Considering the background of ship emission control area, multi-objective ship speed optimization is carried out with fuel cost and charter cost as optimization objectives to reduce ship operating costs and improve ship energy efficiency. A multi-objective optimization method based on the combination of butterfly optimization algorithm and linear weighting is selected to obtain the Pareto optimal solution set. Finally, the ideal point distance method is used as the decision-making approach to determine the best compromise solution from the Pareto optimal solution set. The results show that when optimizing the sailing speed, fuel costs decrease by 1.98%, sailing costs decrease by 17.32%, and overall operating costs for ships can be effectively reduced by 5.62%, greatly improving the economic efficiency of shipowners and shipping companies while meeting environmental requirements.

Reliable and accurate travel time data can provide valuable performance measures to support operational applications in areas of congestion management and routing analysis. The travel time information is also essential to the calibration and validation of travel demand models in order to better model current and future travel for congested area. Historically, the ability to collect travel time data in sufficient quantity to provide reliable, robust evidence has been severely limited, almost to the point of being unattainable. Indeed, to take it further, any aspirations of addressing travel time variability are almost inconceivable. Recent developments with Global Positioning Systems (GPS) data have shed new light in this field. The ability to collect large volumes of data from GPS devices has provided a wealth of data for use in this area. Such data collected and processed by Intelematics for a large proportion of the Greater Metropolitan strategic road network has been reviewed and analysed to examine time of day and day of week variations in travel times by traffic conditions for individual sections of road. Findings from this research could significantly influence the guidelines, processes and procedures for future model validation.