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HF Considerations When Testing and Evaluating ACIVs
Published in Donald L. Fisher, William J. Horrey, John D. Lee, Michael A. Regan, Handbook of Human Factors for Automated, Connected, and Intelligent Vehicles, 2020
One example of a non-public roadway usage for commercial vehicles is drayage. Drayage trucks typically work to transfer cargo between mixed modes of transportation, such as offloading cargo from a ship to then be transported by heavy truck or train. These operations are often conducted in a space with controlled access, such as a port of entry or rail yard facility. These characteristics make the domain attractive for heavy vehicle automation; the operation space is confined, there is little to no mixed traffic, there are relatively fixed transit routes, and there are lower speeds of operation (see Smith, Harder, Huynh, Hutson, & Harrison, 2012, for an overview of drayage operations and facilities). Drayage may also offer unique challenges to automated commercial vehicles as roadway infrastructure could differ significantly from public roads. Additionally, while most driving may take place in areas closed to public traffic, there may be some driving on public roads in order to get the cargo to a nearby destination for further transit. These transitions between private and public space may provide test cases of transfer of control between automated driving and manual driving.
Intermodal Drayage Routing and Scheduling
Published in Petros A. Ioannou, Intelligent Freight Transportation, 2008
Alan L. Erera, Karen R. Smilowitz
Intermodal drayage refers to the local movement of intermodal freight transportation equipment (trailers and containers), both loaded and empty. Drayage service provides the “last-mile” link for the vast majority of freight moving via the international intermodal freight chain. For example, a large international import shipper may receive loaded 40-foot ocean containers at its regional distribution center via drayage service from a local container seaport. Export shippers moving manufactured product overseas may request empty containers and then load them for delivery to the port for export. While ocean carriers may arrange these container transfers, they are executed by drayage trucking services. Transfer of international containers by drayage, however, is not limited to last-mile scenarios. Drayage trucking services are also used to transfer containers between seaports and nearby off-port rail intermodal yards for connection to and from inland customer facilities. In another scenario, drayage services are used to move international ocean containers to transshipment warehouses where containers may be unloaded and their contents repacked into domestic rail containers or truck trailers.
A simulation-based optimization approach for external trucks appointment scheduling in container terminals
Published in International Journal of Modelling and Simulation, 2020
Ahmed Azab, Ahmed Karam, Amr Eltawil
One of the earliest studies that addressed the scheduling of external truck arrivals in CTs was introduced by Murty et al. [6]. They developed a TAS embedded in a computerized decision support system for the Hong Kong International Terminal. The developed appointment system reduced the terminal congestion by determining the optimal quota of trucks that are supposed to arrive at the terminal every 30minutes. Huynh et al. [7] investigated the effect of using truck appointments on the truck turnaround time in container terminals. Their objective was to maximize the number of trucks served during different time windows with achieving a satisfactory truck turnaround time as well as considering the terminal resources’ levels at various arriving times. Namboothiri and Erera [8] developed an integer programming model for planning pickup and delivery operations of external trucks. The model was used to determine the sequence of the drayage operations with the objective of minimizing the transportation cost. Guan and Liu [9] introduced a congestion modeling approach to analyze and estimate the truck waiting cost at terminal gates using a multi-server queuing model. A dramatic decrease in the waiting cost due to the decrease in the congestion levels at the terminal gates was achieved. Chen and Yang [10], introduced an integer programming model with the objective of reducing transportation cost to smooth the traffic arrival patterns in a Chinese container terminal. By optimizing the position and duration length of each time window, the traffic peaks for export container trucks could be flattened efficiently resulting in reducing the terminal congestion.
Railway freight subsidy mechanism based on multimodal transportation
Published in Transportation Letters, 2021
Chuanzhong Yin, Yu Lu, Xingfang Xu, Xuezong Tao
To determine the impact of the freight subsidy policy and evolve a reasonable freight subsidy mechanism, many researchers have analyzed the impact of subsidy policy on the development of multimodal transport, both quantitatively and qualitatively (See Table 2). In terms of the subsidy policy of the CR Express, Kundu and Sheu (2019) proposed a competition model (railway vs. maritime) based on the game-theoretic technique to analyze the effect of the government’s subsidy on the shippers’ mode switching behavior (from maritime to railway). Their study indicated that shippers having high-value and time-sensitive goods always preferred the railway, while those having low-value goods chose the maritime route owing to its cost-effectiveness. Wu et al. (2017) believed that appropriate subsidies for sustainable development were acceptable. However, these subsidies are not a final goal and should be ultimately reduced or even eliminated. Based on a qualitative analysis, they proposed the establishment of a unified fund and a phased dynamic subsidy mechanism that was suitable for the operations. In addition, on the multimodal transport subsidy policy, Larranaga, Arellana, and Senna (2017) adopted a discrete selection model to determine the preferences of the respondents and presented sustainable policies that could increase the regional competitiveness. Their study suggested that investments in increasing the reliability of the intermodal alternatives were more effective in encouraging intermodality than cost reductions. Macharis et al. (2010) established a GIS-based multimodal transportation terminal positioning analysis model in Belgium to study the impact of several fuel price scenarios on a single route transport market area. Their study showed that the growth of market areas could benefit intermodal barge/highway and intermodal railway/highway. Carreira et al. (2012) proposed an optimization model for the multimodal transportation terminal location in inland multimodal transportation systems. Their findings suggested that when the railway or transshipment costs were subsidized by the government, the external costs would decrease, while the competitiveness of the intermodal transport would increase. Santos, Limbourg, and Carreira (2015) proposed a hybrid integer multimodal transportation freight allocation model based on the hub-location theory. Their study indicated that internalizing the external costs could negatively impact the promotion of intermodality. Innovative last-mile transports were needed to reduce the external impacts of drayage operations.