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Dynamic vehicle routing problems (DVRPs) have become a hot research topic due to their significance in logistics, although it is still very challenging for existing algorithms to solve DVRPs due to the dynamically changing customer requests during the optimization. In this paper, we propose a pairwise proximity learning-based ant colony algorithm, termed PPL-ACO, for tackling DVRPs. In PPL-ACO, a pairwise proximity learning method is suggested to predict the local visiting order of customers in the optimal route after the occurrence of changes, which is on the basis of learning from the optimal routes found before the changes occur. A radial basis function network is used to learn the local visiting order of customers based on the proximity between each pair of customer nodes, by which the optimal routes can be quickly tracked after changes occur. Experimental results on 22 popular DVRP instances show that the proposed PPL-ACO significantly outperforms four state-of-the-art approaches to DVRPs. More interestingly, the results on five large-scale DVRP instances demonstrate the superiority of the proposed PPL-ACO in solving large-scale DVPRs with up to 1000 customers. The results on a real case of Nankai Strict, Tianjin, China also verifies that the proposed PPL-ACO is more effective and efficient than the four compared approaches in solving real-world DVRPs.

In this paper, we consider an extension of the Dynamic Vehicle Routing Problem with Backhauls integrated with two-dimensional loading problem called DVRPB with 2D loading constraints (2L-DVRPB). In the VRPB, a vehicle can deliver (Linehaul) then collect goods from customers (backhaul) and bring back to the depot. Once customer demand is formed by a set of two-dimensional items the problem will be treat as a 2L-VRPB. The 2L-VRPB has been studied on the static case. However, in most real-life application, new customer requests can be happen over time of backhaul and thus perturb the optimal routing schedule that was originally invented. This problem has not been analysed sofar in the literature. The 2L-DVRPB is an NP-Hard problem, so, we propose to use a Genetic algorithm for routing and a packing problems. We applied our approach in a real case study of the Regional Post Office of the city of Jendouba in the North of Tunisia. Results indicate that the AGA approach is considered as the best approach in terms of solutions quality for a real world routing system.

This paper addresses the experimental verification of vertical displacement predictions for a highway bridge under dynamic vehicle loads. In the structural health monitoring of bridge structures, the measurements of vertical displacements are relatively difficult than the measurements of axial strains, and the vertical displacements can give an intuitive information for monitoring the structural conditions. Therefore, an artificial neural network (ANN) was introduced for the accurate predictions of the vertical displacements from the axial strains. In the experiments, both the strains and displacements at the different locations were measured during 48 h for obtaining the training (Set 1) and testing (Set 2) data, which were utilized to develop and validate the ANN-based prediction model. The environmental effects such as temperature loads were eliminated from the experimental data using a calibration approach, and the pure contributions of the external vehicle loadings to the 3D highway bridge were considered. The validation results showed that the ANN-based model can accurately predict the vertical displacements. It is expected that the proposed ANN-based model, as a fast and accurate framework, can be utilized for various civil infrastructures during the structural health monitoring.

This paper compares different track model formulations for the simulation of dynamic vehicle–track interaction in switches and crossings (S&C, turnouts) in a multi-body simulation (MBS) environment. The investigations are an extension of the S&C simulation Benchmark with the addition of a finite element model of a 60E1-760-1:15 turnout. This model constitutes a common reference from which four different track formulations are derived: co-running, modal superposition, finite element incorporated into the MBS model and finite element coupled to MBS using a co-simulation approach. For the different track models, the difference in modelling technique, results, simulation time, and suitability for different simulation tasks is compared. A good agreement is found between the different track model formulations for wheel–rail contact forces and rail displacements. This study found a better agreement between co-running and structural track models compared to previous studies in the prediction of wheel–rail contact forces. This appears to be due to the increased complexity of co-running track model used in this study together with a tuning of the co-running track model to the reference model in a wider frequency range. For the reader interested to reproduce the results in this paper, the reference track model is available for download.

In this study 3-DOF quarter car model with the three bumps on the rigid road, the assumption has been modeled with the non-random irregularity. To reduce the excessive vibrations occurred on the vehicle body, an active suspension system with the linear actuator has been considered. Moreover, to control this actuator, an adaptive neuro-fuzzy algorithm is designed. The training and testing data of the ANFIS has been obtained from Proportional Integral Derivative (PID) control algorithm. After that the successful training process, a testing procedure has been applied to ANFIS for the measure of the adaptive neuro-fuzzy system with data that are not considered in the training process. Then, the performance of the ANFIS is compared by the PID algorithm and passive suspension system in terms of vehicle body vertical acceleration, vehicle body vertical displacement, and control force. The road model used in the study has been modeled according to non-random road profile mathematical formulation considering periodical and discrete road profile cases. In this formulation, one can easily determine the height, width, and number of the road defect with the series mathematical formulation. Consequently, with the results obtained from the presented study, it is proven that ANFIS is a very effective controlling algorithm to suppress vibration occurred on the vehicle body due to vehicle road interaction. Furthermore, the performance of the ANFIS has been tested with different parameters, for example, different number membership functions (MF), which used the fuzzification of the input parameters.

A multicircle order acceptance strategy was proposed to decide whether to accept customer requests for specific last-mile delivery modes (including attended home or reception box delivery) and time slots. The strategy was composed of initializing acceptable time slot allocations, reallocating acceptable time slots, matching reference sites, and assessing time slot deviations. A strategy-oriented insertion algorithm for dynamic vehicle routing problems with hard time windows was constructed, thereby showing the proposed strategy achieves a better balance between revenue and distance than the “first come and first served” strategy. Always accepting global optimization result is not significantly better than adopting the result conditionally or based on simulated annealing theory. The distance and revenue gradually increase with the number of reference sites, while revenue/distance ratio decreases. The vehicles are available to serve more attended home delivery orders with a gradual increase in time slot interval, thereby leading to an increase of AHD and total revenue.

The advancement and development of technologies promote more interest in exploring Dynamic Vehicle Routing Problems (DVRP), especially in Home Healthcare. Home healthcare (HHC) has gained more attention from researchers in recent years due to its increasing demand. There are certain cases where some patients may require one or more care services concurrently. In such a situation, synchronization of vehicles within specific time slots is necessary. The inclusion of dynamic patients and synchronization of vehicles without disregarding the time window will transform into a formidable task for HHC organizations. Hence, the investigation of the Dynamic Vehicle Routing problem where Synchronization occurs within the Time Slots (DVRPSTS) becomes a remarkable model in the context of HHC. This paper proposed one such complex endeavour considering multi-objectives such as (i) minimize the total travel time and the number of vehicles utilized (ii) maximize the number of new patients visits in HHC. The accuracy and efficiency of the proposed algorithm Mutated Cuckoo Search Algorithm (MCSA) are validated by comparing its results with existing methods in the literature. Thus, this algorithm outperforms most of the randomly generated test instances. To the best of our knowledge, the proposed MCSA has not yet been modelled for the DVRPSTS in HHC.

A Benchmark of railway multibody dynamics software application to switches and crossings (S&C) is presented, comparing all major commercially available software and a few independent codes. Two different representative S&C have been implemented, using the Manchester Benchmark passenger vehicle. The final results show that all software offer a reliable and efficient way to understand the kinematic and dynamics forces between the wheels and the track elements. The highest challenges are found when modelling a combination of multiple rails in simultaneous contact with a wheel (check-stock or switch-stock), large longitudinal variations in rail shape (crossings) and high lateral steering forces (diverging cases in tight radius). In those cases, the codes able to account for the exact relative motion of each wheels with respect to each rails independently are the most apt. The most significant variations between software are found in the contact prediction with an influence on the detailed contact tangential and normal forces. The user variability is found to be very small, with the most time-consuming and error prone being the task of handling the input data for the variable rails definition. All software could benefit from improvements to assist the user and ensure higher reliability and efficiency generally.

In downtown areas of large cities, it is very challenging for drivers to find available parking spots, even when they are provided with information on parking availability and location information. To overcome this challenge, this paper develops a dynamic vehicle routing system to search for the optimal routes for connected vehicles to find parking spots successfully and to minimize total trip time, including driving time and walking time. The system predicts the probability of each parking lot having available parking spots based on the existing available number of spots and the vehicle arrival and departure rates collected by connected vehicles. This probability is integrated in the search for vehicle routes to minimize total travel and walking times. Numerical experiments indicate that the proposed system can reduce the cruising time spent searching for available parking spots, and the total trip time can be reduced by up to 24%. In addition, the system can decrease the number of re-routing decisions, which reduces the stress of drivers on the road. A sensitivity analysis of the parking probability is also conducted. Some future work based on the proposed system is proposed in the conclusion to this paper.

This paper presents the final description for the S&C Benchmark that was launched at the IAVSD 2019 conference held in Gothenburg, Sweden and completed by eighteen participants by the end of 2020. The purpose of this paper is to allow for the replication of the Benchmark exercise after publication for those who wish to do so in the future, and it includes a link to the repository containing all necessary input data. The original task description, including a description of the Benchmark submission, is presented in full. The results from the Benchmark are available in [Bezin Y, Palsson BA, Kik W, et al. Multibody simulation Benchmark for dynamic vehicle-track interaction in switches and crossings: results and method statements. Submitted to VSD, 2021].

Demand responsive transit (DRT) has the potential to provide passengers with higher accessibility and lower travel time as compared with conventional transit, and at the same time make more efficient use of vehicle capacity than traditional taxi. In many current systems, vehicles are assigned to passengers along travel paths that are chosen myopically. When information on future demand distribution is available, it would be more beneficial to dispatch transit vehicles strategically to areas with a higher probability of generating passengers. This paper proposes a mathematical model for a dynamic DRT vehicle dispatch problem. It determines in real time how the operating vehicles shall be used to serve arriving passenger demand, and which paths the vehicles should choose to achieve a balance between operator and passenger costs. The model is solved by an approximate dynamic programming (ADP) based solution approach. Case studies, including a hypothetical numerical example and a real-world case in Qingdao, China, have been conducted to demonstrate the applicability of the proposed modeling framework. Results show that the proposed ADP solution can significantly improve the overall system performance as compared with myopic benchmarks.

We study the effect of highway congestion on the “arms race” on American roads, which has led to larger and more powerful vehicles that reduce safety and increase fuel consumption. We develop a new methodology to estimate a dynamic vehicle size choice and replacement model and find that congestion delays affect vehicle sizes. We then show that by addressing complementary externalities – congestion and the externalities associated with larger vehicle sizes – congestion pricing could reduce the vehicle fatality rate, generating $25 billion in annual benefits, and could improve vehicle fleet fuel efficiency, generating roughly $10 billion in annual operating cost savings.

The increase in the size of the city and the increase in population mobility have greatly increased the number of vehicles on the road, and at the same time brought considerable challenges to the traffic management department. In recent years, more and more experts and scholars have devoted themselves to applying sensors, network communication and dynamic adaptive technologies to road traffic management systems. At present, people have not completely overcome all kinds of complex problems in traffic supervision. The complexity of traffic information and the defects of identification algorithms have brought great challenges to intelligent traffic management. This article has launched a research on the intelligent Internet of Things traffic management system, with the detection and recognition of stationary vehicles and seat belts as the key analysis targets. When monitoring stationary vehicles, this paper replaces the background difference algorithm commonly used in dynamic vehicle detection with a new recognition algorithm. From the experimental results, the average detection accuracy of the new algorithm is 96.77% higher than the previous 87.56%. When studying the driver's seat belt detection, this paper combines the YOLOv3 target detection algorithm and the lightweight network structure, and proposes a driver-oriented positioning algorithm. With the increase in the number of lightweight templates, the accuracy of the positioning algorithm has increased from 80.57 to 99.98%. But on the other hand, the detection speed has also changed from 78 to 69 frames/s.

Thermal emissions – or anthropogenic heat fluxes (QF) – from human activities impact urban climates at a local and larger scale. DASH considers both urban form and function in simulating QF through the use of an agent-based structure that includes behavioural characteristics of urban residents. This allows human activities to drive the calculation of QF, incorporating dynamic responses to environmental conditions. The spatial resolution of simulations depends on data availability. DASH has simple transport and building energy models to allow simulation of dynamic vehicle use, occupancy and heating–cooling demand, and release of energy to the outdoor environment through the building fabric. Building stock variations are captured using archetypes. Evaluation of DASH in Greater London for periods in 2015 uses a top-down inventory model (GQF) and national energy consumption statistics. DASH reproduces the expected spatial and temporal patterns of QF, but the annual average is smaller than published energy data. Overall, the model generally performs well, including for domestic appliance energy use. DASH could be coupled to an urban land surface model and/or used offline for developing coefficients for simpler/faster models.

Industry 4.0 is a concept that assists companies in developing a modern supply chain (MSC) system when they are faced with a dynamic process. Because Industry 4.0 focuses on mobility and real-time integration, it is a good framework for a dynamic vehicle routing problem (DVRP). This research works on DVRP. The aim of this research is to minimize transportation cost without exceeding the capacity constraint of each vehicle while serving customer demands from a common depot. Meanwhile, new orders arrive at a specific time into the system while the vehicles are executing the delivery of existing orders. This paper presents a two-stage hybrid algorithm for solving the DVRP. In the first stage, construction algorithms are applied to develop the initial route. In the second stage, improvement algorithms are applied. Experimental results were designed for different sizes of problems. Analysis results show the effectiveness of the proposed algorithm., 22 pages, 4 figures, 1 table. Journal of Management Analytics (2020)

After three decades of its introduction, the dynamic vehicle routing problem (DVRP) remains a fertile field for new studies. The technological evolution, which continues to progress day by day, has allowed better communication between different actors of this model and a more encouraging execution time. This encouraged researchers to investigate new variants of the DVRP and use more complicated algorithms for the resolution. Among these variants is the multi-tour DVRP (MTDVRP) with overtime (MTDVRPOT), which is the subject of this article. This paper proposes an approach with a memetic algorithm (MA). The results obtained in this study are better than those of the ant colony system (ACS) applied to the same problem and published in an earlier paper.

A number of technological improvements have prompted a great concern on ‘dynamism’ in vehicle routing problems (VRP). In real-world applications, the dynamic information happens simultaneously with the plan being carried out. In order to effectively solve dynamic VRP (DVRP), many optimization strategies have been introduced in the literature. A new variant of vehicle routing problem is proposed which combines DVRP with time windows and capacity constraints, named capacitated DVRP with time windows (CDVRPTW). Apart from the traditional way of handling the problem, this paper proposes a novel strategy that incorporates improved firefly algorithm (IFA) into the framework of spiking neural P (SN P) systems, named spiking neural firefly optimization (SFO). A mathematical model of the problem is formulated, and the solution scheme is designed by associating a number of SN P systems that work in parallel to find optimal solutions in a reasonable time. Additionally, the parameters in the IFA are optimized by adjusting the rule probabilities using SN P systems. Being a NP-hard problem with real-world applications, the benefits of this study are far-reaching. The proposed scheme has been tested on benchmark instances and proved novelty, feasibility, and potentiality of the system.

This article focuses on the synthetic generation of human mobility data in urban areas. We present a novel application of generative adversarial networks (GANs) for modeling and generating human mobility data. We leverage actual ride requests from ride-sharing/hailing services from four major cities to train our GANs model. Our model captures the spatial and temporal variability of the ride request patterns observed for all four cities over a typical week. Previous works have characterized the spatial and temporal properties of human mobility datasets using the fractal dimensionality and the densification power law , respectively, which we utilize to validate our GANs-generated synthetic datasets. We also validate the synthetic datasets using a dynamic vehicle placement application. Such synthetic datasets can avoid privacy concerns and be extremely useful for researchers and policy makers on urban mobility.

The turning radius of the vehicle is determined based on the coordinates of the center of gravity. When the vehicle is moving at different velocities and driving at different steering angles, the value of the turning radius is also different. This change is nonlinear and continuous and determined by simulation or experimentation process. Previous studies have not established the formula to calculate the value of a turning radius of the vehicle based on input parameters. Therefore, determining the value of a turning radius is complex and time-consuming. This research has shown the dependence between turning radius R on input parameters such as velocity v and steering angle by using a double-track dynamic vehicle model to simulate. Besides, this research also established the state function to calculate the turning radius of the vehicle at different velocities and steering angles. The state function is determined based on the rule of change of the corresponding values. Using only the determined input parameters like velocity and steering angle, the value of the turning radius can be easily calculated with high accuracy through 4 steps. The reference coefficients needed to calculate are also provided in this research. The results of the research can be applied in various computational conditions.

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. In the past years, a number of benchmarks have been developed to characterize dynamic optimization problems (DOPs) consisting of a series of static problems over time. The solutions found for a static problem in a previous environment are required to be completely implemented so that the static problems in future environments are independent of the implementation of the solutions in the previous environment. Nevertheless, there is a wide range of real-world DOPs in which the problems in future environments are considerably influenced by the components of the solutions that are not implemented in previous environments, since the optimization for the problem in each environment continuously proceeds while the solutions are continuously implemented until the end of a working day or makespan. This type of DOPs can be termed as an online DOP (OL-DOP). To compensate for the lack of a systematical OL-DOP test suite, in this study we propose a benchmark generator for online dynamic single-objective and multi-objective optimization problems. Specifically, different types of influences of the solutions found in each environment on the problems in the next environment can be adjusted by different types of functions, and the dynamism degree can be tuned by a set of predefined parameters in these functions. Based on the proposed generator, we suggest a test suite consisting of ten continuous OL-DOPs and two discrete OL-DOPs. The empirical results demonstrate that the suggested OL-DOP test suite is characterized by time-deception in comparison with existing DOP benchmark test suites, and is able to analyze the ability of dynamic optimization algorithms in tackling the influence of the solutions found in each environment on the problem in the succeeding environment.

Because of its inherent difficulties and wide actual application requirements, uncertain vehicle routing problem has attracted extensive attention recently. Unfortunately, few literature have conducted a comprehensive review on this problem. We give a review mainly from the perspective of stochastic and dynamic, and further classify them into several small categories according to the solution methods and application scenarios respectively. In this survey, we ignore the complex mathematic models and algorithms, aiming to give an intuitive understanding on structure, common methods and strategies for beginner in this field.

The determination of not or not precisely measurable driving state variables as velocity over ground, slip angle, yaw, rolling, and pitch angle can be reconstructed by state estimation methods with kinematic or dynamic vehicle models. The estimation of some parameters may also be integrated in a state estimation procedure.

Cooperative Adaptive Cruise Control (CACC) is an advanced technique for organizing and managing a vehicle platoon, which employs the Vehicle-to-Vehicle/Vehicle-to-Infrastructure (V2V/V2I, or V2X) wireless communication to minimize the inter-vehicle distance while guaranteeing string-stability. Consequently, the conventional CACC system relies heavily on the quality of communications, which means that the regular CACC platoon is sensitive to the communication failure. Therefore, in this paper, a Safety Reinforced Cooperative Adaptive Cruise Control (SR-CACC) strategy is proposed to resist unexpected communication failure. Different from the regular CACC system, the safety enhanced platoon control system is embedded with a dual-branch control strategy. When a fatal wireless communication failure is detected and confirmed, the SR-CACC system will automatically activate the alternative sensor-based adaptive cruise control strategy. Moreover, to make the transforming process smooth, a linear smooth transition algorithm is added to the SR-CACC system. Then, to verify the performance of the proposed SR-CACC system, we conducted a simulation experiment with a heterogonous platoon constructed with eight vehicles. The experiments results reveal that, under the extremely poor communication environment, the proposed SR-CACC strategy can significantly improve the safety performance of the organized vehicle platoon.

Recently, the explosion of large amounts of traffic data has guided data scientists to create models with big data for a better decision-making. Big Data applications process and analyze this huge amounts of data (collected from a variety of heterogeneous data sources) that cannot be processed with traditional technologies. In this paper, Big Data frameworks are used for solving an optimization problem known as Dynamic Vehicle Routing Problem (DVRP). Hence, due to the NP-Hardness of the problem and to deal with a large size of data, we develop a parallel Spark Genetic Algorithm named (S-GA). This parallelism aims to take the advantage of Spark’s in-memory computing ability (as a master-slave distribution computing) and GA’s iterations operations. Parallel operations were used for fitness evaluation and genetic operations. Based on the parallel S-GA a decision support system is developed for the DVRP in order to generate the best routes. The experiments show that our proposed architecture is improved due to its capacity when coping with Big Data optimization problems by interconnecting components and deploying on different nodes of a cluster.

Currently, most of the policies for the dynamic demand vehicle routing problem are based on the traditional method for static problems as there is no general method for constructing a real-time optimization policy for the case of dynamic demand. Here, a new approach based on a combination of the rules from the static sub-problem to building real-time optimization policy is proposed. Real-time optimization policy is dividing the dynamic problem into a series of static sub-problems along the time axis and then solving the static ones. The static sub-problems’ transformation and solution rules include: Division rule, batch rule, objective rule, action rule and algorithm rule, and so on. Different combinations of these rules may constitute a variety of real-time optimization policy. According to this general method, two new policies called flexible G/G/m and flexible D/G/m were developed. The competitive analysis and the simulation results of these two policies proved that both are improvements upon the best existing policy.