Allocation optimization of bicycle-sharing stations at scenic spots

Bicycle-sharing system is considered as a green option to provide a better connection between scenic spots and nearby metro/bus stations. Allocating and optimizing the layout of bicycle-sharing system inside the scenic spot and around its influencing area are focused on. It is found that the terrain, land use, nearby transport network and scenery point distribution have significant impact on the allocation of bicycle-sharing system. While the candidate bicycle-sharing stations installed at the inner scenic points, entrances/exits and metro stations are fixed, the ones installed at bus-stations and other passenger concentration buildings are adjustable. Aiming at minimizing the total cycling distance and overlapping rate, an optimization model is proposed and solved based on the idea of cluster concept and greedy heuristic. A revealed preference/stated preference （RP/SP） combined survey was conducted at Xuanwu Lake in Nanjing, China, to get an insight into the touring trip characteristics and bicycle-sharing tendency. The results reveal that 39.81% visitors accept a cycling distance of 1-3 km and 62.50% respondents think that the bicycle-sharing system should charge an appropriate fee. The sttrvey indicates that there is high possibility to carry out a bicycle-sharing system at Xuanwu Lake. Optimizing the allocation problem cluster by cluster rather than using an exhaustive search method significantly reduces the computing amount from O（2^43） to O（43 2）. The 500 m-radius-coverage rate for the alternative optimized by 500 m-radius-cluster and 800 m-radius-cluster is 89.2% and 68.5%, respectively. The final layout scheme will provide decision makers engineering guidelines and theoretical support.

Analysis of temporal and spatial usage patterns of dockless bike sharing system around rail transit station area

In order to study the spatiotemporal characteristics of the dockless bike sharing system(BSS)around urban rail transit stations,new normalized calculation methods are proposed to explore the temporal and spatial usage patterns of the dockless BSS around rail transit stations by using 5-weekday dockless bike sharing trip data in Nanjing,China.First,the rail transit station area(RTSA)is defined by extracting shared bike trips with trip ends falling into the area.Then,the temporal and spatial decomposition methods are developed and two criterions are calculated,namely,normalized dynamic variation of bikes(NDVB)and normalized spatial distribution of trips(NSDT).Furthermore,the temporal and spatial usage patterns are clustered and the corresponding geographical distributions of shared bikes are determined.The results show that four temporal usage patterns and two spatial patterns of dockless BSS are finally identified.Area type(urban center and suburb)has a great influence on temporal usage patterns.Spatial usage patterns are irregular and affected by limited directions,adjacent rail transit stations and street networks.The findings can help form a better understanding of dockless shared bike users behavior around rail transit stations,which will contribute to improving the service and efficiency of both rail transit and BSS.

How do Users Evaluate Service Quality of the Dockless Bicycle Sharing System?

In order to improve the public transit’s accessibility and promote service quality of the public transit,it is necessary to alleviate the“last⁃mile”problem.The combination of the public transit and bicycle⁃sharing system is competitive compared with the private car.In 2016,the dockless bicycle⁃sharing system has been undergoing tremendous development.Because the dockless bicycle⁃sharing system is different from previous bicycle⁃sharing systems,components of service quality of the dockless bicycle⁃sharing system differ.However,there are few articles about the service quality of the dockless bicycle⁃sharing system.This paper examined the evaluation of service quality of the dockless bicycle⁃sharing system along with a measurement of users’satisfaction.A questionnaire about service quality was designed,which includes 15 factors.Through the analysis of travelers’evaluation of factors of the dockless bicycle⁃sharing system with Rasch Model,these original ordinal data(Likert data)was transformed to the interval data:the person parameter(ability)and the item parameter(difficulty).Through the analysis of interviewees’abilities,it was found that only education and monthly consumption have significant influence on interviewees’evaluation of service quality of the dockless bicycle⁃sharing system,while there is no significant influence of gender,weekly use of dockless shared⁃bicycle,private car ownership,or monthly income on the evaluation.Furthermore,it was found that users were dissatisfied with cycling under the adverse weather,disturbance from pedestrians and cars,continuity of the bicycle lane,complaint channel,and staff service the most.

接驳轨道交通的共享单车潮汐均衡时空分布模式

以深圳市为研究区,使用工作日早高峰的接驳骑行订单数据,考虑地铁站和出入口两层面的潮汐均衡性分类,从单车调度角度将出入口划分为无需调度型、站内调度型和站外调度型;使用非负矩阵分解方法划分出入口流量时空分布类别,确定调度时间.结果表明,将接驳轨道交通的潮汐均衡分析聚焦到出入口层面,精确划分出入口调度类型,可确定调度方式和产生调度需求的时间,为单车管理者进行车辆调度提供可靠的数据支持.

基于马尔可夫链的无桩共享单车车辆投放规模分析

为动态优化无桩共享单车投放规模,加强规范化管理,采用马尔可夫随机过程的数学方法研究共享单车的投放问题.分析了无桩共享单车使用过程所具有的不可约、非周期和正常返等性质,为高效求解共享单车站点的稳态规模,通过构造转移概率随机素矩阵,使用向量迭代技术,提出“稀疏矩阵秩-修正算法”,并进一步讨论了稳态规模的依时间段动态求解问题.最后,结合数据对算法进行了验证与分析,得出共享单车马尔可夫链“极限状态(稳态)概率唯一存在且独立于初始概率分布”的结论.可用于解决无桩共享单车投放和调度中的无序问题.

共享单车影响下小汽车出行方式转移机理研究

从小汽车到共享单车参与的出行模式转变是解决交通与环境问题的有效方法之一.本文通过对南京市308名小汽车出行者进行问卷调查,探讨了共享单车影响下心理因素对小汽车出行模式转移的影响.通过建立改进后的技术接受模型,研究了感知有用性,感知易用性和感知健康价值对共享单车的态度,以及对出行方式转移意愿的影响.结果表明,通过作用于共享单车的态度,感知健康价值和感知易用性对转移意愿产生积极影响,感知有用性则产生直接的积极影响.共享单车可以在短途出行中替代小汽车,中距离下出行者最有可能受到激励从小汽车用户转变为潜在的共享单车接驳公共交通使用者,而长距离下共享单车较难影响小汽车出行者的出行方式选择.

基于地理加权回归的共享单车需求影响因素分析

以共享单车订单数据、空间地理数据以及公共交通刷卡数据等多源数据为基础,构建基于地理加权回归的需求影响模型.结果表明:共享单车订单的空间分布具有空间集聚效应;影响因素对共享单车需求的影响程度具有空间非稳态的特征,在大多数交通小区,居住用地、休闲娱乐、购物服务、科教服务、公交登降量、轨道登降量等因素与共享单车需求为正相关关系;办公用地、风景名胜与共享单车需求为负相关关系.本研究可为共享单车的投放规模确定和日常运营调度提供支撑.

共享单车电子围栏应用现状及发展前景

作为共享单车停车管理的新兴技术手段,电子围栏在各大城市越来越受到关注。首先,介绍共享单车管理政策下电子围栏的技术背景,界定电子围栏的基本概念,说明电子围栏的运行规则;其次,通过梳理电子围栏在各大城市的应用情况,以北京、上海、厦门等典型城市案例作为研究对象,详细分析电子围栏的划设办法和停车位供给情况,系统总结在设施布局规划、惩治违停行为、多方参与共治等方面的经验;最后,在分析电子围栏对各利益相关方影响的基础上,提出在设计动态电子围栏系统、优化基于用户行为的停车诱导方案,创新电子围栏与共享单车交互机制等未来发展方向。

基于兴趣点聚类的无桩共享单车时空模式分析

城市无桩共享单车发展迅猛,其方便快捷、经济高效的特点受到人们的推崇。它们产生的数字足迹揭示了城市范围内人们在时间和空间上的活动,使利用共享单车对城市中人们的活动进行定量分析成为可能。利用采集的北京市无桩共享单车数据,提出了一种基于城市兴趣点聚类的方法,对城市空间进行划分,构建城市共享单车的流动网络,并从不同的角度分析单车流动的时空模式。本文的研究有助于了解城市居民的出行特点,帮助城市相关管理人员设计和规划城市交通管理体系。

租赁自行车用户出行特征及方式的影响因素分析

针对我国市面上2种主流租赁自行车(公共自行车与共享单车),以南京市为例,基于共享单车骑行数据、公共自行车智能卡数据和租赁自行车用户问卷调查数据,对比2种租赁自行车用户在出行特征及其影响因素方面的差异.从骑行距离、车辆使用频率与时间分布等方面揭示租赁自行车用户的出行特征差异;构建二元Logistic模型,从用户个人属性和主观感知2个层面探究影响租赁自行车用户出行方式选择的显著性因素.结果表明:相较于公共自行车,共享单车的平均骑行距离和骑行时间更短,但小时使用量更高;2种租赁自行车在工作日均呈现出明显的早晚高峰时段,且使用量均远高于周末.退休人员、企业职员和电动自行车拥有者更倾向于使用公共自行车;高收入群体、对互联网技术以及在线支付服务高度敏感的人则更倾向于使用共享单车.

时间维度下上海市共享单车骑行模式研究

基于上海市摩拜单车一个星期的骑行数据,从时间角度深入挖掘骑行数据背后隐藏的行为模式和规律.统计结果显示,工作日和周末的骑行行为存在显著差异,工作日呈现两个明显骑行高峰时段,周末的骑行分布则比较均匀.在区分工作日和周末的基础上,根据骑行行为对每日的骑行者进行分类,每组骑行者均表现出独特的骑行开始时间和骑行时长.基于周中的一天与骑行开始时间,以及周中的一天与骑行时长这两个组合分别计算赫希曼指数(HHI),证实共享单车骑行高度的时间规律性;周中的一天与骑行开始时间组合的HHI值具有更大的标准差,显示了共享单车的灵活性.对共享单车骑行模式的探究,能够在一定程度上促进城市交通系统的持续改善,吸引更多出行者使用绿色交通方式.

基于时空消耗法的共享单车投放规模研究

为探讨城市共享单车的合理投放规模,以有限的城市道路网承载力为约束条件,考虑城市道路总时空资源与非机动车个体出行时空资源消耗量;结合非机动车中共享单车出行分担比例和周转率,构建时空消耗模型,计算宏观性的共享单车投放规模。最后通过对宁波市的实证调查,确定模型中各参数的取值,计算后宁波市二级路网容量水平下的共享单车投放量为18.32万辆。但目前的投放量严重偏高于合理数量,针对该紧迫形势,建议宁波市严格控制共享单车投放量。时空消耗法在共享单车投放规模计算中具有良好的实用性。

考虑宏微观建成环境的共享单车骑行影响因素分析

针对共享单车骑行影响因素分析中对微观建成环境因素探讨的不足,以北京市五环内及附近街道为研究对象,基于城市多源数据,从公共交通设施、土地利用、人口经济属性、空间设计及道路环境5方面提取宏微观建成环境因素,并构建全局回归模型(global regression,GR)、地理加权回归模型(geographically weighted regression,GWR)及多尺度地理加权回归模型(multi-scale geographically weighted regression,MGWR),对共享单车骑行密度进行分析。研究结果表明:MGWR模型有更好的回归效果,调整拟合优度R 2高达0.91;共享单车骑行密度空间上呈现“高-高”及“低-低”聚类两极分化现象;地铁站点密度为局部影响因素,公交线路密度及隔离护栏出现率为区域影响因素,其他建成环境变量为全局尺度;骑行密度显著影响因素的回归系数,在空间上较为平稳,单一变量的空间影响关系均为正向或负向影响;微观建成环境中,绿视率、交通信号出现率为正向影响,色彩丰富性及路灯出现率为负向影响。

Unravel the spatio-temporal patterns and their nonlinear relationship with correlates of dockless shared bikes near metro stations

The dockless bike-sharing system has rapidly expanded worldwide and has been widely used as an intermodal transport to connect with public transportation.However,higher flexibility may cause an imbalance between supply and demand during daily operation,especially around the metro stations.A stable and efficient rebalancing model requires spatio-temporal usage patterns as fundamental inputs.Therefore,understanding the spatio-temporal patterns and correlates is important for optimizing and rescheduling bike-sharing systems.This study proposed a dynamic time warping distance-based two-dimensional clustering method to quantify spatio-temporal patterns of dockless shared bikes in Wuhan and further applied the multiclass explainable boosting machine to explore the main related factors of these patterns.The results found six patterns on weekdays and four patterns on weekends.Three patterns show the imbalance of arrival and departure flow in the morning and evening peak hours,while these phenomena become less intensive on weekends.Road density,living service facility density and residential density are the top influencing factors on both weekdays and weekends,which means that the comprehensive impact of built-up environment attraction,facility suitability and riding demand leads to the different usage patterns.The nonlinear influence universally exists,and the probability of a certain pattern varies in different value ranges of variables.When the densities of living facilities and roads are moderate and the relationship between job and housing is relatively balanced,it can effectively promote the balanced usage of dockless shared bikes while maintaining high riding flow.The spatio-temporal patterns can identify the associated problems such as imbalance or lack of users,which could be mitigated by corresponding solutions.The relative importance and nonlinear effects help planners prioritize strategies and identify effective ranges on different patterns to promote the usage and efficiency of the bike-sharing system.

基于订单数据分析的共享单车重置调度优化研究

无桩共享单车打破了传统公共自行车基于固定桩点提供骑行服务的限制,但如何重置调度随机分布于整个区域面上的共享单车则成为决策者面临的一大难题,而数据驱动的优化方法则为解决这一问题提供了可能。本文首先通过对居民使用共享单车的订单数据进行深入分析,挖掘用户出行规律,继而界定出区域内共享单车的收车点与放车点;进一步地,借鉴报童模型思想构建放车点单车投放的收益函数,在此基础上建立带取送货的共享单车重置路径规划模型并设计算法进行求解。通过算例测试,本文从重置车辆的最大载重量、单位距离成本、重置车辆里程限制、收车点个数以及收车点所拥有的共享单车数等多角度,为共享单车的重置调度提供有效的决策建议。

接驳地铁站的共享单车源汇时空特征及其影响因素

共享单车是解决"最后一公里"出行的有效方法,然而,人们在利用其进行接驳地铁时,常出现无车可用或车辆淤积的现象。因此,探究用于接驳地铁的共享单车的源汇时空分布特征及其影响因素对实现其供需平衡有一定意义,单车运营公司可据此进行更及时、合理的调度。为了解不同区域的共享单车在接驳地铁时使用模式的差异,本文基于不同时间段的客流特征,对用于接驳北京市地铁站的共享单车所产生的源、汇网格进行了K-均值聚类,并进一步利用地理探测器探究了造成这种空间分异的原因。结果表明:①源、汇网格各被分为5类,分别为高频低流出、高频异常源、中频低流出、低频高流出、低频低流出和高频低流入、中频低流入、低频高流入、低频差异流入、高频异常汇等类型,反映了共享单车源汇的时空分布特征;②在不同聚类中,共享单车的日均流量对应的主导因子有所差别,位于市中心的聚类的车辆主要受距离和交通因子的影响,而在其它聚类中则会同时受到多种POI的显著影响,且在不同时段中影响机制不同;③对于净流入(出)率而言,各聚类的源、汇网格的主导因子则大致相同,车辆的缺少或过剩主要与距地铁站或市中心的距离有关。④从整体源、汇来看,住宅类POI数量与距最近地铁站的距离分别是影响日均流量和净流入(出)率的最强的因子。

基于时空地理加权回归的共享单车需求影响因素分析

基于南京市多源数据对共享单车出行需求进行了分析。构建了时空地理加权回归模型,揭示了公共自行车使用量、土地利用与社会经济人口因素在时间和空间上对共享单车出行需求的影响。研究表明:时空地理加权回归模型的拟合结果显著优于最小二乘回归模型与地理加权回归模型;公共自行车的使用会促进共享单车的需求,特别是在早晚高峰时段和郊区;娱乐型兴趣点在中心城区会促进共享单车的需求,在郊区则抑制共享单车的需求;老年人与小汽车用户不倾向使用共享单车,但在良好骑行环境且租赁自行车理念很好普及的区域更容易选择共享单车出行。

接驳地铁站的共享单车时空均衡性分析与吸引区域优化

共享单车是接驳地铁出行的最有效选择之一,但共享单车的时空分布不均问题对使用者和管理者造成了很大的不便,尤其是早高峰期间出现这种问题更会大大降低2个交通系统的运行效率。因此,研究用于接驳地铁出行的共享单车的时空均衡性特点对提升早高峰期间的通勤效率有一定意义。为了了解接驳地铁骑行的时空均衡性特点,本文以厦门市作为实验区,将厦门市早高峰期间接驳地铁站的骑行作为主要研究对象,基于出行OD提出了新的地铁站吸引区域建立方法,基于出行特点提出了考虑地铁站点的单车聚类方法。本文还从潮汐比统计角度和吸引区域角度对各个地铁站的早高峰整体出行均衡性做了分析,从时空角度对早高峰中不同时段的地铁站接驳骑行的时空均衡性做了分析,通过分析得出了3种角度下各个地铁站点均衡性的相似点与不同点,结果表明:(1)根据潮汐比特点,地铁站对骑行的接驳职能可划分为4类,分别为起始型,均衡型,到达型和不适合接驳地铁出行型,反映了各地铁站点的总体接驳特征;(2)地铁站对接驳骑行的吸引区域与潮汐比特点有所差别,其主要影响因素为地铁站点的地理位置及周边土地利用类型;(3)对于时空均衡性分析结果而言,潮汐比对时空均衡性层面没有显著影响,其最大影响因素为周边土地利用类型。该分析结果能体现出厦门市早高峰期间与各地铁站接驳骑行的共享单车运行的差异性以及与地铁接驳效率的高低,以此来支持共享单车企业的重点区域调度和监管。

Extracting commuter-specific destination hotspots from trip destination data-comparing the boro taxi service with Citi Bike in NYC

Taxi trajectories from urban environments allow inferring various information about the transport service qualities and commuter dynamics.It is possible to associate starting and end points of taxi trips with requirements of individual groups of people and even social inequalities.Previous research shows that due to service restrictions,boro taxis have typical customer destination locations on selected Saturdays:many drop-off clusters appear near the restricted zone,where it is not allowed to pick up customers and only few drop-off clusters appear at complicated crossing.Detected crossings imply recent infrastructural modifications.We want to follow up on these results and add one additional group of commuters:Citi Bike users.For selected Saturdays in June 2015,we want to compare the destinations of boro taxi and Citi Bike users.This is challenging due to manifold differences between active mobility and motorized road users,and,due to the fact that station-based bike sharing services are restricted to stations.Start and end points of trips,as well as the volumes in between rely on specific numbers of bike sharing stations.Therefore,we introduce a novel spatiotemporal assigning procedure for areas of influence around static bike sharing stations for extending available computational methods.

轨道交通站点周边土地利用与共享单车的耦合关系分析——以西安市为例

为促进共享单车接驳轨道交通出行和公交主导型的发展,利用GPS数据确定共享单车接驳轨道交通的范围,在该范围内基于POI数据,利用K均值算法将站点划分为四类。通过BP神经网络的方法研究四类站点下土地利用差异性和共享单车间的关系,并建立四个需求预测模型。结果表明在考虑不同轨道站点周边用地差异性的前提下,四个模型对共享单车需求的预测效果良好,说明轨道交通站点周边共享单车的配置需考虑站点类型和用地差异性。此外,研究结果发现大部分轨道站点对单车的吸引是来源于公司企业、商业服务、零售和住宅用地,且主要分布在市中心;单车数量分布较低的站点主要集中在首末站及其附近站,以及用地单一的站点。