Minimum Travel Time Research Articles

An assistance technology towards maintaining efficient driving strategies of electric vehicle

ABSTRACT In order to overcome the problems of electric vehicle (EV) range anxiety and inadequate charging station, a driver assistance technology is presented here. The proposed technology assists a driver to maintain an efficient driving strategy (EDS) that minimizes energy consumption toward increasing the driving range. Additionally, it provides an anticipated range of EV corresponding to its current battery charge which alerts the driver to take a decision for required charging. EDS is determined by solving a multi-objective optimization problem corresponding to the current route characteristics. Optimization problem considers three primary objectives, minimization of energy consumption and travel time and maximize journey comfort, and follows an EV drive model. Effectiveness of the proposed technology is identified by analyzing the effect of adopting EDS in a micro-trip planning with an E-bus based on experimental drive data. A drive model for the E-bus is formulated and validated through comparative analysis of speed, motor current, and voltage that are obtained in simulation and experimentation. Such analysis was performed independently in four micro-trips identified from E-bus drive data following a typical route. It was found that the driver assistance technology reduces both energy consumption and travel time than that found by following a random driving strategy.

Determining Tourist Destination Priorities Using Website-Based Particle Swarm Optimization Methods (Case Study : North Padang Lawas Regency)

. Optimization of tourist routes is very important to minimize travel time and reduce travel costs. This study focuses on optimizing tourist routes in North Padang Lawas Regency using Multi Attribute Utility Theory (MAUT), and Particle Swarm Optimization (PSO) in the context of the Traveling Salesman Problem (TSP). This study discusses problems such as the lack of priority of tourist destinations and the need for shorter travel times. The research process includes problem identification, literature review, data collection through field observations and interviews, and data processing with MAUT to prioritize destinations. The identified priority destinations are Hotel Sapadia Gunung Tua, Barumun Nagari, Batik Sekar Najogi, Durian and Manggis Agrotourism, Rumah Makan Holat Alhamdulillah, Waterboom Gunung Tua, and Candi Bahal I, II, III. Furthermore, PSO is applied to determine the optimal travel route. PSO finds a route with a total travel time of 351 minutes, although the three-day travel time is extended to 375 minutes.

Optimization of the electric vehicle charging strategy problem for sustainable intercity travels with multiple refueling stops

Electric vehicle (EV) drivers considering long-distance trips still face range anxiety due to the limited range of EVs and the scarcity of charging stations. Thus, it becomes important to ensure the feasibility of the selected route and determine an optimal charging strategy. As a crucial aspect of decision support for EV drivers, this study proposes a mixed integer linear programming (MILP) approach for the EV charging strategy problem (EVCSP), incorporating a piecewise linear approximation technique to address the challenges posed by nonlinear charging times. The proposed optimization model, namely CSPM determines where, when, and how much to charge an EV for a specified route to minimize travel time and cost. The solution time of large-scale test problems and a case study on Türkiye reveal the robustness and reliability of the CSPM. Furthermore, two multi-objective optimization methods (the weighted sum and the lexicographic method) are applied to the case study, and the results are analyzed. The results indicate that the travel cost is more sensitive to the selected charging strategy, with a range of 46.09 % across the applied charging strategies, whereas travel time remains more resilient, with a maximum fluctuation of 19.77 %. A comparative analysis with a full charging strategy reveals that the CSPM reduces the travel time by 60.1 % and improves the cost efficiency by 105.72 %.

Enhancing International Logistics and Supply Chain Management: Deep Learning Strategies for Enhanced Route Planning and Warehouse Optimization

Logistics and Supply Chain Management (SCM) are both key areas in modern industry and commerce which need better route planning and warehouse optimization. Traditional methods that are in practice have often fall short in of addressing the dynamic complexities of modern logistics which results in inefficient travel times, fuel consumption, and space utilization. To counter these limitations this study introduces an integrated model that combines Long Short-Term Memory (LSTM) networks, Radial Basis Function Neural Networks (RBNN), and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) for route planning and warehouse optimization. The proposed model employ LSTM to predict traffic patterns and RBNN to optimize space utilization in warehouse. The NSGA-II model is then utilized for multi-objective optimization of minimizing travel times and maximizing warehouse space utilization. In experiment analysis the proposed model achieved the highest accuracy and least variability in predictions, with mean MAE, RMSE, and MAPE values of 0.57, 1.12, and 5.9%, respectively.

Digital clinics for diagnosing and treating migraine.

Several innovative digital technologies have begun to be applied to diagnosing and treating migraine. We reviewed the potential benefits and opportunities from delivering migraine care through comprehensive digital clinics. There are increasing applications of digitization to migraine diagnosis and management, including e-diaries, and patient self-management, especially after the COVID-19 pandemic. Digital care delivery appears to better engage chronic migraine sufferers who may struggle to present to physical clinics. Digital clinics appear to be a promising treatment modality for patients with chronic migraine. They potentially minimize travel time, shorten waiting periods, improve usability, and increase access to neurologists. Additionally, they have the potential to provide care at a much lower cost than traditional physical clinics. However, the current state of evidence mostly draws on case-reports, suggesting a need for future randomized trials comparing digital interventions with standard care pathways.

Advanced Route Optimization using Hybrid Algorithms and Road-based Distance Calculation

This study proposes a hybrid approach to route optimization, comparing and combining Genetic Algorithms (GA), Ant Colony Optimization (ACO), and Dynamic Programming (DP) to solve the Traveling Salesman Problem (TSP) and similar routing challenges. The objective is to minimize travel time and cost by incorporating real-time road data from OpenRouteService and Google Maps APIs. The hybrid algorithms are tested on large datasets, demonstrating their scalability and adaptability to real-world complexities such as fluctuating traffic conditions. Through mathematical modelling and pseudocode, the performance of each algorithm is compared, highlighting their effectiveness in optimizing logistics operations. The results indicate that this approach significantly reduces computational time and operational costs, providing a robust solution for modern logistics.

Enhancing HIV treatment and support: a qualitative inquiry into client and healthcare provider perspectives on differential service delivery models in Uganda

BackgroundHIV/AIDS continues to be a significant contributor to illness and death, particularly in sub-Saharan Africa. In this study, we conducted a qualitative assessment to understand Client and Healthcare Provider Perspectives on Differential Service Delivery Models in Uganda. The purpose was to establish strengths and weaknesses within the services delivery models, inform policy and decision-making, and to facilitate context specific solutions.MethodsBetween February and April 2023, a qualitative cross-sectional study was utilised to gather insights from a targeted selection of individuals, including People Living with HIV (PLHIV), healthcare workers, HIV focal persons, community retail pharmacists, and various stakeholders. The data collection process included eleven in-depth interviews, nine key informant interviews, and eight focus group discussions carried out across eight districts in Central Uganda. The collected data was analyzed through inductive thematic analysis with the aid of Excel.ResultsThe various Differentiated Service Delivery Models (DSDMs), notably Community-Client-Led Drug Distribution (CCLAD), Community Drug Distribution Point (CDDP), Community Retail Pharmacy Drug Distribution Point (CRPDDP), and the facility-based Facility Based Individual Model (FBIM), were reported to have several positive impacts. These included improved treatment adherence, efficient management of antiretroviral (ARV) supplies, reduced exposure to infectious diseases, enhanced healthcare worker hospitality, minimized travel time for ART refills, stigma reduction, and decreased waiting times. Concern was raised about the lack of improvement in HIV status disclosure, opportunistic infection treatment, adherence to seasonal appointments, and sustainability due to the overreliance of the DSDMs on donor funding, suggesting potential discontinuation without funding. Doubts about health workers’ commitment surfaced. Notably, the CCLAD model displayed self-sustainability, with clients financially supporting group members to collect medicines.ConclusionCommunity-based DSDMs, such as CCLAD and CDDP, improve ART refill convenience, social support, and client experiences. These models reduce travel and waiting times, lowering infection risks. Addressing challenges and enhancing facility-based models is vital. In order to maintain funding after donor funding ends, sustainability measures like cross-subsidization can be used. If well implemented, the DSDMs have the potential to produce better or comparable ART outcomes compared to the FBIM model.

Smarter facility layout design: leveraging worker localisation data to minimise travel time and alleviate congestion

This paper introduces a novel methodology leveraging worker localisation data from ultrawide-band sensors to formulate alternative facility layouts aimed at minimising travel time and congestion in labour-intensive manufacturing systems. The system preprocesses sensor data to discern flow patterns between existing stations within the production facility, such as machine tools, workbenches, and stores. This information about the movement of people and materials informs the generation of optimised layouts through scenario-based optimisation. We explored two methods to devise these new layouts: a mixed-integer linear programming method and a simulated annealing metaheuristic, the latter being specifically developed to find high-quality solutions to the quadratic layout design formulation. Both methods employ biobjective formulations, focussing on the minimisation of travel time and the reduction of congestion risk on the manufacturing floor, an aspect often neglected in prior studies. Our methodology, applied to a real-world manual assembly line case study, demonstrated the potential to reduce travel time by a minimum of 32% and alleviate congestion while maintaining significant safety distances between facilities. This was achieved by automatically identifying design features that position high-traffic facilities closely and align them to eliminate movement overlaps.

Impact of navigation apps on congestion and spread dynamics on a transportation network

In recent years, the widespread adoption of navigation apps by motorists has raised questions about their impact on local traffic patterns. Users increasingly rely on these apps to find better, real-time routes to minimize travel time. This study uses microscopic traffic simulations to examine the connection between navigation app use and traffic congestion. The research incorporates both static and dynamic routing to model user behavior. Dynamic routing represents motorists who actively adjust their routes based on app guidance during trips, while static routing models users who stick to known fastest paths. Key traffic metrics, including flow, density, speed, travel time, delay time, and queue lengths, are assessed to evaluate the outcomes. Additionally, we explore congestion propagation at various levels of navigation app adoption. To understand congestion dynamics, we apply a susceptible–infected–recovered (SIR) model, commonly used in disease spread studies. Our findings reveal that traffic system performance improves when 30–60% of users follow dynamic routing. The SIR model supports these findings, highlighting the most efficient congestion propagation-to-dissipation ratio when 40% of users adopt dynamic routing, as indicated by the lowest basic reproductive number. This research provides valuable insights into the intricate relationship between navigation apps and traffic congestion, with implications for transportation planning and management.

A theoretic analysis of expressed toll lane pricing policy targeting for a guaranteed level of service

This study compares the first-best toll algorithm that is theoretically appealing with the density-based toll algorithm that is more popular in practice. The comparison shows that both toll schemes can serve as a travel demand management strategy. Although the first-best scheme always leads to minimal total travel time, it is not necessarily attractive from toll operators' perspective. The density-based scheme is (1) more reliable for ensuring the federally mandated minimal level of services when the demand is high, (2) simpler for data collection and implementation, and (3) can generate higher revenues. From the users' perspective, the first-best scheme leads to less total costs in many cases, but the density-based scheme could also lead to less total costs under certain conditions. Traffic patterns could converge in both cases under a day-to-day framework. The speed of convergence for traffic dynamics is faster under the density-based scheme in most cases.

Conceptual Study on Car Acceleration Strategies to Minimize Travel Time, Fuel Consumption, and CO2-CO Emissions

A conceptual study was performed on intelligent driving acceleration strategies for vehicles equipped with internal combustion engines. Two archetypal acceleration scenarios of highway driving and urban driving were prescribed. Three trajectories were considered for each scenario. They involved (a) nearly constant acceleration, (b) fast acceleration first and slow acceleration later, and (c) slow acceleration first and fast acceleration later. The selected vehicle was a generic European small–medium passenger car. Engine inlet pressure and ignition time were optimized along each trajectory to minimize fuel consumption, CO, and CO2 emissions, and travel time. The optimization process involved a methodological approach based on the higher-order singular value decomposition of the tensor form of the engine model. The optimized trajectories were analyzed and compared among themselves. Conceptual acceleration design guidelines for intelligent driving were provided that could be of interest when integrating vehicle/engine performance into the surrounding traffic flow.

Allocation of charging stations in Kota city using data analysis/machine learning

ABSTRACT This paper proposes the allocation of charging stations of optimal capacity at appropriate locations within Kota city in Rajasthan state of India. The location of charging stations has been identified by considering various parameters including population density, family income, distribution network performance, number of electric vehicles and transformer capacity. The proposed approach of allocation of charging stations has been modeled mathematically and also by machine learning algorithms such as data mining, clustering with all the possible scenarios and constraints. In identifying suitable places for positioning charging stations in the city by observing different parameters and aspects, i.e. geo-statistical approaches, minimum traveling time and travel distance for charging stations has been included in the proposed approach. The proposed approach has been tested in MATLAB/Simulink environment.

Geographic Accessibility of General Practitioners by Car and Public Transport - A Study Using the Minimum Travel Time Method in a Rural Area in the North-East of Germany

Basic medical care should be accessible to all people without restriction. However, the distribution of practices in rural areas is often limited to the centers, meaning that distances have to be covered. The Federal Joint Committee (G-BA), the highest decision-making body of the joint self-government of physicians, dentists, hospitals and health insurance funds in Germany, only states for cars that 95% of the population should be able to reach general practitioners (GPs) in less than 20 minutes on average. The aim of this study was to determine the geographic accessibility of GP practices by car and public transport. A cross-sectional study was conducted in a rural area in the north-east of Germany called Neustrelitz Central Zone and reported according to the STROBE statement. Geographic data of OpenStreetMap and the Federal Agency for Cartography and Geodesy and GP practices data of the Associations of Statutory Health Insurance Physicians Mecklenburg-Western Pomerania and Brandenburg were used. Based on the method of minimum travel time, the required travel times by car and public transport from the local centers to the three nearest physician locations were determined using various online tools. On average, inhabitants need 10.7 minutes to reach the three nearest GP practices by car, up to a maximum of 19 minutes. The average travel time by public transport for an appointment at 10 a.m. is four hours and twelve minutes. While it is possible for all municipalities (and their inhabitants) to reach the nearest GP practices in time for an appointment at 10 a.m. and to complete the return journey on the same day, this is no longer possible for 35% of municipalities (and their inhabitants) for an appointment at 3 p.m. and for 53% for an appointment at 5 p.m. In the Neustrelitz Central Zone, the accessibility of GP practices by car is ensured in accordance with the G-BA statements. On the other hand, the accessibility of GP practices by public transport can be a problem for people who do not have their own car. This could be remedied, for example, by expanding eHealth or mobile medical practices.

Framework for Assessing the Sustainability Impacts of Truck Routing Strategies

The impact of freight on the transportation system is accentuated by the fact that trucks consume a greater roadway capacity than other vehicles and therefore cause more significant problems including traffic congestion, traffic delays, crashes, and pavement damage. Evaluating the actual repercussions of truck traffic becomes paramount in locales where roadway expansion is unfeasible. Trucks are vital to the economy, providing essential services to commerce and industry, and yet it is crucial that their operation does not contribute to the deterioration of infrastructural quality or compromise public safety. Currently, we lack methodologies in practice for the real-time management of traffic, specifically for truck routing, to minimize travel times and prevent delays due to non-recurrent congestion, such as traffic incidents. Accordingly, this study aimed to devise a truck routing strategy utilizing a traffic micro-simulation model (VISSIM) and to assess its effects on reducing travel delays. This involved the development of real-time truck re-routing simulation models that take into account non-recurrent congestion and the resulting travel delays and fuel consumption. The VISSIM model was applied to the I-75 corridor in Marion County, Florida, focusing on non-recurrent congestion effects on travel delays and fuel consumption. The initial findings suggest that the implementation of a dynamic truck re-routing system can significantly alleviate traffic congestion, resulting in a marked decrease in travel delays and fuel usage, demonstrating the potential for such strategies to enhance the overall efficiency of the transportation system.

Spatial coverage analysis of public transport bus stops through a citizen perception study. Case study: Manizales – Colombia

The development of urban public transport systems today has focused on increasing their operability, trying to minimize travel times and mobilize the greatest number of users by increasing operational speed, renewing the vehicle fleet, and increasing frequencies, among others. However, in addition to restructuring the operating distance and the location of the bus stops, this process of increasing operational capacity has paid little attention to the people's perception of these stop systems’ location and coverage. This causes, largely, users’ apathy to take advantage of the system, promoting the use of other means of transport, or in the least cases, to make stops within the system in unauthorized locations.Considering the above, the aim of this research is to analyze the overage variation of bus-stop sets, based on the perception of users in normal and eager conditions, through the application of an analysis of population and zonal coverage, in which physical and operational variables of interest are considered, complemented with geostatistical models and digital tools that allow implementing a set of bus stops creating an inclusive environment with a broader vision of the ideal conditions for the definition of bus-stop sets. Within the research process, it is necessary to structure a statistical sampling, which allows for characterizing the population of public transport systems. Therefore, Manizales, the capital of the department of Caldas − Colombia, through its 12 district clusters and 378,000 inhabitants, is taken as a focus of study. However, due to the conurbation agreement with the municipality of Villamaría, the inclusion of its urban framework is carried out, resulting in a total population of 424,300 inhabitants.

Solving biobjective traveling thief problems with multiobjective reinforcement learning

This study proposes an end-to-end multiobjective reinforcement learning (MORL) approach to solve the biobjective traveling thief problems (TTP). A TTP involves a thief visiting cities and selecting items to maximize profit while minimizing travel time within a knapsack’s capacity. The study evaluates combinations of two architectures, namely the pointer network (PN) and attention mechanism (AM), with three MORL methods: deep reinforcement learning multiobjective algorithm (DRLMOA), multi-sample Pareto hypernetwork (PHN), and manifold-based policy search (MBPS). However, PN and AM cannot be directly used to predict two different sequences simultaneously: the city tour and the item selection. Therefore, a solution encoding and decoding scheme is proposed to solve TTP without substantially modifying PN and AM. The methods are trained on only small randomly generated problem instances based on Eil76 instances, and their performance is evaluated on various problem instances. The state-of-the-art non-dominated sorting-based customized random-key genetic algorithm (NDS-BRKGA) serves as the baseline. The experimental study demonstrates a competitive performance of the proposed methods compared to the baseline, particularly in instances with a high number of items. The proposed methods, especially PN-DRLMOA and AM-DRLMOA, also show promising generalization capabilities on different and larger graphs. Lastly, the proposed MORL methods significantly outperform NDS-BRKGA in terms of the solution generation running time.

Chaotic Harris Hawks Optimization Algorithm for Electric Vehicles Charge Scheduling

Electric Vehicle (EV) technology and migration are hindered by battery sizing, short driving ranges, and optimal operations. This article focuses on developing a strategy for scheduling EV charging in a specific region, addressing waiting time, charging time, and uneven scheduling due to unevenly distributed charging stations (CS). The proposed approach optimizes CS using separate queues for different levels, reducing waiting time and costs during peak hours. Which considers trade-offs between time-aware fairness and overall waiting time, and factors like reachability, battery state of charge, depth of discharge limits, and charging rate constraints. A bi-objective formulation and online scheduling algorithm based on dynamic schedulable time, energy demand fluctuation and user’s prioritization are proposed. The aim is to allocate a charging station to each EV by considering travel needs and battery specifics, with the objective of minimizing travel time, queue time, recharging time, and energy costs. To achieve this, the scheduling system utilizes the Chaotic Harris Hawks Optimization (CHHO), an enhanced iteration of the previously discussed metaheuristic, the Harris Hawk Optimization. Validation of the system is conducted through Vehicular Ad-hoc Network (VANET) simulation and comparison with alternative algorithms Exponential Harris Hawk Optimization, Grey Wolf Optimizer and Random allocation. The outcomes demonstrate noteworthy decreases in travel time, queue time, recharging time, and energy costs, all while adhering to set constraints.

Cox Regressive Outlier Robust Incremental Extreme Learning Machine based IoT aware Smart Car Parking and Route Optimization for Intelligent Transportation

An Intelligent Transport System (ITS) is a wide-ranging framework that influences advanced technologies to improve efficiency of transportation networks. An ITS integrated with an IoT enhances the capabilities of transportation networks. Owing to quick development of vehicles, traffic congestion during road network is major problems. Therefore, intelligent traffic control system is required to create smart transportation through optimization of vehicle route, and so on. In this paper, Cox Regressive Fuzzified Outlier Robust Incremental Extreme Learning Machine (CRFORIELM) is developed for smart ITS. The IoT technology in IT’S is used to collect traffic data, vehicle information, Parking slot occupancy data. After data collection process, proposed CRFORIELM technique includes two major processing steps namely parking slot availability detection and traffic aware route optimization. The extreme learning machine includes three types of layers such as input, three hidden layers as well as output layer. Initial hidden layer predicts the parking slot availability using Cox regression is performed. With the parking lot availability predictions, integrate this information into a route optimization algorithm. This algorithm considers the real-time traffic conditions as well as parking availability for detecting optimal route by applying fuzzy triangular membership function in the second hidden layer. Finally, nearest route path is effectively determined to minimize travel time in intelligent transport system. Experimental analysis indicates that the CRFORIELM technique achieved 2%, 5%, 10% improvement in accuracy, sensitivity, specificity and reduced error rates, route detection time by 39%, and 36%, compared to existing methods.

Identifying alternative stops for first and last-mile urban travel planning

Urban travelers today are seeking increasingly more information to plan their optimal trip, based on additional factors other than scheduled departure times. Still, some route planning applications provide a simple approach with a few parameter settings (e.g. to minimize travel time between two specific places at a certain time) and without any multimodal solutions. Our approach provides travelers with a set of non-dominated nearby stops that presents a number of traveler preferences in an easily comprehensible and quickly calculable manner. We display first and last-mile stops that fall on a Pareto front based on multiple criteria such as travel time, number of transfers, and frequency of service. Our algorithm combines stop and route-based information to quickly present the traveler with numerous nearby quality options for their itinerary decision making. We expand this algorithm to include multimodal itineraries with the incorporation of free-floating scooters to investigate the change in stop and itinerary characteristics. We then analyze the results on the star-shaped public transportation network of Göttingen, Germany, to show what advantages stops on the Pareto front have as well as demonstrate the increased effect on frequency and service lines when incorporating a broadened multimodal approach.

A reinforcement learning-based hyper-heuristic for AGV task assignment and route planning in parts-to-picker warehouses

Globally, e-commerce warehouses have begun implementing robotic mobile fulfillment systems (RMFS), which can improve order-picking efficiency by using automated guided vehicles (AGVs) to realize operations from parts to pickers. AGVs depart from their initial points, move to a target rack position, and subsequently transport racks to picking stations. The AGVs return the racks to their original positions after the workers pick them up. When all tasks are completed, the AGVs return to their starting point. In this context, the main challenge is the task assignment and route planning of multiple AGVs to minimize travel times. We formulate a mixed-integer linear programming (MILP) model with valid inequalities to solve small problem instances optimally. We introduce a reinforcement learning (RL)-based hyper-heuristic (HH) framework to solve large instances to near-optimality. A typical HH framework comprises two levels: high-level heuristics (HLH) and low-level heuristics (LLH). The framework starts from an initial solution and improves iteratively through LLHs, while the HLH invokes a selection strategy and an acceptance criterion to generate a new solution. We propose a novel selection strategy based on the improved Multi-Armed Bandits algorithm called Co-SLMAB and Exponential Monte Carlo with counters (EMCQ) as the acceptance criterion. The corresponding collision avoidance rules are then formulated for different conflicts to construct a conflict-free traveling route for AGVs. Besides testing the proposed framework’s effectiveness in real-life warehouse layouts, we perform extensive computational experiments and a thorough sensitivity analysis. The results show that (i) the proposed valid inequalities aid in obtaining better lower bounds and significantly speed up the solution process; (ii) the Co-SLMAB-HH framework is quite competitive compared to CPLEX, outperforming the other tested hyper-heuristics and the problem-specific heuristic regarding convergence and computation time; and (iii) a pool of LLHs consisting of a wide range of different operators is advantageous over a limited set of simple operators while solving problems using hyper-heuristics.