The Impact of Fleet Coordination on Taxi Operations

On-demand mobility has existed for more than 100 years in the form of taxi systems. Comparatively recently, ride-hailing schemes have also grown to a significant mode share. Most types of such one-way mobility-on-demand systems allow drivers taking independent decisions. These systems are not or only partially coordinated. In a different operating mode, all decisions are coordinated by the operator, allowing for the optimization of certain metrics. Such a coordinated operation is also implied if human-driven vehicles are replaced by self-driving cars. This work quantifies the service quality and efficiency improvements resulting from the coordination of taxi fleets. Results based on high-fidelity transportation simulations and data sets of existing taxi systems are presented for the cities of San Francisco, Chicago, and Zurich. They show that fleet coordination can strongly improve the efficiency and service level of existing systems. Depending on the operator and the city’s preferences, empty vehicle distance driven and fleet sizes could be substantially reduced, or the wait times could be reduced while maintaining the current fleet sizes. The study provides clear evidence that full fleet coordination should be implemented in existing mobility-on-demand systems, even before the availability of self-driving cars.

Analysis and Control of Autonomous Mobility-on-Demand Systems

Challenged by urbanization and increasing travel needs, existing transportation systems need new mobility paradigms. In this article, we present the emerging concept of autonomous mobility-on-demand, whereby centrally orchestrated fleets of autonomous vehicles provide mobility service to customers. We provide a comprehensive review of methods and tools to model and solve problems related to autonomous mobility-on-demand systems. Specifically, we first identify problem settings for their analysis and control, from both operational and planning perspectives. We then review modeling aspects, including transportation networks, transportation demand, congestion, operational constraints, and interactions with existing infrastructure. Thereafter, we provide a systematic analysis of existing solution methods and performance metrics, highlighting trends and trade-offs. Finally, we present various directions for further research. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 5 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Optimal Operations Management of Mobility-on-Demand Systems

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%.

Analisis Permintaan Bubuk Kayu Manis Indonesia di Pasar Dunia

Bubuk kayu manis (kode HS 090620) adalah salah satu produk turunan rempah yang paling banyak diekspor oleh Indonesia dan volumenya menurun dalam beberapa tahun terakhir dibandingkan negara pesaingnya, yaitu Vietnam. Analisis permintaan dilakukan untuk menilai posisi persaingan pasar bubuk kayu manis Indonesia di pasar dunia menggunakan metode almost ideal demand systems. Hasilnya menunjukkan bahwa pada pasar Amerika Serikat dan Jerman, Indonesia menjadi eksportir tebesar, sedangkan pada pasar Kanada, bubuk kayu manis Vietnam menempati posisi tertinggi. Berdasarkan elastisitas permintaan, bubuk kayu manis Indonesia adalah produk yang bersifat inelastis dan saling bersubtitusi dengan pesaingnya, yaitu Vietnam di pasar importir Amerika Serikat, Jerman, dan Kanada. Agar dapat terus berdaya saing dalam perdagangan bubuk kayu manis, perlu ada perbaikan mutu produk sesuai dengan sertifikasi internasional serta menjalin kerja sama bilateral. Kata kunci: almost ideal demand systems model, bubuk kayu manis, pasar, persaingan