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.

Research on Behavior on Travel Mode, Going to Metro Station at Ho Chi Minh City

This research presents an analysis of travel mode choice for trips to Ho Chi Minh City metro station. Research methods were inherited through a formula to calculate passenger traffic forecasts to predict passengers, going to the station. Based on the collected data of interviews, traffic surveys, and ＂Irwin ＆ Von Cube＂ function, forecast the proportion of travel mode, and use to go to the station and leaving from the station. The results of study are used for the purpose of calculating the size of the metro station parking lots and parking layout plan.

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.

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.

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.

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.

Influence of land use characteristics and trip attributes on commuting mode choice: a case of Nanjing

The effects of socio-demographics, land use characteristics and trip attributes on the commute mode choice are studied with a nested logit （NL） model. Based on the random utility maximum theory, the NL model is formulated. The analysis is carried out in the main area of Nanjing. Direct and cross elasticities are calculated to analyze the effects of travel time and travel cost on the selection of travel mode choice. The results reveal that the non-motorized travel modes are more attractive in the areas with higher housing and employment accessibility and car owners are found to be more likely to commute to work by car. The bus and subway choice probabilities are more sensitive to changes in travel times than to changes in travel costs. In conclusion, a comprehensive public transit system and effective integration of land use and transportation policies help to relieve the traffic congestion levels caused by the increasing urban sprawl.

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。

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。

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。

考虑出行偏好的城市群交通方式选择行为影响因素研究

针对城市群旅客出行方式选择行为影响机理不够明晰的问题,开展旅客城市群交通方式选择行为影响因素研究.设计问卷对关中城市群内出行行为进显性偏好(Revealed Preference,RP)调查,并对调查数据的效度、信度进行检验,基于调查所得出行数据,采用梯度提升决策树(Gradient Boosting Decision Tree,GBDT)模型构建了考虑出行偏好的出行方式选择预测模型,并采用可解释机器学习框架SHapley Additive exPlanations(SHAP)模型对预测结果进行解析,所构建模型结合旅客实际决策逻辑将出行方式多元选择行为解构为多次二元选择行为,以增强模型的可解释性.研究结果表明:所构建考虑出行偏好的GBDT预测模型能有效预测旅客城市群出行方式选择行为;私家车与公共交通之间的二元选择行为,会受到小汽车拥有状况,旅客对方便性、安全性、快捷性、舒适性的偏好程度、家庭居住区位、出行距离的显著影响;高速巴士与铁路系统、普通铁路与高铁动车之间的二元选择行为,均会受到出行距离、旅客对舒适性、安全性、快捷性、准时性的偏好程度的显著影响;小汽车拥有状况、家庭居住区位会显著影响高速巴士与铁路系统的选择行为,收入状况会显著影响普通铁路与高铁动车的选择行为.研究成果可为决策者从旅客出行偏好的角度进行更加切实有效的决策提供参考.

考虑心理偏好的枢纽抵站旅客接续出行方式选择模型

为掌握枢纽抵站旅客的出行方式选择特征,解决接续运输运力配置协同优化与效率提升问题,基于高铁枢纽接续出行问卷调查数据,引入上次接续属性、接续方式主观重要度、枢纽服务满意度、接续出行选择倾向特征等潜在变量因素,构建考虑心理倾向的结构方程模型(Structural Equation Model,SEM)和抵站旅客接续出行方式选择多项Logit(Multinomial Logit,MNL)模型相融合的SEM-MNL模型,定量计算枢纽抵站旅客接续出行分方式选择效用函数,预测不同时段旅客接续出行方式选择概率,并对比分析SEM-MNL模型与MNL模型命中率.研究结果表明:引入心理倾向潜在变量后,模型的整体命中率提升了4.6%;对枢纽环境服务满意度越高、接续方式服务重视程度越高、上次出行的时间和距离更短且使用公共交通的出行者,更倾向于选择公共交通方式.通过方式选择模型预测分方式的接续出行需求,开展对外客运枢纽接续运输保障和多模式运力的协调调度,对于实现枢纽接续客流与运力资源的供需平衡,继而提升接续服务质量具有重要意义.

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。

县域医疗服务空间公平性评价——以沂南县为例

县域人口密度和医疗服务资源分布不均,不同区位居民采取不同出行方式就医时,产生的出行成本差异较大。为探究基于居民出行特征的县域医疗服务空间公平,文章采用高斯两步移动搜索法计算多出行方式下县域村、乡镇、县级医疗服务空间可达性,采用局部莫兰指数分析可达性集聚特征,进而从空间角度对可达性公平进行评价。案例区分析结果表明:县域医疗服务空间公平与医疗服务质量垂直梯度差异关系密切。县城县级、乡/镇/街道级医疗服务可达性高,强强组合为人口密度较高的县城提供医疗服务,村/社区级医疗服务较为薄弱;近郊区县级医疗服务可达性较高,乡镇医疗服务可达性在各镇交界处较低,村/社区级医疗服务参差不齐;远郊区乡镇卫生院呈补强趋势,镇区周边乡镇医疗服务可达性高,远郊乡镇边缘县级、乡镇级医疗服务可达性低的地区,主要依赖村级医疗服务。研究结果对县域医疗服务公平发展具有理论和实践意义。

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。

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。