警车配置及巡逻方案研究

以警车的配置与巡逻方案为研究对象,建立了一套警车巡逻模型,并提出巡逻效果显著度及隐藏性的评价标准,分别针对警车初始位置配置与巡逻方案的制定,提出警车配置优化选址的贪婪算法与基于多Agent的警车巡逻方案设计方法,给出了不同情景下的配置及巡逻方案:①在只考虑警车选址配置的情况下,配置19辆警车可以使全市路网警车覆盖率达到92.8%;②在顾及巡逻效果显著性与隐藏性的情况下,配置25辆警车使全市路网在整个巡逻过程中平均警车覆盖率达到90.9%;③在配置10辆警车的情况下,使得全市路网在整个巡逻过程中平均警车覆盖率达到61.5%.     

A solution method for a car fleet management problem with maintenance constraints

The problem retained for the ROADEF’99 international challenge was an inventory management problem for a car rental company. It consists in managing a given fleet of cars in order to satisfy requests from customers asking for some type of cars for a given time period. When requests exceed the stock of available cars, the company can either offer better cars than those requested, subcontract some requests to other providers, or buy new cars to enlarge the available stock. Moreover, the cars have to go through a maintenance process at a regular basis, and there is a limited number of workers that are available to perform these maintenances. The problem of satisfying all customer requests at minimum cost is known to be NP-hard. We propose a solution technique that combines two tabu search procedures with algorithms for the shortest path, the graph coloring and the maximum weighted independent set problems. Tests on benchmark instances used for the ROADEF’99 challenge give evidence that the proposed algorithm outperforms all other existing methods (thirteen competitors took part to this contest).     

Mean and variance of waiting time and their optimization for alternating traffic control systems

We analyze alternating traffic crossing a narrow one-lane bridge on a two-lane road. Once a car begins to cross the bridge in one direction, arriving cars from the other direction must wait, forming a queue, until all the arrivals in the first direction finish crossing the bridge. Such a situation can often be observed when road-maintenance work is being carried out. Cars are assumed to arrive at the queues according to independent Poisson processes and to cross the bridge in a constant time. In addition, once cars join the queue, each car needs a constant starting delay, before starting to cross the bridge. We model the situation where a signal controls the traffic so that the signal gives a priority to one direction as long as a new car from the same direction arrives in a fixed time. For this model, we get a closed form for the first two moments of the waiting time of cars arriving at the bridge, and then numerically obtain Pareto optimal solutions of holding times to minimize the mean waiting time and its standard deviation. To the memory of our best friend, Yo Ishizuka     

Planning Formula One race strategies using discrete-event simulation

A discrete-event simulation model that imitates most on-track events, including car failures, passing manoeuvres and pit stops during a Formula One race, is presented. The model is intended for use by a specific team. It will enable decision-makers to plan and evaluate their race strategy, consequently providing them with a possible competitive advantage. The simulation modelling approach presented in this paper captures the mechanical complexities and physical interactions of a race car with its environment through a time-based approach. Model verification and validation are demonstrated using three races from the 2005 season. The application of the model is illustrated by evaluating the race strategies employed by a specific team during these three races.     

Valuing the switching flexibility of the ethanol-gas flex fuel car

Renewable energy sources are becoming more important as the world’s supply of fossil fuels decrease and also due to environmental concerns. Since 2003, when the ethanol-gasoline flex fuel car became commercially available in Brazil, the growth of this market has been significant, to the point where currently more than 50% of the fuel consumption of cars in Brazil is from renewable biofuels (ethanol). This has been made possible due to the success of the flex fuel car, which can run on ethanol, gasoline, or any mix of these in the same fuel tank, and which is sold at a premium over the non-flex models.     

The determinants of equity transmission between the new and used car markets: a hedonic analysis

Drawing on a data set containing 371082 observations on new and used cars from 2008, this study employs a hedonic model to estimate the determinants of prices in the primary and secondary car markets in Germany. We are specifically interested in identifying those vehicle attributes that are responsible for retaining the car’s value in the used car market. Beyond parameterizing the influence of technical features and brand name on the retail price, our model simultaneously generates a corresponding set of parameter estimates for the used car price, thereby allowing us to formally compare their magnitudes across the two markets. This comparison reveals that fuel consumption, in particular, is an important determinant of the price, one whose impact is higher in magnitude in the used car market than in the new car market. Large heterogeneity in how cars hold their investment value is also seen to depend on body type and brand/model name.     

Invariance and homogenization of an adaptive time gap car-following model

In this paper we consider a microscopic model of traffic flow called the adaptive time gap car-following model. This is a system of ODEs which describes the interactions between cars moving on a single line. The time gap is the time that a car needs to reach the position of the car in front of it (if the car in front of it would not move and if the moving car would not change its velocity). In this model, both the velocity of the car and the time gap satisfy an ODE. We study this model and show that under certain assumptions, there is an invariant set for which the dynamics is defined for all times and for which we have a comparison principle. As a consequence, we show rigorously that after rescaling, this microscopic model converges to a macroscopic model that can be identified as the classical LWR model for traffic.     

Shunting Minimal Rail Car Allocation

We consider the rail car management at industrial in-plant railroads. Demands for loaded or empty cars are characterized by a track, a car type, and the desired quantity. If available, we assign cars from the stock, possibly substituting types, otherwise we rent additional cars. Transportation requests are fulfilled as a short sequence of pieces of work, the so-called blocks. Their design at a minimal total transportation cost is the planning task considered in this paper. It decomposes into the rough distribution of cars among regions, and the NP-hard shunting minimal allocation of cars per region. We present mixed integer programming formulations for the two problem levels. Our computational experience from practical data encourages an installation in practice.MSC (2000): 90C11, 90C27, 90B06     

Numerical bifurcation analysis of a ‘car‐following’ model

We study a system of ordinary differential equations describing a car‐following model for the motion of N car around a circular highway. All cars behave in the same way. The acceleration of each car is determined as a function of the headway (optimal velocity function). This model is known to have a solution with constant velocities and headways which, in a certain parameter regime, is stable and, varying the density of the cars, the loss of stability is generally due to a super‐ or subcritical Hopf bifurcation. Guided by analytical results, we numerically investigate the global bifurcation diagram for periodic solutions and obtain a complete picture of the dynamics of the model. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

A Multi-agent Based Self-adaptive Genetic Algorithm for the Long-term Car Pooling Problem

Rising vehicles number and increased use of private cars have caused significant traffic congestion, noise and energy waste. Public transport cannot always be set up in the non-urban areas. Car pooling, which is based on the idea that sets of car owners having the same travel destination share their vehicles has emerged to be a viable possibility to reduce private car usage around the world. In this paper, we present a multi-agent based self-adaptive genetic algorithm to solve long-term car pooling problem. The system is a combination of multi-agent system and genetic paradigm, and guided by a hyper-heuristic dynamically adapted by a collective learning process. The aim of our research is to solve the long-term car pooling problem efficiently with limited exploration of the search space. The proposed algorithm is tested using large scale instance data sets. The computational results show that the proposed method is competitive with other known approaches for solving long-term car pooling problem.     

Bringing a non-linear manoeuvringobject to the optimal position in the shortest time

A different game problem with two players (cars), in which one player (car) pursues the other, is considered. The roles of theplayers are fixed, and the functional to be minimized (for player I) and maximixed (for player II) is the maximum value of a given scalar non-negative function (the performance index) of the phase vector along the trajectory of the dynamical system over a fairly long time interval. A zero value of the performance index corresponds to the situation in which the pursuer is behind the evader at a given distance from it, and the velocity vectors are codirectional and lie on the same straight line. A detailed investigation is presented of the special case in which the car being pursued is at rest, and the pursuer is moving in the plane at a velocity of constant magnitude subject to a certain constraint on its turning radius. The game ends when the car is moving in a circle of given radius, in which case its velocity vector must point toward the centre of the circle. The relations of the Pontryagin maximum principle characterizing optimal open-loop controls are written out and analysed. The main result of the paper is the synthesis of an optimal feedback control.     

Optimization of operating strategies in a community solar heating system

Minimization of auxiliary energy costs is discussed for heating in a district solar heating system with effective heat storage. The minimization problem is approached by dynamic programming which gives an optimal operating strategy for the auxiliary energy system. The effects of different pricing schemes of auxiliary energy (electricity) have been studied. The results show that with an adequate heat storage capacity, the optimization of the auxiliary energy use in a community solar heating system may lead to considerable cost savings.     

How Much Should You Oil Your Vehicle

<正>While private cars have become necessary means of communication for families these days,gasoline has become part of living expenditure.In turn,a variety of oil saving tips have been put forward based on personal experience and unique insights,such as controlling the accelerator and the driving speed.Not considering personal driving habits,some people believe that the gasoline stored in the     

A DEA analysis of risk,cost, and revenues in insurance

Insurance companies have to take risk and cost into account when pricing car insurance policies that cover the risk of private use of cars. In this paper we use data from 80?000 car insurance policies in order to assess, once risk and cost have been taken into account, the combinations of risk that generate the highest returns for the company under existing pricing practices. We use data envelopment analysis (DEA) and frame the study within an analysis of experiments context. The results of DEA are interpreted in a multivariate statistical analysis context using factor analysis, and property fitting techniques. The impact of risk factors in the efficiency is explored by means of regression analysis with dummy variables. There are consequences for the pricing policy of the company.     

Memetic algorithm for the Traveling Car Renter Problem: an experimental investigation

The Traveling Car Renter Problem (CaRS) is a generalization of the Traveling Salesman Problem where the tour can be decomposed into contiguous paths that are travelled by different rented cars. When a car is rented in a city and delivered in another, the renter must pay a fee to return the car to its home city. Given a graph G and cost matrices associated to cars available for rent, the problem consists in determining the minimum cost Hamiltonian cycle in G, considering also the cost paid to deliver a car in a city different from the one it was rented. The latter cost is added to the cost of the edges in the cycle. This paper describes the general problem and some related variants. Two metaheuristic approaches are proposed to deal with CaRS: GRASP hybridized with Variable Neighborhood Descent and Memetic Algorithm. A set of benchmark instances is proposed for the new problem which is utilized on the computational experiments. The algorithms are tested on a set of 40 Euclidean and non-Euclidean instances.     

Iterated tabu search for the car sequencing problem

This paper introduces an iterated tabu search heuristic for the daily car sequencing problem in which a set of cars must be sequenced so as to satisfy requirements from the paint shop and the assembly line. The iterated tabu search heuristic combines a classical tabu search with perturbation operators that help escape from local optima. The resulting heuristic is flexible, easy to implement, and fast. It has produced very good results on a set of test instances provided by the French car manufacturer Renault.     

辽宁省私家车保有量增长及影响因素分析

近年来,辽宁省的经济稳步发展,私家车保有量在快速上升.私家车保有量的变化趋势与辽宁省的基础设施建设、城市发展、交通以及环保等政策的制定有着密切联系,因此,准确的预测未来辽宁省私家车保有量有重要意义.本文以辽宁省1996~2015年私家车发展情况为研究对象,运用统计学及计量经济学相关知识,建立多元线性回归模型来分析私家车保有量的影响因素,经过模型检验和修正,进而分析各因素与保有量的影响关系.最后,根据得到的研究结果,对未来几年辽宁省私家车保有量进行预测并针对辽宁特殊的社会经济状况,为改善建成环境中潜藏着的复杂问题提出相关政策和建议.     

Models for Allocating Police Preventive Patrol Effort

This paper develops models that maximize the expected number of occasions per unit of time that a police patrol unit enters a street segment during the time that a crime is visible. Constraints are added that insure a minimum patrol coverage to all streets. The successive visits from street-to-street form a Markov chain. The solution that maximizes the objective function gives a stochastic decision rule which is used with Monte Carlo techniques to generate a random patrol schedule. The problem is posed with one car and several cars patrolling the same region.     

基于层次分析法的几种家用汽车购车模型

目前,随着我国人民生活水平的不断提高,家庭购车逐渐成为一种新的需求.但面对纷繁多变的汽车市场,很多消费者苦于车型的抉择.为了解决家庭购车中的科学决策问题,根据来自汽车销售市场的实际信息,以目前几种10万元以下的主流家用轿车为研究对象,应用层次分析法得到购买家用汽车的数学模型,从而使定量化地解决了车型抉择问题.     

Modeling the number of insureds’ cars using queuing theory

In this paper, we propose to model the number of insured cars per household. We use queuing theory to construct a new model that needs 4 different parameters: one that describes the rate of addition of new cars on the insurance contract, a second one that models the rate of removal of insured vehicles, a third parameter that models the cancellation rate of the insurance policy, and finally a parameter that describes the rate of renewal. Statistical inference techniques allow us to estimate each parameter of the model, even in the case where there is censorship of data. We also propose to generalize this new queuing process by adding some explanatory variables into each parameter of the model. This allows us to determine which policyholder’s profiles are more likely to add or remove vehicles from their insurance policy, to cancel their contract or to renew annually. The estimated parameters help us to analyze the insurance portfolio in detail because the queuing theory model allows us to compute various kinds of useful statistics for insurers, such as the expected number of cars insured or the customer lifetime value that calculates the discounted future profits of an insured. Using car insurance data, a numerical illustration based on a portfolio from a Canadian insurance company is included to support this discussion.