Kriging Geostatistical Methods for Travel Mode Choice: A Spatial Data Analysis to Travel Demand Forecasting

This paper aims to compare the results of two techniques of Kriging (Ordinary Kriging and Indicator Kriging) that are applied to estimate the Private Motorized (PM) travel mode use (car or motorcycle) in several geographical coordinates of non-sampled values of the concerning variable. The data used was from the Origin/Destination and Public Transportation Opinion Survey, carried out in 2007/2008 at S?o Carlos (SP, Brazil). The techniques were applied in the region with 110 sample points (households). Initially, Decision Tree was applied to estimate the probability of mode choice in surveyed households, thus determining the numeric variable to be used in Ordinary Kriging. For application of Indicator Kriging it was used the variable “main travel mode” in a discrete manner, where “1” represented the use of PM travel mode and “0” characterized others travel modes. The results obtained by the two spatial estimation techniques were similar (Kriging maps and cross-validation procedure). However, the Indicator Kriging (KI) obtained the highest number of hit rates. In addition, with the KI it was possible to use the variable in its original form, avoiding error propagation. Finally, it was concluded that spatial statistics was thriving in travel demand forecasting issues, giving rise, for the both Kriging methods, to a travel mode choice surface on a confirmatory way.

Joint analysis of urban shopping destination and travel mode choice accounting for potential spatial correlation between alternatives

In recent years,there have been important developments in the joint analysis of the travel behavior based on discrete choice models as well as in the formulation of increasingly flexible closed-form models belonging to the generalized extreme value class.The objective of this work is to describe the simultaneous choice of shopping destination and travel-to-shop mode in downtown area by making use of the cross-nested logit(CNL) structure that allows for potential spatial correlation.The analysis uses data collected in the downtown areas of Maryland-Washington,D.C.region for shopping trips,considering household,individual,land use,and travel-related characteristics.The estimation results show that the dissimilarity parameter in the CNL model is 0.37 and significant at the 95% level,indicating that the alternatives have high spatial correlation for the short shopping distance.The results of analysis reveal detailed significant influences on travel behavior of joint choice shopping destination and travel mode.Moreover,a Monte Carlo simulation for a group of scenarios arising from transportation policies and parking fees in downtown area,was undertaken to examine the impact of a change in car travel cost on the shopping destination and travel mode switching.These findings have important implications for transportation demand management and urban planning.

Big Data-Driven Residents’ Travel Mode Choice Choice: A Research Overview

The research on residents'travel mode choice mainly studies how traffic flows are shared by different traffic modes,which is the prerequisite for the government to establish transportation planning and policy.Traditional methods based on survey or small data sources are difficult to accurately describe,explain and verify residents'travel mode choice behavior.Recently,thanks to upgrades of urban infrastructures,many real-time location-tracking devices become available.These devices generate massive real-time data,which provides new opportunities to analyze and explain resident travel mode choice behavior more accurately and more comprehensively.This paper surveys the current research status of big data-driven residents'travel mode choice from three aspects:residents'travel mode identification,acquisition of travel mode influencing factors,and travel mode choice model construction.Finally,the limitations of current research and directions of future research are discussed.

A tour-based analysis of travel mode choice accounting for regional transit service

The aim of this work is to explore the impact of regional transit service on tour-based commuter travel behavior by using the Bayesian hierarchical multinomial logit model, accounting for the spatial heterogeneity of the people living in the same area.With two indicators, accessibility and connectivity measured at the zone level, the regional transit service is captured and then related to the travel mode choice behavior. The sample data are selected from Washington-Baltimore Household Travel Survey in 2007,including all the trips from home to workplace in morning hours in Baltimore city. Traditional multinomial logit model using Bayesian approach is also estimated. A comparison of the two different models shows that ignoring the spatial context can lead to a misspecification of the effects of the regional transit service on travel behavior. The results reveal that improving transit service at regional level can be effective in reducing auto use for commuters after controlling for socio-demographics and travel-related factors.This work provides insights for interpreting tour-based commuter travel behavior by using recently developed methodological approaches. The results of this work will be helpful for engineers, urban planners, and transit operators to decide the needs to improve regional transit service and spatial location efficiently.

Comparison of Travel Mode Choice Between Taxi and Subway Regarding Traveling Convenience

In this study, we investigate travel mode choice behavior between taxi and subway with an emphasis on the influence of traveling convenience. In the first stage, we examine the Origin-Destination(OD) points of Beijing taxi trips and compare these locations with the respective nearest subway station. Statistics reveal several interesting conclusions. First, for approximately 24.89% of all trips, no convenient subway connections exist between the OD pairs. As such, a taxi becomes the only viable choice. Second, for 80.23% of the remaining 75.11%of trips(equivalent to 60.26% of all trips), access distance from either the origin or the destination to the nearest subway station is greater than 500 meters. This phenomenon indicates that walking distance plays an important role in travel mode choice. In the second stage, we examine groups of taxi trips with similar travel distances and travel times to reveal common features. We establish a preference rule in terms of travel distance and travel time.This determines whether an individual driver will take a taxi or the subway, using a pairwise comparison-based preference regression model. Tests indicate that more than 95% of taxi trips can be correctly predicted by this preference rule. This conclusion reveals that traveling convenience dominates the travel model choice between taxi and subway. All these findings shed light on the factors that influence travel mode choice behavior.

＂Wheelchair slow transit＂ system-based elderly auxiliary travel mode

Based on research of the current situation and analysis result of a case study in the Nanjing region, this paper proposes a ＂whee（chair slow transit＂（WST） system-based elderly auxiliary travel mode. The system involves three fundamental composition frames, namely, support, transit components, and connection components. Each component is designed as an armrest, ground track, or vertical type to respond to actual demands. Thus, this system may be adaptable to diverse conditions. Taking Xiangpuying community as a case study, the author examined the application of the WST auxi（iary mode in particular communities. The WST system helps to increase the accessibility of existing public areas, creates a safe community traffic environment, improves municipal facilities, and strengthens universal design. The study intends to provide a reference to obtain a complete aged care design and to build a comfortable and livable aged care community environment.

Complex modes and traveling waves in axially moving Timoshenko beams

Complex modes and traveling waves in axially moving Timoshenko beams are studied. Due to the axially moving velocity, complex modes emerge instead of real value modes. Correspondingly, traveling waves are present for the axially moving material while standing waves dominate in the traditional static structures. The analytical results obtained in this study are verified with a numerical differential quadrature method.

Mode of travel and the decision to allow children to walk or bike to schools—The Abu Dhabi experience

The study investigates school travel to and from schools as perceived by parents in Abu Dhabi, United Arab Emirates. Far fewer children are cycling or walking to school, and more and more are being driven to school by car. Traffic conditions, environmental factors, distance to school, road’s infrastructures, walking or biking with groups, schools efforts to educate children about active travel, and preferences of parents to accompany children to school were significantly perceived as important. The absence of active travel culture in the community was seen as a significant factor. Parent’s gender, child’s gender, child’s grade level, number of cars owned by the family, nationality and number of children were significantly associated with parents’ decisions to allow active travel to school. Results show that Abu Dhabi must establish aggressive strategic plans to promote active-commuting to school as the main mode of transportation. Current conditions in Abu Dhabi City are not conducive to active travel to school.

基于出行剩余理论的旅游出行方式选择研究

为揭示旅游出行需求主体选择出行服务方式的规律,研究旅游出行特性,提升研判运输通道上旅游客流选择出行服务方式的准确性,构建基于出行剩余理论的休闲旅游出行方式选择模型。从需求独立价值角度刻画旅游出行人群的出行行为,在旅游出行价值特征方面强调出行服务方式的情感价值和游客空间行为价值,在出行剩余理论基础上对旅游出行方式选择进行细化分析。以西安和成都为旅游出行的OD点进行情境模拟算例研究,根据游客对沿途旅游资源的偏好差异将出行需求主体分为3类,分别计算其出行剩余,根据出行剩余最大化原则讨论其最佳出行服务方式。研究结果表明,由于3类游客对特定旅游资源数量、品质情感响应、偏好等存在差异,所对应的最佳出行服务方式有所不同。

重大节假日出行方式选择模型研究

为优化节假日高速公路免费政策,选取出行者变量、出行方式相关变量和行程特性变量作为效用变量,构建了出行方式位于上层、出行距离位于下层的NL模型。利用弧弹性法分析不同收费标准和收费里程条件下出行者的行为特性,并利用场景仿真评价多种收费组合对出行方式的影响。研究结果表明:提高收费标准,汽车出行比例会显著降低,且高速铁路承担了大部分的转移交通量。随着收费里程的增加,出行者逐渐倾向于出行成本较小的普通铁路。对长距离出行收费可以提升公共交通分担率,缓解节假日高速公路的交通拥堵。

基于机器学习的中型城市居民出行方式选择行为研究

为探索中型城市居民出行特征以及不同因素对出行方式选择行为的影响机制,本文以中国某中型城市居民出行数据为例,综合考虑传统离散选择模型和机器学习模型在预测精度和建模合理性上的优劣,以及机器学习模型超参数求解算法的特点和效率,引入变异程序,提出粒子群优化随机森林的中型城市居民出行方式选择预测模型,采用预测准确率、出行方式比例预测绝对误差和期望模拟误差这3项性能指标,量化对比粒子群优化随机森林模型与多种机器学习模型和多项Logit模型统计学上的预测性能差异,利用SHAP(SHapley Additive exPlanation)模型深入分析个人社会经济属性、出行属性及出行方式属性等相关因素与居民出行方式选择之间的非线性关系。结果表明:粒子群优化随机森林模型整体平均预测准确率最高,为0.856,出行方式比例预测平均绝对误差和期望模拟平均误差最低,分别为0.062和0.306,模型间指标差异在统计学检验下显著;距离对不同出行方式选择的影响最显著,步行和私家车出行对距离敏感性更高,不同距离下,两者选择概率变化超过35%;30岁以下群体不同出行方式选择概率差距大于其他年龄段;性别、是否拥有私家车或公交IC卡等因素显著改变公交车和私家车的选择概率。

时间匹配程度对学龄儿童通学出行方式的影响

改善学生与家长的通勤时间约束、优化或转变儿童通学出行方式是缓解拥堵、提升安全的有益选择。基于时间地理学理论,提出儿童通学时间与家长通勤时间的匹配程度测度方法,划分时间制度约束型、出行时间宽裕型和出行时间约束型三种时间匹配值类型。采用多项Logit模型分析父母不同时间匹配值类型对儿童通学出行方式的影响。结果表明,家长与儿童的时间匹配程度会影响儿童通学出行方式选择。在不同时间匹配值类型条件下,儿童通学出行方式与家长性别高度关联。上学出行中,母亲与儿童在时间制度约束下小汽车出行比例增加;放学出行中父亲受到时间制度约束反而会增加儿童步行通学出行的概率。因此,制定鼓励学生步行通学出行的政策时要重视时间制度与上下学时间情境的影响。

职住学空间测度及儿童通学决策的影响机理——以昆明市双职工家庭为例

当代城市家庭选择居住地时往往将临近学校作为重要参考,在缩小学住距离的同时加剧了职住分离。选取昆明市561个有学龄儿童的双职工家庭为研究对象,分析家庭空间分异对儿童通学决策的影响。首先,基于时间地理学理论构建考虑职住学三个锚点的家庭活动空间及测度理论模型。其次,根据测度结果将家庭活动空间划分为职住学就近型和职住学分离型两类。最后,建立结构方程模型分别探究两种模式下父母工作时长以及父母儿童组合时间窗对儿童通学时空行为的影响。研究结果表明:相对于学住距离,父母的职住距离和工作时长对通学行为的影响更为显著,因此就近入学政策对于促进儿童步行上学的作用有限;对于职住学分离型家庭,父母下班与儿童放学组合时间窗的宽裕程度对儿童出行影响显著,组合时间窗越宽裕,儿童小汽车出行的可能越大,因此弹性放学时间的效果也有待商榷;儿童在通学行为中的性别差异主要在职住学分离型家庭中体现。

航空旅客出行需求强度异质性研究

为了研究旅客出行需求强度问题,采用问卷调查的方式采集数据,对回收的问卷进行数据整理,构建了潜在类别模型,并用软件mplus对出行人群的出行需求强度进行分类,得到结果如下:可通过旅客属性特征调查,将旅客分为4类人群,分别是高需求人群、中高需求人群、中低需求人群、低需求人群。对分类结果采用多元logistic回归分析,结果表示需求强度人群划分影响因素主要为职业、出行目的、票价提高是否出行等。根据回归结果分析可以将4类需求强度异质性做具体的分析。

建成环境对公共交通与小汽车出行方式选择的非线性影响——基于出行链视角

针对城市公共交通竞争力有待提升的现实背景,以及对车外环境和替代出行方式关注不足等问题,文章通过出行链视角,结合武汉市主城区居民出行调查和街景数据,利用百度地图模拟出行路径,使用随机森林模型全面分析了出发地、目的地和车外路段建成环境对公共交通与小汽车出行选择的影响。结果表明:1)细化出行阶段并考虑替代小汽车的公交出行方式,可增强分析深度和模型精度;2)建成环境是影响2种交通方式选择倾向的关键因素,其中车外环境与出发地、目的地的建成环境同等重要;建成环境因素与交通方式选择倾向之间存在非线性关系,且部分因素在出发地和目的地的影响有所差异;3)建成环境对出行方式选择的影响机理可分为弹性调整、有限支撑和直接驱动3种类型。

学生通学出行的家长接送方式的选择

研究家长接送方式选择的影响因素,对于早晚高峰交通出行政策的制定具有重要意义.考虑家长、学生的个人属性与家庭属性,以及后续行程目的、生理成本与延误时间等出行属性因素,基于多项logit(multinomial logit,MNL)模型分析对于家长接送学生交通方式选择的影响因素,并通过中国海宁市居民出行数据进行验证.研究结果表明,家长年龄越大,选择步行接送的可能性越大;学生性别对于家长接送方式的选择没有显著影响;家长性别和学生年龄对接送方式具有显著影响,其中,女性接送者更偏向于选择非机动车接送,学生年龄越小,家长更偏向于选择非机动车接送;后续行程目的为上班的家长更偏向于选择机动车接送;小汽车接送者的接送时间相对较长,但该接送方式承担的生理成本和延误时间相对较少;电动车接送者承担的生理成本和时间成本都较低.依据研究结果,从家庭、学校和交通管制等维度对学生通学出行提出相关改善建议,能够为相关部门从学生通学的角度制定交通规划管理政策、方案提供指导.

交通需求管理的通勤福利效应——以“禁摩限电”为例

在市民个体层面,“禁摩限电”政策的实施效果尚缺乏定量评估。本研究将交通模式特征和土地利用特征整合到离散选择模型中,模拟计算了“禁摩限电”对市民通勤福利(模式转变和成本变化)的影响。研究发现,实施“禁摩限电”后:(1)超过35%的摩托车和电动自行车使用者转向小汽车出行。小汽车分担率由不足30%上升到50%以上;(2)弱势群体的福利损失高于其他群体。单程通勤的时间平均增加6.9min以上,经济成本平均增加0.5元以上。(3)郊区镇居民的单程通勤时间和经济成本分别比其他区域市民多增加20%和0.2元。城市中心周边市民的换乘次数和候车时间增加明显。在我国实施公交优先发展战略,倡导绿色出行的背景下,本研究对于优化相关政策实施具有重要参考价值。

基于生活方式视角的城市青年居民交通出行与居住区位选择行为——以南京市为例

引入生活方式概念并作为城市青年居民细分指标,通过构建潜在类别选择模型,探讨青年居民日常交通出行方式及居住区位选择行为。基于调查数据,采用聚类分析将青年居民生活方式分为“市区+个人公共交通+家庭私家车”导向、“市区+个人出行无偏好+家庭私家车”导向、“市区+个人/家庭私家车”导向、“市郊+个人/家庭公共交通”导向、“市郊+个人/家庭私家车”导向,及“市郊+个人公共交通+宅家”导向6种类型。随后基于模型参数估计结果,研究城市青年居民生活方式、出行方式及居住区位选择间的内在联系。相关研究成果与结论为理解城市青年居民时空选择行为差异提供了理论支持。

城市绿地游憩服务网络特征及不同出行模式的响应差异

【目的】随着经济社会的快速发展,城市居民亲近自然的游憩需求显著增长。在城市尺度提升绿地的蓝色和绿色游憩服务效率成为当前亟须解决的问题。【方法】以武汉市为研究区域,通过提取居民游憩出行的最短路径,利用社会网络分析软件Ucinet 6.0分别构建步行、骑行和驾车3种出行模式下的5 min、10 min和15 min城市绿地的游憩服务网络,并分析网络的整体特征、节点功能和节点类型,进而探究这些特征在不同出行模式下的响应差异。【结果】当出行时长为5 min时,骑行和驾车模式能够组织超过85%的城市绿地节点协同联系形成游憩服务网络,而步行模式下游憩服务则呈零星局部组团分布。随着出行时长的增加,3种出行模式下游憩服务网络的核心集聚功能节点占比呈上升趋势,且节点类型主要以绿色游憩服务为主。骑行和驾车模式下的边缘汇聚功能节点占比则随出行时长增加而逐渐下降,但其蓝色游憩服务型核心集聚节点的占比则逐渐上升。【结论】城市绿地游憩服务网络效率的整体提升须系统考虑不同出行模式下居民的游憩需求,并协同整合蓝色和绿色游憩空间。这些发现可以为城市绿地游憩体系的构建和完善,以及游憩资源的系统整合提供直接支持。