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This study proposes a framework of an urban passenger-and-package sharing (PPS) system that utilizes e-hailing taxis to transport packages in addition to passengers. We examine the interactions between stakeholders and highlight the role of the pricing strategy in the system, including passenger fare discounts and driver incentives for PPS trips. Higher discounts and incentives stimulate more passengers and taxi drivers, respectively, to participate in the PPS system but possibly limit the profit of the service provider. A multi-agent simulation model is developed to analyze the influences of the pricing strategy on the behaviors of the service provider, passengers, and taxi drivers. The real-world case study demonstrates that the service provider, passengers, and taxi drivers benefit from different combinations of the passenger fare discounts and driver incentives. The pricing strategy could be set up to produce the system optimal (SO) situation that maximizes the total benefit, or win-win situations that simultaneously benefit all stakeholders—the service provider gains a higher profit, passengers enjoy lower travel costs, and taxi drivers have higher incomes when compared to the traditional e-hailing taxi system. The public authority is suggested to provide a subsidy to expand the domain of win-win situations to cover the SO situation, such that the system benefits all stakeholders and obtains the maximum total benefit. • Envisions a multi-stakeholder framework for PPS system based on e-hailing taxis. • Pricing concerns passenger fare discount and driver incentive for PPS service. • Analyzes the impact of pricing on system profit, passenger cost and driver income. • Optimal pricing maximizes the total benefit but may distract some stakeholders. • Evaluates the effectiveness of subsidy in coordinating win-win and SO situations. [ABSTRACT FROM AUTHOR]

Highlights • A mixed integer linear model is developed to optimize multi-period tram timetables. • Priorities of improving tram operational efficiency and timetable adherence are considered. • Arterial signal timings are resynchronized when optimizing scheduled tram trajectories. • Tram performance is improved without sacrificing vehicular mobility. Abstract Modern trams run on exclusive rail lanes along urban streets, but they usually share the right of way with general traffic at intersections and often get interrupted by traffic signals. To improve tram operation reliability, this paper develops a methodology to optimize a multi-period tram timetable by simultaneously adjusting bidirectional scheduled tram trajectories and traffic signal timings. The objective balances the operational priorities of minimizing tram running time and maximizing timetable adherence. The scheduled trajectories depict tram movements along the roads and dwell processes at stations. Arterial signal timings are resynchronized to favor tram movements. The proposed methodology was evaluated in a simulation of a real-world tram line. Compared with a traditional approach, the proposed methodology reduced tram running time, number of stops at intersections and schedule delay by 11.1%, 82.4% and 37.5%, respectively. The impact on general traffic could be assumed neutral since cycle lengths, green splits and vehicular bandwidths were kept unchanged. [ABSTRACT FROM AUTHOR]

As transit agencies increasingly adopt the use of Automatic Passenger Count (APC) technologies, a large amount of boarding and alighting data are being amassed on an ongoing basis. These datasets offer opportunities to infer good estimates of passenger origin–destination (OD) flows. In this study, a method is proposed to estimate transit route passenger OD flow matrices for time-of-day periods based on OD flow information derived from labor-intensive onboard surveys and the large quantities of APC data that are becoming available. The computational feasibility of the proposed method is established and its accuracy is empirically evaluated using differences between the estimated OD flows and ground-truth observations on an operational bus route. To interpret the empirical differences from the ground-truth estimates, differences are also computed when using the state-of-the-practice Iterative Proportional Fitting (IPF) method to estimate the OD flows. The empirical results show that when using sufficient quantities of boarding and alighting data that can be readily obtained from APC-equipped buses, the estimates determined by the proposed method are better than those determined by the IPF method when no or a small sample sized onboard OD flow survey dataset is available and of similar quality to those determined by the IPF method when a large sample sized onboard OD flow survey dataset is available. Therefore, the proposed method offers the opportunity to forgo conducting costly onboard surveys for the purpose of OD flow estimation. [ABSTRACT FROM AUTHOR]

Parking scenarios are spatially dense and have a lot of interactions, making predicting vehicles' search behavior crucial and challenging for autonomous driving. Existing data-driven prediction methods struggle to determine vehicles' intents and consider the surrounding environment accurately. This study proposes a novel two-stage framework for parking search behavior prediction, involving parking intent and vehicle trajectory predictions based on imitation learning and deep learning. First, we develop an adversarial inverse reinforcement learning model for parking search intent (PSI-AIRL) learning from measured trajectory data in an actual parking lot. Then, we design an integrated convolutional neural network (CNN) and transformer model to forecast vehicle trajectory using historical observations and the predicted parking search intents. This two-stage framework achieves parking search intent prediction by applying global information about the parking lot while improving vehicle trajectory prediction's accuracy and robustness. Finally, the experiments are conducted on the Dragon Lake Parking (DLP) dataset to compare our framework with state-of-the-art models. The results show that our model outperforms other baseline models in accuracy for parking intent and vehicle trajectory predictions. Moreover, our model shows exceptional accuracy and robustness in predicting across diverse parking scenarios. • A novel two-stage framework for parking search behavior prediction is proposed. • An AIRL model is developed for the first stage of parking intention learning. • Trajectory forecasting is then based on an integrated CNN and transformer model. • The experiments prove the superiority of our model via actual parking datasets. [ABSTRACT FROM AUTHOR]

Bus bridging has been widely used to connect stations affected by metro disruptions such that stranded passengers could resume their journeys. Previous studies generally assumed that a bus operates exclusively on one bridging route with given frequency, which limits the service flexibility and reduce the operational efficiency. We propose a strategy to instruct buses to operate on predefined bridging routes once they are dispatched from depots. Buses are allowed to flexibly serve different bridging routes. Each bus operates based on a bridging plan that lists the stations to serve in sequence instead of route frequencies. A two-stage model is developed to optimize the bridging plans and their assignments to buses with the objectives that balance the operational priorities between minimizing bus bridging time and reducing passenger delay. A Weight Shortest Processing Time first (WSPT) rule based heuristic algorithm is developed to solve the proposed model. The developed model is further incorporated in a rolling horizon framework to handle dynamic passenger arrivals during the disruption period. The effectiveness of the proposed strategy is demonstrated in comparison with alternative strategies in real-world case studies. [ABSTRACT FROM AUTHOR]

• A satellite terminal is integrated into the inter-terminal transportation (ITT) • A time–space network flow model is developed to minimize the ITT cost. • Two approaches are proposed to reduce the solution space of the model. • The influence of the satellite terminal on the Yangshan Port, Shanghai is evaluated. Offshore ports, which are located on islands or away from the hinterland, are usually connected with the hinterland via bridges for truck transportation. With the rapid growth of port throughput, the bridges are likely to become a bottleneck restricting the development of port business. Inland container depots (ICD) have been introduced as satellite terminals to address congestion issues on bridges by allowing a proportion of drayage trucks to pick up and drop off containers at the ICD instead of going to the container terminals of the offshore ports. Containers are transported between the ICD and container terminals by inter-terminal transportation (ITT) trucks owned by port authorities. The ICD is expected to mitigate the bridge congestion by transferring the travel demands of drayage trucks in peak hours to the travel demands of ITT trucks in off-peak hours. We propose a methodology for analyzing the influence of ICDs on port efficiency. An integer linear programming model is developed to optimize the movements of containers, drayage trucks and ITT trucks in an offshore port area with the objective of minimizing the costs of drayage trucks and ITT trucks and the penalty for late delivery of containers. Two model transformation approaches are proposed to reduce the solution space of the model for improving the computational efficiency of the algorithm. The influence of ICDs on port efficiency is evaluated through a real-world case study using the proposed methodology. The results provide insights for port authorities to make strategic decisions. [ABSTRACT FROM AUTHOR]

Data-driven traffic management and control has attracted much attention recently. This paper conducts a series of coherent analyses based on geocoded data to understand the distribution characteristics of bus operational speed and to explore the potential applications of speed distributions. First, an original bipartite model is adopted for capturing instantaneous speed where the suspended and moving states are considered separately and a two-component mixed Weibull distribution is used to model the speed distribution in moving states. The mixed Gaussian distribution with variable components is found to be capable of expressing the speed distribution patterns of different road sections. Second, elaborate analyses on the basis of speed distribution modelling are conducted: (i) regression analyses are conducted to explore the correlations between parameters of instantaneous speed distributions and traffic related factors; (ii) a powerful clustering method using Kullback-Leibler divergence as the distance measure is proposed to grade the road sections of a bus route. These results can be utilized in fields such as bus operations management, bus priority signal control and infrastructure transformation aiming to improve the efficiency of bus operations systems. [ABSTRACT FROM AUTHOR]

• We propose a lifelong framework for data quality monitoring of roadside sensors based on fully instrumented CAVs. • A novel trajectory similarity algorithm of LCSS-TRPS is developed to determine the CAV trajectory in the roadside perception dataset. • The indicators of absolute and relative positioning errors are designed to assess the data accuracy of roadside sensors. • The feasibility and efficiency of the framework are verified in the field experiments on Donghai Bridge, China. To monitor the data quality of roadside sensors in cooperative vehicle-infrastructure systems (CVIS), this study proposes a lifelong framework based on high-precision positioning and perception data of fully instrumented connected and automated vehicles (CAVs). First, a novel trajectory similarity algorithm, called longest common subsequence considering time and relative position sequences (LCSS-TRPS), is developed to match the CAV perception data with roadside perception data. The system time deviation is then calculated, and Kalman filtering is applied to synchronize the sampling time. Finally, indicators are rigorously designed considering absolute and relative positioning errors to assess the data accuracy. Simulation via PreScan and field experiments on Donghai Bridge (China) are conducted to verify the performance and feasibility of the proposed framework. The results show that the algorithms of trajectory matching and time synchronization are efficient and stable under different conditions, and the accuracy of data can be effectively evaluated by the designed indicators. [Display omitted] [ABSTRACT FROM AUTHOR]