Optimal Speed Limit Research Articles

Voyage charterparty arrangement for river tramp shipping: Green and traditional vessels comparison

Voyage charterparty arrangement (VCA) for river shipping is a common agreement between a shipowner and a charterer regarding the transportation of specific cargoes from one port to another via a tramp vessel. Optimizing VCA with the essential terms such as laytime and sailing speed of the vessel, is a crucial task for both parties to reduce potential disputes and achieve win-win outcomes. This paper models the problem from two perspectives: the VCA negotiation stage and execution stage. Firstly, according to the given probability distribution of port time, the theoretically optimal laytime is obtained by using textbook formulation of newsvendor problems (NVP). On this basis, considering the concerns about the inaccuracy of the given probability distribution and the adjustment measures taken by charterers and shipowners in reality, an integrated adjustment model is constructed to determine the optimal laytime in the VCA negotiation stage. Secondly, taking into account the vessel's uncertain lock crossing time, an operations coordination model with information sharing of the berth plan is established to obtain the optimal sailing speed under a certain confidence level in the VCA execution stage. Finally, a case study of chartering tramp vessels on the Yangtze River validates the applicability and effectiveness of the models proposed in this paper, and some important management insights are derived. The results show that establishing lower optimal speed limits and relatively shorter laytime when signing a contract with the shipowner of green river vessels is reasonable for the charterer. By notifying the shipowner of the berth plan after the green river vessel crosses through the lock, the charterer can avoid higher demurrage costs due to speeding-up. Operations coordination would be mutually beneficial without abusing the contract terms. Compared to traditional river vessels, it's more reasonable to reduce the benefits allocated to charterers from the deceleration of green river vessels by shipowners.

Rotating speed pulling-back control and adaptive strategy of doubly-fed variable speed pumped storage unit

When the doubly-fed variable speed pumped storage unit (DFVSPSU) adopts the power priority control, the rotating speed may change excessively during the transient process, potentially causing the rotor side power to exceed the converter capacity, which may damage the converter. Therefore, this study aims to analyze the mechanism underlying rotor side power overlimit and conventional rotating speed pulling-back control (RSPBC), proposing an adaptive rotating speed pulling-back control (ARSPBC). First, the unit's speed operating range and the converter capacity were determined based on the characteristic curve of the pump-turbine. Second, the optimal speed and speed limit were compared, indicating that the unit's operation is closer to the lower speed limit, which increases the likelihood of power overlimit occurrences. Third, the mechanism of rotor side power overlimit and conventional RSPBC were analyzed. It was noted that although conventional RSPBC can prevent rotor side power overlimit, it can result in significant generator power fluctuations. Finally, an ARSPBC incorporating speed change rate feedback was proposed. The numerical simulation showed that the ARSPBC can not only prevent the rotor side power overlimit, but also reduce the generator power fluctuations. Compared to the conventional RSPBC, the power fluctuation was reduced from 70 % to 62.5 %–4 %.

Speed limit management on two-lane rural roads shared by drivers and cyclists to improve safety and traffic operation

Two-lane rural roads require management that integrates all road users, especially cyclists, who have reached significant traffic levels. Management measures must focus on increasing safety and, wherever possible, also improving traffic operation. Speed limit management can be a solution, however it has to be based on scientific criteria. In this study, various speed limits were simulated on a narrow rural road using a traffic microsimulation model and considering a set of scenarios combining motor and cycle traffic levels. As a results of simulations, performance measures were obtained to characterise safety and traffic operation. The optimal speed limit for each traffic level was obtained by a Multi Criteria Decision Making method, using performance measures as criteria. The results point to the groups of cyclists to ride two-abreast, and to a dynamic management of the road speed limit, reducing the speed during periods of peak motorised and bicycle traffic. It is recommended to adapt the study to each rural road considering its traffic and geometric characteristics, calibrating the traffic microsimulation model and obtaining a specific dynamic speed limit management model.

Multi-Lane Differential Variable Speed Limit Control via Deep Neural Networks Optimized by an Adaptive Evolutionary Strategy.

In advanced transportation-management systems, variable speed limits are a crucial application. Deep reinforcement learning methods have been shown to have superior performance in many applications, as they are an effective approach to learning environment dynamics for decision-making and control. However, they face two significant difficulties in traffic-control applications: reward engineering with delayed reward and brittle convergence properties with gradient descent. To address these challenges, evolutionary strategies are well suited as a class of black-box optimization techniques inspired by natural evolution. Additionally, the traditional deep reinforcement learning framework struggles to handle the delayed reward setting. This paper proposes a novel approach using covariance matrix adaptation evolution strategy (CMA-ES), a gradient-free global optimization method, to handle the task of multi-lane differential variable speed limit control. The proposed method uses a deep-learning-based method to dynamically learn optimal and distinct speed limits among lanes. The parameters of the neural network are sampled using a multivariate normal distribution, and the dependencies between the variables are represented by a covariance matrix that is optimized dynamically by CMA-ES based on the freeway's throughput. The proposed approach is tested on a freeway with simulated recurrent bottlenecks, and the experimental results show that it outperforms deep reinforcement learning-based approaches, traditional evolutionary search methods, and the no-control scenario. Our proposed method demonstrates a 23% improvement in average travel time and an average of a 4% improvement in CO, HC, and NOx emission.Furthermore, the proposed method produces explainable speed limits and has desirable generalization power.

Optimal Differential Variable Speed Limit Control in a Connected and Autonomous Vehicle Environment for Freeway Off-Ramp Bottlenecks

When traffic congestion occurs on freeway off-ramp bottlenecks, the traffic state becomes complicated and changeable, which leads to increased vehicle travel time and decreased traffic safety and traffic efficiency. Variable speed limit (VSL) control is an effective method to improve traffic conditions, increase bottleneck throughput, improve traffic efficiency, and reduce emissions. Currently, there is an emerging trend of using connected and autonomous vehicle (CAV) technology to develop VSL control. This paper proposes an optimal differential variable speed limit (DVSL) control strategy under mixed CAVs and human-driven vehicles (HVs) environment for freeway off-ramp bottlenecks. The proposed DVSL control considers the characteristics of on-ramp, off-ramp, and mixed traffic flow (i.e., CAVs coexist with HVs). The proposed optimal DVSL control can describe and forecast the dynamics of traffic flow, and can set different speed limits across each lane with a multiple-objective function of total travel time (TTT) and total travel distance (TTD). A model predictive control (MPC) approach was utilized to optimize the DVSL control algorithm. The designed DVSL control was tested on a real-word freeway section with a simulated off-ramp bottleneck. The simulation results show that the proposed control strategy outperforms other existing methods in terms of improving the mobility of a freeway off-ramp bottleneck and maximizing the environmental benefits. Sensitivity analysis shows that the proposed control strategy can improve performance with the increase of the penetration rate (PR) of CAVs. The proposed methods form the basis of VSL control at off-ramp sections under mixed traffic environment.

Differential Evolution Based Numerical Variable Speed Limit Control Method with a Non-Equilibrium Traffic Model

This paper introduces a numerical variable speed limit (VSL) control method on a motorway, modeled by the system of partial differential equations (PDEs) of a non- equilibrium continuum traffic model. The method consists of a macroscopic simulation (i.e., numerical solution of the system of PDEs of the continuum model), introduction of the solution-based cost function and numerical optimization with a differential evolution algorithm (DE). Due to the numerical solution scheme, the method enables application of a wide range of continuum traffic models without prior discretization of PDEs. In this way, the method overcomes the limitations of the basic continuum models and represents a step towards more accurate traffic modelling in control strategies. In this paper, we determine optimal variable speed limits with the DE algorithm on a motorway section modeled by the modified switching curve model, which is a non-equilibrium continuum model consistent with the three-phase traffic flow theory. The effectiveness of the determined variable speed limits is validated using microsimulations of the test section, which show promising reductions of queue lengths and number of stops.

Optimisation of variable speed limits at the freeway lane drop bottleneck

ABSTRACT The primary objectives of this study were to use variable speed limits (VSL) upstream of freeway lane drop to maintain capacity and reduce congestion. As driving behaviours are the main reasons leading to capacity drop and the microscopic simulation can reflect driving behaviours precisely, microscopic simulations were first used to test lane drop scenarios. The objective function and constraints determined according to traffic engineering practice were optimised using a modified genetic algorithm (GA) based on microscopic simulation to get the optimal speed limit combination. The modified GA can guarantee the solution diversity and optimal results. Then, the cell transmission model, a macroscopic flow model, was used to crosscheck the simulated results. Both microscopic and macroscopic analysis results demonstrated that VSL could only improve lane drop traffic efficiency if speed limits were set appropriately. This study provided a new process from microscopic to macroscopic aspects for analysing traffic problems.

Estimation of Optimal Speed Limits for Urban Roads Using Traffic Information Big Data

The use of an inconsistent speed limit determination method can cause low speed limit compliance. Therefore, we developed an objective methodology based on engineering judgment considering the traffic accident rate in road sections, the degree of roadside development, and the geometric characteristics of road sections in urban roads. The scope of this study is one-way roads with two or more lanes in cities, and appropriate sections were selected among all roads in Seoul. These roads have speed limits of the statutory maximum speed of 80 km/h or lower and are characterized by various speeds according to the function of the road, the roadside development, and traffic conditions. The optimal speed limits of urban roads were estimated by applying the characteristics of variables as adjustment factors based on the statutory maximum speed limit. As a result of investigating and testing various influence variables, the function of roads, the existence of median, the level of curbside parking, the number of roadside access points, and the number of traffic breaks were selected as optional variables that influence the operating speed. The speed limit of one-way roads with two or more lanes in Seoul was approximately 10 km/h lower than the current speed limit. The existing speed limits of the roads were applied uniformly considering only the functional road class. However, considering the road environment, the speed limit should be applied differently for each road. In the future, if the collection scope and real-time collection of road environment information can be determined, the GIS visualization of traffic safety information will be possible for all road sections and the safety of road users can be ensured.

Deriving environment-friendly speed limit scheme using an integrated traffic simulation framework

ABSTRACT Traffic authorities have taken various measures to mitigate vehicular-source pollution emissions, among which setting speed limits has been proved to be effective. However, most of the existing macroscopic research proposes idealized mathematical models to design optimal speed limit from the perspective of network equilibrium, and the simplified pollution emission functions cannot accurately reflect the emission mechanism. In order to improve the accuracy of emission calculation and obtain more reliable speed limit schemes in a large-scale network, an integrated traffic simulation approach was proposed herein to derive environment-friendly road speed limit schemes. First, this article built an integrated simulation framework to portray traffic operations and emissions comprehensively. Subsequently, a toy network was employed to demonstrate how to obtain environment-friendly speed limit schemes using this framework. Then this article utilized it to determine the corresponding optimal speed limit schemes on the Second Ring road network in Beijing under different scenarios. The results indicated that traffic authorities should impose adaptive rather than fixed speed limits according to traffic period and emission reduction priority. Moreover, imposing the derived speed limit schemes, the amounts of total emissions, hydrocarbons, carbon monoxide, nitrogen oxides, carbon dioxide, and fine particulate matter decreased by 2.24%, 0.72%, 0.13%, 1.61%, 2.32%, and 5.35% during peak hour, and by 8.31%, 0.60%, 1.92%, 7.16%, 8.60%, and 8.72% during off-peak hour, respectively. Implications: In order to alleviate traffic pollutions emission issues more effectively from the traffic management perspective, we put forward an integrated traffic simulation approach to derive the environmental friendly road speed limit schemes. We combined MOVES and PARAMICES to build the integrated traffic simulation framework to portray road traffic operations and emissions elaborately. To verify the effectiveness of our method on real large road network, we used this method to find out the corresponding optimal speed limit schemes within the combination scheme set on Second Ring road network in Beijing under different traffic demand scenarios. Comparing with the current speed limit scheme, our derived speed limit scheme contributed to lowering emission. The amounts of total pollution emissions, hydrocarbons (HC), carbon monoxide (CO), nitrogen oxides (NOx), carbon dioxide (CO2), and fine particulate matter (PM2.5) decreased by 2.24%, 0.72%, 0.13%, 1.61%, 2.32%, and 5.35% during peak hours, and by 8.31%, 0.60%, 1.92%, 7.16%, 8.60%, and 8.72% during off-peak hours, respectively.

Variable speed limit strategy with anticipatory lane changing decisions

This paper develops a novel anticipatory Variable Speed Limit (VSL) control strategy that incorporates driver behavior based on trajectory data from probe vehicles. In particular, the research examines how the speed limit controls can be coordinated and optimized to reduce lane changing and overall braking, thus to achieve greater traffic throughput, safety, and sustainability. Driver behavior, such as acceleration/deceleration and lane changing, as reflected in individual trajectory data from GPS enabled probe vehicles are crucial for early detection of shockwave formation and proactive selection of speed limits; and consequently delay, or even eliminate breakdown formation. The core of this approach is the incorporation of a lane changing model which provides a more robust integrated speed limit selection and shockwave detection framework than the one we have developed in an earlier paper. The control principle is formulated in a generic fashion that finds the optimal speed limit control variables for either separate or simultaneous reduction of travel time, crash rates, and fuel consumption over a prediction horizon. The findings from the paper suggested that the VSL strategy was able to result in fewer lane changing rate (LCR) compared to the No-VSL case. Also, the developed algorithm was able to produce higher frequency of lower acceleration and deceleration rates than the No-VSL case, which indicated a smoother acceleration and deceleration pattern that corresponded to lower emission and fuel consumption. The performance of the algorithm was also examined under different probe penetration rates and congestion levels to identify the % of probe needed to achieve simultaneous mobility, safety, and environmental objectives.

Identify Optimal Traffic Condition and Speed Limit for Hard Shoulder Running Strategy

Highway system is experiencing increasing traffic congestion with fast-growing number of vehicles in metropolitan areas. Implementing traffic management strategies such as utilizing the hard shoulder as an extra lane could increase highway capacity without extra construction work. This paper presents a method of determining an optimal traffic condition and speed limit of opening hard shoulder. Firstly, the traffic states are clustered using K-Means, mean shift, agglomerative and spectral clustering methods, and the optimal clustering algorithm is selected using indexes including the silhouette score, Davies-Bouldin Index and Caliski-Harabaz Score. The results suggested that the clustering effect of using K-Means method with three categories is optimal. Then, cellular automata model is used to simulate traffic conditions before and after the hard shoulder running strategy is applied. The parameters of the model, including the probabilities of random deceleration, slow start and lane change, are calibrated using real traffic data. Four indicators including the traffic volume, the average speed, the variance of speed, and the travel time of emergency rescue vehicles during traffic accident obtained using the cellular automata model are used to evaluate various hard shoulder running strategies. By using factor analysis and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) methods, the optimal traffic condition and speed limit of opening hard shoulder could be determined. This method could be applied to highway segments of various number of lanes and different speed limits to optimize the hard shoulder running strategy for highway management.

Reinforcement Learning-Based Variable Speed Limits Control to Reduce Crash Risks Near Traffic Oscillations on Freeways

Traffic oscillation on freeways results in abrupt and frequent vehicle deceleration, which remarkably increases crash risks. Artificial Intelligence based traffic control provides a new opportunity for addressing the safety issues. This study aims at proposing a Reinforcement Learning (RL)-based variable speed limits (VSL) control algorithm to reduce crash risks associated with oscillations. The state, action and reward, which are the critical components in the RL, were designed carefully for safety improvement. The RL was trained to learn the optimal speed limit for various traffic states to achieve a goal of safety optimization. A rear-end crash risk model was applied to assess crash risks associated with oscillations near freeway bottlenecks. The cell transmission model was modified as the simulation platform for evaluating the control effects. The results showed that after the training process, the proposed RL-based VSL control successfully reduced the crash risks by 19.4%. A continuous online learning function was developed in RL to enhance the robustness of our strategy. The results showed that with continuous learning, the RL-based VSL control performed reasonably well under lower driver compliance situations.

Vehicle emission control on road with temporal traffic information using deep reinforcement learning

The increased vehicle usage significantly aggravate the urban air pollution, which have great impact on the public health. Therefore, it is necessary to make proper traffic control policies and reduce traffic emissions. However, it is difficult to establish control strategies based on modeling methods, and carry out online control based on historical traffic information for the complex time-varying characteristics of emissions. In this paper, we present a deep reinforcement learning emission control strategy, which automatically learns the optimal traffic flow and speed limits to reduce traffic emission on the target road segment based on the temporal traffic information. The proposed approach is evaluated on real world vehicle emission data in Hefei. And the results demonstrate the effectiveness of the proposed approach against baseline methods.

Многокритериальный подход к обоснованию выбора проекта приобретения и эксплуатации судна-балкера

Since fuel consumption significantly depends on the vessel‟s speed, operation of vessels at low speeds is widely used in world practice. On the one hand, reducing the speed of vessels allows saving fuel, which helps to reduce the cost of transportation. But, on the other hand, too long cargo delivery time may not always be acceptable for cargo owners. Therefore, studies aimed at choosing an economically viable speed of vessels are particularly relevant. The purpose of the work is to develop measures aimed at improving the operational efficiency of vessels used in tramp shipping, taking into account the possibility of reducing vessels‟ speed, as well as the rationale for choosing an investment project for acquiring and operating vessels using multi-criteria estimates. Studies have shown that when justifying projects for the acquisition and operation of vessels, it is necessary to take into account the speeds in which these vessels are planned to be used. The effectiveness of projects for the acquisition and operation of bulk carrier vessels can be significantly improved by means of the informed choice of the vessels speed. This made it possible to make an informed choice of the investment project for acquiring a bulk vessel and the optimal speed limit for its use, taking into account both the value of economic indicators of the efficiency of use of the vessel and the average time of cargo transportation.

Industry Stakeholder Perspectives on the Adoption of Electric Bicycles in British Columbia

Electric-assist bicycles (e-bikes) are an emerging mode of transportation that offers a sustainable alternative to automobile use in urban areas. Past research on e-bike adoption has focused on user perspectives. Understanding other stakeholder perspectives is also essential to implementing effective e-bike policy. The objectives of this research are to identify alignments and misalignments in perspectives on e-bike adoption across industry stakeholders in British Columbia (BC), including e-bike retailers, manufacturers, cycling coalitions, and government agencies, and to provide recommendations for e-bike policy that account for those perspectives. An online survey was distributed to industry stakeholders to examine perceived barriers to adoption, expected impacts of adoption, and effects of policy on adoption. Questions about regulations discriminated between five e-bike types: pedal-assist, throttle-assist, scooter-style, electric recumbents, and enclosed electric recumbents. Results indicate strong agreement among industry stakeholders that scooter-style e-bikes require separate and additional regulation from other types of e-bikes and from existing regulation in BC. In contrast, there was misalignment in the expected mode shift resulting from e-bike adoption, with government agencies least optimistic about diversion of automobile trips. Industry stakeholders broadly agreed on the need for speed regulation and viewed higher speeds as one of the least important benefits of e-bikes, which contrasts with past research on user perspectives. Policy recommendations include reclassifying scooter-style e-bikes, rebate or tax programs to reduce e-bike costs, further research on optimal e-bike speed limits, and continued support for improvements in general cycling infrastructure (a top priority for industry and user stakeholders).

SmartDrive: Traction Energy Optimization and Applications in Rail Systems

This paper presents the development of SmartDrive package to achieve the application of energy-efficient driving strategy. The results are from collaboration between Ricardo Rail and the Birmingham Centre for Railway Research and Education (BCRRE). Advanced tram and train trajectory optimization techniques developed by BCRRE as part of the UKTRAM More Energy Efficiency Tram project have been now incorporated in Ricardo’s SmartDrive product offering. The train trajectory optimization method, associated driver training and awareness package (SmartDrive) has been developed for use on tram, metro, and some heavy rail systems. A simulator was designed that can simulate the movement of railway vehicles and calculate the detailed power system energy consumption with different train trajectories when implemented on a typical AC or DC powered route. The energy evaluation results from the simulator will provide several potential energy-saving solutions for the existing route. An enhanced Brute Force algorithm was developed to achieve the optimization quickly and efficiently. Analysis of the results showed that by implementing an optimal speed trajectory, the energy usage in the network can be significantly reduced. A driver practical training system and the optimized lineside driving control signage, based on the optimized trajectory were developed for testing. This system instructed drivers to maximize coasting in segregated sections of the network and to match optimal speed limits in busier street sections. The field trials and real daily operations in the Edinburgh Tram Line, U.K., have shown that energy savings of 10%–20% are achievable.

Optimal Variable Speed Limit Control in Connected Autonomous Vehicle Environment for Relieving Freeway Congestion

AbstractThis study presents an optimal variable speed limit (VSL) strategy in a connected autonomous vehicle (CAV) environment for a freeway corridor with multiple bottlenecks. The VSL control was ...

Combining Road Network Data from OpenStreetMap with an Authoritative Database

Computer modeling of road networks requires detailed and up-to-date dataset. This paper proposes a method of combining authoritative databases with OpenStreetMap (OSM) system. The complete route is established by finding paths in the graph constructed from partial data obtained from OSM. In order to correlate data from both sources, a method of coordinate conversion is proposed. The algorithm queries road data from OSM and provides means of locating any point on the route in both datasets. A method of calculating the distance of any route point from the origin, and conversion between the distance and geographic coordinates, is described. Next, the location of any route point in the authoritative database is converted to the calculated route distance, which establishes a relation between the two data sources. Additionally, a method of estimating road curvature is proposed. The algorithm is validated in series of experiments. The proposed algorithm may be beneficial for researchers who collect datasets needed for computer simulations, e.g. for evaluation of optimal speed limits, and it shows usefulness of OSM in transportation related research.

How can the notion of optimal speed limits best be applied in urban areas?

This paper reviews estimates of optimal speed limits made in the past 30 years. A tendency is seen for optimal speed limits to become higher. In the most recent estimates made for Norway, the optimal speed limit was in no case lower than 60 km/h. Adopting a speed limit of 60 km/h on roads in urban areas now having speed limits of 30, 40 or 50 km/h would most likely lead to an increase in the number of accidents and killed or injured road users. It is a political objective in Norway to reduce the number of killed or injured road users and to encourage more walking and cycling. Raising speed limits would conflict with both these objectives. This paper discusses if a re-interpretation of the notion of optimal speed limits can applied to justify low speed limits in urban areas. Traditionally, analyses of optimal speed limits have included motorised travel only. It is shown by means of simple numerical examples, that by including the effects of motorised travel speed on walking and cycling, optimal speed limits tend to be lower than when only motorised travel is included.

Reinforcement Learning-Based Variable Speed Limit Control Strategy to Reduce Traffic Congestion at Freeway Recurrent Bottlenecks

The primary objective of this paper was to incorporate the reinforcement learning technique in variable speed limit (VSL) control strategies to reduce system travel time at freeway bottlenecks. A Q-learning (QL)-based VSL control strategy was proposed. The controller included two components: a QL-based offline agent and an online VSL controller. The VSL controller was trained to learn the optimal speed limits for various traffic states to achieve a long-term goal of system optimization. The control effects of the VSL were evaluated using a modified cell transmission model for a freeway recurrent bottleneck. A new parameter was introduced in the cell transmission model to account for the overspeed of drivers in unsaturated traffic conditions. Two scenarios that considered both stable and fluctuating traffic demands were evaluated. The effects of the proposed strategy were compared with those of the feedback-based VSL strategy. The results showed that the proposed QL-based VSL strategy outperformed the feedback-based VSL strategy. More specifically, the proposed VSL control strategy reduced the system travel time by 49.34% in the stable demand scenario and 21.84% in the fluctuating demand scenario.