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Optimal distribution of regional centers with corresponding reachable locations throughout the street map of Manhattan, New York City, considering a maximal hiring delay of 150s at 30km/h.

Optimal distribution of regional centers with corresponding reachable locations throughout the street map of Manhattan, New York City, considering a maximal hiring delay of 150s at 30km/h.

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Article
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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...

Citations

... It can reduce traffic congestion and energy consumption by reducing the number of cars in public spaces and large cities [2]. It can also reduce the time spent searching for parking and travel costs [3]. Additionally, ridesharing has high positive impacts on environmental factors such as greenhouse gas (GHG) emissions reduction [4,5]. ...
... Recent studies show that travel time is a crucial factor in ride-sharing systems, and matching models consider time constraints in finding suitable rides [2]. However, in most of the studied works, a time window was considered in vehicle routing optimization [3,23], or travel time was considered deterministic [24][25][26][27][28]. To minimize the travel cost, [29] presented an optimization model for ride-sharing routes and cost-sharing problems. ...
... Constraint (3) guarantees that each passenger is picked up by exactly one vehicle. Inequality (4) states that the number of passengers in each vehicle must not exceed the vehicle's capacity. ...
Article
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Ride-sharing services are one of the top growing sustainable transportation trends led by mobility-as-a-service companies. Ridesharing is a system that provides the ability to share vehicles on similar routes for passengers with similar or nearby destinations on short notice, leading to decreased costs for travelers. At the same time, though, it takes longer to get from place to place, increasing travel time. Therefore, a fundamental challenge for mobility service providers should be finding a balance between cost and travel time. This paper develops an integer bi-objective optimization model that integrates vehicle assignment, vehicle routing, and passenger assignment to find a non-dominated solution based on cost and time. The model allows a vehicle to be used multiple times by different passengers. The first objective seeks to minimize the total cost, including the fixed cost, defined as the supply cost per vehicle, and the operating cost, which is a function of the distance traveled. The second objective is to minimize the time it takes passengers to reach their destination. This is measured by how long it takes each vehicle to reach the passenger’s point of origin and how long it takes to get to the destination. The proposed model is solved using the AUGMECON method and the NSGA II algorithm. A real case study from Sioux Falls is presented to validate the applicability of the proposed model. This study shows that ridesharing helps passengers save money using mobility services without significant change in travel time.
... For example, emerging mobility-on-demand platforms aim to satisfy different service quality thresholds for different customer segments (e.g. business, standard, budget) (Beirigo et al. 2019). ...
Article
Full-text available
Vehicle routing problems have been the focus of extensive research over the past sixty years, driven by their economic importance and their theoretical interest. The diversity of applications has motivated the study of a myriad of problem variants with different attributes. In this article, we provide a concise overview of existing and emerging problem variants. Models are typically refined along three lines: considering more relevant objectives and performance metrics, integrating vehicle routing evaluations with other tactical decisions, and capturing fine-grained yet essential aspects of modern supply chains. We organize the main problem attributes within this structured framework. We discuss recent research directions and pinpoint current shortcomings, recent successes, and emerging challenges.
... For example, emerging mobility-on-demand platforms aim to satisfy different service quality thresholds for different customer segments (e.g. business, standard, budget) (Beirigo et al. 2019). ...
Preprint
Full-text available
Vehicle routing problems have been the focus of extensive research over the past sixty years, driven by their economic importance and their theoretical interest. The diversity of applications has motivated the study of a myriad of problem variants with different attributes. In this article, we provide a brief survey of existing and emerging problem variants. Models are typically refined along three lines: considering more relevant objectives and performance metrics, integrating vehicle routing evaluations with other tactical decisions, and capturing fine-grained yet essential aspects of modern supply chains. We organize the main problem attributes within this structured framework. We discuss recent research directions and pinpoint current shortcomings, recent successes, and emerging challenges.
Thesis
Current mobility services cannot compete on equal terms with self-owned mobility products concerning service quality. Due to supply and demand imbalances, ridesharing users invariably experience delays, price surges, and rejections. Traditional approaches often fail to respond to demand fluctuations adequately since service levels are, to some extent, bounded by fleet size. With the emergence of autonomous vehicles (AVs), however, the characteristics of mobility services change, and new opportunities to overcome the prevailing limitations arise. This thesis proposes a series of learning- and optimization-based strategies to help autonomous transportation providers meet the service quality expectations of diversified user bases. We show how autonomous mobility-on-demand (AMoD) systems can develop to revolutionize urban transportation, improving reliability, efficiency, and accessibility.