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Adding battery locomotives to traditional diesel locomotives to form a hybrid consist has the potential to reduce fuel consumption and emissions in freight rail operations. This paper provides route-based estimates of diesel (hpd/ton) and battery (hpb/ton) power requirements and diesel (gal/ton) and battery (hpbhr/ton) energy requirements for hybrid consists. Given a route-specific power profile, the power split between the battery pack and the diesel engine is optimized to minimize fuel consumption by running the diesel engine at maximum efficiency during the entire route. Simulations for 200-mile round trips between Chicago and Harrisburg show that the diesel power is maximum in low-gradient regions at 1.05 (hpd/ton) and battery power is maximum in hilly regions at 2 (hpb/ton) for discharge and 6.5 (hpb/ton) for charge. Maximum fuel efficiency gains of approximately 60% are seen in the mountains, where 2.09 (hpbhr/ton) of battery energy is needed. Minimum fuel economy gains are observed in flat regions, with 20% fuel consumption reduction. With battery current and voltage limits, the battery pack size increases, and fuel savings decrease. [ABSTRACT FROM AUTHOR]

Emerging shared mobility systems are gaining popularity due to their significant economic and environmental benefits. In this paper, we present a network-based approach for predicting travel demand between stations (e.g., whether two stations have sufficient trips to form a strong connection) in shared mobility systems to support system design decisions. In particular, we answer the research question of whether local network information (e.g., the network neighboring station's features of a station and its surrounding points of interest (POI), such as banks, schools, etc.) would influence the formation of a strong connection or not. If so, to what extent do such factors play a role? To answer this question, we propose using graph neural networks (GNNs), in which the concept of network embedding can capture and quantify the effect of local network structures. We compare the results with a regular artificial neural network (ANN) model that is agnostic to neighborhood information. This study is demonstrated using a real-world bike sharing system, the Divvy Bike in Chicago. We observe that the GNN prediction gains up to 8% higher performance than the ANN model. Our findings show that local network information is vital in the structure of a sharing mobility network, and the results generalize even when the network structure and density change significantly. With the GNN model, we show how it supports two crucial design decisions in bike sharing systems, i.e., where new stations should be added and how much capacity a station should have. [ABSTRACT FROM AUTHOR]