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Mobility-on-demand (MoD) systems consist of a fleet of shared vehicles that can be hailed for one-way point-to-point trips. The total distance driven by the vehicles and the fleet size can be reduced by employing ridesharing, i.e., by assigning multiple passengers to one vehicle. However, finding the optimal passenger-vehicle assignment in an MoD system is a hard combinatorial problem. In this work, we demonstrate how the VGA method, a recently proposed systematic method for ridesharing, can be used to compute the optimal passenger-vehicle assignments and corresponding vehicle routes in a massive-scale MoD system. In contrast to existing works, we solve all passenger-vehicle assignment problems to optimality, regularly dealing with instances containing thousands of vehicles and passengers. Moreover, to examine the impact of using optimal ridesharing assignments, we compare the performance of an MoD system that uses optimal assignments against an MoD system that uses assignments computed using insertion heuristic and against an MoD system that uses no ridesharing. We found that the system that uses optimal ridesharing assignments subject to the maximum travel delay of 4 minutes reduces the vehicle distance driven by 57% compared to an MoD system without ridesharing. Furthermore, we found that the optimal assignments result in a 20% reduction in vehicle distance driven and 5% lower average passenger travel delay compared to a system that uses insertion heuristic. [ABSTRACT FROM AUTHOR]

Sustainability is the ability to maintain and preserve natural and manmade systems for the benefit of current and future generations. The three pillars of sustainability are social, economic, and environmental. These pillars are interdependent and interconnected, meaning that progress in one area can have positive or negative impacts on the others. This calls for smart methods to balance such benefits and find solutions that are optimal with respect to all the three pillars of sustainability. By using AI methods, in particular, genetic algorithms for multiobjective optimisation, we can better understand and manage complex systems in order to achieve sustainability. In the context of sustainability-oriented ridesharing, genetic algorithms can be used to optimise route finding in order to lower the cost of transportation and reduce emissions. This work contributes to this domain by using AI, specifically genetic algorithms for multiobjective optimisation, to improve the efficiency and sustainability of transportation systems. By using this approach, we can make progress towards achieving the goals of the three pillars of sustainability. [ABSTRACT FROM AUTHOR]

This paper considers the problem of supply-demand imbalances in Mobility-on-Demand (MoD) services. These imbalances occur due to uneven stochastic travel demand and can be mitigated by proactively rebalancing empty vehicles to areas where the demand is high. To achieve this, we propose a method that takes into account uncertainties of predicted travel demand while minimizing pick-up time and rebalance mileage for autonomous MoD ride-hailing. More precisely, first travel demand is predicted using Gaussian Process Regression (GPR) which provides uncertainty bounds on the prediction. We then formulate a stochastic model predictive control (MPC) for the autonomous ride-hailing service and integrate the demand predictions with uncertainty bounds. In order to guarantee constraint satisfaction in the optimization under estimated stochastic demand prediction, we employ a probabilistic constraining method with user-defined confidence interval, using Chance Constrained MPC (CCMPC). The benefits of the proposed method are twofold. First, travel demand uncertainty prediction from data can naturally be embedded into the MoD optimization framework, allowing us to keep the imbalance at each station below a certain threshold with a user-defined probability. Second, CCMPC can be relaxed into a Mixed-Integer-Linear-Program (MILP) and the MILP can be solved as a corresponding Linear-Program, which always admits an integral solution. Our transportation simulations show that by tuning the confidence bound on the chance constraint, close to optimal oracle performance can be achieved, with a median customer wait time reduction of 4% compared to using only the mean prediction of the GPR.

On-demand mobility services are promising to revolutionise urban travel, but preliminary studies are showing they may actually increase total vehicle miles travelled, worsening road congestion in cities. In this study, we assess the demand for on-demand mobility services in urban areas, using a stated preference survey, to understand the potential impact of introducing on-demand services on the current modal split. The survey was carried out in the Netherlands and offered respondents a choice between bike, car, public transport and on-demand services. 1,063 valid responses are analysed with a multinomial logit and a latent class choice model. By means of the latter, we uncover four distinctive groups of travellers based on the observed choice behaviour. The majority of the sample, the Sharing-ready cyclists (55%), are avid cyclists and do not see on-demand mobility as an alternative for making urban trips. Two classes, Tech-ready individuals (27%) and Flex-ready individuals (9%) would potentially use on-demand services: the former is fairly time-sensitive and would thus use on-demand service if they were sufficiently fast. The latter is highly cost-sensitive, and would therefore use the service primarily if it is cheap. The fourth class, Flex-sceptic individuals (9%) shows very limited potential for using on-demand services. [ABSTRACT FROM AUTHOR]

This paper presents an Aggregate Matching and Pick-up (AMP) model to delineate the matching and pick-up processes in mobility-on-demand (MoD) service markets by explicitly considering the matching mechanisms in terms of matching intervals and matching radii. With passenger demand rate, vehicle fleet size and matching strategies as inputs, the AMP model can well approximate drivers' idle time and passengers' waiting time for matching and pick-up by considering batch matching in a stationary state. Properties of the AMP model are then analyzed, including the relationship between passengers' waiting time and drivers' idle time, and their changes with market thickness, which is measured in terms of the passenger arrival rate (demand rate) and the number of active vehicles in service (supply). The model can also unify several prevailing inductive and deductive matching models used in the literature and spell out their specific application scopes. In particular, when the matching radius is sufficiently small, the model reduces to a Cobb–Douglas type matching model proposed by Yang and Yang (2011) for street-hailing taxi markets, in which the matching rate depends on the pool sizes of waiting passengers and idle vehicles. With a zero matching interval and a large matching radius, the model reduces to Castillo model developed by Castillo et al. (2017) that is based on an instant matching mechanism, or a bottleneck type queuing model in which passengers' matching time is derived from a deterministic queue at a bottleneck with the arrival rate of idle vehicles as its capacity and waiting passengers as its customers. When both the matching interval and matching radius are relatively large, the model also reduces to the bottleneck type queuing model. The performance of the proposed AMP model is verified with simulation experiments. • We propose a Aggregate Matching and Pick-up (AMP) model. • The AMP model can approximate passenger waiting and pickup times. • The AMP model can unify a few prevailing matching models. • The accuracy of AMP model is validated by simulation studies. • We derive a few managerial insights helpful for ride-sourcing platforms. [ABSTRACT FROM AUTHOR]

• Combined modal-split assignment model with autonomous taxis (ATs) was developed. • Policy analysis was conducted in Nagoya considering fare, delay, and dedicated lanes. • Trips using of taxis will increase by a factor of 11 when ATs are adopted. • Rail usage will decrease by 1.5%, while usage of other modes will decrease by 4%–5%. In recent years, the implementation of autonomous vehicles has been widely discussed worldwide. In particular, urban transportation demand is expected to change significantly when autonomous taxis (ATs) are introduced. Thus, planners must anticipate changes in traffic conditions and the number of users of other transport modes. Therefore, changes in travel behavior and traffic conditions must be quantified with respect to comprehensive changes in the service level of ATs, including changes in fares and the possibility of delays. However, previous studies have not sufficiently considered factors such as the intention to use ATs, the interrelationship between mode choice and traffic congestion, and the impact of ATs on public transit. Therefore, they are not applicable to city-level transportation demand forecasts. The purpose of this study was to propose an easily implementable method for forecasting urban transportation demand when AT services are adopted, which overcomes such problems. In this study, we developed a combined modal split-assignment model and analyzed the effects of various AT service implementation scenarios in Nagoya, Japan. The results showed that the number of trips using taxis will increase by a factor of 11 when AT services are implemented. Additionally, the usage of other modes is expected to decrease by 4%–5%, except for rail usage with a decrease of 1.5%. [ABSTRACT FROM AUTHOR]

In ride-sharing services, travel time uncertainty significantly impacts the quality of matching solutions for both the drivers and the riders. This paper studies a one-to-many ride-sharing matching problem where travel time between locations is uncertain. The goal is to generate robust ride-sharing matching solutions that minimize the total driver detour cost and the number of unmatched riders. To this end, we formulate the ride-sharing matching problem as a robust vehicle routing problem with time window (RVRPTW). To effectively capture the travel time uncertainty, we propose a deep learning-based data-driven approach that can dynamically estimate the uncertainty sets of travel times. Given the NP-hard nature of the optimization problem, we design a hybrid meta-heuristic algorithm that can handle large-scale instances in a time-efficient manner. To evaluate the performance of the proposed method, we conduct a set of numeric experiments based on real traffic data. The results confirm that the proposed approach outperforms the non-data-driven one in several important performance metrics, including a proper balance between robustness and inclusiveness of the matching solution. Specifically, by applying the proposed data-driven approach, the matching solution violation rate can be reduced up to 85.8%, and the valid serving rate can be increased up to 42.3% compared to the non-data-driven benchmark.

Time-sensitive parcel deliveries—shipments requested for delivery in a day or less—are an increasingly important aspect of urban logistics. It is challenging to deal with these deliveries from a carrier perspective. These require additional planning constraints, preventing the efficient consolidation of deliveries that is possible when demand is well known in advance. Furthermore, such time-sensitive deliveries are requested to a wider spatial scope than retail centers, including homes and offices. Therefore, an increase in such deliveries is considered to exacerbate negative externalities, such as congestion and emissions. One of the solutions is to leverage spare capacity in passenger transport modes. This concept is often denominated as cargo hitching. While there are various system designs, it is crucial that such a solution does not deteriorate the quality of service of passenger trips. This research aims to evaluate the use of mobility-on-demand (MOD) services that perform same-day parcel deliveries. To test the MOD-based solutions, we utilize a high-resolution agent- and activity-based simulation platform of passenger and freight flows. E-commerce demand carrier data collected in Singapore are used to characterize simulated parcel delivery demand. We explore operational scenarios that aim to minimize the adverse effects of fulfilling deliveries with MOD service vehicles on passenger flows. Adverse effects are measured in fulfillment, wait, and travel times. A case study on Singapore indicates that the MOD services have potential to fulfill a considerable amount of parcel deliveries and decrease freight vehicle traffic and total vehicle kilometers travelled without compromising the quality of MOD for passenger travel. Insights into the operational performance of the cargo-hitching service are also provided.

New mobility-on-demand services together with the emerging technology of autonomous vehicles (AV) aim to revolutionize urban transportation systems, by introducing autonomous driving and sophisticated sharing and routing schemes for efficiently serving individual’s needs and requirements. On the other hand, the COVID-19 pandemic has disrupted travel patterns due to the emerging trends of social distancing and teleworking. In this paper, we aim at investigating users’ perception on autonomous vehicles, mobility on demand schemes as well as on the future transportation landscape using data collected through a questionnaire survey in the Metropolitan Area of Athens, Greece conducted after the first COVID-19 pandemic wave. First, a statistical analysis of the responses is performed and, then, a clustering approach is followed to identify user profiles based on daily mobility patterns and attitudes towards autonomous vehicles. Subsequently, the identified profiles are exploited in the development of a Bayesian Network to reveal interrelations between user profiling, attitudes and perceptions for future mobility services. Regarding the acceptance of Autonomous Mobility on Demand (AMoD) services, as well as travelers’ level of happiness concerning future scenarios of urban transportation, results have shown that the majority of travelers in Athens will be more than happy in the case where the entire transportation system is served with AMoD services.

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. [ABSTRACT FROM AUTHOR]

This paper studies congestion-aware route-planning policies for intermodal Autonomous Mobility-on-Demand (AMoD) systems, whereby a fleet of autonomous vehicles provides on-demand mobility jointly with public transit under mixed traffic conditions (consisting of AMoD and private vehicles). First, we devise a network flow model to jointly optimize the AMoD routing and rebalancing strategies in a congestion-aware fashion by accounting for the endogenous impact of AMoD flows on travel time. Second, we capture the effect of exogenous traffic stemming from private vehicles adapting to the AMoD flows in a user-centric fashion by leveraging a sequential approach. Since our results are in terms of link flows, we then provide algorithms to retrieve the explicit recommended routes to users. Finally, we showcase our framework with two case-studies considering the transportation sub-networks in Eastern Massachusetts and New York City, respectively. Our results suggest that for high levels of demand, pure AMoD travel can be detrimental due to the additional traffic stemming from its rebalancing flows. However, blending AMoD with public transit, walking and micromobility options can significantly improve the overall system performance by leveraging the high-throughput of public transit combined with the flexibility of walking and micromobility. [ABSTRACT FROM AUTHOR]

With the popularization of ride-sharing transportation and increasing penetration of electric vehicles (EVs) in recent years, the electric mobility-on-demand (EMoD) system is emerging as a promising means to provide ride-sharing services in the context of sustainable cities. In this paper, we focus on sequential decision-making for the operator of an EMoD system by considering both the passengers’ utility and system revenue. Specifically, we design a pricing mechanism to incentivize passengers with spatially and temporally different demand to make different mobility choices. After the passengers’ demand is realized, the operator makes operational decisions, including dispatching and repositioning EVs between service regions, and recharging EVs to maintain their energy levels. Therefore, a bi-level and dynamic programming problem is formulated to model these decisions. To solve this problem, we first transform the bi-level problem into a single-level one based on the structural properties of the formulation. Furthermore, we rigorously prove the coordinate-wise concavity of the single-level formulation and efficiently obtain near-optimal solutions based on approximation. Numerical tests show that the proposed dynamic pricing mechanism achieves a significantly better performance than static pricing and other existing model-free approaches (e.g., Q-learning). [ABSTRACT FROM AUTHOR]

In a coordinated mobility-on-demand system, a fleet of vehicles is controlled by a central unit and serves transportation requests in an on-demand fashion. An emerging field of research aims at finding the best way to operate these systems given certain targets, e.g., customer service level or the minimization of fleet distance. In this work, we introduce a new element of fleet operation: the assignment of idle vehicles to a limited set of parking spots. We present two different parking operating policies governing this process and then evaluate them individually and together on different parking space distributions. We show that even for a highly restricted number of available parking spaces, the system can perform quite well, even though the total fleet distance is increased by 20% and waiting time by 10%. With only one parking space available per vehicle, the waiting times can be reduced by 30% with 20% increase in total fleet distance. Our findings suggest that increasing the parking capacity beyond one parking space per vehicle does not bring additional benefits. Finally, we also highlight possible directions for future research such as to find the best distribution of parking spaces for a given mobility-on-demand system and city.

• Developed a model for Mobility-as-a-Service platform equilibrium. • Captures on-demand mobility, coopetition between operators, user equilibrium. • Algorithm converges to global optimum when outcomes are non-empty. • Stability guarantee is ensured in algorithmic design that may introduce subsidy. • Tests on 748-link network can output firm market entry, inequities, and congestion. Mobility-as-a-Service (MaaS) systems are two-sided markets, with two mutually exclusive sets of agents, i.e., travelers/users and operators, forming a mobility ecosystem in which multiple operators compete or cooperate to serve customers under a governing platform provider. This study proposes a MaaS platform equilibrium model based on many-to-many assignment games incorporating both fixed-route transit services and mobility-on-demand (MOD) services. The matching problem is formulated as a convex multicommodity flow network design problem under congestion that captures the cost of accessing MOD services. The local stability conditions reflect a generalization of Wardrop's principles that include operators' decisions. Due to the presence of congestion, the problem may result in non-stable designs, and a subsidy mechanism from the platform is proposed to guarantee local stability. A new exact solution algorithm to the matching problem is proposed based on a branch and bound framework with a Frank-Wolfe algorithm integrated with Lagrangian relaxation and subgradient optimization, which guarantees the optimality of the matching problem but not stability. A heuristic which integrates stability conditions and subsidy design is proposed, which reaches either an optimal MaaS platform equilibrium solution with global stability, or a feasible locally stable solution that may require subsidy. For the heuristic, a worst-case bound and condition for obtaining an exact solution are both identified. Two sets of reproducible numerical experiments are conducted. The first, on a toy network, verifies the model and algorithm, and illustrates the differences local and global stability. The second, on an expanded Sioux Falls network with 82 nodes and 748 links, derives generalizable insights about the model for coopetitive interdependencies between operators sharing the platform, handling congestion effects in MOD services, effects of local stability on investment impacts, and illustrating inequities that may arise under heterogeneous populations. [ABSTRACT FROM AUTHOR]

With Mobility-as-a-Service platforms moving toward vertical service expansion, we propose a destination recommender system for Mobility-on-Demand (MOD) services that explicitly considers dynamic vehicle routing constraints as a form of a “physical internet search engine”. It incorporates a routing algorithm to build vehicle routes and an upper confidence bound based algorithm for a generalized linear contextual bandit algorithm to identify alternatives which are acceptable to passengers. As a contextual bandit algorithm, the added context from the routing subproblem makes it unclear how effective learning is under such circumstances. We propose a new simulation experimental framework to evaluate the impact of adding the routing constraints to the destination recommender algorithm. The proposed algorithm is first tested on a 7 by 7 grid network and performs better than benchmarks that include random alternatives, selecting the highest rating, or selecting the destination with the smallest vehicle routing cost increase. The RecoMOD algorithm also reduces average increases in vehicle travel costs compared to using random or highest rating recommendation. Its application to Manhattan dataset with ratings for 1,012 destinations reveals that a higher customer arrival rate and faster vehicle speeds lead to better acceptance rates. While these two results sound contradictory, they provide important managerial insights for MOD operators. [ABSTRACT FROM AUTHOR]

With advances in digitization and automation, autonomous mobility on demand services have the potential to disrupt the future mobility system landscape. Ridepooling services in particular can both decrease land consumption by reducing the need for parking and increase transportation efficiency by increasing the average vehicle occupancy. Nevertheless, because ridepooling services require a sufficient user base for pooling to take effect, their performance can suffer if multiple operators offer such a service and must split the demand. This study presents a simulation framework for evaluating the impact of competition and cooperation among multiple ridepooling providers. Two different kinds of interaction via a broker platform are compared with the base cases of a single monopolistic operator and two independent operators with divided demand. In the first, the broker presents trip offers from all operators to customers (similar to a mobility-as-a-service platform), who can then freely choose an operator. In the second, a regulated broker platform can manipulate operator offers with the goal of shifting the customer-operator assignment from a user equilibrium towards a system optimum. To model adoptions of the service design depending on the different interaction scenario, a game setting is introduced. Within alternating turns between operators, operators can adapt parameters of their service (fleet size and objective function) to maximize profit. Results for a case study based on Manhattan taxi data, show that operators generate the highest profit in the broker setting while operating the largest fleet. Additionally, pooling efficiency can nearly be maintained compared to a single operator. The regulated competition benefits not only operators (profit) and cities (increased pooling efficiency), but customers also experience higher service rate, though they need to accept slightly increased waiting and travel time due to increased pooling efficiency. Contrarily, when users can decide freely, the lowest pooling efficiency and operator profit is observed.

The growth of mobility on demand (MOD) services has raised partnership opportunities between transit agencies and transportation network companies (TNCs) in the US. However, there is still a need to recognize how MOD programs confront different challenges during the implementation of pilot projects, and to what extent they are successful in promoting mobility efficiency and providing multiple mobility options. This study aims to evaluate the potential opportunities of public-private partnerships for MOD planning while presenting an overview of the challenges and lessons learned during the implementation of the Federal Transit Administration (FTA) Sandbox Program projects. Following a comprehensive review of MOD's background, we identify the goals and scopes of the 11 FTA Sandbox Program projects. The programs are classified into four categories: service to people with disabilities, first/last mile solutions, mobile application targeting one non-transit mode, and mobile application to integrate public and private transportation services on one app. Emphasizing particular FTA Sandbox Program projects, we determine the challenges and technical lessons learned during the implementation of the programs. Finally, this study identifies fundamental factors to a well-integrated public transit system that uses app-based on-demand technology. Our findings provide new insights, which could reinforce future partnerships among public-private transportation services.

Shared Mobility-on-Demand using automated vehicles can reduce energy consumption and cost for future mobility. However, its full potential in energy saving has not been fully explored. An algorithm to minimize fleet fuel consumption while satisfying customers’ travel time constraints is developed in this article. Numerical simulations with realistic travel demand and route choice are performed, showing that if fuel consumption is not considered, the Mobility-on-demand (MOD) service can increase fleet fuel consumption due to increased empty vehicle mileage. With fuel consumption as part of the cost function, we can reduce total fuel consumption by 7% while maintaining a high level of mobility service. [ABSTRACT FROM AUTHOR]

This simulation work investigates new means to decrease the modal share of motorized transport in a large urban area in Hamburg, Germany. This was deemed necessary in order to cut down CO 2 emissions. The five scenarios simulated with the MATSim [13] framework including an adapted mode choice model strongly suggest that making public transport more attractive is not sufficient to reach this goal, the results display a meager 3%-point change in the share of motorized transport. With introducing additional means to repel motorized transport, an 8%-point change may be within reach. The results also show that by making bike riding more safe, a considerably higher share of biking is possible (+8%-points). [ABSTRACT FROM AUTHOR]

Serving both passenger and freight demand with the same vehicle fleet is an ambition that led to the development of several innovative vehicle concepts [10, 26]. This study proposes a simulation-based methodology to investigate the execution of freight tours with an On-Demand fleet of autonomous, modular vehicles while giving priority to passenger transport. Based on assumptions regarding the operational scheme, tour pricing and delivery time windows, the software Multi-Agent Transport Simulation (MATSim) is extended. The developed methodology is then applied to a Berlin-wide freight delivery scenario. The results show that when using a relatively large vehicle fleet, passenger waiting time statistics barely change. However, it has to be noted that the study should be repeated with different (smaller) fleet sizes. A rough cost analysis for the freight operator suggests that there is a large saving potential when using autonomous On-Demand vehicles instead of an owned fleet. Because of the uncertainty of price composition, further studies to quantify this saving potential have to be made. [ABSTRACT FROM AUTHOR]

In this paper, we present a software architecture that extends the MATSim mobility simulation framework by providing an external rebalancing server, which offers a set of rebalancing algorithms. This allows to implement complex rebalancing algorithms with minimal changes introduced into MATSim's code base. We are specifically interested in the potential of using algorithms based on reinforcement learning, we present and implement in our architecture a simple Q-learning rebalancing algorithm. Our initial results show the potential of this approach. Although they are not better than existing rebalancing algorithm in MATSim, this approach enables more developments in this area and the implementation of other more complex algorithms for mobility on-demand operation in MATSim. [ABSTRACT FROM AUTHOR]

As an alternative means of convenient and smart transportation, mobility-on-demand (MOD), typified by online ride-sharing and connected taxicabs, has been rapidly growing and spreading worldwide. The large volume of complex traffic and the uncertainty of market supplies/demands have made it essential for many MOD service providers to proactively dispatch vehicles towards ride-seekers. To meet this need effectively, we propose STRide, an MOD coordination learning mechanism reinforced spatio-temporally with capsules. We formalize the adaptive coordination of vehicles into a reinforcement learning framework. STRide incorporates spatial and temporal distributions of supplies (vehicles) and demands (ride requests), customers’ preferences and other external factors. A novel spatio-temporal capsule neural network is designed to predict the provider’s rewards based on MOD network states, vehicles and their dispatch actions. This way, the MOD platform adapts itself to the supply-demand dynamics with the best potential rewards. We have conducted extensive data analytics and experimental evaluation with five large-scale datasets ($\sim$ ∼ 27 million rides from Uber, NYC/Chicago Taxis, Didi and Car2Go). STRide is shown to outperform state-of-the-arts, substantially reducing request-rejection rate and passenger waiting time, and also increasing the service provider’s profits. [ABSTRACT FROM AUTHOR]

The rapid popularity growth of shared e-scooters creates the necessity of understanding the determinants of shared e-scooter usage. This paper estimates the impacts of temporal variables (weather data, weekday/weekend, and gasoline prices) and time-invariant variables (socio-demographic, built environment, and neighborhood characteristics) on the shared e-scooter demand by using four months (June 2019- October 2019) period of data from the shared e-scooter pilot program in Chicago. The study employs a random-effects negative binomial (RENB) model that effectively models shared e-scooter trip origin and destination count data with over-dispersion while capturing serial autocorrelation in the data. Results of temporal variables indicate that shared e-scooter demand is higher on days when the average temperature is higher, wind speed is lower, there is less precipitation (rain), weekly gasoline prices are higher, and during the weekend. Results related to time-invariant variables indicate that densely populated areas with higher median income, mixed land use, more parks and open spaces, public bike-sharing stations, higher parking rates, and fewer crime rates generate a higher number of e-scooter trips. Moreover, census tracts with a higher number of zero-car households and workers commuting by public transit generate more shared e-scooter trips. On the other hand, results reveal mixed relationships between shared e-scooter demand and public transportation supply variables. This study's findings will help planners and policymakers make decisions and policies related to shared e-scooter services. [ABSTRACT FROM AUTHOR]

The emergence of the sharing economy in urban transportation networks has enabled new fast, convenient and accessible mobility services referred to as Mobilty-on-Demand systems (e.g., Uber, Lyft, DiDi). These platforms have flourished in the last decade around the globe and face many operational challenges in order to be competitive and provide good quality of service. A crucial step in the effective operation of these systems is to reduce customers' waiting time while properly selecting the optimal fleet size and pricing policy. In this paper, we jointly tackle three operational decisions: (i) fleet size, (ii) pricing, and (iii) rebalancing, in order to maximize the platform's profit or its customers' welfare. To accomplish this, we first devise an optimization framework which gives rise to a static policy. Then, we elaborate and propose dynamic policies that are more responsive to perturbations such as unexpected increases in demand. We test this framework in a simulation environment using three case studies and leveraging traffic flow and taxi data from Eastern Massachusetts, New York City, and Chicago. Our results show that solving the problem jointly could increase profits between 1% and up to 50%, depending on the benchmark. Moreover, we observe that the proposed fleet size yield utilization of the vehicles in the fleet is around 75% compared to private vehicle utilization of 5%.

Enabling Ride-sharing (RS) in Mobility-on-demand (MoD) systems allows reduction in vehicle fleet size while preserving the level of service. This, however, requires an efficient vehicle to request assignment, and a vehicle rebalancing strategy, which counteracts the uneven geographical spread of demand and relocates unoccupied vehicles to the areas of higher demand. Existing research into rebalancing generally divides the coverage area into predefined geographical zones. Division is done statically, at design-time, impeding adaptivity to evolving demand patterns. To enable more accurate dynamic rebalancing, this paper proposes a Dynamic Demand-Responsive Rebalancer (D2R2) for RS systems. D2R2 uses Expectation-Maximization (EM) technique to recalculate zones at each decision step based on current demand. We integrate D2R2 with a Deep Reinforcement Learning multi-agent MoD system consisting of 200 vehicles serving 10,000 trips from New York taxi dataset. Results show a more fair workload division across the fleet when compared to static pre-defined equiprobable zones. [ABSTRACT FROM AUTHOR]

We formalize one aspect of reliability in the context of Mobility-on-Demand (MoD) systems by acknowledging the uncertainty in the pick-up time of these services. This study answers two key questions: i) how the difference between the stated and actual pick-up times affect the propensity of a passenger to choose an MoD service? ii) how an MoD service provider can leverage this information to increase its ridership? We conduct a discrete choice experiment in New York to answer the former question and adopt a micro-simulation-based optimization method to answer the latter question. In our experiments, the ridership of an MoD service could be increased by up to 10% via displaying the predicted wait time strategically. [ABSTRACT FROM AUTHOR]

App-based services and ridesharing in the field of mobility-on-demand (MoD) create a new mode of transport between motorized individual transport and public transportation whose long-term role in the urban mobility landscape and within public transport systems is not fully understood as of today. For the public transport industry, these new services offer new chances but also risks, making planning models and tools for integrated intermodal network planning indispensable. We develop a strategic network planning optimization model for bus lines that allows for intermodal trips with MoD as a first or last leg. Starting from an existing public transport network, we decide simultaneously on the use of existing line segments in the future fixed-route network, on areas of the city where an integrated MoD service should be offered, on how MoD interacts with the fixed-route network via transfer points, and on passenger routes fulfilling given service-level requirements. The main challenges from a modeling point of view are to capture the interplay between MoD services and the fixed public network, as well as the approximation of MoD costs taking into account that vehicle utilization is a key factor influencing these costs. We develop a path-based formulation and a branch-and-price algorithm, as well as an enhanced enumeration-based approach, to solve real-world instances to proven optimality. The solution methods are tested on instances generated with the help of real-world data from a medium-sized German city, Göttingen, that currently operates around 20 bus lines. [ABSTRACT FROM AUTHOR]

Public transport lines, especially train lines, have historically played an important role as economic lifelines of rural areas. They are one of the most important factors contributing to economic prosperity as they provide access to mobility for all the inhabitants of these regions. Maintaining such rural public transport lines can be a challenge due to the low utilization inherent to rural areas. Today, with the emergence of fully self-driving cars, on-demand mobility schemes in which autonomous robotic taxis transport passengers, are becoming possible. In this work, we analyze if rural public transport lines with low utilization can be replaced with autonomous mobility-on-demand systems. More specifically, we compare the existing public transportation infrastructure to hypothetical mobility-on-demand systems both in terms of cost and service level. We perform our analysis, which focuses on the operational aspects, using a simulation approach in which unit-capacity robotic taxis are operated in a street network taking into account congestion effects and state-of-the-art control (dispatching and rebalancing) strategies. Our study considers the case of four rural train lines in Switzerland that operate at low utilization and cost coverage. We show that a unit-capacity mobility-on-demand service with self-driving cars reduces both travel times and operational cost in three out of four cases. In one case, even a service with human driven vehicles would provide higher service levels at lower cost. The results suggest that centrally coordinated mobility-on-demand schemes could be a very attractive option for rural areas. [ABSTRACT FROM AUTHOR]

This paper presents an empirical investigation on demand for TNC services (e.g., Uber) in the Greater Toronto and Hamilton Areas (GTHA) through the application of an innovative discrete choice model. The proposed model combines Independent Availability Logit (IAL) and Constrained Multinomial Logit (CMNL) model formulation to reap the unique features of both. The proposed model is thus a Semi Compensatory Independent Availability Logit (SCIAL) model. For the empirical investigation, it uses a dataset of trip mode choices that suitable to represent ride-hailing service (e.g., Uber). Such trips are named as hailable trips in the dataset, which is drawn from a large scale household travel survey conducted in the region in 2016. To have a clear understanding of behavioural processes involved in the choice of travel mode of hailable trips, the proposed SCIAL model jointly models probabilistic choice set formation and conditional semi-compensatory choice. The empirical model does not reveal any evident competition between Uber and the private car, public transit, or non-motorized modes. It indicates that urban taxi is its main competitor, but there are notable differences in socio-demographic profiles of taxi and Uber users. For example, a taxi is preferred by older people, but younger people prefer uber, and there is no gender difference in such a pattern. In terms of the relationship between considering Uber as a feasible mode and choosing it for a trip, Uber has similarities to the car passenger mode. Merely accepting it as a feasible option has a significant influence on the final choice to use it. This indicates a potential new segment of the travel market, generated primarily for the advent of TNC service, e.g., Uber in Toronto. [ABSTRACT FROM AUTHOR]

• Proposed a framework to design mobility-on-demand services with endogenous demand. • Used Bayesian optimization to select the supply-side parameters (e.g. fleet size). • Calibrated the framework using NYC's taxi data and illustrated policy implications. • Mode choice model was calibrated using state preferences of New Yorkers. • Showed existence of supply-demand equilibrium numerically. Mobility-on-Demand (MoD) systems are generally designed and analyzed for a fixed and exogenous demand, but such frameworks fail to answer questions about the impact of these services on the urban transportation system, such as the effect of induced demand and the implications for transit ridership. In this study, we propose a unified framework to design, optimize and analyze MoD operations within a multimodal transportation system where the demand for a travel mode is a function of its level of service. An application of Bayesian optimization (BO) to derive the optimal supply-side MoD parameters (e.g., fleet size and fare) is also illustrated. The proposed framework is calibrated using the taxi demand data in Manhattan, New York. Travel demand is served by public transit and MoD services of varying passenger capacities (1, 4 and 10), and passengers are predicted to choose travel modes according to a mode choice model. This choice model is estimated using stated preference data collected in New York City. The convergence of the multimodal supply-demand system and the superiority of the BO-based optimization method over earlier approaches are established through numerical experiments. We finally consider a policy intervention where the government imposes a tax on the ride-hailing service and illustrate how the proposed framework can quantify the pros and cons of such policies for different stakeholders. [ABSTRACT FROM AUTHOR]

• Design a new rideshare strategy while leveraging transfers to/from transit networks. • Test different anticipatory dispatch and relocation algorithms in a bimodal network. • Large-scale case study of the Long Island Railroad accessing New York City. We propose a ridesharing strategy with integrated transit in which a private on-demand mobility service operator may drop off a passenger directly door-to-door, commit to dropping them at a transit station or picking up from a transit station, or to both pickup and drop off at two different stations with different vehicles. We study the effectiveness of online solution algorithms for this proposed strategy. Queueing-theoretic vehicle dispatch and idle vehicle relocation algorithms are customized for the problem. Several experiments are conducted first with a synthetic instance to design and test the effectiveness of this integrated solution method, the influence of different model parameters, and measure the benefit of such cooperation. Results suggest that rideshare vehicle travel time can drop by 40–60% consistently while passenger journey times can be reduced by 50–60% when demand is high. A case study of Long Island commuters to New York City (NYC) suggests having the proposed operating strategy can substantially cut user journey times and operating costs by up to 54% and 60% each for a range of 10–30 taxis initiated per zone. This result shows that there are settings where such service is highly warranted. [ABSTRACT FROM AUTHOR]

Pricing in mobility-on-demand (MOD) networks, such as Uber, Lyft, and connected taxicabs, is done adaptively by leveraging the price responsiveness of drivers (supplies) and passengers (demands) to achieve such goals as maximizing drivers' incomes, improving riders' experience, and sustaining platform operation. Existing pricing policies only respond to short-term demand fluctuations without accurate trip forecast and spatial demand-supply balancing, thus mismatching drivers to riders and resulting in loss of profit. We propose CAPrice, a novel adaptive pricing scheme for urban MOD networks. It uses a new spatio-temporal deep capsule network (STCapsNet) that accurately predicts ride demands and driver supplies with vectorized neuron capsules while accounting for comprehensive spatio-temporal and external factors. Given accurate perception of zone-to-zone traffic flows in a city, CAPrice formulates a joint optimization problem by considering spatial equilibrium to balance the platform, providing drivers and riders/passengers with proactive pricing "signals." We have conducted an extensive experimental evaluation upon over 4.0× 108 MOD trips (Uber, Didi Chuxing, and connected taxicabs) in New York City, Beijing, and Chengdu, validating the accuracy, effectiveness, and profitability (often 20% ride prediction accuracy and 30% profit improvements over the state-of-the-arts) of CAPrice in managing urban MOD networks. [ABSTRACT FROM AUTHOR]

Highlights • Comparison of four fleet operational strategies for automated mobility. • Rebalancing-based strategies allow for acceptable wait times and costs. • Fleet sizing for an automated mobility system in Zurich, Switzerland. Abstract We evaluate performance of four different operational policies to control an automated mobility-on-demand system with sequential vehicle-sharing in one public open-source-accessible, agent-based, high fidelity simulation environment. Detailed network dynamics on a road level of precision are taken into account. The case study is conducted in a simulation scenario of Zurich city. The results indicate that automated vehicles' shared mobility systems can provide approximately six times higher occupancy rates than conventional private cars, but that their costs – in Switzerland – are considerably higher than those of subsidized public transport or private cars in the short term. However, these services are predicted to be considerably cheaper than the full costs of owning and using a private car, which makes the long-term adoption of automated taxi services likely. Simulations demonstrate that choice of fleet operational policy determining customer-vehicle assignment and repositioning of empty vehicles (rebalancing) heavily influences system performance, e.g., wait times and cost. This aspect must thus be regarded as an important factor in the interpretation of existing and future simulation studies. [ABSTRACT FROM AUTHOR]

• Joint dispatching of autonomous mobility-on-demand system and power system. • Multi-spatiotemporal scale dispatching models in power-traffic coordination. • Dispatching performance validated on a large-scale real-world case. • 15% reduction of total carbon emissions with vehicle-to-grid (V2G) considered. • Sensitivity analysis on fleet size, renewable energy profile, power system capacity. Electrified autonomous mobility-on-demand (E-AMoD) systems will play a crucial role in future cities, which couple the carbon emissions generated by the transportation system and the power system. This paper proposes a joint dispatching framework to facilitate city decarbonization by coordinating the fleet operator and power system operator. Given the substantial differences in spatial and temporal dispatching scales between the two systems, this framework effectively integrates micro vehicle-level service dispatching with macro fleet-level charging scheduling. Firstly, a novel bipartite matching model efficiently solves the short-term passenger serving and rebalancing dispatching at the vehicle level. The service dispatching results are then aggregated as spatial-temporal transition flows across different areas and time intervals. Incorporating these mobility constraints, the long-term charging-discharging scheduling at the fleet level is captured as an expanded network flow model and integrated with the power system dispatching model, which aims at renewable energy utilization and carbon emission mitigation. Finally, the fleet-level scheduling results are disaggregated to the serving-rebalancing-charging sequences of each vehicle, for practical implementation. To validate the proposed framework, numerical experiments are conducted on a large-scale real-world case. Multiple charging schedule scenarios, including heuristic charging, smart charging, and smart charging-discharging, are investigated under both centralized and distributed supplied power systems. (1) The framework proves to be an effective solution for addressing the differences in dispatching spatiotemporal scales, calculation efficiencies, and privacy concerns between power and traffic systems. (2) Through power-traffic coordination, the E-AMoD fleet demonstrates its ability to alleviate 8% of average renewable energy curtailment and reduce nearly 15% of the total emissions in a power system installed with renewable distributed generators. (3) Furthermore, the extent of emission mitigation effects is influenced by critical factors such as fleet size, renewable energy profile, and power system capacity. Overall, the E-AMoD system show great potential as mobile storage devices to provide spatial-temporal flexibility to the power system and contribute to city decarbonization. [ABSTRACT FROM AUTHOR]

One-way car-sharing systems are becoming increasingly popular, and the introduction of autonomous vehicles could make these systems even more widespread. Shared Autonomous Electric Vehicles could also allow for more controllable charging compared to private electric vehicles, allowing large scale demand response and providing essential ancillary services to the electric grid. In this work, we develop a simulation methodology for evaluating a Shared Autonomous Electric Vehicle system interacting with passengers and charging at designated charging stations using a heuristic-based charging strategy. The influence of fleet size is studied in terms of transport service quality and break-even prices for the system. We test the potential of the system to supply operating reserve by formulating an optimization problem for the optimal deployment of vehicles during a grid operator request. The results of the simulations for the case study of Tokyo show that a fleet of Shared Autonomous Electric Vehicles would only need to be about 10%–14% of a fleet of private cars providing a comparable level of transport service, with low break-even prices. Moreover, we show that the system can provide operating reserve under several operational conditions even at peak transport demand without significant disruption to transport service. [ABSTRACT FROM AUTHOR]

This paper proposes and simulates an integrated autonomous vehicle (AV) and public transportation (PT) system. After discussing the attributes of and the interaction among the prospective stakeholders in the system, we identify opportunities for synergy between AVs and the PT system based on Singapore’s organizational structure and demand characteristics. Envisioning an integrated system in the context of the first-mile problem during morning peak hours, we propose to preserve high demand bus routes while repurposing low-demand bus routes and using shared AVs as an alternative. An agent-based supply-side simulation is built to assess the performance of the proposed service in fifty-two scenarios with different fleet sizes and ridesharing preferences. Under a set of assumptions on AV operation costs and dispatching algorithms, the results show that the integrated system has the potential of enhancing service quality, occupying fewer road resources, being financially sustainable, and utilizing bus services more efficiently. [ABSTRACT FROM AUTHOR]