Modelling users’ behaviour in inter-urban carsharing program: A stated preference approach

In this paper, the effects of a inter-urban carsharing program on users’ mode choice behaviour were investigated and modelled through specification, calibration and validation of different modelling approaches founded on the behavioural paradigm of the random utility theory. To this end, switching models conditional on the usually chosen transport mode, unconditional switching models and holding models were investigated and compared. The aim was threefold: (i) to analyse the feasibility of a inter-urban carsharing program; (ii) to investigate the main determinants of the choice behaviour; (iii) to compare different approaches (switching vs. holding; conditional vs. unconditional); (iv) to investigate different modelling solutions within the random utility framework (homoscedastic, heteroscedastic and cross-correlated closed-form solutions). The set of models was calibrated on a stated preferences survey carried out on users commuting within the metropolitan area of Salerno, in particular with regard to the home-to-work trips from/to Salerno (the capital city of the Salerno province) to/from the three main municipalities belonging to the metropolitan area of Salerno. All of the involved municipalities significantly interact each other, the average trip length is about 30 km a day and all are served by public transport. The proposed carsharing program was a one-way service, working alongside public transport, with the possibility of sharing the same car among different users, with free parking slots and free access to the existent restricted traffic areas. Results indicated that the inter-urban carsharing service may be a substitute of the car transport mode, but also it could be a complementary alternative to the transit system in those time periods in which the service is not guaranteed or efficient. Estimation results highlighted that the conditional switching approach is the most effective one, whereas travel monetary cost, access time to carsharing parking slots, gender, age, trip frequency, car availability and the type of trip (home-based) were the most significant attributes. Elasticity results showed that access time to the parking slots predominantly influences choice probability for bus and carpool users; change in carsharing travel costs mainly affects carpool users; change in travel costs of the usually chosen transport mode mainly affects car and carpool users.     

Stability of transportation networks under adaptive routing policies

Growing concerns regarding urban congestion, and the recent explosion of mobile devices able to provide real-time information to traffic users have motivated increasing reliance on real-time route guidance for the online management of traffic networks. However, while the theory of traffic equilibria is very well-known, fewer results exist on the stability of such equilibria, especially in the context of adaptive routing policy. In this work, we consider the problem of characterizing the stability properties of traffic equilibria in the context of online adaptive route choice induced by GPS-based decision making. We first extend the recent framework of “Markovian Traffic Equilibria” (MTE), in which users update their route choice at each intersection of the road network based on traffic conditions, to the case of non-equilibrium conditions, while preserving consistency with known existence and uniqueness results on MTE. We then exhibit sufficient conditions on the network topology and the latency functions for those MTEs to be stable in the sense of Lyapunov for a single destination problem. For various more restricted classes of network topologies motivated by the observed properties of travel patterns in the Singapore network, under certain assumptions we prove local exponential stability of the MTE, and derive analytical results on the sensitivity of the characteristic time of convergence to network and traffic parameters. The results proposed in this work are illustrated and validated on synthetic toy problems as well as on the Singapore road network with real demand and traffic data.     

冰雪条件下中国驾驶员跟驰行为及模型研究

分析驾驶员在冰雪条件下的驾驶行为特性，建立考虑驾驶员行为特性的跟驰模型，有助于丰富现有交通流理论.通过招募驾驶员开展实车跟驰试验，对比分析正常条件与冰雪条件下的驾驶行为差异.进而基于任务难度均衡理论构建包含人类因素参数的任务难度模块，引入改进后的智能驾驶员模型，并采用车辆轨迹数据对模型进行标定和有效性验证.研究表明：驾驶员在跟驰行驶过程中受外界刺激及自身驾驶能力影响时会对车辆行驶状态进行动态调整，试图保持期望间距，且速度与前车一致的状态；冰雪条件下驾驶员采取风险补偿行为，其车头时距波动幅度较正常条件收窄，模型引入人类因素参数可以较好地描述其差异性. 模型有效性验证表明，新模型在6个仿真场景中的表现都优于传统智能驾驶员模型，且表现出更好的鲁棒性.研究结果可为冰雪条件下的交通管理措施制定提供理论支持.     

建成环境对城市停车需求影响的非线性模型

为精细化把握城市建设项目在微观空间尺度下的停车需求规律，从空间视角探究停车需 求与建成环境之间的关系。通过高峰小时建筑物单位面积的停车生成数表征停车需求，以土地 利用混合度、路网密度、公交服务水平等9个因子描述建成环境，分别构建建成环境对停车需求影 响的普通最小二乘(Ordinary Least Squares，OLS)模型与梯度提升迭代决策树(Gradient Boosting Decision Tree，GBDT)模型。以保定市主城区停车调查数据中的商业类配建停车场为对象，基于 停车调查数据、兴趣点数据(Point of Interst，POI)、道路网络数据等多源异构数据进行模型实证分 析。结果表明，考虑非线性效应的GBDT模型比OLS模型具有更好的拟合度。从影响贡献度来 看，配建指标(18.92%)与区位(15.23%)是影响停车需求的最重要建成环境因素，交叉口密度 (5.19%)贡献度最小；在非线性关系方面，建成环境因子与停车需求均具有非线性关系与阈值效 应，除交叉口密度及人口密度与停车需求呈现U型关系，其余因素与停车需求的关系整体上保持 正相关或负相关。     

驾驶人“感知-决策-操控”行为模型

为准确模拟驾驶人跟车行为，提出基于隐马尔可夫模型(Hidden Markov Model，HMM)的驾驶人“感知-决策-操控”行为模型。建立描述驾驶意愿的HMM模型，模拟驾驶人感知过程，获得期望的车间距；预测模块模拟驾驶人根据交通环境和自身生理、心理状态预测车辆未来轨迹，即决策过程；优化模块描述驾驶人为使预测的车辆轨迹跟踪上期望的车辆间距而采取的操控汽车的执行动作，即操控过程。上述3个模块的滚动过程实现了对驾驶人跟车行为的模拟。利用自然驾驶数据进行算例分析，结果表明，本文模型预测车间距平均误差仅为1.47%，证明了所建模型的有效性及准确性。本文为驾驶行为建模方法的理论研究和应用拓宽了思路。     

盾构隧道管片接头缩尺试验模拟方法

由于盾构隧道管片接头刚度影响因素繁多复杂，使得缩尺模型管片接头制作困难、模拟效果较差。为此，提出一种采用特制弹簧与预埋螺栓共同工作的管片接头模拟方法来模拟原型管片接头，并分别对该方法设计的模型管片接头以及原型管片螺栓连接接头的轴向、剪切及弯曲刚度进行计算分析。通过对比分析可以得出设计管片模型接头与原型管片螺栓连接接头的等效条件，在满足上述等效条件的情况下，经相似关系换算可认为缩尺模型管片接头刚度与原型管片接头刚度满足试验设定的相似关系。最后，采用堆载试验对模型管片进行可行性验证。经理论分析及模型试验表明： 提出的盾构隧道管片接头缩尺试验模拟方法可使模型接头刚度等效于原型管片接头，能较为全面地满足模拟原型管片的要求。     

数据驱动跟驰模型综述

车辆跟驰模型是被交通科学与交通工程领域广泛认可的微观交通流模型，是交通流理论 的基础。近年来，信息感知与获取、大数据、人工智能等技术快速发展，推动了数据驱动跟驰模型 的快速发展。数据驱动跟驰模型，是以真实的车辆行驶数据为基础，利用数据科学与机器学习等 理论和方法，通过样本数据的训练、学习、迭代、进化，挖掘车辆跟驰行为的内在规律。本文系统 回顾了数据驱动跟驰模型在过去20余年的发展历程以及由神经网络和深度学习带动的两次研究 热潮，归纳了基于传统机器学习理论的跟驰模型、基于深度学习的跟驰模型、模型与数据混合驱 动的跟驰模型3类数据驱动跟驰模型，并分别介绍了其中的典型代表。分析数据源发现，尽管各 种高精度轨迹数据不断涌现，目前研究仍多使用美国于2006年发布的Next Generation Simulation (NGSIM)高精度车辆轨迹数据，模型的可移植性和泛化能力值得思考与研究。提出关于模型输 入、输出的3个问题：如何考虑更多驾驶行为变量，是否有必要考虑更多行为变量，现有输入、输出 是否可替换。在模型测试与验证方面，发现并讨论了目前测试不充分、对比不完整、缺少统一测 试集与测试标准等问题。最后，探讨了数据驱动跟驰模型原创性与成功的关键因素等问题。期 望通过本文的梳理，帮助研究者更好地了解数据驱动跟驰模型的过去与现状，促进相关研究的快 速发展。     

基于滚动时域优化的共享自动驾驶汽车动态调度方法

共享自动驾驶汽车被视为未来城市交通系统的重要组成部分。本文考虑随机订单需求研究共享自动驾驶汽车的动态调度优化方法。通过建立车辆调度时空网络，分别针对订单分配与空车移位生成车辆运行时间弧，提出车辆调度问题的刻画方法。基于马尔科夫决策框架，以时空节点流量为状态，以时空弧流量为决策变量，建立最大化系统净收益的车辆动态调度优化模型。 采取滚动时域优化思想，建立含前视时间窗的随机规划模型，并利用CPLEX优化引擎，滚动求解车辆动态调度决策结果。Sioux Falls网络算例结果表明，滚动时域优化方法可保证车辆动态调度决策效果，提升系统运营效率。在计算时间限制下，滚动时域方法应优先采用长时间窗中等规模 样本。在最大化系统净收益的同时进一步最小化乘客等待时间，可有效提升车辆动态调度决策效果。     

基于SCP理论的我国港口物流绩效定量分析

运用SCP产业组织理论的研究方法,分析我国港口物流产业绩效的整体现状,同时试图构造一个"结构-行为-绩效"的分析框架,为产业现状的科学评价提供服务.研究结果表明:我国港口物流产业目前正处于一种一定程度垄断性质的竞争市场结构;市场行为体现出局部垄断和竞争合作的两重性,其出现的适应性反应,影响了市场结构和市场绩效;另外,市场绩效也造成了产业结构和市场行为的变化.通过技术统计及理论分析,认为我国港口物流产业正处于快速成长时期,其经营绩效显著优于其他物流行业,呈现出较好的发展势头.     

Bi-phasic Simulation of Metal Injection Moulding： Constitutive Determinations

IntroductionMetalinjectionmolding (MIM )isexpectedtobeveryefficienttoproducesmalland 3Dcomplicatedpartsinlargeseries .Thistechnologycombinesthewell knownpolymerinjectionmouldingandpowdermetallurgytechnology .AnMIM processincludesfourmainstages[1,2 ] :them…