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Rotterdam regions (Noord and Zuid) and the seven train stations that are the destinations of all first-mile trips. We show that a proper cost scheme setup can overcompensate the rebalancing bias towards densely populated and high-income areas improving mobility choices in underserviced areas.
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Residents of cities' most disadvantaged areas face significant barriers to key life activities, such as employment, education, and health-care, 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...
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Citations
... Zhang et al. (2015) also model the user's willingness to share rides and adapt their waiting tolerances from random hourly incomes. Beirigo et al. (2020), on the other hand, consider equity aspects of service quality for AMoD in areas with limited 145 public transit access. Addressing heterogeneous user requirements is also common outside transportation-on-demand formulations. ...
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.
Transport poverty (TP) is a widespread problem that affects individuals and communities all over the world, restricting access to services, education, job prospects, and social contacts. However, advancement of technology has emerged in easing TP by improving accessibility, affordability, and efficiency in transport systems. The advancements in electric and hybrid vehicles, ridesharing platforms, autonomous vehicles, and micro-mobility have contributed to addressing the challenges faced by communities experiencing TP. By promoting these technological innovations and inclusive transport policies, societies can potentially strive towards a more equitable and sustainable future. The paper studies the influence of technology on TP and its potential to ameliorate issues related. Moreover, it discusses various aspects of novel technologies on TP and presents carefully selected (dis)advantages of certain solutions’ applications.
This paper shows that the prediction of vessel arrival times with AIS (Automatic Identification System) is increasing the number of vessels a port can handle without additional superstructure. The Port of Hamburg is used as a case study to show the difference between the as-is situation and one with the integrated information system. The simulation shows improvements with two different risk levels to prove the concept. The simulation uses simplified versions of an algorithm that assigns vessels to free berths without disrupting the normal terminal usage. It was possible to clear up to 44% more ships each day just with an additional system that utilises already existing data for achieving more efficiency within the port.
This paper presents a solution for a real world roadside assistance problem. Roadside assistance companies must allocate their specialised resources to minimize the operating cost associated with servicing when incidents occur. In this process, the location of these resources plays an important role. Therefore, this work proposes a study on the forecasting of incidents and their impact on the location of resources and operating costs. To do this, we have built a machine learning model competition enriched with new features drawn from traditional time series methods and external data such as weather, holidays, and client portfolios. The results show a significant reduction in operating costs thanks to the forecasting of incidents.
The craft beer supply chain in the USA differs from the supply chain of macro breweries in its structure, handled volumes and product shelf-life. In this work, we study how these smaller craft breweries can benefit from transparency in their supply chain. We consider additional information sharing of orders and inventories at downstream nodes. The levels that we investigate grant the brewery incremental access to distributor, wholesaler, and retailer data. We show how this knowledge can be incorporated effectively into the brewery’s production planning strategy. Extending the well-known beer game, we conduct a simulation study using real-world craft beer supply chain parameters and demand. We quantify the impact of information sharing on the craft brewery’s sales, spoilage, and beer quality. Our model is designed to directly support the brewery when evaluating the value of downstream information and negotiating data purchases with brokers. Through a computational analysis, we show that the brewery’s benefits increase almost linearly with every downstream node that it gets data from. Full transparency allows to halve the missed beer sales, and beer spoilage can even be reduced by 70% on average.