arXiv subjects

Ravi Seshadri

Publications and source records attributed to Ravi Seshadri.

22 records · Page 2Linked to original sources

arXiv2020

Network Impacts of Automated Mobility-on-Demand: A Macroscopic Fundamental Diagram Perspective

Technological advancements have brought increasing attention to Automated Mobility on Demand (AMOD) as a promising solution that may improve future urban mobility. During the last decade, extensive research has been conducted on the design and evaluation of AMOD systems using simulation models. This paper adds to this growing body of literature by investigating the network impacts of AMOD through high-fidelity activity- and agent-based traffic simulation, including detailed models of AMOD fleet operations. Through scenario simulations of the entire island of Singapore, we explore network traffic dynamics by employing the concept of the Macroscopic Fundamental Diagram (MFD). Taking into account the spatial variability of density, we are able to capture the hysteresis loops, which inevitably form in a network of this size. Model estimation results at both the vehicle and passenger flow level are documented. Environmental impacts including energy and emissions are also discussed. Findings from the case study of Singapore suggest that the introduction of AMOD may bring about significant impacts on network performance in terms of increased VKT, additional travel delay and energy consumption, while reducing vehicle emissions, with respect to the baseline. Despite the increase in network congestion, production of passenger flows remains relatively unchanged.

cs.CY↗

arXiv2020

E-Commerce Delivery Demand Modeling Framework for An Agent-Based Simulation Platform

The e-commerce delivery demand has grown rapidly in the past two decades and such trend has accelerated tremendously due to the ongoing coronavirus pandemic. Given the situation, the need for predicting e-commerce delivery demand and evaluating relevant logistics solutions is increasing. However, the existing simulation models for e-commerce delivery demand are still limited and do not consider the delivery options and their attributes that shoppers face on e-commerce order placements. We propose a novel modeling framework which jointly predicts the average total value of e-commerce purchase, the purchase amount per transaction, and delivery option choices. The proposed framework can simulate the changes in e-commerce delivery demand attributable to the changes in delivery options. We assume the model parameters based on various sources of relevant information and conduct a demonstrative sensitivity analysis. Furthermore, we have applied the model to the simulation for the Auto-Innovative Prototype city. While the calibration of the model using real-world survey data is required, the result of the analysis highlights the applicability of the proposed framework.

econ.EM↗

arXiv2020

A simulation-based evaluation of a Cargo-Hitching service for E-commerce using mobility-on-demand vehicles

Time-sensitive parcel deliveries, shipments requested for delivery in a day or less, are an increasingly important research subject. It is challenging to deal with these deliveries from a carrier perspective since it entails additional planning constraints, preventing an efficient consolidation of deliveries which is possible when demand is well known in advance. Furthermore, such time-sensitive deliveries are requested to a wider spatial scope than retail centers, including homes and offices. Therefore, an increase in such deliveries is considered to exacerbate negative externalities such as congestion and emissions. One of the solutions is to leverage spare capacity in passenger transport modes. This concept is often denominated as cargo-hitching. While there are various possible system designs, it is crucial that such solution does not deteriorate the quality of service of passenger trips. This research aims to evaluate the use of Mobility-On-Demand services to perform same-day parcel deliveries. For this purpose, we use SimMobility, a high-resolution agent-based simulation platform of passenger and freight flows, applied in Singapore. E-commerce demand carrier data are used to characterize simulated parcel delivery demand. Operational scenarios that aim to minimize the adverse effect of fulfilling deliveries with Mobility-On-Demand vehicles on Mobility-On-Demand passenger flows (fulfillment, wait and travel times) are explored. Results indicate that the Mobility-On-Demand services have potential to fulfill a considerable amount of parcel deliveries and decrease freight vehicle traffic and total vehicle-kilometers-travelled without compromising the quality of Mobility On-Demand for passenger travel.

cs.MA↗

arXiv2019

Real-Time Predictive Control Strategy Optimization

Traffic congestion has lead to an increasing emphasis on management measures for a more efficient utilization of existing infrastructure. In this context, this paper proposes a novel framework that integrates real-time optimization of control strategies (tolls, ramp metering rates, etc.) with guidance generation using predicted network states for Dynamic Traffic Assignment systems. The efficacy of the framework is demonstrated through a fixed demand dynamic toll optimization problem which is formulated as a non-linear program to minimize predicted network travel times. A scalable efficient genetic algorithm is applied to solve this problem that exploits parallel computing. Experiments using a closed-loop approach are conducted on a large scale road network in Singapore to investigate the performance of the proposed methodology. The results indicate significant improvements in network wide travel time of up to 9% with real-time computational performance.

eess.SY↗