Optimal Travel Time Research Articles

Optimal steering of a smart active particle

We formulate the theory for steering an active particle with optimal travel time between two locations and apply it to the Mexican hat potential without brim. For small heights the particle can cross the potential barrier, while for large heights it has to move around it. Thermal fluctuations in the orientation strongly affect the path over the barrier. Then we consider a smart active particle and apply reinforcement learning. We show how the active particle learns in repeating episodes to move optimally. The optimal steering is stored in the optimized action-value function, which is able to rectify thermal fluctuations.

Penentuan Jarak Terpendek Pada Jalur Pengiriman Musae Chips Dengan Menggunakan Algoritma Genetika

Combination of good path distribution by land can optimize travel time and costs. However, not all of these path distribution combinations will provide the best solution. The study was conducted to determine the distribution of goods so that the best solution is achieved. To simplify the process of determining the goods distribution channel, it is supported by software development. Genetic algorithms that have reliability in producing optimal solutions can be used to solve this problem. The application of the genetic algorithm method is applied in software. In the software that is built, there are several inputs needed, namely: cities destination distribution as the initial chromosome number, number of generations, crossover probability and probability of mutation. The result of processing is a combination of goods distribution lines to be taken which represent the solution to this problem. Only the best chromosomes will be given as a result. Through the software that was built, the determination of goods distribution lines is expected to be better and can optimize the time and cost of travel. Based on the research, 5 road combinations are used as chip distribution routes used for testing. This study results from the first fitness gene get the highest fitness 7.4, fitness lowest 5.6 and the second gene get the highest fitness 9.3, fitness lowest 5.6.

Railway Travel-Time Optimization System

Railway Travel-Time Optimization System

Effects of random motion in traveling and grazing herds

We examine the role that randomness or noise in individual motion may play in forming effective grazing strategies for herd members as they collectively move toward a destination. Through a model where animals are attracted to Voronoi neighbors as well as a destination endpoint, we show that including a significant random motion component can speed up the movement of a herd toward this destination, increase the efficiency that food is acquired during the travel, and facilitate a natural herd shape that mitigates predation risk. Specifically, if the influence of the Voronoi neighbors on individual motion is equal to the pull toward the destination, we find that optimal travel time and food consumption efficiency occurs for noise approximately twice as strong as the influence of herd members to each other, in a range of herd sizes from 10 to 100. We find that reducing the destination influence lowers this optimal noise only slightly, with random motion still exceeding the influence of neighbors. For a destination influence exceeding that of the Voronoi neighbors, an additional travel mode appears with minimal noise and aligned velocities in which the herd marches directly toward the endpoint. Our results are consistent with observational evidence of random motion in several animal groups, and motivate its generalization to traveling and grazing herds.

Optimizing Travel Time to Outpatient Interventional Radiology Procedures in a Multi-Site Hospital System Using a Google Maps Application.

The purpose of this study is to determine whether a custom Google Maps application can optimize site selection when scheduling outpatient interventional radiology (IR) procedures within a multi-site hospital system. The Google Maps for Business Application Programming Interface (API) was used to develop an internal web application that uses real-time traffic data to determine estimated travel time (ETT; minutes) and estimated travel distance (ETD; miles) from a patient's home to each a nearby IR facility in our hospital system. Hypothetical patient home addresses based on the 33 cities comprising our institution's catchment area were used to determine the optimal IR site for hypothetical patients traveling from each city based on real-time traffic conditions. For 10/33 (30%) cities, there was discordance between the optimal IR site based on ETT and the optimal IR site based on ETD at non-rush hour time or rush hour time. By choosing to travel to an IR site based on ETT rather than ETD, patients from discordant cities were predicted to save an average of 7.29min during non-rush hour (p = 0.03), and 28.80min during rush hour (p < 0.001). Using a custom Google Maps application to schedule outpatients for IR procedures can effectively reduce patient travel time when more than one location providing IR procedures is available within the same hospital system.

How accurate is your travel time reliability?—Measuring accuracy using bootstrapping and lognormal mixture models

ABSTRACTAs with travel time collection, the accuracy of observed travel time and the optimal travel time data quantity should be determined before using travel time reliability (TTR) data. The statistical accuracy of TTR measures should be evaluated so that the statistical behavior and belief can be fully understood. More specifically, this issue can be formulated as a question: using a certain amount of travel time data, how accurate is the TTR for a specific freeway corridor, time of day, and day of week? A framework for answering this question has not been proposed previously. Our study proposes a framework based on bootstrapping to evaluate the accuracy of TTR measures and answer the question. Bootstrapping is a computer-based method for assigning measures of accuracy to multiple types of statistical estimators without requiring a specific probability distribution. Three scenarios representing three traffic flow conditions (free-flow, congestion, and transition) were used to evaluate the accuracy of TTR measures under different traffic conditions and quantities of data. The results of the accuracy measurements demonstrated that: (1) the proposed framework supports assessment of TTR accuracy and (2) stabilization of the TTR measures did not necessarily correspond to statistical accuracy. The findings in our study also suggested that moment-based TTR measures may not be statistically sufficient for measuring freeway TTR. Additionally, our study suggested that 4 or 5 weeks of travel time data is sufficient for measuring freeway TTR under free-flow conditions, 40 weeks for congested conditions, and 35 weeks for transition conditions.

Optimization of Personnel Assignment Problem Based on Traveling Time by Using Hungarian Methods: Case Study on the Central Post Office Bandung

In this paper, traveling time workers from the central post office Bandung in delivering the package to the destination location was optimized by using Hungarian method. Sensitivity analysis against data changes that may occur was also conducted. The sampled data in this study are 10 workers who will be assigned to deliver mail package to 10 post office delivery centers in Bandung that is Cikutra, Padalarang, Ujung Berung, Dayeuh Kolot, Asia- Africa, Soreang, Situ Saeur, Cimahi, Cipedes and Cikeruh. The result of this research is optimal traveling time from 10 workers to 10 destination locations. The optimal traveling time required by the workers is 387 minutes to reach the destination. Based on this result, manager of the central post office Bandung can make optimal decisions to assign tasks to their workers.

АЛГОРИТМ ОРГПРОЕКТИРОВАНИЯ ГАРНИЗОНА ПРОТИВОПОЖАРНОЙ СЛУЖБЫ ГОРОДА

Purpose. The purpose and objective of the study is to develop an algorithm for the organizational design of the garrison of the city fire department. Methods. The methodological basis of the study is the existing theory of modeling fire services. So far, in the Republic of Kazakhstan, there have been no studies on the development of scientifically based standards for the design of fire fighting units. Results. According to the above algorithm for determining the required number of fire trucks, fire depots and personnel, it was established that with an average time to the place of a call in 3 minutes, a large number of fire departments will be required to ensure the fire safety of the city. At the same time, on average, one unit will have from 3 to 10 visits per year, which is not economically feasible. The optimal travel time to the place of call in cities should be on average 7 minutes, and 14 minutes for a settlement. The maximum travel time to the call site will be 18 minutes in cities and rural areas 28 minutes, which will be economically viable. Scope of scientific results. The obtained results can be used in the development of regulatory acts in the field of design of fire fighting units of cities and settlements, as well as local executive bodies and authorized bodies in the field of fire safety in the development of appropriate management decisions on the design of fire services. Conclusion. The analysis of regulatory legal acts of the Republic of Kazakhstan in the field of design of fire-fighting units showed that these norms are borrowed in the boundary camps and do not have scientific justification. In order to improve the fire safety of cities and towns of the Republic of Kazakhstan, taking into account the socio-economic characteristics of the country, the most optimal time to the call site in cities is determined, which is 7 minutes.

Multiclass, Multicriteria Dynamic Traffic Assignment with Path-Dependent Link Cost and Entropy-Based Risk Preference

Challenges arise in dynamic traffic assignment (DTA) when heterogeneous users evaluate choices on the basis of multiple interrelated criteria such as travel time and travel time uncertainty. This paper proposes a density-based formulation along with a stochastic quasigradient projection (SQGP) solution scheme with the aid of a traffic simulator. Path-dependent link cost is proposed to allow for the objective function formulation and more tractable analysis. The criteria in the discussion and the case study are travel time (link-additive), monetary cost (non additive), and travel time uncertainty (path-dependent link-additive). An information entropy-based uncertainty measure is proposed because of concerns about using conventional measures such as variability and reliability. The case study shows stochastic and efficient convergence, demonstrates the ability of SQGP to bypass local optima, and exemplifies the significant effect of using path-independent and path-dependent link costs to forecast traffic pattern and toll revenue. The results also suggest that a pricing strategy aimed at optimizing travel time and reliability for different user classes should consider travel time correlations between toll segments and the adjacent no-toll segments if enumerating paths is practically infeasible.

Parsimonious shooting heuristic for trajectory design of connected automated traffic part II: Computational issues and optimization

Advanced connected and automated vehicle technologies enable us to modify driving behavior and control vehicle trajectories, which have been greatly constrained by human limits in existing manually-driven highway traffic. In order to maximize benefits from these technologies on highway traffic management, vehicle trajectories need to be not only controlled at the individual level but also coordinated collectively for a stream of traffic. As one of the pioneering attempts to highway traffic trajectory control, Part I of this study (Zhou et al., 2016) proposed a parsimonious shooting heuristic (SH) algorithm for constructing feasible trajectories for a stream of vehicles considering realistic constraints including vehicle kinematic limits, traffic arrival patterns, car-following safety, and signal operations. Based on the algorithmic and theoretical developments in the preceding paper, this paper proposes a holistic optimization framework for identifying a stream of vehicle trajectories that yield the optimum traffic performance measures on mobility, environment and safety. The computational complexity and mobility optimality of SH is theoretically analyzed, and verifies superior computational performance and high solution quality of SH. A numerical sub-gradient-based algorithm with SH as a subroutine (NG-SH) is proposed to simultaneously optimize travel time, a surrogate safety measure, and fuel consumption for a stream of vehicles on a signalized highway section. Numerical examples are conducted to illustrate computational and theoretical findings. They show that vehicle trajectories generated from NG-SH significantly outperform the benchmark case with all human drivers at all measures for all experimental scenarios. This study reveals a great potential of transformative trajectory optimization approaches in transportation engineering applications. It lays a solid foundation for developing holistic cooperative control strategies on a general transportation network with emerging technologies.

PENILAIAN LALUAN PERKHIDMATAN BAS; ANTARA BILANGAN PENUMPANG MAKSIMUM ATAU KEPADATAN PENUMPANG PER LALUAN

The main issue in Putrajaya’s bus services is related to time and cost. Optimal travel time require optimal routes and shortest distance. Minimizing travel time means minimizing operational cost. This paper reviews and evaluates the density of passengers and maximum number of passengers as parameters to obtain an optimum route. The mathematical model was utilized to analyze the passenger density with the improvements of the constraint to the number of passengers. The study based on data from intra-city bus services during off-peak hours. The main focus of this optimization is to compare the passenger density and the number of passengers as the parameters in decision making for bus routes. The result shows that the passenger density is better than the number passengers to assess bus routes.

Nonhomogeneous Time Mixed Integer Linear Programming Formulation for Traffic Signal Control

Urban traffic congestion is on the increase worldwide; therefore, it is critical to maximize the capacity and throughput of the existing road infrastructure with optimized traffic signal control. For that purpose, this paper builds on the body of work in mixed integer linear programming (MILP) approaches that attempt to optimize traffic signal control jointly over an entire traffic network and specifically on improving the scalability of these methods for large numbers of intersections. The primary insight in this work stems from the fact that MILP-based approaches to traffic control used in a receding horizon control manner (that replan at fixed time intervals) need to compute high-fidelity control policies only for the early stages of the signal plan. Therefore, coarser time steps can be used to see over a long horizon to adapt preemptively to distant platoons and other predicted long-term changes in traffic flows. To that end, this paper contributes the queue transmission model (QTM), which blends elements of cell-based and link-based modeling approaches to enable a nonhomogeneous time MILP formulation of traffic signal control. Experimentation is then carried out with this novel QTM-based MILP control in a range of traffic networks, and it is demonstrated that the nonhomogeneous MILP formulation achieves ( a) substantially lower delay solutions, ( b) improved per vehicle delay distributions, and ( c) more optimal travel times over a longer horizon in comparison with the homogeneous MILP formulation with the same number of binary and continuous variables.

Determinants of delays in travelling to an emergency obstetric care facility in Herat, Afghanistan: an analysis of cross-sectional survey data and spatial modelling.

BackgroundWomen’s delays in reaching emergency obstetric care (EmOC) facilities contribute to high maternal and perinatal mortality and morbidity in low-income countries, yet few studies have quantified travel times to EmOC and examined delays systematically. We defined a delay as the difference between a woman’s travel time to EmOC and the optimal travel time under the best case scenario. The objectives were to model travel times to EmOC and identify factors explaining delays. i.e., the difference between empirical and modelled travel times.MethodsA cost-distance approach in a raster-based geographic information system (GIS) was used for modelling travel times. Empirical data were obtained during a cross-sectional survey among women admitted in a life-threatening condition to the maternity ward of Herat Regional Hospital in Afghanistan from 2007 to 2008. Multivariable linear regression was used to identify the determinants of the log of delay.ResultsAmongst 402 women, 82 (20%) had no delay. The median modelled travel time, reported travel time, and delay were 1.0 hour [Q1-Q3: 0.6, 2.2], 3.6 hours [Q1-Q3: 1.0, 12.0], and 2.0 hours [Q1-Q3: 0.1, 9.2], respectively. The adjusted ratio (AR) of a delay of the “one-referral” group to the “self-referral” group was 4.9 [95% confidence interval (CI): 3.8-6.3]. Difficulties obtaining transportation explained some delay [AR 2.1 compared to “no difficulty”; 95% CI: 1.5-3.1]. A husband’s very large social network (> = 5 people) doubled a delay [95% CI: 1.1-3.7] compared to a moderate (3-4 people) network. Women with severe infections had a delay 2.6 times longer than those with postpartum haemorrhage (PPH) [95% CI: 1.4-4.9].ConclusionsDelays were mostly explained by the number of health facilities visited. A husband’s large social network contributed to a delay. A complication with dramatic symptoms (e.g. PPH) shortened a delay while complications with less-alarming symptoms (e.g. severe infection) prolonged it. In-depth investigations are needed to clarify whether time is spent appropriately at lower-level facilities. Community members need to be sensitised to the signs and symptoms of obstetric complications and the urgency associated with them. Health-enhancing behaviours such as birth plans should be promoted in communities.

Travel time resilience of roadway networks under disaster

A bi-level, three-stage Stochastic Mathematical Program with Equilibrium Constraints (SMPEC) is proposed for quantifying and optimizing travel time resilience in roadway networks under non-recurring natural or human-induced disaster events. At the upper-level, a sequence of optimal preparedness and response decisions is taken over pre-event mitigation and preparedness and post-event response stages of the disaster management life cycle. Assuming semi-adaptive user behavior exists shortly after the disaster and after the implementation of immediate response actions, the lower-level problem is formulated as a Partial User Equilibrium, where only affected users are likely to rethink their routing decisions. An exact Progressive Hedging Algorithm is presented for solution of a single-level equivalent, linear approximation of the SMPEC. A recently proposed technique from the literature that uses Schur’s decomposition with SOS1 variables in creating a linear equivalent to complementarity constraints is employed. Similarly, recent advances in piecewise linearization are exploited in addressing nonseparable link travel time functions. The formulation and solution methodology are demonstrated on an illustrative example.

Exploring sensitive dependence and transitivity to optimize travel time in chaotic systems

Transitivity and sensitive dependence on initial conditions are the main characteristics of chaotic behavior. The latter one can be exploited so that small controlled perturbations in system parameters may imply a faster transfer in time from a desired start point to a neighborhood of a desired final state. In this study three targeting approaches are evaluated: The first one uses a geometric approach to find the proper perturbation which allows a faster transfer between two desired points; The second, an evolutionary algorithm called GEO (Generalized External Optimization), is adapted to search for optimized orbits; The third one, uses successive perturbations along the path in order to direct the orbits to the final desired point in a short time interval. These three methods are evaluated regarding performance and implementation complexity.

Detection probability criterion on the path for mobile object control problem in conflict environment

A problem to design probabilistic optimality criterion for mobile object in conflict environment is considered. Probability of the event that the object would not be detected during motion along the path is represented as a certain functional of the path. The relationship between detection probability of the object and its traveling time along the path is derived. Optimal traveling time along the path is evaluated.

Algorithm for Determining Most Reliable Travel Time Path on Network with Normally Distributed and Correlated Link Travel Times

Transportation networks are subject to significant travel time uncertainty as a result of traveler behavior, recurring congestion, capacity variability (construction zones, traffic incidents), variation in demand, and so on. Therefore, interest is growing in modeling and optimizing travel time reliability in such networks. This paper proposes an efficient algorithm to compute the path of maximum travel time reliability on a network with normally distributed and correlated link travel times. For this optimal reliability path (ORP) problem, it is shown that the subpath optimality condition for the deterministic shortest path problem does not hold, and consequently, a new bounds-based optimality criterion is proposed using the K shortest expected time paths and the minimum path variance on the network. An algorithm is developed to solve the ORP problem on the basis of the proposed optimality criterion and an efficient path generation procedure. Computational experiments on various test networks show the proposed algorithm to be efficient, requiring limited path enumeration. With as few as five shortest paths and 50 Monte Carlo draws, the proposed algorithm is able to find the most reliable path for realistic network sizes. Empirical investigations highlight the unreliability of the least expected time path and suboptimality of the independence assumption. The study also underscores the role of risk attitudes (reflected by reliability threshold) on the benefits of the ORP. The algorithm and empirical results have important applications for developing reliability-based routing applications for congestion mitigation and intelligent transportation systems.

A novel modeling approach for the fleet deployment problem within a short-term planning horizon

This paper is concerned with model development for a short-term fleet deployment problem of liner shipping operations. We first present a mixed integer nonlinear programming model in which the optimal vessel speeds for different vessel types on different routes are interpreted as their realistic optimal travel times. We then linearize the proposed nonlinear model and obtain a mixed integer linear programming (MILP) model that can be efficiently solved by a standard mixed integer programming solver such as CPLEX. The MILP model determines the optimal route service frequency pattern and take into account the time window constraints of shipping services. Finally, we report our numerical results and performance of CPLEX on randomly generated instances.

Optimal Route Based on Dynamic Programming for Road Networks

One of the main functions of the traffic navigation systems is to find the optimal route to the destination. In this paper, we propose an iterative Q value updating algorithm, Q method, based on dynamic programming to search the optimal route and its optimal traveling time for a given Origin-Destination (OD) pair of road networks. The Q method uses the traveling time information available at adjacent intersections to search for the optimal route. The Q value is defined as the minimum traveling time to the destination when a vehicle takes the next intersection. When the Q values converge, the optimal route to the destination can be determined by choosing the minimum Q value at each intersection. The Q method gives us the solutions from multiple origins to a single destination. The proposed method is not restricted to find a single solution, but, if there exist multiple optimal routes with the identical traveling time to the destination, the proposed method can find all of it. In addition to that, when the traveling time of the road sections changes, an alternative optimal route can be found easily starting with the already obtained Q values. We compared the Q method with Dijkstra algorithm and the simulation results showed that the Q method can give better performances, depending on the situations, when the traveling time of the road sections changes.

An assignment-based heuristic for vehicle routing with time windows

In this paper, we consider the typical vehicle routing problem with time window constraints (VRPTW). The problem is approached via mathematical decomposition and solved using a three-stage method. First, we formulate the generalized assignment problem, which provides an approximation to the sequencing of customers that partially respects the time windows and apply the Hungarian method to obtain optimal solutions. Subsequently, we address the split of infeasible routes resulting from the assignment solution using a simple, time window-based decomposition heuristic. The best of these routes, in terms of traveling and vehicle waiting times, form part of the final solution, which is completed by the routes provided by a look-ahead heuristic applied to the remainder of the customers. The proposed method is applied to a standard literature data set, and provides very good results with respect to both the number of vehicles and the total travel time. Furthermore, the approach offers useful insights on the effect of employing optimal travel time solutions resulting from the assignment relaxation to derive partial route sets of VRPTW.