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Titlebook: Developments and Applications in SmartRail, Traffic, and Transportation Engineering; Proceedings of ICSTT Limin Jia,Said Easa,Yong Qin Conf

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https://doi.org/10.1007/978-3-476-04128-9 the network and their respective service status. In addition to this, it is common that waste collection is done with a fleet of vehicles, many of which may be covering more than one collection route a day. Using the statistics generated for each collection route, normalized objective function valu
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https://doi.org/10.1007/978-3-476-99972-6y cars, and the degree of influence decreased in turn. Perceived risk has a significant negative effect on consumers’ willingness to use unmanned delivery cars. Demographic characteristics and pick-up characteristics play moderating roles between influencing factors and intention to use.
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way freight volume is distributed on the road network. Finally, taking C-HZB port area as an example, the throughput from 2021 to 2030 is forecasted, the feasibility of the method is verified by predicting the new port throughput reliably.
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Developments and Applications in SmartRail, Traffic, and Transportation EngineeringProceedings of ICSTT
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Weekday Travel Demand Distribution Pattern of Ride-Hailing Service Based on Didi Daily Order Data in was processed to estimate volume by mode for links in the roadway network by different time of day. Ride hailing service users travel along major freeway corridor during peak hours to major employer and commercial centers in city proper area. Results of volume forecasting can be validated using scr
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