Ridesourcing Services Research Articles

Assessing the VMT effect of ridesourcing services in the US

Abstract The net effect of ridesourcing (RS) services on vehicle miles traveled (VMT) is ambiguous. With 2017 US National Household Travel Survey data, this study measures and compares the heterogeneous VMT effects of RS across population groups with various levels of household vehicle access and RS usage. A propensity score matching method was implemented to match RS non-users, occasional users, and frequent users based on observable sociodemographic traits. The results suggest that among drivers with household vehicle access, frequent RS users generate the least VMT, but occasional users actually generate more VMT. Those without a driver’s license or a household vehicle generate lower VMT while use more transit, and increasing RS use uniformly increases their overall VMT generation. We estimate that overall, RS generated a net increase of 7.8 million daily VMT in the US, compared with a counterfactual case in which all NHTS 2017 respondents were non-users of RS.

Regulating ridesourcing services with product differentiation and congestion externality

This paper proposes a model of the ridesourcing market in presence of traffic congestion and with the provision of both solo and pooling services. Our analysis of the first-best solution shows that, under a highly congested scenario, the ridesourcing platform may enjoy non-negative profits. However, when congestion is low, the ridesourcing market must be subsidized due to low marginal costs of operation. This mirrors previous findings in the traditional taxi literature. We also demonstrate that a commission cap on the solo service combined with a congestion toll (however small) on ridesourcing vehicles can induce any desired, sustainable equilibrium under the assumption of homogeneous value of travel time and sufficient supply of homogeneous drivers. Furthermore, numerical experiments suggest that the most important problem that a regulator should address when faced with a monopoly may not be that of congestion but rather that of market power. Indeed, when congestion is high, similar to previous findings in the literature, decisions by the monopolist tend to mirror that of the regulator. This is because customers on the platform must also bear the congestion cost, which hurts the platform’s revenues. Additionally, numerical examples reveal that, even when accounting for heterogeneity in the value of travel time, the commission cap is able to achieve the second-best–whether combined with a toll or not. This confirms the effectiveness of the commission cap strategy illustrated in previous literature and provides decision makers with a simple, non-intrusive mechanism for regulating the market.

Has ridesourcing reduced haze? An analysis using the Didi app.

Environmental degradation, often evidenced by haze, has not only increased the cost of China's economic development, but its unfair distribution among regional development, social hierarchy, and generations has also reduced quality of life. Hence, this study uses cross-sectional data from Didi, a Chinese ridesourcing service company, from 2017 to study the impact mechanism of shared mobility on haze. First, it applies the generalized spatial two-stage least squares model after controlling for urban economic, geographic, transportation, meteorological, and policy variables. We then use urbanization depth (urban agglomeration) and breadth (urban sprawl) variables, which represent the intensity and scope of human activities, respectively, as mediators to test the transmission mechanism from shared mobility to haze. Finally, differences based on urban heterogeneity were tested in the end. The study finds that the scale of ridesourcing and haze show a significant U-shaped relationship: the initial development of ridesourcing has a significant effect on reducing haze, but will then intensify haze as it develops to scales such as those of Beijing and Shenzhen. Moreover, the depth of urbanization is the main transmission mechanism of haze. This conclusion is verified in Tier 3 cities and above; however, the transmission mechanism in Tier 4 and Tier 5 cities is not obvious, indicating that the scale of ridesourcing should be compatible with the scale of urban development. Therefore, the design of emission reduction policies should be considered from the perspective of urban agglomeration.

Modeling and Analysis of Optimal Strategies for Leveraging Ride-Sourcing Services in Hurricane Evacuation

In many large-scale evacuations, public agencies often have limited resources to evacuate all citizens, especially vulnerable populations such as the elderly and disabled people, and the demand for additional transportation means for evacuation can be high. The recent development of ride-sourcing companies can be leveraged in evacuations as an additional and important resource in future evacuation planning. In contrast to public transit, the availability of ride-sourcing drivers is highly dependent on the price, since surge pricing will occur when the demand is high and the supply is low. The key challenge is thus to find the balance between evacuation demand and driver supply. Based on the two-sided market theory, we propose mathematical modeling and analysis strategies that can help balance demand and supply through a pricing mechanism designed for ride-sourcing services in evacuation. A subsidy is considered in the model such that lower-income and vulnerable individuals could benefit from ride-sourcing services. A hypothetical hurricane evacuation scenario in New York City in the case study showed the feasibility of the proposed method and the applicability of subsidies for ride-sourcing services in evacuation. The methodology and results given in this research can provide useful insights for modeling on-demand ride-sourcing for future evacuation planning.

Quantifying Environmental Benefits of Ridesplitting based on Observed Data from Ridesourcing Services

The increasing emissions from the transportation sector pose substantial hazards to the environment and human health around the world. With the rapid development of information and communication technologies, ridesplitting, a form of ridesourcing service accessed via smartphone applications, enables passengers with similar origins and destinations to be matched to the same driver and share the ride. This is regarded as a promising travel mode that could mitigate air pollution. However, because of a lack of quantitative analysis, the environmental benefits of ridesplitting have not been rigorously justified. As vast amounts of observed data of ridesourcing have become increasingly available, this study quantifies the environmental benefits of ridesplitting based on the global positioning system (GPS) trajectory and trip order datasets of DiDi Chuxing in Chengdu, China. First, the saved distances of ridesplitting are calculated by analyzing the travel distances of both ridesplitting trips and the corresponding trips under non-ridesplitting conditions. Then, the emission factors of CO, NOx, and HC are estimated by a localized MOVES model. Combining the saved distances and emission factors, the emission reductions from each ridesplitting trip can be calculated. The results show that ridesplitting can decrease the travel distance by 22% on average compared with non-ridesplitting. As a consequence, the average emission reductions per ridesplitting trip are 10.601 g of CO, 0.691 g of NOx, and 1.424 g of HC, respectively. This study provides a better understanding of the environmental benefits of ridesplitting and theoretical guidance for the government’s decision-making in green transport planning.

Optimal contract design for ride-sourcing services under dual sourcing

To cope with the uncertainty of labor supply from freelance/self-scheduling drivers, some ride-sourcing platforms recruit contractual drivers, who are paid a fixed salary for pre-specified work schedules. This paper develops an aggregate modeling framework to examine the practicability of such a dual-sourcing strategy. We investigate the optimal contract design of dual sourcing under demand uncertainty, varying price sensitivity of freelancers, and heterogeneity in drivers’ risk attitude. Our results uncover the conditions under which dual sourcing benefits both the platform and drivers. We show that the platform’s staffing and pricing decisions are most responsive to freelancers’ price sensitivity. When the price sensitivity stays adequately low, both the platform and drivers can be better off under dual sourcing compared to the self-scheduling counterpart. On the contrary, with moderate price sensitivity, freelancers will be made worse off by dual sourcing. The dual-sourcing contracts are most effective in markets where drivers are risk-averse.

Exploring nonlinear effects of the built environment on ridesplitting: Evidence from Chengdu

Ridesplitting, a form of ridesourcing services that matches riders with similar routes to the same driver, is a high occupancy travel mode that can bring considerable benefits. However, the current ratio of ridesplitting in the ridesourcing services is relatively low and its influencing factors remain unrevealed. Therefore, this paper uses a machine learning method, gradient boosting decision tree (GBDT) model, to explore the nonlinear effects of built environment on the ridesplitting ratio of origin–destination pairs (census tract to census tract). The GBDT model also provides the relative importance ranking of all the built environment factors. The results indicate that distance to city center, land use diversity and road density are the key influencing factors of ridesplitting ratio. In addition, the non-linear thresholds of built environment factors are identified based on partial dependence plots, which could provide policy implications for the government and transportation network companies to promote ridesplitting.

Equilibrium in taxi and ride-sourcing service considering the use of e-hailing application

With the advent of e-hailing applications, ride-sourcing service has become popular and replaced a substantial portion of taxi services. It is crucial to study the impact of different management strategies and regulation policies on taxi and ride-sourcing market performance. We build a taxi network service model involving taxi and ride-sourcing service to study the effect of pricing strategies, fleet size regulation, and customer-driver meeting ways on the equilibrium and the market performance. We prove that there is a solution for the network model and propose a solution algorithm based on the fixed-point iteration method. Numerical experiments are performed to analyze the market performance of taxi and ride-sourcing services under different fleet sizes and pricing strategies. This study provides valuable references for designing policies and measures to regulate taxi and ride-sourcing.

Effects of Air Emission Externalities on Optimal Ridesourcing Fleet Electrification and Operations.

Ridesourcing services from transportation network companies, like Uber and Lyft, serve the fastest growing share of U.S. passenger travel demand.1 Ridesourcing vehicles' high use intensity is economically attractive for electric vehicles, which typically have lower operating costs and higher capital costs than conventional vehicles. We optimize fleet composition (mix of conventional vehicles (CVs), hybrid electric vehicles (HEVs), and battery electric vehicles (BEVs)) and operations to satisfy demand at minimum cost and compare findings across a wide range of present-day and future scenarios for three cities. In nearly all cases, the optimal fleet includes a mix of technologies, HEVs and BEVs make up the majority of distance traveled, and CVs are used primarily for periods of peak demand (if at all). When life cycle air pollution and greenhouse gas emission externalities are internalized via a Pigovian tax, fleet electrification increases and externalities decrease, suggesting a role for policy. Externality reductions vary from 10% in New York (where externality costs for both gasoline and electricity consumption are relatively high and a Pigovian tax induces a partial shift to BEVs), to 22% in Los Angeles (where high gasoline and low electric grid externalities lead a Pigovian tax to induce a near-complete shift to BEVs).

The evolution, usage and trip patterns of taxis & ridesourcing services: evidence from 2001, 2009 & 2017 US National Household Travel Survey

Given the rapid adoption of ridesourcing services (RS), it is critical for transportation planners and policymakers to understand their impacts and keep policies up to date. This study contributes to the literature by using representative samples captured in the 2001, 2009 and 2017 National Household Travel Surveys to explore how taxis and ridesourcing (T/R) services have evolved and shaped people’s travel behavior pre- and post-disruption at the US national level. It characterizes and visualizes the asymmetries in demand spatially and temporally for T/R trips, showing that ridesourcing has greatly increased T/R trips from flexible and optional activity locations to home, which vary by times of day. It also characterizes tours involving T/R services, showing that while simple optional tours (such as home–recreation–home) represent the largest share of tours involving T/R, the fastest growth has been in simple mandatory tours (such as home–work–home). Tours involving T/R grew from 0.4% of all tours in 2009 to 1% of all tours in 2017, mostly within densely populated and transit-oriented regions. Although less than 1% of T/R trips involved a direct transfer to or from transit, one-third of all tours containing T/R also included transit. However, at the same time, 40% of T/R-containing tours also involved auto trip(s). Overall, this study reveals the complex relationships among their underlying sociodemographic characteristics, RS adoption and usage behavior, and daily tour patterns.

Equilibrium analyses and operational designs of a coupled market with substitutive and complementary ride-sourcing services to public transits

The emerging on-demand ride-sourcing programs provided by transportation network companies (TNCs) have been reshaping the transportation industry. Research efforts in this area have covered topics such as supply-demand equilibrium, pricing, matching, dispatching, but have not fully spread to the potential impacts of ride-sourcing on public transit in multi-modal transportation systems. On the one hand, ride-sourcing services act as convenient feeders to solve first-mile/last-mile problems for public transit riders. On the other hand, direct origin-to-destination ride-sourcing services may also absorb passengers from public transit. In this paper, we propose a user equilibrium based mathematical model to analyze complement and substitution of ride-sourcing to public transit. Through both analytical and numerical discussions, we find that the fleet size of ride-sourcing vehicles can critically affect the complementary and substitutive relationship between ride-sourcing and public transit, and ride-sourcing service fares affect the market share between first-mile/last-mile (i.e., from origin to the transportation hub or from the hub to destination) and direct (i.e., from origin to destination) ride-sourcing services. We also examine the optimal strategies to maximize the TNC’s profit and/or social welfare and find that the TNC can implement a Pareto-efficient strategy that makes both the two objectives better off.

A General Matching Function for Ride-Sourcing Services

This paper presents a general matching function to delineate the on-demand matching process in ride-sourcing markets under different matching mechanisms with different matching time intervals and matching radius. The matching function characterizes a stationary matching equilibrium for the ride-sourcing market in which passengers can be matched in consecutive batches, and drivers are in either an idle, pick-up, or delivery state. The function well approximates the expected driver idle time and the expected passenger matching time and pick-up time under different supply-demand conditions and platform matching strategies. The general function also unifies several prevailing inductive and deductive matching functions developed in the literature and demonstrates their specific application scope. In particular, when the matching radius is sufficiently small, the function reduces to the Cobb-Douglas-type matching function proposed by Yang and Yang (2011) for street-hailing taxi markets, in which the matching rate depends on the pool size of waiting passengers and idle drivers. Under an instant matching mechanism with a large matching radius, the function reduces to the model of Castillo et al. (Castillo et al. 2017) or a variant of the Cobb-Douglas-type matching function, in which the matching rate depends on the pool size of waiting passengers and drivers in the pick-up phase. In addition, when both the matching time interval and matching radius are relatively large, the function reduces to a variant of the Cobb-Douglas-type matching function.

A review of Ride-Matching strategies for Ridesourcing and other similar services

ABSTRACT Ridesourcing services have emerged as a popular alternative for commuters in metropolitan areas. There is a significant spatio-temporal variation of demand and supply for such services, which requires efficient ride-matching strategies to ensure optimal allocation of trips to drivers and users. This paper reviews different ride-matching techniques/strategies that highlight the outlook of different stakeholders, such as, drivers, users, and service providers and summarises the impacts of the matching process on the ideologies of the stakeholders. The review found that searching techniques guide the primary stakeholders like riders and drivers, and the assignment techniques ensure trip allotment. We also observed that fleet size is an important attribute to ensure availability as well as the assignment of ridesourcing services in an urban area.

On the inefficiency of ride-sourcing services towards urban congestion

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.

The travel behaviour of ride-sourcing users, and their perception of the usefulness of ride-sourcing based on the users' previous modes of transport: A case study in Bandung City, Indonesia

Research provides evidence that ride-sourcing has not only substituted for private transport but also public transport. For further investigation of this substitution effect, this paper explores the travel behaviour of ride-sourcing users and those users' socio-demographic characteristics as well as perception of the usefulness of ride-sourcing based on the users' previous modes of transport before ride-sourcing existed. For these purposes, the study collected data using a questionnaire survey in Bandung City in 2018. We found that substitution from public transport exists for younger travellers, while substitution from private transport is most likely associated with infrequent and higher-income travellers. We also found open spaces, a green environment and high interaction society in the residential environments that have more association with high-income travellers, thus associated with lower use of ride-sourcing for former private transport users. This study found that travellers who resided in areas with similar, high-income characteristics tend to have a positive appreciation of ride-sourcing. It appears that the increasing perceived usefulness does not straightforwardly increase the frequency of usage. Therefore, travellers who lived in a more high-end and active social environment might appreciate the role of ride-sourcing services in some of their trips that complement another daily trip.

Predicting origin-destination ride-sourcing demand with a spatio-temporal encoder-decoder residual multi-graph convolutional network

With the rapid development of mobile-internet technologies, on-demand ride-sourcing services have become increasingly popular and largely reshaped the way people travel. Demand prediction is one of the most fundamental components in supply-demand management systems of ride-sourcing platforms. With an accurate short-term prediction for origin-destination (OD) demand, the platforms make precise and timely decisions on real-time matching, idle vehicle reallocations, and ride-sharing vehicle routing, etc. Compared to the zone-based demand prediction that has been examined in many previous studies, OD-based demand prediction is more challenging. This is mainly due to the complicated spatial and temporal dependencies among the demand of different OD pairs. To overcome this challenge, we propose the Spatio-Temporal Encoder-Decoder Residual Multi-Graph Convolutional network (ST-ED-RMGC), a novel deep learning model for predicting ride-sourcing demand of various OD pairs. Firstly, the model constructs OD graphs, which utilize adjacent matrices to characterize the non-Euclidean pair-wise geographical and semantic correlations among different OD pairs. Secondly, based on the constructed graphs, a residual multi-graph convolutional (RMGC) network is designed to encode the contextual-aware spatial dependencies, and a long-short term memory (LSTM) network is used to encode the temporal dependencies, into a dense vector space. Finally, we reuse the RMGC networks to decode the compressed vector back to OD graphs and predict the future OD demand. Through extensive experiments on the for-hire-vehicles datasets in Manhattan, New York City, we show that our proposed deep learning framework outperforms the state-of-arts by a significant margin.

Reducing ridesourcing empty vehicle travel with future travel demand prediction

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.

Testing Curbside Management Strategies to Mitigate the Impacts of Ridesourcing Services on Traffic

Increased use of ridesourcing leads to increased pick-up and drop-off activity. This may slow traffic or cause delays as vehicles increase curb use, conduct pick-up and drop-off activity directly in the travel lane, or slow to find and connect with passengers. How should cities respond to this change in an effort to keep travel lanes operating smoothly and efficiently? This research evaluates two strategies in Seattle, WA, in an area where large numbers of workers commute using ridesourcing services: (i) a change of curb allocation from paid parking to passenger load zone (PLZ), and (ii) a geofencing approach by transportation network companies (TNCs) which directs their drivers and passengers to designated pick-up and drop-off locations on a block. An array of data on street and curb activity along three study blockfaces was collected, using video and sensor technology as well as in-person observations. Data were collected in three phases: (i) the baseline, (ii) after the new PLZs were added, expanding total PLZ curb length from 20 ft to 274 ft, and (iii) after geofencing was added to the expanded PLZs. The added PLZs were open to any passenger vehicle (not just TNC vehicles), weekdays 7:00–10:00 a.m. and 2:00–7:00 p.m. The results showed that the increased PLZ allocation and geofencing strategy reduced the number of pick-ups/drop-offs in the travel lane, reduced dwell times, increased curb use compliance, and increased TNC passenger satisfaction. The two strategies, however, had no observable effect on travel speeds or traffic safety in the selected study area.

Emergence of Application Based Transportation and its Impacts on Travelling Trends: A Case Study of Lahore

The urban transportation system impacts the sustainable development of a country. Ride sourcing is a transportation model that operates under the notion of sharing economy. This study attempts to identify the changes in travel patterns of the users, particularly female users and their access to space. Focusing on how for the women in Lahore, the mobility has changed? The data for this research has been collected from passengers and drivers of ride-sourcing in Lahore through structured questionnaires. Structural equation modelling (SEM) was used to do the econometric analysis of consumers and drivers. Main findings indicate that for females, there is a significant shift in travel patterns from conventional modes (family car, public transportation) towards ride-sourcing. The results indicate that Uber and Careem has improved mobility as women feel secure in ride-sourcing services compared to public transportation. The female population of Lahore have started taking more trips because of car availability. The paper also tries to calculate the carbon emissions of ride-sourcing. The increasing number of cars is contributing to the city's worsening air pollution as the concept of 'one person in one car' prevails. The social impacts are positive, where women have become more mobile and independent because of app-based transportation.

Exploring ridesourcing trip patterns by fusing multi-source data: A big data approach

Ridesourcing service has been playing an increasingly important role in urban travel, which it can be regard as a promising path towards urban sustainability because of its nature of “sharing”. This paper investigated the travel behavior of ridesourcing users, by considering pick-up and drop-off locations fusing Didi ridesourcing data and online Point of Interest (POI) data based on the Latent Dirichlet Allocation (LDA) model. A case study was conducted in Chengdu, an Asian city. Firstly, the study area was tessellated with hexagons and then clustered into five hexagon types based on POI data. Then researchers analyzed information on spatial distribution of POI data in five hexagon types and temporal distribution of boarding and alighting ridership for five hexagon types. The validation results indicated that the expression “Nine to Ten” represents the mode of life for people in Chengdu. Ridesourcing is not yet being used as a commuter tool for most people. LDA model results also verified that many people work overtime in the evenings and especially on Saturday, even though Chengdu is often thought of as a “slow city”. The study will be useful for practitioners and government to implement effective policies about multi-modal transportation.