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摘要：“打车难”问题是国民关注的热点问题之一。随着“互联网+”时代的到来，各种打车软件应运而生，并推出了多种出租车补贴方案，各种补贴方案是否能缓解打车难，仍有待研究。为此我们搜集相关数据，利用数学建模的方法，研究了“互联网+”时代的出租车资源配置问题，获得了一些有价值的结论。我们的主要工作如下： 由于题目未提供数据，通过查找资料，选择2011年同济大学数学建模夏令营D题提供的深圳市出租车GPS数据进行研究。因数据过于庞大且存在错误，故先要剔除空间、状态上不合理的数据，然后按照时间、经纬度、行驶状态进行归类整理。 问题一要求建立出租车资源供求匹配程度模型。我们选择出租车总量供需比 、出租车拥有量（万人） 、出租车空驶率 三项指标对深圳市出租车匹配程度进行研究。首先，基于出租车总量供需比研究出租车的供求匹配程度，通过查找资料得到出租车需求量的测定模型，代入数据得到2011年深圳市出租车需求量为26110辆，与实际出租车供给量15035辆作比，求得出租车总量供需比 为0.5758，表明2011年深圳市出租车总量的供需匹配程度较差。然后，基于出租车拥有量（万人）研究不同空间出租车的供求匹配程度，数据中深圳市各地区载客点数目比重可评估各地区出租车供给量，与该地区常住人口数（万人）求比值，得到深圳市不同空间出租车拥有量（万人），其中罗湖区、福田区和南山区的出租车拥有量大，出租车供求匹配程度高，而坪山新区和光明新区的出租车拥有量小，出租车供求匹配程度低。最后，基于出租车空驶率研究不同时间出租车资源的供求匹配程度。将一天分为24个时间单位，由数据得到各时间段的空驶时间和运营时间，两者相比得到各时间段的小时平均空驶率，其中在23:00—7:00出租车空驶率最高，出租车供求匹配程度较低，而在8:00—10:00和14:00—18:00空驶率较低，出租车供求匹配程度较高。 问题二通过建立补贴方案吸引力模型，对“缓解打车难”的帮助程度进行评估。我们认为吸引力越大，软件使用越广泛，对“缓解打车难”帮助越大。首先，以每笔补贴金额为指标，利用模糊数学中隶属函数建立补贴金额与吸引力隶属关系式，其中吸引力因子为6.414。结合各公司出租车补贴方案，求得当单笔补贴方案为10元和2元时吸引力分别为0.91220，0.0927。由于补贴方案随时间的推移，其吸引力会发生变化，构建修正模型，得到补贴方案的综合吸引力模型，求得在每笔补贴方案为10元和2元时其值分别为：6.4880,0.6595。 问题三要求设计打车软件补贴方案，并评价方案的合理性。其实质是构建评价补贴方案合理性模型。该模型主要由公司经营成本，顾客满意度和吸引力三因素决定。顾客满意度受到补贴金额影响，因为当补贴金额较高时，司机将会拒载沿边扬招乘客而使其满意度下降。利用非线性规划求解该模型，得到每笔补贴6.53元时结果最优。对于偏远地区、拥堵路段的打车难问题，本文通过建立司机满意度模型并与公司成本的实际相结合，得到对单个出租车的月补贴为860—1035元较合适。 关键词：供求匹配程度 补贴方案吸引力 满意度 “打车难”

12306monitor

监控12306特定种类的票

2018-tencent-ad-competition-baseline

2018腾讯广告算法大赛baseline 线上0.73

2019-taida-jdata-top3

accuracy-first-differential-privacy

Code for NIPS'2017 paper

adatrace

Utility-aware synthesis of differentially private and attack-resilient location traces

adversarial

Code and hyperparameters for the paper "Generative Adversarial Networks"

aip

Adversarial Image Perturbation for Privacy Protection -- A Game Theory Perspective, ICCV'17

algorithm

alphaclean

A Tree Search Library for Data Cleaning

als-alternating_least_squares

This is the projcet of course Statistical Signal Processing (2017-2018,Fall) in Tsinghua University

als-wr-tutorial

Demonstration of the speedup potential when taking advantage of sparsity in ALS-WR algorithm

awesome-machine-learning

A curated list of awesome Machine Learning frameworks, libraries and software.

awesome-ml-for-cybersecurity

:octocat: Machine Learning for Cyber Security

baselines

OpenAI Baselines: high-quality implementations of reinforcement learning algorithms

basicnmftool

Series Algorithms for Standard Non-negative Matrix Factorization (Matlab Code)

bbs

BBS技术论坛

bbs_forum

专业设计项目——BBS论坛

bes

Differential Privacy + Data Streaming Aggregation + Storm

beyond-frontal-faces

Implement of 'Beyond Frontal Faces: Improving Person Recognition Using Multiple Cues' by Ning Zhang, et al

blockchain

Blockchain technology has been linked with Internet of Things for a long time now. There are many issues that are hinder the implementation of IoT applications at a large scale. Surveys and studies from multiple sources reveal that security threats and data privacy are still the primary concerns. These problems are well known and solutions exist for these problems in the IT industry. However, traditional IT security solutions cannot be applied to IoT for various reasons spanning from type of devices to sheer volume of devices. Unfortunately, like in any other industry, security is often disregarded in the IoT domain as well, and most of the resources are allocated to application development and device hardware. So, the search for a silver bullet to overcome these inhibitors has been going on for a while. After Bitcoin became prominent, people started to realize the potential of the underlying distributed ledger (blockchain) technology and considered it as a true innovation. Rather than facilitating a peer-to-peer digital payment system involving a cryptocurrency, the blockchain technology is viewed as a mechanism that provides device identity, secure data transfer, and immutable data storage. All these features can be implemented without any centralized authority and a completely transparent system with auditable cryptographic proofs. Our aim through this research project is to get a deep level understanding of the blockchain technology and study some of the widely used blockchain frameworks including Ethereum, Eris, and IOTA. We will further examine the exclusive features offered by each of these frameworks and define their target use cases. While researching about each framework, we plan to deploy a blockchain in the local network i.e., private blockchain and operate on it from different devices running on various operating systems. In each deployment, we will observe the functional issues and benchmark system requirements for running different types of nodes. Also, we will study different algorithms involved in each framework, compare them with each other, and derive their suitability for IoT. Ultimately, our aim is to determine the most suitable blockchain architecture for the IoT ecosystem. A high-level comparison of the researched architectures will be provided so that managers and developers can quickly decide on a suitable framework for their application or use case depending upon the requirements. For each architecture, a set of sample use cases and on-going projects will be discussed to get an idea of the usage of that architecture in the real world.

blockchain-1

区块链 - 中文资源

blog

Some notes on things I find interesting and important.

bop2017

编程之美2017资格赛—基于文档的问答

bp_finance

基于BP神经网络的高频金融时间序列分析 (毕设)

bp_regression_fireworksalg

Hybrid modeling in neural network and nonlinear regression. Fireworks algorithm is used to optimize the neural network to make a nice initialization value of weight and bias.

buildingmachinelearningsystemswithpython

Source Code for the book Building Machine Learning Systems with Python

chaotic-pso-nn-training

Research paper - Investigating Chaotic Particle Swarm Optimisation for Neural Network Training

chatterbot

ChatterBot is a machine learning, conversational dialog engine for creating chat bots

cleverhans

An adversarial example library for constructing attacks, building defenses, and benchmarking both