居民出行调查中的抽样技术研究

居民出行调查的数据是城市交通规划的基础,它的准确度直接影响着规划的质量。恰当的调查方法和抽样率对保证数据精度和节省费用至关重要。目前,我国绝大多数城市在进行居民出行调查时,根据国外经验来确定抽样率,理论依据不充分,无法估计调查数据的精度和预测数据的准确性。运用抽样技术理论,首先对居民出行调查中用的抽样方法和抽样率的计算做理论上的阐述,然后对常用的抽样率计算公式进行推导,并讨论其他相关问题,最后给出计算实例。     

Electric vehicles: How much range is required for a day’s driving?

One full year of high-resolution driving data from 484 instrumented gasoline vehicles in the US is used to analyze daily driving patterns, and from those infer the range requirements of electric vehicles (EVs). We conservatively assume that EV drivers would not change their current gasoline-fueled driving patterns and that they would charge only once daily, typically at home overnight. Next, the market is segmented into those drivers for whom a limited-range vehicle would meet every day’s range need, and those who could meet their daily range need only if they make adaptations on some days. Adaptations, for example, could mean they have to either recharge during the day, borrow a liquid-fueled vehicle, or save some errands for the subsequent day. From this analysis, with the stated assumptions, we infer the potential market share for limited-range vehicles. For example, we find that 9% of the vehicles in the sample never exceeded 100 miles in one day, and 21% never exceeded 150 miles in one day. These drivers presumably could substitute a limited-range vehicle, like electric vehicles now on the market, for their current gasoline vehicle without any adaptation in their driving at all. For drivers who are willing to make adaptations on 2 days a year, the same 100 mile range EV would meet the needs of 17% of drivers, and if they are willing to adapt every other month (six times a year), it would work for 32% of drivers. Thus, it appears that even modest electric vehicles with today’s limited battery range, if marketed correctly to segments with appropriate driving behavior, comprise a large enough market for substantial vehicle sales. An additional analysis examines driving versus parking by time of day. On the average weekday at 5 pm, only 15% of the vehicles in the sample are on the road; at no time during the year are fewer than 75% of vehicles parked. Also, because the return trip home is widely spread in time, even if all cars plug in and begin charging immediately when they arrive home and park, the increased demand on the electric system is less problematic than prior analyses have suggested.     

An empirical investigation on the dynamic processes of activity scheduling and trip chaining

Investigation of the dynamic processes of activity scheduling and trip chaining has been an interest of transportation researchers over the past decade because of its relevance to the effectiveness of congestion management and intelligent transportation systems. To empirically examine the processes, a computerized survey instrument is developed to collect household activity scheduling data. The instrument is unique in that it records the evolution of activity schedules from intentions to final outcomes for a weekly period. This paper summarizes the investigation on the dynamic processes of activity scheduling and trip chaining based on data collected from a pilot study of the instrument. With the data, ordered logit models are applied to identify factors that are pertinent to the scheduling horizon of activities. Results of the empirical analysis show that a daily schedule often starts with certain activities occupying a portion of the schedule and other activities are then arranged around these pre-occupants. Activities of shorter duration are more likely to be opportunistically inserted in a schedule already anchored by their longer duration counterparts. Persons with children often expect more constraining activities than those with no children. The analysis also shows that female respondents tend to be more structured in terms of how the week is planned. Additionally, analysis of travel patterns reveals that many trip-chains are formed opportunistically. Travel time required to reach an activity is positively related to the scheduling horizon for the activity, with more distant stops being planned earlier than closer locations.     

基于GIS的公交网络模型及其在公交线路查询中的应用

分析了公交网络的特点，研究了如何利用GIS技术建立基于道路网的公交网络模型，提出了基于最优化路径条件下的换乘搜索方法，并探讨了其在公交线路查询中的应用．该方法可用于计划出行路线和提供在线信息，有效综合了线路、费用、时间及相关的动态交通数据．这样的系统可建立在互联网GIS系统中，为出行者提供交互式的服务．     

基于BP神经网络技术的居民机动车出行分布预测研究

以各交通小区区位势、路阻、出行产生量、出行吸引量作为输出参数,输出得到出行分布量.结果表明该模型具有较高的预测精度.     

Transport for older people: Characteristics and solutions

Entering the 21st century, one of the most significant demographic changes in developed countries is the aging of the population. Travel is an important aspect of older people's economic well-being, so with the aging of the population, improving transport for older people is increasingly important. This article presents the results of a study of older people's travel behaviour based largely but not exclusively on LATS (London Area Travel Survey) 2001 data. The focus is on older people's trip chaining behaviour, including trip chain complexity, trip purpose sequence and mode choice in a chain. After identifying the policy implications it looks at the role of Special Transport Services in improving the supply of transport for older people, taking the London Borough of Camden as a case study.     

重力模型在TRANSCAD中的应用研究

论述了重力模型在TransCAD软件中的工作原理,针对云南省中小城市的交通特点,探讨了利用TransCAD重力模型进行重力模型参数标定和交通分布预测的整个流程。     

城市公共自行车交通出行特性分析

本文根据佛山市南海区公共自行车出行相关数据,从使用者的年龄、职业、收入以及公共自行车的出行目的、出行方式、出行时耗、出行距离等方面,对城市公共自行车交通出行特性进行了详细的分析,并为公共自行车系统规划和发展提出建议.     

西安市客运交通结构调查与分析

城市客运交通结构调查与研究是合理规划交通出行方式、提高城市交通效率和制定交通政策的重要环节。首先采用发放调查问卷的形式进行出行调查,通过数据处理与分析得出西安市出行方式结构比例,与2000年居民出行调查结果进行对比,分析了交通结构比例的变化和影响变化的因素。根据客运交通结构的实际,结合西安的城市建设、交通现状及道路条件,提出以TOD模式为主导,以轨道交通为骨干,积极发展自行车交通的建议,合理分布客运交通结构。尤其是通过设立公共自行车以及自行车与公共交通设施的衔接,充分发挥自行车交通的功能,带动整个城市交通的良性发展。