城市道路交通网络优化理论分析与实证研究
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摘要
近年来,随着经济的发展和城市人口激增,中国许多城市,尤其是大城市和省会城市,交通拥堵状况日益严重,优先发展公共交通体系成为城市可持续发展的必然选择。作为城市交通研究领域中的热点,交通网络优化正在受到越来越多的关注。交通供需矛盾突出表现在车路发展的不平衡上。在悠久历史的大中城市内,所能进行路网改造的余地不大,市区内所能提供的交通供给非常有限。随着不断增加的机动车流量,仅靠修路已不能有效改变供需紧张的恶性循环,因此解决交通供需矛盾的出路在于科学合理地规划道路并优化城市交通网络。
本文综述了国内外交通网络优化的研究现状,通过引入交通网络优化相关模型,从传统网络规划模型中推导出网络规划的多目标优化模型,扩展了网络规划研究方法的范围。在本文的研究中,还用差分进化算法来求解多目标网络优化模型,为多目标网络优化模型提供了一条新的求解路径:即在多目标优化的框架下提出了稳定性多目标公交网络优化模型及其多目标算法的解法。然后将上述研究结果应用于南昌市公共交通网络改造。通过实证研究,比较了理论模型与现实使用中方法的差异。为改进将来的公共交通优化提供了一定的理论依据。本论文的主要内容和结论包括:(1)从传统的三种网络优化模型出发,推导出网络规划的多目标优化模型;并针对传统交通网络设计和优化中不能达到全局最优、只侧重改善局部交通网络以及方案不够精确的缺点,本文将不确定性和多目标性引入到稳定性网络优化中,提出了长期不确定性下多目标网络优化的改进模型。改进后,模型可以比传统模型更好的反映现实情况。从而为将来的网络改造投资决策提供了一个很好的参考。(2)为解决传统求解网络优化方法过于复杂的缺点,本文引入了一种遗传算法和改进的差分进化算法来求解这个多目标稳定性网络优化模型问题。通过数值实验对比其它算法得出:改进后的差分进化算法具有更好的鲁棒性;对于高维测试问题改进后的差分进化算法能够以非常高的可靠度收敛到全局最优解;另外,从单目标连续实域优化问题的角度看,改进的差分进化算法比遗传算法具有更好的计算性能。(3)调查分析了南昌市城市交通现状(包括居民出行、车辆出行、公共交通、城市交通设施及设施管理、城市交通管理、道路交通状况调查、城市交通安全状况等)。对所提出的模型使用了两个模拟网络和一个现实网络的改造项目进行了检验。在使用上述模拟网络时,我们对算法的有效性进行了检验。计算结果表明,我们提出的多目标稳定性网络优化模型比传统的网络优化结果有相当程度的改善。同时,我们使用本模型计算了南昌市公共交通网络改造相关的问题,结果表明本模型对现实网络有相当的适应性和稳定性。最后,我们以南昌市机动车辆管理处累计的历史数据为例,用广义矩方法实证检验了我们在使用多目标稳定性网络优化模型中提出的假设,实证结果基本上支持本文提出的检验结果,即多目标网络优化模型对网络的稳定性和改造费用的具有一定的作用。
In recent years, with economic development and urban population increase, many cities in China, particularly the major cities and provincial cities, traffic congestion is becoming increasingly serious, give priority to developing urban public transport system to become sustainable and harmonious at the inevitable choice. As city traffic study in the field of hot spots, transportation network optimization is being more and more attention. The contradiction between supply and demand of the traffic is prominently shown on the uneven development of the road. Have a long history in the large and medium-sized cities, to carry out the transformation of roads little room for the urban area can provide traffic supply is very limited. With the ever-increasing vehicle traffic, the road has been alone can not effectively change the vicious cycle of the tension between supply-demand, it lies in a scientific and rational planning, optimizing the use of urban transport and road networks.
In this paper, we summarized the research status of optimization for urban traffic network at home and abroad. By introducing the relevant model of optimization for urban traffic network, the multiple objects optimal model for network planning is deduced by using the traditional model of network planning. And we solve the model of optimization of multi-objective network with the genetic algorithm in this study, which provides a new way for the multi-objective network. Namely, we put forward a new solution to the model of optimization of stability multi-objective traffic network and the multi-objective algorithm of this model. At last, we use the above results to analysis the reform of public transportation network in Nanchang city. According to the empirical study, the difference between theoretical model and practical application of it provides a theoretical base for the improvements on optimization of the future public transportation. The main results of this paper are as follows: (1) Unattainalbe global optimum, only placing the extra emphasis on local traffic-network, and the scheme is not very precise are three weak points of traditional traffic network design and optimization. Taking into consideration the above weak points, we proposed the improved optimum model of multi-objective network under long-term uncertainty by introducing uncertainty and multi-objective into optimization of stability network. Results of the improved model may agree better with the practical situation better in contrast to traditional model, which provides a good reference for the future investment decision of the network reform. (2) We introduced basic structure, research overview and overall classification of multi-objective optimization algorithm and proposed the key problem of it. Because the traditional approach to solving network optimization is too complicated, we tried to solve the optimum model of multi-objective stability network in order to make for the above weak point by introducing a genetic algorithm and the improved differential evolution algorithm. According to the numerical experiment and comparing to other algorithm, we have the conclusions about the improved differential evolution algorithm as follows: it has better robustness; with regard to the high-dimensional test problem, it could convergence to the overall optimal point with very high reliability; what's more, from the standpoint of optimization of single-object continuous real domain problem, it has better calculated performance than genetic algorithm. (3) We investigated and analyzed the current situation of traffic of Nanchang city (including resident trip, vehicle trip, public traffic, urban transport facilities and facility management, municipal traffic management, research of traffic of the roads, and situation of municipal traffic safety, etc.). The above proposed model was checked by using renovation project of two analog and one real network. We checked the validity of the algorithm when using the above two analog network. The results show that the proposed optimum model of multi-objective stability network has some improvement in comparison with traditional network optimization. Meanwhile, we calculated the relevant problems of the reform of public traffic network of Nanchang city by using this model, and the results indicate that our model has considerable suitability and stability to the real network. At last, we took the historical datas of Nanchang city accumulated by Department of Motor Vehicles (DMV) for example, and tested the proposed assumption through the using of Generalized Method of Moments when using optimum model of multi-objective stability network. The results basically support the testing result proposed in this paper, namely, the optimum model of multi-objective network has some improvement in stability and reform cost of the network.
本文综述了国内外交通网络优化的研究现状,通过引入交通网络优化相关模型,从传统网络规划模型中推导出网络规划的多目标优化模型,扩展了网络规划研究方法的范围。在本文的研究中,还用差分进化算法来求解多目标网络优化模型,为多目标网络优化模型提供了一条新的求解路径:即在多目标优化的框架下提出了稳定性多目标公交网络优化模型及其多目标算法的解法。然后将上述研究结果应用于南昌市公共交通网络改造。通过实证研究,比较了理论模型与现实使用中方法的差异。为改进将来的公共交通优化提供了一定的理论依据。本论文的主要内容和结论包括:(1)从传统的三种网络优化模型出发,推导出网络规划的多目标优化模型;并针对传统交通网络设计和优化中不能达到全局最优、只侧重改善局部交通网络以及方案不够精确的缺点,本文将不确定性和多目标性引入到稳定性网络优化中,提出了长期不确定性下多目标网络优化的改进模型。改进后,模型可以比传统模型更好的反映现实情况。从而为将来的网络改造投资决策提供了一个很好的参考。(2)为解决传统求解网络优化方法过于复杂的缺点,本文引入了一种遗传算法和改进的差分进化算法来求解这个多目标稳定性网络优化模型问题。通过数值实验对比其它算法得出:改进后的差分进化算法具有更好的鲁棒性;对于高维测试问题改进后的差分进化算法能够以非常高的可靠度收敛到全局最优解;另外,从单目标连续实域优化问题的角度看,改进的差分进化算法比遗传算法具有更好的计算性能。(3)调查分析了南昌市城市交通现状(包括居民出行、车辆出行、公共交通、城市交通设施及设施管理、城市交通管理、道路交通状况调查、城市交通安全状况等)。对所提出的模型使用了两个模拟网络和一个现实网络的改造项目进行了检验。在使用上述模拟网络时,我们对算法的有效性进行了检验。计算结果表明,我们提出的多目标稳定性网络优化模型比传统的网络优化结果有相当程度的改善。同时,我们使用本模型计算了南昌市公共交通网络改造相关的问题,结果表明本模型对现实网络有相当的适应性和稳定性。最后,我们以南昌市机动车辆管理处累计的历史数据为例,用广义矩方法实证检验了我们在使用多目标稳定性网络优化模型中提出的假设,实证结果基本上支持本文提出的检验结果,即多目标网络优化模型对网络的稳定性和改造费用的具有一定的作用。
In recent years, with economic development and urban population increase, many cities in China, particularly the major cities and provincial cities, traffic congestion is becoming increasingly serious, give priority to developing urban public transport system to become sustainable and harmonious at the inevitable choice. As city traffic study in the field of hot spots, transportation network optimization is being more and more attention. The contradiction between supply and demand of the traffic is prominently shown on the uneven development of the road. Have a long history in the large and medium-sized cities, to carry out the transformation of roads little room for the urban area can provide traffic supply is very limited. With the ever-increasing vehicle traffic, the road has been alone can not effectively change the vicious cycle of the tension between supply-demand, it lies in a scientific and rational planning, optimizing the use of urban transport and road networks.
In this paper, we summarized the research status of optimization for urban traffic network at home and abroad. By introducing the relevant model of optimization for urban traffic network, the multiple objects optimal model for network planning is deduced by using the traditional model of network planning. And we solve the model of optimization of multi-objective network with the genetic algorithm in this study, which provides a new way for the multi-objective network. Namely, we put forward a new solution to the model of optimization of stability multi-objective traffic network and the multi-objective algorithm of this model. At last, we use the above results to analysis the reform of public transportation network in Nanchang city. According to the empirical study, the difference between theoretical model and practical application of it provides a theoretical base for the improvements on optimization of the future public transportation. The main results of this paper are as follows: (1) Unattainalbe global optimum, only placing the extra emphasis on local traffic-network, and the scheme is not very precise are three weak points of traditional traffic network design and optimization. Taking into consideration the above weak points, we proposed the improved optimum model of multi-objective network under long-term uncertainty by introducing uncertainty and multi-objective into optimization of stability network. Results of the improved model may agree better with the practical situation better in contrast to traditional model, which provides a good reference for the future investment decision of the network reform. (2) We introduced basic structure, research overview and overall classification of multi-objective optimization algorithm and proposed the key problem of it. Because the traditional approach to solving network optimization is too complicated, we tried to solve the optimum model of multi-objective stability network in order to make for the above weak point by introducing a genetic algorithm and the improved differential evolution algorithm. According to the numerical experiment and comparing to other algorithm, we have the conclusions about the improved differential evolution algorithm as follows: it has better robustness; with regard to the high-dimensional test problem, it could convergence to the overall optimal point with very high reliability; what's more, from the standpoint of optimization of single-object continuous real domain problem, it has better calculated performance than genetic algorithm. (3) We investigated and analyzed the current situation of traffic of Nanchang city (including resident trip, vehicle trip, public traffic, urban transport facilities and facility management, municipal traffic management, research of traffic of the roads, and situation of municipal traffic safety, etc.). The above proposed model was checked by using renovation project of two analog and one real network. We checked the validity of the algorithm when using the above two analog network. The results show that the proposed optimum model of multi-objective stability network has some improvement in comparison with traditional network optimization. Meanwhile, we calculated the relevant problems of the reform of public traffic network of Nanchang city by using this model, and the results indicate that our model has considerable suitability and stability to the real network. At last, we took the historical datas of Nanchang city accumulated by Department of Motor Vehicles (DMV) for example, and tested the proposed assumption through the using of Generalized Method of Moments when using optimum model of multi-objective stability network. The results basically support the testing result proposed in this paper, namely, the optimum model of multi-objective network has some improvement in stability and reform cost of the network.
引文
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