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Mean number of passengers per vehicle for four different vehicle types (capacity one, two, four, and ten). We show four one-week time series for different fleet sizes and maximum waiting time: (A) 1000 vehicles and Ω = 2 min; (B) 1000 vehicles and Ω = 7 min; (C) 3000 vehicles and Ω = 2 min; and (D) 3000 vehicles and Ω = 7 min. At night, most vehicles wait, and during rush hour, the mean occupancy decreases as the fleet gets larger. Larger maximum waiting time enables more opportunities for ride-sharing.  

Mean number of passengers per vehicle for four different vehicle types (capacity one, two, four, and ten). We show four one-week time series for different fleet sizes and maximum waiting time: (A) 1000 vehicles and Ω = 2 min; (B) 1000 vehicles and Ω = 7 min; (C) 3000 vehicles and Ω = 2 min; and (D) 3000 vehicles and Ω = 7 min. At night, most vehicles wait, and during rush hour, the mean occupancy decreases as the fleet gets larger. Larger maximum waiting time enables more opportunities for ride-sharing.  

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Ride-sharing services are transforming urban mobility by providing timely and convenient transportation to anybody, anywhere, and anytime. These services present enormous potential for positive societal impacts with respect to pollution, energy consumption, congestion, etc. Current mathematical models, however, do not fully address the potential of...

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... For TNCs, sharing rides with other ridehailing users during rush hours can increase the order response rate and ridesplitting matching success rate, and improve the operation efficiency of the platform and the attraction of the ridesplitting system (Agatz et al., 2011;Alonso-González et al., 2020). For cities, compared with solo ride-hailing trips, ridesplitting can reduce the use of vehicles (Alonso-Mora et al., 2017), and thus reduce traffic congestion, traffic accidents, and parking demand on the roads (Morris et al., 2019). This service can also bring various environmental benefits, such as reducing energy consumption and harmful gas emissions (e.g. ...
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Ridesplitting, a form of ride-hailing service where passengers with similar travel routes are matched to the same driver, can reduce the negative effects of solo ride-hailing trips and bring various environmental and social benefits. However, limited efforts were made to examine the spatial variation of ridesplitting trips, which was not conducive to the formulation of ridesplitting policies. To fill the gap, this work investigates the spatial variation of ridesplitting adoption rate (the proportion of ride-hailing trips with shared trip authorized, RAR) and its association with built environment and socioeconomic factors at the census tract level, using the ride-hailing trip data in Chicago. To addressing the spatial heterogeneity, geographically weighted regression models are established to detect the factors influencing the RAR during different time periods, such as weekday, weekend, weekday morning peak and evening peak. Modeling results show that GWR models outperform the traditional global models in terms of model fit. The census tract level factors including subway station density, frequency of transit, land use mix, homicide density, percent female, the share of nonwhite, and percent zero-vehicle households have impacts on RAR, and the coefficient estimates of each explanatory variable vary across regions. The research results can help urban planners and transportation network companies develop refined policies to promote shared ride-hailing trips.
... One approach is to construct a shareability graph between vehicles and requests for the ridesharing [10], [11]. Based on this approach, Alonso-Mora et al. create a Requests-Trip-Vehicle (RTV) assignment graph [12]. The large ride-sharing problem is encoded using integer linear programming (ILP) and solved almost in real-time. ...
... Fig. 1: The road network corresponding to part of mid-Manhattan is shown. Travel duration estimates are inferred from real taxi travel data in hourly increments [12]. ...
... The mobility-on-demand ride-sharing problem can be translated to an ILP problem through the construction of a shareability graph and an assignment (RTV) graph [10], [12]. Then we can apply graph search algorithms and provide efficient solutions. ...
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Mobility-on-demand systems are transforming the way we think about the transportation of people and goods. Most research effort has been placed on scalability issues for systems with a large number of agents and simple pick-up/drop-off demands. In this paper, we consider fair multi-vehicle route planning with streams of complex, temporal logic transportation demands. We consider an approximately envy-free fair allocation of demands to limited-capacity vehicles based on agents' accumulated utility over a finite time horizon, representing for example monetary reward or utilization level. We propose a scalable approach based on the construction of assignment graphs that relate agents to routes and demands, and pose the problem as an Integer Linear Program (ILP). Routes for assignments are computed using automata-based methods for each vehicle and demands sets of size at most the capacity of the vehicle while taking into account their pick-up wait time and delay tolerances. In addition, we integrate utility-based weights in the assignment graph and ILP to ensure approximative fair allocation. We demonstrate the computational and operational performance of our methods in ride-sharing case studies over a large environment in mid-Manhattan and Linear Temporal Logic demands with stochastic arrival times. We show that our method significantly decreases the utility deviation between agents and the vacancy rate.
... Hence, it is critical to design effective passenger matching algorithms or strategies to avoid significant waiting or detours. In so doing, a large body of studies have focused on optimizing ride-sharing services, mainly at the operational level, e.g., dynamic matching algorithms (Santi et al., 2014;Alonso-Mora et al., 2017;Di Febbraro et al., 2013;Bei and Zhang, 2018;Zeng et al., 2020), and demand management and pricing Jacob and Roet-Green, 2021;Zhu et al., 2020). Readers are referred to Tafreshian et al. (2020) for a comprehensive review of recent efforts. ...
Preprint
This study presents a multi-zone queuing network model for steady-state ride-sharing operations that serve heterogeneous demand, and then builds upon this model to optimize the design of ride-sharing services. Spatial heterogeneity is addressed by partitioning the study region into a set of relatively homogeneous zones, and a set of criteria are imposed to avoid significant detours among matched passengers. A generalized multi-zone queuing network model is then developed to describe how vehicles' states transition within each zone and across neighboring zones, and how passengers are served by idle or partially occupied vehicles. A large system of equations is constructed based on the queuing network model to analytically evaluate steady-state system performance. Then, we formulate a constrained nonlinear program to optimize the design of ride-sharing services, such as zone-level vehicle deployment, vehicle routing paths, and vehicle rebalancing operations. A customized solution approach is also proposed to decompose and solve the optimization problem. The proposed model and solution approach are applied to a hypothetical case and a real-world Chicago case study, so as to demonstrate their applicability and to draw insights. These numerical examples not only reveal interesting insights on how ride-sharing services serve heterogeneous demand, but also highlight the importance of addressing demand heterogeneity when designing ride-sharing services.
... The goal of the optimization is to treat all currently active requests in a batch and find the best possible solution for the corresponding static problem based on the current fleet state. In FleetPy a variant of the algorithm proposed by [Alonso-Mora et al., 2017] is implemented for the ride-pooling use case. By exploiting time constraints for pick-up and maximum in-vehicle time, all feasible VehiclePlans for all vehicles serving a specific set of requests can be created. ...
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The market share of mobility on-demand (MoD) services strongly increased in recent years and is expected to rise even higher once vehicle automation is fully available. These services might reduce space consumption in cities as fewer parking spaces are required if private vehicle trips are replaced. If rides are shared additionally, occupancy related traffic efficiency is increased. Simulations help to identify the actual impact of MoD on a traffic system, evaluate new control algorithms for improved service efficiency and develop guidelines for regulatory measures. This paper presents the open-source agent-based simulation framework FleetPy. FleetPy (written in the programming language "Python") is explicitly developed to model MoD services in a high level of detail. It specially focuses on the modeling of interactions of users with operators while its flexibility allows the integration and embedding of multiple operators in the overall transportation system. Its modular structure ensures the transferabillity of previously developed elements and the selection of an appropriate level of modeling detail. This paper compares existing simulation frameworks for MoD services and highlights exclusive features of FleetPy. The upper level simulation flows are presented, followed by required input data for the simulation and the output data FleetPy produces. Additionally, the modules within FleetPy and high-level descriptions of current implementations are provided. Finally, an example showcase for Manhattan, NYC provides insights into the impacts of different modules for simulation flow, fleet optimization, traveler behavior and network representation.
... Furthermore, a driver with matched/onboard passengers (from previous matching rounds) may be matched again with new passengers (Agatz et al., 2012). Alonso-Mora et al. (2017a) propose a general many-to-many dynamic multi-passenger vehicle assignment framework based on the idea of shareability networks (Santi et al., 2014), and show their algorithm's ability and efficiency of handling large demands. Simonetto et al. (2019) simplify the ridesharing matching problem and solve it efficiently using linear programming. ...
... In this section, we illustrate the importance of considering dynamic supply-demand interactions in designing and evaluating ridesharing systems, by explicitly comparing the performance metrics with three models: 1) static model (as in Yao & Bekhor, 2021); 2) dynamic model without interactions (e.g., Alonso-Mora et al., 2017a;Herbawi & Weber, 2012;Masoud & Jayakrishnan, 2017;Simonetto et al., 2019); 3) dynamic model with interactions (this paper). We randomly select one OD pair of the Winnipeg network and generate the same set of driver and passenger agents for the 3 models. ...
... Buses have fixed routes and fixed timetables. of which are based on the dynamic DARP. Alonso-Mora et al. (2017) solve an on-demand high capacity ride-sharing problem via dynamic trip vehicle assignment and constrained optimization. They test their algorithm on the New York City taxicab data set and find that even without ride-sharing (this means only one person per ride), they can decrease the number of taxis from 13.000 to 3000, contributing to the fight against congestion. ...
Article
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.
... Internet-based ride-hailing services, which connect drivers and passengers in real time, have attracted much interest as travel options for residents in recent years (Vazifeh et al., 2018, Xu et al., 2020. Compared to a traditional taxi service, with a ride-hailing service, passengers can book orders online in advance through a mobile app instead of standing on the side of the road and spending time waiting for a taxi to arrive; this improves the mobility of vehicles and the service level of travel (Alonso-Mora et al., 2017). With the collection and analysis of large amounts of user order data and vehicle trajectory data, ride-hailing services are constantly updating and evolving (Alisoltani et al., 2021), thereby becoming a disruptive force to the traditional transportation industry (Wang and Yang, 2019). ...
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The problem of learning from imbalanced ride-hailing demand data with spatiotemporal heterogeneity and highly skewed demand distributions is a relatively new challenge. Current prediction methods usually filter out some spatiotemporal partitions with sparse demands by setting a minimum ride-hailing demand threshold, where the dataset is always assumed to be well balanced in terms of its spatiotemporal partitions, with equal misprediction costs. However, this widely used assumption results in large prediction biases. To achieve better prediction performance, we propose a bagging learning approach based on hexagonal convolutional long short-term memory (H-ConvLSTM), which combines three components. 1) By setting multiple minimum ride-hailing demand thresholds, several subdatasets with different majority ride-hailing demand prediction ranges are obtained. The H-ConvLSTM regression model is applied to each undersampled dataset to train multiple submodels with their respective biased ride-hailing demand prediction ranges. 2) The H-ConvLSTM classification model is trained on the total ride-hailing demand dataset to predict the potential demand range for a certain partition at a future time. 3) The submodel with the best performance with respect to the potential demand range is selected to predict the future demand for this partition. Experiments conducted on order data obtained from Didi Chuxing in Chengdu, China, are conducted. The results show that the proposed approach achieves significantly improved prediction performance relative to that of other models.
... Similar to standard ride-hailing, on-demand ride-pooling services typically act as doorto-door transport for passengers, matching similar passenger requests to each other or to vehicles already on route, ideally without any detour for the passengers (Fig. 1a,b). In contrast to ride-hailing services, however, the assignment of passenger requests to ride-pooling vehicles is much more complex [23,24] due to the restrictions of the routes of the vehicles by already assigned passengers. The resulting complex collective dynamics of the ride-pooling fleet [25,26] and the intricate dependence of the service efficiency on the system parameters [23,27,28] are far from fully understood. ...
Preprint
Ride-pooling (or ride-sharing) services combine trips of multiple customers along similar routes into a single vehicle. The collective dynamics of the fleet of ride-pooling vehicles fundamentally underlies the efficiency of these services. In simplified models, the common features of these dynamics give rise to scaling laws of the efficiency that are valid across a wide range of street networks and demand settings. However, it is unclear how constraints of the vehicle fleet impact such scaling laws. Here, we map the collective dynamics of capacity-constrained ride-pooling fleets to services with unlimited passenger capacity and identify an effective fleet size of available vehicles as the relevant scaling parameter characterizing the dynamics. Exploiting this mapping, we generalize the scaling laws of ride-pooling efficiency to capacity-constrained fleets. We approximate the scaling function with a queueing theoretical analysis of the dynamics in a minimal model system, thereby enabling mean-field predictions of required fleet sizes in more complex settings. These results may help to transfer insights from existing ride-pooling services to new settings or service locations.
... Some ride-sharing studies bore similarities with DRT by considering high-capacity services (11). Dynamic trip assignment was considered with real-time optimal routes generated based on demand and vehicle locations. ...
Article
This paper investigates the potential of autonomous minibuses which take on-demand directional routes for pick-up and drop-off in a grid network of wider area with low density, followed by fixed routes in areas with greater demand. Mathematical formulation for generalized costs demonstrates its benefits, with indicators proposed to select existing bus routes for conversion with the options of zonal express and parallel routes. Simulations on modeled scenarios and case studies with bus routes in Chicago show reductions in both passenger costs and generalized costs compared with existing fixed-route bus services between suburban areas and the central business district.
... It has unique properties compared to other modes, including (1) pooling, (2) TNC operating, and (3) on-demand. Before the term was coined, researchers used various combinations of pooled on-demand ride services (Lazarus et al. 2021), shared ridehailing (Malik et al. 2021;Tirachini 2019), pooled ridehailing (Gehrke et al. 2021;Young et al. 2020), ride-pooling (Soria and Stathopoulos 2021), on-demand pooled mobility services (Wadud and Mattioli 2021), or on-demand ride-sharing (Alonso- Mora et al. 2017). Tirachini et al. pointed out that research on ridesplitting is still rare (Tirachini et al. 2020), while existing studies provided encouraging results on energy saving and emission reduction. ...
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Ridesplitting is a pooled version of a ridesourcing service that can reduce emissions per trip compared with exclusive ridehailing. However, it only takes a small share of the ridesourcing market currently. Fiscal and social incentives are potential leverage tools to attract passengers from solo hailed rides. This study aims to quantitatively identify the effects of carbon credits (CC) and monetary rewards (MR) on people's willingness to adopt ridesplitting. The theory of planned behavior realized by structural equation modeling decomposes the factors which affect passengers' intention and actual action on choosing ridesplitting. Confirmatory factor analysis suggests that pro-environmental attitudes do not directly force people to ridesplitting. Path effect analysis shows that subjective norms and perceived behavioral control significantly affect ridesplitting intentions. Immediate effect regression reveals that CC has greater direct effects on ridesplitting intention than MR (4.5 times more effective). Moderating effect analysis shows the MR effect reduces while the CC effect enhances when increasing the incentive value. The results encourage policymakers and operation designers to consider better marketing schemes combining monetary and social incentives to promote ridesplitting and to gain better fiscal and environmental benefits from ridesourcing systems.