Dynamic Ridesharing Research Articles

Snapshot-Optimal Real-Time Ride Sharing

Ridesharing effectively tackles urban mobility challenges by providing a service comparable to private vehicles while minimising resource usage. Our research primarily concentrates on dynamic ridesharing, which conventionally involves connecting drivers with passengers in need of transportation. The process of one-to-one matching presents a complex challenge, particularly when addressing it on a large scale, as the substantial number of potential matches make the attainment of a global optimum a challenging endeavour. This paper aims to address the absence of an optimal approach for dynamic ridesharing by refraining from the conventional heuristic-based methods commonly used to achieve timely solutions in large-scale ride-matching. Instead, we propose a novel approach that provides snapshot-optimal solutions for various forms of one-to-one matching while ensuring they are generated within an acceptable timeframe for service providers. Additionally, we introduce and solve a new variant in which the system itself provides the vehicles. The efficacy of our methodology is substantiated through experiments carried out with real-world data extracted from the openly available New York City taxicab dataset.

Utility-Aware Dynamic Ridesharing in Spatial Crowdsourcing

With smartphones and geo-locating devices widely used around the world, ridesharing, as a main application field of spatial crowdsourcing, has been fast expanding its widespread adoption and potentially brings great benefits to human society and the environment. However, ridesharing has so far not been as popular as expected. A recent survey shows that notable obstacles come from concerns about social comfort and price fairness when riding with strangers. To defeat these obstacles, in this paper, we study a novel problem of utility-aware ride matching (URM) for dynamic ridesharing, where drivers and riders are matched in batches by considering their social comfort and price revenue. While the URM problem is of practical usefulness, we prove that this problem is Nondeterministic Polynomial Hard (NP-hard). To tackle the problem optimally and find exact answers, we present a novel bipartite matching algorithm by integrating an effective Driver-Rider Graph (DR-Graph) index. To balance the effectiveness and efficiency, we propose two efficient algorithms to solve the URM problem with only a small loss of utility. Leveraging a bounded Driver-Rider-group Graph (DRg-Graph) and several useful pruning bounds for matching utility and travel cost, we develop an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -refining algorithm to find an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -approximation matching utility of the optimal answer. Extensive experiments on real datasets showed that our proposed algorithms achieved effective matching results of social-pricing based utilities and the efficient performance of quick matching under various parameter settings.

Exploration of Factors That Influence Willingness to Consider Pooled Rideshare

Ridesharing has become an increasingly prevalent form of transportation. Although transportation network companies such as Uber and Lyft initially started as a personal rideshare service where individuals ride alone or with people they know, rideshare services have been expanded to pooled rideshare—a dynamic rideshare system where an individual rides with passengers they do not know. Despite the growth in rideshare services worldwide, the use of pooled rideshare in the U.S.A. is relatively low compared to other forms of transportation. A national U.S. survey ( N = 5385) was conducted to investigate reasons why individuals are willing or unwilling to consider pooled rideshare. Exploratory and confirmatory factor analyses were performed, where the exploratory factor analysis suggests five factors, specifically, service experience, time/cost, traffic/environment, privacy, and safety. Model fit indices of the confirmatory factor analysis verified that these five factors can represent the factors behind riders’ willingness to consider pooled rideshare. Furthermore, a binomial logistic regression was conducted to explore how the five factors influence riders’ willingness to consider pooled rideshare. The three factors that influence riders’ willingness to consider pooled rideshare were service experience ( B = 1.05), traffic/environment ( B = .38), and time/cost ( B = .26), while a lack of privacy ( B = −1.46) can be a deterrent for pooled rideshare. Safety is important for those who are both willing and unwilling to consider the use of pooled rideshare. Understanding these factors is important for the future of pooled rideshare services in the U.S.A.

En-route transfer-based dynamic ridesharing

In the last few decades, the continuous growth of travel demand increased urban traffic congestion. The situation demands an efficient way of handling resources like roads and vehicles. Parallelly, the advancement of technology proposed more innovative forms of shared mobility, such as flexible pods. These autonomous, modular vehicles possess the unique capability to autonomously merge and split during their journeys, facilitating en-route transfer of passengers. Formulating a precise mathematical model that comprehensively captures the intricacies of such a complex system is quite challenging. This paper introduces an alternative approach rooted in Reinforcement Learning that circumvents the need for a predetermined mathematical model. Instead, it enables the system to learn its dynamics through interactions with the environment. The primary goal of this research is to acquire an optimized passenger transfer policy. The experimental findings indicate that the transfer-based pod service outperforms private taxi services and Demand Responsive Transport services, boosting vehicle utility by 18% and 8%, respectively. Remarkably, the pod service achieves these gains with only one-third of the vehicles required for private taxis and 13% fewer vehicles than the Demand Responsive Transport service. However, it's important to note that the transfer-based pod service does result in a modest increase in passenger travel time, ranging from 2% to 13%. Therefore, the transfer-based pod service offers a promising opportunity to alleviate traffic congestion while accepting a compromise on passenger travel times.

Joint Order Dispatching and Vehicle Repositioning for Dynamic Ridesharing

Joint Order Dispatching and Vehicle Repositioning for Dynamic Ridesharing

Correction: Optimizing Dynamic Ride Sharing with Multiple Objectives

Correction: Optimizing Dynamic Ride Sharing with Multiple Objectives

Optimizing Dynamic Ride Sharing with Multiple Objectives

Optimizing Dynamic Ride Sharing with Multiple Objectives

Willingness to Consider Pooled Rideshare?: An Exploratory Study on Influential Factors

Rideshare use has grown significantly, beginning with solo riders and evolving to pooled rideshare. Pooled rideshare involves sharing a ride with stranger(s). Despite the growth in rideshare services worldwide, the use of pooled rideshare in the U.S. is relatively low within all rideshare trips and compared to other forms of transportation, e.g., driving one's personal vehicle. A national survey of 5,385 individuals was conducted to identify factors influencing riders' willingness to consider pooled rideshare. Exploratory and confirmatory factor analyses were performed. The survey results indicated five factors: service experience, time/cost, traffic/environment, privacy, and safety. Understanding these factors is crucial for the future of dynamic ridesharing services in the U.S.

Passenger‐perception dynamic ridesharing service based on parallel technology

AbstractWith the emergence of new information and communication technology, mobile‐based ridesharing services have received more attention and have improved. They offer timely and convenient service and exert an enormous influence on pollution and congestion by allowing passengers to share overlapping routes. However, ridesharing services have also been discussed and called into question by people with respect to their attractiveness, their comfort, and passenger safety. This paper proposes a ridesharing algorithm with a perceived mechanism by introducing multiple dimensions of perceived value to overall assess passengers’ perceptions. The approach involved can continuously track the psychological changes in passengers during travel, capture the dynamic and comprehensive perceptions of passengers, and make real‐time adjustments on this basis. Furthermore, the proposed partition‐based parallel strategy can ensure high‐quality services and further enhance computational efficiency. Finally, numerical experiments are conducted based on the urban transportation network and the actual trip data from Manhattan to evaluate the performance of the proposed algorithm. The results show that it is important to effectively capture the passengers’ perception of ridesharing services and apply them to optimization mechanisms, which will contribute to improving the perception of ride experiences and attracting potential passengers.

Dynamic Ridesharing With Minimal Regret: Towards an Enhanced Engagement Among Three Stakeholders

In dynamic ridesharing, the platform serves as the mediator by tailoring the assignment result between workers and riders with a focus on a certain objective. Existing studies generally focus on either one or two stakeholders when modelling the problem while the wellbeing of the other parties may be ignored or even undermined. For example, purely maximizing the total revenue of the ridesharing platform may cause the loss of riders and in turn lead to a low served rate, because those expensive orders will be processed in priority. In this paper, we for the first time study how to incorporate the willingness of all stakeholders (i.e., the platform, workers and riders). Given a set of workers and a set of rider requests, we aim to return the matchable worker-rider pairs in order to minimize the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">regret</i> . Specifically, two types of regret are defined: (i) the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">served rate regret</i> , which refers to the rate of unserved requests, catering for the reputation and profit of the platform and workers; (ii) the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">revenue regret</i> , which considers the portion of revenue loss from unserved riders, catering for the focus of workers and riders in the trip schedule. We prove the NP-hardness of this problem. To tackle this problem, we first propose a dynamic programming insertion algorithm to improve the efficiency of inserting a rider request into a trip schedule of a worker. Furthermore, two kinds of heuristic algorithms are devised to match rider requests with workers effectively. Comprehensive experiments on two real-world datasets verify the effectiveness, efficiency and scalability of our solutions in dealing with different supply-demand relationships in practice.

Usage Intention of Shared Autonomous Vehicles with Dynamic Ride Sharing on Long-Distance Trips

Technology advancements have paved the way for public access to shared autonomous vehicles (SAVs), but there is still no travel survey examining how SAVs with dynamic ride sharing (DRS) affect long-distance (LD) trips. Given the growth in these trips and the higher importance of travel time and cost on LD trips, assessing potential impacts of SAVs could be a vital tool in planning for a sustainable transportation system. This paper examines the impact of various attitudinal, sociodemographic, and travel-related characteristics on the usage intention of SAVs with DRS on LD trips. We have designed and conducted a web-based survey for this purpose and based on a representative sample of 723 individuals in 2021, a Generalized Ordered Logit model is estimated. Estimation results highlight the key importance of following psychological factors in a descending order: price evaluation, perceived usefulness, consumer innovativeness, sharing attitude, and privacy concern. Further, key factors among sociodemographic and travel-related characteristics are gender, education level, driving license, household car ownership, generational element, and crash history. These findings provide crucial insights into the likely effects of SAVs with DRS on LD trip behavior, based on which a number of practical implications are proposed for facilitating policy-making.

Budget-balanced and strategy-proof auctions for ridesharing

Ridesharing services have become widespread, and pricing the rides is a crucial problem for these systems. We propose and analyze a budget-balanced and strategy-proof auction, the Weighted Minimum Surplus (WMS) auction, for the dynamic ridesharing problem with multiple passengers per ride. Under the assumption of downward closed alternatives, we obtain lower bounds for the surplus welfare and surplus profit of the WMS auction. We also propose and analyze a budget-balanced version of the well-known VCG mechanism, the V CGs. Encouraging experimental results were obtained for both the WMS auction and the V CGs.

A column generation approach for a dynamic ridesharing problem

ABSTRACT Recent advances in GPS and telecommunications technologies have made ridesharing a widespread practice around the world. By matching drivers and passengers with near-distance destinations and similar time schedules, ridesharing saves individuals travel costs by enabling them to share vehicles. It is also an effective way to reduce traffic congestion and greenhouse gas emissions. A ridesharing problem can be modeled as a dial-a-ride problem with time windows. This study looks at a solution to the dynamic ridesharing problem in which passengers share travel costs at the same ratio. We formulate the problem as a mixed-integer programming model and suggest a column generation approach. As the system’s status is updated in real time, riders and drivers are matched, and new paths are created via column generation. Computational experiments show that our approach is superior to an existing algorithm when it is tested on instances of various sizes.

Measuring taxi ridesharing effects and its spatiotemporal pattern in Seoul, Korea

Most studies on dynamic ridesharing, which allows any driver to ferry separate passengers with overlapping routes in a single trip, have focused on developing efficient algorithms. While dynamic ridesharing demands extensive computing infrastructure, this paper suggests a simple taxi ridesharing approach that allows only passengers from certain taxi hotspots to share a ride with another who has a similar destination and measures the effects, spatiotemporal patterns and its benefits. Compared to dynamic ridesharing, the proposed method does not require dedicated driver fleets, networked communication system, or monopoly of all passenger information. We identify taxi pickup hotspots and analyze the spatiotemporal patterns of the simple ridesharing approach. The results show that 48 % of rides from hotspots could be shared, reducing the overall vehicle-km traveled by 1.2 km for each shared ride. We also find that spatiotemporal patterns of the ridesharing could represent urban characteristics. For example, places with high ridesharing potential and low saved-trip distances could imply low public transportation accessibility while areas with high shareability during working hours on both weekdays and weekends could represent public transportation hubs. The proposed method is expected to be useful to identify taxi stands that have high ridesharing opportunities. Policy makers can use our approach to support simple ridesharing scheme. Moreover, with the characterized taxi stands, such as longer saved-trip distance and nightlife peaks, the proposed method could be used as decision support tools for temporary allowed ridesharing. In addition, spatiotemporal patterns of the taxi stands would be used in designing public transportation systems.

An agent-based model for assessing the financial viability of autonomous mobility on-demand systems used as first and last-mile of public transport trips: A case-study in Rotterdam, the Netherlands

The continuing urbanization and corresponding increase in transport demand are putting pressure on the accessibility, safety, sustainability, livability, and efficiency of urbanized regions. Public transport is regarded as a sustainable mode of transport for these regions and therefore transport policies aim to increase its attractiveness. However, public transport is facing last-mile connectivity problems. The application of Autonomous Mobility on-Demand (AMoD) as a feeder service for public transport hubs can potentially improve the first and last-mile trip leg which increases the attractivity of public transport. However, will such a system be financially viable when applied in an urban area? and what kind of operation will lead to the highest system performance? In this research, this question is addressed by proposing a method that connects macro transport modeling and agent-based modeling (ABM). An existing gravity-based travel demand estimation model built in a macro simulation tool is used to predict passenger demand across all the OD pairs of a city. For those OD pairs that can use the AMoD as first /last mile this is modeled using an agent-based rationale to be able to simulate the behavior of passengers and vehicles within that specific area of the city. The simulation model is applied to the case study area of the south of Rotterdam, in The Netherlands, where metro Station Zuidplein and the rail Station Lombardijen function as two AMoD hubs. Using the case study, the impact of relocation, ridesharing, and charging strategy is assessed in regards to financial viability. Among other insights, results show that the AMoD service leads to a profit on a typical business day for the operating companies despite the high-quality level of the service (very low average waiting time for a vehicle). If this particular system would not consist of automated vehicles and one would have to pay a salary to drivers, it would not be possible to make a profit on a typical business day. Moreover, results show that activating dynamic ridesharing and using wireless fast chargers at the stations results in the most financially viable operation. Activating automatic relocations results in the most costly operation.

How Many Vehicles Do We Need? Fleet Sizing for Shared Autonomous Vehicles With Ridesharing

Fleet sizing is critical for shared autonomous vehicle(SAV) fleet management to reduce maintenance costs and traffic congestions. As the passenger demands are dynamic, how to make the demand-aware dynamic ridesharing and calculate the fleet size is an important and challenging problem. In this paper, we propose a minimum fleet sizing method called Fleet Sizing for demand-aware Dynamic Ridesharing (FSDR) to accurately determine the fleet size for ridesharing enabled SAV system. Specifically, the travel demands are first predicted by an ensemble method that takes account of temporal correlations between regions. Then a concept called demand utility is proposed to measure the travel demands when planning vehicle paths, and the ride-matching dispatches vehicles to high travel demand regions by maximizing the demand utility along the vehicle path. Based on the ride-matching result, the minimum fleet size is calculated based on a trip graph by the Hopcroft-Karp algorithm. FSDR is evaluated on two real GPS taxi trajectory datasets from Wuhan, China, and San Francisco, USA. The result validates that the proposed demand-aware ridesharing in FSDR can significantly reduce the fleet size compared to the existing ride-matching methods. Moreover, the result shows that by allowing ridesharing, the vehicle fleet size can be reduced by 30%.

Impact of New Mobility Solutions on Travel Behaviour and Its Incorporation into Travel Demand Models

Advancement in the fields of electrification, automation, and digitalisation and emerging social trends are fuelling the transformation of road transport resulting in the introduction of various innovative mobility solutions. Yet the reaction of people to many of the new solutions is still vastly unknown. This creates an unprecedented quandary for transport planners who are requested to design future transport systems and create the related investment plans without fully validated models to base the assessment upon. As some evidence on citizens’ behaviour concerning new mobility solutions starts to be progressively made available, first attempts to update the existing models begin to emerge. Nevertheless, a lot more is needed as some of the transpiring mobility solutions have not yet reached the market, making the corresponding behaviour changes imponderable. In this context, the main purpose of this paper is to provide a review on how travel behaviour changes linked to the deployment of new mobility solutions have been considered in travel demand models. The new mobility solutions studied include carsharing, dynamic ridesharing, micromobility sharing services, and personal and shared autonomous vehicles. An overview and comparison of relevant studies implementing activity or trip-based demand models and other methodologies are presented. The analysis shows that the results of the different studies heavily depend on the extent to which behavioural changes are considered. The results of the review thus point to the need for holistic demand models that carefully mimic the urban reality with everything it has to offer and account for the importance of individual traits in the decision-making processes. Such models need an in-depth understanding of the microscopic mechanisms leading to the travel behaviour shifts linked to the most innovative mobility solutions. To achieve this level of detail, mobility living labs and their real-life experiments and experience with citizens, which are flourishing in Europe, are suggested to play a crucial role in the years to come.

Dynamic ride-sharing impacts of greater trip demand and aggregation at stops in shared autonomous vehicle systems

Sharing vehicles and rides may become the norm with public use of fully-automated self-driving vehicles in the near future, assuming pandemic-related health concerns fade. Dynamic ride-sharing (DRS) or ride-pooling of trips can significantly improve system performance by lowering empty vehicle-miles traveled (eVMT) and increasing average vehicle occupancy (AVO). With several cities looking to promote efficient curb space use, especially with the use of pickup and drop-off locations (PUDOs), this study explores the impacts of PUDOs on DRS rates and AVO values. Various PUDO spacings and trip-demand densities were studied, across the Bloomington, Illinois region, using the agent-based simulator POLARIS. Results reveal that greater PUDO spacing or distances between stops and higher levels of SAV use or trip demand increase AVO (by up to 20% per 4-seater SAV, on average) and decrease SAV VMT (by up to 27%) compared to door-to-door SAV fleet operations without DRS or PUDOs. A quarter-mile PUDO spacing is recommended in downtown regions, similar to current transit stop spacing, to keep walking trips short and demand relatively high. At 0.25 mi PUDO spacings (thoughtfully placed, using origin and destination clusters), travelers walked less than 5 min at either trip end, on average, while 0.5 mi spacings led to another 1 min (approximately) of walking. More evenly distributed and higher SAV demand can save up to 39% total VMT from use of DRS and PUDO stops. It is also important to prepare for queuing areas at PUDOs in settings of high trip density, to limit curbside congestion.

A joint demand modeling framework for ride-sourcing and dynamic ridesharing services: a geo-additive Markov random field based heterogeneous copula framework

Promoting sustainable transportation, ride-sourcing and dynamic ridesharing (DRS) services have transformative impacts on mobility, congestion, and emissions. As emerging mobility options, the demand for ride-sourcing and DRS services has rarely been simultaneously examined. This study contributes to filling this gap by jointly analyzing the demand for ride-sourcing and DRS services and examining how it varies across neighborhood-level built environment, transit accessibility and crime, behavioral, and sociodemographic factors. To achieve these objectives, unique geo-coded data containing millions of ride-sourcing and DRS trips in Chicago are spatially joined with up-to-date data on the built environment, transit accessibility, crime, active travel, and demographic factors. A novel Markov Random Field-based joint heterogeneous geo-additive copula framework is presented to simultaneously capture random, systematic, and spatial heterogeneity. Characterized by a Frank copula structure, the demand for ride-sourcing and DRS services exhibited a non-linear stochastic dependence pattern. With spatial heterogeneity and spillover effects, the stochastic dependence of ride-sourcing and DRS demand varied across time of day and was the strongest in compact and dense neighborhoods. Key aspects of the built environment related to urban design (pedestrian-oriented infrastructure), density, and land-use diversity were positively associated with ride-sourcing and DRS demand—suggesting that sustainable mobility goals can be achieved by continuing to invest in more walkable neighborhoods. Active travel and telecommuting were positively linked with ride-sourcing and DRS demand. Complementary and substitutive effects for transit accessibility were found. Results show that increasing transit accessibility in areas with low levels of accessibility (compared to those with high transit levels) could be more helpful in increasing the adoption of ride-sourcing and DRS services. Relative to ride-sourcing, the demand for DRS services appeared more responsive to improvements in pedestrian-infrastructure and transit accessibility. Quantification of non-linear associations with ceiling and overdose effects for the built environment, vehicle ownership, and transit accessibility provided deeper insights. The findings can help guide the development of policy interventions and investment decisions to further accelerate the adoption of mobility-on-demand systems.

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.