Abstract

Most solution methods for the vehicle routing problem with time windows (VRPTW) develop routes from the earliest feasible departure time. However, in practice, temporal traffic congestions make that such solutions are not optimal with respect to minimizing the total duty time. Furthermore, VRPTW solutions do not account for complex driving hours regulations, which severely restrict the daily travel time available for a truck driver. To deal with these problems, we consider the vehicle departure time optimization (VDO) problem as a post-processing step of solving a VRPTW. We propose an ILP-formulation that minimizes the total duty time. The obtained solutions are feasible with respect to driving hours regulations and they account for temporal traffic congestions by modeling time-dependent travel times. For the latter, we assume a piecewise constant speed function. Computational experiments show that problem instances of realistic sizes can be solved to optimality within practical computation times. Furthermore, duty time reductions of 8 percent can be achieved. Finally, the results show that ignoring time-dependent travel times and driving hours regulations during the development of vehicle routes leads to many infeasible vehicle routes. Therefore, vehicle routing methods should account for these real-life restrictions.

Abstract

Daily traffic congestions form major problems for businesses such as logistical service providers and distribution firms. They cause late arrivals at customers and additional hiring costs for the truck drivers. The additional costs of traffic congestions can be reduced by taking into account and avoid well-predictable traffic congestions within off-line vehicle route plans. In the literature, various strategies are proposed to avoid traffic congestions, such as selecting alternative routes, changing the customer visit sequences, and changing the vehicle-customer assignments. We investigate the impact of these and other congestion avoidance strategies in off-line vehicle route plans on the performance of these plans in reality. For this purpose, we develop a set of VRP instances on real road networks, and a speed model that inhabits the main characteristics of peak hour congestion. The instances are solved for different levels of congestion avoidance using a modified Dijkstra algorithm and a restricted dynamic programming heuristic. Computational experiments show that 99% of late arrivals at customers can be eliminated if traffic congestions are accounted for off-line. On top of that, almost 70% of the extra duty times caused by the traffic congestions can be eliminated by clever avoidance strategies.

Abstract

This paper presents the design of the offshore energy simulation CEL as a flow network, and its integration in the MSP Challenge 2050 simulation game platform. This platform is designed to aid learning about the key characteristics and complexity of marine or maritime spatial planning (MSP). The addition of CEL to this platform greatly AIDS MSP authorities in learning about and planning for offshore energy production, a highly topical and big development in human activities at sea. Rather than a standard flow network, CEL incorporates three additions to accommodate for the specificities of energy grids: an additional node for each team's expected energy, a split of each node representing an object into input and output parts to include the node's capacity, and bidirectional edges for all cables to enable more complex energy grid designs. Implemented with Dinic's algorithm it takes less than 30ms for the simulation to run for the average amount of grids included in an MSP Challenge 2050 game session. In this manner CEL enables MSP authorities and their energy stakeholders to use MSP Challenge 2050 for designing and testing more comprehensive offshore energy grids.

Abstract

International audience; We introduce an electric vehicle routing problem combining conventional, plug-in hybrid, and electric vehicles. Electric vehicles are constrained in their service range by their battery capacity, and may require time-consuming recharging operations at some specific locations. Plug-in hybrid vehicles have two engines, an internal combustion engine and an electric engine using a built-in rechargeable battery. These vehicles can avoid visits to recharging stations by switching to fossil fuel. However, this flexibility comes at the price of a generally higher consumption rate and utility cost. To solve this complex problem variant, we design a sophisticated metaheuristic which combines a genetic algorithm with local and large neighborhood search. All route evaluations, within the approach, are based on a layered optimization algorithm which combines labeling techniques and greedy evaluation policies to optimally insert recharging stations visits in a fixed trip and to select the fuel types. The metaheuristic is finally hybridized with an integer programming solver, over a set partitioning formulation, so as to recombine high-quality routes from the past search into better solutions. Extensive experimental analyses are conducted, highlighting the good performance of the algorithm and the contribution of each of its main components. Finally, we investigate the impact of fuel and energy cost on fleet composition decisions. Our experiments show that a careful use of a mixed fleet can significantly reduce operational costs in a large variety of price scenarios, in comparison with the use of a fleet composed of a single vehicle class.

Abstract

The fundamental diagram of a road, including free flow capacity and queue discharge rate, is very important for traffic engineering purposes. In the real word, most traffic measurements come from stationary loop detectors. This paper proposes a method to fit Wu’s fundamental diagram to loop detector data. Wu’s fundamental diagram is characterised by five parameters, being the free flow speed, wave speed, free flow capacity, queue discharge rate and jam density. The fits appear to be not very sensitive to the right value for the wave speed or the free flow speed. The proposed method therefore entails fixing these two parameters. The method consists of two steps. We first use a triangular fundamental diagram to separate the congested branch from the free flow branch. Then, the remaining three parameters of Wu’s fundamental diagram are fitted on each of the branches using a least square fit. This method is shown to be robust for cases tested in real life, and hence very noisy, data.

Abstract

This paper presents the design of the offshore energy simulation CEL as a flow network, and its integration in the MSP Challenge 2050 simulation game platform. This platform is designed to aid learning about the key characteristics and complexity of marine or maritime spatial planning (MSP). The addition of CEL to this platform greatly AIDS MSP authorities in learning about and planning for offshore energy production, a highly topical and big development in human activities at sea. Rather than a standard flow network, CEL incorporates three additions to accommodate for the specificities of energy grids: an additional node for each team's expected energy, a split of each node representing an object into input and output parts to include the node's capacity, and bidirectional edges for all cables to enable more complex energy grid designs. Implemented with Dinic's algorithm it takes less than 30ms for the simulation to run for the average amount of grids included in an MSP Challenge 2050 game session. In this manner CEL enables MSP authorities and their energy stakeholders to use MSP Challenge 2050 for designing and testing more comprehensive offshore energy grids.

Abstract

Introduction:"/jats:title""jats:p"An acoustic signal-based tunnel accident detection system was developed in this study. In a tunnel environment, the sound diffusion effect is minimized and thanks to that, discrimination of accident sounds (crash and skid) from other noises can apparently be accomplished."/jats:p""/jats:sec""jats:sec""jats:title"Discussion:"/jats:title""jats:p"The system is composed of three parts: algorithm, field device, and center system. To distinguish accident-related acoustic signals such as a crash or skid among various other sounds in a tunnel, a delicate algorithm that can discriminate those signals from other normal signals generated from moving vehicles was created."/jats:p""/jats:sec""jats:sec""jats:title"Conclusion:"/jats:title""jats:p"The developed algorithm processes acoustic signals to filter out noises and to identify accident-related signals. The field device, installed in a tunnel, collects analog sounds, transforms them into digital signals, and transmits the digital signals to the server in the tunnel traffic management center. Lastly, in the tunnel traffic management center, the acoustic signal processing algorithm described above, installed in a server system, can instantaneously detect accidents. Once confirmed by the system operators, the information on the detected accidents is intended to be provided to drivers following behind as well as relevant agencies to prevent secondary accidents and to respond promptly. The developed system was evaluated in a real tunnel environment using traffic accident sounds acquired from real crash tests. The detection rates were 95, 91, and 80% at distances of 10, 30, and 50 m, respectively with a detection duration less than 1.4 s. Compared to conventional detection systems using loop detectors or video images that have a long detection time of around 1 min, the developed system can be regarded as superior in that it has an extremely short detection time, which, of course, is one of the most important factors for automatic incident detection systems."/jats:p""/jats:sec

Abstract

Congestion in intercity corridors of metropolitan area has been increasing steadily. To alleviate congestion, many major investment projects, such as the high speed railway projects, were proposed by agency. To evaluate the adequacy and efficiency of these projects, the intercity travel behavior should be analyzed in metropolitan area. The paper constructed a Multiple Indicators and Multiple Causes (MIMIC) model according to an expanded theory of planned behavior (TPB) to study the travel behavior of choosing from the choice set of the traditional train, the high speed railway and the coach by demographic and psychological factors. Through empirical data collection and analysis, we found that demographic factors of travelers indeed positively engender the latent variables in MIMIC, and descriptive norm and habit had direct or indirect significant effect on travel behavior and intention. On the basis of the effect of psychological constructors of the expanded TPB on the intercity travel behavior and differentiation of traveler's demographic characteristics, the agency can make reasonable policies and proper information for the intercity transportation. The results will support the basic theory of optimizing the transportation system in metropolitan area. Implications for researchers and suggestions for future research are also addressed in this study. Document type: Article

Abstract

Multi-product pipelines are a significant and extensive mean of transporting petroleum based products from refineries to distribution centers. Previous contributions on tree-like pipeline scheduling problem have considered a simple structure with a single refinery connected to a mainline and some secondary lines only emerged from the mainline. In practice, however, a tree-like pipeline may also have several branches on a secondary line resulting in a complex structure, the so called multi-level tree-like pipeline. This paper addresses the short-term scheduling of multi-level tree-like pipelines with multiple refineries through a continuous time mixed-integer linear programming (MILP) model that considers multiple intermediate due dates for product demands. The objective is to satisfy product demands on time at the minimum operational costs, such as pumping, interface and backorder costs. The proposed model performance’s is shown by solving four examples. Document type: Article

Abstract

Variable message signs (VMSs), as one of the important ITS devices, provide real-time traffic information of road network to drivers in order to improve route choice and relieve the traffic congestion. In this study, the effectiveness of VMS on driving behavior was tested based on a driving simulation experiment. A road network with three levels of VMS location to route-diverging intersection and three types of VMS information format was designed in a high fidelity driving simulator platform. Fifty-two subjects who were classified by driver age, gender, and vocation successfully completed this experiment. The experimental results showed that driver characteristics, VMS location, and information format profoundly influence driving behaviors. Based on the research findings, it is suggested that VMS would be positioned between 150 m and 200 m upstream of the diverging point to balance the VMS effects on traffic safety and operation and the graphic information VMS format is better than the format with text massage only. Document type: Article