Category Archives: Research

  • September 10, 2018
  • 0

Work on Big Data Analytics and Urban Computing

Category : Research

I have worked on big data analytics for different transportation-related topics. Here is a list of publications in this field.

  • Xiao-Feng Xie and Zun-Jing Wang. Examining travel patterns and characteristics in a bikesharing network and implications for data-driven decision supports: Case study in the Washington DC area. Journal of Transport Geography, 71: 84-102, 2018. [PDF] [DOI] [Bibtex]
    @Article{Xie2018Bike,
Title = {Examining travel patterns and characteristics in a bikesharing network and implications for data-driven decision supports: Case study in the {Washington DC} area},
Author = {Xiao-Feng Xie and Zun-Jing Wang},
Journal = {Journal of Transport Geography},
Volume = {71},
Pages = {84--102},
PDF={http://www.wiomax.com/team/xie/paper/JTRG18Pre.pdf},
Doi = {10.1016/j.jtrangeo.2018.07.010},
Year = {2018}
}
  • Xiao-Feng Xie and Zun-Jing Wang. Multiscale crash analysis: A case study of integrating FARS, Maryland’s crash data, and Montgomery County’s traffic violation data. In Transportation Research Board (TRB) Annual Meeting, number 18-2283, Washington, DC, 2018. [PDF] [DOI] [Bibtex]
    @InProceedings{xie2018multiscale,
title={Multiscale crash analysis: A case study of integrating {FARS}, {Maryland}'s crash data, and {Montgomery County}'s traffic violation data},
author={Xie, Xiao-Feng and Wang, Zun-Jing},
Booktitle = {Transportation Research Board (TRB) Annual Meeting},
number={18-2283},
Address = {Washington, DC},
LNK={https://trid.trb.org/View/1495254},
PDF={http://www.wiomax.com/team/xie/paper/TRB18.pdf},
year={2018}
}
  • Yiming Gu, Zhen Qian, and Xiao-Feng Xie. An unsupervised learning approach for analyzing traffic impacts under arterial road closures: Case study of East Liberty in Pittsburgh. Journal of Transportation Engineering, 142(9): 4016033, 2016. [DOI] [Bibtex]
    @Article{Gu2016,
Title = {An unsupervised learning approach for analyzing traffic impacts under arterial road closures: Case study of {East Liberty} in {Pittsburgh}},
Author = {Yiming Gu and Zhen Qian and Xiao-Feng Xie},
journal = {Journal of Transportation Engineering},
Year = {2016},
Number = {9},
Volume = {142},
Pages = {04016033},
Doi={10.1061/(ASCE)TE.1943-5436.0000860},
publisher={American Society of Civil Engineers}
}
  • Xiao-Feng Xie and Zun-Jing Wang. An empirical study of combining participatory and physical sensing to better understand and improve urban mobility networks. In Transportation Research Board (TRB) Annual Meeting, number 3238, Washington, DC, 2015. [PDF] [PPT] [DOI] [Bibtex]
    @InProceedings{Xie2015,
Title = {An empirical study of combining participatory and physical sensing to better understand and improve urban mobility networks},
Author = {Xiao-Feng Xie and Zun-Jing Wang},
Booktitle = {{Transportation Research Board (TRB) Annual Meeting}},
number={3238},
PDF={http://www.wiomax.com/team/xie/paper/TRB15LBSN.pdf},
PPT={http://www.wiomax.com/team/xie/demo/TRB15_demo_BigData_UrbanInformatics.pdf},
LNK={https://trid.trb.org/View/1337999},
Year = {2015},
Address = {Washington, DC}
}
  • X. Xie and Z. J. Wang, “Uncovering Urban Mobility and City Dynamics from Large-Scale Taxi Origin-Destination (O-D) Trips: Case Study in Washington DC Area,” WIOMAX, WIO-TR-18-003, 2018. [PDF] [DOI] [Bibtex]
    @TechReport{xie2018uncovering,
title = {Uncovering Urban Mobility and City Dynamics from Large-Scale Taxi Origin-Destination ({O-D}) Trips: Case Study in {Washington DC} Area},
author = {Xiao-Feng Xie and Wang, Zunjing Jenipher},
year = {2018},
number = {WIO-TR-18-003},
Institution={WIOMAX},
PDF = {http://www.wiomax.com/doc/report/WIO-TR-18-003.pdf},
DOI = {10.13140/RG.2.2.32170.72644},
urldate = {2018-07-25},
upddate = {2018-07-25}
}
  • February 26, 2017
  • 0

Ant Colony Optimization (ACO) Algorithms

Tags :

Category : Research , Software

Ant colony optimization (ACO), or ant system (AS), is a class of metaheuristic optimization algorithms inspired by the emergent search behavior using pheromone trails in natural ants.

We present CGO-AS, a generalized ant system (AS) implemented in the framework of cooperative group optimization (CGO), to show the leveraged optimization with a mixed individual and social learning. Ant colony is a simple yet efficient natural system for understanding the effects of primary intelligence on optimization. However, existing AS algorithms are mostly focusing on their capability of using social heuristic cues while ignoring their individual learning. CGO can integrate the advantages of a cooperative group and a low-level algorithm portfolio design, and the agents of CGO can explore both individual and social search. In CGO-AS, each ant (agent) is added with an individual memory, and is implemented with a novel search strategy to use individual and social cues in a controlled proportion. The presented CGO-AS is therefore especially useful in exposing the power of the mixed individual and social learning for improving optimization. The optimization performance is tested with instances of the traveling salesman problem (TSP). The results prove that a cooperative ant group using both individual and social learning obtains a better performance than the systems solely using either individual or social learning. The best performance is achieved under the condition when agents use individual memory as their primary information source, and simultaneously use social memory as their searching guidance. In comparison with existing AS systems, CGO-AS retains a faster learning speed toward those higher-quality solutions, especially in the later learning cycles. The leverage in optimization by CGO-AS is highly possible due to its inherent feature of adaptively maintaining the population diversity in the individual memory of agents, and of accelerating the learning process with accumulated knowledge in the social memory.

  • Xiao-Feng Xie and Zun-Jing Wang. Cooperative group optimization with ants (CGO-AS): Leverage optimization with mixed individual and social learning. Applied Soft Computing, 50: 223-234, 2017. [PDF] [DOI] [Bibtex]
    @Article{Xie2017Ants,
Title = {Cooperative group optimization with ants (CGO-AS): Leverage optimization with mixed individual and social learning},
Author = {Xiao-Feng Xie and Zun-Jing Wang},
Journal = {Applied Soft Computing},
Year = {2017},
Pages = {223--234},
Doi = {10.1016/j.asoc.2016.11.018},
PDF={http://www.wiomax.com/team/xie/paper/ASOC17.pdf},
Volume = {50}
}
  • December 26, 2014
  • 0

Differential Evolution (DE)

Tags :

Category : Research

Differential Evolution (DE) is an optimizatin method developed by Price and Storn in 1995. DE is original proposed as an evolution strategy (ES). It may also be considered as an swarm intelligence (SI) based method.

DE has been implemented into the Cooperative Group Optimization System (CGOS). DEPSO is a effective algorithm to combine the advantages of both DE and PSO.

  • Xiao-Feng Xie, Jiming Liu, and Zun-Jing Wang. A cooperative group optimization system. Soft Computing, 18(3): 469-495, 2014. [PDF] [DOI] [Bibtex]
    @Article{xie2014cooperative,
Title = {A cooperative group optimization system},
Author = {Xie, Xiao-Feng and Liu, Jiming and Wang, Zun-Jing},
Journal = {Soft Computing},
Year = {2014},
Number = {3},
Pages = {469--495},
Volume = {18},
PDF={http://www.wiomax.com/team/xie/paper/SOCO14.pdf},
DOI={10.1007/s00500-013-1069-8},
Publisher = {Springer}
}
  • Xiao-Feng Xie and Jiming Liu. A compact multiagent system based on autonomy oriented computing. In IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT), pages 38-44, Compiegne, France, 2005. IEEE. [PDF] [DOI] [Bibtex]
    @InProceedings{Xie:2005p1406,
Title = {A compact multiagent system based on autonomy oriented computing},
Author = {Xiao-Feng Xie and Jiming Liu},
Booktitle = {IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT)},
Address = {Compiegne, France},
Year = {2005},
PDF={http://www.wiomax.com/team/xie/paper/IAT05.pdf},
DOI={10.1109/IAT.2005.6},
Pages = {38--44},
Publisher = {IEEE}
}
  • Xiao-Feng Xie and Wen-Jun Zhang. SWAF: Swarm algorithm framework for numerical optimization. In Genetic and Evolutionary Computation Conference (GECCO), pages 238-250, Seattle, WA, USA, 2004. Springer. [PDF] [DOI] [Bibtex]
    @InProceedings{Xie:2004p238,
Title = {{SWAF}: Swarm algorithm framework for numerical optimization},
Author = {Xiao-Feng Xie and Wen-Jun Zhang},
Booktitle = {Genetic and Evolutionary Computation Conference (GECCO)},
Year = {2004},
PDF={http://www.wiomax.com/team/xie/paper/GECCO04_SWAF.pdf},
DOI={10.1007/978-3-540-24854-5_21},
Address = {Seattle, WA, USA},
Pages = {238--250},
Publisher = {Springer}
}
  • Xiao-Feng Xie, Wen-Jun Zhang, and De-Chun Bi. Handling equality constraints by adaptive relaxing rule for swarm algorithms. In Congress on Evolutionary Computation (CEC), pages 2012-2016, Portland, OR, USA, 2004. [PDF] [DOI] [Bibtex]
    @InProceedings{Xie:2004p2012,
Title = {Handling equality constraints by adaptive relaxing rule for swarm algorithms},
Author = {Xiao-Feng Xie and Wen-Jun Zhang and De-Chun Bi},
Year = {2004},
Pages = {2012--2016},
Booktitle = {Congress on Evolutionary Computation (CEC)},
PDF={http://www.wiomax.com/team/xie/paper/CEC04_ECH.pdf},
DOI={10.1109/CEC.2004.1331143},
Address = {Portland, OR, USA}
}
  • Wen-Jun Zhang and Xiao-Feng Xie. DEPSO: Hybrid particle swarm with differential evolution operator. In IEEE International Conference on Systems, Man, and Cybernetics, pages 3816-3821, Washington, DC, USA, 2003. IEEE. [PDF] [Code] [DOI] [Bibtex]
    @InProceedings{Zhang:2003p3816,
Title = {{DEPSO}: Hybrid particle swarm with differential evolution operator},
Author = {Wen-Jun Zhang and Xiao-Feng Xie},
Booktitle = {IEEE International Conference on Systems, Man, and Cybernetics},
Year = {2003},
PDF={http://www.wiomax.com/team/xie/paper/SMCC03.pdf},
DOI={10.1109/ICSMC.2003.1244483},
Code={http://www.wiomax.com/depso},
Address = {Washington, DC, USA},
Pages = {3816--3821},
Publisher = {IEEE}
}
  • December 21, 2014
  • 0

中文论文

Tags :

Category : 中文论文

 

摘要：首先基于一些实例研究了差异演化(DE)的参数选择问题;然后在分析DE特点的基础上,将缩放因子F由固定数值设为随机函数,实现了一个简化的DE版本(SDE).该方法不仅减少了需调整的参数,而且对CR的参数选择更为宽松.与已有文献中遗传算法的带约束型数值优化问题的实验结果对比,表明SDE能在较少的计算次数内获得较好的结果.
* 差异演化算法(Differential Evolution, DE)，又称为差分演化算法或差分进化算法 (相关论文和源代码)

摘要：讨论微粒群算法的开发与应用.首先回顾从1995年以来的开发过程,然后根据一些已有的测试结果对其参数设置进行系统地分析,并讨论一些非标准的改进手段,如簇分解、选择方法、邻域算子、无希望/重新希望方法等.介绍了一些常用的测试函数,以及与其他演化算法的比较.最后讨论了一些已经开发和在将来有希望的领域中的应用.
* 微粒群算法(Particle Swarm Optimization, PSO)，又称为粒子群优化算法、粒子群算法、或微粒群优化算法 (相关论文和源代码)

摘要：随着器件尺寸的缩小,器件特性空间变得越来越复杂.如果仍采用手工参数调整的方法,不仅需要有较好的器件物理知识,而且也不一定能得到合适的结果.为节约设计时间,对半导体器件建模与优化系统(MOSSED)进行了研究与实现.该系统可以对半导体器件进行有效地建模、优化和综合,以得到所需要的器件.通过一些实例对部分功能进行了说明,并和一些已有的系统进行了比较.

摘要：通过对浮点遗传算法早熟收敛现象的分析,提出了一种新的父代选择策略,即使用当前代的子代个体作为下代的父代个体,可使交叉算子持续地探索和开发新空间.引入对个体的代数保护策略,即在它发生变异前保证有足够的演化,可以避免对新空间不成熟的开发.通过与其它父代选择策略的对比,并通过实验和GENOCOP系统比较,表明本方法能得到较好的结果.

摘要：综合技术作为一项重要的研究方法，不仅在电路设计中获得广泛应用，而且在器件和工艺的研究上同样获得应用。利用器件与工艺综合的思想，我们开发出自顶向下的新的器件和工艺设计方法，和实现了该设计方法的MOSPAD 软件，并利用MOSPAD 系统做出一定的综合结果。本文分别做出关于器件与工艺综合的两个实例，即对FIB 器件的器件综合和对阱形成工艺模块进行的工艺综合，并证明了自顶向下的器件与工艺综合思想的可行性。

摘要：本文对应用于器件综合系统的遗传算法GENOCOP进行了改进.将实数设计空间根据参数的工艺精度影响转换为整型空间,并加入适应性复合算子利用已经得到的点来扩展和开发准可行空间.使其保持有效搜索到可行解的特性的同时,在同等的算法设置下,提高了对可行空间的覆盖率(约2.87倍),可以帮助设计人员更有效的设计可工作的器件.

摘要：为实现器件综合，即从期望的器件性能出发得到优化的器件设计参数，最关键的是要选用有效的优化搜索算法。文章将遗传算法应用于实现一个器件综合的原型系统，并通过对FIBMOS器件的综合设计，验证了遗传算法和该器件综合原型系统的有效性。器件的参数化表示也被作为器件综合的重要问题进行了讨论。

 

  • October 13, 2012
  • 0

Work on Smart Urban Traffic Control published on Transportation Research Part C

Category : Activities , Research

Schedule-driven intersection control (SchIC) is the core control engine (the brain) of the smart and scalable urban traffic control system.

  • Xiao-Feng Xie, S. Smith, Liang Lu, and G. Barlow. Schedule-driven intersection control. Transportation Research Part C, 24: 168-189, 2012. [PDF] [DOI] [Bibtex]
    @Article{Xie2012,
Title = {Schedule-driven intersection control},
Author = {Xiao-Feng Xie and S. Smith and Liang Lu and G. Barlow},
Journal = {Transportation Research Part C},
Year = {2012},
Pages = {168-189},
Volume = {24},
PDF={http://www.wiomax.com/team/xie/paper/TRC12.pdf},
Doi = {10.1016/j.trc.2012.03.004}
}

Model-based intersection optimization strategies have been widely investigated for distributed traffic signal control in road networks. Due to the form of ‘‘black-box’’ optimization that is typically assumed, a basic challenge faced by these strategies is the combinatorial nature of the problem that must be solved. The underlying state space is exponential in the number of time steps in the look-ahead optimization horizon at a given time resolution. In this paper, we present a schedule-driven intersection control strategy, called SchIC, which addresses this challenge by exploiting the structural information in non-uniformly distributed traffic flow. Central to our method is an alternative formulation of intersection control optimization as a scheduling problem, which effectively reduces the state space through use of an aggregate representation on traffic flow data in the prediction horizon. A forward recursive algorithm is proposed for solving the scheduling problem, which makes use of a dominance condition to efficiently eliminate most states at early stages. SchIC thus achieves near optimal solutions with a polynomial complexity in the prediction horizon, and is insensitive to the granularity of time resolution that is assumed. The performance of SchIC with respect to both intersection control and implicit coordination between intersections is evaluated empirically on two ideal scenarios and a real-world urban traffic network. Some characteristics and possible real-world extensions of SchIC are also discussed.

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