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Title: The Value of Incorporating Social Preferences in Dynamic Ridesharing

Paper content:
Dynamic ridesharing services (DRS) play a major role in improving the efficiency of urban transportation.
User satisfaction in dynamic ridesharing is determined by multiple factors such as travel time, cost, and social compatibility with co-passengers.
Existing DRS optimize profit by maximizing the operational value for service providers or minimize travel time for users but they neglect the social experience of riders, which significantly influences the total value of the service to users.
We propose DROPS, a dynamic ridesharing framework that factors the riders' social preferences in the matching process so as to improve the quality of the trips formed.
Scheduling trips for users is a multi-objective optimization that aims to maximize the operational value for the service provider, while simultaneously maximizing the value of the trip for the users.
The user value is estimated based on compatibility between co-passengers and the ride time.
We then present a real-time matching algorithm for trip formation.
Finally, we evaluate our approach empirically using real-world taxi trips data, and a population model including social preferences based on user surveys.
The results demonstrate improvement in riders' social compatibility, without significantly affecting the vehicle miles for the service provider and travel time for users.
Dynamic ridesharing services, such as UberPool and LyftLine, are becoming an increasingly popular means of commute, especially in large cities BID6 BID2 .
Dynamic ridesharing is characterized by matching multiple requests that arrive in real-time, for a one-way and one-time trip.
We consider a setting in which a service provider operates a vehicle fleet and schedules cars to pick up and drop off passengers in response to a stream of requests, which includes matching requests with each other.
There are two important factors that explain the growing attractiveness of DRS for customers:
(i) cost effectiveness and
(ii) ease of finding a ride in large cities where it is comparatively hard to find a taxi otherwise.
For a service provider, dynamic ridesharing helps serve customers with possibly fewer vehicles, thus reducing their operational cost.A common objective for optimizing riders' satisfaction in existing ridesharing systems is to minimize travel time BID14 BID0 BID2 .
In practice, however, there are many other factors that affect user satisfaction in dynamic ridesharing, apart from travel time.
Since a user could be traveling with strangers in the ride, their compatibility plays a major role in the user's satisfaction.
In fact, there is growing evidence that desire for personal space and security when riding with strangers pose a major barrier to using ridesharing for many users (Tao and Wu 2008; BID0 . For example, a female passenger may prefer to ride only with female co-passengers. The user may have a different set of preferences depending on the time of day and the location -preferences are tripspecific and not necessarily user-specific. Consider a scenario with three requests where r 1 and r 2 are male and r 3 is a female passenger. Let these requests arrive at the same time FIG0 ), such that optimizing the operational value for the service provider forms a trip with these requests (1(a)).
However, this may violate the users' social preferences and the trip may need to be altered to satisfy the preferences, such as the following:• If the passengers prefer riding with co-passengers of the same gender but are indifferent to riding with copassengers of a different gender, then it is desirable to minimize their ride time overlap in the vehicle by altering the pick up and drop off order FIG0 ); and If the service does not provide a mechanism to express such social preferences and forms trips that violate these preferences (as in 1(a)), the customers may not use the service.
Current DRS, however, do not account for social preferences in their optimization, despite being indicated as a major concern for users in several surveys BID0 BID15 BID10 BID21 .
We present DROPS (Dynamic Ridesharing Optimization using social PreferenceS), a dynamic ridesharing framework that facilitates incorporating social preferences of the users in the trip formation process.
A weight vector over preferences indicates the importance of each factor in determining the trip value to the user.
The goal is to form trips that optimize both operational value for the service provider and value of the trip to the passengers, which incentivizes them to continue using the service and benefits the service provider.
The value of a trip to a user is calculated based on their social compatibility with other co-passengers, the ride time, and ride cost.
We solve this bi-objective optimization problem using scalarization BID18 , which solves a linear combination of the multiple objectives.
The relative importance of each objective can be controlled using the weight vector for the objectives.
Given a set of riders, we evaluate their potential shared trip using an optimal trajectory planning algorithm.
Candidate trips are formed using our real-time greedy algorithm that adds customers to a trip only if the trip's value is above a certain threshold.We consider three basic social factors -age, gender, and user rating-along with a time preference indicating if the user is in a rush.
The viability of factoring social preferences into the trips scheduling process is evaluated empirically.
The experiments examine the impact of matching with social preferences (social matching) on users and the service provider.
We test our approach on a real-world taxi trips dataset and compare the results with that of three baselines, each focusing on optimizing different components of the objective for trip formation.
The population model and preferences used in our experiments were acquired using webbased user surveys, which was conducted in two phases and had 489 responses.
The survey was conducted specifically to determine how different potential riders evaluate social ridesharing.
Our results show that incorporating social preferences of users in the trip formation improves the overall user satisfaction, without significantly affecting the operational cost for the service provider.Our primary contributions are:
(i) presenting DROPS, a system for dynamic ridesharing with social preferences;
(ii) proposing a real-time greedy algorithm for trip formation; and
(iii) extensive empirical evaluation showing the benefits of social matching in dynamic ridesharing using real-world taxi data and a population model based on user surveys.
Dynamic ridesharing is an increasingly appealing commuter option.
However, numerous surveys have indicated that users' concerns, primarily about the social characteristics of co-passengers, pose a major barrier to using ridesharing for a segment of the population.
We present the DROPS system for optimizing dynamic ridesharing with social preferences and present an efficient real-time matching algorithm that can handle effectively high density zones.
Our results demonstrate that factoring social preferences into the matching process helps improve the user value, without significantly affecting the operational value to the service provider.
Furthermore, survey results indicate that services that perform social matching are likely to incentivize more individuals to use the service.
We conclude that while social matching is beneficial overall, it is not always guaranteed to result in improved performance.
Factoring social preferences into the matching process is most beneficial in zones with a high request density per decision cycle and greater compatibility among ridesharing users.In the future, we aim to examine ways to extend the matching model to consider nearby trips that have already been dispatched and are currently en-route.
We will also consider more complex ways to factor the competing objectives using more general multi-objective planning algorithms BID23 .
Additionally, based on the performance analysis of our approach with that of a hindsight trip formation, we aim to employ a predictive model for future requests to improve the user value.
While we anticipate some performance gains, we do not expect the relative benefits of social matching to diminish.

assistant: text: We propose a novel dynamic ridesharing framework to form trips that optimizes both operational value for the service provider and user value to the passengers by factoring the users' social preferences into the decision-making process.