Integrated simulation of activity-based demand and multi-modal dynamic supply for energy assessment

Abstract

The development of a large scale agent-based simulation model for the Greater Boston Area is presented, closing the gap between state-of-the art integrated demandsupply modeling techniques (SimMobility) with advanced energy estimation models (TripEnergy) and shedding light on its practical application to large urban areas. This paper describes the technical details of its three key components (activity-based demand, multi-modal dynamic supply, and trajectory-based energy models), the used data, the model estimation, integration and calibration processes. The proposed model can simulate any day with and without congestion in order to capture changes in energy use across all dimensions of a mobility system, namely temporal, spatial, modal or functional. For an average 24h in the Greater Boston Area the simulated travel of 4.5-million people resulted in 15-million trips and a total vehicle energy consumption of 548 thousand equivalent gallons of gasoline. Our proposed platform allows for the comprehensive and consistent assessment of energy related policies, technologies and services affecting traveler behavior, the transportation system's and vehicle energy performances.

Integrated population synthesis and workplace assignment using an efficient optimization-based person-household matching method

“…Such an integrated approach can be easily integrated in current common practice in existing models in the United States and elsewhere. The specific application described in this paper synthesized a population of 4.6-million people and 1.7-million households in the Greater Boston Area, which is ultimately utilized for an energy assessment simulation of an activity-based demand and multi-modal supply simulation (Fournier et al, 2018).…”

Sustainable Automated Mobility-On-Demand Strategies in Dense Urban Areas: A Case Study of the Tel Aviv Metropolis in 2040

“…Nonetheless, the outlook for AMoD availability is uncertain, and its relationship with existing mobility patterns and its influence on system performance has not been fully ascertained. Optimistic studies project that AMoD will potentially reduce costs [6], VKT [7,8], and parking demand [9][10][11][12], and improve traffic management [13][14][15]. AMoD, under environmentally conscious regulation, has also been demonstrated to reduce energy usage and greenhouse gas (GHG) emissions [16].…”

“…Numerous trials have showcased the potential effects of AMoD on urban mobility across a range of scenarios, encompassing factors such as cost, demand, supply (fleet control and management), and policy considerations. These studies have, however, been modeled on either specific cities [7,8,[29][30][31][32][33][34][35][36] or on virtual cities [9,[35][36][37] under idealized conditions. The result is that the implications of these studies are neither readily nor broadly applicable to a wide range of cities.…”