Abstract Summary
Against the backdrop of the expansion and densification of metropolitan areas around the world and the recent evolutions in logistics (such as demand-driven supply chain practices), the traffic and environmental impact of urban freight movements is a significant concern. A small share of goods vehicle movements among all vehicle movements accounts for a disproportionally large share of environmental impacts in a metropolitan area (Coulombel et al., 2018). A new set of logistics solutions, including those taking advantages of technological innovations, have been proposed to mitigate the negative impacts associated with urban freight flows (Taniguchi et al., 2016) and, for fulfilling the needs of designing and evaluating them, a number of novel urban freight modeling frameworks have been proposed (Chow et al., 2010; Comi et al., 2012; De Jong et al., 2013). Such models often take a disaggregated, “agent-based” approach, partially at least, to simulate the behaviors of multiple agents that are engaged in urban freight-related activities, and interactions of thereof, for enhancing model capabilities beyond traditional aggregate models. However, urban freight simulators using such agent-based models are still limited in replicating logistics decisions at the level of detail to evaluate various types of innovative logistics solutions. Furthermore, a number of the novel modeling frameworks still remain in isolation and there is a need for their integration. Moreover, to the best of our knowledge, the integration between the agent-based freight and passenger models that considers the same agents (e.g. individuals, establishments, and drivers) consistently, is not yet available for real-world policy analysis (Schroder and Liedtke, 2017). We will present the framework of an urban freight simulator, SimMobility Freight, which is newly developed as a part of SimMobility, an agent-based urban transportation simulation platform (Adnan et al., 2016). SimMobility involves multiple interacting agents on multiple temporal dimensions and its passenger travel simulation follows an activity-based paradigm. To date, it has been implemented for two metropolitan areas in the U.S., Boston and Baltimore, and Singapore. Together with the other parts in SimMobility, SimMobility Freight aims at the integration of disaggregate, agent-based freight and passenger simulation models. The core components of SimMobility Freight predict commodity contracts, logistics planning, and goods vehicle operations and, therefore, capture the connections among land use, commodity flow, goods vehicle traffic, and transportation network conditions. Furthermore, the adaptive modular approach allows for the extensions regarding key decisions which impact urban freight; SimMobility Freight also predicts overnight parking choices and pickup/delivery parking decisions. The disaggregated nature of the models allows for measuring the impacts of the changes in various aspects in logistics operations and evaluating solutions, such as crowd-shipping, delivery consolidation, carrier cooperation, and delivery demand management with/without extension modules. SimMobility Freight is currently available for Singapore, one of the largest metropolitan areas in Asia, and has been used for evaluating a series of logistics solutions (Gopalakrishnan et al., 2019; Mepparambath, 2019).
