The fast advances in autonomous driving technology prompt the question of suitable operational models for future autonomous vehicles. A key determinant of the viability of such operational models is the competitiveness of their cost structures. Using a comprehensive analysis of the respective cost structures, this research shows that public transportation (in its current form) will only remain economic in situations which allow substantial bundling of demand. In all other cases, shared and pooled vehicles serve the travel demand more efficiently. In contrast to the general opinion, shared fleets may not be the most efficient alternative due to higher efforts for vehicle cleaning. Moreover, a substantial share of vehicles may remain in private possession and use.

Cost-based analysis of autonomous mobility services

Motivated by the growth of ridesourcing services and the expected advent of fully-autonomous vehicles (AVs), this paper defines, models, and compares assignment strategies for a shared-use AV mobility service (SAMS). Specifically, the paper presents the on-demand SAMS with no shared rides, defined as a fleet of AVs, controlled by a central operator, that provides direct origin-to-destination service to travelers who request rides via a mobile application and expect to be picked up within a few minutes. The underlying operational problem associated with the on-demand SAMS with no shared rides is a sequential (i.e. dynamic or time-dependent) stochastic control problem. The AV fleet operator must assign AVs to open traveler requests in real-time as traveler requests enter the system dynamically and stochastically. As there is likely no optimal policy for this sequential stochastic control problem, this paper presents and compares six AV-traveler assignment strategies (i.e. control policies). An agent-based simulation tool is employed to model the dynamic system of AVs, travelers, and the intelligent SAMS fleet operator, as well as, to compare assignment strategies across various scenarios. The results show that optimization-based AV-traveler assignment strategies, strategies that allow en-route pickup AVs to be diverted to new traveler requests, and strategies that incorporate en-route drop-off AVs in the assignment problem, reduce fleet miles and decrease traveler wait times. The more-sophisticated AV-traveler assignment strategies significantly improve operational efficiency when fleet utilization is high (e.g. during the morning or evening peak); conversely, when fleet utilization is low, simply assigning traveler requests sequentially to the nearest idle AV is comparable to more-advanced strategies. Simulation results also indicate that the spatial distribution of traveler requests significantly impacts the empty fleet miles generated by the on-demand SAMS.

Dynamic Autonomous Vehicle Fleet Operations: Optimization-Based Strategies to Assign AVs to Immediate Traveler Demand Requests

Three design problems are discussed in this article. First, it is shown that the network design problem with congestion reduces to an all-or nothing traffic assignment problem under some assumptions on the congestion function and the investment cost function. Second, the land use design problem is formulated as an extension of the Koopmans-Beckmann problem and a heuristic is proposed to solve this problem. Third, it is shown that the seemingly more complex problem of designing jointly a land-use plan and a transportation network reduces to a pure land-use design problem. All that is needed to solve the joint optimization problem is a shortest path algorithm and a heuristic to solve the land use design problem. Computational experience is reported for each algorithm.

A Discrete-Convex Programming Approach to the Simultaneous Optimization of Land Use and Transportation

Providing mobility services effectively to residents and visitors is a complex socio-technical system task to city public managers. Smart mobility systems aim to support the efficient exploitation of city transport facilities and sustainable mobility within the urban environment. People need to travel quickly and conveniently between locations at different scales, ranging from a few blocks within a city to a journey across cities. At the same time, goods need to be timely delivered, considering both the users and the businesses’ needs. Several cities indicated an interest in using Autonomous Vehicles (AV) for the “last-mile” mobility services in the last few years. With them, it seems to be easier to get people and goods around using fewer vehicles. In this context, Autonomous Shuttles (AS) are beginning to be thought of as a new mobility/delivery service into the city center where narrow streets are not easily served by traditional buses. They allow them to perform critical areas with minimal new infrastructure and reduce noise and pollution. The article analyses the state-of-art on autonomous shuttles by proposing four application scenarios targeting the last-mile delivery of goods, the tourist experiences, and the shared and integrated mobility. Furthermore, we contribute with the proposition of the Autonomous Shuttles-as-a service (ASaaS) concept as the key pillar for the realization of innovative and sustainable proximity mobility. Our research proposed new research challenges for ASaaS, and we discuss social implications and governance challenges that consider user engagement and sustainability. It also recommended extending new research to focus on simulation and machine learning techniques for last-mile mobility planning and explore the journeys tracking certification via artificial intelligence and blockchain-based techniques.

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Autonomous Shuttle-as-a-Service (ASaaS): Challenges, Opportunities, and Social Implications

The use of smartphone applications (apps) to acquire real-time information for trip planning has become and progressively continues becoming a more instinctive behavior among public transport (PT) users. Thus, it becomes an integral part of the design and management of PT systems, but corresponding transit assignment models for improving the prediction of passenger ridership have yet to be developed. This work proposes a novel stochastic transit assignment model that predicts passenger ridership. Two new features are incorporated into a transit assignment model, namely, personalization and bounded rationality. Personalization refers to a personalized route-ranking methodology so that the app recommends paths with respect to a traveler’s preference considering various PT attributes. Bounded rationality is modeled over three route-choice strategies representing different levels of cognitive effort exercised by a traveler in selecting a path from the set of paths recommended by the app. The transit assignment model is formulated as a fixed-point problem. Because the mapping function of the fixed-point formulation is not necessarily continuous, the model constructs an approximated fixed point existing under certain measures of discontinuity. The method of successive averages (MSA) is applied to solve the problem. Numerical studies are conducted to demonstrate the properties of the new transit assignment model, the effect of demand on the path choice probability, and the effect of passengers’ heterogeneity on the convergence of the algorithm. The results reveal that, with a personalized path recommendation, passenger’s preferences could stabilize the differences of path choice probability when adopting route-choice strategies relying on the path order. In addition, although the MSA may not always converge and oscillate, the fluctuation is below the derived measure of discontinuity, indicating that an approximated fixed point can be found.

Incorporating personalization and bounded rationality into stochastic transit assignment model

As Public Transport (PT) becomes more dynamic and demand-responsive, it increasingly depends on predictions of transport demand. But how accurate need such predictions be for effective PT operation? We address this question through an experimental case study of PT trips in Metropolitan Copenhagen, Denmark, which we conduct independently of any specific prediction models. First, we simulate errors in demand prediction through unbiased noise distributions that vary considerably in shape. Using the noisy predictions, we then simulate and optimize demand-responsive PT fleets via a commonly used linear programming formulation and measure their performance. Our results suggest that the optimized performance is mainly affected by the skew of the noise distribution and the presence of infrequently large prediction errors. In particular, the optimized performance can improve under non-Gaussian vs. Gaussian noise. We also obtain that dynamic routing can reduce trip time by at least 23% vs. static routing. This reduction is estimated at 809,000 EUR per year in terms of Value of Travel Time Savings for the case study.

Curb Your Normality: On the Quality Requirements of Demand Prediction for Dynamic Public Transport.

Fairness is an important criterion for achieving sustainable urban development. While most existing studies focus on accessing or evaluating the fairness condition of a given transit network, this ...

Reliability-based equitable transit frequency design

https://backend.orbit.dtu.dk/ws/files/247617544/Melju_1_s2.0_S0377221721004768_main.pdf

Lexicographic Multi-Objective Road Pricing Optimization Considering Land Use and Transportation Effects

Unlike traditional bus fleets, autonomous mobility services are naturally amenable to dynamic, demand-responsive adaptation of itinerary. Accurate prediction of demand for such services can thus improve their utilization and decrease their operational costs. Although demand for transit services is inherently stochastic, models of demand often reduce its distribution to point estimates, thus losing useful information for subsequent decision making. In this paper, we advocate for preserving the full predictive distribution through quantile regression, so that the structure of uncertainty in future demand is preserved. To demonstrate our approach, we present a real-world case study of an autonomous shuttle service in a Danish university campus, for which we have several weeks of crowd movement counts, as reconstructed from campus WiFi records. We devise several types of quantile regression models for demand prediction, analyze their performance, and discuss their applicability to the case study. Our modeling methodology can be extended to autonomous fleets of higher scale, thus promoting sustainable shared mobility.

Yu Jiang

Papers

Incorporating personalization and bounded rationality into stochastic transit assignment model

Curb Your Normality: On the Quality Requirements of Demand Prediction for Dynamic Public Transport.

Reliability-based equitable transit frequency design

Lexicographic Multi-Objective Road Pricing Optimization Considering Land Use and Transportation Effects

Preserving Uncertainty in Demand Prediction for Autonomous Mobility Services