基于数据驱动的物流电动汽车充电行为分析

为研究城市配送中物流电动汽车用户的充电行为规律,本文采集70辆物流电动车2014年一年的充放电数据,并采用数据挖掘相关分析方法,建立考虑物流电动汽车的充电电量状态及充电时刻的充电行为模型.研究结果表明:用户一般在SOC为30%~50%时为车辆进行充电,车辆开始充电时的剩余电量服从μ=0.48、σ=0.22的正态分布;车辆开始充电时刻主要集中在14:00-16:00之间.通过数据实验证明本文所建立的充电行为模型具有较高的精确性,同时具有较好的实用性,为车辆的充电调度和用户的出行安排提供科学的决策支持.     

考虑行驶速度波动的进口单放绿波协调控制模型

考虑车队实际行驶速度在一定范围随机波动的特性,分析了满足车队队首车辆高速与队尾车辆低速行驶时均不受阻的约束条件,给出了相应的速度波动百分比计算公式,并以双向绿波带宽之和最大为一级目标,以速度波动百分比之和最大为二级目标,建立了一种绿波协调控制目标规划模型,并设计了序贯式算法及遗传算法求解该模型.算例分析结果表明,本文提出的模型能够较好地考虑行驶速度波动的特性,能够直接生成适于行驶速度波动的协调控制方案,能够使更多的车辆处在绿波带宽之内.     

基于贝叶斯网的城市迁居者通勤方式变迁模型

随着中国城市空间布局的扩展与郊区化现象的加剧,城市内部居住迁移导致居民出行交通结构发生演变.国内关于迁居—出行行为的研究主要集中于对迁居人群机动化通勤趋势及变化成因的宏观描述上,从微观角度分析城市内部迁居人群通勤方式转变的研究尚为少见.本文以南京为例,构建基于贝叶斯网络的迁居人群通勤方式转移模型,模拟并分析个人家庭信息、迁居属性,以及小区建成环境感知变化对原非机动(步行、自行车或电动车)通勤人群出行方式转移的影响.结果表明:家庭汽车、个人收入、新车的购置、住房类型、购房方式、迁居类型、地铁通勤便捷度和地铁步站距离是影响原非机动通勤人群转向小汽车出行的主要因素.研究结果可为城市规划者制定迁居通勤机动化的建成环境调控政策提供一定科学依据.     

中国大城市交通问题的空间解读与对策

城市交通大多从城市人口平均特征出发进行研究。然而在市场的作用下,受收入、职业等因素的影响,不同社会阶层在城市空间中的居住和就业选址(城市的社会空间)存在差异。这是改善公共交通服务、缓解交通拥堵等很多大城市交通问题的根源之一。从中国快速城镇化过程中大城市新增人口和中低收入人口的居住空间分布入手,分析大城市通勤出行距离快速增长导致的交通拥堵、公共交通服务水平提高困难等大城市交通问题的成因。提出大城市空间应基于分区模式构建多中心的城市空间,并提出对应的交通系统,实现大城市交通低碳和公平的目标。     

随机扰动项呈离散分布的库存路径和定价问题的模型与算法

针对产品需求价格函数随机扰动项呈离散分布的库存路径和定价问题(Inventory Routing and Pricing Problem,IRPP),利用随机扰动项的离散分布率和零售商库存服务水平要求,以供货商期望收益最大化为目标,构建IRPP优化模型,将禁忌搜索算法嵌入改进的粒子群算法中求解模型.3组不同规模的算例分...     

观测路径出行时间下随机网络交通需求估计

道路网络起讫点(OD)需求是城市决策长期交通规划和短期交通管理中的基础参数,准确的交通需求更是实施交通拥堵控制、限行限速、路径诱导等措施的先决条件.综合运用观测的轨迹已知和未知路径出行时间,建立随机网络交通需求估计双层规划模型.上层广义最小二乘模型最小化历史交通需求与待估交通需求、观测路径出行时间与待估路径出行时间之间...     

Joint leisure travel optimization with user-generated data via perceived utility maximization

The lack of personalized solutions for managing the demand of joint leisure trips in cities in real time hinders the optimization of transportation system operations. Joint leisure activities can account for up to 60% of trips in cities and unlike fixed trips (i.e., trips to work where the arrival time and the trip destination are predefined), leisure activities offer more optimization flexibility since the activity destination and the arrival times of individuals can vary.To address this problem, a perceived utility model derived from non-traditional data such as smartphones/social media for representing users’ willingness to travel a certain distance for participating in leisure activities at different times of day is presented. Then, a stochastic annealing search method for addressing the exponential complexity optimization problem is introduced. The stochastic annealing method suggests the preferred location of a joint leisure activity and the arrival times of individuals based on the users’ preferences derived from the perceived utility model. Test-case implementations of the approach used 14-month social media data from London and showcased an increase of up to 3 times at individuals’ satisfaction while the computational complexity is reduced to almost linear time serving the real-time implementation requirements.     

Exploring the capacity of social media data for modelling travel behaviour: Opportunities and challenges

In the past few years, the social science literature has shown significance attention to extracting information from social media to track and analyse human movements. In this paper the transportation aspect of social media is investigated and reviewed. A detailed discussion is provided about how social media data from different sources can be used to indirectly and with minimal cost extract travel attributes such as trip purpose, mode of transport, activity duration and destination choice, as well as land use variables such as home, job and school location and socio-demographic attributes including gender, age and income. The evolution of the field of transport and travel behaviour around applications of social media over the last few years is studied. Further, this paper presents results of a qualitative survey from travel demand modelling experts around the world on applicability of social media data for modelling daily travel behaviour. The result of the survey reveals positive view of the experts about usefulness of such data sources.     

Development and validation of energy demand uncertainty model for electric city buses

The prediction of electric city bus energy demand is crucial in order to estimate operating costs and to size components such as the battery and charging systems. Unfortunately, there are unpredictable dynamic factors that can cause variation in the energy demand, particularly concerning driver choices and traffic levels. The impact of these factors on energy demand has been difficult to study since fast computing sufficiently accurate dynamic simulation models have been missing, properly quantified in terms of relevant inputs which contribute to energy demand. The objective is to develop and validate a novel electric city bus model for computing the energy demand, to study the nature and impact of various input factors. The developed equation-based model predicted real-world electric city bus energy consumption within 0.1% error. The most crucial unmeasurable input factors were the driven bus route, the number of stops, the elevation profile, the traffic level and the driving style. This understanding can be used to specify routes and stops for a given electric bus battery capacity. Worst-case scenarios are also necessary for electric bus sizing analysis. The best- and worst-case levels of the crucial factors were identified and with them synthetic best- and worst-case speed profiles were generated to demonstrate their effect to the energy demand. While the measured nominal consumption was 0.70 kWh/km, the computed range of variation was between 0.19 kWh/km and 1.34 kWh/km. For design sizing purposes, an electric city bus can have a broad range of possible energy consumption rates due to mission condition variations.     

Walking,cycling and the urban form: A Heckman selection model of active travel mode and distance by young adolescents

Physical inactivity of children and adolescents is a major public health challenge of the modern era but, when adequately promoted and nurtured, active travel offers immediate health benefits and forms future sustainable and healthy travel habits. This study explores jointly the choice and the extent of active travel of young adolescents while considering walking and cycling as distinct travel forms, controlling for objective urban form measures, and taking both a “street-buffer” looking at the immediate home surroundings and a “transport-zone” looking at wider neighborhoods. A Heckman selection model represents the distance covered while cycling (walking) given the mode choice being bicycle (walk) for a representative sample of 10–15 year-olds from the Capital Region of Denmark extracted from the Danish national travel survey. Results illustrate the necessity of different urban environments for walking and cycling, as the former relates to “street-buffer” urban form measures and the latter also to “transport-zone ” ones. Results also show that lessening the amount and the density of car traffic, diminishing the movement of heavy vehicles in local streets, reducing the conflict points with the density of intersections, and intervening on crash frequency and severity, would increase the probability and the amount of active travel by young adolescents. Last, results indicate that zones in rural areas and at a higher percentage of immigrants are likely to have lower probability and amount of active travel by young adolescents.