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<section class="xmteditor" style="display:none;" data-tools="gulangu" data-label="powered by gulangu"><br  /></section><section style="box-sizing: border-box;font-style: normal;font-weight: 400;text-align: justify;font-size: 16px;"><section style="margin: 10px 0% 8px;box-sizing: border-box;" powered-by="gulangu"><section style="display: inline-block;width: 100%;vertical-align: top;border-left: 3px solid rgb(160, 160, 160);border-bottom-left-radius: 0px;padding: 0px 0px 0px 8px;background-color: rgb(250, 250, 250);box-sizing: border-box;"><section style="font-size: 14px;box-sizing: border-box;" powered-by="gulangu"><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><strong style="box-sizing: border-box;">主要作者：</strong></p><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><strong style="box-sizing: border-box;">张宇</strong>，北京市城市规划设计研究院，教授级高级工程师；</p><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><strong style="box-sizing: border-box;">王建光</strong>，百度地图高级研发工程师；</p><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><strong style="box-sizing: border-box;">郑猛</strong>，北京市城市规划设计研究院交通所副所长，教授级高级工程师；</p><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><strong style="box-sizing: border-box;">阚长城</strong>，百度地图资深研发工程师。</p></section></section></section><section style="margin-top: 10px;margin-bottom: 10px;padding-right: 2.6em;box-sizing: border-box;" powered-by="gulangu"><section style="text-align: right;box-sizing: border-box;"><section style="display: inline-block;vertical-align: bottom;margin: 0px -2.6em -2.6em 0px;border-width: 3px;border-style: solid;box-sizing: border-box;width: 5em;height: 5em;border-radius: 100%;border-color: rgb(95, 156, 239);background-position: center center;background-repeat: no-repeat;background-size: cover;background-image: url("http://mmbiz.qpic.cn/mmbiz_gif/ia8F4bZicuKMsW1aVm82gddZoiaYiaatD36FHkRFCicZSd9a2V2nLBN8PVeckicws95gomcsUDNnO83LXksWkEGk7IEw/640?wx_fmt=gif");overflow: hidden;"><br  /></section><span style="padding: 0.3em 2.6em 0.3em 0.5em;border-radius: 0.8em 0.8em 0px 0px;display: block;background-color: rgb(95, 156, 239);width: 100%;text-align: left;color: white;box-sizing: border-box;"><p style="margin: 0px;padding: 0px;box-sizing: border-box;"><strong style="box-sizing: border-box;">【大数据与AI驱动的交通规划方法创新】</strong></p></span> </section><section style="border-width: 1px;border-style: solid;border-color: rgb(95, 156, 239);border-radius: 0px 0px 0.8em 0.8em;padding: 10px;box-sizing: border-box;"><section style="font-size: 14px;box-sizing: border-box;" powered-by="gulangu"><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;">《大数据与AI驱动的交通规划方法创新》是响应国家新一代人工智能发展规划的号召，由北京市城市规划设计研究院交通规划所联合百度地图慧眼、中国科学院自动化研究所、智慧足迹科技有限公司、等研究机构共同开展的，基于大数据结合人工智能算法在城市交通规划领域的应用方法探索。希望借助大数据和人工智能算法的融合为城市交通规划行业带来新空气，甚至革命性变革。进而为城市规划注入新活力。</p></section></section></section><section style="box-sizing: border-box;" powered-by="gulangu"><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p></section><section style="box-sizing: border-box;" powered-by="gulangu"><section style="display: inline-block;width: 100%;vertical-align: top;box-sizing: border-box;"><section style="text-align: center;box-sizing: border-box;" powered-by="gulangu"><section style="display: inline-block;width: 300px;height: 60px;vertical-align: top;overflow: hidden;padding: 0px 10px;box-sizing: border-box;"><section style="opacity: 0.1;box-sizing: border-box;" powered-by="gulangu"><section style="font-size: 120px;color: rgb(15, 76, 129);line-height: 1;box-sizing: border-box;"><p style="margin: 0px;padding: 0px;box-sizing: border-box;"><strong style="box-sizing: border-box;">摘要</strong></p></section></section></section></section><section style="box-sizing: border-box;" powered-by="gulangu"><section style="display: inline-block;width: 100%;vertical-align: top;border-top: 1px solid rgb(229, 229, 229);border-top-left-radius: 0px;border-bottom: 1px solid rgb(229, 229, 229);border-bottom-right-radius: 0px;box-shadow: rgb(0, 0, 0) 0px 0px 0px;padding: 15px;box-sizing: border-box;"><section style="box-sizing: border-box;" powered-by="gulangu"><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><span style="font-size: 14px;box-sizing: border-box;">北京上班族，自称“世界最奔波”的上班族群，其真实的生活状态、出行状态如何一直都是重要的城市话题，也是城市规划需要解决好的核心问题之一。北京市城市规划设计研究院与百度地图慧眼联合课题组使用慧眼常住人口、岗位、通勤职住和出行OD大数据识别出北京上班族，并挖掘他们的生活与出行状态，总结出北京上班族的空间分布与出行特征的七大特点。为未来的规划中给北京上班族提供更好的服务打下基础。<span style="color: rgb(160, 160, 160);box-sizing: border-box;">（注：相关数据处理各环节均匿名化，各环节及输出均不涉及个体隐私）</span></span><br style="box-sizing: border-box;"  /></p></section></section></section><section style="text-align: center;box-sizing: border-box;" powered-by="gulangu"><section style="display: inline-block;width: 300px;height: 80px;vertical-align: top;overflow: hidden;padding: 0px 10px;box-sizing: border-box;"><section style="opacity: 0.1;margin: -60px 0% 0px;box-sizing: border-box;" powered-by="gulangu"><section style="font-size: 120px;color: rgb(15, 76, 129);line-height: 1;box-sizing: border-box;"><p style="margin: 0px;padding: 0px;box-sizing: border-box;"><strong style="box-sizing: border-box;">摘要</strong></p></section></section></section></section></section></section><section style="box-sizing: border-box;" powered-by="gulangu"><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><br  /></p></section><section style="text-align: left;margin: 10px 0%;box-sizing: border-box;" powered-by="gulangu"><section style="display: inline-block;width: auto;vertical-align: top;min-width: 10%;max-width: 100%;height: auto;border-left: 3px solid rgb(15, 76, 129);border-bottom-left-radius: 0px;padding: 0px 0px 0px 10px;line-height: 0;box-sizing: border-box;"><section style="margin: 0px 0%;transform: translate3d(-13px, 0px, 0px);-webkit-transform: translate3d(-13px, 0px, 0px);-moz-transform: translate3d(-13px, 0px, 0px);-o-transform: translate3d(-13px, 0px, 0px);box-sizing: border-box;" powered-by="gulangu"><section style="display: inline-block;width: 25px;height: 10px;vertical-align: top;overflow: hidden;line-height: 0;border-style: solid solid none;border-width: 3px 3px 2px;border-radius: 0px;border-color: rgb(15, 76, 129);box-sizing: border-box;"><br  /></section></section><section style="transform: translate3d(-1px, 0px, 0px);-webkit-transform: translate3d(-1px, 0px, 0px);-moz-transform: translate3d(-1px, 0px, 0px);-o-transform: translate3d(-1px, 0px, 0px);box-sizing: border-box;" powered-by="gulangu"><section style="text-align: justify;color: rgb(15, 76, 129);font-size: 20px;line-height: 1.5;box-sizing: border-box;"><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><strong style="box-sizing: border-box;">特点一：北京上班族平均通勤距离13公里</strong></p></section></section><section style="margin: 0px 0%;transform: translate3d(-13px, 0px, 0px) rotateX(180deg);-webkit-transform: translate3d(-13px, 0px, 0px) rotateX(180deg);-moz-transform: translate3d(-13px, 0px, 0px) rotateX(180deg);-o-transform: translate3d(-13px, 0px, 0px) rotateX(180deg);box-sizing: border-box;" powered-by="gulangu"><section style="display: inline-block;width: 24px;height: 10px;vertical-align: top;overflow: hidden;line-height: 0;border-style: solid solid none;border-width: 3px 3px 2px;border-radius: 0px;border-color: rgb(15, 76, 129);box-sizing: border-box;"><br  /></section></section></section></section><section style="box-sizing: border-box;" powered-by="gulangu"><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p></section><section style="box-sizing: border-box;" powered-by="gulangu"><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;">据百度慧眼大数据识别统计，<strong style="box-sizing: border-box;">北京上班族平均通勤距离为13.0公里</strong>。5公里以内的上班族占到1/3<em style="box-sizing: border-box;">（这1/3的上班族应该是非机动化出行方式应该努力争取的通勤人群）</em>，10公里以内的上班族占到一半，15公里以内的上班族则占到70%（通勤距离15公里以上的近1/3的上班族应是轨道交通优势范围服务人群），20公里以内的上班族则占到80%，40公里以上的上班族已经算小概率事件了（<5%），然而的确还有近1%的上班族通勤距离超过了70公里，相当于单程绕北京四环路一圈还多一点。</p><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p></section><section style="text-align: center;margin-top: 10px;margin-bottom: 10px;box-sizing: border-box;" powered-by="gulangu"><section style="max-width: 100%;vertical-align: middle;display: inline-block;line-height: 0;box-sizing: border-box;"><img data-ratio="1.0719424" src="http://mmbiz.qpic.cn/mmbiz_png/icKFe0wqE4eMibMSum0iathzeE0gEvKu3ZVn3Iu665EIknjfvMmOvnncObnSVKpibWg4ymSGPuS2qyQzpBLJibKaxNQ/640?wx_fmt=png" data-type="png" data-w="556" style="vertical-align: middle;max-width: 100%;box-sizing: border-box;"></section></section><section style="box-sizing: border-box;" powered-by="gulangu"><p style="text-align: center;white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><span style="font-size: 14px;box-sizing: border-box;"><em style="box-sizing: border-box;"><strong style="box-sizing: border-box;">北京上班族通勤距离（公里）分布曲线图</strong></em></span></p><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><br  /></p><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;">对比国际大都市，上海平均通勤距离8.2公里（2013年），东京平均通勤距离11.0公里（2000年），伦敦平均通勤距离9.1公里（2001年）。北京13公里的通勤距离 “名列前茅”。与旁边的邻居天津市对比也高出近30%（天津中心城区2017年平均通勤距离10.2公里）。对比可见，北京的职住分布及城市功能布局还有很大的优化空间。</p><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;">不同年龄段上班族的平均通勤距离比较可见，25-34岁的上班族平均通勤距离最长，达到14公里以上。比55岁以上的上班族长了1/4。按年龄段推断这些人群大多是“有娃在上学”一族，“以娃上学为中心”的居住地选择依据可能是导致他们通勤距离较长的一个主要原因。而即使对于55岁以上的高龄上班族，其平均通勤距离依然达到11公里，比北京二环路的直径还要长，可见北京高龄上班族的“体格子”还是不错的。</p><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p></section><section style="text-align: center;margin-top: 10px;margin-bottom: 10px;box-sizing: border-box;" powered-by="gulangu"><section style="max-width: 100%;vertical-align: middle;display: inline-block;line-height: 0;box-sizing: border-box;"><img data-ratio="0.4778973" src="http://mmbiz.qpic.cn/mmbiz_png/icKFe0wqE4eMibMSum0iathzeE0gEvKu3ZVu4M2Ww2fjvdDh5o0lv0Y78sVITHEcCM75DJgY7vLGkJDlocpxz2PQg/640?wx_fmt=png" data-type="png" data-w="837" style="vertical-align: middle;max-width: 100%;box-sizing: border-box;"></section></section><section style="box-sizing: border-box;" powered-by="gulangu"><p style="text-align: center;white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><strong style="box-sizing: border-box;"><em style="box-sizing: border-box;"><span style="font-size: 14px;box-sizing: border-box;">不同年龄阶段上班族平均通勤距离对比图</span></em></strong><br style="box-sizing: border-box;"  /></p><p style="text-align: center;white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><strong style="box-sizing: border-box;"><em style="box-sizing: border-box;"><span style="font-size: 14px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></span></em></strong></p></section><section style="text-align: left;margin: 10px 0%;box-sizing: border-box;" powered-by="gulangu"><section style="display: inline-block;width: auto;vertical-align: top;min-width: 10%;max-width: 100%;height: auto;border-left: 3px solid rgb(15, 76, 129);border-bottom-left-radius: 0px;padding: 0px 0px 0px 10px;line-height: 0;box-sizing: border-box;"><section style="margin: 0px 0%;transform: translate3d(-13px, 0px, 0px);-webkit-transform: translate3d(-13px, 0px, 0px);-moz-transform: translate3d(-13px, 0px, 0px);-o-transform: translate3d(-13px, 0px, 0px);box-sizing: border-box;" powered-by="gulangu"><section style="display: inline-block;width: 25px;height: 10px;vertical-align: top;overflow: hidden;line-height: 0;border-style: solid solid none;border-width: 3px 3px 2px;border-radius: 0px;border-color: rgb(15, 76, 129);box-sizing: border-box;"><br  /></section></section><section style="transform: translate3d(-1px, 0px, 0px);-webkit-transform: translate3d(-1px, 0px, 0px);-moz-transform: translate3d(-1px, 0px, 0px);-o-transform: translate3d(-1px, 0px, 0px);box-sizing: border-box;" powered-by="gulangu"><section style="text-align: justify;color: rgb(15, 76, 129);font-size: 20px;line-height: 1.5;box-sizing: border-box;"><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><strong style="box-sizing: border-box;">特点二：北京“最奔波”上班族就业地</strong></p></section></section><section style="margin: 0px 0%;transform: translate3d(-13px, 0px, 0px) rotateX(180deg);-webkit-transform: translate3d(-13px, 0px, 0px) rotateX(180deg);-moz-transform: translate3d(-13px, 0px, 0px) rotateX(180deg);-o-transform: translate3d(-13px, 0px, 0px) rotateX(180deg);box-sizing: border-box;" powered-by="gulangu"><section style="display: inline-block;width: 24px;height: 10px;vertical-align: top;overflow: hidden;line-height: 0;border-style: solid solid none;border-width: 3px 3px 2px;border-radius: 0px;border-color: rgb(15, 76, 129);box-sizing: border-box;"><br  /></section></section></section></section><section style="box-sizing: border-box;" powered-by="gulangu"><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p></section><section style="box-sizing: border-box;" powered-by="gulangu"><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;">为了可以更好的描述北京上班族的空间分布特征，我们将北京“最奔波”上班族和上班族“最扎堆”聚集地，按职住位置进行识别，洞见其空间分布特征。</p><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;">为反映“最奔波”上班族就业地，我们首先将上班族就业地聚集到交通小区尺度，并对一定就业规模交通小区（2000人以上）的上班族平均通勤距离进行统计，得出北京“最奔波”就业地主要为：<strong style="box-sizing: border-box;">海淀区的上地、北京城市副中心的北京行政办公区、房山区的大学城、顺义区的首都机场临空经济区、昌平区的乐多港与八达岭奥莱地区</strong>等区域，几个区域的平均通勤距离均在20公里以上，大多超过25公里。</p><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;">细分可见，乐多港与八达岭奥莱地区属于近几年打造的文化旅游区、房山大学城、北京行政办公区等属于近几年功能疏解的功能承接地，目前处于刚刚搬迁后的“调整”期，随着今后功能配套的完善，长距离通勤有望得到缓解。而上地和首都机场临空经济区则成为相对稳定的“最奔波”上班族就业地。</p><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p></section><section style="text-align: center;margin-top: 10px;margin-bottom: 10px;box-sizing: border-box;" powered-by="gulangu"><section style="max-width: 100%;vertical-align: middle;display: inline-block;line-height: 0;box-sizing: border-box;"><img data-ratio="1.7748538" src="http://mmbiz.qpic.cn/mmbiz_png/icKFe0wqE4eMibMSum0iathzeE0gEvKu3ZVeyEPwIoBqIpPXbs6MqmmG7uKDD6q5hlmziaUezbUWtXmd027wGib3teQ/640?wx_fmt=png" data-type="png" data-w="684" style="vertical-align: middle;max-width: 100%;box-sizing: border-box;"></section></section><section style="box-sizing: border-box;" powered-by="gulangu"><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p></section><section style="text-align: left;margin: 10px 0%;box-sizing: border-box;" powered-by="gulangu"><section style="display: inline-block;width: auto;vertical-align: top;min-width: 10%;max-width: 100%;height: auto;border-left: 3px solid rgb(15, 76, 129);border-bottom-left-radius: 0px;padding: 0px 0px 0px 10px;line-height: 0;box-sizing: border-box;"><section style="margin: 0px 0%;transform: translate3d(-13px, 0px, 0px);-webkit-transform: translate3d(-13px, 0px, 0px);-moz-transform: translate3d(-13px, 0px, 0px);-o-transform: translate3d(-13px, 0px, 0px);box-sizing: border-box;" powered-by="gulangu"><section style="display: inline-block;width: 25px;height: 10px;vertical-align: top;overflow: hidden;line-height: 0;border-style: solid solid none;border-width: 3px 3px 2px;border-radius: 0px;border-color: rgb(15, 76, 129);box-sizing: border-box;"><br  /></section></section><section style="transform: translate3d(-1px, 0px, 0px);-webkit-transform: translate3d(-1px, 0px, 0px);-moz-transform: translate3d(-1px, 0px, 0px);-o-transform: translate3d(-1px, 0px, 0px);box-sizing: border-box;" powered-by="gulangu"><section style="text-align: justify;color: rgb(15, 76, 129);font-size: 20px;line-height: 1.5;box-sizing: border-box;"><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><strong style="box-sizing: border-box;">特点三：北京上班族“最扎堆”就业地</strong></p></section></section><section style="margin: 0px 0%;transform: translate3d(-13px, 0px, 0px) rotateX(180deg);-webkit-transform: translate3d(-13px, 0px, 0px) rotateX(180deg);-moz-transform: translate3d(-13px, 0px, 0px) rotateX(180deg);-o-transform: translate3d(-13px, 0px, 0px) rotateX(180deg);box-sizing: border-box;" powered-by="gulangu"><section style="display: inline-block;width: 24px;height: 10px;vertical-align: top;overflow: hidden;line-height: 0;border-style: solid solid none;border-width: 3px 3px 2px;border-radius: 0px;border-color: rgb(15, 76, 129);box-sizing: border-box;"><br  /></section></section></section></section><section style="box-sizing: border-box;" powered-by="gulangu"><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p></section><section style="box-sizing: border-box;" powered-by="gulangu"><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;">根据识别的上班族就业所在地，进行就业岗位强度统计可见，最集中的办公地，主要为<strong style="box-sizing: border-box;">海淀区的</strong><strong style="box-sizing: border-box;">中关村、西城区的金融街、东城区的王府井和东单地区、朝阳区的CBD</strong>等区域。就业岗位密度均在5万个/平方公里以上。此外，如海淀区的上地、丰台区的丰台科技园、朝阳区的望京等地就业岗位密度也已达到2万个/平方公里以上，已形成了部分就业次中心。</p><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;">就业中心的总体分布上，城市北部就业中心明显高于南部。沿东西长安街两侧形成了相对明显的就业中心轴带。</p><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;">与上面“最奔波”就业地叠加来看，只有作为次就业中心的上地地区属于就业强度较大同时通勤距离较长的地区，看来“码农们”在完成了一天长长的代码编写后，还有漫长的回家路等着他们。建议在后续的规划中给予他们“特别关照”。</p><p style="text-align: center;white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p></section><section style="text-align: center;margin-top: 10px;margin-bottom: 10px;box-sizing: border-box;" powered-by="gulangu"><section style="max-width: 100%;vertical-align: middle;display: inline-block;line-height: 0;box-sizing: border-box;"><img data-ratio="1.4342105" src="http://mmbiz.qpic.cn/mmbiz_png/icKFe0wqE4eMibMSum0iathzeE0gEvKu3ZViaVedyfRku09pMDoOibsD8EncEbaw5FDBOWg5BFJa2XF3ibcvy075cyag/640?wx_fmt=png" data-type="png" data-w="684" style="vertical-align: middle;max-width: 100%;box-sizing: border-box;"></section></section><section style="box-sizing: border-box;" powered-by="gulangu"><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p></section><section style="text-align: left;margin: 10px 0%;box-sizing: border-box;" powered-by="gulangu"><section style="display: inline-block;width: auto;vertical-align: top;min-width: 10%;max-width: 100%;height: auto;border-left: 3px solid rgb(15, 76, 129);border-bottom-left-radius: 0px;padding: 0px 0px 0px 10px;line-height: 0;box-sizing: border-box;"><section style="margin: 0px 0%;transform: translate3d(-13px, 0px, 0px);-webkit-transform: translate3d(-13px, 0px, 0px);-moz-transform: translate3d(-13px, 0px, 0px);-o-transform: translate3d(-13px, 0px, 0px);box-sizing: border-box;" powered-by="gulangu"><section style="display: inline-block;width: 25px;height: 10px;vertical-align: top;overflow: hidden;line-height: 0;border-style: solid solid none;border-width: 3px 3px 2px;border-radius: 0px;border-color: rgb(15, 76, 129);box-sizing: border-box;"><br  /></section></section><section style="transform: translate3d(-1px, 0px, 0px);-webkit-transform: translate3d(-1px, 0px, 0px);-moz-transform: translate3d(-1px, 0px, 0px);-o-transform: translate3d(-1px, 0px, 0px);box-sizing: border-box;" powered-by="gulangu"><section style="text-align: justify;color: rgb(15, 76, 129);font-size: 20px;line-height: 1.5;box-sizing: border-box;"><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><strong style="box-sizing: border-box;">特点四：北京“最奔波”上班族居住地</strong></p></section></section><section style="margin: 0px 0%;transform: translate3d(-13px, 0px, 0px) rotateX(180deg);-webkit-transform: translate3d(-13px, 0px, 0px) rotateX(180deg);-moz-transform: translate3d(-13px, 0px, 0px) rotateX(180deg);-o-transform: translate3d(-13px, 0px, 0px) rotateX(180deg);box-sizing: border-box;" powered-by="gulangu"><section style="display: inline-block;width: 24px;height: 10px;vertical-align: top;overflow: hidden;line-height: 0;border-style: solid solid none;border-width: 3px 3px 2px;border-radius: 0px;border-color: rgb(15, 76, 129);box-sizing: border-box;"><br  /></section></section></section></section><section style="box-sizing: border-box;" powered-by="gulangu"><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p></section><section style="box-sizing: border-box;" powered-by="gulangu"><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;">“最奔波”上班族居住地同样将上班族居住地聚集到交通小区尺度，并将2000人以上的一定居住规模交通小区的上班族平均通勤距离进行统计，得出北京“最奔波”居住地。整体来看，随着居住地与市中心距离的增长，其通勤距离也随之增长。仅有北部部分外围圈层的新城（如延庆新城等），目前还呈现相对独立的新城内近距离通勤状态。</p><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;">因此，“最奔波”的上班族集中居住地主要分布在近郊新城内。平均通勤距离前三甲的代表性居住地分别为：<strong style="box-sizing: border-box;">房山新城的良乡地区</strong>（平均通勤距离26.9公里）、<strong style="box-sizing: border-box;">昌平新城的南邵地区</strong>（平均通勤距离25.7公里）、<strong style="box-sizing: border-box;">大兴新城的天宫院地区</strong>（平均通勤距离22.2公里）。</p><p style="text-align: center;white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p></section><section style="text-align: center;margin-top: 10px;margin-bottom: 10px;box-sizing: border-box;" powered-by="gulangu"><section style="max-width: 100%;vertical-align: middle;display: inline-block;line-height: 0;box-sizing: border-box;"><img class="" data-croporisrc="http://mmbiz.qpic.cn/mmbiz_png/icKFe0wqE4eMibMSum0iathzeE0gEvKu3ZVKRo7KBLwicwfJRmiazvryDmZ4GOna1ZwYRianMbSQGSVdaRUQEVyotcYg/?wx_fmt=png" data-cropx1="0" data-cropx2="681" data-cropy1="0" data-cropy2="1208.9529616724737" data-ratio="1.775330396475771" src="http://mmbiz.qpic.cn/mmbiz_jpg/icKFe0wqE4eMibMSum0iathzeE0gEvKu3ZVWQnUG23T7mClzyZtO2jyMlRefic0EDtKu3jUwkwuTCD1DriaXjb900kg/640?wx_fmt=jpeg" data-type="jpeg" data-w="681" style="vertical-align: middle;max-width: 100%;box-sizing: border-box;width: 574px;height: 1019px;"></section></section><section style="box-sizing: border-box;" powered-by="gulangu"><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p></section><section style="text-align: left;margin: 10px 0%;box-sizing: border-box;" powered-by="gulangu"><section style="display: inline-block;width: auto;vertical-align: top;min-width: 10%;max-width: 100%;height: auto;border-left: 3px solid rgb(15, 76, 129);border-bottom-left-radius: 0px;padding: 0px 0px 0px 10px;line-height: 0;box-sizing: border-box;"><section style="margin: 0px 0%;transform: translate3d(-13px, 0px, 0px);-webkit-transform: translate3d(-13px, 0px, 0px);-moz-transform: translate3d(-13px, 0px, 0px);-o-transform: translate3d(-13px, 0px, 0px);box-sizing: border-box;" powered-by="gulangu"><section style="display: inline-block;width: 25px;height: 10px;vertical-align: top;overflow: hidden;line-height: 0;border-style: solid solid none;border-width: 3px 3px 2px;border-radius: 0px;border-color: rgb(15, 76, 129);box-sizing: border-box;"><br  /></section></section><section style="transform: translate3d(-1px, 0px, 0px);-webkit-transform: translate3d(-1px, 0px, 0px);-moz-transform: translate3d(-1px, 0px, 0px);-o-transform: translate3d(-1px, 0px, 0px);box-sizing: border-box;" powered-by="gulangu"><section style="text-align: justify;color: rgb(15, 76, 129);font-size: 20px;line-height: 1.5;box-sizing: border-box;"><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><strong style="box-sizing: border-box;">特点五：北京上班族“最扎堆”居住地</strong></p></section></section><section style="margin: 0px 0%;transform: translate3d(-13px, 0px, 0px) rotateX(180deg);-webkit-transform: translate3d(-13px, 0px, 0px) rotateX(180deg);-moz-transform: translate3d(-13px, 0px, 0px) rotateX(180deg);-o-transform: translate3d(-13px, 0px, 0px) rotateX(180deg);box-sizing: border-box;" powered-by="gulangu"><section style="display: inline-block;width: 24px;height: 10px;vertical-align: top;overflow: hidden;line-height: 0;border-style: solid solid none;border-width: 3px 3px 2px;border-radius: 0px;border-color: rgb(15, 76, 129);box-sizing: border-box;"><br  /></section></section></section></section><section style="box-sizing: border-box;" powered-by="gulangu"><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p></section><section style="box-sizing: border-box;" powered-by="gulangu"><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;">根据识别的上班族居住所在地，进行就业人口居住地强度统计可见，最集中的居住地，主要集中在城市南部地区，分布为<strong style="box-sizing: border-box;">丰台区的</strong><strong style="box-sizing: border-box;">马连道、马家堡、成寿寺、朝阳区的潘家园</strong>，以及北部<strong style="box-sizing: border-box;">昌平区的</strong><strong style="box-sizing: border-box;">天通苑地区</strong>。居住的就业人口密度均在2万人/平方公里以上。</p><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;">就业人口的居住地总体分布与就业中心正好相反，呈现南部高、北部低。东南三环沿线分布较为集中的特点。</p><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;">在周边新城中，虽然未形成2万人/平方公里以上的强就业人口聚集区，但通州新城、通州马驹桥、门头沟新城等新城均有1.5-2万人/平方公里的较强就业人口聚集区。其他新城也均有1.0-1.5万人/平方公里的中等强度就业人口聚集区。</p><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;">与上面“最奔波”上班族集中居住地叠加来看，房山新城、大兴新城、昌平新城的部分区域均属于聚集度与通勤距离均较大的区域，应该在后续的规划中予以重视。</p><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p></section><section style="text-align: center;margin-top: 10px;margin-bottom: 10px;box-sizing: border-box;" powered-by="gulangu"><section style="max-width: 100%;vertical-align: middle;display: inline-block;line-height: 0;box-sizing: border-box;"><img data-ratio="1.4342105" src="http://mmbiz.qpic.cn/mmbiz_png/icKFe0wqE4eMibMSum0iathzeE0gEvKu3ZV8NvozRsGEOMhujwTicHrndGJzW9EOMdwhdfPjL1B9Bevlz339dA0VIQ/640?wx_fmt=png" data-type="png" data-w="684" style="vertical-align: middle;max-width: 100%;box-sizing: border-box;"></section></section><section style="box-sizing: border-box;" powered-by="gulangu"><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p></section><section style="text-align: left;margin: 10px 0%;box-sizing: border-box;" powered-by="gulangu"><section style="display: inline-block;width: auto;vertical-align: top;min-width: 10%;max-width: 100%;height: auto;border-left: 3px solid rgb(15, 76, 129);border-bottom-left-radius: 0px;padding: 0px 0px 0px 10px;line-height: 0;box-sizing: border-box;"><section style="margin: 0px 0%;transform: translate3d(-13px, 0px, 0px);-webkit-transform: translate3d(-13px, 0px, 0px);-moz-transform: translate3d(-13px, 0px, 0px);-o-transform: translate3d(-13px, 0px, 0px);box-sizing: border-box;" powered-by="gulangu"><section style="display: inline-block;width: 25px;height: 10px;vertical-align: top;overflow: hidden;line-height: 0;border-style: solid solid none;border-width: 3px 3px 2px;border-radius: 0px;border-color: rgb(15, 76, 129);box-sizing: border-box;"><br  /></section></section><section style="transform: translate3d(-1px, 0px, 0px);-webkit-transform: translate3d(-1px, 0px, 0px);-moz-transform: translate3d(-1px, 0px, 0px);-o-transform: translate3d(-1px, 0px, 0px);box-sizing: border-box;" powered-by="gulangu"><section style="text-align: justify;color: rgb(15, 76, 129);font-size: 20px;line-height: 1.5;box-sizing: border-box;"><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><strong style="box-sizing: border-box;">特点六：早晚高峰通勤出行最集中</strong></p></section></section><section style="margin: 0px 0%;transform: translate3d(-13px, 0px, 0px) rotateX(180deg);-webkit-transform: translate3d(-13px, 0px, 0px) rotateX(180deg);-moz-transform: translate3d(-13px, 0px, 0px) rotateX(180deg);-o-transform: translate3d(-13px, 0px, 0px) rotateX(180deg);box-sizing: border-box;" powered-by="gulangu"><section style="display: inline-block;width: 24px;height: 10px;vertical-align: top;overflow: hidden;line-height: 0;border-style: solid solid none;border-width: 3px 3px 2px;border-radius: 0px;border-color: rgb(15, 76, 129);box-sizing: border-box;"><br  /></section></section></section></section><section style="box-sizing: border-box;" powered-by="gulangu"><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p></section><section style="box-sizing: border-box;" powered-by="gulangu"><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;">通过对北京通勤人群出行目的的识别与统计发现，通勤出行在早晚高峰的出行比例最高，在早高峰时段（6点到9点）三个小时发生的上班出行占比近50%，其中高峰小时发生的上班出行占比近20%。下班回家的晚高峰时段（16点至19点）发生的下班回家出行占比也近50%，其中晚高峰小时发生的下班出行占比为19%。与其他出行目的对比，通勤出行的早晚高峰集中度明显大于其他几类出行。</p><p style="margin: 0px;white-space: normal;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p></section><section style="text-align: center;margin-top: 10px;margin-bottom: 10px;box-sizing: border-box;" powered-by="gulangu"><section style="max-width: 100%;vertical-align: middle;display: inline-block;line-height: 0;box-sizing: border-box;"><img data-ratio="1.8729583" src="http://mmbiz.qpic.cn/mmbiz_png/icKFe0wqE4eMibMSum0iathzeE0gEvKu3ZVqY7MljWt9fRnlODwlPogFF71fO8nlVxib7TD4IyWRRQ9Y5q0buz8wAg/640?wx_fmt=png" data-type="png" data-w="551" style="vertical-align: middle;max-width: 100%;box-sizing: border-box;"></section></section><section style="box-sizing: border-box;" powered-by="gulangu"><p style="text-align: center;white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><span style="text-align: justify;box-sizing: border-box;"> </span><br style="box-sizing: border-box;"  /></p><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;">上班族各类出行绝对量在时间轴上的累计分布可见，早7点至8点和晚17点至18点为上班族出行总量的双峰值，均接近270万人次，一小时出行量占上班族全日出行总量的近9%。</p><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><br  /></p></section><section style="text-align: center;margin-top: 10px;margin-bottom: 10px;box-sizing: border-box;" powered-by="gulangu"><section style="max-width: 100%;vertical-align: middle;display: inline-block;line-height: 0;box-sizing: border-box;"><img data-ratio="1.9695817" src="http://mmbiz.qpic.cn/mmbiz_png/icKFe0wqE4eMibMSum0iathzeE0gEvKu3ZVf5ew5FeOVobUbTsFiazp2KsoRBJS9G1gX9s9ibPbicFtYDGWfO1OD4IeA/640?wx_fmt=png" data-type="png" data-w="526" style="vertical-align: middle;max-width: 100%;box-sizing: border-box;"></section></section><section style="box-sizing: border-box;" powered-by="gulangu"><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p></section><section style="text-align: left;margin: 10px 0%;box-sizing: border-box;" powered-by="gulangu"><section style="display: inline-block;width: auto;vertical-align: top;min-width: 10%;max-width: 100%;height: auto;border-left: 3px solid rgb(15, 76, 129);border-bottom-left-radius: 0px;padding: 0px 0px 0px 10px;line-height: 0;box-sizing: border-box;"><section style="margin: 0px 0%;transform: translate3d(-13px, 0px, 0px);-webkit-transform: translate3d(-13px, 0px, 0px);-moz-transform: translate3d(-13px, 0px, 0px);-o-transform: translate3d(-13px, 0px, 0px);box-sizing: border-box;" powered-by="gulangu"><section style="display: inline-block;width: 25px;height: 10px;vertical-align: top;overflow: hidden;line-height: 0;border-style: solid solid none;border-width: 3px 3px 2px;border-radius: 0px;border-color: rgb(15, 76, 129);box-sizing: border-box;"><br  /></section></section><section style="transform: translate3d(-1px, 0px, 0px);-webkit-transform: translate3d(-1px, 0px, 0px);-moz-transform: translate3d(-1px, 0px, 0px);-o-transform: translate3d(-1px, 0px, 0px);box-sizing: border-box;" powered-by="gulangu"><section style="text-align: justify;color: rgb(15, 76, 129);font-size: 20px;line-height: 1.5;box-sizing: border-box;"><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><strong style="box-sizing: border-box;">特点七：夜间经济时间最活跃群体——年轻上班族</strong></p></section></section><section style="margin: 0px 0%;transform: translate3d(-13px, 0px, 0px) rotateX(180deg);-webkit-transform: translate3d(-13px, 0px, 0px) rotateX(180deg);-moz-transform: translate3d(-13px, 0px, 0px) rotateX(180deg);-o-transform: translate3d(-13px, 0px, 0px) rotateX(180deg);box-sizing: border-box;" powered-by="gulangu"><section style="display: inline-block;width: 24px;height: 10px;vertical-align: top;overflow: hidden;line-height: 0;border-style: solid solid none;border-width: 3px 3px 2px;border-radius: 0px;border-color: rgb(15, 76, 129);box-sizing: border-box;"><br  /></section></section></section></section><section style="box-sizing: border-box;" powered-by="gulangu"><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p></section><section style="box-sizing: border-box;" powered-by="gulangu"><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;">虽然前面几个特点显示了北京上班族的辛苦，但数据显示年轻的上班族还是北京夜间经济时间最活跃的群体，可见他们“苦中作乐”的特质还是很明显的。</p><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;">根据不同年龄段全日非通勤出行时间分布对比可见，夜生活阶段，<strong style="box-sizing: border-box;">越年轻的上班族晚上活动越活跃。显示了他们才是北京夜间经济当之无愧的“主力军”。</strong></p><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><strong style="box-sizing: border-box;">而随着上班族的年龄增大，在日间平峰的活动比例越高。</strong></p><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;">相较而言，45-54岁群体整体的非通勤活动强度相对较低。</p></section><section style="box-sizing: border-box;" powered-by="gulangu"><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p></section><section style="text-align: center;margin-top: 10px;margin-bottom: 10px;box-sizing: border-box;" powered-by="gulangu"><section style="max-width: 100%;vertical-align: middle;display: inline-block;line-height: 0;box-sizing: border-box;"><img data-ratio="1.8544601" src="http://mmbiz.qpic.cn/mmbiz_png/icKFe0wqE4eMibMSum0iathzeE0gEvKu3ZVELJUicHqMLmucZyRTicx4734NbTa49hMOiaUkSBd6I53GFjMiczcJiccK8Q/640?wx_fmt=png" data-type="png" data-w="639" style="vertical-align: middle;max-width: 100%;box-sizing: border-box;"></section></section><section style="box-sizing: border-box;" powered-by="gulangu"><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p></section><section style="text-align: center;margin: 10px 0%;box-sizing: border-box;" powered-by="gulangu"><section style="display: inline-block;width: auto;vertical-align: top;min-width: 10%;max-width: 100%;height: auto;border-style: none;border-left-width: 0px;border-radius: 0px;border-left-color: rgb(15, 76, 129);padding: 0px 10px;line-height: 0;border-right-width: 0px;border-right-color: rgb(15, 76, 129);box-sizing: border-box;"><section style="margin: 0px 0%;box-sizing: border-box;" powered-by="gulangu"><section style="display: inline-block;width: 25px;height: 10px;vertical-align: top;overflow: hidden;line-height: 0;border-style: solid solid none;border-width: 3px 3px 2px;border-radius: 0px;border-color: rgb(15, 76, 129);box-sizing: border-box;"><br  /></section></section><section style="box-sizing: border-box;" powered-by="gulangu"><section style="display: flex;flex-flow: row nowrap;box-sizing: border-box;"><section style="display: inline-block;vertical-align: middle;width: auto;flex: 100 100 0%;align-self: center;height: auto;padding: 0px 10px 0px 0px;box-sizing: border-box;"><section style="transform: translate3d(-1px, 0px, 0px);-webkit-transform: translate3d(-1px, 0px, 0px);-moz-transform: translate3d(-1px, 0px, 0px);-o-transform: translate3d(-1px, 0px, 0px);text-align: right;box-sizing: border-box;" powered-by="gulangu"><section style="text-align: center;color: rgba(15, 76, 129, 0.4);font-size: 12px;line-height: 1.5;box-sizing: border-box;"><p style="margin: 0px;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p></section></section></section><section style="display: inline-block;vertical-align: middle;width: auto;flex: 0 0 auto;align-self: center;min-width: 10%;max-width: 100%;height: auto;padding: 0px;box-sizing: border-box;"><section style="transform: translate3d(1px, 0px, 0px);-webkit-transform: translate3d(1px, 0px, 0px);-moz-transform: translate3d(1px, 0px, 0px);-o-transform: translate3d(1px, 0px, 0px);box-sizing: border-box;" powered-by="gulangu"><section style="color: rgb(15, 76, 129);font-size: 20px;line-height: 1.5;box-sizing: border-box;"><p style="margin: 0px;padding: 0px;box-sizing: border-box;"><strong style="box-sizing: border-box;">写在后面</strong></p></section></section></section><section style="display: inline-block;vertical-align: middle;width: auto;flex: 100 100 0%;align-self: center;height: auto;padding: 0px 0px 0px 10px;box-sizing: border-box;"><section style="transform: translate3d(-1px, 0px, 0px);-webkit-transform: translate3d(-1px, 0px, 0px);-moz-transform: translate3d(-1px, 0px, 0px);-o-transform: translate3d(-1px, 0px, 0px);text-align: right;box-sizing: border-box;" powered-by="gulangu"><section style="text-align: center;color: rgba(15, 76, 129, 0.4);font-size: 12px;line-height: 1.5;box-sizing: border-box;"><p style="margin: 0px;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p></section></section></section></section></section><section style="margin: 0px 0%;transform: rotateX(180deg);-webkit-transform: rotateX(180deg);-moz-transform: rotateX(180deg);-o-transform: rotateX(180deg);box-sizing: border-box;" powered-by="gulangu"><section style="display: inline-block;width: 24px;height: 10px;vertical-align: top;overflow: hidden;line-height: 0;border-style: solid solid none;border-width: 3px 3px 2px;border-radius: 0px;border-color: rgb(15, 76, 129);box-sizing: border-box;"><br  /></section></section></section></section><section style="box-sizing: border-box;" powered-by="gulangu"><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p></section><section style="text-align: left;margin: 10px 0%;box-sizing: border-box;" powered-by="gulangu"><section style="display: inline-block;width: auto;vertical-align: top;min-width: 10%;max-width: 100%;height: auto;box-sizing: border-box;"><section style="box-sizing: border-box;" powered-by="gulangu"><section style="display: flex;flex-flow: row nowrap;box-sizing: border-box;"><section style="display: inline-block;vertical-align: bottom;width: auto;align-self: flex-end;flex: 0 0 auto;min-width: 10%;max-width: 100%;height: auto;box-sizing: border-box;"><section style="text-align: right;box-sizing: border-box;" powered-by="gulangu"><section style="display: inline-block;width: auto;vertical-align: top;min-width: 10%;max-width: 100%;height: auto;border-bottom: 4px solid rgb(15, 76, 129);border-bottom-right-radius: 0px;padding: 0px 10px 0px 0px;box-sizing: border-box;"><section style="text-align: center;font-size: 24px;color: rgb(15, 76, 129);box-sizing: border-box;" powered-by="gulangu"><p style="margin: 0px;padding: 0px;box-sizing: border-box;"><em style="box-sizing: border-box;"><strong style="box-sizing: border-box;">01</strong></em></p></section></section></section></section><section style="display: inline-block;vertical-align: bottom;width: auto;align-self: flex-end;flex: 100 100 0%;height: auto;border-width: 2px 3px 4px 4px;border-radius: 0px;border-style: solid solid solid none;border-color: rgb(15, 76, 129);padding: 5px 10px 5px 0px;box-sizing: border-box;"><section style="text-align: justify;color: rgb(15, 76, 129);box-sizing: border-box;" powered-by="gulangu"><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;">背后最用功的上班族识别</p></section></section></section></section></section></section><section style="box-sizing: border-box;" powered-by="gulangu"><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p></section><section style="box-sizing: border-box;" powered-by="gulangu"><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;">本研究使用的职住数据和出行OD数据由百度地图慧眼提供。百度地图慧眼依托百度地图开放平台，具备位置大数据采集存储、挖掘分析、可视化输出等完整闭环能力。截止目前，百度地图开放平台每日响应超过1200亿次位置请求，覆盖智能设备月活数超过11亿台，数据覆盖全国。</p><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;">通勤是交通规划的重要场景，而职住分布和出行OD是探究通勤场景的重要基础数据。百度地图慧眼利用海量位置数据完成了职住挖掘模型和出行OD挖掘模型，对人群的通勤规律进行探究。<span style="color: rgb(160, 160, 160);box-sizing: border-box;">（注：研究数据均经过脱敏处理，不涉及个人隐私问题）</span></p><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p></section><section style="text-align: center;margin-top: 10px;margin-bottom: 10px;box-sizing: border-box;" powered-by="gulangu"><section style="max-width: 100%;vertical-align: middle;display: inline-block;line-height: 0;box-sizing: border-box;"><img data-ratio="0.7773632" src="http://mmbiz.qpic.cn/mmbiz_png/icKFe0wqE4eMibMSum0iathzeE0gEvKu3ZVZobnscrBZpmwibqg61nsA5BPQfRP95icOM43M8ZrMWbBPxicjSXO5WRsQ/640?wx_fmt=png" data-type="png" data-w="804" style="vertical-align: middle;max-width: 100%;box-sizing: border-box;"></section></section><section style="box-sizing: border-box;" powered-by="gulangu"><p style="text-align: center;white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><strong style="box-sizing: border-box;"><em style="box-sizing: border-box;"><span style="font-size: 14px;box-sizing: border-box;">基于位置大数据的职住挖掘</span></em></strong><br style="box-sizing: border-box;"  /></p><p style="text-align: center;white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><strong style="box-sizing: border-box;"><em style="box-sizing: border-box;"><span style="font-size: 14px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></span></em></strong></p><p style="margin: 0px;white-space: normal;padding: 0px;box-sizing: border-box;">职住挖掘基于人群长期的位置信息，通过聚簇方法得到人群经常停留的地点，然后根据停留时长、定位点时间分布、停留地用地类型等特征判定停留地为家、工作地或其他类型，进一步就得到城市各区域通勤人群的职住分布。结合用户画像数据，可以进一步分析通勤人群的各维度属性，如年龄分布、行业分布等。</p><p style="margin: 0px;white-space: normal;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;">出行OD挖掘的主要工作是根据用户位置的时空信息捕捉其出行行为，包括出发地、目的地、出发时间，出行耗时等。挖掘算法的处理流程如下图。</p><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p></section><section style="text-align: center;margin-top: 10px;margin-bottom: 10px;box-sizing: border-box;" powered-by="gulangu"><section style="max-width: 100%;vertical-align: middle;display: inline-block;line-height: 0;box-sizing: border-box;"><img data-ratio="1.0700809" src="http://mmbiz.qpic.cn/mmbiz_png/icKFe0wqE4eMibMSum0iathzeE0gEvKu3ZVYlzDyjqHaibgrARBntGFkClKh1DIicQ8G98s5fc6SatvPwSGGDRlVOYQ/640?wx_fmt=png" data-type="png" data-w="742" style="vertical-align: middle;max-width: 100%;box-sizing: border-box;"></section></section><section style="box-sizing: border-box;" powered-by="gulangu"><p style="text-align: center;white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><strong style="box-sizing: border-box;"><em style="box-sizing: border-box;"><span style="font-size: 14px;box-sizing: border-box;">出行OD挖掘算法流程</span></em></strong><br style="box-sizing: border-box;"  /></p><p style="text-align: center;white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><strong style="box-sizing: border-box;"><em style="box-sizing: border-box;"><span style="font-size: 14px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></span></em></strong></p><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;">首先对数据进行脱敏处理，后续对数据的处理均不涉及个体隐私。将定位点按时间排序，去除轨迹中的异常定位点；使用密度聚类算法，将定位轨迹在空间上聚成若干簇；根据定位信息、时间和空间范围，计算每个空间簇的状态，将停留状态的簇标识为O或D，此处辅助常访地和场景化信息判定；停留状态的簇在时间上的切换构成出行OD。</p><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p></section><section style="text-align: left;margin: 10px 0%;box-sizing: border-box;" powered-by="gulangu"><section style="display: inline-block;width: auto;vertical-align: top;min-width: 10%;max-width: 100%;height: auto;box-sizing: border-box;"><section style="box-sizing: border-box;" powered-by="gulangu"><section style="display: flex;flex-flow: row nowrap;box-sizing: border-box;"><section style="display: inline-block;vertical-align: bottom;width: auto;align-self: flex-end;flex: 0 0 auto;min-width: 10%;max-width: 100%;height: auto;box-sizing: border-box;"><section style="text-align: right;box-sizing: border-box;" powered-by="gulangu"><section style="display: inline-block;width: auto;vertical-align: top;min-width: 10%;max-width: 100%;height: auto;border-bottom: 4px solid rgb(15, 76, 129);border-bottom-right-radius: 0px;padding: 0px 10px 0px 0px;box-sizing: border-box;"><section style="text-align: center;font-size: 24px;color: rgb(15, 76, 129);box-sizing: border-box;" powered-by="gulangu"><p style="margin: 0px;padding: 0px;box-sizing: border-box;"><em style="box-sizing: border-box;"><strong style="box-sizing: border-box;">02</strong></em></p></section></section></section></section><section style="display: inline-block;vertical-align: bottom;width: auto;align-self: flex-end;flex: 100 100 0%;height: auto;border-width: 2px 3px 4px 4px;border-radius: 0px;border-style: solid solid solid none;border-color: rgb(15, 76, 129);padding: 5px 10px 5px 0px;box-sizing: border-box;"><section style="text-align: justify;color: rgb(15, 76, 129);box-sizing: border-box;" powered-by="gulangu"><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;">人群扩样与出行特征校验</p></section></section></section></section></section></section><section style="box-sizing: border-box;" powered-by="gulangu"><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p></section><section style="box-sizing: border-box;" powered-by="gulangu"><p style="margin: 0px;white-space: normal;padding: 0px;box-sizing: border-box;">经过北京市城市规划设计研究院与百度地图慧眼课题组人员的反复核验，最终选用上述出行识别算法中出行生成约束的中尺度方法作为北京出行生成的判断标准方法。由此得到的有出行样本人群日均出行次数约3.0次/日。与传统调查数据及其他口径大数据对比，结论基本一致。</p><p style="margin: 0px;white-space: normal;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;">在此基础上，将通勤人群与北京的人口普查年龄分段数据进行对比分段扩样。有出行不同年龄分段通勤人群扩样见下图。</p><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p></section><section style="text-align: center;margin-top: 10px;margin-bottom: 10px;box-sizing: border-box;" powered-by="gulangu"><section style="max-width: 100%;vertical-align: middle;display: inline-block;line-height: 0;box-sizing: border-box;"><img data-ratio="0.4472222" src="http://mmbiz.qpic.cn/mmbiz_png/icKFe0wqE4eMibMSum0iathzeE0gEvKu3ZVmtWErRRPvm4vtD0DVhRZpnjWOpLodaoTw8VvO7ibZ96w9AZc3HAXcJw/640?wx_fmt=png" data-type="png" data-w="1080" style="vertical-align: middle;max-width: 100%;box-sizing: border-box;"></section></section><section style="box-sizing: border-box;" powered-by="gulangu"><p style="text-align: center;white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><strong style="box-sizing: border-box;"><em style="box-sizing: border-box;"><span style="font-size: 14px;box-sizing: border-box;">不同年龄阶段百度有出行人口数据与普查有出行人口数据对比及扩样系数</span></em></strong><br style="box-sizing: border-box;"  /></p><p style="text-align: center;white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><strong style="box-sizing: border-box;"><em style="box-sizing: border-box;"><span style="font-size: 14px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></span></em></strong></p><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;">上图可见，19-44岁的几个年龄段人口百度数据的整体分布与普查数据较为吻合，扩样系数基本稳定在2-3之间。45-54岁段扩样系数则在4.5左右，55-64岁扩样系数则达到20以上。分年龄段扩样后，保证了上述分析更好的代表性。</p><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p></section><section style="text-align: left;margin: 10px 0%;box-sizing: border-box;" powered-by="gulangu"><section style="display: inline-block;width: auto;vertical-align: top;min-width: 10%;max-width: 100%;height: auto;border-left: 3px solid rgb(15, 76, 129);border-bottom-left-radius: 0px;padding: 0px 0px 0px 10px;line-height: 0;box-sizing: border-box;"><section style="margin: 0px 0%;transform: translate3d(-13px, 0px, 0px);-webkit-transform: translate3d(-13px, 0px, 0px);-moz-transform: translate3d(-13px, 0px, 0px);-o-transform: translate3d(-13px, 0px, 0px);box-sizing: border-box;" powered-by="gulangu"><section style="display: inline-block;width: 25px;height: 10px;vertical-align: top;overflow: hidden;line-height: 0;border-style: solid solid none;border-width: 3px 3px 2px;border-radius: 0px;border-color: rgb(15, 76, 129);box-sizing: border-box;"><br  /></section></section><section style="transform: translate3d(-1px, 0px, 0px);-webkit-transform: translate3d(-1px, 0px, 0px);-moz-transform: translate3d(-1px, 0px, 0px);-o-transform: translate3d(-1px, 0px, 0px);box-sizing: border-box;" powered-by="gulangu"><section style="text-align: justify;color: rgb(15, 76, 129);font-size: 20px;line-height: 1.5;box-sizing: border-box;"><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><strong style="box-sizing: border-box;">预判未来：百度地图慧眼大数据与交通模型的融合分析</strong></p></section></section><section style="margin: 0px 0%;transform: translate3d(-13px, 0px, 0px) rotateX(180deg);-webkit-transform: translate3d(-13px, 0px, 0px) rotateX(180deg);-moz-transform: translate3d(-13px, 0px, 0px) rotateX(180deg);-o-transform: translate3d(-13px, 0px, 0px) rotateX(180deg);box-sizing: border-box;" powered-by="gulangu"><section style="display: inline-block;width: 24px;height: 10px;vertical-align: top;overflow: hidden;line-height: 0;border-style: solid solid none;border-width: 3px 3px 2px;border-radius: 0px;border-color: rgb(15, 76, 129);box-sizing: border-box;"><br  /></section></section></section></section><section style="box-sizing: border-box;" powered-by="gulangu"><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p></section><section style="box-sizing: border-box;" powered-by="gulangu"><p style="margin: 0px;white-space: normal;padding: 0px;box-sizing: border-box;">通过扩样和校核，课题组认为百度地图慧眼的通勤人群分布特征与出行特征比较符合对城市的认知与判断，可以辅助城市决策分析，尤其对城市通勤人群的认知再现较为理想。因此，课题组将百度地图慧眼的通勤人群的职住地分布及相关出行分布与北京市域战略交通模型（BJTM-V2.0）进行融合，使用百度地图慧眼的通勤人群出行特征代替部分传统模型的演算结果，提高了模型对复杂城市的出行行为的整体模拟精度。具体流程见下图。</p><p style="margin: 0px;white-space: normal;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p></section><section style="text-align: center;margin-top: 10px;margin-bottom: 10px;box-sizing: border-box;" powered-by="gulangu"><section style="max-width: 100%;vertical-align: middle;display: inline-block;line-height: 0;box-sizing: border-box;"><img data-ratio="0.5188771" src="http://mmbiz.qpic.cn/mmbiz_png/icKFe0wqE4eMibMSum0iathzeE0gEvKu3ZVIjl8ELyrD1lib3A2noMiafGRp6wbjJdicsm2SjgzNVV7MT47urZ2vGIFQ/640?wx_fmt=png" data-type="png" data-w="1033" style="vertical-align: middle;max-width: 100%;box-sizing: border-box;"></section></section><section style="box-sizing: border-box;" powered-by="gulangu"><p style="text-align: center;white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><strong style="box-sizing: border-box;"><em style="box-sizing: border-box;"><span style="font-size: 14px;box-sizing: border-box;">使用百度地图慧眼大数据进行四阶段模型混合预测流程图</span></em></strong></p></section><section style="box-sizing: border-box;" powered-by="gulangu"><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p></section><section style="text-align: left;margin: 10px 0% -50px;transform: translate3d(-10px, 0px, 0px);-webkit-transform: translate3d(-10px, 0px, 0px);-moz-transform: translate3d(-10px, 0px, 0px);-o-transform: translate3d(-10px, 0px, 0px);box-sizing: border-box;" powered-by="gulangu"><section style="display: inline-block;width: 110px;height: 80px;vertical-align: top;overflow: hidden;background-image: url("http://mmbiz.qpic.cn/mmbiz_png/Xib4DdGhHLlFVdTu6jyuYOhydggRzU9A6cwia0wJbOMW1LoRK48iayzCbQtpkvcyVbSialAOWLiaESHz283XZEkkZ9A/640?wx_fmt=png");background-position: 50% 50%;background-repeat: no-repeat;background-size: contain;background-attachment: scroll;box-sizing: border-box;"><section style="margin: 20px 0% 0px;box-sizing: border-box;" powered-by="gulangu"><section style="text-align: center;box-sizing: border-box;"><p style="margin: 0px;padding: 0px;box-sizing: border-box;"><strong style="box-sizing: border-box;">致谢</strong></p></section></section></section></section><section style="text-align: right;margin: 0px 0% 10px;box-sizing: border-box;" powered-by="gulangu"><section style="display: inline-block;width: 98%;vertical-align: top;border-style: none;border-width: 0px;border-radius: 0px;border-color: rgb(62, 62, 62);padding: 4px;box-shadow: rgb(0, 0, 0) 0px 0px 0px;background-image: url("http://mmbiz.qpic.cn/mmbiz_png/xCuOy6snHDEPENqVIAic4bzThibuzeWAmpG5DhtSaa67vhI0aHxo0lcAPBg3nHdkXgDLGH5C3lBCkjE9rMWul7vw/640?wx_fmt=png");background-position: 0% 0%;background-repeat: repeat;background-size: auto;background-attachment: scroll;box-sizing: border-box;"><section style="box-sizing: border-box;" powered-by="gulangu"><section style="display: inline-block;width: 100%;vertical-align: top;background-color: rgb(255, 255, 255);border-style: solid;border-width: 1px;border-radius: 0px;border-color: rgb(62, 62, 62);padding: 10px;box-sizing: border-box;"><section style="text-align: justify;box-sizing: border-box;" powered-by="gulangu"><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;"><br style="box-sizing: border-box;"  /></p></section><section style="margin: 20px 0% 0px;box-sizing: border-box;" powered-by="gulangu"><section style="text-align: justify;font-size: 15px;line-height: 1.8;box-sizing: border-box;"><p style="white-space: normal;margin: 0px;padding: 0px;box-sizing: border-box;">鸣谢上海市城市规划设计研究院张天然、天津市城市规划设计研究院万涛提供相关城市通勤对比数据。</p></section></section></section></section></section></section></section>
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