Ride-sharing services are transforming urban mobility by providing timely and convenient transportation to anybody, anywhere, and anytime. These services present enormous potential for positive societal impacts with respect to pollution, energy consumption, congestion, etc. Current mathematical models, however, do not fully address the potential of ride-sharing. Recently, a large-scale study highlighted some of the benefits of car pooling but was limited to static routes with two riders per vehicle (optimally) or three (with heuristics). We present a more general mathematical model for real-time high-capacity ride-sharing that (i) scales to large numbers of passengers and trips and (ii) dynamically generates optimal routes with respect to online demand and vehicle locations. The algorithm starts from a greedy assignment and improves it through a constrained optimization, quickly returning solutions of good quality and converging to the optimal assignment over time. We quantify experimentally the tradeoff between fleet size, capacity, waiting time, travel delay, and operational costs for low- to medium-capacity vehicles, such as taxis and van shuttles. The algorithm is validated with ∼3 million rides extracted from the New York City taxicab public dataset. Our experimental study considers ride-sharing with rider capacity of up to 10 simultaneous passengers per vehicle. The algorithm applies to fleets of autonomous vehicles and also incorporates rebalancing of idling vehicles to areas of high demand. This framework is general and can be used for many real-time multivehicle, multitask assignment problems.

https://www.pnas.org/content/pnas/114/3/462.full.pdf

On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment

Optimization for dynamic ride-sharing: A review

▶ Addresses the accessibility, usability, and, ultimately, acceptability of Information Society Technologies by anyone, anywhere, at anytime, and through any media and device. ▶ Focuses on theoretical, methodological, and empirical research, of both technological and non-technological nature. ▶ Features papers that report on theories, methods, tools, empirical results, reviews, case studies, and best-practice examples.

Universal access in the information society

Although ridesharing can provide a wealth of benefits, such as reduced travel costs, congestion, and consequently less pollution, there are a number of challenges that have restricted its widespread adoption. In fact, even at a time when improving communication systems provide real-time detailed information that could be used to facilitate ridesharing, the share of work trips that use ridesharing has decreased by almost 10% in the past 30 years. In this paper we present a classification to understand the key aspects of existing ridesharing systems. The objective is to present a framework that can help identify key challenges in the widespread use of ridesharing and thus foster the development of effective formal ridesharing mechanisms that would overcome these challenges and promote massification.

/pdf/ridesharing-the-state-of-the-art-and-future-directions-4pv1nzkocg.pdf

Ridesharing: the state-of-the-art and future directions

In this review, we provide an overview of emerging trends and challenges in the field of intelligent and autonomous, or self-driving, vehicles. Recent advances in the field of perception, planning,...

Planning and Decision-Making for Autonomous Vehicles

Smartphone technology enables dynamic ride-sharing systems that bring together people with similar itineraries and time schedules to share rides on short-notice. This paper considers the problem of matching drivers and riders in this dynamic setting. We develop optimizationbased approaches that aim at minimizing the total system-wide vehicle miles and individual travel costs. To assess the merits of our methods we present a simulation study based on 2008 travel demand data from metropolitan Atlanta. The simulation results indicate that the use of sophisticated optimization methods instead of simple greedy matching rules may substantially improve the performance of ride-sharing systems. Furthermore, even with relatively low participation rates, it appears that sustainable populations of dynamic ride-sharing participants may be possible even in relatively sprawling urban areas with many employment centers.

/pdf/the-value-of-optimization-in-dynamic-ride-sharing-a-q2nsobe0lt.pdf

The Value of Optimization in Dynamic Ride-Sharing: a Simulation Study in Metro Atlanta

textRide-share systems, which aim to bring together travelers with similar itineraries and time schedules, may provide significant societal and environmental benefits by reducing the number of cars used for personal travel and improving the utilization of available seat capacity. Effective and efficient optimization technology that matches drivers and riders in real-time is one of the necessary components for a successful ride-share system. We formally define dynamic ride-sharing and outline the optimization challenges that arise when developing technology to support ride-sharing. We hope that this paper will encourage more research by the transportation science and logistics community in this exciting, emerging area of public transportation.

/pdf/sustainable-passenger-transportation-dynamic-ride-sharing-s36eiw5nh9.pdf

Sustainable Passenger Transportation: Dynamic Ride-Sharing

Ride-share systems, which aim to bring together travelers with similar itineraries and time schedules, may provide significant societal and environmental benefits by reducing the number of cars used for personal travel and improving the utilization of available seat capacity. Effective and efficient optimization technology that matches drivers and riders in real-time is one of the necessary components for a successful ride-share system. We formally define dynamic ride-sharing and outline the optimization challenges that arise when developing technology to support ride-sharing. We hope that this paper will encourage more research by the transportation science and logistics community in this exciting, emerging area of public transportation.

The trend towards shorter delivery lead-times reduces operational efficiency and increases transportation costs for internet retailers. Mobile technology, however, creates new opportunities to organize the last-mile. In this paper, we study the concept of crowdsourced delivery that aims to use excess capacity on journeys that already take place to make deliveries. We consider a peer-to-peer platform that automatically creates matches between parcel delivery tasks and ad-hoc drivers. The matching of tasks and drivers gives rise to a new variant of the dynamic pick-up and delivery problem. We propose a rolling horizon framework and develop an exact solution approach to solve the various subproblems. In order to investigate the potential benefit of crowdsourced delivery, we conduct a wide range of computational experiments. The experiments provide insights into the viability of crowdsourced delivery under various assumptions about the environment and the behavior of the ad-hoc drivers. The results suggest that the use of ad-hoc drivers has the potential to make the last-mile more cost-efficient and environmentally friendly.

Crowdsourced Delivery - a Pickup and Delivery Problem with Ad-hoc Drivers

The growth of online retail leads to increasing last-mile delivery operations that contribute to various negative externalities, such as traffic congestion and air pollution, especially in urban areas. One way to improve urban delivery operations is to use public transport capacity to move goods to intermediate transfer locations from which they can be delivered by (small) vehicles to the final customers. We study the distance savings that can be achieved by such a two-tier urban delivery system. In particular, we focus on determining which transit stop is best located to be used as a transfer location. We present several special cases to get insights into the transfer location decisions. Moreover, we present a mixed-integer linear programming formulation and a heuristic to solve it. To evaluate the different approaches, we run several computational studies. We also perform a sensitivity analysis to assess the impact of different system parameters on the location decisions and system performance. For very conservative benchmarks, the results show that savings up to 7.1 percent are possible from using public transport capacity to support urban delivery. The savings increase with the distance to the depot, tighter deadlines and customers that are clustered around the transit line.

Niels Agatz

Papers

The Value of Optimization in Dynamic Ride-Sharing: a Simulation Study in Metro Atlanta

Sustainable Passenger Transportation: Dynamic Ride-Sharing

Sustainable Passenger Transportation: Dynamic Ride-Sharing

Crowdsourced Delivery - a Pickup and Delivery Problem with Ad-hoc Drivers

Designing integrated urban delivery systems using public transport