Private Car Travel Characteristics and Influencing Factors in Chinese Cities——A Case Study of Guangzhou in Guangdong,China

Taking Guangzhou as a case,this paper adopted a questionnaire survey to gather first-hand data and analyzed the characteristics and influencing factors of private car travel in Chinese cities.As the research indicated,trip purposes of private car travel are mainly commute and business affairs with a more flexible trip in the urban core area.And trip intensities are concentrated in a certain extent,with trip frequency being lower in the urban core area than the peripheral area.In addition,the trip time has two significant peaks occurring in the morning and afternoon,and one trough in the midday.And trip spatial distribution is mainly within commute with both residence and employment in urban area and inward commute with residence in suburban area while employment in urban area.Both kinds of commutes direct to the urban area.The study also shows that the characteristics of private car travel are principally influenced by two aspects:travelers' attributes and urban characteristics.The main travelers' social and economic attributes influenced it include the gender,education attainment,age,driving experience and per capita monthly household income.The urban characteristics influenced it mainly cover the land use pattern,public traffic facilities and spatial attributes of residential environment.

Application of Disaggregated Logit Modeling with Stated Preference Method in Park and Ride Behavior for Non-local Private Car Travelers in Big Events

During big events, non-local private car travelers can be divided into two types which were returning in one day and in several days. It was demonstrated that those two kinds of travelers have distinct behavior on park and ride （P＆R）, due to their different travel demand and behavior attributes. In this paper focusing on the travelers returning in several days, the travel attributes and requirements for P＆R were analyzed with stated preference survey. A P＆R choice behavior disaggregated logit model was established and calibrated based on random utility theory. The model concludes three variables, which were travel time, parking fee and comprehensive attractiveness index for suburban satellite towns comparing to urban district. The results revealed that for travelers returning in several days the primary key point is increasing the attractiveness of suburban satellite towns.

Evaluation of floating car technologies for travel time estimation

Travel times have been traditionally estimated from data collected by roadway sensors. Recently, new tech- nologies, such as cell phone tracking, license plate matching, automatic vehicle identifications and video detection, are employed for this purpose. In this study, the data collected by TRANSMIT readers, Bluetooth sensors, and INRIX are assessed by comparing each to the ＂ground truth＂ travel times collected by probe vehicles carrying GPS-based naviga- tion devices. Travel times of probe vehicles traveling on the study segment of 1-287 in New Jersey were collected in 2009. Statistical measures, such as standard deviation, average absolute speed error, and speed error bias, were used to make an in-depth analysis. The accuracy of each travel time estimation method is analyzed. The data collected by Bluetooth sensors and the TRANSMIT readers seem more consistent with the ground true data, and slightly outperform the data reported by 1NRIX. This study established a procedure for analyzing the accuracy of floating car data （FCD） collected by different technologies.

Floating Car Data Based Nonparametric Regression Model for Short-Term Travel Speed Prediction

A K-nearest neighbor （K-NN） based nonparametric regression model was proposed to predict travel speed for Beijing expressway. By using the historical traffic data collected from the detectors in Beijing expressways, a specically designed database was developed via the processes including data filtering, wavelet analysis and clustering. The relativity based weighted Euclidean distance was used as the distance metric to identify the K groups of nearest data series. Then, a K-NN nonparametric regression model was built to predict the average travel speeds up to 6 min into the future. Several randomly selected travel speed data series, collected from the floating car data （FCD） system, were used to validate the model. The results indicate that using the FCD, the model can predict average travel speeds with an accuracy of above 90%, and hence is feasible and effective.

基于网约车出行的长三角城市群空间关联网络特征与影响因素识别

文章基于长三角城市群内城市、乡镇街道尺度的流空间视角,利用网约车出发地―目的地(OD)出行数据等多源数据,结合复杂网络模型和二次指派程序(QAP)分别对城市群空间关联网络结构特征及其驱动机制进行分析。研究表明:1)基于网约车流的长三角空间关联网络具有空间依赖性和层级特征,网络关联强度与经济发展程度较为耦合;2)长三角空间关联网络具有空间溢出效应,整体形成南部发展均衡性高,北部单点发展能力强的格局;3)长三角空间关联网络特征在乡镇街道与城市尺度上呈一定差异,部分交通网络中的高层级乡镇街道在城市层面未能充分发挥带动作用;4)乡镇街道间经济发展状况、人口活力、城市建设水平、行政区划归属和地理区位差异对乡镇街道间空间关联网络结构均有显著影响,其中行政区划归属差异影响最重要。

考虑共享单车接驳的共享汽车站点选址模型构建及应用研究

共享汽车是共享经济背景下的一种绿色出行模式,它不但能满足人们出行用车需求,还能实现城市低碳绿色发展。但是,很多城市也出现了共享汽车站点布局不合理、站点汽车数量和用户需求不匹配、停车取车难等一系列问题。在组合出行的背景下,基于停车场、城市POI及出租车(包括快车)订单等多源数据,以最大化用户需求满足和最小化取车还车成本为目标,构建考虑共享单车接驳的共享汽车站点选址模型,并实证分析得到最优的站点布局。研究结果表明,从用户需求情况来看,在共享汽车站点最大服务范围一定的前提下,可以通过增加站点数量和共享单车可用率来提高用户需求,让共享汽车用户在出行时做到有车可用。从用户取还车便捷性来看,在共享汽车站点最大服务范围较大时,随着共享单车可用率的增加,用户使用取还共享汽车花费的时间明显减少。这在一定程度上说明共享单车可用率增加可有效提高共享汽车用户取还车的便捷性,缩短用户的取还车时间,提高其用车意愿。

新型冠状病毒感染疫情对网约车出行行为的影响

为探究新型冠状病毒感染疫情对居民选择网约车出行的影响,通过问卷调查获取影响居民选择网约车出行的潜在因素,对出行者选择网约车的出行行为特征进行分析,揭示疫情前后居民出行方式选择的变化;建立复工后居民出行选择的二元Logit模型,识别影响选择网约车出行的主要因素。结果表明:复工后,出行者对公共交通产生排斥心理,公司职工通勤倾向于选择私家车和网约车;出行者更关注网约车的车辆消毒、安全报警功能、车内隔离防护等措施;网约车用户中男性是女性用户的2.081倍;年轻群体(20~30岁)是使用网约车出行的主体;无车人群更倾向于选择网约车出行;网约车的出行费用优惠于传统出租车成为吸引出行者的主要优势。但网约车的乘车安全问题,对出行者选择网约车出行有消极作用。研究成果能够为提升网约车服务质量提供参考。

TOD模式缓解小汽车依赖的作用机制探析

面对小汽车高普及带来的交通拥堵、空气质量恶化、道路资源浪费、社会空间分异等问题,TOD理论被提出并广泛运用于实践中,以期优化城市空间,缓解小汽车依赖。文章通过建立指标评价体系,探析TOD模式缓解小汽车依赖的作用机制,认为TOD模式通过改善客观出行环境,即构建合理的空间结构、提供多样的出行方式、采用混合开发的土地利用模式,以及营造良好的慢行空间,逐渐构造了不依赖小汽车的城市空间,通过提供多样的出行方式,改善出行的客观环境,降低小汽车出行被选择的概率。

Accessibility Comparison and Spatial Differentiation of Xi’an Scenic Spots with Different Modes Based on Baidu Real-time Travel

A study of the accessibility of a city’s scenic spots via different travel modes can contribute to optimization of tourism-related transportation while improving tourists’ travel-related satisfaction levels and advancing tourism. We systematically analyzed the accessibility of 56 scenic spots in Xi’an City, China, via car and public transport travel modes using the real-time travel function of the Baidu Maps API(Application Programming Interface) along with spatial analysis methods and the modal accessibility gap index of scenic spots. We obtained the following results. First, maximum and minimum travel times using public transport exceeded those using cars. Moreover, the accessibility of scenic spots via cars and public transport presented a circular spatial pattern of increasing travel time from the center to the periphery. Contrasting with travel by public transport, car travel showed a clear time-space compression effect. Second, accessibility of the scenic spots via cars and public transport showed some spatial heterogeneity, with no clear advantages of car accessibility in the central urban area. However, advantages of car accessibility were increasingly evident moving from the center to the periphery. Third, whereas the correlation of the modal accessibility gap index of scenic spots in Xi’an with global space was significantly positive, local spatial interdependence was only evident in some inner city areas and in marginal areas. Moreover, spatial heterogeneity was evident in two regions but was insignificant in other areas, indicating that the spatial interdependence of the modal accessibility gap index in most scenic spots was not apparent in terms of the overall effect of public transport routes, road networks, and the distribution of scenic spots. The improvement of public transport coverage in marginal areas and the optimization of public transport routes in central urban areas are essential tasks for improving travel using public transport in the future.

Optimal Path Finding Method Study Based on Stochastic Travel Time

Finding optimal path in a given network is an important content of intelligent transportation information service. Static shortest path has been studied widely and many efficient searching methods have been developed, for example Dijkstra’s algorithm, Floyd-Warshall, Bellman-Ford, A* et al. However, practical travel time is not a constant value but a stochastic value. How to take full use of the stochastic character to find the shortest path is a significant problem. In this paper, GPS floating car is used to detect road section’s travel time. The probability distribution of travel time is estimated according to Bayes estimation method. The combined probability distribution of a feasible route is calculated according to probability operation. The objective function is to find the route that has the biggest probability to arrive for desired time thresholds. Improved Genetic Algorithm is used to calculate the optimal path. The efficiency of the proposed method is illustrated with a practical example.

基于卡诺模型的亲子互动玩具车设计

旨在促进良好亲子关系的发展,通过亲子互动玩具促进幼儿的配合与动手能力的提高。以亲子互动玩具为媒介,将亲子互动贯穿于亲子玩耍过程中。通过实地调研和网络问卷调查的方式,分析亲子互动玩具的现状。利用KANO模型对用户需求进行分析,确定重要程度的用户需求,并进行排序。研究出亲子户外游乐过程中的亲子互动玩具设计模型与设计原则,并将其应用到设计实践中。为户外亲子互动玩具设计提供设计方法与流程。使户外亲子互动玩具的互动性和可玩性得到提升,达到户外环境游玩时亲子良好互动交流的效果,在培养良好亲子关系的过程中发挥更积极的作用。

“新冠”疫情对私家车出行特征的影响

受新冠肺炎疫情的影响,北京市私家车辆在2020年的出行特征,包括出行频率,出行时间以及出行地点均表现出了不同于往年的变化.使用安装在志愿者车辆上的车载盒子收集车辆行驶的浮动车数据,通过对比2020年6月与2019年6月同期的私家车辆出行特征,在不同功能的出行区域内、以出行终点为研究对象,对私家车辆的出行特征进行分析.经过对数据的筛选和处理,进而从出行目的及出行意愿角度对“新冠”疫情影响私家车辆出行特征的改变进行总结.

网约车与巡游车混合模式下的居民出行选择意愿影响研究

网约车的出现为居民出行提供了一个全新的选择。长久来看,网约车和巡游车混合经营将成为未来很长一段时间的主流经营模式。为了探究在这种模式下影响居民出行意愿的影响因素,在居民出行方式选择的研究上集中于网约车和巡游车两种。通过对出行者特征、出行特征和居民感知属性三大方面设计调查问卷,建立了居民网约车和巡游车选择差异的二元Logistic模型,利用SPSS软件进行数据回归分析。回归分析结果显示,在出行者特征中年龄、性别对居民出行选择影响显著;出行特征中,夜晚时间、有人同行、高峰时段影响显著;居民感知属性上,安全性影响显著。为了进一步探究影响因素,获取了网约车和巡游车一个月的订单数据,根据订单数据分析出行时间、出行距离和出行地区对居民选择意愿的影响。

面向小汽车出行的代客泊车支付意愿影响因素分析

代客泊车作为城市停车产业可拓展的服务方向之一,推动其合理、有序地产业化发展可隐性提高城市停车资源的利用效率.本文通过RP/SP问卷调查方法,搜集福州市小汽车出行者数据信息,构建以出行目的为上层模型、支付意愿水平为下层模型的Nested Logit模型,进而解析个体代客泊车支付意愿的影响因素及作用机理.结果表明,相较于通勤,出行者更愿意在休闲旅行时为代客泊车支付更高的服务费,具体表现在出行成本的增加和代泊风险的减少都会提高休闲个体的代泊支付意愿,当助泊过程风险每降低1个等级,支付意愿程度会比之前提升1.24倍;并且在休闲旅行时,年轻男性企业员工的代泊支付愿意要比女性高0.82倍,尤其是在带领家庭成员集体外出游玩时,其支付意愿在原有基础之上还会有一定幅度的提升.此外,代客泊车平台通过整合停车场附近的加油充电场,将进一步丰富代泊服务范畴,提高出行者的代泊支付意愿,从而促进代泊服务体系的产业化发展.

机械式立体停车场行走机构设计

随着经济的高速发展,私家车的数量不断增多,但交通及道路的荷载量、面积是固定的,这导致停车的难度与日俱增。为此,需加强对机械立体停车装置的优化配置,以满足停车需求,改善并解决停车难问题。本文设计了机械式立体停车场行走机构,以期提高停车场的停车便捷性、安全性。

西安市私家车出行特征及其影响因素

伴随着汽车保有量的快速增加,城市交通拥堵与环境污染问题日益严重。同时随着放宽户籍限制政策使得城市人口数量大幅度增加,交通问题愈发严重。文章以陕西省西安市私家车为研究对象,对这座国内典型城市的私家车出行强度和出行时间的分布特征进行了研究。对不同驾驶员个人属性所影响的出行强度建立了合适的结构方程模型,分析个人属性对出行强度的影响。然后对不同驾驶员属性所影响的出行时间进行卡方检验,筛选出对出行特征有明显影响的属性,接着对数据进行可视化处理并分析这些属性对出行特征的影响规律,最后结合分析结果提出管理建议。分析结果表明,年龄、职业对出行强度呈现显著的正相关性,教育程度对出行强度呈现显著的负相关性。私家车工作日和周末的出行特征明显不同;同时,年龄、职业、受教育程度是影响私家车出行时间特征的主要因素。

出行方式转移行为特征分析——以北京市为例

对规划与政策可控变量组合情景下的转移行为特征实证分析在现有出行方式转移研究中较为少见。以北京市为例,对3 351名出行者进行出行方式转移意向调查(SP调查),识别出4类转移意愿群体。首先分析出行时间比和停车费用两个变量对不同群体的影响。然后,对比不同组合情景下出行方式转移率的变化,以反映公共交通出行效率改善和小汽车使用成本提高对不同群体出行方式转移意愿的影响程度。结果表明:降低出行时间比对于提高公共交通乘客依赖度和减少乘客流失效果较好,但是很难吸引对小汽车高度依赖的出行者;提高停车费用对于引导摇摆群体从小汽车出行向公共交通转移效果显著。两个变量所产生的出行方式转移率受到现状基准值的影响,在制定政策时需统筹考虑两者的互动关系。最后,提出应结合出行调查数据制定分区域、分群体、分目的的公共交通吸引力提升策略,探索精细化、可实施、重统筹的公共交通发展及交通治理路径。

基于GPS浮动车的路段行程时间估计方法比较

以长春市局部路网的地理信息为基础,运用实测GPS数据分别对基于GPS浮动车的路段行程时间的直接估计方法与间接估计方法进行编程实现和测试分析,并且依据所得路段行程时间数据对比分析了两种方法各自的特点及适用范围。结果表明,两种方法适用范围的差别比较明显。

基于居民通勤行为的私家车对居住空间影响研究——以大连市为例

私家车的出现改变了交通结构,增强了空间可达性,从而影响居住空间的格局和规模。以大连市为例,基于居住—就业之间的通勤行为研究私家车对居住空间扩展的作用,通过居民的社会属性、通勤行为和心理偏好等信息,利用GIS技术进行模拟和分析。结果表明,居民购车情况与居住—就业空间分布情况有一定的相关性。由于城市扩展区居住—就业的不均衡性,通勤不便是城市边缘区居民的主要问题,私家车增强了通勤的便利性,在距城市中心5km以外,私家车随居住空间的扩展而增多,并通过循环与累计效应使城市的整体规模扩大。同时,通过计量模型计算,在1990~2004年,影响居住空间扩展的诸多因子中,私家车的贡献率平均达到2.14%。

基于浮动车数据的行程时间可靠度特征研究

针对正态分布或对数正态分布往往不能有效地描述小间隔行程时间分布的问题,提出了基于浮动车数据的行程时间算法。通过基于浮动车数据的行程时间经验分布,得到了以15min为间隔的不同出发时段行程时间的可靠度指标。以广深高速公路同乐—宝安机场段为例,对不同日、不同出发时段的行程时间可靠度特征进行了分析。该研究方法对于描述交通条件变化比较频繁的城市交通环境的行程时间可靠度有较强的适应性。