通勤者活动—出行选择行为研究
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摘要
交通拥堵及由此引发的社会和环境问题已成为制约城市可持续发展的瓶颈，其根本原因是交通供需不平衡。单纯通过加大投入、加快交通基础设施建设并不能从根本上解决交通供需不平衡的矛盾。因此，在适度交通建设规模下，通过诱导出行、调整交通需求时空分布的交通需求管理（TDM）策略的制定和实施为解决交通系统供需矛盾提供了新思路。
出行行为分析是交通科学研究的一个前沿领域，全面、系统、深入的研究个体出行选择行为的本质是诊断城市交通问题、科学制定和实施TDM策略的关键。基于活动的分析方法寻求出行的内在原因，是最有前景的出行行为分析方法之一。出行决策机理对出行行为分析和需求预测有重要影响，这一领域的研究在一些发达国家和地区已经起步，但是由于我国在经济发展水平、城市用地结构、居民活动和出行特征、交通服务水平上的差异，所表现出来的出行决策机理和属性变量之间的相互关系也会不同，需要针对我国城市的具体情况采用适当的方法进行研究。
通勤出行带来的早晚高峰时段的交通拥堵是城市交通最为突出的问题之一。根据通勤者活动-出行特征，有针对性的制定相应的TDM措施，对提高出行效率、缓解城市交通拥堵意义重大。为此，本研究以通勤者为研究对象，对在出行需求分析和TDM策略评价中有关决策机制的活动-出行属性进行分析，即：出行方式、出行链结构、时间使用模式、工作、非工作活动与出行安排，并在此基础上进一步揭示这些选择之间的相互作用规律，以期在理论上推动基于活动的出行行为研究的发展、使出行需求预测更接近出行者实际决策过程，在实践方面为有效预测TDM策略对交通和环境的影响、为制定和实施TDM策略提供科学有效的分析工具。围绕上述研究目标，论文进行了以下几方面的研究：
(1)从基于活动的出行行为分析方法的数据要求出发，根据传统的基于出行的居民出行调查数据的特点，提出数据分析方法，包括数据有效性检验规则和数据整理规则，提取出行方式及换乘特征、出行时耗特征、活动时间和时长特征，将基于出行的调查数据转换为由活动、停驻和出行组成的出行链数据，建立适用于出行行为研究的基础数据库。
(2)城市交通问题突出表现为早晚高峰通勤时段的交通拥堵，对通勤出行方式选择的研究是诊断城市交通问题、引导和调控通勤出行需求的重要分析依据和手段。针对已有研究的不足，探讨选择集的有效形式、模型参数的合理设定和基于预测结果的模型改进方法，将改进后的出行方式选择模型用于分析小汽车拥有水平变化所带来的通勤出行方式选择的影响，验证了新方法的有效性。
(3)通勤出行链的复杂化对出行方式在服务时间、路线灵活性方面的要求削弱了公共交通的竞争力。为了更好的理解公共交通方式对通勤出行链的影响，从通勤出行方式和通勤出行链的相互关系出发，建立方式选择和出行链安排的离散选择模型，通过引入选择结果哑元变量和模型随机项间的相关关系量化分析两选择量之间的相互影响模式，利用连续和离散解释变量的边际效用分析出行者个人及家庭社会经济属性、工作活动特征、出行链复杂程度对出行方式选择的直接和间接影响，探讨研究结论对交通需求预测和管理工作的意义。
(4)交通需求管理和控制与时间因素密切相关，出行者时间分配模式在一定程度上决定了日活动和出行安排，是出行行为分析的核心内容。论文建立通勤者时间分配模型，分析了通勤者在家活动、外出非工作活动和出行上的时间安排，利用模型讨论了压缩工作周和错时上下班策略的实施对通勤者活动和出行安排的影响；用持续时间模型分析通勤者日出行时间，建立出行时间投入与出行者社会经济属性、活动和出行特征之间的数量关系，通过活动与出行之间派生和竞争关系的描述部分反映了通勤者的时间分配行为。
(5)受工作活动在时间和空间上的限制，通勤者的非工作活动主要安排在上下班途中。发生在通勤途中的非工作活动增加了高峰时段出行需求，将加剧交通运输的供需矛盾。论文以单个通勤者为研究对象，将非工作活动发生时段数据看作截取样本，回答了两个重要问题，即：具有什么属性的通勤者会参加非工作活动；在有非工作活动安排的通勤者中，哪些人更倾向于在上下班途中完成。模型由选择方程和结果方程组成，通过两个方程随机项之间的相关关系引入两个选择的相互关联，能有效分析样本选择对通勤者非工作活动时段选择的影响。
(6)家庭成员共同分享收入、居住空间、交通工具等家庭资源，成员之间的相互关系以多种方式影响到其活动和出行选择。论文应用结构方程模型分析了活动和出行在家庭主要成员间的分配情况，讨论了存在于同一成员和不同成员各次活动和出行中的替代、补充、陪同等影响关系及其对成员个体活动-出行选择行为的影响；用离散选择模型分析了通勤者关于居住环境和位置、职业和工作地两方面选择的权衡，探讨了家庭主要成员在通勤出行长度选择之间存在的替代、互补关系。
论文具有创新性的主要研究成果包括以下几个方面：
(1)提出了考虑不可选择方案和不同出行者群体差异的出行方式选择分析方法，并应用于小汽车拥有水平提高后的出行方式预测中。新方法针对出行方式选择已有研究的不足，在选择集表达、模型设定、预测结果分析方面进行了改进，在提高预测精度的同时能更好的揭示方式选择行为的细节。
(2)研究了通勤出行方式选择和出行链结构安排之间的相互影响机制，采用递归联立方程双变量Probit模型，较好的描述了出行方式选择和出行链结构之间的相互作用，为了解出行决策机制、准确预测出行需求提供了有力支持。模型将两个选择变量置于同一模型体系中，从Mode Chain和Chain Mode两个方向分析了出行方式选择和出行链结构之间的相互作用模式，分析表明Chain Mode影响模式在通勤者中占主导地位，出行链的安排是根本，通勤方式选择不仅与出行方式的吸引力有关，还受到通勤出行链中非工作活动安排的影响。
(3)提出了通勤者出行时耗分析的持续时间模型，为出行时耗的定量研究提供了更准确有效的分析工具，对于准确把握通勤者时间分配行为有重要意义。模型用已投入出行时间下出行结束的条件概率更准确的描述了出行的动态过程，建立了出行时间随社会经济属性、活动和出行特征变化的连续分布模型，从活动和出行之间的派生和竞争关系部分的反映了通勤者时间分配行为，分析表明出行时间最小化的行为假设对大多数通勤者成立。
(4)将活动参与和时间安排数据视为截取样本，构建了通勤者非工作活动参与和时间安排分析模型，有效的分析了样本选择对通勤者非工作活动时段选择的影响，更真实的反映了两种选择之间的相互影响关系，是Probit模型对截取样本分析用于活动-出行选择行为研究的重要补充。
(5)从基于活动分析方法中的协作性限制出发，研究了家庭主要成员活动-出行选择之间的相互影响模式，利用结构方程和双变量有序Probit模型分析了家庭主要成员之间的相互依赖关系对活动安排和出行选择的影响。分析表明出行需要是从活动需求派生出来的，活动对出行存在着可量化的影响。对通勤者而言，占主要地位的工作活动和出行影响着其他外出活动和出行的安排；受时间和空间约束的限制，替代、补充等影响关系存在于同一成员的各次活动和出行中；由于成员对收入、居住空间、交通工具等资源的共享，类似的替代、补充、陪同关系也存在于不同成员的活动和出行中；同一成员以及不同成员的各次活动和出行之间的相互作用还受到家庭和成员社会经济属性的影响和制约。
Traffic congestion and subsequent social and environmental problems have becomethe bottlenecks for sustainable urban development. The main cause is the imbalance on thedemand and supply of urban traffic system. Therefore, there’s increasing interest in thedevelopment and implementation of travel demand management (TDM) strategies. Thesestrategies are aimed at effectively managing and distributing travel demand in both spatialand temporal dimensions.
Travel behavior analysis is one of the frontiers of transportation science. It issupported that a systematic and comprehensive study on traveler behavior analysis isessential for the diagnosis and treatment of urban traffic problems and the scientificplanning and implementation of TDM measures. Activity-based approach, whichexplicitly recognizes that travel demand is derived from the need to pursue activities, is amost promising alternative to the current travel behavior analysis methodology. In the pasttwo decades great progress has been made in activity-based travel forecasting and TDMstrategy evaluation both at home and abroad. But research on the joint and causalrelationships among multiple endogenous variables, which plays an important role in theunderstanding of travel decision mechanism, is relatively insufficient. Some studies in thisfield have already been carried out in developed countries but fewer insights are available in the literature in the context of developing countries. However, the characteristics ofeconomy level, land use, household socio-demographics, travel and activity attributes andtraffic system service level in China are all different from those of the western countriesand it is necessary to study the interrelationships among traveler activity and travelattributes conforming to the local situation.
The analysis of commuter travel behavior is of great importance since it has a decisiveimpact on peak-period congestion, which is now the foremost transportation problem.Motivated by the relevance of the subject in the current context, the focus of this researchis to present a comprehensive analysis of commuter activity-travel attributes and theinterrelationship among them based on the processing and analysis of household travelsurvey data, which may shed considerable light on travel demand forecasting and TDMmeasure evaluation. Results from this study will expand and enrich the theoreticalframework and analysis methods of travel behavior analysis and contribute to theestablishment and carrying out of high performance TDM measures by presentingscientific and effective decision analysis methods. The main research contents aresummarized as follows:
(1) Guided by the data requirement of activity-based travel behavior analysis, dataprocessing method including rules about trip data validity judgment and data arrangementis presented according to the characteristics of traditional trip-based travel survey data.Then travel mode and interchange pattern, characteristics of travel time and the timing andduration of activity episodes are examined. The trip-based travel data are converted to tripchaining records composed of activities, stops and trips, which are used as the basic database for the following research.
(2) Urban traffic problems are typically characterized by peak-period trafficcongestion during commuting time thus the research of commute travel mode choice canprovide practical tools for the diagnosis of urban transportation problems and theregulation and control of commute travel demand. The present study is aimed to provide anew method addressing some of the shortcomings of existing researches by taking thefollowing factors on mode choice into consideration: alternative means of choice setrepresentation, reasonable model parameter identification and the improvement based onthorough analysis of forecasting results. The validity of the provided method in regard to policy and planning scenarios is confirmed by the analysis of the mode shift under anincrease in vehicle ownership.
(3) As commute trip chains become more complex, flexibility of travel mode becomesmore important and the mobility service offered by public transport is less attractive. Inorder to obtain a better understanding of the impact of public transport on commute tripchain, this paper investigates the interaction between commute mode choice and thecomplexity of trip chaining pattern. The relationship between the two choices isrepresented in a recursive simultaneous bivariate probit model by the introduction ofdummy variables indicating choice results and the correlation coefficient between the tworandom errors. Then the direct and indirect effects of individual and householdsocio-economic attributes, the characteristics of work activity and the trip chaining patternon the choice of commute travel mode are examined based on the analysis of the marginaleffects of a series of discrete and continuous explanatory variables. And the importantimplications of the research findings for travel demand forecasting and management arediscussed.
(4) Travel demand management strategies and transportation control measures areinherently linked to the time dimension. People’s time allocation patterns, havingprofound impact on the daily activity and travel arrangement, are the research focus oftravel behavior analysis. This dissertation first applies the fractional split distributionmodel to investigate commuters’ allocation of time to in-home and out-of-home non-workactivities and demonstrates the applicability of the model for determining activity andtravel behavior adjustments through numerical situations of compressed work week andflexible working hours. Then the duration model approach which integrates the notion ofthe temporal dynamics is applied to the analysis of commuters’ daily travel time cost. Therelationships between daily travel time and socio-economic attributes and activity andtravel characteristics are analyzed and the time allocation pattern is partly represented bythe derivative and competing relationships of activity and travel.
(5) Commuters often introduce non-work activities to the basic home-work-homechain because of the temporal-spatial constraints from work activity. This commute tripchaining may explain the rise in non-work trips occurring in peak periods and is posited asone reason for increased congestion problem in peak periods. Therefore, this dissertation focuses on workers’ decisions of activity participation and timing. Taking non-workactivity timing data as a censored sample a censored probit model is established to answerthe following questions: what influences the decision to participate in a non-work activityand among those who pursue non-work activities what contributes to the chaining of theactivity to work. The model consists of the selection equation and the outcome equation,and the interrelationship between activity participation and timing is represented as thecorrelation between the two random errors. The model provides a better understanding ofhow workers make non-work activity decision in relation to work, which is a majorrequisite to improving the performance of travel demand modeling as well as thedevelopment of congestion relief policies.
(6) Members in a household share various household resources such as income, livingspace and transportation tools and play different roles in the household. As a result,household members interact in daily activity-travel choices. A structural equation model isused to explain household allocation of activity and travel between household heads. Themodel attempts to capture the substitution, companion and complementary relationshipsinvolving both activities and the associated travel both within and between the twomembers. Since the trip to work is a reflection of location decisions made regarding bothhousing and employment locations by all members of a household there may be complexinterrelationships among individual household members. A bivariate ordered probit modelis applied to analyze the interrelationships between spousal commuting decisions. Inparticular it is further explored whether spousal trips to work appear to be substitutes orcomplements for one another.
The main contributions of this dissertation research are as follows:
(1) Aimed at addressing some of the shortcomings of current mode choice models thispaper investigates alternative choice set representation and reasonable model identification.It is demonstrated that an explicit model of unavailability of some alternatives provides abetter fit and captive effects are observed among commuter segmentations. A predictedoutcome matrix is constructed to distinguish those aspects of travel behavior that are notwell-captured by the model. This process is designed to improve the performance of themodel.
(2) The relationship between the use of public transport and the complexity of commute trip chaining pattern is analyzed in a recursive simultaneous bivariate probitmodel, which provides a powerful analysis tool for the examination of interactions andmutual restrictions between two discrete choices. Research findings from this effort haveimportant implications for the development of activity-based travel forecasting systemsand for the understanding of travel decision process.
(3) Commuters’ daily travel time is analysed in a hazard duration model, whichenriches the quantitative analysis method of travel time and is of great importance for theprecise understanding of commuters’ time allocation behavior. It models the conditionalprobability of the end-of-duration of travel, given that it has lasted to a specified time andpermits the likelihood of ending to be dependent on the length of elapsed time. Theresearch results demonstrate that the behavior of most commuters can be represented bytravel time minimization mechanism.
(4) The participation and timing of non-work activities of commuters are analyzed bycensored probit model, where the non-work activity choice data are taken as censoredsample. The model is composed of two equations: activity participation is analyzed in theselection equation, activity timing is analyzed in the outcome equation and theinterrelation between participation and timing is accounted by the correlation between thetwo random errors. This provides an exploration to extend probit model for censoredsample analysis in activity-travel behavior analysis.
(5) Models of activity engagement and time allocation among household heads aredeveloped and estimated in order to identify the trade-offs and complementaryrelationships among household members’ activity and travel engagement patterns. Ingeneral it is an attempt for the understanding of the intra-person and inter-personinteractions in the context of non-work activity and travel patterns and provides basis forfurther studies of various interactions among all household members simultaneously.
出行行为分析是交通科学研究的一个前沿领域，全面、系统、深入的研究个体出行选择行为的本质是诊断城市交通问题、科学制定和实施TDM策略的关键。基于活动的分析方法寻求出行的内在原因，是最有前景的出行行为分析方法之一。出行决策机理对出行行为分析和需求预测有重要影响，这一领域的研究在一些发达国家和地区已经起步，但是由于我国在经济发展水平、城市用地结构、居民活动和出行特征、交通服务水平上的差异，所表现出来的出行决策机理和属性变量之间的相互关系也会不同，需要针对我国城市的具体情况采用适当的方法进行研究。
通勤出行带来的早晚高峰时段的交通拥堵是城市交通最为突出的问题之一。根据通勤者活动-出行特征，有针对性的制定相应的TDM措施，对提高出行效率、缓解城市交通拥堵意义重大。为此，本研究以通勤者为研究对象，对在出行需求分析和TDM策略评价中有关决策机制的活动-出行属性进行分析，即：出行方式、出行链结构、时间使用模式、工作、非工作活动与出行安排，并在此基础上进一步揭示这些选择之间的相互作用规律，以期在理论上推动基于活动的出行行为研究的发展、使出行需求预测更接近出行者实际决策过程，在实践方面为有效预测TDM策略对交通和环境的影响、为制定和实施TDM策略提供科学有效的分析工具。围绕上述研究目标，论文进行了以下几方面的研究：
(1)从基于活动的出行行为分析方法的数据要求出发，根据传统的基于出行的居民出行调查数据的特点，提出数据分析方法，包括数据有效性检验规则和数据整理规则，提取出行方式及换乘特征、出行时耗特征、活动时间和时长特征，将基于出行的调查数据转换为由活动、停驻和出行组成的出行链数据，建立适用于出行行为研究的基础数据库。
(2)城市交通问题突出表现为早晚高峰通勤时段的交通拥堵，对通勤出行方式选择的研究是诊断城市交通问题、引导和调控通勤出行需求的重要分析依据和手段。针对已有研究的不足，探讨选择集的有效形式、模型参数的合理设定和基于预测结果的模型改进方法，将改进后的出行方式选择模型用于分析小汽车拥有水平变化所带来的通勤出行方式选择的影响，验证了新方法的有效性。
(3)通勤出行链的复杂化对出行方式在服务时间、路线灵活性方面的要求削弱了公共交通的竞争力。为了更好的理解公共交通方式对通勤出行链的影响，从通勤出行方式和通勤出行链的相互关系出发，建立方式选择和出行链安排的离散选择模型，通过引入选择结果哑元变量和模型随机项间的相关关系量化分析两选择量之间的相互影响模式，利用连续和离散解释变量的边际效用分析出行者个人及家庭社会经济属性、工作活动特征、出行链复杂程度对出行方式选择的直接和间接影响，探讨研究结论对交通需求预测和管理工作的意义。
(4)交通需求管理和控制与时间因素密切相关，出行者时间分配模式在一定程度上决定了日活动和出行安排，是出行行为分析的核心内容。论文建立通勤者时间分配模型，分析了通勤者在家活动、外出非工作活动和出行上的时间安排，利用模型讨论了压缩工作周和错时上下班策略的实施对通勤者活动和出行安排的影响；用持续时间模型分析通勤者日出行时间，建立出行时间投入与出行者社会经济属性、活动和出行特征之间的数量关系，通过活动与出行之间派生和竞争关系的描述部分反映了通勤者的时间分配行为。
(5)受工作活动在时间和空间上的限制，通勤者的非工作活动主要安排在上下班途中。发生在通勤途中的非工作活动增加了高峰时段出行需求，将加剧交通运输的供需矛盾。论文以单个通勤者为研究对象，将非工作活动发生时段数据看作截取样本，回答了两个重要问题，即：具有什么属性的通勤者会参加非工作活动；在有非工作活动安排的通勤者中，哪些人更倾向于在上下班途中完成。模型由选择方程和结果方程组成，通过两个方程随机项之间的相关关系引入两个选择的相互关联，能有效分析样本选择对通勤者非工作活动时段选择的影响。
(6)家庭成员共同分享收入、居住空间、交通工具等家庭资源，成员之间的相互关系以多种方式影响到其活动和出行选择。论文应用结构方程模型分析了活动和出行在家庭主要成员间的分配情况，讨论了存在于同一成员和不同成员各次活动和出行中的替代、补充、陪同等影响关系及其对成员个体活动-出行选择行为的影响；用离散选择模型分析了通勤者关于居住环境和位置、职业和工作地两方面选择的权衡，探讨了家庭主要成员在通勤出行长度选择之间存在的替代、互补关系。
论文具有创新性的主要研究成果包括以下几个方面：
(1)提出了考虑不可选择方案和不同出行者群体差异的出行方式选择分析方法，并应用于小汽车拥有水平提高后的出行方式预测中。新方法针对出行方式选择已有研究的不足，在选择集表达、模型设定、预测结果分析方面进行了改进，在提高预测精度的同时能更好的揭示方式选择行为的细节。
(2)研究了通勤出行方式选择和出行链结构安排之间的相互影响机制，采用递归联立方程双变量Probit模型，较好的描述了出行方式选择和出行链结构之间的相互作用，为了解出行决策机制、准确预测出行需求提供了有力支持。模型将两个选择变量置于同一模型体系中，从Mode Chain和Chain Mode两个方向分析了出行方式选择和出行链结构之间的相互作用模式，分析表明Chain Mode影响模式在通勤者中占主导地位，出行链的安排是根本，通勤方式选择不仅与出行方式的吸引力有关，还受到通勤出行链中非工作活动安排的影响。
(3)提出了通勤者出行时耗分析的持续时间模型，为出行时耗的定量研究提供了更准确有效的分析工具，对于准确把握通勤者时间分配行为有重要意义。模型用已投入出行时间下出行结束的条件概率更准确的描述了出行的动态过程，建立了出行时间随社会经济属性、活动和出行特征变化的连续分布模型，从活动和出行之间的派生和竞争关系部分的反映了通勤者时间分配行为，分析表明出行时间最小化的行为假设对大多数通勤者成立。
(4)将活动参与和时间安排数据视为截取样本，构建了通勤者非工作活动参与和时间安排分析模型，有效的分析了样本选择对通勤者非工作活动时段选择的影响，更真实的反映了两种选择之间的相互影响关系，是Probit模型对截取样本分析用于活动-出行选择行为研究的重要补充。
(5)从基于活动分析方法中的协作性限制出发，研究了家庭主要成员活动-出行选择之间的相互影响模式，利用结构方程和双变量有序Probit模型分析了家庭主要成员之间的相互依赖关系对活动安排和出行选择的影响。分析表明出行需要是从活动需求派生出来的，活动对出行存在着可量化的影响。对通勤者而言，占主要地位的工作活动和出行影响着其他外出活动和出行的安排；受时间和空间约束的限制，替代、补充等影响关系存在于同一成员的各次活动和出行中；由于成员对收入、居住空间、交通工具等资源的共享，类似的替代、补充、陪同关系也存在于不同成员的活动和出行中；同一成员以及不同成员的各次活动和出行之间的相互作用还受到家庭和成员社会经济属性的影响和制约。
Traffic congestion and subsequent social and environmental problems have becomethe bottlenecks for sustainable urban development. The main cause is the imbalance on thedemand and supply of urban traffic system. Therefore, there’s increasing interest in thedevelopment and implementation of travel demand management (TDM) strategies. Thesestrategies are aimed at effectively managing and distributing travel demand in both spatialand temporal dimensions.
Travel behavior analysis is one of the frontiers of transportation science. It issupported that a systematic and comprehensive study on traveler behavior analysis isessential for the diagnosis and treatment of urban traffic problems and the scientificplanning and implementation of TDM measures. Activity-based approach, whichexplicitly recognizes that travel demand is derived from the need to pursue activities, is amost promising alternative to the current travel behavior analysis methodology. In the pasttwo decades great progress has been made in activity-based travel forecasting and TDMstrategy evaluation both at home and abroad. But research on the joint and causalrelationships among multiple endogenous variables, which plays an important role in theunderstanding of travel decision mechanism, is relatively insufficient. Some studies in thisfield have already been carried out in developed countries but fewer insights are available in the literature in the context of developing countries. However, the characteristics ofeconomy level, land use, household socio-demographics, travel and activity attributes andtraffic system service level in China are all different from those of the western countriesand it is necessary to study the interrelationships among traveler activity and travelattributes conforming to the local situation.
The analysis of commuter travel behavior is of great importance since it has a decisiveimpact on peak-period congestion, which is now the foremost transportation problem.Motivated by the relevance of the subject in the current context, the focus of this researchis to present a comprehensive analysis of commuter activity-travel attributes and theinterrelationship among them based on the processing and analysis of household travelsurvey data, which may shed considerable light on travel demand forecasting and TDMmeasure evaluation. Results from this study will expand and enrich the theoreticalframework and analysis methods of travel behavior analysis and contribute to theestablishment and carrying out of high performance TDM measures by presentingscientific and effective decision analysis methods. The main research contents aresummarized as follows:
(1) Guided by the data requirement of activity-based travel behavior analysis, dataprocessing method including rules about trip data validity judgment and data arrangementis presented according to the characteristics of traditional trip-based travel survey data.Then travel mode and interchange pattern, characteristics of travel time and the timing andduration of activity episodes are examined. The trip-based travel data are converted to tripchaining records composed of activities, stops and trips, which are used as the basic database for the following research.
(2) Urban traffic problems are typically characterized by peak-period trafficcongestion during commuting time thus the research of commute travel mode choice canprovide practical tools for the diagnosis of urban transportation problems and theregulation and control of commute travel demand. The present study is aimed to provide anew method addressing some of the shortcomings of existing researches by taking thefollowing factors on mode choice into consideration: alternative means of choice setrepresentation, reasonable model parameter identification and the improvement based onthorough analysis of forecasting results. The validity of the provided method in regard to policy and planning scenarios is confirmed by the analysis of the mode shift under anincrease in vehicle ownership.
(3) As commute trip chains become more complex, flexibility of travel mode becomesmore important and the mobility service offered by public transport is less attractive. Inorder to obtain a better understanding of the impact of public transport on commute tripchain, this paper investigates the interaction between commute mode choice and thecomplexity of trip chaining pattern. The relationship between the two choices isrepresented in a recursive simultaneous bivariate probit model by the introduction ofdummy variables indicating choice results and the correlation coefficient between the tworandom errors. Then the direct and indirect effects of individual and householdsocio-economic attributes, the characteristics of work activity and the trip chaining patternon the choice of commute travel mode are examined based on the analysis of the marginaleffects of a series of discrete and continuous explanatory variables. And the importantimplications of the research findings for travel demand forecasting and management arediscussed.
(4) Travel demand management strategies and transportation control measures areinherently linked to the time dimension. People’s time allocation patterns, havingprofound impact on the daily activity and travel arrangement, are the research focus oftravel behavior analysis. This dissertation first applies the fractional split distributionmodel to investigate commuters’ allocation of time to in-home and out-of-home non-workactivities and demonstrates the applicability of the model for determining activity andtravel behavior adjustments through numerical situations of compressed work week andflexible working hours. Then the duration model approach which integrates the notion ofthe temporal dynamics is applied to the analysis of commuters’ daily travel time cost. Therelationships between daily travel time and socio-economic attributes and activity andtravel characteristics are analyzed and the time allocation pattern is partly represented bythe derivative and competing relationships of activity and travel.
(5) Commuters often introduce non-work activities to the basic home-work-homechain because of the temporal-spatial constraints from work activity. This commute tripchaining may explain the rise in non-work trips occurring in peak periods and is posited asone reason for increased congestion problem in peak periods. Therefore, this dissertation focuses on workers’ decisions of activity participation and timing. Taking non-workactivity timing data as a censored sample a censored probit model is established to answerthe following questions: what influences the decision to participate in a non-work activityand among those who pursue non-work activities what contributes to the chaining of theactivity to work. The model consists of the selection equation and the outcome equation,and the interrelationship between activity participation and timing is represented as thecorrelation between the two random errors. The model provides a better understanding ofhow workers make non-work activity decision in relation to work, which is a majorrequisite to improving the performance of travel demand modeling as well as thedevelopment of congestion relief policies.
(6) Members in a household share various household resources such as income, livingspace and transportation tools and play different roles in the household. As a result,household members interact in daily activity-travel choices. A structural equation model isused to explain household allocation of activity and travel between household heads. Themodel attempts to capture the substitution, companion and complementary relationshipsinvolving both activities and the associated travel both within and between the twomembers. Since the trip to work is a reflection of location decisions made regarding bothhousing and employment locations by all members of a household there may be complexinterrelationships among individual household members. A bivariate ordered probit modelis applied to analyze the interrelationships between spousal commuting decisions. Inparticular it is further explored whether spousal trips to work appear to be substitutes orcomplements for one another.
The main contributions of this dissertation research are as follows:
(1) Aimed at addressing some of the shortcomings of current mode choice models thispaper investigates alternative choice set representation and reasonable model identification.It is demonstrated that an explicit model of unavailability of some alternatives provides abetter fit and captive effects are observed among commuter segmentations. A predictedoutcome matrix is constructed to distinguish those aspects of travel behavior that are notwell-captured by the model. This process is designed to improve the performance of themodel.
(2) The relationship between the use of public transport and the complexity of commute trip chaining pattern is analyzed in a recursive simultaneous bivariate probitmodel, which provides a powerful analysis tool for the examination of interactions andmutual restrictions between two discrete choices. Research findings from this effort haveimportant implications for the development of activity-based travel forecasting systemsand for the understanding of travel decision process.
(3) Commuters’ daily travel time is analysed in a hazard duration model, whichenriches the quantitative analysis method of travel time and is of great importance for theprecise understanding of commuters’ time allocation behavior. It models the conditionalprobability of the end-of-duration of travel, given that it has lasted to a specified time andpermits the likelihood of ending to be dependent on the length of elapsed time. Theresearch results demonstrate that the behavior of most commuters can be represented bytravel time minimization mechanism.
(4) The participation and timing of non-work activities of commuters are analyzed bycensored probit model, where the non-work activity choice data are taken as censoredsample. The model is composed of two equations: activity participation is analyzed in theselection equation, activity timing is analyzed in the outcome equation and theinterrelation between participation and timing is accounted by the correlation between thetwo random errors. This provides an exploration to extend probit model for censoredsample analysis in activity-travel behavior analysis.
(5) Models of activity engagement and time allocation among household heads aredeveloped and estimated in order to identify the trade-offs and complementaryrelationships among household members’ activity and travel engagement patterns. Ingeneral it is an attempt for the understanding of the intra-person and inter-personinteractions in the context of non-work activity and travel patterns and provides basis forfurther studies of various interactions among all household members simultaneously.
引文
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