Ridesourcing Services Research Articles

Optimization-based trip chain emulation for electrified ride-sourcing charging demand analyses

ABSTRACT Range anxiety remains one of the key concerns for ride-sourcing drivers to adopt battery electric vehicles (BEVs). To investigate the feasibility of using BEVs for ride-sourcing services, we propose an optimization-based methodology to estimate the daily driving trip patterns of ride-sourcing vehicles based on widely available non-identifiable trip data. Furthermore, we investigate the charging needs of electrified ride-sourcing vehicles using agent-based simulation. The methodologies are illustrated through a case study in the city of Chicago. Through sensitivity analysis on driver working hours and initial charging status, we quantify the range of daily average vehicle miles traveled (VMT) per car and identify the hot spots of current public charging demand and potential unsatisfied charging demand. This study can be used to determine the priorities of future charging infrastructure investment to further mitigate range anxiety and promote adoption of electrified ride-sourcing services.

Ride-sourcing Service Pricing and Cancellation Penalty under Service Arrival Time Uncertainty

This study investigates ride-sourcing service pricing and order cancellation penalty in a duopoly market under service arrival time uncertainty. Order cancellation is wasteful to the service provider and society as a whole due to the incurred unproductive congestion and therefore, should be minimized either by penalty or regulation. Certain cancellations are inevitable, due to customer trip changes; others, however, arise from ordering from two service providers, necessitating the need for cancellation. We model the service arrival time perception via an inhomogeneous Poisson process and derive the cancellation behavior in a stochastic manner. A game-theoretic approach is then developed to formulate the competitive market equilibrium. For comparison, we also study the monopoly market as a benchmark. The monopoly market is found to be better in terms of order cancellations and service waiting times, but worse in trip fares for large markets. Based on these results, we propose possible regulatory interventions to reduce wasteful order cancellations.

Ride acceptance behaviour of ride-sourcing drivers

The performance of ride-sourcing services such as Uber and Lyft is determined by the collective choices of individual drivers who are not only chauffeurs but private fleet providers. In such a context, ride-sourcing drivers are free to decide whether to accept or decline ride requests assigned by the ride-hailing platform. Drivers’ ride acceptance behaviour can significantly influence system performance in terms of riders’ waiting time (associated with the level of service), drivers’ occupation rate and idle time (related to drivers’ income), and platform revenue and reputation. Hence, it is of great importance to identify the underlying determinants of the ride acceptance behaviour of drivers. To this end, we collected a unique dataset from ride-sourcing drivers working in the United States and the Netherlands through a cross-sectional stated preference experiment designed based upon disparate information conveyed to the respondents. Using a choice modelling approach, we estimated the effects of various existing and hypothetical attributes influencing the ride acceptance choice. Employment status, experience level with the platform, and working shift are found to be the key individual-specific determinants. Part-time and beginning drivers who work on midweek days (Monday-Thursday) have a higher tendency to accept ride offers. Results also reveal that pickup time, which is the travel time between the driver’s location and the rider’s waiting spot, has a negative impact on ride acceptance. Moreover, the findings suggest that a guaranteed tip (i.e., the minimum amount of tip that is indicated upfront by the prospective rider, a feature that is currently not available) and an additional income due to surge pricing are valued noticeably higher than trip fare. The provided insights can be used to develop customised matching and pricing strategies to improve system efficiency. Since the study has been conducted during the COVID-19 crisis, the potential implications of the pandemic on ride acceptance behaviour have been examined using an Integrated Choice and Latent Variable (ICLV) model. The results show that drivers with a higher sensitivity to the COVID-19 effects tend to have a lower acceptance rate.

Spatial pricing of ride-sourcing services in a congested transportation network

We investigate the impacts of spatial pricing for ride-sourcing services in a Stackelberg framework considering traffic congestion. In the lower level, we use combined distribution and assignment approaches to explicitly capture the interactions between drivers’ relocation, riders’ mode choice, and all travelers’ routing decisions. In the upper level, a single transportation network company (TNC) determines spatial pricing strategies to minimize imbalance in a two-sided market. We show the existence of the optimal pricing strategies for locational imbalance minimization, and propose effective algorithms with reliable convergence properties. Furthermore, the optimal pricing is unique and can be solved in a convex reformulation when waiting time is small compared to travel time. We conduct numerical experiments on different scales of transportation networks with different TNC objectives to generate policy insights on how spatial pricing could impact transportation systems.

Analysis of ride-sourcing drivers' working Pattern(s) via spatiotemporal work slices: A case study in Hangzhou

Ride-sourcing services have become an integral part of urban transportation systems. In a cooperative relationship with the platform, ride-sourcing drivers have the flexibility to work according to their habits or preferences, which could constitute heterogeneous working pattern(s) among drivers. Based on real-world ride-sourcing data, we analyze drivers' working pattern(s) via their work slices (WSs). A WS is defined as a time window during which a driver is continuously online to take ride-sourcing orders. We use the Gaussian mixture model to classify the WSs according to the start time/start place attributes and obtain 9 clusters with significant spatiotemporal differences. It is found that WSs starting from rural areas that are closed to urban areas show no significant spatiotemporal grouping and mediocre performance in terms of fare income and fare-cost ratio, while WSs starting from distant rural areas show the worst performance; WSs starting from urban/suburban areas reflect more diverse spatiotemporal groupings and significant differences in attributes of delivery time and fares earned. We also analyze drivers' working pattern(s) by summarizing their habitual WS clusters. Findings include that drivers with suburban-dominant/rural-dominant patterns have a disadvantage in working efficiency in terms of the shorter order distance and longer cruising time. If geometrical distance allows, they can improve their income by switching to urban WS clusters occasionally. However, frequently switching between WS clusters can lead to a decrease in fares income. According to our results, the platform can introduce differentiated policies, including subsidies and order-matching algorithms, to better handle the spatiotemporal inequality, ultimately improving the service performance and overall benefits.

Real-Time Driver-Request Assignment in Ridesourcing

Online on-demand ridesourcing service has played a huge role in transforming urban transportation. A central function in most on-demand ridesourcing platforms is to dynamically assign drivers to rider requests that could balance the request waiting times and the driver pick-up distances. To deal with the online nature of this problem, existing literature either divides the time horizon into short windows and applies a static offline assignment algorithm within each window or assumes a fully online setting that makes decisions for each request immediately upon its arrival. In this paper, we propose a more realistic model for the driver-request assignment that bridges the above two settings together. Our model allows the requests to wait after their arrival but assumes that they may leave at any time following a quitting function. Under this model, we design an efficient algorithm for assigning available drivers to requests in real-time. Our algorithm is able to incorporate future estimated driver arrivals into consideration and make strategic waiting and matching decisions that could balance the waiting time and pick-up distance of the assignment. We prove that our algorithm is optimal ex-ante in the single-request setting, and demonstrate its effectiveness in the general multi-request setting through experiments on both synthetic and real-world datasets.

Charging infrastructure planning for ride-sourcing electric vehicles considering drivers’ value of time

ABSTRACT A reliable-charging network is urgently demanded to support electrified ride-sourcing services due to their shorter dwell time, longer daily vehicle miles traveled, and concerns of sacrificing revenue for charging activities. We developed an integer programming (IP) model for the optimal allocation of charging stations and charging plugs to minimize the total investment costs and spatio-temporal varying drivers’ value of time (VOT) for charging activities. The trip chain data of the RideAustin ride-sourcing services have been used as a test case, based on which we estimated the charging needs of ride-sourcing EVs and identified candidate charging locations to fulfill the daily travel needs of ride-sourcing drivers. Through numerical study and sensitivity analyses, we analyze the impacts of different charger types, fleet sizes, government incentives, and VOT considerations on the optimal investment plans and system costs, and show the importance of considering ride-sourcing drivers’ VOT into charging infrastructure planning.

Minimizing the Effects of Urban Mobility-on-Demand Pick-Up and Drop-Off Stops: A Microscopic Simulation Approach

Short-term disruptions can have a long-lasting negative effect on traffic flow exceeding the duration of the disruption itself. This is especially the case when traffic demand approaches the network’s capacity. On-demand ride-sourcing services like ride-hailing and ride-pooling do not only have an impact on the overall kilometers driven in a network, but also conduct frequent stopping maneuvers to let passengers board and alight. As further growth of such services is expected, municipalities will need to find ways to organize and, if needed, regulate such activities. This paper proposes, evaluates, and discusses two possible methods that can be part of a holistic strategy to mitigate the impacts of frequent mobility-on-demand curbside stops in an urban environment. The first method adapts the positions of stops at an intersection according to real-time signal timings without adding another variable to the already quite complex traffic signal optimization. The second method discusses a temporary reduction of the number of allowed stopping maneuvers on saturated street sections or in other sensitive areas. Both methods are evaluated using microscopic traffic simulation and result in significant reductions of average vehicle delay as well as standard deviation thereof in all investigated traffic demand scenarios. These results indicate that the proposed methods can help to preserve a stable traffic state in situations close to the capacity limit, which is to the benefit of all stakeholders involved.

To What Extent May Transit Stop Spacing Be Increased before Driving Away Riders? Referring to Evidence of the 2017 NHTS in the United States

With the emergence of ride-sourcing and ride-splitting services, more options are available to support shifts away from transit, where maintaining transit ridership increases requirements for transit service quality, so as to promote high-capacity and sustainable transport systems. In this endeavor, proper transit stop spacing is critical for both service accessibility and in-vehicle trip efficiency, as well as operation cost. This research explores acceptable stop spacing for three kinds of transit services from the perspective of travel behavior, drawing on the 2017 National Household Travel Survey in the United States. A stochastic frontier model is developed to infer passengers’ unobservable vertex of acceptable transit access times on the basis of observed walk time, which can be converted to the tolerance with respect to stop spacing with the average walking speed. Significant explanatory variables on the vertex of acceptable transit stop spacing are further identified with their quantified impacts, including household density, household income, wait time, trip distance, transfer, and maintenance purpose, while the inefficiency variance is significantly related to traveler age, wait time, secondary walk time, and trip frequency. Recommended response strategies follow. Findings from this study provide insights, guidelines, and implementation plans for different transit agencies when considering stop spacing redesign, to strengthen transit service appeal and to promote cooperative and sustainable multi-modal urban transport systems.

Exploring the operational characteristics of ride-sourcing in an urban area

The emergence of ride-sourcing services has radically changed travel behavior and has important implications for future urban mobility. However, despite the growing market for mobility-on-demand (MOD), empirical studies on the operational characteristics of ride-sourcing systems are sparse. This paper provides an exploratory analysis of the operations of ride-sourcing using large-scale dis-aggregate mobility service data and documents the findings. From a case study in Singapore, we found that the demand follows reproducible patterns, which allows for realistic estimates of fleet operations over time and space. High travel demand during peak periods is observed along with a surge in prices (by 1.6 times on average). Shift behavior such as number of shifts, shift duration, and choices of shift start and end time affect fleet operations and fleet utilization rate. From the user metrics, we found that passengers may experience higher waiting and travel time due to the longer distance required to pickup in residential areas, the network congestion in commercial and business districts, and the detouring effects for sharing. Finally, the driver's total and marginal revenue were analyzed, which indicated a lower average revenue per unit time, distance, and service item in a shared ride compared to a single ride. These empirical observations can help understand the spatio-temporal characteristics of ride-sourcing operations in an urban area and provide insights for transportation planning and operations.

Intentions to use ride-sourcing services in Vietnam: What happens after three months without COVID-19 infections?

The COVID-19 pandemic has brought new risks and stress for paid transport users worldwide. COVID-19 has changed mobility dynamics worldwide, including low- and middle-income countries (e.g., Vietnam). The present study aims to provide an in-depth understanding of ride-sourcing passengers' behavioural intentions when COVID-19 pandemic management successfully prevented community transmission by extending the TPB with two constructs: perceived virus infection risk and problem-focused coping. Using self-administered questionnaires, data were collected from ride-sourcing customers in Ho Chi Minh City (Vietnam). A total of 540 responses were used for validating the proposed theorethical model. The structural equation model results indicate that problem-focused coping is a multi-faceted construct with two dimensions: problem-solving and self-protection. Also, problem-focused coping has the highest total effect on the intention to use ride-sourcing services following a period of COVID-19 suppression (3 months without identified cases). The findings also reveal that attitude partially mediates the link between problem-focused coping and behavioural intention. The results of this study could be used to develop strategies to promote ride-sourcing services in the aftermath of the COVID-19 pandemic.

Calibration and validation of matching functions for ride-sourcing markets

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.

Coordinating ride-sourcing and public transport services with a reinforcement learning approach

Combining ride-sourcing and public transit services (with ride-sourcing service to address the first/last-mile issues) can bring many benefits, such as saving passengers’ trip fares, improving drivers’ earnings, reducing gas emissions, and alleviating traffic congestion. However, it still remains a challenging issue to coordinate ride-sourcing and public transit services through real-time order dispatching. In this paper, we model the order dispatching in a multi-modal transportation system as a large-scale sequential decision-making problem. A centralized algorithm is then proposed to dispatch idle drivers to arriving passenger orders and determine whether to advise passengers to use a combined mode of ride-sourcing and public transit services (if yes, the algorithm also needs to recommend an appropriate transportation hub). In particular, our proposed algorithm contains a reinforcement learning approach that estimates the long-term expected rewards, and an Integer Linear Programming (ILP) that matches idle drivers and waiting passengers in real-time based on both immediate revenue and the estimated long-term rewards. By evaluation on the real-world on-demand data and metro system in Manhattan, the proposed method shows remarkable improvement on the system’s efficiency under different density of supply and demands.

Optimal fare and fleet size regulation in a taxi/ride-sourcing market with congestion effects, emission externalities, and gasoline/electric vehicles

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.

Pricing strategy of ride-sourcing services under travel time variability

Ride-sourcing services are burgeoning urban mobility services provided by transportation network companies (TNCs), consisting of non-pooling service and ride-pooling service. By receiving online orders and offering efficient door-to-door services, ride-sourcing services are becoming popular alternatives to public transit. Meanwhile, unignorable travel time variability influences passengers’ travel behavior and TNC’s pricing strategy. To investigate the effect of travel time variability, this paper proposes a bi-level framework. The mode choice of passengers, zone choice of searching drivers and the subsequent route choice traffic equilibrium in a transportation network is formulated as the lower level. And the upper level investigates the optimal pricing strategy. In the network formulation, passengers encounter variability of waiting time, detour time and highway travel time. Two pricing strategies are proposed to accommodate the risk of unsuccessful pairing. Pricing strategy 1 charges a fixed price even when a ride-pooling request becomes a non-pooling service due to unsuccessful pairing; whereas pricing strategy 2 increases the price of a ride-pooling request to that of non-pooling service if pairing is unsuccessful. The results show that pricing strategy 1 produces more profit and induces more ride-pooling demand. In addition, under more reliable traffic conditions, the optimal prices for ride-sourcing services are higher and the network travel time decreases. The results illustrate the influence of travel time variability on the ride-sourcing market and provide guidance on the company’s pricing strategy regarding a network under travel time variability.

Shared parking for ride-sourcing platforms to reduce cruising traffic

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.

Modeling and Managing Mixed On-Demand Ride Services of Human-Driven Vehicles and Autonomous Vehicles

We model a monopoly ride-sourcing market where the platform adopts the service types of human-driven vehicles (HVs) and autonomous vehicles (AVs). Both congestion externality under mixed traffic flow and heterogeneity on riders' perceived utility of the ride-sourcing service are considered when formulating the mode choice behavior of riders. We analyze the impact of the platform's fleet size and its price for riders on demand rates and riders' waiting time in the market equilibrium state. The analytical results show that the demand rates of mixed on-demand ride service types are not necessarily monotonous to the price for riders or the fleet size, due to the congestion externality and existence of a wild goose chase regime. Under either profit maximization or welfare maximization strategies, numerical results demonstrate that a higher pure AV traffic flow capacity benefits human ride-sourcing drivers and both types of riders. The platform should arrange more AVs than HVs even under the high AV depreciation cost. In a surging demand scenario, the platform should encourage riders to switch from the HV service to the AV service through price regulation. Moreover, an extended scenario considering the integrated service is discussed. The economic analysis and gained managerial insights benefit the on-demand ride services platform's decision making on operational strategies of HVs and investment in AVs with mixed traffic congestion effects.

How does the suspension of ride-sourcing affect the transportation system and environment?

• Study the alternative mode choice of ride-sourcing users based on survey data. • 54.71% of respondents choose public transit when the ride-sourcing is suspended. • Age, gender, income, car ownership, and trip purpose are significant determinants. • More metro stations around the trip origin could reduce private vehicle use. • Suspension of ride-sourcing services would lead to lower vehicle emissions. Despite the popularity of ride-sourcing services among the public, some cities consider suspending ride-sourcing services because of repeated safety failures and protests from taxi drivers. The impact of such a policy on the transportation system and environment is rarely studied. To fill this gap, we performed a stated preference survey in Chengdu, China, to investigate the alternative mode choice (AMC) of ride-sourcing users. We collected data of 435 trips from 340 respondents. The analysis results show that with suspension, 53.71%, 27.36%, 10.80%, and 7.13% of ride-sourcing trips would shift to public transit, taxi, active mode (walking and cycling), and private car trips, respectively. The mixed logit modeling results show that age, gender, household income, number of cars per person, trip purpose, and transit accessibility significantly influence AMC. Results of Monte-Carlo Markov Chain simulations indicate that vehicle emissions related to ride-sourcing trips will decrease by more than 50% after the suspension.

Ridesourcing vs. traditional taxi services: Understanding users’ choices and preferences in Spain

Both the traditional taxi and ridesourcing market provide similar on-demand door-to-door transportation services from the users’ viewpoint, although operate under different legal and regulatory frameworks. Ridesourcing has experienced notable growth in urban mobility in the last few years since it gives a convenient, on-demand door-to-door service, provides app-based real-time information about the trip, and offers flexible prices that vary according to the level of supply and demand at each moment. This new actor in urban mobility directly competes with taxis, a more regulated mode that has traditionally provided door-to-door trips. While competition between taxi and ridesourcing has generated great controversy in the public debate, little attention has been paid to the users’ viewpoint. Additionally, most of the scientific literature on travel behavior and ridesourcing focus on specific characteristics of these services, while scarce efforts have been devoted to study users’ choices and preferences towards ridesourcing vs. its main competitor, taxi services. To bridge this gap, this paper investigates the main factors (individual sociodemographic, mobility-related characteristics, psychological attitudes, etc.) determining individuals’ choices between ridesourcing and traditional taxis. To that end, a Generalized Structural Equation Model (GSEM) is carried out, based on the information collected from a survey campaign conducted in Spain. The results show that, from a behavioral perspective, people opened to technological innovation and with liberal thought (in the sense of being favorable to market openness) tend to use ridesourcing services more often than taxis. Our results also suggest a higher tendency to use ridesourcing among women, young people, and people using hailing services for leisure, recreational or social purposes. Interestingly, individuals having already used both taxi and ridesourcing in Spain, tend to rate higher the quality-of-service performance (driver and/or vehicle fleet) provided by ridesourcing compared to taxis.

Government Regulations for Ride-Sourcing Services as Substitute or Complement to Public Transit

Government Regulations for Ride-Sourcing Services as Substitute or Complement to Public Transit