Minimum Travel Time Research Articles

Deep Reinforcement Learning Based Optimal Route and Charging Station Selection

This paper proposes an optimal route and charging station selection (RCS) algorithm based on model-free deep reinforcement learning (DRL) to overcome the uncertainty issues of the traffic conditions and dynamic arrival charging requests. The proposed DRL based RCS algorithm aims to minimize the total travel time of electric vehicles (EV) charging requests from origin to destination using the selection of the optimal route and charging station considering dynamically changing traffic conditions and unknown future requests. In this paper, we formulate this RCS problem as a Markov decision process model with unknown transition probability. A Deep Q network has been adopted with function approximation to find the optimal electric vehicle charging station (EVCS) selection policy. To obtain the feature states for each EVCS, we define the traffic preprocess module, charging preprocess module and feature extract module. The proposed DRL based RCS algorithm is compared with conventional strategies such as minimum distance, minimum travel time, and minimum waiting time. The performance is evaluated in terms of travel time, waiting time, charging time, driving time, and distance under the various distributions and number of EV charging requests.

AN INTEGER PROGRAMMING APPROACH FOR SINGLE TRUCK ROUTING-AND-SCHEDULING PROBLEMS TO ISLANDS WITH TIME-VARYING FERRY SCHEDULES

This study aims to develop a solving model for the single trucks routing-and-scheduling problems to islands with variations in ferry schedules. In this problem, the travel time is asymmetric and the truck routing is based on the sequence of island visits, known and unknown. The models are developed using an integer programming approach. Integer non-linear programming is formulated to solve problems where the sequence is unknown, whereas integer linear programming for the sequence is known. Besides, a delivery day scenario is built to determine the optimal route and schedule with minimum total travel time on each departure day. Numerical experiments were carried out on the case of a small distribution of a small industry in Central Moluccas, Indonesia. The results showed that the model developed could provide solutions to solve problems.

Travel time to health-care facilities, mode of transportation, and HIV elimination in Malawi: a geospatial modelling analysis

UNAIDS has prioritised Malawi and 21 other countries in sub-Saharan Africa for fast-tracking the end of their HIV epidemics. UNAIDS' elimination strategy requires achieving a treatment coverage of 90% by 2030. However, many individuals in the prioritised countries have to travel long distances to access HIV treatment and few have access to motorised transportation. Using data-based geospatial modelling, we investigated whether these two factors are barriers to achieving HIV elimination in Malawi and assessed the effect of increasing bicycle availability on expanding treatment coverage. We built a data-based geospatial model that we used to estimate the minimum travel time needed to access treatment, for every person living with HIV in Malawi. We constructed our model by combining a spatial map of health-care facilities, a map that showed the number of HIV-infected individuals per km2, and an impedance map. We quantified impedance using data on road and river networks, land cover, and topography. We estimated travel times for the existing coverage of 70%, and the time that HIV-infected individuals would need to spend travelling in order to achieve a coverage of 90%, whether driving, bicycling, or walking. We identified a quantitative relationship between the maximum achievable coverage of treatment and the minimum travel time to the nearest health-care facility. At 70% coverage, health-care facilities can be reached within approximately 45 min if driving, 65 min if bicycling, and 85 min if walking. Increasing coverage above 70% will become progressively more difficult. To reach 90% coverage, many HIV-infected individuals (who have yet to initiate treatment) will need to travel for almost twice as long as those already on treatment. Bicycling, rather than walking, in rural areas would substantially increase the maximum achievable coverage. The long travel times needed to reach health-care facilities coupled with little motorised transportation in rural areas are substantial barriers to reaching 90% coverage in Malawi. Increased bicycle availability could help eliminate HIV. US National Institute of Allergy and Infectious Diseases.

Appraisal of cycling infrastructure investments using a transport model with focus on cycling

Cost-benefit analysis (CBA) for cycling infrastructure investments are less sophistically developed compared to the ones for private cars and public transport, and one of main reasons is the lack of “well-developed” transport models for cycling. In this study, a dedicated transport model for cycling is used to appraise cycling infrastructure investments in Stockholm, Sweden. The model captures the impact of a change in cycling infrastructure on cycling route choice, mode choice, destination choice and trip generation and calculates cycling flow on link level. the generalised cost measure defined in the route choice model captures the impact of cycling infrastructure. Results suggest that although cycling flow on the links with investment may increase substantially, only a small share comes from modal shift and thus the external effects such as reducing car congestion and emissions are marginal. For all three scenarios investigated, over 97% of the benefits measured in the unit of generalised cost belong to the existing cyclists. The route choice model does not minimize travel time but generalised cost which also measures health, safety benefits and other possible benefits that may be considered by the cyclists when they choose to cycle. In fact, travel time saving benefits of the investments evaluated in this paper are all negative. The existing effect evaluation models therefore need to be adjusted to be more consistent with the transport model.

Shortest Path from Bandar Tun Razak to Berjaya Times Square using Dijkstra Algorithm

The shortest path is an issue that involves the route from one point (nodes) to another. It is to find a path with a minimum travelling time. Nowadays, traffic problems have affected many transport users especially in Kuala Lumpur area. The time wasted on the road causes a lot of problems to the users. Furthermore, the costs between two destinations are rather expensive. Therefore, the inability of users to use the shortest path has attracted the researcher to propose several travel alternatives to overcome this problem. In addition, this study will help to improve the efficiency of the road and make people want to use it more often. The objectives of this study are to find the shortest path from Bandar Tun Razak to Berjaya Times Square and to cut down the cost between these two destinations. The time of the shortest path problem and the cost problem are drawn separately. Moreover, Dijkstra algorithm is applied to find the shortest path. The shortest path is calculated by using C programming of Dev C++. Nevertheless, both time and cost of shortest path are constructed in different paths. The time and cost of the journey are described by driving a car from Bandar Tun Razak to Lebuhraya SMART to Kampung Pandan, then Berjaya Times Square. The total time taken is 23 minutes (RM8.00), whereas, the cost is based on the shortest path from Bandar Tun Razak to Taman Maluri to Seasons Tower and Berjaya Times Square. The minimum cost is RM4.00 (30 minutes).

Optimal 3D time-energy trajectory planning for AUVs using ocean general circulation models

This paper develops a new approach for solving optimal time and energy trajectory planning problems for Autonomous Underwater Vehicles (AUVs) in transient, 3D, ocean currents. Realistic forecasts using an Ocean General Circulation Model (OGCM) are used for this purpose. The approach is based on decomposing the problem into a minimal time problem, followed by minimal energy subproblems. In both cases, a non-linear programming (NLP) formulation is adopted. The scheme is demonstrated for time-energy trajectory planning problems in the Gulf of Aden. In particular, the numerical experiments illustrate the capability of generating Pareto optimal solutions in a broad range of mission durations. In addition, the analysis also highlights how the methodology effectively exploits both the vertical structure of the current field, as well as its unsteadiness, namely to minimize travel time and energy consumption.

Regional dynamic traffic assignment with bounded rational drivers as a tool for assessing the emissions in large metropolitan areas

Data-driven surveys show that drivers do not always choose the shortest-path for their travels. The ideas of bounded rationality have been used to model this behavior, and relax the main assumption of travel time minimization of the User Equilibrium principle. In this paper, we propose an extension of an existing dynamic traffic assignment framework, for aggregated traffic models based on the Macroscopic Fundamental Diagram and regional networks, that extends the principle of the User Equilibrium to account for bounded rational drivers. We focus on drivers with indifferent preferences, and with preferences for more reliable travel times. The network equilibrium is calculated through Monte Carlo simulations and the classical Method of Successive Averages. We first investigate how the drivers' preferences for reliable travel times influences the traffic dynamics in the regional network. We then discuss a potential application example of the proposed methodological framework for estimating the emissions of Carbon Dioxide CO2 and Monoxide NOx at the network level. The results shed light on the importance of properly accounting for more realistic drivers' behavior for estimating emissions. The main contributions of this study lie on the edge between the disciplines of traffic flow theory and network modeling, with a great potential of application for practitioners to assess traffic emissions on large metropolitan areas.

Topology Optimization of High-Speed Flexible Robot Arms. (Dept. M.)

There are growing demands in the robot industry for light structures in order to increase the speed of the robot motion and thus the productivity. Size and shape optimization are carried out previously to achieve minimum traveling time of the robot arm taking into consideration its flexibility. In this work, topology optimization is introduced for obtaining optimum robot arm to achieve minimum traveling time from both structure and control viewpoints. Method of Moving Asymptotes is used as optimization technique, because it can handle arbitrary number of constrains and considered to be a general and flexible optimization method. A comparison is accomplished between the optimal resulted design and its initial design for different dimensions of robot arm neglecting air damping. It is found that the reduction ratio of the travelling time reaches 62% and the reduction ratio of the weight reaches 70%. A comparison between this work using topology optimization of flexible robot arm outperforms the size and the shape optimization when considering the real condition that takes into account the air damping. The optimum topological design in this case gives reduction ratio of travelling time equals 44.8%, while previous work gives 23.5%.

A polynomial algorithm for minimizing travel time in consistent time‐dependent networks with waits

AbstractWe consider a time‐dependent shortest path problem with possible waiting at some nodes of the graph and a global bound W on the total waiting time. The goal is to minimize the time traveled along the edges of the path, not including the waiting time. We prove that the problem can be solved in polynomial time when the travel time functions are piecewise linear and continuous. The algorithm relies on a recurrence relation characterized by a bound ω on the total waiting time, where 0 ≤ ω ≤ W. We show that only a small number of values ω1, ω2, …, ωK need to be considered, where K depends on the total number of breakpoints of all travel time functions.

Fuzzy Programming Approach to a Multi-Objective Fuzzy Stochastic Routing and Siting Hazardous Wastes

The aim of the research article is not only to propose a solution procedure to solve multi-objective fuzzy stochastic programming problem by using genetic-algorithm-based fuzzy programming method, but also to apply the computational techniques for transportation of the hazardous waste materials. In this article, routing and siting problems for nuclear hazardous waste material are studied and solved. The amount of waste materials generated in the nuclear reactors follows normal distribution. The two considered objective functions are about route selection which includes minimum travel time and minimum number of houses along the way, taking the safety measures into consideration. A multi-objective fuzzy stochastic mathematical model is formulated with the above mentioned objective functions and the route selection as the constraints. The proposed solution procedure is illustrated by a numerical example and a case study.

Development of Solid Waste Management Plan to Solve the Transport Routes Problem in Baghdad City

The transportation cost problem of solid waste presents the biggest part of the budget allocated by municipalities for SWM. So, there is no comprehensive plan to address transport routes optimization problems in SWM that including the transfer of solid waste from transfer stations to final landfill sites. Therefore, the aim of the study finding a scientific method to solve the transportation problem of solid waste transport suitable Baghdad city that tries to find feasible solutions that ensure reducing total transport costs and leads to an effective solid waste management system. In this research, a new methodology has been developed to select the optimal transport routs of SWM in Baghdad city which involves determining the best-supposed scenario. the proposed methodology includes integration of Global Positioning System (GPS) technologies with Network Analysis model (NA). Therefore, this work provides an advanced framework of decision-makers for analysis and simulation of the optimal transport routs problem related to SWM. Applying these modeling tools to select the scenario that can provide economic benefits by minimizing travel time, travel distance and reduction of total transportation costs. The Results of work implementation show that all solutions that include current state S1 and suggested scenarios have been evaluated. The scenarios generated include (S2, S3) by applying the proposed technique for analyzed and identified the optimal routes. The solutions of scenario S2, specified with two landfill sites while scenarios S3 specified with four landfill sites. Finally, this work shows the Scenario S3 is the best scenario of the solution, that include applied GPS and Network Analysis for four landfill sites.

Variable fixing heuristics for the capacitated multicommodity network flow problem with multiple transport lines, a heterogeneous fleet and time windows

ABSTRACT In this paper, we investigate a new variant of the multi-commodity network flow problem, taking into consideration multiple transport lines and time windows. This variant arises in a city logistics environment, more specifically in a long-haul passenger transport system that is also used to transport urban freight. We propose two mixed integer programming models for two objective functions: minimization of network operational costs and minimization of travel times. Since the problems under study are NP-hard, we propose three size reduction heuristics. In order to assess the performance of the proposed algorithms, we carried out computational experiments on a set of synthetic problem instances. We use the relative percentage deviation as performance criterion. For the cost objective function, a LP-and-Fix algorithm outperforms other methods in most tested instances, but for the travel time, a hybrid method (size reduction with LP-and-Fix algorithm) is, in general, better than other approaches.

Optimizing snowplow routes using all-new perspectives: road users and winter road maintenance operators

Snowplowing is an indispensable part of winter road maintenance because it contributes to improving drivers’ mobility and safety. Most prior studies, however, limit focus to operational aspects without considering interaction with road users. This paper aims to minimize travel time in the traffic system while still maintaining the efficiency of plowing activities. The k-truck plowing with the precedence model was formulated to link road users with snowplow activities. The tabu search algorithm then followed to optimize the order of precedence of snowplow routes. Furthermore, the discrete-event simulation was used to quantify the effectiveness of the proposed method based on different scenarios and fleet sizes, and to illustrate the potential benefit of integrating both the operators’ and users’ perspectives. The methodological framework developed herein can be used to design a routing strategy that improves the performance of the snowplow trucks by reducing both plowing completion and road users’ total travel time.

Eco-Driving System for Connected Automated Vehicles: Multi-Objective Trajectory Optimization

This study aims to leverage the advances of connected automated vehicle (CAV) technology to design an eco-driving and platooning system that can improve both fuel and operational efficiency of vehicles on the freeways. The proposed algorithm optimizes CAVs’ trajectories with three objectives, including travel time minimization, fuel consumption minimization, and traffic safety improvement, following a two-stage control logic. The first stage, designed for CAV trajectory planning, is carried out with two optimization models. The first model functions to predict the freeway traffic states in the near future and accordingly optimize CAVs’ desired speed profile to minimize total freeway travel time. Notably, the interactions between CAVs and human-driven vehicles (HVs) are described in the embedded traffic flow model and the optimization can fully account for CAVs’ impact to HVs’ speeds. Then grounded on the obtained speed profile, the second eco-driving model would further update it so as to platoon CAVs and minimize their fuel consumption. The second stage, for real-time control purpose, is developed to ensure the operational safety of CAVs. Particularly, based on the speed profile from the first stage, real-time adaptions would be placed on CAVs to dynamically adjust speeds, in response to local driving conditions. To evaluate the proposed algorithms, this study selects a freeway segment of I-15 in Salt Lake City as the study site. The extensive numerical simulation results confirmed the effectiveness of the proposed framework in both mitigating freeway congestion and reducing vehicles’ fuel consumption.

A Joint Service Migration and Mobility Optimization Approach for Vehicular Edge Computing

The vehicular edge computing is considered an enabling technology for intelligent and connected vehicles since the optimization of communication and computing on edge has a significant impact on driving safety and efficiency. In this paper, with the road traffic assignment to “proactively” reshape the spatiotemporal distribution of resource demands, we investigate the joint service migration and mobility optimization problem for vehicular edge computing. The goal is to meet the service delay requirements of vehicular edge computing with minimum migration cost and travel time. As service migration and mobility optimization are coupled, the joint scheduling problem suffers from the curse of dimensionality, which cannot be solved in real time by centralized algorithms. To this end, a multi-agent deep reinforcement learning (MADRL) algorithm is proposed to maximize the composite utility of communication, computing, and route planning in a distributed way. In the MADRL algorithm, a two-branch convolution based deep Q -network is constructed to coordinate migration action and routing action. Extensive experimental results show that the proposed algorithm is scalable and substantially reduces service delay, migration cost and travel time as compared with the existing baselines.

Evaluating the Ability of FARSITE to Simulate Wildfires Influenced by Extreme, Downslope Winds in Santa Barbara, California

Extreme, downslope mountain winds often generate dangerous wildfire conditions. We used the wildfire spread model Fire Area Simulator (FARSITE) to simulate two wildfires influenced by strong wind events in Santa Barbara, CA. High spatial-resolution imagery for fuel maps and hourly wind downscaled to 100 m were used as model inputs, and sensitivity tests were performed to evaluate the effects of ignition timing and location on fire spread. Additionally, burn area rasters from FARSITE simulations were compared to minimum travel time rasters from FlamMap simulations, a wildfire model similar to FARSITE that holds environmental variables constant. Utilization of two case studies during strong winds revealed that FARSITE was able to successfully reconstruct the spread rate and size of wildfires when spotting was minimal. However, in situations when spotting was an important factor in rapid downslope wildfire spread, both FARSITE and FlamMap were unable to simulate realistic fire perimeters. We show that this is due to inherent limitations in the models themselves, related to the slope-orientation relative to the simulated fire spread, and the dependence of ember launch and land locations. This finding has widespread implications, given the role of spotting in fire progression during extreme wind events.

Automatic Storage and Retrieval System using the Optimal Path Algorithm

The demand for application of mobile robots in performing boring and extensive tasks are increasing rapidly due to unavailability of human workforce. Navigation by humans within the warehouse is one among such repetitive and exhaustive task. Autonomous navigation of mobile robots for picking and dropping the shelves within the warehouse will save time and money for the warehousing business. Proposing an optimization model for automated storage and retrieval systems by the goals of its planning is investigated to minimize travel time in multi-robot systems. This paper deals with designing a system for storing and retrieving a group of materials within an environment arranged in rows and columns. Its intersections represent storage locations. The title of any subject is indicated by the row number and the column in it. A method was proposed to store and retrieve a set of requests (materials) using a number of robots as well as one receiving and delivery port. Several simulation results are tested to show this improvement in length of path and time of arrival.

Foraging distances in six species of solitary bees with body lengths of 6 to 15 mm, inferred from individual tagging, suggest 150 m-rule-of-thumb for flower strip distances

Bees require suitably close foraging and nesting sites to minimize travel time and energy expenditure for brood provisioning. Knowing foraging distances in persistent (‘healthy’) populations is therefore crucial for assessing harmful levels of habitat fragmentation. For small bees, such distances are poorly known because of the difficulty of individual tagging and problems with mark-recapture approaches. Using apiarist’s number tags and colour codes, we marked 2689 males and females of four oligolectic and two polylectic species of Osmiini bees (Megachilidae, genera Chelostoma, Heriades, Hoplitis, Osmia) with body lengths of 6 to 15 mm. The work was carried out in 21 ha-large urban garden that harbours at least 106 species of wild bees. Based on 450 re-sightings, mean female flight distances ranged from 73 to 121 m and male distances from 59 to 100 m. These foraging distances suggest that as a rule of thumb, flower strips and nesting sites for supporting small solitary bees should be no further than 150 m apart.

ISSUES OF DESIGNING AND RESTRUCTURING THE ROUTE NETWORKS OF PUBLIC PASSENGER URBAN GROUND TRANSPORT

The urban passenger public transport as an important element of social infrastructure of megalopolises directly influences the quality of life of city residents, development of town-planning, social and economic potential. Efficiency of urban transport directly depends on the right choice and well-sought designing of its routes.The objective of the research was to develop based on the overview of existing methods for designing route networks of urban public passenger transport an updated technique allowing to restructure existing routes. The analysis of route network design methods permitted to reveal their advantages and disadvantages. An improved methodology for designing route networks is proposed, aimed at solving the problems of restructuring existing route networks and at practical implementation of the results considering existing constraints. The technique to design route networks of city districts and of small towns is based on the model of road and street network, shown as a detailed graph with subsequent consideration of the arrays of connections of all the vertices of the graph with several points of gravity. QGIS geoinformatics software used to build the graph allows to analyze, visualize, and facilitate the operations with the detailed graph of the transport network of a city district.Identifying of rational itineraries between points of departure and of gravity is executed by purposed selection of options by criterion of minimum travel time spending. Shaping out of routes is based on principles of integration of the most possible number of points (vertices of the graph) into the route.The application of an improved technique will allow to consider the changing demand of the population for transport services, make adjustments to the existing route network, thereby satisfying the population’s need for transportation and increasing the efficiency of urban transport networks and motor transport enterprises.

Multi-objective optimization of customized bus routes based on full operation process

In order to rationally formulate a customized bus route plan to improve the operation efficiency of customized buses, in view of the problem of ignoring the distance between the boarding area and the alighting area, and setting it as a fixed value when performing custom bus route optimization modeling, as well as solving the problem of multiple custom bus parking lots. This paper proposes a method based on the NSGA-II algorithm using a three-stage hybrid coding method to solve. First, according to real life, the entire operation process of customized buses is divided into four phases. Second, based on the four stages, a multi-objective optimization model of customized bus routes that satisfies multiple parking lots, multiple cars, and multiple boarding and alighting stations is constructed to pursue the minimum total travel time of passengers and minimizes the operating costs of customized bus companies. Third, the NSGA-II algorithm is employed to solve the model, including three-stage hybrid coding, segmented crossover, and segmented mutation operators. Finally, the local road network in Lanzhou City is adopted for simulation research. The results show that it is feasible to use NSGA-II algorithm to solve the problem of customizing bus routes for multiple parking lots, multiple vehicles, and multiple boarding and alighting stations. The research results are of great value for exploring the optimization methods of customized bus routes and improving the operating efficiency of custom buses.