Dynamic Ridesharing Research Articles

Determinants of consumers’ continuance intention to use dynamic ride-sharing services

Dynamic ride-sharing has been widely regarded as a potential way to alleviate traffic congestion and reduce environmental pollution . Prior studies have focused mainly on motivating consumers to adopt ride-sharing, but few have attempted to investigate continuance intention. This study constructs a comprehensive theoretical framework based on expectation confirmation theory to explore the critical factors influencing consumers’ continuance intention to use dynamic ride-sharing services. The empirical results indicate that the determinants of continuance intention are satisfaction, perceived usefulness, economic benefits, environmental awareness , and platform incentives. Female users’ satisfaction has a more important effect on continuance intention than male users’, whereas perceived usefulness has a relatively small effect. Unexpectedly, the effect of platform incentives on the continuance intention of female users is non-significant. The social experience of users without private cars has a stronger effect on satisfaction than that of users who have private cars.

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.

Impacts of shared automated vehicles on airport access and operations, with opportunities for revenue recovery: Case Study of Austin, Texas

With rising use of ridesourcing apps and, eventually, self-driving vehicles, demands for airport parking spaces, rental cars, and, ultimately, air travel is expected to fall everywhere, relative to background trends. This study uses publicly available ridesourcing demand data for the Austin, Texas area to pursue simulations of a fleet of shared autonomous vehicles or “SAVs”, and quantify airport revenue and operations impacts. Results suggest that dynamic ride-sharing (DRS) of centrally-dispatched vehicles across strangers (with current travel patterns) may reduce airport-related ground travel by up to 30% while reducing airport-access revenues by 46%, assuming ridesourcing permits continue to be charged $2 per trip. A time-varying zone-based toll around the airport can help offset lost parking and car-rental (but not seat-mile) revenues and potentially double present-day airport-access revenues. Such policies can come at the cost of adding non-revenue ridesourcing and SAV miles to the rest of the network, when incentivizing SAVs to leave the airport zone after a dropoff, in order to avoid curbside congestion. A combination of DRS and use of access fees on all commercial vehicles dropping off or picking up travelers can achieve a useful middle ground.

A system of shared autonomous vehicles for Chicago: Understanding the effects of geofencing the service

With autonomous vehicles (AVs) still in the testing phase, researchers and planners must resort to simulation techniques to explore possible futures regarding shared and automated mobility. An agent-based discrete-event transport simulator, POLARIS, is used in this study to simulate travel in the 20-county Chicago region with a shared AV (SAV) mobility option. Using this framework, the effect of an SAV fleet on system performance when constrained to serve within geofences is studied under four distinct scenarios: service restricted to the city, to the city plus suburban core, to the core plus exurban areas, and to the entire region — along with the choice of dynamic ridesharing (DRS) versus solo travel in an SAV. Results indicate that service areas need a balanced mix of trip generators and attractors, and an SAV fleet’s empty VMT (eVMT) can be noticeably reduced through suitable geofencing and DRS. Geofences can also help lower response times, reduce systemwide VMT across all modes, and ensure uniform access to SAVs. DRS is most useful in lowering VMT and %eVMT that arises from sprawled land development, but with insufficient demand to share rides, savings from the use of geofences is higher. Geofences targeting neighborhoods with high trip density bring about low response times and %eVMT, but fleet sizes in these regions need to be designed for uniformly low response times throughout a large region, as opposed to maximizing vehicle use in a 24-hour day.

Shared mobility: Characteristics, impacts, and improvements

Dynamic ride-sharing has been widely regarded as a potential way to alleviate traffic congestion and reduce environmental pollution. Prior studies have focused mainly on motivating consumers to adopt ride-sharing, but few have attempted to investigate continuance intention. This study constructs a comprehensive theoretical framework based on expectation confirmation theory to explore the critical factors influencing consumers’ continuance intention to use dynamic ride-sharing services. The empirical results indicate that the determinants of continuance intention are satisfaction, perceived usefulness, economic benefits, environmental awareness, and platform incentives. Female users’ satisfaction has a more important effect on continuance intention than male users’, whereas perceived usefulness has a relatively small effect. Unexpectedly, the effect of platform incentives on the continuance intention of female users is non-significant. The social experience of users without private cars has a stronger effect on satisfaction than that of users who have private cars.

America’s fleet evolution in an automated future

Cost reductions and technological advancements are priming autonomous, electric, and shared vehicles for rapid growth, which may improve safety and mobility, but may also increase vehicle-miles traveled (VMT). This study seeks to improve upon existing fleet evolution work, by simulating the adoption of autonomous, electric, and shared vehicles in a single fleet evolution simulation, calibrated with results of a recent survey of Americans. Statistical models are used in the household-level microsimulation to model decisions regarding vehicle transactions, travel behavior, and land use decisions.The simulation predicts a shift in the powertrain makeup of the United States fleet, with hybrid-electric and plug-in hybrid vehicles each comprising over 40% of the private fleet by 2050. The rate of decline of the price premium for self-driving vehicle technology has a significant effect on autonomous vehicle (AV) adoption rates. Allowing the ability to retain a human-driving option in self-driving vehicles increases AV adoption rates by roughly 10% by 2050. Shared autonomous vehicle (SAV) use rises to roughly 35% of overall VMT by 2050, about one third of which is via dynamic ride-sharing (DRS). VMT among private vehicles roughly follows adoption rates, though technologies with greater adoption generally experience disproportionately higher VMT.

An Incentive Based Dynamic Ride-Sharing System for Smart Cities

Connected and automated vehicle (CAV) technology, along with advanced traffic control systems, cannot ensure congestion-free traffic when the number of vehicles exceeds the road capacity. To address this problem, in this paper, we propose a dynamic ride-sharing system based on incentives (for both passengers and drivers) that incorporates travelers of similar routes and time schedules on short notice. The objective is to reduce the number of private vehicles on urban roads by utilizing the available seats properly. We develop a mobile-cloud architecture-based system that enables real-time ride-sharing. The effectiveness of the proposed system is evaluated through microscopic traffic simulation using Simulation of Urban Mobility (SUMO) considering the traffic flow behavior of a real smart city. Moreover, we develop a lab-scale experimental prototype in the form of Internet of Things (IoT) network. The simulation results show that the proposed system reduces fuel consumption, CO2 and CO emissions, and average waiting time of vehicles significantly, while increasing the vehicle’s average speed. Remarkably, it is found that only 2–10% ride-sharing can improve the overall traffic performance.

Can dynamic ride-sharing reduce traffic congestion?

Can dynamic ride-sharing reduce traffic congestion? In this paper we show that the answer is yes if the trip density is high, which is usually the case in large-scale networks but not in medium-scale networks where opportunities for sharing in time and space become rather limited. When the demand density is high, the dynamic ride-sharing system can significantly improve traffic conditions, especially during peak hours. Sharing can compensate extra travel distances related to operating a mobility service. The situation is entirely different in small and medium-scale cities when trip shareability is small, even if the ride-sharing system is fully optimized based on the perfect demand prediction in the near future. The reason is simple, mobility services significantly increase the total travel distance, and sharing is simply a means of combating this trend without eliminating it when the trip density is not high enough. This paper proposes a complete framework to represent the functioning of the ride-sharing system and multiple steps to tackle the curse of dimensionality when solving the problem. We address the problem for two city scales in order to compare different trip densities. A city scale of 25 km2 with a total market of 11,235 shareable trips for the medium-scale network and a city scale of 80 km2 with 205,308 demand for service vehicles for the large-scale network over a 4-hour period with a rolling horizon of 20 minutes. The solutions are assessed using a dynamic trip-based macroscopic simulation to account for the congestion effect and dynamic travel times that may influence the optimal solution obtained with predicted travel times. This outperforms most previous studies on optimal fleet management that usually consider constant and fully deterministic travel time functions.

Optimal fast charging station locations for electric ridesharing with vehicle-charging station assignment

Electrified shared mobility services need to handle charging infrastructure planning and manage their daily charging operations to minimize total charging operation time and cost. However, existing studies tend to address these problems separately. A new online vehicle-charging assignment model is proposed and integrated into the fast charging location problem for dynamic ridesharing services using electric vehicles. The latter is formulated as a bi-level optimization problem to minimize the fleet’s daily charging operation time. A surrogate-assisted optimization approach is proposed to solve the combinatorial optimization problem efficiently. The proposed model is tested on a realistic flexible bus service in Luxembourg. The results show that the proposed online charging policy can effectively reduce the charging delays of the fleet compared to the state-of-the-art methods. With 10 additional DC fast chargers installed, charging operation time can be reduced up to 27.8% when applying the online charging policy under the test scenarios.

Integrating probabilistic tensor factorization with Bayesian supervised learning for dynamic ridesharing pattern analysis

In the era of transportation big data, the analysis of mobility patterns generally involves large quantities of datasets with high-dimensional variables recording individual travelers’ activities and socio-economic attributes, bringing new challenges to researchers. Conventional regression-based models commonly require complex structures in depicting random or fixed effects with a considerable number of parameters to estimate, and state-of-the-art machine learning models are regarded as black-boxes that are not clear in interpreting the mechanism in human mobility. To overcome the challenges of capturing complex high-order relationships among variables of interest, this paper proposes a Bayesian supervised learning tensor factorization (BSTF) model for the classification of travel choices in the mobility pattern analysis. The BSTF model induces a hierarchical probabilistic structure between predictor variables and the dependent variable, which offers a nature supervised learning foundation via Bayesian inference. Latent class (LC) variables are considered in the BSTF model to discover hidden preferences/states among travelers associated with their mobility patterns. We apply the BSTF model to analyze passenger-side choice patterns between diverse service options on a ride-sourcing platform, drawing increasing attention during recent years. A case study with a real-world dynamic ridesharing dataset in Hangzhou, China, is conducted. Different cases of training sizes are utilized to fit the proposed BSTF model as well as some other state-of-the-art machine learning models. By identifying significant variables and derive their probabilistic relationship between service types (i.e., ridesharing, non-sharing, and taxi), the proposed BSTF model offers good performance in both classification accuracy and the interpretability of shared mobility.

Shared autonomous vehicle fleet performance: Impacts of trip densities and parking limitations

This study micro-simulates 2% and 5% of the region’s 9.5 million daily person-trips and 20% of trips in the central Twin Cities with shared autonomous vehicles (SAVs) in the 7-county Minneapolis–Saint Paul region using MATSim to appreciate the effects of different trip-making densities and curb-use restrictions. Results suggest the average SAV in this region can serve at most 30 person-trips per day with less than 5 min average wait time, but generates 13% more vehicle-miles traveled (VMT). With dynamic ride-sharing (DRS), SAV VMT fell, on average, by 17% and empty VMT (eVMT) fell by 26%. Compared to idling-at-curb scenarios, parking-restricted scenarios generated 8% more VMT. Relying on 52 mi/gallon hybrid electric SAVs, as opposed to a 31 mi/gallon conventional drivetrain SAV, is estimated to lower travelers’ energy use by 21% and reduce tailpipe emissions by 30%, assuming no new or longer trips. Similarly, a 106 mi/gallon equivalent battery-electric fleet does much better by lowering energy use by 64%.

An Efficient Insertion Operator in Dynamic Ridesharing Services

Dynamic ridesharing refers to services that arrange one-time shared rides on short notice. It underpins various real-world intelligent transportation applications such as car-pooling, food delivery and last-mile logistics. A core operation in dynamic ridesharing is the “ <i>insertion operator</i> ”. Given a worker and a feasible route which contains a sequence of origin-destination pairs from previous requests, the insertion operator inserts a new origin-destination pair from a newly arrived request into the current route such that certain objective is optimized. Common optimization objectives include minimizing the maximum/sum flow time of all requests and minimizing the total travel time of the worker. Despite its frequent usage, the insertion operator has a time complexity of <inline-formula><tex-math notation="LaTeX">$O(n^3)$</tex-math></inline-formula> , where <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> is the number of all requests assigned to the worker. The cubic running time of insertion fundamentally limits the efficiency of urban-scale dynamic ridesharing based applications. In this paper, we propose a novel partition framework and a dynamic programming based insertion with a time complexity of <inline-formula><tex-math notation="LaTeX">$O(n^2)$</tex-math></inline-formula> . We further improve the time efficiency of the insertion operator to <inline-formula><tex-math notation="LaTeX">$O(n)$</tex-math></inline-formula> harnessing efficient index structures, such as fenwick tree. Evaluations on two real-world large-scale datasets show that our methods can accelerate insertion by 1.5 to 998.1 times.

Anticipating land-use impacts of self-driving vehicles in the Austin, Texas, region

This paper used an implementation of the land-use model SILO in Austin, Texas, over a 27-year period with an aim to understand the impacts of the full adoption of self-driving vehicles on the region’s residential land use. SILO was integrated with MATSim for the Austin region. Land-use and travel results were generated for a business-as-usual case (BAU) of 0% self-driving or “autonomous” vehicles (AVs) over the model timeframe versus a scenario in which households’ value of travel time savings (VTTS) was reduced by 50% to reflect the travel-burden reductions of no longer having to drive. A third scenario was also compared and examined against BAU to understand the impacts of rising vehicle occupancy (VO) and/or higher roadway capacities due to dynamic ride-sharing (DRS) options in shared AV (SAV) fleets. Results suggested an 8.1% increase in average work-trip times when VTTS fell by 50% and VO remained unaffected (the 100% AV scenario) and a 33.3% increase in the number of households with “extreme work-trips” (over 1 hour, each way) in the final model year (versus BAU of 0% AVs). When VO was raised to 2.0 and VTTS fell instead by 25% (the “Hi-DRS” SAV scenario), average work-trip times increased by 3.5% and the number of households with “extreme work-trips” increased by 16.4% in the final model year (versus BAU of 0% AVs). The model also predicted 5.3% fewer households and 19.1% more available, developable land in the city of Austin in the 100% AV scenario in the final model year relative to the BAU scenario’s final year, with 5.6% more households and 10.2% less developable land outside the city. In addition, the model results predicted 5.6% fewer households and 62.9% more available developable land in the city of Austin in the Hi-DRS SAV scenario in the final model year relative to the BAU scenario’s final year, with 6.2% more households and 9.9% less developable land outside the city.

Dynamic Ridesharing in Peak Travel Periods

In this paper, we study a variant of the dynamic ridesharing problem with a specific focus on peak hours: Given a set of drivers and rider requests, we aim to match drivers to each rider request by achieving two objectives: maximizing the served rate and minimizing the total additional distance, subject to a series of spatio-temporal constraints. Our problem can be distinguished from existing work in three aspects: (1) Previous work did not fully explore the impact of peak travel periods where the number of rider requests is much greater than the number of available drivers. (2) Existing solutions usually rely on single objective optimization techniques, such as minimizing the total travel cost. (3) When evaluating the overall system performance, the runtime spent on updating drivers' trip schedules as per incoming rider requests should be incorporated, while it is excluded by most existing solutions. We propose an index structure together with a set of pruning rules and an efficient algorithm to include new riders into drivers' existing trip schedule. To answer new rider requests effectively, we propose two algorithms that match drivers with rider requests. Finally, we perform extensive experiments on a large-scale test collection to validate the proposed methods.

Matching algorithm for improving ride-sharing by incorporating route splits and social factors.

Increasing traffic congestion and the advancements in technology have fostered the growth of alternative transportation modes such as dynamic ride-sharing. Smartphone technologies have enabled dynamic ride-sharing to thrive, as this type of transportation aims to establish ride matches between people with similar routes and schedules on short notice. Many automated matching methods are designed to improve system performance; such methods include minimizing process time, minimizing total system cost or maximizing total distance savings. However, the results may not provide the maximum benefits for the participants. This paper intends to develop an algorithm for optimizing matches when considering participants’ gender, age, employment status and social tendencies. The proposed matching algorithm also splits unmatched parts of drivers’ routes and creates new travel requests to find additional matches using these unmatched parts. Accordingly, this paper performs an extensive simulation study to assess the performance of the proposed algorithm. The simulation results indicate that route splits may increase the number of matches significantly when there is a shortage of drivers. Furthermore, the paper demonstrates the effects and potential benefits of utilizing a social compatibility score in the objective function.

Trip-based graph partitioning in dynamic ridesharing

A dynamic ridesharing system is a platform that connects drivers who use their personal vehicles to travel with riders who are in need of transportation, on a short notice. Since each driver/rider may have several potential matches, to achieve a high performance level, the ridesharing operator needs to make the matching decisions based on a global view of the system that includes all active riders and drivers. Consequently, the ride-matching problem that needs to be solved can become computationally expensive, especially when the system is operating over a large region, or when it faces high demand levels during certain hours of the day. This paper develops a graph partitioning methodology based on the bipartite graph that arises in the one-to-one ride-matching problem. The proposed method decomposes the original graph into multiple sub-graphs with the goal of reducing the overall computational complexity of the problem as well as providing high quality solutions. We further show that this methodology can be extended to more complex ride-matching problems in a dynamic ride-sharing system. Using numerical experiments, we showcase the advantages of the new partitioning method for different forms of ride-matching problems. Moreover, a sensitivity analysis is conducted to show the impact of different parameters on the quality of our solution.

A decomposition algorithm to solve dynamic city-scale ridesharing problem

This paper proposes a computational efficient, rapid, dynamic ridesharing algorithm. Firstly, a designed pre-processing procedure reduces size of the input date of potential nodes for vehicle to pass through. The proposed decomposition algorithm decomposes the large-scale problem with pre-processing date into independent sub-problems, and the method of parallel computing is adopted to solve the sub-problems. We call the dynamic programming algorithm to deal with the vehicle routing problem involved in sub-problems. Greedy random adaptive search algorithm is compared with the decomposition algorithm to verify the algorithm. Finally, a mathematical example shows that the algorithm can effectively solve large-scale dynamic ridesharing problems.

Exploring the Ridesharing Efficiency of Taxi Services

The application of ridesharing strategy to autonomous taxi system holds great promise for improving the efficiency in the future on-demand ride-hailing services. Prior to the implementation of dispatching strategies to the autonomous taxi system, it is necessary to gain insight into the performance of the dispatching strategies. This study aims to solve the dynamic ridesharing problem and conduct a comprehensive quantity analysis of the ridesharing efficiency for various demand levels and carrying capacities in a metropolitan area. We quantify the success rate of serviced requests, the trip travel time, the discount rate of taxi fare, and total energy consumption for different carrying capacities and demand levels in the road network of Shenzhen city. The simulation results show that the ride-matching success rate within 3 minutes enables to increase by more than 13% in the ridesharing mode, and over 80% of the passengers can be served within 6 minutes if the carrying capacity is set to four. The trip travel time and energy consumption also show a significant downward trend as the capacity of the taxi increases in the ridesharing mode.

Exploring the Performance of Different On-Demand Transit Services Provided by a Fleet of Shared Automated Vehicles: An Agent-Based Model

Automated vehicles used as public transport show a great promise of revolutionizing current transportation systems. Still, there are many questions as to how these systems should be organized and operated in cities to bring the best out of future services. In this study, an agent-based model (ABM) is developed to simulate the on-demand operations of shared automated vehicles (SAVs) in a parallel transit service (PTS) and a tailored time-varying transit service (TVTS). The proposed TVTS system can switch service schemes between a door-to-door service (DDS) and a station-to-station service (SSS) according to what is best for the service providers and the travelers. In addition, the proposed PTS system that allows DDS and SSS to operate simultaneously is simulated. To test the conceptual design of the proposed SAV system, simulation experiments are performed in a hypothetical urban area to show the potential of different SAV schemes. Simulation results suggest that SAV systems together with dynamic ridesharing can significantly reduce average waiting time, the vehicle kilometres travelled and empty SAV trips. Moreover, the proposed optimal vehicle assignment algorithm can significantly reduce the empty vehicle kilometres travelled (VKT) for the pickups for all tested SAV systems up to about 40% and improve the system capacity for transporting the passengers. Comparing the TVTS system, which has inconvenient access in peak hours, with the PTS systems, which always makes available door-to-door transport, we conclude that the latter could achieve a similar system performance as the former in terms of average waiting time, service time and system capacity.