Estimating Path Choice Models through Floating Car Data

The path choice models play a key role in transportation engineering, especially when coupled with an assignment procedure allowing link flows to be obtained. Their implementation could be complex and resource-consuming. In particular, such a task consists of several stages, including (1) the collection of a large set of data from surveys to infer users’ path choices and (2) the definition of a model able to reproduce users’ choice behaviors. Nowadays, stage (1) can be improved using floating car data (FCD), which allow one to obtain a reliable dataset of paths. In relation to stage (2), different structures of models are available; however, a compromise has to be found between the model’s ability to reproduce the observed paths (including the ability to forecast the future path choices) and its applicability in real contexts (in addition to guaranteeing the robustness of the assignment procedure). Therefore, the aim of this paper is to explore the opportunities offered by FCD to calibrate a path/route choice model to be included in a general procedure for scenario assessment. The proposed methodology is applied to passenger and freight transport case studies. Significant results are obtained showing the opportunities offered by FCD in supporting path choice simulation. Moreover, the characteristics of the model make it easily applicable and exportable to other contexts

통행시간 신뢰도의 개별 리스크 선호도를 고려한 경로선택행태 모형

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 건설환경공학부, 2018. 2. 고승영.The route choice problem is an important factor in traffic operation and transportation planning. There have been many studies to analyze the route choice behavior using travel data. There was a limit in constructing a route choice model by generating an appropriate choice set due to the limitations of the data. In this study, we construct a choice set generation model and a stochastic route choice model using the observed data. This study estimates the parameters incorporating travelers heterogeneity according to the choice set from the choice set generation model and the route choice model. We define the individual confidence level according to perceived travel time distribution to reflect travelers heterogeneity on choice set generation model. In addition, the parameters were estimated using the mixed path-size correction logit model (MPSCL) considering the path overlapping and individual risk preference in the route choice model. We compare the experienced paths and the derived choice set to construct choice set generation model. In addition, it is possible to estimate better parameters incorporating travelers heterogeneity for choice set generation model and route choice model. We compare the choice set from the developed model with that of the conventional choice set generation model. This study shows the superior prediction result in route choice model reflecting the individual behaviors of the route choice in the urban area on the transportation demand forecasting and traffic operation.1. Introduction 1 1.1 Backgrounds 1 1.2 Research Purpose 4 1.3 Main contents 5 1.4 Research Scope 9 2. Literature Review 11 2.1 Choice Set Generation 13 2.2 Route Choice Model 30 2.3 Review result and limitation 58 2.4 Research Contributions 62 3. Modelling Framework 66 3.1 Overview 66 3.2 Terminology 68 3.3 Choice set generation model 76 3.4 Route choice model 92 4. Revealed Preference Routing Data 100 4.1 Data Characteristics 100 4.2 Data Collection & Description 106 4.3 Data Processing 109 4.4 Missing Data Correction 113 5. Model Estimation & Validation 120 5.1 Overview 120 5.2 Choice set generation 121 5.3 Model estimation & Validation 124 5.4 Model verification 135 5.5 Discussion 138 6. Conclusion 140 6.1 Conclusion 140 6.2 Further research 142 REFERENCES 145 APPENDIX 158Docto

Investigating the potential of the combination of random utility models (CoRUM) for discrete choice modelling and travel demand analysis

People make choices every day. Many choices have a strong impact on the quality or their life.Each day a person wakes up and chooses which action he wants to do before, what to have for breakfast, what to wear, what time to go outside, how to manage his/her day by virtue of the budget and time constraints, which place to move to and how, which activity to do, which one to do before or after and so forth. There are choices that are not made every day, but they have a strong impact on the decision maker’s well-being. In fact, sooner or later, a person will decide his household location, whether to own a car, the typology and the vehicle model, whether to own a pet, which breed or size, how many children to have, in which school register them and much more. Some of the above-mentioned choice examples involve mobility. Thus, it is easy to recognize why these kind of choices form the basis for the planning and policy actions in the transport field. What is called, at aggregate level, congestion or traffic, represents the sum of individual choices that everyone makes at different levels: do I move? What time do I move? Where I want to go? Which transport mode do I want to use? What itinerary do I travel? This kind of choices, that can be termed transport choices, relating the so-called travel behaviour, are characterized by a significant modelling complexity. The random utility theory represents the most widely used paradigm in modelling the behaviour of people who make choices. This thesis investigates the potential of the combination of random utility models (CoRUM; Papola, 2016) for travel demand analysis and discrete choice modelling in general. In the current work, several theoretical advances and some specific transport-field applications are carried out. The CoRUM framework, in fact, is very general and allows for handling several discrete choice modelling crucial issues. The thesis is structured as follows: Chapter 2 reviews the state of the art on random utility theory and its application to route choice. In particular, the Section 2.1 provides the basic setup for the description of RUMs; Section 2.2 reviews the random utility models available in the literature, with reference to the two main problems of the error structure (inter-correlations and heteroskedasticity problems) and the inter/intra-respondent taste variation; Section 2.3 briefly summarizes the main applications of the random utility theory to the route choice problem; Section 2.4 describes the main assumptions of the Combination of random utility models (CoRUM) as a general framework for modelling discrete choices, with particular reference to travel choices. Chapter 3 investigates more general specifications of the CoRUM than those previously analysed, allowing accommodating also the taste heterogeneity and the heteroscedasticity, in particular by combining mixtures of RUMs. To this end, the chapter proposes a theoretical generalization of the CoRUM framework and a real-world application on data collected on a stated survey of 1688 observations of 211 respondents. Chapter 4 represents an estimation exercise with applications on future scenarios on the main closed form random utility models, on synthetic datasets with variable sample sizes and complex underlying correlation scenarios. Such correlation scenarios, on the other hand, can be representative of typical mode choice or route choice contexts. The aim of this chapter is investigating the potential of the CoNL (and the other models) in terms of forecasting, and comparing it with the models goodness of fit performances. Chapter 5 proposes a new route choice model obtained under the CoRUM framework. It describes an algorithm to generate a CoNL specification, allowing detecting a set and a composition for the components of the model, and a way to compute all the structural model parameters, whatever the network. Chapter 6 is currently an original contribution of this thesis and describes several advance compared to the published work in Chapter 5. In particular, an implicit enumeration algorithm theoretically consistent with the CoRUM route choice model,is proposed and tested on toy networks; an in-depth analysis of the complex route choice models is carried out on their ability to reproduce complex correlation scenarios, drawing important conclusions, both theoretical and applicative, on the novel CoNL route choice model, proposed in Chapter 5, and on the existent Link Nested Logit model; some practical advance on the original route choice model is proposed and tested both on toy networks and on a real network (Region Campania network). The goodness of fit of the CoNL route choice has been analysed and compared with the one of the other route choice models, using real observations collected by means of GPS detection of about 200 trajectories. Chapter 7 reports a summary of the conclusions reached in the whole thesis and proposes several ideas for future research steps

Active Commuting and Active Transportation

This book focuses on active transport as a way to increase physical activity levels. Active commuting and active transportation on foot or by bicycle create opportunities for physical activity, provide transportation options for those without a car, encourage social cohesion, and reduce contributions to air pollution