Dynamic Ridesharing Systems Research Articles

A scalable vehicle assignment and routing strategy for real-time on-demand ridesharing considering endogenous congestion

On-demand ridesharing has been recognised as an effective way to meet travel needs while significantly reducing the number of required vehicles. As the growth of on-demand mobility services and the advent of shared autonomous vehicles are envisioned to boost the presence of ridesharing vehicles, these may soon significantly affect traffic patterns in our cities. However, most previous studies investigating dynamic ridesharing systems overlook the effects on travel times due to the assignment of requests to vehicles and their routes. In order to assign the ridesharing vehicles while considering network traffic dynamics, we propose a strategy that incorporates time-dependent link travel time predictions into the request–vehicle assignment to avoid or mitigate traffic congestion. In particular, we formulate an efficient linear assignment problem that considers multiple path alternatives and accounts for the impact on travel times, which may be potentially caused by vehicles assigned to specific routes. Simulation experiments reveal that using an appropriate congestion avoidance ridesharing strategy can remarkably reduce passenger average travel and waiting times by alleviating endogenous congestion caused by ridesharing fleets.

Adaptive forecast-driven repositioning for dynamic ride-sharing

AbstractIn dynamic ride-sharing systems, intelligent repositioning of idle vehicles often improves the overall performance with respect to vehicle utilization, request rejection rates, and customer waiting times. In this work, we present a forecast-driven idle vehicle repositioning algorithm. Our approach takes a demand forecast as well as the current vehicle fleet configuration as inputs and determines suitable repositioning assignments for idle vehicles. The core part of our approach is a mixed-integer programming model that aims to maximize the acceptance rate of anticipated future trip requests while minimizing vehicle travel times for repositioning movements. To account for changes in current trip demand and vehicle supply, our algorithm adapts relevant parameters over time. We embed the repositioning algorithm into a planning service for vehicle dispatching. We evaluate our forecast-driven repositioning approach through extensive simulation studies on real-world datasets from Hamburg, New York City, Manhattan, and Chengdu. The algorithm is tested assuming a perfect demand forecast and applying a naïve forecasting model. These serve as an upper and lower bound on state-of-the-art forecasting methods. As a benchmark algorithm, we utilize a reactive repositioning scheme. Compared to this, our forecast-driven approach reduces trip request rejection rates by an average of 3.5 percentage points and improves customer waiting and ride times.

A business class for autonomous mobility-on-demand: Modeling service quality contracts in dynamic ridesharing systems

With the popularization of transportation network companies (TNCs) (e.g., Uber, Lyft) and the rise of autonomous vehicles (AVs), even major car manufacturers are increasingly considering themselves as autonomous mobility-on-demand (AMoD) providers rather than individual vehicle sellers. However, matching the convenience of owning a vehicle requires providing consistent service quality, taking into account individual expectations. Typically, different classes of users have different service quality (SQ) expectations in terms of responsiveness, reliability, and privacy. Nonetheless, AMoD systems presented in the literature do not enable active control of service quality in the short term, especially in light of unusual demand patterns, sometimes allowing extensive delays and user rejections. This study proposes a method to control the daily operations of an AMoD system that uses the SQ expectations of heterogeneous user classes to dynamically distribute service quality among riders. Additionally, we consider an elastic vehicle supply, that is, privately-owned freelance AVs (FAVs) can be hired on short notice to help providers meeting user service-level expectations. We formalize the problem as the dial-a-ride problem with service quality contracts (DARP-SQC) and propose a multi-objective matheuristic to address real-world requests from Manhattan, New York City. Applying the proposed service-level constraints, we improve user satisfaction (in terms of reached service-level expectations) by 53% on average compared to conventional ridesharing systems, even without hiring additional vehicles. By deploying service-quality-oriented on-demand hiring, our hierarchical optimization approach allows providers to adequately cater to each segment of the customer base without necessarily owning large fleets.

Matching and pricing in ride-sharing: Optimality, stability, and financial sustainability

In ride-sharing, a sustainable transportation mode, drivers share idle seats with riders. This paper addresses two key subproblems to find a ride-sharing solution: matching drivers and riders to achieve the system-wide optimality and allocating the resulting travel-cost saving among them by considering their self interests or the stability of the solution. Solutions to either subproblem significantly affect the efficiency of overall dynamic ride-sharing systems measured in total travel-cost saving and individual benefits of people involved. We evaluate the worst-case performance of the best stable solutions associated with four ridematching-defined pricing methods existing in the literature and widely used in practice. We then analyze a Vickrey-Clarke-Groves (VCG)-based pricing mechanism that lets the system achieve optimal system efficiency, prove its stability, and identify its drawbacks such as budget unbalance. We formulate the matching subproblem to maximize the total travel-cost saving and propose a simple and consistent pricing method to guarantee that the resulting ride-sharing solution not only is stable and system-wide optimal but also makes the system deficit-free and thus financially sustainable. Furthermore, such a ride-sharing solution can be obtained in polynomial time. In the numerical experiments, we evaluate our method against existing ones through a real-life instance and randomly generated instances in a dynamic environment.

Pricing and allocation algorithm designs in dynamic ridesharing system

Traffic congestion and car pollution are becoming serious plagues nowadays. High travel cost brings a great burden to people and society. A ridesharing system mitigates traffic congestion and car pollution by allowing passengers to share their travel costs with others. Traditional ridesharing platforms usually require passengers to submit their plans in advance and then design schedules for the drivers who would like to offer a ride. Nowadays, with the development of the smartphone technology, dynamic ridesharing systems enable passengers request a car anytime and anywhere. This paper mainly considers the problems of how to allocate passengers to drivers, how to charge the passengers and how to design feasible schedules for the driver in such online environment. The allocation problem is modeled as an online weighted matching problem with the graph changing over time. Firstly, we give a fair pricing method which is easy to be understood and accepted by the passengers. We develop a greedy algorithm called LiqMax_Gre for the purpose of maximizing liquidity and an algorithm called UtiMax for the purpose of maximizing utility. LiqMax_Gre achieves a competitive ratio of 1λ+1, where λ is the maximal number of passengers a car can take. In general, we prove that no online algorithm can have a good guarantee for the design goal of maximizing utility. Innovatively, UtiMax considers not only the current maximal utility, but also the opportunity cost, which is the utility contributed by occupying a seat. It reflects the utility in the future and thus can be used to handle the difficulty in online design. We prove that, our algorithm has a competitive ratio of 1/3 in a special case. The schedule problem is NP-hard and we design a heuristic nearest neighbor algorithm to solve it.

Sequential Individual Rationality in Dynamic Ridesharing

In dynamic ridesharing systems, both operational and economic policies impact Quality-of-Service (QoS). Field experiments have found that firms benefit from proactively compensating users whose QoS expectations are violated. This motivates a broader analytical study of how behavioral perceptions of QoS impact operational and economic policy design in ridesharing systems. We introduce a novel, QoS-centric framework with the following elements: (a) users’ state-dependent utility model that bridges operational (detours) and economic effects (prices or cost shares), (b) dynamic QoS notion, sequential individual rationality, defined on the sequence of (dis)utilities from successive stages of a shared ride, influenced by behavioral drivers (reference/recency effects, loss aversion), and (c) QoS-sensitive economic objectives (profit or fairness) that endogenize users’ choices and QoS constraints. Our framework can be used to extract key operational insights from QoS-sensitive economic objectives in two different environments: (i) commercial ridesharing, which involves pricing exclusive/shared service to maximize profit (considering penalties for QoS-violations), and (ii) community carpooling, which involves designing fair cost sharing schemes. In the commercial setting, we characterize a ride’s optimal shareable region, and show that it may be optimal for a QoS-sensitive service provider to violate QoS and get penalized, no matter how strong the users’ loss aversion. In the carpooling setting, we characterize routes that admit budget-balanced, QoS-compliant cost sharing schemes, resulting in a ride’s QoS-compliant shareable region. We define sequential fairness and characterize fair, QoS-compliant schemes that bring out insightful structural properties, including a strong requirement that commuters must compensate each other for the detour-inconveniences they cause.

Adoption of dynamic ridesharing system under influence of information on social network

Abstract In an era of highly developed information and communication technologies, social networks play an important role in all aspects of businesses, including transportation. The use of dynamic ridesharing systems, which are systems in which travelers can find partner(s) to share their upcoming rides in real time, is also expected to be affected by the information propagated by friends on social networks. In this study, we investigate how such information in both online and offline social networks affects travelers’ decisions pertaining to ridesharing over days. Because travelers can choose which information to obtain, we also investigate whether the information collected only from nearby friends (in physical space) affects their decisions. To do this, we formulate a day-to-day behavior-based model of a dynamic ridesharing system. Results obtained from the numerical experiments, which are based on the formulated model, show that with the information propagation on social networks, the more friends that a traveler has, the greater is the number of travelers who use dynamic ridesharing systems.

Modular dynamic ride-sharing transport systems

It is widely recognised that digital technology – and in particular dynamic ride sharing systems (DRS) – is leading to fundamental changes to vehicular transport and in particular, offers the prospect of substantially more cost effective forms of transport. A much anticipated transformational wave is that of autonomous vehicles (AVs) which carry the prospect of considerably cheaper and far more convenient individualised transport. That eventuality when married to DRS in the form of shared AVs (SAVs) — is seen as individualised transport’s ‘killer app’ with the attendant risk of its convenience and low cost drawing clients away from public transport systems.To address this problem digital technology is now enabling hybrid modular transport systems (MTS) designed to act variously as a rapid (bus like) transit system, a feeder role for mass transit systems or, alternatively, a low density urban DRS service.In this article a road based modular DRS (MDRS) is described which is comprised of low capacity (up to 10 people) pods which can be articulated into a single vehicle or dynamically separated into individual transport units. There is, therefore, the potential when the pods are detached, to be used for off-peak low capacity last mile feeder services. When the pods are coupled together in bus length vehicles, they can act as a quasi-rapid transit system or as a feeder to them. Such a modular DRS system (MDRS) is shown to have the potential to reduce congestion through decreasing the size and number of vehicles at particular periods of the urban transport cycle. A further major increase in cost effectiveness is envisaged to be an urban based autonomous MDRS system which captures the key cost and convenience benefits of AVs and SAVs while curbing excess use of individual vehicles and abandonment of public transport.

Is There a Limit to Adoption of Dynamic Ridesharing Systems? Evidence from Analysis of Uber Demand Data from New York City

Recent technological advances have paved the way for new mobility alternatives within established transportation networks, including on-demand ride hailing/sharing (e.g., Uber, Lyft) and citywide bike sharing. Common across these innovative modes is a lack of direct ownership by the user; in each of these mobility offerings, a resource not owned by the end users’ is shared for fulfilling travel needs. This concept has flourished and is being hailed as a potential option for autonomous vehicle operation moving forward. However, substantial investigation into how new shared modes affect travel behaviors and integrate into existing transportation networks is lacking. This paper explores whether the growth in the adoption and usage of these modes is unbounded, or if there is a limit to their uptake. Recent trends and shifts in Uber demand usage from New York City were investigated to explore the hypothesis. Using publicly available data about Uber trips, temporal trends in the weekly demand for Uber were explored in the borough of Manhattan. A panel-based random effects model accounting for both heteroscedasticity and autocorrelation effects was estimated wherein weekly demand was expressed as a function of a variety of demographic, land use, and environmental factors. It was observed that demand appeared to initially increase after the introduction of Uber, but seemed to have stagnated and waned over time in heavily residential portions of the island, contradicting the observed macroscopic unbounded growth. The implications extend beyond already existing fully shared systems and also affect the planning of future mobility offerings.

Stable Matching for Dynamic Ride-Sharing Systems

Dynamic ride-sharing systems enable people to share rides and increase the efficiency of urban transportation by connecting riders and drivers on short notice. Automated systems that establish ride-share matches with minimal input from participants provide convenience and the most potential for system-wide performance improvement, such as reduction in total vehicle-miles traveled. Indeed, such systems may be designed to match riders and drivers to maximize system performance improvement. However, system-optimal matches may not provide the maximum benefit to each individual participant. In this paper, we consider a notion of stability for ride-share matches and present several mathematical programming methods to establish stable or nearly stable matches, where we note that ride-share matching optimization is performed over time with incomplete information. Our numerical experiments using travel demand data for the metropolitan Atlanta region show that we can significantly increase the stability of ride-share matching solutions at the cost of only a small degradation in system-wide performance.

Stable Matching for Dynamic Ride-Sharing Systems

Dynamic ride-sharing systems enable people to share rides and increase the efficiency of urban transportation by connecting riders and drivers on short notice. Automated systems that establish ride-share matches with minimal input from participants provide the most convenience and the most potential for system-wide performance improvement, such as reduction in total vehicle-miles traveled. Indeed, such systems may be designed to match riders and drivers to maximize system performance improvement. However, system-optimal matches may not provide the maximum benefit to each individual participant. In this paper we consider a notion of stability for ride-share matches and present several mathematical programming methods to establish stable or nearly-stable matches, where we note that ride-share matching optimization is performed over time with incomplete information. Our numerical experiments using travel demand data for the metropolitan Atlanta region show that we can significantly increase the stability of ride-share matching solutions at the cost of only a small degradation in system-wide performance.

Dynamic ridesharing and information and communications technology: past, present and future prospects

Using a case study approach situated historically, this paper examines how dynamic ridesharing (DRS) has evolved through time in tandem with changes in information and communication technologies (ICTs). Informed speculation is then offered on the future of DRS. A chronological review of a sample of DRS systems reveals the strengths and weaknesses of various technologies. Recent shifts in the software development industry's business and technical models have changed the type of applications being created, development processes and uses. The prospects of these changes on DRS are discussed, focusing on the issues of security/privacy and adoption. The roles of old and new stakeholders, such as automobile manufacturers, software developers and end users are also discussed. The increasing technical confidence and competence of the end user is identified as essential to the success of any contemporary DRS application.

Optimization of Dynamic Ridesharing Systems

Internet-enabled technologies are becoming more widespread; users are constantly connected to the network in every place and daily activity. Access to transportation-related features—mobile payment systems, Global Positioning System connections, real-time public transit timetables or traffic congestion information, and so on—is easy. This access results in new ways to plan mobility. In the innovative mobility systems implemented and developed with these technologies, the new real-time capabilities of dynamic ridesharing, an extended version of traditional ridesharing, can play a key role if the relevant performance is improved. In other words, although ridesharing is not a new idea, recent technological advances should increase its popularity. In this paper, a proposed ridesharing system considers the interactions between drivers or riders and the system manager and the interactions between drivers and riders. The positions and speeds of the shared vehicles and the traffic flows in which such vehicles travel are omitted. To optimize the performance of the ridesharing system, a discrete event, dynamic pickup and delivery model that represents the considered dynamics and an optimal matching problem that optimally allocates an empty seat in a vehicle to a rider are proposed. The dynamic model represents the behavior of the ridesharing system and computes the relevant performance; the optimization problem finds the best match and path in the considered transportation network to minimize the difference between the desired departure and arrival times. In this paper, after the introduction of the ridesharing model, the discussion of the solution to the optimal matching problem, a simulation model is described. A real world case study is then presented and discussed.

Dynamic ride-sharing: A simulation study in metro Atlanta

Smartphone technology enables dynamic ride-sharing systems that bring together people with similar itineraries and time schedules to share rides on short-notice. This paper considers the problem of matching drivers and riders in this dynamic setting. We develop optimization-based approaches that aim at minimizing the total system-wide vehicle miles incurred by system users, and their individual travel costs. To assess the merits of our methods we present a simulation study based on 2008 travel demand data from metropolitan Atlanta. The simulation results indicate that the use of sophisticated optimization methods instead of simple greedy matching rules substantially improve the performance of ride-sharing systems. Furthermore, even with relatively low participation rates, it appears that sustainable populations of dynamic ride-sharing participants may be possible even in relatively sprawling urban areas with many employment centers.

Dynamic Ride-Sharing: a Simulation Study in Metro Atlanta

Smartphone technology enables dynamic ride-sharing systems that bring together people with similar itineraries and time schedules to share rides on short-notice. This paper considers the problem of matching drivers and riders in this dynamic setting. We develop optimization-based approaches that aim at minimizing the total system-wide vehicle miles incurred by system users, and their individual travel costs. To assess the merits of our methods we present a simulation study based on 2008 travel demand data from metropolitan Atlanta. The simulation results indicate that the use of sophisticated optimization methods instead of simple greedy matching rules substantially improve the performance of ride-sharing systems. Furthermore, even with relatively low participation rates, it appears that sustainable populations of dynamic ride-sharing participants may be possible even in relatively sprawling urban areas with many employment centers