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Accurate forecasting of bus travel time and its uncertainty is critical to service quality and operation of transit systems: it can help passengers make informed decisions on departure time, route choice, and even transport mode choice, and it also support transit operators on tasks such as crew/vehicle scheduling and timetabling. However, most existing approaches in bus travel time forecasting are based on deterministic models that provide only point estimation. To this end, we develop in this paper a Bayesian probabilistic model for forecasting bus travel time and estimated time of arrival (ETA). To characterize the strong dependencies/interactions between consecutive buses, we concatenate the link travel time vectors and the headway vector from a pair of two adjacent buses as a new augmented variable and model it with a mixture of constrained multivariate Gaussian distributions. This approach can naturally capture the interactions between adjacent buses (e.g., correlated speed and smooth variation of headway), handle missing values in data, and depict the multimodality in bus travel time distributions. Next, we assume different periods in a day share the same set of Gaussian components, and we use time-varying mixing coefficients to characterize the systematic temporal variations in bus operation. For model inference, we develop an efficient Markov chain Monte Carlo (MCMC) algorithm to obtain the posterior distributions of model parameters and make probabilistic forecasting. We test the proposed model using the data from two bus lines in Guangzhou, China. Results show that our approach significantly outperforms baseline models that overlook bus-to-bus interactions, in terms of both predictive means and distributions. Besides forecasting, the parameters of the proposed model contain rich information for understanding/improving the bus service, for example, analyzing link travel time and headway correlation using covariance matrices and understanding time-varying patterns of bus fleet operation from the mixing coefficients. Funding: This research is supported in part by the Fonds de Recherche du Quebec-Societe et Culture (FRQSC) under the NSFC-FRQSC Research Program on Smart Cities and Big Data, the Canadian Statistical Sciences Institute (CANSSI) Collaborative Research Teams grants, and the Natural Sciences and Engineering Research Council (NSERC) of Canada. X. Chen acknowledges funding support from the China Scholarship Council (CSC). Supplemental Material: The e-companion is available at https://doi.org/10.1287/trsc.2022.0214. [ABSTRACT FROM AUTHOR]

The authors analyzed the navigation along the Northern Sea Route (NSR) and developed the pathfinding algorithm for its strategic assessment. It creates a framework connecting weather observations with shipping operational parameters: travel speed, travel time, travel distance, the necessity of icebreaker escort. It is based on the A* algorithm, the Arctic Ice Regime Shipping System (AIRSS) methodology, and empirical relationships between ship's speed and environmental data. The idea is to minimize travel time since sailing through areas with severe weather conditions requires speed reduction. The objective function finds the fastest safe path with minimal icebreaker escort. We used 2019–2020 environmental data to test the algorithm and analyzed daily optimal NSR transit routes for Eastbound and Westbound directions. It was shown that environmental parameters significantly affect the vessel's path, while ice parameters make the largest contribution. Depending on the ship's ice class and sailing season, it takes between 11 and 38 days to complete the entire route. All obtained operational parameters are valuable for assessing transportation costs, profitability, and riskiness of NSR. We provide their seasonal, monthly, and weekly values, and statistical parameters. [ABSTRACT FROM AUTHOR]

With the significant growth of world economy and container transportation, large seaports are under the pressure of higher requirements for handling efficiency and often involved in recurrent congestion. For those large seaports, shuttle vessels are usually run between terminals in order to fulfill container inter-terminal transshipment demand as well as to alleviate seaports' congestion effectively. This paper focuses on planning the shuttle vessel routing and scheduling, in which shuttle vessels connect a main terminal and neighboring terminals for container pickup. Two mixed-integer programming formulations are developed. The first model aims to minimize the total operational cost and the second further takes into account the time window requirement. A two-phase heuristic algorithm is devised to find a near-optimal solution within the short computational time for large-scale problems. According to the computational results, the first model shows high searching efficiency if cargo owners do not have time requirement. The two-phase heuristic algorithm shows both efficiency and effectiveness in searching near-optimal solution of the second model for large-scale instances considering time windows. The routing analysis indicates that terminal operators focus on time or no-load rate, respectively, when the waiting penalty shift from low to high. [ABSTRACT FROM AUTHOR]

As a sustainable transportation alternative, cycling has been developed by many large cities as one essential measure for addressing the "last mile problem" in urban areas with high mobility demand. Developing a safe and friendly lane network for cycling has become an urgent task for governments, especially for many Chinese cities encountering a rapid increase in the use of "dockless" shared bikes, such as Mobike and Ofo. The emergence of such kind of app-driven dockless bike sharing systems results in a fast growth in the cycling mobility demand, and leads to a gap between the growing demand and the existing cycling infrastructure. Therefore, it is imperative to have a good understanding of the cycling travel demand and its infrastructure, and in turn to bridge the supply-demand gap. In this paper, we employ data mining techniques including graphic clustering algorithm, Louvain Method, on a large data set from one of the largest bike sharing companies in China. Then we applied the methodology in two cases in Shanghai, including a campus and an urban area. Typical cycling patterns in the spatial and temporal dimensions are identified automatically by the cluster analysis. It is also found that the construction of the cycling infrastructure is closely related to three factors: non-negligible impact of points of interest (POIs), geographical barriers, and temporal variance of the network. Managerial insights and policy measures are proposed accordingly for improving the cycle lane network. [ABSTRACT FROM AUTHOR]

Nowadays, the significance of developing sustainable transport systems is being reconsidered due to the impact produced by an extensive use of private cars. In this scenario, bicycles are becoming more popular. To encourage cycling, bike-sharing systems have been developed. However, as safety is people's main concern of cycling, bike-sharing systems should be part of an integral project that includes not only docking stations but a dedicated cycle path network connecting them. A two-stage stochastic programming model that maximizes the travel demand covered by the system is developed. Various demand scenarios in terms of different time periods of a day, travel intensities and levels of commuting demand are considered in the model, in order to obtain robust solutions. A real-world case study based on the city of Montevideo in Uruguay is conducted. The obtained solution shows that the model tends to develop bicycle lane network with good connectivity between transit stations and normal stations, and the obtained network possesses good robustness. [ABSTRACT FROM AUTHOR]

This study provides a cargo contribution yield management model to solve the ship capacity control problem for the container liner shipping industry. We propose a new objective to optimize cargo contribution to replace the focus on total revenue or average revenue in the current research. We reflect the special characteristics of yield management in container liner shipping, and all cost items were identified and calculated to develop a new cargo contribution evaluating system. We propose a mathematical model for service route segments' allocation distribution based on cargo contribution. We use a genetic algorithm to solve the model further with comparative analysis with actual practice. The study cultivates new ground in the current literature with a wide range of innovative applications at a practical level. [ABSTRACT FROM AUTHOR]

In container terminals, operators normally form an initial plan for berth allocation before the arrival of incoming vessels. However, during the real operations, unforeseen incidents (e.g. delays, equipment breakdowns) happen frequently, which hinder the implementation of initial plans. In response to disruptions, terminal operators usually employ an experience-based strategy for berth allocation recovery. However, this practice may not work well in challenging situations, especially when extensive transshipment operations need to be conducted, and the berthing for mother and feeder vessels should be coordinated. This paper studies the disruption recovery problem for berth allocation in transshipment terminals, and develops decision support tools for terminal operators. A mixed integer linear programming model is established to adjust original berthing plan with the objective of minimizing recovery cost caused by deviations from the original plan. Quay crane assignment decision is also taken into account during disruption recovery. Transshipment connections between correlated feeder and mother vessels are also considered specifically in order to reduce the missing of transshipment connection opportunities. Squeaky Wheel Optimization heuristic is devised to find near-optimal solutions under large problem scales. Extensive computational experiments validate the effectiveness and the efficiency of designed method, and the results show practical benefit from incorporating transshipment consideration when rescheduling berth allocation. [ABSTRACT FROM AUTHOR]