Shared Rides Research Articles

What is the elasticity of sharing a ridesourcing trip?

Transportation network companies (TNCs) offer a ride-splitting option for ridesourcing trips, allowing users to share the vehicle with others at a lower fare. While encouraging shared rides has environmental benefits, little is known about how price affects the decision to share. Using TNC trip data from Chicago, we investigate the temporal and spatial distribution of authorized ride-splitting trips in 2019. We found that the willingness to share TNC trips differed across neighborhoods with different demographics, socioeconomic status, and built environment characteristics. The willingness to share was related to price and trip duration. We estimate logistic regression and random forest models to determine the marginal price and time effects on the decision to share. The results indicate the probability of authorizing a ride-splitting trip is highly elastic to the price per mile and the random forest model had better predictive accuracy than the logistic model. Additionally, we examine the importance and marginal effects of total price and trip duration. We use two data preprocessing methods to address rounding errors in the price and demonstrate the robustness of the results. Policy implications for increasing shared trips are discussed based on the findings.

A Comparison of Three Ridesharing Cost Savings Allocation Schemes Based on the Number of Acceptable Shared Rides

Shared mobility based on cars refers to a transportation mode in which travelers/drivers share vehicles to reduce the cost of the journey, emissions, air pollution and parking demands. Cost savings provide a strong incentive for the shared mobility mode. As cost savings are due to cooperation of the stakeholders in shared mobility systems, they should be properly divided and allocated to relevant participants. Improper allocation of cost savings will lead to dissatisfaction of drivers/passengers and hinder acceptance of the shared mobility mode. In practice, several schemes based on proportional methods to allocate cost savings have been proposed in shared mobility systems. However, there is neither a guideline for selecting these proportional methods nor a comparative study on effectiveness of these proportional methods. Although shared mobility has attracted much attention in the research community, there is still a lack of study of the influence of cost saving allocation schemes on performance of shared mobility systems. Motivated by deficiencies of existing studies, this paper aims to compare three proportional cost savings allocation schemes by analyzing their performance in terms of the numbers of acceptable rides under different schemes. We focus on ridesharing based on cars in this study. The main contribution is to develop theory based on our analysis to characterize the performance under different schemes to provide a guideline for selecting these proportional methods. The theory developed is verified by conducting experiments based on real geographical data.

Effect of Price and Time on Private and Shared Transportation Network Company Trips

Transportation network companies (TNCs) offer two types of service: private-party ridehailing and shared ridehailing. Policymakers have an interest in encouraging shared over private ridehailing to promote more efficient use of the transportation network. While transportation researchers have analyzed ridehailing behavior before, there is limited literature describing the effect of price and time on a rider’s choice between private-party and shared ridehailing. This paper fills this gap by analyzing revealed preferences for private-party and shared ridehailing trips in 15 American cities coupled with a survey of 4,365 users of a large TNC that includes stated preference questions focused on various alternative options for their most recent trip choice. This study finds that an increase in the relative price difference of $1 per mile increases an individual’s probability of sharing by over 8%, while a decrease in the relative travel time difference of 1 min per mile increases the probability of sharing by over 33%. The survey results also show that that a sizable proportion of private-party TNC trips (approximately 35%) will be difficult or even impossible to convert to shared rides through a price-based incentive. Market segmentation analysis reveals user and trip types where price- and time-based incentives have a relatively greater effect on the choice between private and shared rides. Finally, heterogeneity in user time versus money trade-offs suggests new product possibilities that would increase TNC sharing.

A Note on Principal-Agent Problem in a Stochastic System

We consider a principal (e.g., a ridesharing platform such as Uber or Lyft) who receives two types of jobs (e.g., passengers requesting solo or shared rides) according to a Poisson process. The principal first decides which jobs to admit and then assigns an agent (e.g., driver) to perform them. The agent who is assigned the job has preference between the two types of jobs. The agent can independently decide whether to accept or reject a job which is assigned to them. The principal and the agent receive different rewards from each job thus resulting in incentives misalignment. The research questions are: (1) which job(s) should the principal admit? (2) How much should the principal pay the agent? To answer these questions, we model the agent as an M / M / 1 loss system. Using a Markov decision process and dynamic programming, we find the optimal wage the principal should pay the agent and a threshold admission policy (also known as trunk-reservation or switching-curve policy). Prior literature did not consider two players (agent and principal) with misaligned objectives and each making dynamic decisions. We contribute to the literature by adding another layer of decision making and by introducing server (agent) independence wherein the servers have preferences regarding the type of job they wish to perform.

Integrating Passenger Incentives to Optimize Routing for Demand-Responsive Customized Bus Systems

The customized bus (CB) is an alternative public transportation mode that extends the flexibility and coverage of the fixed-route transit networks. It allows passengers to make reservations for trips and arranges vehicles to serve shared rides. However, the operation performance is limited to vehicle routing, the number of stops, and the length of detour times. Extra detours and stops would not be significantly avoided if deploying high-capacity vehicles. The demand control aspect is a possible way out. Releasing incentives to passengers can attract them to aggregated locations and reduce the vehicle detour times. How to determine an appropriate incentive scheme for passengers is the critical problem. This paper presents an approach to integrating the disaggregated trip choice model with the vehicle routing model to determine incentive schemes. First, the discrete choice model is established to bridge the passengers' trip choice probabilities with the influence of monetary incentives, walking time, and travel time. A vehicle routing model based on the pickups and deliveries problem is then adopted to generate the routes and schedules of vehicles to serve the influenced passengers. The result shows that the proposed approach can reduce the total running kilometers, shorten the onboard time, and increase the profits. The analysis also suggests that passengers' sensitivity towards incentives is decisive to the result.

Gospodarka współdzielenia a zachowania polskich młodych singli: przypadek BlaBlaCar

This article is a research exercise. The purpose of the study is to provide an insight into what young singles know about the sharing economy and what their key motives are for using the BlaBlaCar platform, the most popular sharing economy venture for passenger transport. In the theoretical part of the text, based on a critical analysis of the literature, the concept and essence of the sharing economy are explained, the BlaBlaCar transport service is characterised as a flagship example of the sharing economy, and an attempt is made to explain the terms “single” and “young single”. The empirical section focuses on the research conceptualisation and a description of the research sample and its characteristics. Subsequently, following the author’s own research, the knowledge of young singles about the sharing economy is analysed, and the most important motives encouraging them to use the BlaBlaCar platform are discussed. The analysis is based on a survey questionnaire administered from May 1 to July 30, 2018 in a sample of 826 young singles who made independent decisions in the market. The survey shows that young singles demonstrated fairly good knowledge of sharing economy ventures and the most popular car sharing websites. However, they are confused about the rich terminology associated with collaborative platforms and often misunderstand the sharing economy, identifying it with the access economy, gift economy or on-demand economy. BlaBlaCar is undoubtedly one of the most popular examples of the practical application of sharing economy fundamentals. The survey reveals that almost three-fifths of respondents use the BlaBlaCar platform. Its active participants are most often men in the 18–23 age group, with a bachelor’s or engineering degree, earning a monthly income not exceeding PLN 3,000, and living in cities with over 500,000 inhabitants. Lower travel costs, better travel conditions, the possibility of reaching a destination quickly and directly and environmental considerations proved to be the most important motives for the respondents to use BlaBlaCar. In turn, social relations that are established among the users of websites such as BlaBlaCar.pl are a kind of hybrid of relations developed in direct interaction during shared rides and those built up in cyberspace.

SRide: An Online System for Multi-Hop Ridesharing

In the context of smart cities, ridesharing in urban areas is gaining researchers’ interest and is considered to be a sustainable transportation solution. In this paper, we present SRide (Shared Ride), a multi-hop ridesharing system as a mode of sustainable transportation. Multi-hop ridesharing is a type of ridesharing in which a rider travels in multiple hops to reach a destination, transferring from one driver to another between hops. The key problem in multi-hop ridesharing is to find an optimal itinerary or route plan for a rider from an origin to a destination in a dynamic, online setting. SRide adopts a novel approach to finding itineraries for riders suited to the online nature of the problem. The system represents ride offers as a time-dependent directed graph and finds itineraries dynamically by updating the graph incrementally and decrementally as ride offers are updated in the system. The system’s distinguishing feature is its incremental and decremental operation, which is enabled by employing dynamic single-source shortest-path algorithms. We conducted two extensive simulation studies to evaluate its performance. Metrics, including the matching rate, savings in total system-wide vehicle-miles, and total system-wide driving times were measured. In the first study, SRide’s dynamic update algorithms were compared with their non-dynamic versions. Results show that SRide’s algorithms run up to thirteen times faster than their non-dynamic versions. In the second study, we used data from the travel demand model for metropolitan Atlanta in the US state of Georgia, to assess the benefits of multi-hop ridesharing. Results show that matching rates increase up to 68%, saving in total system-wide vehicle-miles of up to 12%, and reduction in the total system-wide driving time of up to 12.86% is achieved.

Exact matching of attractive shared rides (ExMAS) for system-wide strategic evaluations

The premise of ride-sharing is that service providers can offer a discount, so that travellers are compensated for prolonged travel times and induced discomfort, while still increasing their revenues. While recently proposed real-time solutions support online operations, algorithms to perform strategic system-wide evaluations are crucially needed. We propose an exact, replicable and demand-, rather than supply-driven algorithm for matching trips into shared rides. We leverage on delimiting our search for attractive shared rides only, which, coupled with a directed shareability multi-graph representation and efficient graph searches with predetermined node sequence, narrows the (otherwise exploding) search-space effectively enough to derive an exact solution. The proposed utility-based formulation paves the way for model integration in travel demand models, allowing for a cross-scenario sensitivity analysis, including pricing strategies and regulation policies. We apply the proposed algorithm in a series of experiments for the case of Amsterdam, where we perform a system-wide analysis of the ride-sharing performance in terms of both algorithm computations of shareability under alternative demand, network and service settings as well as behavioural parameters. In the case of Amsterdam, 3000 travellers offered a 30% discount form 1900 rides achieving an average occupancy of 1.67 and yielding a 30% vehicle-hours reduction at the cost of halving service provider revenues and a 17% increase in passenger-hours. Benchmarking against time-window constrained approaches reveals that our algorithm reduces the search-space more effectively, while yielding solutions that are substantially more attractive for travellers.

Analytical and Agent-Based Model to Evaluate Ride-Pooling Impact Factors

On-demand ride-pooling (ODRP) services have the potential to improve traffic conditions in cities and at the same time offer user-centric mobility services. Recently, an analytical model, which investigates the influence of service quality parameters, such as detour, maximum waiting time, and boarding time, on the fraction of trips which could potentially be shared (a quantity called shareability), has been presented. The aim of this study is to test this model with a simulation framework that models an ODRP service in different levels of detail. The results show that by increasing the modeling complexity, in which we consider network topology, trip distribution patterns, optimization objectives, and changing velocity, the theoretical value of shareability and the actual experienced shared rides are decreased. It is observed that the shareability predicted by the mathematical model could be confirmed by a certain simulation setup with the objective to maximize shared rides. Nevertheless, changing the optimization objective to optimizing the total kilometers driven has the highest impact on shareability, decreasing it by up to 50%. By using a fitting procedure within this simulation setup, we can still exploit the analytical model to predict the influence of service quality parameters. This study may be useful for other researchers who plan to model ride-pooling systems and for operators who want to have an estimation of the level of shared rides they can achieve in an operating area.

Operational benefits and challenges of shared-ride automated mobility-on-demand services

Abstract This paper presents a quantitative analysis of the operations of shared-ride automated mobility-on-demand services (SRAMODS). The study identifies (i) operational benefits of SRAMODS including improved service quality and/or lower operational costs relative to automated mobility-on-demand services (AMODS) without shared rides; and (ii) challenges associated with operating SRAMODS. The study employs an agent-based stochastic dynamic simulation framework to model the operational problems of AMODS. The agents include automated vehicles (AVs), on-demand user requests, and a central AV fleet controller that can dynamically change the plans (i.e. routes and AV-user assignments) of AVs in real-time using optimization-based control policies. The agent-based simulation tool and AV fleet control policies are used to test the operational performance of AMODS under a variety of scenarios. The first set of scenarios vary user demand and a parameter constraining the maximum user detour distance. Results indicate that even with a small maximum user detour distance parameter value, allowing shared rides significantly improves the operational efficiency of the AV fleet, where the efficiency gains stem from economies of demand density and network effects. The second set of scenarios vary the mean and coefficient of variation of the curbside pickup time parameter; i.e. how long an AV must wait curbside at a user’s pickup location before the user gets inside the AV. Results indicate that increases in mean curbside pickup time significantly degrade operational performance in terms of user in-vehicle travel time and user wait time. The study quantifies the total system (user plus fleet controller) cost as a function of mean curbside pickup time. Finally, the paper provides an extensive discussion of the implications of the quantitative analysis for public-sector transportation planners and policy-makers as well as for mobility service providers.

Assignment and Pricing of Shared Rides in Ride-Sourcing Using Combinatorial Double Auctions

Transportation Network Companies employ dynamic pricing methods at periods of peak travel to incentivise driver participation and balance supply and demand for rides. Surge pricing multipliers are commonly used and are applied following demand and estimates of customer and driver trip valuations. Combinatorial double auctions have been identified as a suitable alternative, as they can achieve maximum social welfare in the allocation by relying on customers and drivers stating their valuations. A shortcoming of current models, however, is that they fail to account for the effects of trip detours that take place in shared trips and their impact on the accuracy of pricing estimates. To resolve this, we formulate a new shared-ride assignment and pricing algorithm using combinatorial double auctions. We demonstrate that this model is reduced to a maximum weighted independent set model, which is known to be APX-hard. A fast local search heuristic is also presented, which is capable of producing results that lie within 10% of the exact approach for practical implementations. Our proposed algorithm could be used as a fast and reliable assignment and pricing mechanism of ride-sharing requests to vehicles during peak travel times.

Shared autonomous vehicle simulation and service design

Today, driverless cars, as a new technology that allows a more accessible, dynamic and intelligent form of Shared Mobility, are expected to revolutionize urban transportation. One of the conceivable mobility services based on driverless cars is shared autonomous vehicles (SAVs). This service could merge cabs, carsharing, and ridesharing systems into a singular transportation mode. However, the success and competitiveness of future SAV services depend on their operational models, which are linked intrinsically to the service configuration and fleet specification. In addition, any change in operational models will result in a different demand. Using a comprehensive framework of SAV simulation in a multimodal dynamic demand system with integrated SAV user taste variation, this study evaluates the performance of various SAV fleets and vehicle capacities serving travelers across the Rouen Normandie metropolitan area in France. Also, the impact of ridesharing and rebalancing strategies on service performance is investigated.Research results suggest that the performance of SAV is strongly correlated with the fleet size and the strategy of individual or shared rides. Further analysis indicates that for the pricing scheme proposed in this study (i.e., 20% lower for ridesharing scenario), the standard 4-seats car with shared ride remains the best option among all scenarios. The results also underline that enabling vehicle-rebalancing strategies may have an important effect on both user and service-related metrics. The estimated SAV average and maximum driven distance prove the importance of vehicle range and charging station deployment.

Efficient and Privacy-Preserving Ridesharing Organization for Transferable and Non-Transferable Services

Ridesharing allows multiple persons to share one vehicle for their trips instead of using multiple vehicles. Ridesharing can reduce the number of vehicles in the street, which consequently can reduce air pollution, traffic congestion, and transportation cost. However, ridesharing organization requires passengers to report sensitive location information about their trips to a trip organizing server (TOS) which creates a serious privacy issue. The existing ridesharing organization schemes are neither flexible nor scalable in the sense that they require a driver and a rider to have exactly the same trip to share a ride, and they are inefficient if applied to large geographic areas. In this paper, we propose two efficient privacy-preserving ridesharing organization schemes for Non-transferable Ridesharing Service (NRS) and Transferable Ridesharing Service (TRS). In NRS, a rider shares a ride from his/her trip's start to the destination with only one driver, whereas, in TRS, a rider can transfer between multiple drivers while en route until he reaches his destination. In the proposed schemes, the ridesharing area is divided into a number of small geographic areas, called cells, and each cell has a unique identifier. Each driver/rider should encrypt his/her trip's data with modified kNN encryption scheme, and send an encrypted ridesharing offer/request to the TOS. In NRS scheme, Bloom filters are used to represent the trip information compactly before encryption. Then, the TOS can measure the similarity of the encrypted trips to organize shared rides without revealing either the users' identities or the locations. In TRS scheme, drivers report their encrypted routes, and then the TOS builds a directed graph that is passed to a modified version of Dijkstra's shortest path algorithm to search for an optimal path for rides that can achieve a set of preferences prescribed by the riders. Although TRS can be used to organize non-transferable trips, performance evaluation shows that NRS requires less communication overhead than TRS. Our formal privacy proof and analysis demonstrate that the proposed schemes can preserve users privacy and our experimental results using routes extracted from real maps show that the proposed schemes can be used efficiently for large cities.

Understanding the Relationships between Demand for Shared Ride Modes: Case Study using Open Data from New York City

The concept of shared travel, making trips with other users via a common vehicle, is far from novel. However, a changing technological climate has laid the tracks for new dynamically shared modes in the form of transportation network companies (TNCs), to substantially impact travel behavior. The current body of research on how these modal offerings impact the demand for existing shared modes (e.g., bikeshare, transit) is growing. However, a comprehensive investigation of the temporal evolution of the demand for TNCs and their relationship to other shared modes, is lacking. This research tackles this important limitation by analyzing ridership data for TNCs, taxi, subway, and Citi Bike in New York City using daily ridership data from January 2015 through June 2017. The primary objective was to understand the relationship between TNCs and other shared modal offerings while accounting for the influence of temporal trends and other exogenous factors. A dynamic linear modeling framework was formulated to accommodate time-dependent trends, periodicity, and time-varying exogenous factors on the demand for TNCs. As a preliminary work, the findings of this study reinforce the observed substitution relationship between taxis and TNCs. The results may also indicate a substitutional relationship between TNCs and Citi Bike, and a complementary relationship with subway, however these results still need to be explored further. With potentially impactful findings for planning and policymakers, the predictive model developed in the study can be used to carry out forecasting in support of short- and long-term operations and planning applications.

Discriminatory attitudes between ridesharing passengers

Prior studies have provided evidence of discrimination between drivers and passengers in the context of ridehailing. This paper extends prior research by investigating passenger-to-passenger discriminatory attitudes in the context of ridesharing. We conducted a survey of 1110 Uber and Lyft users in the US using Mechanical Turk, 76.5% of whom have used uberPOOL or Lyft Shared rides, and estimated two structural equation models. The first model examines the influence of one’s demographic, social and economic characteristics on discriminatory attitudes toward fellow passengers in ridesharing, and how such influence varies by the targets of discrimination (i.e., race and class). The second model examines the influence of one’s generic social dominance orientation on discriminatory attitudes in the ridesharing context. We find that discriminatory attitudes toward fellow passengers of differing class and race in the shared ride are positively correlated with respondents that are male or are women with children. A respondent’s race does not have a significant effect on discriminatory attitudes, but white respondents that live in majority white counties are more likely to hold discriminatory attitudes with regard to race (no effect is observed regarding class preferences). The same is true of respondents that live in counties in which a larger share of the electorate voted for the Republican candidate in the 2016 presidential election. Conversely, higher-income respondents appear more likely to hold discriminatory attitudes regarding class, but no effect is observed regarding racial preferences. We also find that one’s generic social dominance orientation strongly influences his/her discriminatory attitudes in ridesharing, supporting the claim that behavior in shared mobility platforms reflects long-standing social dominance attitudes. Further research is required to identify policy interventions that mitigate such attitudes in the context of ridesharing.

Privately Owned Autonomous Vehicle Optimization Model Development and Integration with Activity-Based Modeling and Dynamic Traffic Assignment Framework

This paper presents a first-order approach integrated with activity-based modeling and dynamic traffic assignment framework to model the impact of autonomous vehicles on household travel and activity schedules. By considering shared rides among household members, mode choices, re-planning of departure times, and the rescheduling of activity sequences, two optimization models—basic personal owned autonomous vehicle (POAV) model and enhanced POAV model—are presented. The proposed approach is tested for the different models at the household level with different household sizes. The activity schedules of each household were generated in the Chicago sub-area network. The results show that each POAV can effectively replace multiple conventional vehicles, however, using POAV will lead to more vehicle miles traveled because of detour trips. The proposed enhanced POAV model considers mode choice decision with a household-based approach instead of a trip-based approach to capture the impacts of repositioning trips on mode choice. The results show that, if the generalized travel cost of POAV remains at the same level as conventional vehicles, more passengers will choose to use transit because the repositioning trips increase the total cost.

Characterization of ridesplitting based on observed data: A case study of Chengdu, China

With the development of mobile internet technology, on-demand ridesourcing services have rapidly spread across the world and have caused debates in the transportation industry. While many researchers have begun to study the characteristics and impacts of ridesourcing, there are few published studies specifically on ridesplitting, a ridesourcing service that matches riders with similar origins or destinations to the same ridesourcing driver and vehicle in real time. This paper aims to explore the characteristics and effects of ridesplitting using observed ridesourcing data provided by DiDi Chuxing that contain complete datasets of the ridesourcing trajectories and orders in the city of Chengdu, China. First, a ridesplitting trip identification (RTI) algorithm is developed to separate the shared rides from the single rides (non-ridesplitting orders) and derive ridesplitting scales. Second, a ridesplitting trajectory reconstruction (RTR) algorithm is proposed to estimate the ridesplitting effects on delays and detours. Then, we analyze and compare the scales, spatiotemporal patterns and travel characteristics between shared rides and single rides, which are very different. The results show that the current percentage of ridesplitting in ridesourcing is still low (6–7%), which may be explained by the extra delay (about 10 min on average), detour (about 1.55 km on average), and degraded travel time reliability caused by ridesplitting. In addition, built environment factors, such as density, diversity, and development, are also correlated with ridesplitting demand and delay. The findings of this study can help better understand the features of ridesplitting and develop strategies for improving its use in emerging ridesourcing services.

Dynamic autonomous vehicle fleet operations: Optimization-based strategies to assign AVs to immediate traveler demand requests

Motivated by the growth of ridesourcing services and the expected advent of fully-autonomous vehicles (AVs), this paper defines, models, and compares assignment strategies for a shared-use AV mobility service (SAMS). Specifically, the paper presents the on-demand SAMS with no shared rides, defined as a fleet of AVs, controlled by a central operator, that provides direct origin-to-destination service to travelers who request rides via a mobile application and expect to be picked up within a few minutes. The underlying operational problem associated with the on-demand SAMS with no shared rides is a sequential (i.e. dynamic or time-dependent) stochastic control problem. The AV fleet operator must assign AVs to open traveler requests in real-time as traveler requests enter the system dynamically and stochastically. As there is likely no optimal policy for this sequential stochastic control problem, this paper presents and compares six AV-traveler assignment strategies (i.e. control policies). An agent-based simulation tool is employed to model the dynamic system of AVs, travelers, and the intelligent SAMS fleet operator, as well as, to compare assignment strategies across various scenarios. The results show that optimization-based AV-traveler assignment strategies, strategies that allow en-route pickup AVs to be diverted to new traveler requests, and strategies that incorporate en-route drop-off AVs in the assignment problem, reduce fleet miles and decrease traveler wait times. The more-sophisticated AV-traveler assignment strategies significantly improve operational efficiency when fleet utilization is high (e.g. during the morning or evening peak); conversely, when fleet utilization is low, simply assigning traveler requests sequentially to the nearest idle AV is comparable to more-advanced strategies. Simulation results also indicate that the spatial distribution of traveler requests significantly impacts the empty fleet miles generated by the on-demand SAMS.

Spatial welfare effects of shared taxi operating policies for first mile airport access

Abstract With increasing availability of alternative mobility options for first/last mile, it is necessary to better understand how shared taxis are impacting airport access demand and consumer surplus. However, no study has been conducted to evaluate the welfare effects of the range of shared taxi matching and fare allocation policies for airport access. Using several data sources primarily from Port Authority of NY and NJ and The Taxi and Limousine Commission, a mode choice model is estimated for access to John F. Kennedy International Airport in New York City. The baseline model and data show that passengers have a value of time of $101 per hour, consistent with Harvey’s study from 1986. Airport taxi travelers are also elastic to cost in a similar manner to public transit. The model is used to evaluate two policies: a first (we call this wait-share policy) where taxis can offer shared rides for two passengers from the same zip code, incorporating an endogenous expected wait time variable; and a second (we call this space-share policy) where taxis match randomly arriving passengers from any zip codes in the city. These two policies reflect extreme ends of a spectrum of policies between waiting and detouring. Findings suggest that having a shared taxi option benefit passengers in NYC going to JFK airport by at least 10% increase in consumer surplus. However, the increase in taxi ridership comes at a cost to transit ridership. Furthermore, the population in NYC that benefits most is highly dependent on the type of shared taxi policy. A wait-share policy benefits passengers from the dense parts of Manhattan most, while a space-share policy distributes the benefits more to other boroughs. These insights can help policymakers set regulations in providing first/last mile ride-sharing taxi options in different cities around the world.

How much is trust: The cost and benefit of ridesharing with friends

Ridesharing with social contacts (i.e., ‘friends’) is substantially more accepted than with strangers. However, limiting ridesharing to friends while rejecting strangers also reduces ride choices and increases detour costs. This work studies, from a theoretical perspective, whether the additional detour costs of limiting shared rides to social network contacts would be prohibitive. It proposes a social network based ridesharing algorithm with heterogeneous detour tolerances for varied social contacts. The theoretical matching rates and detour costs are compared in a simulation for three levels of social connectivity: travelling with direct contacts only, with direct and indirect contacts, or with anyone. The simulation allows for a systematic and comprehensive testing of system behaviour when varying the parameters of social network structure, detour tolerance, and spatial distribution of friendship. Results show that for a clustered friendship – the expected spatial distribution of a social network growing with a ridesharing network – ridesharing with friends does not cause significantly higher costs. Furthermore, the algorithm prioritising friends can substantially increase the matching of friends. An empirical study justifies the findings.