An important, but seldom investigated, issue in the field of dynamic vehicle routing and dispatching is how to exploit information about future events to improve decision making. In this paper, we address this issue in a real-time setting with a strategy based on probabilistic knowledge about future request arrivals to better manage the fleet of vehicles. More precisely, the new strategy introduces dummy customers (representing forecasted requests) in vehicle routes to provide a good coverage of the territory. This strategy is assessed through computational experiments performed in a simulated environment.

Exploiting Knowledge About Future Demands for Real-Time Vehicle Dispatching

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

A steerable audio system can be realized using parametric array. However, the available steerable angle is often limited by the sampling interval used in the digital system. As such, the smallest steerable angle is large (/spl sim/26/spl deg/) for several hundred kilohertz of sampling frequency. Although there are some fractional delay or frequency domain algorithms can be used to improve the steering angle, most of the algorithms are either computational intensive or introduce error during the process. In this paper, an algorithm is proposed to rectify this problem by applying separate delays to the carrier and sideband frequencies. Different weighting functions also added to the carrier and sideband frequencies to control the difference frequency's beamwidth and sidelobe. Most importantly, the proposed system can steer the difference frequency to a small angle with minimal computation.

A digital beamsteerer for difference frequency in a parametric array

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

The parametric loudspeaker provides an effective means of projecting sound in a highly directional manner without using large loudspeaker arrays to form sharp directional beams. It can be augmented with conventional loudspeakers to create a more immersive audio soundscape. Deployment of parametric loudspeakers in many public places where private messaging can make a difference in attracting attention, conveying messages without needing headphones, and creating private listening zones to reduce noise pollution. Digital signal processing plays a significant role in enhancing the aural quality of the parametric loudspeakers, and array processing can help to shape and steer the beam electronically. In addition, other signal processing techniques can also be applied to add more flexibility and improve the performance of parametric loudspeakers. These developments rely heavily on the latest techniques in acoustics and audio signal processing to overcome some of the current limitations in nonlinear acoustics modeling and ultrasonic transducers' technology. A useful feature in sound projection is to realize a highaccuracy digital beamsteering capability in air using an array of parametric loudspeakers. An in-depth study into the theoretical model of wave steering capability in parametric array in air can provide some hints on how we can best steer the demodulated signal in an efficient manner. As seen from this article, digital signal processing provides the main engine to achieve directional sound projection, and new digital processing techniques will be devised to provide a better quality, controllable audio beaming, and efficient sound focusing device in the future.

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Audio projection : directional sound and its applications in immersive communication

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 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 find that dynamic routing could reduce trip time by at least 23% vs. static routing. This reduction is estimated at 809,000 €/year in terms of Value of Travel Time Savings for the case study.

On the quality requirements of demand prediction for dynamic public transport

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.

A bandwidth-efficient recursive implementation of pth-order equalization is developed in order to correct the inherent baseband distortion in parametric loudspeakers. Assuming that the nonlinear system is largely quadratic, a substitution of it can be made by using an analytic pair of all-pass sections cascaded with a squared magnitude block. The all-pass sections comprise of a delay block and an approximate linear-phase infinite impulse response (IIR) Hilbert transformer to give a Hilbert transform pair. In this way, the distortion terms produced are due to the difference frequencies only and therefore within the bandwidth of the original input signal. When used together with a single-sideband (SSB) amplitude modulation (SSB-AM) scheme, this new method allows the equalized SSB output signal to be faithfully reproduced to correct the distortion. Simulation results in this paper show that the new method is able to suppress residual in-band distortion components by -70 dB or lower.

Bandwidth-efficient recursive pth-order equalization for correcting baseband distortion in parametric loudspeakers

Modeling Nonlinearity of Air with Volterra Kernels for Use in a Parametric Array Loudspeaker

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.

Kelvin Lee

Papers

On the quality requirements of demand prediction for dynamic public transport

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

Bandwidth-efficient recursive pth-order equalization for correcting baseband distortion in parametric loudspeakers

Modeling Nonlinearity of Air with Volterra Kernels for Use in a Parametric Array Loudspeaker

Preserving Uncertainty in Demand Prediction for Autonomous Mobility Services