毕业论文外文文献翻译模板1

广东工业大学华立学院本科毕业设计论文外文参考文献译文及原文 系 部 建设学部 专 业 土地资源管理 年 级 2021级 班级名称 08土地资源管理1班 学 号 学生姓名 指导教师 陈 静 2012 年 5 月 目录、外文文献译文1、外文文献原文12微观仿真在城市停车设施规划中的应用作者简介：Pete Sykes(1956 )，男，英国爱丁堡人，工商管理硕士MBA，微观仿真市场和开发部主任，主要研究方向：S-Paramics 微观仿真软件开发。摘要为研究停车场规划和可达性对城市路网的影响，在世界范围内的三个不同城市应用微观仿真模型模拟停车设施规划。描述了三个城市采用的不同研究方法及得到的研究结果。三项研究都试图找到由驾驶人寻找停车位而造成的城市交通拥堵的解决方法。同时，在测试设计方案时，都使用了S-Paramics微观仿真模型。关键词：交通模型；微观仿真；矩阵细化；停车场；规划对市区驾驶人来说，停车设施供给是最具争议的问题之一。停车位难找、停车费用昂贵是主要问题。距离最近、最方便的停车场往往有很多车辆排队等候，驾驶人必须转而找寻其他停车场。城市中心区的一些商家认为，缺乏适宜的停车设施已成为顾客选择市外购物中心的重要原因。2002 年，在美国纽黑文(New Haven)市最大的停车场改造庆典上，该市市长Rowland 先生说：“如果没有停车场，什么措施都不会有效1。驾驶人在道路上循环驾驶寻找停车位，可能是城市中心区拥堵的主要原因。2006 年， 曼哈顿(Manhattan)的调查显示，道路上行驶的车辆中，正在寻找停车位的小汽车比例达26%， 在布鲁克林区(Brooklyn，位于纽约市西南部)那么高达46%。这种状况并不是近期才出现。1927 年，底特律(Detroit)市有两个区做了类似调查，相关数据分别为19% 和34%2。这一由来已久的问题现在或许可以借助技术手段解决：移动网络 (iPhone)用户可以彼此告知停车位信息3；交通规划人员那么可以利用交通模型领域的最新研究成果微观仿真来辅助停车场的规划设计。城市规划政策中的停车场选址主要侧重于优化停车场、驾驶人及其与目的地间的关系。优化停车场选址获得的潜在利益之一是通过改善目标消费群体的可达性，实现对城市商业中心的改善。制定的政策既包含以供给为主导对停车位进行积极管理，也包含以需求为主导简单增加停车位数量4。积极的管理政策正在被广泛采用，以限制停车位数量、鼓励可持续的交通方式5。城市规划政策考虑停车场的收费机制，交通规划政策那么对此加以补充，着重关注停车场的可达性以及停车场与路网、交通拥堵的关系。收费机制可用来降低停车设施对当地造成的不便，并且在分配停车位时区别对待不同停车类型的驾驶人。?英国道路设计指南?对停车场的可达性做了说明。英国交通部关于停车诱导信息系统的建议中包含的案例报告说明，通过安装可变信息标志(VMS)显示停车位状态可获得量化效益6。量化效益可用时间节省表示，非量化效益那么表现为公共形象和驾驶人的平安性。英国交通部?交通分析指南?(WebTAG)7简要地提及了这一问题，即在讨论出行本钱时应包含停车本钱(理论上包括寻找和排队等待停车位以及步行到达最终目的地的时间)。停车场的可达性通常在城市设计完成以及停车政策确定后考虑。目前缺乏的环节是，在城市早期规划阶段交通规划政策对停车场设施及其可达性的影响研究。最近，在世界范围内的三个不同城市对这一明显缺乏进行了探讨，目的是寻求停车场规划政策和可达性对路网影响的研究方法，探讨内容的共同之处集中在两方面：1)试图找到降低因驾驶人寻找停车位造成的城市交通拥堵的解决方法；2)使用S-Paramics 微观仿真模型测试设计方案。荷兰新维根市(Nieuwegein) 所做的一项研究模拟了交通量大幅增长时，在城市中心区一个重要的重建工程增加停车设施后其周边交通情况；为使驾驶人得悉停车场停车情况和到达路线，该研究在微观仿真模型中引入了智能交通系统(ITS)。另一项在英格兰罗克代尔市(Rockdale)的研究(见图1)仿真了停车位分布与城市中心区开展规划的关系，其目标是优化停车设施与其周边土地利用的关系；在设计过程的早期阶段，通过改变停车场车位的供给情况来控制城市中心区的交通拥堵状况。第三项是在新西兰北岸市(North ShoreCity)的塔卡普纳区(Takapuna)进行的城市中心区扩张对城市交通的影响研究；该研究使用专门的软件仿真停车场需求，并使用微观交通仿真模型将其需求分配到路网中；其目的同样是了解停车政策效果，缓解城市中心区交通拥堵状况。1 停车场交通状况微观仿真典型的微观仿真设计方案包括：改变道路布局、公交优先措施、信号优化、交通需求变化等，仿真中任一车辆在驶向目的地时，都会对采取的设计方案及其造成的拥堵做出反响。对停车政策效果的仿真测试重点从路网变化带来的影响，转到车辆出行目的地变化对交通造成的影响。因此，仿真模型必须具有区别驾驶人目的地和车辆停车位置的能力，并能在停车场间进行动态选择。1.1 车辆到达停车场在微观仿真模型中是一个整体，与目的地小区相连，且一个停车场可效劳多个小区。通过限定使用停车场的车辆出行目的，对停车位进行分配。由于每一类型车辆可能会有不同的本钱系数， 因此， 模型中以一般化出行本钱(the generalised trip cost)来表示停车费用以及停车场与相关小区间的距离。建模人员可据此区分接受较长步行距离和较高费用的驾驶人。如果某停车场已满，模型中的驾驶人在入口处等待一定时间后，会重新进行停车场选择评估，并可能驶向其他停车场。通过外部软件控制器可以监控模型中停车场的占有率，并能在车辆到达停车场等待队列之前改变其行驶方向。下面用一个例子说明在停车政策模型中如何应用这种方法。假设某零售业和商业混合的城市中心区有多个停车场在合理的步行距离内，根据停车时间和停车费用结构，驾驶人会优先考虑某一停车场，且某些驾驶人可能有停车许可证。一个停车场可能有多个相邻的入口，为让车辆支付适当的停车费用，每个入口都有相应的限制条件。仿真结果显示，短暂停留的车辆，驾驶人会使用距离目的地较近且停车费用较低的停车场。长时间停留的车辆，驾驶人会使用费用较高的入口进入停车场或能接受较长的步行时间。通过调整不同停车场的入口费用或停车许可证的许可水平可以测试驾驶人对停车条件变化的反响。通过调整使用某特定区域相关停车场的驾驶人和车辆类型的比例，可以仿真停车场对土地利用变化的影响。1.2 车辆离开在S-Paramics 微观仿真模型中，路网的车辆分配是通过详细的(5 min)时间释放分布图控制的。应用于停车场规划时，最简单的情形是依据最低出行本钱(包括最短出行时间)或停车场面积确定车辆始发停车场。对于更复杂的情形，如为使同一停车场到达和驶离车辆相匹配，可通过与模型相连的外接控制器来控制车辆出发，并与停车场占有率监测系统相连，利用特定算法确定车辆的离开时间和地点，从而到达到达和驶离车辆相匹配的目标。2 仿真对策2.1 数据收集新维根市交通需求矩阵来自于已有宏观模型，并使用调查数据进行调整。同时，通过进一步调查确定主要停车场的使用情况，如车辆平均停放时间和商店停止营业后停车场存留的车辆。罗克代尔市和塔卡普纳区的要求较为复杂，有必要收集更为全面的数据。罗克代尔市的交通需求矩阵数据主要来自路边访问，显示了出行的真实目的。由访问数据可确定停车种类(如长时间/短暂停留、路边/专用停车场停车、是否有停车许可证等)和可能的停车时间(基于出行目的)。为了将停车场和目的地小区联系起来，需利用市中心所有停车场的数据清单及各停车场的占有率建立停车场位置模型。首先采用简易几何方法确定每一停车场到每个目的地小区的步行时间，并以此作为模型校准参数。停车场数据清单对准确估计不同类型停车场(如长时间/短暂停留、路边/专用、私人/公共、收费/免费停车场等)的容量十分必要。数据清单包含市中心和周边的所有停车场，同时还包括邻近市中心的居民停车区。由于不收取停车费用，尽管居民停车区距目的地有较长的步行距离，通勤车辆仍常常使用。建模时，对接送车辆下客率较高的区域以私人停车场类型对其进行模拟。假设需要对模型做进一步改良，可通过在需求矩阵中同时包含接送车辆的往返方向实现。停车场数据清单也包括每一停车场的收费信息。结合各停车场实际调查数据，发现停车场收费情况可简单归为短暂停留和长时间停留两种。这种简化较适用于罗克代尔市，但假设不同停车场的收费差异十分显著，那么须区别对待。仿真时，罗克代尔市的停车场数据较适用于顶峰时段，假设对停车场进出车辆进行全天详细调查，那么会对模型更加有益。在塔卡普纳区和新维根市，收集的停车场数据包括车辆到达时间、停留时间和停车场占有率。这些数据与收费信息一起用于仿真驾驶人对停车场的选择情况。2.2 需求矩阵细化停车需求矩阵细化使建模人员能够控制不同类型车辆驶离停车场的时间。细化程度取决于已有调查数据，同时，停车场分类的详细程度应与模型输入数据相适应。在罗克代尔市，小汽车分为通勤车辆、非通勤车辆和公务车辆，停车需求矩阵可由此导出。根据调查数据，可把通勤车辆、公务车辆进一步分为非居民私人停车(Private Non Residential, PNR)和合同停车contract parking)。由于需求矩阵已被明确定义，对于难以估计的PNR 车位供给量，模型不予限定，并假设市中心周边地区所有驾驶人均在其目的地停车。塔卡普纳区采用了类似方法，停车需求细分为长时间和短暂停车两类。依据工作场所是否有停车位，将长时间停车进一步分为工作场所就地停车(on site)或公共停车场停车两类。长时间和短暂停车的需求量可由宏观交通模型的出行目的矩阵导出，并通过停车场记录的车辆数进行调整。2.3 出行链接为了仿真同一停车场的车辆到达和离开，必须把进入和驶离城市中心区的出行联系起来，并基于车辆先前到达的停车场及停留时间选择其始发停车场和驶离时间。传统的OD矩阵法不能满足此要求，需要使用更复杂的控制方法来确定车辆的驶离时间和地点。新维根市的模型研究时段为某周六下午的购物顶峰时段，并包含了一个前期准备阶段，以便为模型中的停车场预停一些车辆，同时对ITS 控制器进行初始化。为了仿真相链接的车辆出行，需要从OD 矩阵中删除所有从市中心始发的车辆。利用外部软件控制器监测模型中的停车场占有率，以确定车辆到达时间的分布情况，在停留一段适宜的时间(如1 h)后，让与到达车辆相匹配的返程车辆出发。塔卡普纳区模型基于早顶峰的停车场占有率，在仿真停车场晚顶峰运行情况之前，利用一个独立的需求模型生成停车场晚顶峰车辆出发时间分布图。塔卡普纳区每个分区包含有车辆出发时间分布情况的出行需求量，OD 矩阵即基于此生成。在车辆选择某一特定停车场出发时，需求模型中的车辆出发分区与停车车辆所在分区相匹配。匹配关系基于停留时间长短，并按照车辆到达时间分布预计出发时间。假设此过程中发现某车辆的出发时间与预计出发时间的误差在20%以内，那么该车辆将被添加到出发时段分布图中。假设没有找到匹配车辆，那么转到下一出发时段分布图，并重复搜索过程。罗克代尔市模型对车辆到达和驶离停车场的仿真也划分为早晚顶峰两个时段。模型对晚顶峰时段出发停车场的选择使用了一般化出行本钱和“出口本钱(exit cost)，以帮助区分停车场选择。通过与观测数据进行比照，发现使用“出口本钱可以更加成功地校准模型。 2.4 停车场的寻找上述三个模型的研究对象均为停车场对城市交通拥堵的影响，因此，研究成功的关键是模型把车辆分配到停车场的策略以及车辆寻找停车场的过程。新维根市的研究工程是为测试停车诱导系统的效果而设计。停车场可变信息标志指示牌(见图2)分布于车辆进入城市中心区的所有入口处(见图3)，给进城车辆提供停车场相关信息，以便车辆做出选择。根据城市停车管理系统(the TownParking Manager)的经验记录，ITS 系统的设置是仅20%的驾驶人遵循停车诱导系统的建议，其余80%的驾驶人将驶向自己的首选停车场，假设该停车场已满，再改变方向驶向其他停车场。罗克代尔市和塔卡普纳区的模型更加注重停车场的可达性，其次才是诱导功能。在罗克代尔市，停车场的位置选择根据政策确定，即规划分区时对停车场的位置予以限制，如：合同停车区域(contract parking areas)设于与工作有关而不是与购物有关的分区。在塔卡普纳区，停车限制政策较少，停车场可以与所有分区相连，因而减少了预先设定停车空间的情况。车辆到达某一停车场后会排队等候，等待一定时间后，基于出行本钱、步行本钱以及停车本钱会驶向其他停车场。塔卡普纳区模型通过外接软件控制器管理停车场车位，同时对车辆寻找停车场的条件进行限定(寻找界限)，从而对车辆选择停车场的过程进行补充。寻找界限反映了驾驶人在寻找停车位的过程中，在数量众多的停车场间循环行驶的意愿，即设定驾驶人尝试找寻停车位的次数，之后，便放弃尝试而驶向最有可能有空闲车位的停车场。车位控制器可监测停车场的使用情况，任何停车场停满车辆10 min 后会从控制器名单中去除。一旦有车辆从该停车场离开，那么会重新回到名单中供待停车辆选择。3 首选停车场问题首选停车场问题出现在罗克代尔市和塔卡普纳区模型中。当车辆的首选停车场容量较小且很快就停满时会出现首选停车场问题。大多数驾驶人基于出行本钱最小选择该停车场，但不得不改变路线驶向次选停车场。现实生活中，即使知道该停车场通常停满车辆，很多驾驶人依然重复同样的出行，因此不得不选择一个有更多时机找到空闲车位的较大停车场作为首选。针对这一问题，塔卡普纳区提出了两种解决方法：1)使用停车场车位使用情况控制器覆盖驾驶人的选择，为驾驶人提供替代停车场。2)对较小的停车场(车位数一般小于50 个)进行分组，同时调整停车场对出行目的地的覆盖范围和步行时间，以防止过多的驾驶人把某一特定停车场作为首选。罗克代尔市模型那么通过细化需求矩阵来分配停车场，提供了另一种解决方法。按照驾驶人对出行目的地周边停车场的熟悉度对矩阵做进一步细化。假设驾驶人得悉较小的停车场已停满，便会防止使用它们，而把较大的停车场作为首选。4 仿真结果4.1 新维根市新维根市根底模型基于周六下午的典型状况进行校准，比拟了未来年的两个仿真，其测试方案包括城市中心区新规划的开发区。没有停车诱导系统时，大量车辆在最有吸引力的停车场前排队。有停车诱导系统的情况下，待停车辆那么按照停车场容量更加均衡地分布，且大多数停车场会有空闲车位，从而减少寻找停车位的车辆，城市中心区道路上的车辆数也相应减少。这一效果是在20%的驾驶人遵循停车诱导系统的情况下实现的。下一步进行研究时，将扩大ITS 系统的覆盖范围，并把某些路段设置为步行区。新维根市的仿真方案是基于2021 年的情况进行的，城市中心区的详细规划可能会与仿真情况有所差异，所以，在评估设计方案的预期效果时进行了保守解释。尽管如此，仿真结果依然说明在新维根市投资建设停车诱导系统的方案是合理的，可以充分利用停车场的容量。4.2 罗克代尔市罗克代尔市模型针对方案于2021 年实施的城市中心区改造的一局部进行测试。主要包括：搬迁位于中心区的公共汽车站和政府办公地点，撤除中心区的多层停车场，重新设计穿越中心区的A58 干路的主要交叉口。下一步研究将使用与塔卡普纳区和新维根市模型类似的外接软件控制器，同时在路线选择点安装停车诱导设施，并考虑停车场车位的时机本钱(opportunity cost)。可以在设置首选停车场之前将时机本钱添加到每一停车场的一般化本钱中，尽管需要通过数据调查对此进行校准，但在停车场选择中考虑时机本钱，可以将每一停车场可能的空闲车位完全纳入选择。4.3 塔卡普纳区塔卡普纳区S-Paramics 模型是塔卡普纳中心区所有重要规划申请和地区性规划变更的交通运行评估工具，以保证所有评估在同一仿真体系下完成。该方法已成功应用于北岸市的另外两个开发区。已经校准的塔卡普纳区停车场模型是其交通仿真模型的一局部，二者又是整个评估体系的一局部。不过，受最近经济滑坡的影响，该地区局部业主已停止营业或是将物业出售，相关机构也推迟了规划、评估工作，但在经济复苏时该体系即可使用。5 结论1) 停车设施规划对减轻城市中心区的交通拥堵至关重要。新维根市研究说明，即便针对局部停车问题采用某种解决方法，也能带来相应效果。罗克代尔市和塔卡普纳区的测试结果显示，在模型体系中同时包含停车策略，可以显著影响设计方案的效果。2) 三项研究都说明把同一停车场的到达和出发车辆连接起来非常必要，但却采用了三种不同的解决方法。三个模型中驾驶人选择停车场的方法也有差异，但都是以矩阵细化为根底。通过设定停车费用和限制条件来测试驾驶人对不同收费政策的反响。三项研究都设法解决首选停车场的问题。3) 三个城市中心区从不同角度对停车场模型进行了探讨，对一些共同问题提出了多种有创意的解决方法，尤其是针对驾驶人寻找停车位造成的交通拥堵问题。这些研究说明利用微观仿真，可以测试停车策略的效果。参考文献：1 Joseph Straw. Rowland Dedicates Parking GarageN. New Haven Register, 20020911(B3).2 Donald Shoup. Cruising for Parking J. TransportPolicy, 2006, 13(6): 479486.3 Spotswitch Link, Spot Switch EB/OL. 202120210301. :/ spotswitch .4 Gaston Serge Tchang. Parking Policy to Improve Accessibility in Industrial AreasEB/OL. 20072021 03 5 SPP 172005 Scottish Planning Policy 17: Planning for Transport S.6 UKs Department for Transport. Traffic Advisory Leaflet ITS4/03 EB/OL. 20032021 03 01.7 UKs Department for Transport. Transport Models TAG Unit EB/OL. 20052021 03 01.unit_Planning urban car park provision usingMicrosimulationPete Sykes SIAS Ltd (UK), Falco De Jong Grontmij BV (NL), Richard Bradley ANSA Consultants (UK), GerardJennings MicroNet Limited (UK), Greig McDonnell North Shore Council (NZ)AbstractIn three different locations around the world, city planners have sought to investigate the effects on the road network of car park planning policy and accessibility. All have looked for methods to minimise urban congestion caused by drivers searching for a car park space. All have used an SParamics microsimulation model to test the design options. This paper describes how they went about it and what they achieved.104INTRODUCTIONThe provision of available car parking is one of the most contentious issues for city drivers. Car park spaces can be hard to find and expensive to use. There may be queues to get into the most convenient car parks which require drivers to move on to alternative car parks. Some city centre traders regard the lack of suitable car parking as a significant reason for shoppers to prefer out of town shopping centres. In New Haven Connecticut, Gov. Rowland at a ceremony celebrating the renovation of the citys largest car park in 2002 declared: if you dont have parking, nothing else works.1Car park hunting, the circulation of drivers looking for a parking space, can be a major contribution to city centre congestion. The proportion of cars searching for a space was found to be 26% when surveyed in Manhattan in 2006, while in Brooklyn it was 45%. The situation is not new. In 1927, a similar survey in Detroit found the figures to be 19% and 34% in separate locations 2. This long standing problem may at last be assisted by technology. While iPhone users can now notify each other as spaces become available 3, traffic planners can now take advantage of recent developments in traffic modelling, which demonstrate that car park access can be included in road traffic simulation models to support the design process. Car park location in urban planning policy is largely concerned with optimising the relationship between car parks, drivers and their destinations. Charging regimes may be used to reduce localised inconvenience caused by parked cars and to favour one class of driver over another in allocating spaces. The perceived benefits include improvements to a citys commercial centre through better accessibility for the target consumer. Policies may be supply- led by actively managing spaces or demand-led by simply increasing the number of spaces 4. Increasingly, active management policies are used to ration spaces and encourage sustainable travel patterns 5.Urban planning policy considers the charging regimes for car parks. Transport planning policy complements this and considers access to the car parks. It is concerned with the relationship between car parks, the road network and congestion.Accessibility of car parks is addressed in road design guidelines. UK Department for Transport advice on parking guidance and information systems includes reports of case studies that show that there are quantifiable benefits to be derived from installing variable message signs indicating car parking space availabilty 6. Benefits are described as quantitative, in terms of time saved, and qualitative in terms of public image and driver safety. WebTAG 7 guidance touches on the subject briefly in discussion of travel costs by including parking “costs(which notionally include time spent searching and queuing for a space and walking to the final destination). The authors perception is that car park accessibility isnormally considered after the urban design is complete and car park policy has been determined. The missing link is in the transport planning policy contribution to the initial design of urban areas with respect to car park provision and accessibility. This apparent deficiency has recently been addressed in three different locations reported here. Each has sought ways to investigate the effects on the road network of car park planning policy and accessibility. All have looked for methods to minimize urban congestion caused by drivers searching for a car park space. All have used an S-Paramics microsimulation model to test the design options.A study in Nieuwegein (The Netherlands) modelled a large expansion in travel demand and the provision of car park spaces for a major town centre redevelopment, where Saturday afternoon shopping was the critical period. It incorporated ITS within the microsimulation model to deliver information to drivers on availability of spaces and routes to car parks. Another study, in Rochdale (England), models the distribution of spaces in conjunction with major town centre development plans. The goal is to optimise the provision of car parks with respect to adjacent land use and to minimise town centre congestion by considering car park access early in the design process. The third study, in Takapuna (New Zealand), is also investigating the effect of city centre expansion. It uses bespoke software to model the car park demand and a microsimulation model to assign the demand to the network. Once again the goal is to understand the effect of car park policy and minimise city centre congestion.CAR PARK MODELLING IN MICROSIMULATIONTypical design option tests for a microsimulation model include changes to road layout, public transport priority schemes, optimisation of signals, or changes in demand. Each individual vehicle in the simulation will react to these changes, and the congestion they cause, as it moves to its destination. When testing the effect of car park policy decisions, the emphasis moves from examination of the effect of changes to the road network to examination of the effect of changes in the destination for that part of the trip undertaken in a car. The simulation model must now include the capability to distinguish between the drivers destination and the vehicles parking location and make dynamic choices between these locations.Figutr 1：Rochdale city centre carparksArrivalsCar parks are an entity within the microsimulation model, and are linked to zone destinations and car parks may serve more than one zone. Allocation of vehicles to car parks is undertaken by limiting car park access to specific trip purposes. The model includes car parking charges and the distances between car parks and associated zones as components of the generalised trip cost. As each vehicle type may have different cost coefficients, the modeler may differentiate between drivers who will accept a longer walk and those who will accept a higher charge. If a car park is full then vehicle drivers within the simulation wait at the entrance for a predetermined time, after which they re-assess their choice of car park and possibly proceed to another. Using an external software controller it is possible to monitor car park occupancy within the simulation and change a vehicles destination before it reaches the queue.As an example of how this methodology can be used to implement a car park policy model, consider a city centre zone with a mix of retail and commercial use with several car parks available within reasonable walking distance. Drivers will have a preferred location based on their proposed length of stay and the car park charging structure.Some drivers may have a contract for permit parking. A car park may have multiple adjacent entrances, each coded with a restriction to force vehicles to accept the appropriate parking charge. The effect in the simulation is that short stay vehicles enter car parks closer to their destination or with a lower charge. The long stay vehicles enter via the entry links with the higher charges or accept a longer walk time. The modeller can test responses to car parking changes by adjusting entry charges for different car parks or by varying the level of permit parking. Land use changesmay be modelled by adjusting the proportion of driver and vehicle types using a particular zone and related car parks.DeparturesThe assignment of all vehicles to an S-Paramics road network is controlled by a detailed (5 minute) time release profile. In its simplest form of use, the journey origin car park is determined by finding the minimum journey cost,which includes the walk time, or vehicles may simply be released in proportion to the size of the car park.If more control is required, such as the ability to match departures to arrivals at the same car park, the release may be triggered by an external software controller linked to the simulation model which uses an algorithm to determine when to release vehicles and where they originate on the network. This may be associated with a car park occupancy monitoring system and be used to match vehicle arrivals with a subsequent departure.NIEUWEGEIN PLANNED DEVELOPMENTSNieuwegein is a town just to the south of the city of Utrecht in the centre of the Netherlands with good economic prospects. To make the most of this, the municipality wants to restructure their city centre to include new developments. New multi-story car parks are planned to cope with the increased demand for parking spaces and a system for dynamic parking advice will attempt to minimise queueing at the car park entrances.Grontmij was asked to build an S-Paramics model of the city centre to review the effects of the new developments on the citys road network including the parking advisory system. The results of the simulation showed congestion at the three car parks closest to the city centre. This was in accordance with the city managers expectations. To bring the remaining capacity of the two other car parks into use, a parking advisory system was implemented in the simulation to redirect vehicles to the available parking capacity and gain insight into the effect of the system.MODELLING SOLUTIONSData collectionTravel demand matrices for the Nieuwegein model were derived from a pre-existing macroscopic model and refined with survey data. Further surveys were undertaken to determine the usage of the main car parks, the average length of stay and the residual numbers after the shops were closed.Because of the complex requirements of the Rochdale and Takapuna studies, more extensive data collection was necessary. Demand matrices for Rochdale were developed primarily from roadside interview data which identified the true destination of the journey. The interview data was used to determine the parking type (eg long/short stay, on/off street, contract), and the likely parking duration (based on journey purpose).A full parking inventory of the town centre and occupancy data provided input to a car park location model, used to link car parks to destination zones. The time to walk to each destination from each car park was initially estimated from simple geometry and later used as an important calibration parameter.Parking inventory data was essential to provide accurate capacity estimates categorised by: short or long stay, on or off-street; public or private, and charged or free. This included all car parks within, or adjacent to, the town centre. Residential parking areas adjacent to the town centre, were also included as these provided free parking, with longer walk distances, and were often used by commuters. Areas with high drop-off trips were modelled as a private parking type at their destination but could have been improved by having both the inbound and outbound legs of the drop-off trip in the matrix.The car park data also provided charging information for each car park. When combined with the car park interview data it was found to be possible to group charges into a single short stay and long stay charge. This simplifica