The Merits of Sharing a Ride

The culture of sharing instead of ownership is sharply increasing in individuals behaviors. Particularly in transportation, concepts of sharing a ride in either carpooling or ridesharing have been recently adopted. An efficient optimization approach to match passengers in real-time is the core of any ridesharing system. In this paper, we model ridesharing as an online matching problem on general graphs such that passengers do not drive private cars and use shared taxis. We propose an optimization algorithm to solve it. The outlined algorithm calculates the optimal waiting time when a passenger arrives. This leads to a matching with minimal overall overheads while maximizing the number of partnerships. To evaluate the behavior of our algorithm, we used NYC taxi real-life data set. Results represent a substantial reduction in overall overheads

On the interaction between Autonomous Mobility-on-Demand systems and the power network: models and coordination algorithms

We study the interaction between a fleet of electric, self-driving vehicles servicing on-demand transportation requests (referred to as Autonomous Mobility-on-Demand, or AMoD, system) and the electric power network. We propose a model that captures the coupling between the two systems stemming from the vehicles' charging requirements and captures time-varying customer demand and power generation costs, road congestion, battery depreciation, and power transmission and distribution constraints. We then leverage the model to jointly optimize the operation of both systems. We devise an algorithmic procedure to losslessly reduce the problem size by bundling customer requests, allowing it to be efficiently solved by off-the-shelf linear programming solvers. Next, we show that the socially optimal solution to the joint problem can be enforced as a general equilibrium, and we provide a dual decomposition algorithm that allows self-interested agents to compute the market clearing prices without sharing private information. We assess the performance of the mode by studying a hypothetical AMoD system in Dallas-Fort Worth and its impact on the Texas power network. Lack of coordination between the AMoD system and the power network can cause a 4.4% increase in the price of electricity in Dallas-Fort Worth; conversely, coordination between the AMoD system and the power network could reduce electricity expenditure compared to the case where no cars are present (despite the increased demand for electricity) and yield savings of up $147M/year. Finally, we provide a receding-horizon implementation and assess its performance with agent-based simulations. Collectively, the results of this paper provide a first-of-a-kind characterization of the interaction between electric-powered AMoD systems and the power network, and shed additional light on the economic and societal value of AMoD.Comment: Extended version of the paper presented at Robotics: Science and Systems XIV and accepted by TCNS. In Version 4, the body of the paper is largely rewritten for clarity and consistency, and new numerical simulations are presented. All source code is available (MIT) at https://dx.doi.org/10.5281/zenodo.324165

On the interaction between Autonomous Mobility-on-Demand systems and the power network: models and coordination algorithms

We study the interaction between a fleet of electric, self-driving vehicles servicing on-demand transportation requests (referred to as Autonomous Mobility-on-Demand, or AMoD, system) and the electric power network. We propose a model that captures the coupling between the two systems stemming from the vehicles' charging requirements and captures time-varying customer demand and power generation costs, road congestion, battery depreciation, and power transmission and distribution constraints. We then leverage the model to jointly optimize the operation of both systems. We devise an algorithmic procedure to losslessly reduce the problem size by bundling customer requests, allowing it to be efficiently solved by off-the-shelf linear programming solvers. Next, we show that the socially optimal solution to the joint problem can be enforced as a general equilibrium, and we provide a dual decomposition algorithm that allows self-interested agents to compute the market clearing prices without sharing private information. We assess the performance of the mode by studying a hypothetical AMoD system in Dallas-Fort Worth and its impact on the Texas power network. Lack of coordination between the AMoD system and the power network can cause a 4.4% increase in the price of electricity in Dallas-Fort Worth; conversely, coordination between the AMoD system and the power network could reduce electricity expenditure compared to the case where no cars are present (despite the increased demand for electricity) and yield savings of up $147M/year. Finally, we provide a receding-horizon implementation and assess its performance with agent-based simulations. Collectively, the results of this paper provide a first-of-a-kind characterization of the interaction between electric-powered AMoD systems and the power network, and shed additional light on the economic and societal value of AMoD.Comment: Extended version of the paper presented at Robotics: Science and Systems XIV, in prep. for journal submission. In V3, we add a proof that the socially-optimal solution can be enforced as a general equilibrium, a privacy-preserving distributed optimization algorithm, a description of the receding-horizon implementation and additional numerical results, and proofs of all theorem

A dynamic ridesharing dispatch and idle vehicle repositioning strategy with integrated transit transfers

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

CoRide: Joint Order Dispatching and Fleet Management for Multi-Scale Ride-Hailing Platforms

How to optimally dispatch orders to vehicles and how to tradeoff between immediate and future returns are fundamental questions for a typical ride-hailing platform. We model ride-hailing as a large-scale parallel ranking problem and study the joint decision-making task of order dispatching and fleet management in online ride-hailing platforms. This task brings unique challenges in the following four aspects. First, to facilitate a huge number of vehicles to act and learn efficiently and robustly, we treat each region cell as an agent and build a multi-agent reinforcement learning framework. Second, to coordinate the agents from different regions to achieve long-term benefits, we leverage the geographical hierarchy of the region grids to perform hierarchical reinforcement learning. Third, to deal with the heterogeneous and variant action space for joint order dispatching and fleet management, we design the action as the ranking weight vector to rank and select the specific order or the fleet management destination in a unified formulation. Fourth, to achieve the multi-scale ride-hailing platform, we conduct the decision-making process in a hierarchical way where a multi-head attention mechanism is utilized to incorporate the impacts of neighbor agents and capture the key agent in each scale. The whole novel framework is named as CoRide. Extensive experiments based on multiple cities real-world data as well as analytic synthetic data demonstrate that CoRide provides superior performance in terms of platform revenue and user experience in the task of city-wide hybrid order dispatching and fleet management over strong baselines.Comment: CIKM 201

대안 승하차지점 탐색을 통한 승차공유 서비스의 배차 알고리즘 개발

학위논문(석사) -- 서울대학교대학원 : 공과대학 건설환경공학부, 2023. 2. 김동규.Ride-pooling has significantly enhanced the system efficiency in current on-demand ride-sharing services. However, as the numbers of on-board passengers increase, more detours inevitably occur since it provides door-to-door service for everyone. To solve this problem, we focus on rider-participating dispatch by searching walking points, equivalent to alternative pick-up points from origins and alternative drop-off points from destinations. Based on the existing framework for large-scale ride-pooling, we develop our walking point search algorithm, which finds cost-minimizing alternatives. In addition, our approach enables the model to reflect the sensitivity of riders to given walking points by introducing the probability of riders acceptance. We conduct a simulation with the Yellow Cap Taxi dataset in New York City to validate and compare with the base model, which does not include walking. The results show an increase from 69.56% to 77.84% in the service rate, an improvement of 18.2% in delay time, and 8.6% in in-vehicle time. With the increased service rate, the average travel times of vehicles are reduced by 1.5%, allowing drivers to spend more time rebalancing. Furthermore, we show that the effect of walking is maximized in high-demand areas during peak hours. This study demonstrates that walking can substantially enhance operational efficiency, mitigating the supply-demand imbalance with limited fleets. The proposed model can also be utilized in optimizing the meeting points for various high-capacity vehicles, such as on-demand shuttles.Ride-pooling 서비스는 기존의 ride-sharing 서비스의 시스템 효율성을 크게 증대시켰다. 하지만 여러 명의 승객들이 하나의 차량에 동승하여 운행하는 특성으로 인해, 동승하는 승객들이 많을수록 승객당 통행 지체 시간이 길어진다는 단점이 존재한다. 이러한 문제를 해결하기 위해, 본 연구는 승객들의 기존 승하차 지점으로부터 걸어서 도달 가능한 대안 승하차 지점을 최적화하는 알고리즘을 제안한다. 기존의 승객-차량 배정 프레임워크를 기반으로, 비용을 최소화하는 대안 승하차지점 탐색 알고리즘을 구현하였다. 또한, 도보 이동 거리에 대한 승차 수락률 모델을 통해 승객들의 도보 이동에 대한 민감도를 반영하였다. 뉴욕시티의 옐로우캡 택시 데이터를 이용한 하루 동안의 시뮬레이션을 통해 제안한 모델을 검증하고 기존 모델과의 비교를 수행하였다. 시뮬레이션 결과, 서비스율은 69.56%에서 77.84%로 증가하였으며, 통행 지체 시간은 평균적으로 18.2% 감소하였고, 차내 시간은 8.6% 감소하였다. 서비스율의 증가와 함께, 차량들의 평균 총 운행 시간은 1.5% 감소하였고 이는 차량 재배치 시간의 증가로 이어짐을 확인하였다. 또한, 시뮬레이션 결과를 시공간 상에서 분석함으로서, 피크 시간대에 수요가 밀집되는 지역에서 도보 이동의 효과가 극대화됨을 보였다. 본 연구는 도보 이동을 통한 대안 승하차지점 이용이 ride-pooling 서비스의 운영 효율성을 향상시키며, 제한된 수의 공급 대수로 수요-공급의 불균형을 해소시킴을 증명하였다. 제안된 모델은 택시 뿐 아니라 수요응답형 셔틀 등 다양한 종류의 다인승 차량 서비스에서 승객과 차량의 승하차 지점을 최적화하는 데에 활용될 수 있다.Chapter 1. Introduction 1 Chapter 2. Literature Review 4 Chapter 3. Methods 9 3.1 Preliminaries 9 3.2 General Formulation of the Ride-Pooling Problem 9 3.3 Walking Points Search Algorithm 11 3.4 Rider Acceptance Probability with Walking Distance 14 Chapter 4. Results 17 4.1 Simulation Settings 17 4.2 Simulation Results 18 4.2.1 Comparison with the base model 18 4.2.2 Analysis of WSM 24 Chapter 5. Conclusion 28 Bibliography 30 Abstract in Korean 35석

Dynamic pricing or not?:pricing models of Finnish taxi dispatch centers under the act on transport services

Abstract. In 1.7.2018, Finnish government liberalized Finnish taxi markets to create possibilities to introduce new technology, digitalization and new business models into transport sector. Also allowing usage of dynamic pricing in Finnish taxi markets was specifically mentioned. Before the Act on Transport Services came into effect, Finnish regulations specified maximum limits for fares, taxi licenses and operational area where dispatch centers were allowed to operate. In this study, I will look into how pricing models have evolved after the Act on Transport Services came into effect by collecting data from internet and conducting interviews to understand purposes of the changes more in-depth. Also, I looked into how pricing models have become more dynamic compared to old pricing model, and how dynamic pricing is described in literature. Lastly, I combined list of different aspects that affect to implementation of dynamic pricing. Currently, none of the Finnish dispatch centers have implemented similar dynamic pricing based on demand and supply in real-time as what Uber uses. But based on results, Finnish dispatch center’s pricing models have evolved to be more dynamic even though Finnish dispatch center’s do not consider them to be dynamic. Also, there are obstacles related to willingness, technology and regulations why Finnish dispatch centers do not consider dynamic pricing similar to what Uber uses to be currently possible to implement