Dynamic Ridesharing Research Articles

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.

Non-myopic relocation of idle mobility-on-demand vehicles as a dynamic location-allocation-queueing problem

Operation of on-demand services like taxis, dynamic ridesharing services, or vehicle sharing depends significantly on the positioning of idle vehicles to anticipate future demand and operational states. A new queueing-based formulation is proposed for the problem of relocating idle vehicles in an on-demand mobility service. The approach serves as a decision support tool for future studies in urban transport informatics and design of new types of urban mobility systems like carsharing, ridesharing, and smart taxis. A Lagrangian Decomposition heuristic is developed and compared with a relaxed lower bound solution. Using New York taxicab data, the proposed algorithm reduces the cost by up to 27% compared to the myopic case.

The morning commute problem with ridesharing and dynamic parking charges

This paper studies the traffic flow patterns in a single bottleneck corridor with a dynamic ridesharing mode and dynamic parking charges. Schemes with different ridesharing payments and shared parking prices are investigated. Besides the scheme with constant parking charges and ridesharing payments, dynamic parking charges and ridesharing payments are derived to achieve congestion-free traffic in the corridor. With the dynamic ridesharing ratios, it is found that genuinely nonlinear departure rates and travel time functions can be generated in certain ridesharing cases, which was not observed in the traditional ADL model (Arnott et al., 1990) for the morning commute problems without ridesharing or with constant ridesharing ratios. Moreover, comparing different configurations of ridesharing arrangements and parking charges, the results show that constant parking charges with constant ridesharing payments may not significantly improve system performance over the traditional morning commute with solo-drivers, while dynamic parking charges with properly selected constant ridesharing payments can achieve better system performance in terms of vehicle-miles-traveled, vehicle-hours-traveled and total travel costs, by encouraging ridesharing and spreading vehicular demand over time to eliminate queuing delays.

A general framework for modeling shared autonomous vehicles with dynamic network-loading and dynamic ride-sharing application

Shared autonomous vehicles (SAVs) could provide low-cost service to travelers and possibly replace the need for personal vehicles. Previous studies found that each SAV could service multiple travelers, but many used unrealistic congestion models, networks, and/or travel demands. The purpose of this paper is to provide a method for future research to use realistic flow models to obtain more accurate predictions about SAV benefits. This paper presents an event-based framework for implementing SAV behavior in existing traffic simulation models. We demonstrate this framework in a cell transmission model-based dynamic network loading simulator. We also study a heuristic approach for dynamic ride-sharing. We compared personal vehicles and SAV scenarios on the downtown Austin city network. Without dynamic ride-sharing, the additional empty repositioning trips made by SAVs increased congestion and travel times. However, dynamic ride-sharing resulted in travel times comparable to those of personal vehicles because ride-sharing reduced vehicular demand. Overall, the results show that using realistic traffic flow models greatly affects the predictions of how SAVs will affect traffic congestion and travel patterns. Future work should use a framework such as the one in this paper to integrate SAVs with established traffic flow simulators.

Survey on Dynamic Ride Sharing in Big Data Era

Survey on Dynamic Ride Sharing in Big Data Era

Uber for Seniors?: Exploring Transportation Options for the Future

By 2030, the number of adults 65 and older in the United States will surpass 72 million, almost doubling the number in 2010. This population continues to rely on automobiles to satisfy transportation needs; however, with the onset of aging, physical, cognitive, and motor abilities decline and affect those people’s ability to drive and their level of mobility and independence. Lack of mobility and accessibility affect the quality of life and the ability to age in place and could lead to social isolation and a reduction in activity level. The shift to transit is often difficult because of a lack of familiarity and accessibility. Dynamic ridesharing is surging as a viable transportation alternative. This study investigated a unique integration of a dynamic ridesharing system into a local government transportation portfolio to satisfy the mobility needs of older adults by subsidizing the cost of trips on the basis of income level. Over a 9-month period, 40 older adults enrolled in the program. Older women constituted 83% of the people enrolled and were less likely to discontinue use. The majority of trips over the first 6 months were for social purposes, followed by shopping and medical purposes. Fear of driver variability did not seem to affect the level of use. The use of dynamic ridesharing seems to be a feasible low-cost transportation alternative to meet the transportation challenges of older adults and may have policy implications for the future of public transportation systems. Enhanced levels of outreach and training could lead to higher enrollment.

Analysis and Operational Challenges of Dynamic Ride Sharing Demand Responsive Transportation Models

Abstract: There is a wide evidence that sustainable mobility is not only a technological question, automotive technology will be part of the solution as a necessary but not sufficient condition, sufficiency is emerging as a combination of a paradigm shift from car ownership to vehicle usage consequence of socio-economic changes, with the application of Information and Communication Technologies (ICT) that make possible for a user to have access to a mobility service from anywhere to anywhere at any time. Among the many emergent mobility services Multiple Passenger Ridesharing and its variants look the more promising. However, implementations of these systems accounting specifically for time dependencies, and time windows reflecting users' needs raise challenges in terms of real-time fleet dispatching and dynamic route calculation. On the other hand the feasibility and impacts analysis in terms of the many factors influencing the behavior of the system, as for example the service demand, the size of the service fleet, the capacity of the shared vehicles, the time windows requirements, soft or tight. This paper analyzes both aspects. The first is approached in terms of a Decision Support System whose solutions are computed in terms of ad hoc heuristics of variants of Pick Up and Delivery Problems with Time Windows and Feasibility and Profitability criteria rooted on Dynamic Insertion Heuristics. For the evaluation of the applications a Simulation Framework is proposed based on a microscopic simulation model that emulates real-time traffic conditions and a real traffic information system, and interacts with the Decision Support System feeding it with the required data to make the decisions that are implemented in the simulation to emulate the behavior of the shared fleet. The proposed simulation framework has been implemented in a model of Barcelona's Central Business District. The paper is completed with the discussion of the achieved results.

Greenhouse Gas Emission Savings with Dynamic Ride-sharing

Ride-sharing can reduce the greenhouse gas emission in noncommercial passenger highway vehicles by grouping individuals into fewer vehicles and reducing the number of miles that vehicles must travel. In this paper, we first present an integer programming model to maximize the system greenhouse gas emission savings for the dynamic ride-sharing system. Then adopt the existed optimization methods and simulation environment to estimate the potential greenhouse gas emission savings that could result from an increase in ride-sharing. Finally, we firstly calculate the total emission savings for the given simulation environment. From our analysis, we can draw a conclusion that the system greenhouse gas emission savings come from two aspects, firstly, the total vehicle travel miles will be shorten through participating the ride-sharing system, thus the emissions will be largely reduced. Secondly, the different travel speeds result in different emission rates, while the ride-sharing systems can ease the traffic congestion, which is relate to the travel speed of vehicles, there are emission savings with the speed increase.

Public Vehicles for Future Urban Transportation

This paper advocates a new paradigm of transportation systems for future smart cities, namely, public vehicles (PVs), that provides dynamic ridesharing trips at requests. Passengers will enjoy more convenient and flexible transportation services with much less expense. In the PV system, both the number of vehicles and required parking spaces will be significantly reduced. There will be less traffic congestion, less energy consumption, and less pollution. In this paper, the concept, method, and algorithm for the PV system are described. The key issue of effectively implementing the PV system is to design efficient planning and scheduling algorithms. The PV-path problem is formulated, which is NP-complete. Then, a practical approach is proposed, which can serve people anywhere and anytime. The simulation results show that, to achieve the same performance (e.g., total time, waiting time, and travel time), the number of vehicles in the PV system can be reduced by around 90% and 57% compared with the conventional vehicle system and Uber Pool, respectively, and the total traveling distance can be reduced by 34% and 14%.

Dynamic ride-sharing and fleet sizing for a system of shared autonomous vehicles in Austin, Texas

Shared autonomous (fully-automated) vehicles (SAVs) represent an emerging transportation mode for driverless and on-demand transport. Early actors include Google and Europe’s CityMobil2, who seek pilot deployments in low-speed settings. This work investigates SAVs’ potential for U.S. urban areas via multiple applications across the Austin, Texas, network. This work describes advances to existing agent- and network-based SAV simulations by enabling dynamic ride-sharing (DRS, which pools multiple travelers with similar origins, destinations and departure times in the same vehicle), optimizing fleet sizing, and anticipating profitability for operators in settings with no speed limitations on the vehicles and at adoption levels below 10 % of all personal trip-making in the region. Results suggest that DRS reduces average service times (wait times plus in-vehicle travel times) and travel costs for SAV users, even after accounting for extra passenger pick-ups, drop-offs and non-direct routings. While the base-case scenario (serving 56,324 person-trips per day, on average) suggest that a fleet of SAVs allowing for DRS may result in vehicle-miles traveled (VMT) that exceed person-trip miles demanded (due to anticipatory relocations of empty vehicles, between trip calls), it is possible to reduce overall VMT as trip-making intensity (SAV membership) rises and/or DRS users become more flexible in their trip timing and routing. Indeed, DRS appears critical to avoiding new congestion problems, since VMT may increase by over 8 % without any ride-sharing. Finally, these simulation results suggest that a private fleet operator paying $70,000 per new SAV could earn a 19 % annual (long-term) return on investment while offering SAV services at $1.00 per mile for a non-shared trip (which is less than a third of Austin’s average taxi cab fare).

Preferences for shared autonomous vehicles

Shared autonomous vehicles (SAVs) could provide inexpensive mobility on-demand services. In addition, the autonomous vehicle technology could facilitate the implementation of dynamic ride-sharing (DRS). The widespread adoption of SAVs could provide benefits to society, but also entail risks. For the design of effective policies aiming to realize the advantages of SAVs, a better understanding of how SAVs may be adopted is necessary. This article intends to advance future research about the travel behavior impacts of SAVs, by identifying the characteristics of users who are likely to adopt SAV services and by eliciting willingness to pay measures for service attributes. For this purpose, a stated choice survey was conducted and analyzed, using a mixed logit model. The results show that service attributes including travel cost, travel time and waiting time may be critical determinants of the use of SAVs and the acceptance of DRS. Differences in willingness to pay for service attributes indicate that SAVs with DRS and SAVs without DRS are perceived as two distinct mobility options. The results imply that the adoption of SAVs may differ across cohorts, whereby young individuals and individuals with multimodal travel patterns may be more likely to adopt SAVs. The methodological limitations of the study are also acknowledged. Despite a potential hypothetical bias, the results capture the directionality and relative importance of the attributes of interest.

A Discounted Trade Reduction Mechanism for Dynamic Ridesharing Pricing

Dynamic ridesharing arranges shared rides for passengers and drivers in real time. Since, in many dynamic ridesharing services, drivers are crowdsourced, the price mechanism design for such services is a fundamental problem. In this paper, a discounted trade reduction mechanism is designed for dynamic ridesharing pricing. Properties of the proposed mechanism are investigated. We also conduct experiments to evaluate the price mechanism. Experimental results show that our mechanism outperforms three double auction baseline mechanisms and illustrate the effect of adjusting the parameter values of our mechanism.

Dynamic ridesharing

Ridesharing, which offers empty seats in a car to other passengers, is an efficient way of transportation. In this way, the utilization of seats can be improved and the number of cars used can be reduced. Ridesharing has the potential to solve the problems of congestion, pollution, high travel cost, and energy. The development of internet, smart phone, GPS allows dynamic matchings of travel requests with available cars through real-time travel planning systems. However, matching requests and cars under certain constrains in large scale remains challenging. In this paper, we formally address the problem of dynamic ridesharing and introduce the solution framework of filter and refine, under which we summarize existing state-of-the-art works. Finally, we point out possible research directions and problems needed to be solved.

A Multi-modal Routing Approach Combining Dynamic Ride-sharing and Public Transport

Ride-sharing facilitates cheap and eco-friendly mobility. In contrast to classical ride-sharing that basically works like a notice-board, dynamic ride-sharing allows a passenger to get a lift on a section of a driver's route and, if necessary, re-routes the driver. On a dynamic ride-sharing platform, drivers provide their routes and passengers specify queries consisting of departure and arrival location as well as a time for the journey. The platform computes suitable matches of driver routes and passenger queries, and proposes them to both parties.State-of-the-Art platforms for public transport routing and dynamic ride-sharing provide unimodal connections but do not combine both transport modes. The challenge is that the driver routes are not static but could be changed significantly if the driver accepts the detour to pick up the passenger and drop him / her off at his / her destination. Thus, a driver's route may result in a number of dynamic ride-sharing offers, namely all possible connections between pick up and drop off points with an acceptable detour for the driver. In this paper, we present a solution that integrates dynamic ride-sharing into our existing multi-criteria intermodal travel information system. We solve two challenges: First, we allow dynamic ride-sharing between two train rides by connecting public transport stations by dynamic ride-sharing offers of drivers. For this, we integrate driver offers into our graph model, which represents the public transport timetable. Second, we find suitable dynamic ride-sharing offers of drivers who can take the passenger from his / her start location to a public transport station or from a station to the queried destination location. In our computational study, compared to unimodal train connections, we obtain a significant improvement of the results by combining public transport and dynamic ride-sharing.

A Matching Algorithm for Dynamic Ridesharing

Ridesharing is an important component of sustainable urban transportation as it increases vehicle utilization while reducing road utilization. By sharing rides, drivers offer free seats in their vehicles to passengers who want to travel in similar directions. Traditional ridesharing approaches are suitable for long-distance travel, especially inter-city travel, yet they are not flexible enough for short routes within cities. The aim of our research is to develop a service that enables dynamic ridesharing as part of sustainable urban mobility. Dynamic ridesharing refers to a service that automatically matches ride requests and ride offers on short notice without prior agreement between driver and passenger. We present the implementation and evaluation of a dynamic ridesharing service. The implementation part requires an automated matching algorithm that checks whether a driver can take a passenger with him without violating the maximum detour constraint he has set. As this matching algorithm needs to automatically match a relatively large number of ride offers and ride requests in real-time, we focused on building a high-performance algorithm. After implementing the algorithm, we evaluated its performance on a data set with random ride offers in and around the Munich city that is matched with different ride requests. For 10,000 rides in the system, it took less than 0.4seconds on average to identify the best match.

Dynamic Ridesharing: Exploration of Potential for Reduction in Vehicle Miles Traveled

It is widely recognized that new vehicle and fuel technologies are necessary but not sufficient to meet deep greenhouse gas reduction goals in the United States. Demand management strategies, such as land use, transit, and auto pricing policies, are also needed. These measures, however, have historically faced political challenges and have been difficult to implement. Emerging ridesharing systems now suggest the possibility of a new demand management strategy that may be more politically palatable and reduce the number of vehicle miles traveled (VMT). To date, however, little research has evaluated their potential travel effects, especially on a regional scale. This study used the San Francisco, California, Bay Area activity-based travel demand model to simulate business-as-usual, transit-oriented development, and auto pricing scenarios with and without high, medium, and low ridesharing participation levels. The analysis suggests that relatively large VMT reductions are possible from moderate and high participation levels, but at low participation levels, VMT reductions are negligible. Moderate dynamic ridesharing alone compares favorably, with a 9% reduction in VMT, to transit-oriented development and auto pricing scenarios. The analysis also suggests a potentially promising policy combination: a moderately used regional dynamic ridesharing system with a 10- to 30-cent increase in the per mile cost of auto travel, which together may reduce VMT on the order of 11% to 19%.

Car2work: Shared Mobility Concept to Connect Commuters with Workplaces

Over the past decade there has been a surge of shared-use mobility concepts that are redefining how people move in urban areas. In this context, a new shared-use mobility concept, Car2work, that fills the gap between the existing approaches by integration of those approaches with the transit network is proposed. Car2work differs from the traditional dynamic ridesharing approaches in the following ways: ( a) it is designed for recurring trips; ( b) the concept of drivers is dropped; instead, vehicles that carry at least one commuter are used; ( c) commuters announce their trips in advance; and ( d) multiple trips per commuter are allowed during the day. The main goal is to connect commuters with workplaces but to guarantee a trip home and offer some degree of flexibility. The proposed shared mobility system is modeled as a pure binary problem that is solved with an exact solution method. The solution method decomposes the original problem into a master problem and a subproblem, aggregating over the vehicles and reducing the number of decision variables and constraints. A link reduction strategy based on spatiotemporal constraints is also implemented to reduce the number of decision variables. Numerical experiments were performed for two scenarios. The first scenario included 10 commuters with two trips each, three workplaces, two transit stations, and 15 transfer points. The second scenario comprised 25 commuters with two trips each, four workplaces, two transit stations, and 31 transfer points. It is demonstrated that consideration of the transit network increases the matching rate and reduces vehicle costs.

A Partition-Based Match Making Algorithm for Dynamic Ridesharing

Ridesharing offers the opportunity to make more efficient use of vehicles while preserving the benefits of individual mobility. Presenting ridesharing as a viable option for commuters, however, requires minimizing certain inconvenience factors. One of these factors includes detours which result from picking up and dropping off additional passengers. This paper proposes a method which aims to best utilize ridesharing potential while keeping detours below a specific limit. The method specifically targets ridesharing systems on a very large scale and with a high degree of dynamics which are difficult to address using classical approaches known from operations research. For this purpose, the road network is divided into distinct partitions which define the search space for ride matches. The size and shape of the partitions depend on the topology of the road network as well as on two free parameters. This allows optimizing the partitioning with regard to sharing potential utilization and inconvenience minimization. Match making is ultimately performed using an agent-based approach. As a case study, the algorithm is applied to investigate the potential for taxi sharing in Singapore. This is done by considering about 110 000 daily trips and allowing up to two sharing partners. The outcome shows that the number of trips could be reduced by 42% resulting in a daily mileage savings of 230 000 km. It is further shown that the presented approach exceeds the mileage savings achieved by a greedy heuristic by 6% while requiring 30% lower computational efforts.

Agent based model for dynamic ridesharing

Dynamic ridesharing involves a service provider that matches potential drivers and passengers with similar itineraries allowing them to travel together and share the costs. Centralized (binary integer programming) and decentralized (dynamic auction-based multi-agent) optimization algorithms are formulated to match passengers and drivers. Numerical experiments on the decentralized approach provides near optimal solutions for single-driver, single-passenger cases with lower computational burden. The decentralized approach is then extended to accommodate both multi-passenger and multi-driver matches. The results indicate higher user cost savings and vehicle kilometers traveled (VKT) savings when allowing multi-passenger rides. Sensitivity analysis is conducted to test the impact of the service provider commission rate on revenue and system reliability. While short term revenue can be maximized at a commission rate of roughly 50% of each trip’s cost, the resulting drop in system reliability would be expected to reduce patronage and revenues in the longer term.

Taxi and Ride Sharing: A Dynamic Dial-a-Ride Problem with Money as an Incentive

This paper deals with a combinatorial optimization problem that models situations of both dynamic ride-sharing and taxi-sharing. Passengers who want to share a taxi or a ride, use an app to specify their current location, destination and further information such as the earliest departure time, the latest arrival time and the maximum cost they are willing to pay for the ride. Car owners also specify their origin, destination, the leaving time and the maximum accepted delay. Taxi drivers report their location and the time they will start and end the service. All drivers need to define a price per kilometer. The problem is to compute routes, matching requests to vehicles in such a way that ride-sharing is allowed as long as some restrictions are satisfied, such as: the capacity of the vehicle, maximum trip cost of each passenger and maximum delay. The problem is dynamic since new requests arrive on-line and routes can be modified in order to attend them. To solve this dynamic problem, the day is divided in time periods. For each period, an instance of a static problem is created and solved by a greedy randomized adaptive search procedure (GRASP). Experiments with instances based on real data were made to evaluate the heuristics and the proposed method. In our simulations with taxis, passengers paid, on average, almost 30% less than they would pay on private rides.