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.

无人驾驶汽车技术发展对未来城市轨道交通影响研究

[目的]探讨无人驾驶汽车技术的发展对未来城市轨道交通可能产生的影响,并分析无人驾驶汽车在城市交通系统中的作用及其对现有城市交通结构的影响。[方法]概述了无人驾驶汽车的通行特征,分析了无人驾驶汽车对城市交通结构的影响,详细讨论了无人驾驶汽车对城市轨道交通的具体影响。[结果及结论]无人驾驶汽车能提高交通效率和乘客体验,可能会导致城市轨道交通短途客流下降,但长途客流对其依赖性仍很强。无人驾驶汽车的普及将促使城市轨道交通规划更注重速度和效率,更注重优化中心城与卫星城间的交通联系方式,也更注重促进各种交通方式信息系统的互通与信息交互,提升城市交通一体化的强度。无人驾驶汽车的发展将引发城市交通的重大变革,需要城市规划者和交通管理者采取相应措施以适应技术发展。

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。

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。

基于浮动车数据的行程速度估计算法及优化

依托重点实验室构建的浮动车采集处理实验系统,从行程速度的交通工程学定义出发,利用实时上传的浮动车数据,构建了以浮动车的驻留时间和路段长度为基础的行程速度估计算法．通过对比现场跟车实验测得的行程速度与算法计算结果,验证了该算法的行程速度结果精度达到90％以上,从而为交通信息服务系统提供了简单、准确的区间速度计算方法．最后,对算法进行了优化,有效地提高了算法效率,为满足大规模应用奠定了基础．

浮动车数据在车辆路径问题中的应用

利用浮动车信息采集系统预测路段行程时间,实现对带时间窗的混合车辆配送路径选择的优化.提出了带时间窗的混合车辆路径选择优化问题的求解模型;设计了浮动车地图匹配和路段行程时间预测算法,以实现对路段行程时间的预测,并通过给出的成都市浮动车数据证明了所提出的算法比同类算法更有效——地图匹配率提高6%,路段行程时间预测值与实测值的拟合度更高,运输总费用节约24%.

旅行社与汽车俱乐部经营自驾车旅游的比较研究

在国内自驾车旅游快速发展的今天,旅行社和汽车俱乐部作为该领域中两大类主要经营实体,在组织自驾车旅游时,凭借着各自的优势正在发挥着越来越大的作用。但是,由于它们所属行业特点等原因,二者在许多方面尚存在问题。本文从不同角度,将旅行社与汽车俱乐部所经营的自驾车旅游进行了比较研究,因此对相关企业、行业乃至政府相关部门的决策都是十分有意义的。

城市低碳交通研究

通过论证和整理,找到现有技术条件下实现减少城市交通温室气体排放的方法和措施。减少城市交通温室气体排放最理想的办法是降低交通负荷,其次是用低碳出行方式代替小轿车出行,再次是使用低排放的汽车,其中关键是促使人们采用绿色出行。实践中应该多措施并举。列举了一些利用已有技术就可以实现的措施。

城市过量短距离小汽车出行研究

以北京市为例,挑选具有代表性的两类居民小区,对小区出行环境和居民出行行为展开调研.首先,利用GPS和测距仪等设备收集出行环境数据,结合地理信息系统,对小区内部和扩展区域的机动车和非机动车环境进行分析,指出了两类小区出行环境的特点;然后,利用调查问卷收集居民出行行为数据,在分析不同小区的短距离出行特征和同类居民的出行行为后,指出出行环境对出行行为具有显著的影响.最后,给出了不同小区居民偏好小汽车短距离出行的影响因素,以及在不同出行目的下,居民出行方式的转换意愿.研究成果有助于制定合理引导小汽车出行的交通需求管理对策.

基于低采样频率浮动车的行程车速信息实时采集技术

智能运输系统(ITS)是当前解决交通问题的最佳途径之一,而动态交通信息的发布与预测是ITS的关键所在.然而由于在各种外部因素下,道路交通状态呈现出随机变化的特性,使得动态交通信息的发布与预测成为一大难点.结合某城市的实际情况,本文建立了在定位误差、采样间隔、车辆类型、道路类型等因素影响下的行程车速估计模型,并用实测数据对模型进行精度验证.结合出租车车辆的特殊信息,改进了低采样频率浮动车技术的估计算法,建立了空车数据处理模型;针对城市复杂和相似路段问题,提出了基于距离、方向角、连通性、历史数据以及车流方向等约束条件下的地图匹配算法;针对低采样频率浮动车数据,提出了考虑交叉口影响下的行程车速估计算法,通过实测数据评估了算法的性能;最后,针对不同检测技术的特征,提出了数据融合算法.