportation planners and engineers know far less about the potential impact of such technologies on urban form and land use patterns. This paper attempts to address those issues by simulating the operation of shared AVs (SAVs) in the city of Atlanta, Georgia, by using the real transportation network with calibrated link-level travel speeds and a travel demand origin–destination matrix. The model results suggest that the SAV system can reduce parking land by 4.5% in Atlanta at a 5% market penetration level. In charged-parking scenarios, parking demand will move from downtown to adjacent low-income neighborhoods. The results also reveal that policy makers may consider combining charged-parking policies with additional regulations to curb excessive vehicle miles traveled and alleviate potential social equity problems.
In this article, we introduce and study a two-stage stochastic optimization problem suitable to solve strategic optimization problems of car-sharing systems that utilize electric cars. By combining the individual advantages of car-sharing and electric vehicles, such electric car-sharing systems may help to overcome future challenges related to pollution, congestion, or shortage of fossil fuels. A time-dependent integer linear program and a heuristic algorithm for solving the considered optimization problem are developed and tested on real world instances from the city of Vienna, as well as on grid-graph-based instances. An analysis of the influence of different parameters on the overall performance and managerial insights are given. Results show that the developed exact approach is suitable for medium sized instances such as the ones obtained from the inner districts of Vienna. They also show that the heuristic can be used to tackle very-large-scale instances that cannot be approached successfully by the integer-programming-based method.
Recent platforms, like Uber and Lyft, offer service to consumers via “self-scheduling” providers who decide for themselves how often to work. These platforms may charge consumers prices and pay providers wages that both adjust based on prevailing demand conditions. For example, Uber uses a “surge pricing” policy, which pays providers a fixed commission of its dynamic price. With a stylized model that yields analytical and numerical results, we study several pricing schemes that could be implemented on a service platform, including surge pricing. We find that the optimal contract substantially increases the platform’s profit relative to contracts that have a fixed price or fixed wage (or both), and although surge pricing is not optimal, it generally achieves nearly the optimal profit. Despite its merits for the platform, surge pricing has been criticized because of concerns for the welfare of providers and consumers. In our model, as labor becomes more expensive, providers and consumers are better off with surge pricing because providers are better utilized and consumers benefit both from lower prices during normal demand and expanded access to service during peak demand. We conclude, in contrast to popular criticism, that all stakeholders can benefit from the use of surge pricing on a platform with self-scheduling capacity.
The e-companion is available at https://doi.org/10.1287/msom.2017.0618.
Recent platforms, like Uber and Lyft, offer service to consumers via “self-scheduling” providers who decide for themselves how often to work. These platforms may charge consumers prices and pay providers wages that both adjust based on prevailing demand conditions. For example, Uber uses a “surge pricing” policy, which pays providers a fixed commission of its dynamic price. With a stylized model that yields analytical and numerical results, we study several pricing schemes that could be implemented on a service platform, including surge pricing. We find that the optimal contract substantially increases the platform’s profit relative to contracts that have a fixed price or fixed wage (or both), and although surge pricing is not optimal, it generally achieves nearly the optimal profit. Despite its merits for the platform, surge pricing has been criticized because of concerns for the welfare of providers and consumers. In our model, as labor becomes more expensive, providers and consumers are better off with surge pricing because providers are better utilized and consumers benefit both from lower prices during normal demand and expanded access to service during peak demand. We conclude, in contrast to popular criticism, that all stakeholders can benefit from the use of surge pricing on a platform with self-scheduling capacity.