Current mobility services cannot compete on equal terms with self-owned mobility products concerning service quality. Because of supply and demand imbalances, ridesharing users invariably experience delays, price surges, and rejections. Traditional approaches often fail to respond to demand fluctuations adequately because service levels are, to some extent, bounded by fleet size. With the emergence of autonomous vehicles, however, the characteristics of mobility services change and new opportunities to overcome the prevailing limitations arise. In this paper, we consider an autonomous ridesharing problem in which idle vehicles are hired on-demand in order to meet the service-level requirements of a heterogeneous user base. In the face of uncertain demand and idle vehicle supply, we propose a learning-based optimization approach that uses the dual variables of the underlying assignment problem to iteratively approximate the marginal value of vehicles at each time and location under different availability settings. These approximations are used in the objective function of the optimization problem to dispatch, rebalance, and occasionally hire idle third-party vehicles in a high-resolution transportation network of Manhattan, New York City. The results show that the proposed policy outperforms a reactive optimization approach in a variety of vehicle availability scenarios while hiring fewer vehicles. Moreover, we demonstrate that mobility services can offer strict service-level contracts to different user groups featuring both delay and rejection penalties.

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This study considers an integrated water- and land-based transportation (IWLT) system for waste collection. Research on the issue is motivated by increased heavy street movements that damage quay walls as well as congestion. We present a novel two-echelon vehicle routing problem with satellite synchronization based on a two-index formulation and evaluate it on small-sized instances for 10 waste points and 4 hubs. We compare the proposed synchronized IWLT approach with three benchmarks that can reduce issues associated with heavy loads. It is shown that the proposed system can provide better solutions with less collection cost, reduced street movements and lightweight garbage vehicles.@en

With the popularization of transportation network companies (TNCs) (e.g., Uber, Lyft) and the rise of autonomous vehicles (AVs), even major car manufacturers are increasingly considering themselves as autonomous mobility-on-demand (AMoD) providers rather than individual vehicle sellers. However, matching the convenience of owning a vehicle requires providing consistent service quality, taking into account individual expectations. Typically, different classes of users have different service quality (SQ) expectations in terms of responsiveness, reliability, and privacy. Nonetheless, AMoD systems presented in the literature do not enable active control of service quality in the short term, especially in light of unusual demand patterns, sometimes allowing extensive delays and user rejections. This study proposes a method to control the daily operations of an AMoD system that uses the SQ expectations of heterogeneous user classes to dynamically distribute service quality among riders. Additionally, we consider an elastic vehicle supply, that is, privately-owned freelance AVs (FAVs) can be hired on short notice to help providers meeting user service-level expectations. We formalize the problem as the dial-a-ride problem with service quality contracts (DARP-SQC) and propose a multi-objective matheuristic to address real-world requests from Manhattan, New York City. Applying the proposed service-level constraints, we improve user satisfaction (in terms of reached service-level expectations) by 53% on average compared to conventional ridesharing systems, even without hiring additional vehicles. By deploying service-quality-oriented on-demand hiring, our hierarchical optimization approach allows providers to adequately cater to each segment of the customer base without necessarily owning large fleets.

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The shared autonomous vehicle (SAV) is a new concept that meets the upcoming trends of autonomous driving and changing demands in urban transportation. SAVs can carry passengers and parcels simultaneously, making use of dedicated passenger and parcel modules on board. A fleet of SAVs could partly take over private transport, taxi, and last-mile delivery services. A reduced fleet size compared to conventional transportation modes would lead to less traffic congestion in urban centres. This paper presents a method to estimate the optimal capacity for the passenger and parcel compartments of SAVs. The problem is presented as a vehicle routing problem and is named variable capacity share-a-ride-problem (VCSARP). The model has a MILP formulation and is solved using a commercial solver. It seeks to create the optimal routing schedule between a randomly generated set of pick-up and drop-off requests of passengers and parcels. The objective function aims to minimize the total energy costs of each schedule, which is a trade-off between travelled distance and vehicle capacity. Different scenarios are composed by altering parameters, representing travel demand at different times of the day. The model results show the optimized cost of each simulation along with associated routes and vehicle capacities.

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Autonomous vehicles (AVs) have been heralded as the key to unlock a shared mobility future where transportation is more efficient, convenient, and cheaper. However, the AV utopia can only come to fruition if the majority of users trust that autonomous mobility-on-demand (AMoD) systems are on a par with owning a vehicle in terms of service quality. Once the perception of quality is highly subjective, we propose a more personalized approach to on-demand mobility, in which users are segmented into service quality classes. These classes comprise minimum requirements regarding responsiveness and privacy, allowing us to model a series of user profiles formalized using strict service quality contracts. By honoring these contracts, providers can build users' trust and gain their loyalty, which on a grander scheme can contribute to a faster transition to a shared mobility future. This thesis presents a series of strategies to guaranteeing service quality throughout operational scenarios arising in the timeline of AV technology deployment. First, a precondition to providing service quality in autonomous transportation is safety. During a transition phase to full automation, AV operation will likely be restricted to areas where safe operations are guaranteed, leading to the formation of hybrid street networks comprised of autonomous and non-autonomous vehicle zones. In this setting, meeting user service quality expectations is primarily a matter of coverage, once mobility services will have to access both AV-ready and not AV-ready areas. Accordingly, this thesis proposes solutions to overcome the challenges entailed by such a transition scenario, where infrastructures, regulatory measures, and AV technology are gradually evolving. Then, assuming that widespread automated driving is the new status quo, we set out to model rich autonomous transportation scenarios comprised of heterogeneous users and vehicles. Central to our analysis is finding an adequate tradeoff between fleet size and service quality. In traditional AMoD systems, providers can do only so much to prevent user dissatisfaction since, to some extent, this is a matter of having enough vehicles. When the demand outstrips the supply, users inevitably experience longer delays or even rejections, ultimately undermining trust in the service. However, these shortcomings may plague future transportation systems only if setting the fleet size and mix remains a strategic decision. In contrast to most related literature, this thesis investigates a disseminated AV ownership scenario, where ridesharing platforms can occasionally hire available privately-owned AVs on-demand. In this scenario, customers can simultaneously own and share AVs, a setup that better resembles the operation of today's transportation network companies (TNCs), which rely entirely on micro-operators. As a result, AMoD systems can increase and decrease vehicle supply in the short term, thus shifting fleet sizing to the operational planning level. Moreover, analogously to other transportation modes, we consider that the system must deal with a diversified user base with different service quality expectations. This setup allows providers greater leeway to explore requests' delay tolerances to design efficient routes. To balance user expectations and avoid an oversupply of vehicles, we propose a multi-objective matheuristic that dynamically hires third-party AVs to meet the demand. Our approach adds to recent literature by allowing providers to prioritize different customer segments, besides choosing the exact tradeoff between meeting each segment's needs and hiring extra vehicles. This way, when vehicles are lacking, the optimization process can steer the ride-matching solution towards addressing user requests in order of importance (e.g., most lucrative first). To make the most of currently working vehicles, we also design a repositioning algorithm that fixes supply and demand imbalances using users' service level violations as stimuli. Further, to enable anticipatory decision making, this thesis incorporates the stochastic information surrounding both privately-owned AV supply and heterogeneous passenger demand in the fleet management process. We propose a learning-based optimization approach that uses the underlying assignment problem's dual variables to iteratively approximate the marginal value of vehicles at each time and location under different availability settings. In turn, these approximations are used in the optimization problem's objective function to weigh the downstream impact of dispatching, rebalancing, and occasionally hiring vehicles. By harnessing the historical knowledge regarding both demand and supply patterns, we show that AMoD providers are substantially better equipped to meet user needs without necessarily having to own large AV fleets. Typically, learning-based fleet management strategies end up reinforcing biases present in the demand data, therefore frequently moving towards cities' most affluent and densely populated areas, where alternative mobility choices already abound. Although lucrative for providers, this fleet management strategy runs counter to a broader city goal of equitably distributing accessibility across all regions and population demographics. To counterbalance the demand biases, we investigate the extent to which fare subsidization policies can drive the learning process towards sending vehicles to targeted regions where accessibility is lacking. Our results suggest that by using an adequate scheme of incentives, policymakers can orchestrate transportation providers to diminish the insidious effects of ``cream-skimming'' practices, thus using AVs in favor of mobility equity. Lastly, once we have designed strategies that balance the goals of cities, independent owners, fleet owners, and users, we focus on a different approach to maximizing fleet productivity in urban environments. No matter how efficient a fleet optimization method can be, by limiting AVs to service a single commodity type (i.e., people), fleet utilization and consequently profits are bounded by passenger demand patterns. As autonomous technology evolves, however, new opportunities to improve asset utilization arise. We end this thesis with a model for a versatile transportation system where mixed-purpose compartmentalized AVs can address both passengers and goods simultaneously. With the growth of e-commerce and same-day deliveries, our approach provides a starting point to study more flexible short-haul integration systems to consolidate passenger and freight flows.@en

Residents of cities’ most disadvantaged areas face significant barriers to key life activities, such as employment, education, and healthcare, due to the lack of mobility options. Shared autonomous vehicles (SAVs) create an opportunity to overcome this problem. By learning user demand patterns, SAV providers can improve regional service levels by applying anticipatory relocation strategies that take into consideration when and where requests are more likely to appear. The nature of transportation demand, however, invariably creates learning biases towards servicing cities’ most affluent and densely populated areas, where alternative mobility choices already abound. As a result, current disadvantaged regions may end up perpetually underserviced, therefore preventing all city residents from enjoying the benefits of autonomous mobility-on-demand (AMoD) systems equally. In this study, we propose an anticipatory rebalancing policy based on an approximate dynamic programming (ADP) formulation that processes historical demand data to estimate value functions of future system states iteratively. We investigate to which extent manipulating cost settings, in terms of subsidies and penalties, can adjust the demand patterns naturally incorporated into value functions to improve service levels of disadvantaged areas. We show for a case study in the city of Rotterdam, The Netherlands, that the proposed method can harness these cost schemes to better cater to users departing from these disadvantaged areas, substantially outperforming myopic and reactive benchmark policies.

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Autonomous vehicles (AVs) are expected to widely re-define mobility in the future, transforming many solutions into autonomous services. Nonetheless, this development requires an expected transition phase of several decades in which some regions will provide sufficient infrastructure for AV movements, while others will not support AVs yet. In this work, we propose an operational planning model for mobility services operating in regions with AV-ready and not AV-ready zones. To this end, we model detailed automated driving areas and consider a heterogeneous fleet comprised of three vehicle types: autonomous, conventional, and dual-mode. While autonomous and conventional vehicles can only operate in their designated areas, dual-mode vehicles service zone-crossing demands in which both human and autonomous driving are required. For such a hybrid network, we introduce a new mathematical planning model based on a site-dependent variant of the heterogeneous dial-a-ride problem (HDARP). With a numerical study for the city of Delft, The Netherlands, we provide insights into how operational costs, service levels, and fleet utilization develop under 405 scenarios of multiple infrastructural settings and technology costs.

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In the realm of human urban transportation, many recent studies have shown that comparatively smaller fleets of shared autonomous vehicles (SAVs) are able to provide efficient door-to-door transportation services for city dwellers. However, because of the steady growth of e-commerce and same-day delivery services, new city logistics approaches will also be required to deal with last-mile parcel delivery challenges. We focus on modeling a variation of the people and freight integrated transportation problem (PFIT problem) in which both passenger and parcel requests are pooled in mixed-purpose compartmentalized SAVs. Such vehicles are supposed to combine freight and passenger overlapping journeys on the shared mobility infrastructure network. We formally address the problem as the share-a-ride with parcel lockers problem (SARPLP), implement a mixed-integer linear programming (MILP) formulation, and compare the performance of single-purpose and mixed-purpose fleets on 216 transportation scenarios. For 149 scenarios where the solver gaps of the experimental results are negligible (less than 1%), we have shown that mixed-purpose fleets perform in average 11% better than single-purpose fleets. Additionally, the results indicate that the busier is the logistical scenario the better is the performance of the mixed-purpose fleet setting.

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