Ridesharing System Research Articles

Improving Acceptability of Cost Savings Allocation in Ridesharing Systems Based on Analysis of Proportional Methods

Due to its potential for cutting down energy consumption, sharing transport costs, and reducing negative impacts on the environment, ridesharing has been adopted as a viable model by many cities all over the world to achieve sustainability goals. Although there have been many studies on ridesharing systems, most of these studies have focused on the optimization of performance directly linked to saving energy consumption costs or travel distance. The methods used to divide the cost savings among the ridesharing participants have been less explored. In practice, proportional methods are commonly used to allocate cost savings in ridesharing systems. However, the effectiveness of proportional methods has not been well studied. The goal of this paper is to study and compare three proportional cost savings allocation methods to provide a guideline for choosing an effective method based on an analysis of the properties and performance in terms of the number of acceptable rides and the number of ridesharing participants. The properties were verified by experimental results. This study showed that how cost savings are divided among ridesharing participants has a significant influence on participants’ willingness to adopt the ridesharing transport mode. The properties and experimental results provide a rule and principle that can be used by policy makers and service providers to move towards their sustainability goals by promoting ridesharing through adopting the right proportional cost savings allocation method in ridesharing systems.

Heatmap-Based Decision Support for Repositioning in Ride-Sharing Systems

In ride-sharing systems, platform providers aim to distribute the drivers in the city to meet current and potential future demand and to avoid service cancellations. Ensuring such distribution is particularly challenging in the case of a crowdsourced fleet, as drivers are not centrally controlled but are free to decide where to reposition when idle. Thus, providers look for alternative ways to ensure a vehicle distribution that benefits users, drivers, and the provider. We propose an intuitive mean to improve idle ride-sharing vehicles’ repositioning: repositioning heatmaps. These heatmaps highlight driver-specific earning opportunities approximated based on the expected future demand, current and expected future fleet distribution, and the location of the specific driver. Based on the heatmaps, drivers make decentralized yet better-informed repositioning decisions. As our heatmap policy changes the driver distribution in the future, we propose an adaptive learning algorithm for designing our heatmaps in large-scale ride-sharing systems. We simulate the system and generate heatmaps based on the previously learned policy in every iteration. We then update the policy based on the simulation’s outcome and use it in the next iteration. We test our heatmap design in a comprehensive case study on New York ride-sharing data. We show that carefully designed heatmaps reduce service cancellations and therefore, revenue loss for the platform and drivers significantly while leading to a better service level for the users and to a fairer treatment of drivers. History: This paper has been accepted for the Transportation Science Special Issue on Machine Learning Methods and Applications in Large-Scale Route Planning Problems. Funding: This research is funded by the German Research Foundation (Deutsche Forschungsgemeinschaft) [Grant 494812908]. M. W. Ulmer’s work is funded by the German Research Foundation Emmy Noether Programme [Grant 444657906]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.1202 .

A systematic review and meta-synthesis of the complex and interconnected factors that influence planning for driving retirement

Introduction: With an aging population there are more older drivers than ever before. To reduce unnecessary road accidents and assist older drivers to successfully transition to non-driving, a better understanding of the factors that influence planning for driving retirement is required. This review explores documented factors that may influence older adults in planning for driving retirement, thus providing new understandings that can inform future preventative road safety measures, interventions, and policies. Method: A systematic search was performed using four databases to locate qualitative studies on the factors that influenced older drivers to plan for driving retirement. To identify factors influencing planning for driving retirement, a thematic synthesis approach was utilized. Identified themes were categorized in relation to elements of the Social Ecological Model theoretical framework. Results: The systematic search resulted in 12 included studies from 4 countries. Four major themes and 11 subthemes were identified regarding planning driver retirement. Each subtheme denotes a factor that may facilitate and/or impede older drivers planning for driving retirement. Conclusion: These results indicate that it is vitally important to encourage older drivers to plan for driving retirement as early as possible. Stakeholders involved in the safety of older drivers (including family, clinicians, road authorities, and policy makers) should work together on interventions and policies that empower older drivers to successfully plan for driving retirement in order to improve road safety and quality of life. Practical Applications: Introducing conversations about driving retirement via medical appointments, family, media, and peer-support groups could facilitate planning for driving retirement. Community-based ride-sharing systems and subsidized private transport options are needed to ensure continued mobility of older adults, especially in rural and regional areas that lack alternate transport services. When devising urban and rural planning, transport, license renewal, and medical testing rules, policy makers should consider older drivers’ safety, mobility, and quality of life after driving retirement.

Enhancing Blockchain-based Ride-Sharing Services using IPFS

• A decentralized ride-sharing system is proposed and a prototype of it is implemented as a decentralized application (DApp) on the top of Ethereum, a real-world public blockchain platform, to allow users to access the system in a decentralized manner. • Following that, this paper proposed the integration of IPFS with the blockchain in the proposed system. For that, a proposed methodology is presented which provide a complete scenario of how to integrate IPFS and blockchain in RSS. • In the proposed system, we build and deploy a smart contract, and we investigate the expenses incurred by employing such design for both drivers and riders. • This smart contract is evaluated by deploying it to a local network Ganache. • Lastly, extensive evaluation of the proposed system has been performed. Our experimental results demonstrate the scalability, verifiability, and superior performance of the proposed ride-sharing system. Ride-sharing services (RSSs) using centralization methods experience various challenges like single point-of-failure, privacy violation, lack of security, and distributed denial of services (DDoS) attack, etc. So, blockchain-based RSSs mitigate such problems through decentralization. Relying on the blockchain only leads to problems such as increase in application response time, chain size, and a high computational cost due to the increase in data storage in blockchain and thus increase the service costs to end users. Additionally, the blockchain lacks to scalability of data because of the inability to store large-sized data and accommodate the grows of ride-sharing data. To overcome these problems, a novel decentralized ride-sharing system that exploits blockchain and Interplanetary File System (IPFS) is proposed. The goal of the proposed system is to move all ride-sharing data outside the blockchain and replacing it with a small hash. The blockchain manages the application state and users. In addition, it automates processes through smart contracts. While the IPFS stores data for blockchain in immutable and integral way. Wherefore, the proposed ride-sharing system integrates IPFS with blockchain for RSSs to retain the provided assurance by the blockchain and provide efficient service to end users. Experimental results proved the applicability and efficiency of RSS based on blockchain and IPFS which provides efficient storage of ride-sharing data, immutable history, and generally better efficiency in a decentralized manner.

Blockchain Meets Sharing Economy: A Case of Smart Contract Enabled Ridesharing Service

The ideas of the sharing economy have facilitated innovative business applications, such as Uber and Airbnb. As an example of a sharing economy application, ridesharing services take advantage of underutilized resources to create economic value. However, the unruly design of ridesharing systems may make urban traffic more congested and cause other technology-organization-environment issues. This study explores the application of blockchain and smart contract technologies to enhance ridesharing services by harvesting the blockchain benefits of transaction traceability, process transparency, system automation and disintermediation. After presenting system design and implementation details for building and deploying a blockchain-based system to support the reengineered ridesharing service with required business functions, we conduct functionality/performance tests and theory-based comparative analysis to confirm its feasibility and applicability. The results reveal that our system with blockchain-enabled benefits is superior to incumbent ridesharing systems. Moreover, while prior research rarely reports the design and implementation details of blockchain-based systems to support sharing economy services, this paper primarily contributes to extant literature by not only proposing a layered system architecture adapting blockchain and smart contracts into the desired ridesharing service but also demonstrating the design and implementation details, covering the development tools, the deployment environment and the deployed smart contracts.

Development and Comparison of Ten Differential-Evolution and Particle Swarm-Optimization Based Algorithms for Discount-Guaranteed Ridesharing Systems

Savings on transportation costs provide an important incentive for shared mobility models in smart cities. Therefore, the problem of maximizing cost savings has been extensively studied in the ridesharing literature. Most studies on ridesharing focus on the maximization of the overall savings on transportation costs. However, the maximization of the overall savings on transportation costs may satisfy users’ expectations for cost savings. For people to adopt ridesharing as a means to reduce costs, a minimal expected cost savings discount must be offered. There is obviously a gap between the existing studies and the real problems faced by service providers. This calls for the development of a study to formulate a ridesharing model that guarantees the satisfaction of a minimal expected cost savings discount. In this paper, we considered a discount-guaranteed ridesharing model that ensures the provision of a minimal expected cost savings discount to ridesharing participants to improve users’ satisfaction with the ridesharing service in terms of cost savings. The goal was to maximize the overall cost savings under certain capacity, spatial, and time constraints and the constraint that the discount offered to ridesharing participants could be no lower than the minimal expected cost savings discount. Due to the complexity of the optimization problem, we adopted two evolutionary computation approaches, differential evolution and particle swarm optimization, to develop ten algorithms for solving the problem. We illustrated the proposed method by an example. The results indicated that the proposed method could guarantee that the discount offered to ridesharing participants was greater than or equal to the minimal expected cost savings discount. We also conducted two series of experiments to assess the performance and efficiency of the different solution algorithms. We analyzed the results to provide suggestions for selecting the appropriate solution algorithm based on its performance and efficiency.

Stable Matching of Users in a Ridesharing Model

A ridesharing system is a transport mode where two or more users share the same vehicle and divide the trip’s expenses based on similar routes and itineraries. Popular ridesharing systems, such as Uber, Flinc, and Lyft, define a matching among users based only on the coincidence of routes. However, these systems do not guarantee a stable matching (i.e., a matching in which no user prefers another different from the assigned one). In this work, a new ridesharing system model is proposed, including three types of trips: identical, inclusive, and partial. This model is used to introduce a new algorithm to address the stable matching problem for ridesharing systems. Finally, a set of experimental simulations of the proposed algorithm is conducted. Experimental results show that the proposed algorithm always produces a stable matching.

Alternative Pickup Locations in Taxi-Sharing: A Feasibility Study

Taxi-sharing is a known solution for reducing congestion and is more beneficial when the traveled miles, imposed by detours while serving additional passengers, are minimized. This study proposes incorporation of alternative meeting points in taxi-sharing routes to boost the efficiency of the system by eliminating unnecessary detours and improving the chances of passengers being matched. Unlike most ridesharing systems, in the proposed approach, passengers are not necessarily picked up or dropped off at their original location, but at a walkable distance from their origin. The proposed framework deals with practical challenges of including meeting points in a real-world high-demand taxi-sharing system with hundreds of requests per minute. This includes efficient selection of alternative pick up locations and incorporation of these alternatives into a novel mixed integer linear programming (MILP) formulation to find the optimal schedule. Using the 2015 New York City (NYC) yellow cab dataset, first, the potential benefits of introducing meeting points in Manhattan road network are demonstrated. Given the Nondeterministic Polynomial-time Hard (NP-hard) nature of the associated optimization problem, the problem is then broken down into smaller-sized problems forming clusters of passengers with high potential of sharing a ride. Then, the proposed MILP model is used to find the optimal route for each cluster, while selecting the best pickup point for each passenger. Testing on a sample of the NYC dataset, it is shown that the proposed methodology improves the efficiency of the taxi-sharing system by reducing the wait times by about 50% while considerably reducing total travel times and the number of vehicles used.

The real-time on-demand bus routing problem: The cost of dynamic requests

The real-time on-demand bus routing problem (ODBRP) supports the online routing of buses in a large-scale ride-sharing system. Given are a set of buses with fixed capacity, a set of bus stations and a set of transportation requests, only part of which are known before the planning horizon. A request consists of a set of possible departure and arrival stations, as well as an earliest departure and latest arrival time. The aim is to (1) assign each passenger to a departure and arrival bus station and (2) develop a set of bus routes to fulfill each request within its time window while minimizing the sum of the total waiting time and the total user ride time.Including the possibility for requests to be issued after the start of the planning horizon, i.e., when buses have already started servicing other requests, requires a dynamic re-optimization of a partially executed solution. Compared to the case in which all requests are known beforehand, the solution quality, expressed as the total waiting and user ride time, is expected to decline. This decline in objective function value can be seen as the “cost” of the dynamic requests. In this paper, we introduce the real-time ODBRP as an extension of its static problem variant and present a heuristic to deal with dynamic requests. In addition, an extensive set of experiments allows us to conclude that dynamic requests indeed lead to higher waiting and user ride times, especially for passengers who submit their request at the last minute. Passengers are therefore encouraged, if possible, to send their request well in advance, as this results in lower and more stable service promises, higher customer satisfaction, and higher revenues for the operating on-demand bus company.

General stochastic ridesharing user equilibrium problem with elastic demand

In this study, we propose a general stochastic ridesharing user equilibrium (SRUE) problem with elastic demand (ED) for urban transportation network analysis with ridesharing activities by extending the deterministic ridesharing user equilibrium (DRUE) problem, which is referred to as the general SRUE-ED problem. We develop a novel variational inequality (VI) model in terms of the Lagrange multipliers corresponding to the ride-matching constraints for the general SRUE-ED problem. The existence and uniqueness of the SRUE-ED solution are demonstrated under mild conditions. We proceed to put up a global convergent method to solve the developed VI model by incorporating the weighted cost averaging (WCA) method. The purpose of WCA method is to solve a parametric fixed-point model formulated for the general SRUE-ED problem without the ride-matching constraints. Finally, we use three numerical examples to provide practical insights into the operation and management of the ridesharing systems and to examine the effectiveness of the proposed model and solution method.

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.

Benefits and costs of shared, modular automated vehicles for freight and passenger transport: the case of U-Shift

ABSTRACT This study analyses the costs and benefits of fully automated vehicles, operated as part of a ride-sharing system and it compares two alternative technological solutions. For the first solution, automated driving is enabled by hardware and software fully incorporated in the vehicle. For the second solution, automated driving of vehicles is supported by ‘smart roads’ and vehicle movements are coordinated by a central traffic management centre. The study conducts a cost–benefit analysis of these options and a Base Case. The results demonstrate the economic viability of both technological alternatives and show that benefits from improvements in road safety, air pollution and CO2 emissions outweigh costs. The results further demonstrate that the infrastructure-based automation approach is a more cost-efficient way to enable full automation of driving, compared to the current industry-driven approach which is based on vehicles where automated driving tasks are not supported by the road infrastructure.

Trust-Based Recommendation for Shared Mobility Systems Based on a Discrete Self-Adaptive Neighborhood Search Differential Evolution Algorithm

Safety is one concern that hinders the acceptance of ridesharing in the general public. Several studies have been conducted on the trust issue in recent years to relieve this concern. The introduction of trust in ridesharing systems provides a pragmatic approach to solving this problem. In this study, we will develop a trust-aware ridesharing recommender system decision model to generate recommendations for drivers and passengers. The requirements of trust for both sides, drivers and passengers, are taken into consideration in the decision model proposed in this paper. The decision model considers the factors in typical ridesharing systems, including vehicle capacities, timing, location and trust requirements, etc. The decision model aims to determine the shared rides that minimize cost while respecting the trust and relevant constraints. As the decision problem is a nonlinear integer programming problem, we combine a self-adaptive neighborhood search with Differential Evolution to develop an algorithm to solve it. To assess the effectiveness of the proposed algorithm, several other evolutionary computation approaches are also applied to solve the same problem. The effectiveness assessment is done based on the performance of applying different algorithms to find solutions for test cases, to provide a guideline for selecting a proper solution approach.

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.

Putting ridesharing to the test: efficient and scalable solutions and the power of dynamic vehicle relocation

We study the optimization of large-scale, real-time ridesharing systems and propose a modular design methodology, Component Algorithms for Ridesharing (CAR). We evaluate a diverse set of CARs (14 in total), focusing on the key algorithmic components of ridesharing. We take a multi-objective approach, evaluating 10 metrics related to global efficiency, complexity, passenger, and platform incentives, in settings designed to closely resemble reality in every aspect, focusing on vehicles of capacity two. To the best of our knowledge, this is the largest and most comprehensive evaluation to date. We (i) identify CARs that perform well on global, passenger, or platform metrics, (ii) demonstrate that lightweight relocation schemes can significantly improve the Quality of Service by up to 50%, and (iii) highlight a practical, scalable, on-device CAR that works well across all metrics.

Environmental and social implications of incorporating carpooling service on a customized bus system

This study addresses one of the most challenging issues in designing a sustainable and efficient ride-sharing service. This paper uses an extensive computational study to quantify the behavior of carpooling in customized bus routing problems. This mechanism allows organizations to draw on the potential of their employees’ private cars to provide convenient alternative rides for other employees, thereby reducing air pollution and greenhouse gas emissions as well as increasing overall satisfaction with the transportation system offered. The objective functions minimize: (i) total transportation costs and incentives paid to drivers of private cars, (ii) dissatisfaction as determined by staff walking distance, travel time, and delays in arriving at work, and (iii) total carbon emissions generated by commuting. We propose a resolution algorithm based on Pareto Strength Ant Colony Optimization (PSACO) as an effective meta-heuristic method for solving the multi-objective mathematical model and compare it with the results obtained by an exact method. The effectiveness and applicability of the proposed problem have been evaluated by performing computational experiments on a real case study in Paris using a number of comparative metrics with appropriate assumptions. Different parameters affecting the performance of the algorithm are also investigated. The concluding section presents a comparison of the results achieved. The test outcomes confirm that the formulation and the solution methods can be useful references for practice. The insights obtained from the research could provide the basis for designing incentive schemes and information campaigns aimed at making ride-sharing systems more successful and improving their performance.

A Traveler Incentive Program for Promoting Community-Based Ridesharing

Traffic congestion has become a serious issue around the globe, partly owing to single-occupancy commuter trips. Ridesharing can present a suitable alternative for serving commuter trips. However, there are several important obstacles that impede ridesharing systems from becoming a viable mode of transportation, including the lack of a guarantee for a ride back home as well as the difficulty of obtaining a critical mass of participants. This paper addresses these obstacles by introducing a traveler incentive program (TIP) to promote community-based ridesharing with a ride back home guarantee among commuters. The TIP program allocates incentives to (1) directly subsidize a select set of ridesharing rides and (2) encourage a small, carefully selected set of travelers to change their travel behavior (i.e., departure or arrival times). We formulate the underlying ride-matching problem as a budget-constrained min-cost flow problem and present a Lagrangian relaxation-based algorithm with a worst-case optimality bound to solve large-scale instances of this problem in polynomial time. We further propose a polynomial-time, budget-balanced version of the problem. Numerical experiments suggest that allocating subsidies to change travel behavior is significantly more beneficial than directly subsidizing rides. Furthermore, using a flat tax rate as low as 1% can double the system’s social welfare in the budget-balanced variant of the incentive program.

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.

PShare: Privacy-Preserving Ride-Sharing System with Minimum-Detouring Route

Online ride-hailing (ORH) services allow people to enjoy on-demand transportation services through their mobile devices in a short responding time. Despite the great convenience, users need to submit their location information to the ORH service provider, which may incur unexpected privacy problems. In this paper, we mainly study the privacy and utility of the ride-sharing system, which enables multiple riders to share one driver. To solve the privacy problem and reduce the ride-sharing detouring waste, we propose a privacy-preserving ride-sharing system named pShare. To hide users’ precise locations from the service provider, we apply a zone-based travel time estimation approach to privately compute over sensitive data while cloaking each rider’s location in a zone area. To compute the matching results along with the least-detouring route, the service provider first computes the shortest path for each eligible rider combination, then compares the additional traveling time (ATT) of all combinations, and finally selects the combination with minimum ATT. We designed a secure comparing protocol by utilizing the garbled circuit, which enables the ORH server to execute the protocol with a crypto server without privacy leakage. Moreover, we apply the data packing technique, by which multiple data can be packed as one to reduce the communication and computation overhead. Through the theoretical analysis and evaluation results, we prove that pShare is a practical ride-sharing scheme that can find out the sharing riders with minimum ATT in acceptable accuracy while protecting users’ privacy.

Multi-Objective Optimization in a Ridesharing System

Multi-Objective Optimization in a Ridesharing System