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This paper studies an optimal dynamic lane reversal and traffic control (DLRTC) strategy in the presence of autonomous vehicles (AVs). A centralized controller is set to change lane directions dynamically and regulate traffic flow on a motorway network. Through vehicle to infrastructure (V2I) communication, the roadside sensors can send lane reversal information and flow control actions to the AVs which can perform lane-changing behaviors and adjust travel speed. To model the traffic dynamics under DLRTC, we propose a novel multi-lane cell transmission model (CTM). A logit model is used to characterize the lane-changing behaviors under uncontrolled cases. A mixed integer linear programming model (MILP) is formulated for DLRTC, and optimal control actions are implemented in a framework of model predictive control (MPC). The numerical experiments based on the Ayer Rajah Expressway (AYE) in Singapore are conducted to demonstrate the effectiveness of the proposed methods. The results show that the DLRTC strategy can effectively reduce road congestion and achieve better system performance compared to the benchmark method.

This study proposes an autonomous truck (AT) based scheduling problem for container transshipment between two seaport terminals, in which ATs can travel in platoons with short inter-vehicle distance. The following vehicles in a platoon can save fuel consumption because of the reduced air resistance. Based on a fuel consumption function with respect to travel speed and platoon formation, we formulate a mixed-integer second-order-cone programming (MISOCP) model to minimize the total operation cost. To address the extra computational challenges caused by the requirements of platoon coordination, we develop a column-generation-based heuristic method in which two types of columns representing AT schedules and platoon schemes are generated and added into the master problem. According to the practice of container transshipment between two port terminals in Singapore, we conduct extensive numerical experiments and evaluate the performance of the proposed method up to the instance of 50 requests in a two-hour time limit. The results show that the proposed CG heuristic can outperform the state-of-the-art solver by around 10% for the instances with 40–50 requests.

We in this paper investigate navigation strategies of two cross-moving connected and autonomous vehicles (CAVs) at an unsignalised intersection. As highly intelligent and automated entities, CAVs could make decisions independently or behave in a cooperative manner. A Nash game with discrete decision strategy is formulated to characterize the non-cooperative behaviour and a cooperative game is formulated to model the cooperative control mechanism. Results show that (i) pure-strategy Nash equilibria (NEs) for the non-cooperative game always exist and NEs hold if and only if at least one CAV takes its dominant strategy; (ii) more than two pure-strategy NE solutions may exist, but at most two different payoffs could arrive for each player at pure-strategy NEs; (iii) the optimal solution to the cooperative game must be in the NE solution set. These interesting findings provide useful managerial insights to CAV operators and transport authorities, and also enable us to tailor a branch & bound (B&B) algorithm to efficiently solve the models. We also extend the proposed methodology to the n-player case ( n ≥ 3 ) and give some more generalized insights. Numerical experiments are demonstrated in the end to test the computational accuracy and efficiency of the B&B method and show that our models and algorithm can be readily incorporated into future real-time CAV decision system to help navigate through unsignalised intersections.

The tugboat is the vessel that helps to maneuver large ships for berthing and un-berthing operations. To achieve efficient tugboat operations, investigating the features of tugboat activities is of crucial importance. This study aims to use automatic identification system (AIS) data to identify the maneuver services and analyze the characteristics of tugboat activities. A two-stage algorithm is developed to extract the time, locations, and involved tugboats for berthing and un-berthing operations from AIS data. The AIS data from Tianjin port, China, are used in the case study to demonstrate the effectiveness of the proposed method and analyze the pattern of tugboat activities. First, some important features of tugboat jobs are presented, such as the daily number of jobs and the spatial distribution of jobs. Then, a temporal and spatial analysis is conducted to investigate tugboat assignment, service time, tugboat utilization, and locations of berthing and un-berthing operations. The obtained results and implications could shed light on the deployment of tugboat berths, tugboat scheduling, and evaluation of tugboat fleet operation.

The morphology and skeleton architecture of photosynthetic corals modulates the light capture and functioning of the coral-algal symbiosis on shallow-water corals. Since corals can thrive on mesophotic reefs under extreme light-limited conditions, we hypothesized that microskeletal coral features optimize light capture under low-light environments. Using micro-computed tomography scanning, we conducted a comprehensive three-dimensional (3D) assessment of small-scale skeleton morphology of the depth-generalist coral Stylophora pistillata collected from shallow (5 m) and mesophotic (45 m) depths. We detected a high phenotypic diversity between depths, resulting in two distinct morphotypes, with calyx diameter, theca height, and corallite marginal spacing contributing to most of the variation between depths. To determine whether such depth-specific morphotypes affect coral light capture and photosynthesis on the corallite-scale, we developed 3D simulations of light propagation based on photosynthesis-irradiance parameters. We found that corals associated with shallow morphotypes dissipated excess light through self-shading microskeletal features; while mesophotic morphotypes facilitated enhanced light absorption and photosynthesis under low-light conditions. We conclude that the mesophotic coral architecture provides a greater ability to trap solar energy and efficiently exploit the limited light conditions, and suggest that morphological modifications play a key role in the photoadaptation response to low-light.

This study proposes an autonomous vehicle incentive program design (AV-IPD) problem for a local government, which aims to promote the adoption of autonomous vehicles (AVs) by deploying AV lanes and subsidizing the purchase of AVs with the objective of system optimum subject to a fixed budget. As it is difficult to anticipate the changing AV market conditions in the future, we take into account the uncertainty in AV purchase price. For the AV-IPD problem, we firstly decide which regular lanes should be converted into exclusive AV lanes. Secondly, we determine the AV purchase subsidy for each realization of AV purchase price with a given AV lane deployment scheme. A binary logit model is applied to characterize the vehicle choice behavior of users. The AV-IPD problem is formulated as a two-stage stochastic programming model with equilibrium constraints. To solve the AV-IPD problem, we develop a solution method based on linear approximation techniques and duality theories. Numerical experiments are conducted to demonstrate the effectiveness of the proposed model and solution method.

This study deals with the bus & driver scheduling problems with mealtime windows for a single public transport bus route. Firstly, we develop three explicit Integer Linear Programming (ILP) models to formulate: the bus driver scheduling, bus & driver scheduling, and bus & driver scheduling with mealtime windows, respectively. These ILP models enable bus operators to solve their bus & driver scheduling problems by directly invoking an available optimization solver such as IBM ILOG CPLEX. However, our numerical experiments do not show remarkable improvements, as CPLEX takes more than 12, 17, and 35 h respectively to reduce the optimality gap to at most 0.5% for solving the above three models in an over 12-hour bus & driver scheduling problem. To improve the computational efficiency, we develop a valid inequality approach that can generate valid cuts incorporating with the CPLEX. An investigation into the valid inequality approach proves its high computational efficiency. In addition, we develop a self-adaptive search method for determining the upper and lower bounds of driver group and bus fleet sizes. Finally, we test our models and approaches on a real round-trip bus route. The results indicate that compared with the only use of CPLEX to solve the above three models, the valid inequality approach can address large-scale instances in a reasonable CPU time.

Brain Machine Interfaces (BMIs) hold promise to restore impaired motor function and serve as powerful tools to study learned motor skill. While limb-based motor prosthetic systems have leveraged nonhuman primates as an important animal model,(1–4) speech prostheses lack a similar animal model and are more limited in terms of neural interface technology, brain coverage, and behavioral study design.(5–7) Songbirds are an attractive model for learned complex vocal behavior. Birdsong shares a number of unique similarities with human speech,(8–10) and its study has yielded general insight into multiple mechanisms and circuits behind learning, execution, and maintenance of vocal motor skill.(11–18) In addition, the biomechanics of song production bear similarity to those of humans and some nonhuman primates.(19–23) Here, we demonstrate a vocal synthesizer for birdsong, realized by mapping neural population activity recorded from electrode arrays implanted in the premotor nucleus HVC onto low-dimensional compressed representations of song, using simple computational methods that are implementable in real time. Using a generative biomechanical model of the vocal organ (syrinx) as the low-dimensional target for these mappings allows for the synthesis of vocalizations that match the bird’s own song. These results provide proof of concept that high-dimensional, complex natural behaviors can be directly synthesized from ongoing neural activity. This may inspire similar approaches to prosthetics in other species by exploiting knowledge of the peripheral systems and the temporal structure of their output.

A continuous time and mixed state branching process is constructed by a scaling limit theorem of two-type Galton-Watson processes. The process can also be obtained by the pathwise unique solution to a stochastic equation system. From the stochastic equation system we derive the distribution of local jumps and give the exponential ergodicity in Wasserstein-type distances of the transition semigroup. Meanwhile, we study immigration structures associated with the process and prove the existence of the stationary distribution of the process with immigration.

The operational modes of ships reflect the status of ship engines and thus greatly impact the rate of emissions. To capture the characteristics of ship emissions, this study proposes an operational-mode-based method for estimating ship emission inventory . The operational modes of ships are classified according to automatic identification system (AIS) data. An effective algorithm is tailored to identify the operational modes of tugs (berthing, un-berthing, and maneuver). For cargo ships and passenger ships, three key operational modes are considered (transit, maneuver, and hotelling). Based on the identified operational modes, different methods (propeller-law-based method or empirical rules) are applied for emission estimation. In the case study, the ship emissions in the waters of the Port of Dalian are calculated. The results imply that port operators can reduce the emissions by limiting the sulfur content in fuel oil and requiring tugs to perform pushing and pulling operations with a lower engine load.

One important objective of urban traffic signal control is to reduce individual delay and improve safety for travelers in both private car and public bus transit. To achieve signal control optimization from the perspective of all users, this paper proposes a platoon-based adaptive signal control (PASC) strategy to provide multimodal signal control based on the online connected vehicle (CV) information. By introducing unified phase precedence constraints, PASC strategy is not restricted by fixed cycle length and offsets. A mixed-integer linear programming (MILP) model is proposed to optimize signal timings in a real-time manner, with platoon arrival and discharge dynamics at stop line modeled as constraints. Based on the individual passenger occupancy, the objective function aims at minimizing total personal delay for both buses and automobiles. With the communication between signals, PASC achieves to provide implicit coordination for the signalized arterials. Simulation results by VISSIM microsimulation indicate that PASC model successfully reduces around 40% bus passenger delay and 10% automobile delay, respectively, compared with signal timings optimized by SYNCHRO. Results from sensitivity analysis demonstrate that the model performance is not sensitive to the number fluctuation of bus passengers, and the requested CV penetration rate range is around 20% for the implementation.

In this study, we revisit the container drayage problem for heterogeneous trucks with multiple loads in view of its practical significance. We first propose a tailored mixed-integer linear programming (MILP) model explicitly to reflect the reality of container drayage operations. To solve medium-scale problems more effectively, two relaxed MILP models are built to provide a solution that is close to optimal with less computational time. These two relaxed models are proven to work as intended and provide optimal solutions under certain conditions. Moreover, an effective hybrid heuristic method incorporating the cheapest feasible insertion mechanism and the variable neighborhood search scheme is designed to solve large-scale problems. Extensive numerical experiments demonstrate the scalability of the heuristic method in real-world applications.

In urban public transportation system, operators tend to set a series of time points and scheduled travel time (STT) to monitor bus operation. Meanwhile, bus drivers are generally supposed to adjus...

When compared to large cities in developed countries, the shares of public transportation in most Chinese cities are low. Increasing the competitiveness of urban public transportation remains an urgent problem. A capable evaluation method for public transportation is required to assist the development of urban transit systems. This paper focuses on the bus system. Being devoid of standard criteria, it is difficult to determine the efficiency of a transit system or any bus line using a single evaluation index. This paper proposes a comparative analysis to evaluate bus lines so as to filter out candidates for further optimization. From the viewpoints of transit planning, operation and quality of service, this paper establishes 10 subordinate evaluation indices and then uses geographical information system tools, global positioning system data and smart card data to assist the index definition and calculation. Super-efficient data envelopment analysis (DEA) method is adopted for the proposed single f...

Gas reverse circulation drilling which has a wide application prospect in complex strata of deep and ultra-deep wells can effectively improve drilling speed, protect the environment, reduce costs, and reduce downhole accidents, but its string structure is different from that of conventional drilling. In this paper, the design of integral and splitter head-in to side-out rotary sand deflecting device consists of rotating inner tube and non-rotating outer deflecting chamber, so that it can be effectively connected to the double-wall drill pipe system to ensure the normal sand deflecting of the gas reverse circulation drilling system. At the same time, the test well was verified and analyzed, and the sand-carrying efficiency was close to 92.53%. After drilling, there was no sand-settling at the bottom of the well. The average carrying rock diameter is about 10-15mm, and the maximum can be up to 30-40mm, indicating that the top-injection side-exit gas sand guide can be used for gas reverse-circulation drilling.

This paper studies the purchase subsidy design problem for human-driven electric vehicles (HDEVs) and autonomous electric vehicles (AEVs). The proposed range- and mode-specific purchase subsidy aims to maximize the social benefits from vehicle electrification and automation. In this study, we first classify electric vehicles (EVs) into several classes based on electric driving ranges. Each EV class contains two driving modes, i.e., human driving and automated driving. We provide a simplified model to estimate the greenhouse gas (GHG) emission and the inconvenience costs of vehicle charging. The nested logit model is used to characterize users’ vehicle choice behaviors. A mixed integer nonlinear programming (MINLP) model is formulated for the purchase subsidy design problem. A customized branch-and-bound (B&B) method is developed to seek a globally optimal solution to the formulated MINLP model. The numerical examples show that the developed solution method can effectively solve the proposed problem in a reasonable time. The local search strategy embedded in the customized B&B method helps reduce 7% computation time on average. Some managerial insights obtained from the numerical experiments are discussed, which can help the government agency to achieve a reasonable budget allocation between HDEVs and AEVs with different electric driving ranges.

The performance of an adaptive signal control system largely depends on the embedded traffic flow prediction and control algorithm. This paper proposes an improved adaptive control method, comprised of a vehicle arrival estimation model and a signal optimization algorithm. Firstly, a microscopic model was developed to capture vehicle arrival dynamics both in red and green durations. Based on the upstream detection information, vehicle arrival time is predicted taking account of signal timing, vehicle trajectory and variable queue length. The real-time signal control algorithm was formulated based on a Dynamic Programming (DP) procedure, supporting NEMA phase configuration. Three objective functions were considered respectively, namely minimization of delay, queue length and maximization of throughput. The proposed adaptive control method was implemented in VISSIM on a real signalized intersection. It is shown that DP with NEMA generally outperforms DP with 4-phase and the signal timings generated by SYNCHRO. Different objective functions lead to different control performance in terms of various demands and planning horizons. It is reasonably believed that the model has potential applicability in real time traffic adaptive signal control systems.