Pick‐up locations and bus allocation for transit‐based evacuation planning with demand uncertainty

This paper develops a decision‐support model for transit‐based evacuation planning under demand uncertainty. Demand uncertainty refers to the uncertainty associated with the number of transit‐dependent evacuees. A robust optimization model is proposed to determine the optimal pick‐up points for evacuees to assemble, and allocate available buses to transport the assembled evacuees between the pick‐up locations and different public shelters. The model is formulated as a mixed‐integer linear program and is solved via a cutting plane scheme. The numerical example based on the Sioux Falls network demonstrates that the robust plan yields lower total evacuation time and is reliable in serving the realized evacuee demand. Copyright © 2012 John Wiley & Sons, Ltd.     

Applying multiple kernel learning and support vector machine for solving the multicriteria and nonlinearity problems of traffic flow prediction

This article proposes to develop a prediction model for traffic flow using kernel learning methods such as support vector machine (SVM) and multiple kernel learning (MKL). Traffic flow prediction is a dynamic problem owing to its complex nature of multicriteria and nonlinearity. Influential factors of traffic flow were firstly investigated; five‐point scale and entropy methods were employed to transfer the qualitative factors into quantitative ones and rank these factors, respectively. Then, SVM and MKL‐based prediction models were developed, with the influential factors and the traffic flow as the input and output variables. The prediction capability of MKL was compared with SVM through a case study. It is proved that both the SVM and MKL perform well in prediction with regard to the accuracy rate and efficiency, and MKL is more preferable with a higher accuracy rate when under proper parameters setting. Therefore, MKL can enhance the decision‐making of traffic flow prediction. Copyright © 2012 John Wiley & Sons, Ltd.     

班车通勤次数选择行为研究

通过对私家车通勤出行者RP和SP调查,分析了性别、年龄、驾龄、燃油价格等因素对班车通勤次数选择行为的影响,建立了基于Ordinal Logit（OL）模型的私家车转向班车通勤出行的频率选择模型。模型系数表明,驾龄小于3a的私家车出行者,在燃油价格上涨时,更倾向于班车通勤出行;驾龄大于3a的私家车通勤出行者,随着驾龄的增加,受燃油价格的影响越小,转向班车出行的频率越低。     

基于动力定型的驾驶人应激训练及实验验证

由于我国交通系统的复杂性,驾驶人在行驶中无法完全避免紧急情况的发生,这种状态诱发的高度应激状态对驾驶人行车安全有着极其重要影响.从认知科学、生理学和心理学的角度研究驾驶人应激训练理论,提出系统性的＂动力定型＂的驾驶人应激训练理论.采用VC＋＋与OpenGL等技术设计并实现了驾驶人应激训练系统,选择了30名被试者进行仿真实验,并通过生物反馈技术采集心率指标作为实验参数.实验数据分析结果表明,随着训练次数的增加,驾驶人在经过多次应激训练后＂动力定型＂基本形成,再次经历该场景时紧张程度下降,并且在紧急情况下驾驶人能够安全避险.     

小汽车出行群体向公交出行转移的可行性研究

近几年一些大中型城市机动车保有量迅速增加,城市交通拥堵问题越来越严重,小汽车出行过多地占用了城市道路资源,促使部分小汽车出行者转向公交出行,能够提高道路资源利用率,缓解城市交通拥堵问题。选取小汽车出行群体为研究对象,经过预调查得出了影响小汽车出行和公交出行的不同因素,利用正交设计方法,设计了调查场景并进行了SP／RP调查。根据调查数据建立了MNL模型及修正SP＆RP模型,通过对模型的敏感性分析得出,在一定范围内降低公交票价和缩短公交出行时间能够提高公交吸引力,吸引部分小汽车出行者转向公交出行。     

城镇道路交通拥堵问题及改善措施

机动车数量的快速增长,城镇人口密度的不断增加,导致城镇交通发展问题日益凸显,如果不能得到有效解决和根本治理,必将对我国经济的持续、快速、健康发展构成严重威胁。道路交通的基本要素是人、车、路。通过交通三要素特性分析,找出交通拥堵问题的具体原因,并从政策方面、驾驶员行为方面和道路规划建设方面提出了相应的措施建议,为交通拥堵现象的缓解提供参考。     

类BP神经网络的公交一卡通跨区消费推算模型

结合BP神经网络的建模方法,首次提出跨区消费分布矩阵的推算模型,并根据实际应用情况来设置误差阀值和学习率等关键参数,通过训练实际一卡通消费数据,得到跨区消费分布矩阵,实际输出数据误差在允许的范围内.实验表明该模型适用于跨区消费分布情况的预测,可为省域公交一卡通提供重要的决策支撑数据.     

两种典型公交站点候车时间模型对比研究

乘客候车时间可直接反映公交运输系统的运营状态和服务水平.将常规公交站点分类为是否与轨道交通有衔接,对27路、111路白石桥东站,以及运通105路和87路城铁西直门站进行实地调研,获取各线路晚高峰的乘客候车时间数据,对数据进行拟合与分析.结果表明,客流到站时间分别服从对数正态分布和伽马分布.基于客流到站时间分布函数,反推出乘客候车时间模型,同时对模型进行对比分析,并得出相应结论.