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Abstract

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.

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The market potential of a fleet of shared autonomous electric vehicles (SAEVs) is explored by using a multinomial logit mode choice model in an agent-based framework and different fare settings. The mode share of SAEVs in the simulated midsize city (modeled roughly after Austin, Texas) is predicted to lie between 14% and 39% when the SAEVs compete with privately owned, manually driven vehicles and city bus service. The underlying assumptions are that SAEVs are priced between $0.75/mi and $1.00/mi, which delivers significant net revenues to the fleet owner– operator under all modeled scenarios; that they have an 80-mi range and that Level 2 charging infrastructure is available; and that automation costs are up to $25,000 per vehicle. Various dynamic pricing schemes for SAEV fares indicate that specific fleet metrics can be improved with targeted strategies. For example, pricing strategies that attempt to balance available SAEV supply with anticipated trip demand can decrease average wait times by 19% to 23%. However, trade-offs exist within this price setting: fare structures that favor higher revenue-to-cost ratios— by targeting travelers with a high value of travel time (VOTT)—reduce SAEV mode shares, while those that favor larger mode shares—by appealing to a wider VOTT range—produce lower payback.
Article
Shared autonomous vehicles (SAVs) could provide inexpensive mobility on-demand services. In addition, the autonomous vehicle technology could facilitate the implementation of dynamic ride-sharing (DRS). The widespread adoption of SAVs could provide benefits to society, but also entail risks. For the design of effective policies aiming to realize the advantages of SAVs, a better understanding of how SAVs may be adopted is necessary. This article intends to advance future research about the travel behavior impacts of SAVs, by identifying the characteristics of users who are likely to adopt SAV services and by eliciting willingness to pay measures for service attributes. For this purpose, a stated choice survey was conducted and analyzed, using a mixed logit model. The results show that service attributes including travel cost, travel time and waiting time may be critical determinants of the use of SAVs and the acceptance of DRS. Differences in willingness to pay for service attributes indicate that SAVs with DRS and SAVs without DRS are perceived as two distinct mobility options. The results imply that the adoption of SAVs may differ across cohorts, whereby young individuals and individuals with multimodal travel patterns may be more likely to adopt SAVs. The methodological limitations of the study are also acknowledged. Despite a potential hypothetical bias, the results capture the directionality and relative importance of the attributes of interest.
Article
This paper deals with a combinatorial optimization problem that models situations of both dynamic ride-sharing and taxi-sharing. Passengers who want to share a taxi or a ride, use an app to specify their current location, destination and further information such as the earliest departure time, the latest arrival time and the maximum cost they are willing to pay for the ride. Car owners also specify their origin, destination, the leaving time and the maximum accepted delay. Taxi drivers report their location and the time they will start and end the service. All drivers need to define a price per kilometer. The problem is to compute routes, matching requests to vehicles in such a way that ride-sharing is allowed as long as some restrictions are satisfied, such as: the capacity of the vehicle, maximum trip cost of each passenger, maximum delay, etc. The problem is dynamic since new requests arrive on-line and routes can be modified in order to attend them. To solve this dynamic problem, the day is divided in time periods. For each period, an instance of a static problem is created and solved by a greedy randomized adaptive search procedure (GRASP). Experiments with instances based on real data were made to evaluate the heuristics and the proposed method. In our simulations with taxis, passengers paid, on average, almost 30% less than they would pay on private rides.
Article
Taxi services are a vital part of urban transportation, and a considerable contributor to traffic congestion and air pollution causing substantial adverse effects on human health. Sharing taxi trips is a possible way of reducing the negative impact of taxi services on cities, but this comes at the expense of passenger discomfort quantifiable in terms of a longer travel time. Due to computational challenges, taxi sharing has traditionally been approached on small scales, such as within airport perimeters, or with dynamical ad hoc heuristics. However, a mathematical framework for the systematic understanding of the tradeoff between collective benefits of sharing and individual passenger discomfort is lacking. Here we introduce the notion of shareability network, which allows us to model the collective benefits of sharing as a function of passenger inconvenience, and to efficiently compute optimal sharing strategies on massive datasets. We apply this framework to a dataset of millions of taxi trips taken in New York City, showing that with increasing but still relatively low passenger discomfort, cumulative trip length can be cut by 40% or more. This benefit comes with reductions in service cost, emissions, and with split fares, hinting toward a wide passenger acceptance of such a shared service. Simulation of a realistic online system demonstrates the feasibility of a shareable taxi service in New York City. Shareability as a function of trip density saturates fast, suggesting effectiveness of the taxi sharing system also in cities with much sparser taxi fleets or when willingness to share is low.
Conference Paper
Taxi ridesharing can be of significant social and environmental benefit, e.g. by saving energy consumption and satisfying people's commute needs. Despite the great potential, taxi ridesharing, especially with dynamic queries, is not well studied. In this paper, we formally define the dynamic ridesharing problem and propose a large-scale taxi ridesharing service. It efficiently serves real-time requests sent by taxi users and generates ridesharing schedules that reduce the total travel distance significantly. In our method, we first propose a taxi searching algorithm using a spatio-temporal index to quickly retrieve candidate taxis that are likely to satisfy a user query. A scheduling algorithm is then proposed. It checks each candidate taxi and inserts the query's trip into the schedule of the taxi which satisfies the query with minimum additional incurred travel distance. To tackle the heavy computational load, a lazy shortest path calculation strategy is devised to speed up the scheduling algorithm. We evaluated our service using a GPS trajectory dataset generated by over 33,000 taxis during a period of 3 months. By learning the spatio-temporal distributions of real user queries from this dataset, we built an experimental platform that simulates user real behaviours in taking a taxi. Tested on this platform with extensive experiments, our approach demonstrated its efficiency, effectiveness, and scalability. For example, our proposed service serves 25% additional taxi users while saving 13% travel distance compared with no-ridesharing (when the ratio of the number of queries to that of taxis is 6).
Article
Carsharing programs that operate as short-term vehicle rentals (often for one-way trips before ending the rental) like Car2Go and ZipCar have quickly expanded, with the number of US users doubling every 1–2 years over the past decade. Such programs seek to shift personal transportation choices from an owned asset to a service used on demand. The advent of autonomous or fully self-driving vehicles will address many current carsharing barriers, including users’ travel to access available vehicles.This work describes the design of an agent-based model for shared autonomous vehicle (SAV) operations, the results of many case-study applications using this model, and the estimated environmental benefits of such settings, versus conventional vehicle ownership and use. The model operates by generating trips throughout a grid-based urban area, with each trip assigned an origin, destination and departure time, to mimic realistic travel profiles. A preliminary model run estimates the SAV fleet size required to reasonably service all trips, also using a variety of vehicle relocation strategies that seek to minimize future traveler wait times. Next, the model is run over one-hundred days, with driverless vehicles ferrying travelers from one destination to the next. During each 5-min interval, some unused SAVs relocate, attempting to shorten wait times for next-period travelers.Case studies vary trip generation rates, trip distribution patterns, network congestion levels, service area size, vehicle relocation strategies, and fleet size. Preliminary results indicate that each SAV can replace around eleven conventional vehicles, but adds up to 10% more travel distance than comparable non-SAV trips, resulting in overall beneficial emissions impacts, once fleet-efficiency changes and embodied versus in-use emissions are assessed.
Article
This paper extends the model of urban taxi services in congested networks to the case of multiple user classes, multiple taxi modes, and customer hierarchical modal choice. There are several classes of customers with different values of time and money, and several modes of taxi services with distinct combinations of service area restrictions and fare levels. The multi-class multi-mode formulation allows the modeling of both mileage-based and congestion-based taxi fare charging mechanisms in a unified framework, and can more realistically model most urban taxi services, which are charged on the basis of both time and distance. The introduction of multiple taxi modes can also be used to model the differentiation between luxury taxis and normal taxis by their respective service areas and customer waiting times. We propose a simultaneous mathematical formulation of two equilibrium sub-problems for the model. One sub-problem is a combined network equilibrium model (CNEM) that describes the hierarchical logit mode choice model of occupied taxis and normal traffic, together with the vacant taxi distributions in the network. The other sub-problem is a set of linear and nonlinear equations (SLNE), which ensures the satisfaction of the relation between taxi and customer waiting times, the relation between customer demand and taxi supply for each taxi mode, and taxi service time constraints. The CNEM can be formulated as a variational inequality program that is solvable by means of a block Gauss–Seidel decomposition approach coupled with the method of successive averages. The SLNE can be solved by a Newtonian algorithm with a line search. The CNEM is formulated as a special case of the general travel demand model so that it is possible to incorporate the taxi model into an existing package as an add-on module, in which the algorithm for the CNEM is built in practice. Most of the parameters are observable, given that such a calibrated transport planning model exists. A numerical example is used to demonstrate the effectiveness of the proposed methodology.
Article
Abstract The Dial-a-Ride Problem (DARP) consists of designing vehicle routes and schedules for n users who specify pickup and delivery requests between origins and destinations. The aim is to plan a set of m minimum,cost vehicle routes capable of accommodating,as many users as possible, under a set of constraints. The most common example arises in door-to-door transportation for elderly or disabled people. The purpose of this article is to review the scientific literature on the DARP. The main features of the problem are described and a summary,of the most important models and algorithms is provided. Key Words: Dial-a-ride problem – survey – static and dynamic pickup and delivery
Article
In the dial-a-ride problem, users formulate requests for transportation from a specific origin to a specific destination. Transportation is carried out by vehicles providing a shared service. The problem consists of designing a set of minimum cost vehicle routes satisfying capacity, duration, time window, pairing, precedence and ride time constraints. This paper introduces a mixed-integer programming formulation of the problem and a branch-and-cut algorithm. The algorithm uses new valid inequalities for the dial-a-ride problem as well as known valid inequalities for the pickup and delivery and the vehicle routing problems. Computational experiments performed on randomly generated instances show that the proposed approach can be used to solve small to medium size instances. Subject classifications: Transportation: vehicle routing. Programming: cutting plane. Area of review: Transportation.
Article
Few studies have been conducted on the quality of service provided by the organizations responsible for the operation of dial-a-ride services for people with reduced mobility. In order to study quality in such contexts, the paper first provides several definitions of quality in the service sector. Then, some models based on the definitions of quality in marketing will be quickly surveyed, and various measurement scales will be presented. After a brief review of the tools and practices used in the service sector to improve quality, the notion of quality in dial-a-ride services is discussed more in depth. In particular, the dimensions and attributes of various scales of measurement used by researchers in dial-a-ride studies are reviewed. Finally, the impact on quality of various elements, like the size and type of organization and the operational rules used, are discussed.
Article
In the last decade, there has been an increasing body of research in dynamic vehicle routing problems. This article surveys the subclass of those problems called dynamic pickup and delivery problems, in which objects or people have to be collected and delivered in real-time. It discusses some general issues as well as solution strategies.
Autonomous vehicle implementation predictions
  • T Litman
Litman, T., 2017. Autonomous vehicle implementation predictions.
To build the RV graph, we determine suitable pairs for 1120 requests in parallel, whereas to build ERTV, we create vehicle visiting plans in parallel
  • A Appendix
Appendix A. Execution time vs. solution quality We decrease execution time by parallelizing the construction of both the RV and ERTV graphs. To build the RV graph, we determine suitable pairs for 1120 requests in parallel, whereas to build ERTV, we create vehicle visiting plans in parallel, exploring increasingly longer plans for each vehicle separately.