Modeling mode choice behavior incorporating household and individual sociodemographics and travel attributes based on rough sets theory

Most traditional mode choice models are based on the principle of random utility maximization derived from econometric theory. Alternatively, mode choice modeling can be regarded as a pattern recognition problem reflected from the explanatory variables of determining the choices between alternatives. The paper applies the knowledge discovery technique of rough sets theory to model travel mode choices incorporating household and individual sociodemographics and travel information, and to identify the significance of each attribute. The study uses the detailed travel diary survey data of Changxing county which contains information on both household and individual travel behaviors for model estimation and evaluation. The knowledge is presented in the form of easily understood IF-THEN statements or rules which reveal how each attribute influences mode choice behavior. These rules are then used to predict travel mode choices from information held about previously unseen individuals and the classification performance is assessed. The rough sets model shows high robustness and good predictive ability. The most significant condition attributes identified to determine travel mode choices are gender, distance, household annual income, and occupation. Comparative evaluation with the MNL model also proves that the rough sets model gives superior prediction accuracy and coverage on travel mode choice modeling

The limited potential of regional electricity marketing – Results from two discrete choice experiments in Germany

The German energy transition has led to a strong expansion of renewable energies in recent years. As a result, the German population is increasingly coming into contact with generation facilities. To increase local acceptance for new installations and to create new sales channels for energy suppliers, the legislature has established the “System for Guarantees of Regional Origin” in 2019, which allows the marketing of electricity from subsidized facilities as “electricity generated in the region”. However, regional electricity comes with additional costs on the procurement and sales side of energy suppliers, and it is unclear whether and to what extent consumers are willing to pay a premium for electricity generated regionally. This study investigates the willingness to pay (WTP) of residential customers based on two samples of 838 and 59 respondents, respectively. Our model results show that, on average, WTP for regional electricity generation is positive, especially among female, younger and better-educated customers, although differences in WTP between these sociodemographic characteristics are small. Factors that are more relevant are the current type of electricity tariff, differentiated into non-green and green, with the latter having a positive influence, but also the tariff switching behavior of the past, which is a proxy for price sensitivity. Although WTP is positive, it is severely limited, and only pertains to a subgroup of electricity customers. Hence, it is not surprising that our simulation shows that including a regional green electricity tariff in an energy supplier\u27s portfolio is likely to lead to product cannibalization, meaning that mainly green electricity customers will choose this tariff. From an energy supplier\u27s perspective, these results raise the question of whether offering a regional electricity tariff is economically viable. Future research could further investigate what underlying factors drive preferences for regionally generated electricity and how it can contribute to local acceptance

Modelling the effects of social networks on activity and travel behaviour

Activity-based models of transport demand are increasingly used by governments, engineering firms and consultants to predict the impact of various design and planning decisions on travel and consequently on noise emissions, energy consumption, accessibility and other performance indicators. In this context, non-discretionary activities, such as work and school, can be relatively easily explained by the traveller’s sociodemographic characteristics and generalised travel costs. However, participation in, and scheduling of, discretionary and joint activities are not so easily redicted. Understanding the social network that lies on top of the spatial network could lead to better prediction of social activity schedules and better forecasts of travel patterns for joint activities. Existing models of activity-travel behaviour do not consider joint activities in detail, except within households to a limited extent. A recent attempt developed at ETH Zurich to incorporate social networks in a single-day optimisation scheduling model did not model joint activities as such, rather rewarding individuals for scheduling activities at the same location and at the same time as their friends. Realistic social networks were also not incorporated. The aim of this thesis is to contribute to this rapidly expanding field by developing a simulation of activity and travel behaviour incorporating social processes and joint activities to investigate the effects on activity and travel behaviour over a simulated period of weeks. The model developed is intended as a proof-of-concept. In order to achieve this aim, an agent-based simulation was designed, implemented in Java, and calibrated and partly verified with real-world data. The model generates activities on a daily basis, including the time of day and duration of the activity. An interaction protocol has been developed to model the activity decision process. Data collected in Eindhoven on social and joint activities and social networks has been used for calibration and verification. Alongside the model development, several issues are addressed, such as exploring which parameters are useful and their effects, the data required for the validation of agent-based travel behaviour models, and whether the addition of social networks to models of this type makes adifference. Sensitivity testing was undertaken to explore the effects of parameters, which was applied to increasingly more complex versions of the model (starting from one day of outputs with no interactions between individuals and finishing with full interactions over many days). This showed that the model performed as expected when certain parameters were altered. Due to the components included in the model, scenarios of interest to policy makers (such as changes in population, land-use changes, and changes in institutional contexts) can be explored. Altering the structure of the in- put social networks and the interaction protocols showed that these inputs do have a difference on the outputs of the model. As a result, these elements of the model require data collection on the social network structure and the decision processes for each local instantiation. Two more "traditional" transport planning policy scenarios, an increase in free time and an increase in travel cost, showed that the model performs as expected for these scenarios. It is shown that the use of agent-based modelling is useful in permitting the incorporation of social networks. The social network can have a significant impact on model results and therefore the decisions made by planners and stakeholders. The model can be extended further in several different directions as new theories are developed and data sets are collected

Development of a Mode Choice Model to understand the potential impacts of LRT on Mode Shares in the Region of Waterloo

A new light rail transit system (LRT), ION, began operations in the Region of Waterloo in the June of 2019, and the second phase is yet to begin construction. The main thrust of this growth management project for the region was achieving sustainability goals by promoting denser development and boosting transit ridership. The LRT is integrated within the existing transit system, and this study intends to understand its impact on transit mode shares. To understand the potential impact of introduction of a new transit system on mode shares, an analytical modelling approach is required. This research conducts spatial analysis in ARCMap to describe the current commuting patterns in the region of Waterloo, highlighting the spatial distribution of modal shares, top trip origins and destinations and trip distribution patterns by different modes. Furthermore, many nested logit and multinomial logit models were estimated to relate various socio economic, spatial and trip attributes to mode choice behaviour. The nested logit models did not prove to be a good fit for the available data, and the best multinomial logit model was finally used to understand the potential impacts of LRT. The final model estimation projects 0.09% increase in commuter transit ridership for the estimated average decrease of 0.14% in travel time, which is a result of both, introduction of ION and realignment of transit routes. It is however, essential to note that ION is a driver of urban growth and development with potential to attract denser and mixed land use developments, which are both key to increasing transit ridership. This study is a start towards understanding the impacts of LRT and findings may prove to be a valuable resource to discerning spatial distribution of commuter trips in the region. Additionally, the model serves as flexible template, which can be employed to assess the impacts of LRT in the future and inform transit policy decisions

運輸部門と家庭部門を横断した世帯のエネルギー消費の統合分析 : モデルの構築と適用

広島大学(Hiroshima University)博士(工学)Doctor of Engineeringdoctora

Analysis of Relative Frequency of Commuting Modes During COVID-19 Pandemic

At the end of December 2019, a new coronavirus spread in Wuhan, China, and worldwide and the World Health Organization (WHO) declared this outbreak of the COVID-19 virus a pandemic on March 11, 2020. Different states and cities implemented various strategies including school closure, working from home, and restaurant and shop closures to control the virus spread, resulting in reduced travel demand. COVID-19 provided an opportunity to understand the differential impacts of a pandemic on travel demand. This study investigates the changes in the U.S. transportation mode use and factors influencing changes in mode use frequency for commuting during the coronavirus pandemic compared to pre-coronavirus period. Researchers conducted three waves of surveys in four metropolitan areas: New York, Washington D.C, Miami, and Houston in the United States and received 2800 responses from each wave. For this thesis, respondents had to commute at least one day/week to be included in the analysis. Ordered logistic models for relative frequency of use of commuting modes such as owned/leased vehicles, rideshare, bus and walk were created. Larger household size was positively associated with the more frequent use of owned/leased vehicles. Coronavirus risk perception was negatively associated with more frequent use of buses. Vehicle ownership was negatively associated with more frequent use of rideshare mode

Transportation Systems Analysis and Assessment

The transportation system is the backbone of any social and economic system, and is also a very complex system in which users, transport means, technologies, services, and infrastructures have to cooperate with each other to achieve common and unique goals.The aim of this book is to present a general overview on some of the main challenges that transportation planners and decision makers are faced with. The book addresses different topics that range from user's behavior to travel demand simulation, from supply chain to the railway infrastructure capacity, from traffic safety issues to Life Cycle Assessment, and to strategies to make the transportation system more sustainable

Understanding travel mode choice: A new approach for city scale simulation

Understanding travel mode choice behaviour is key to effective management of transport networks, many of which are under increasing strain from rising travel demand. Conventional approaches to simulating mode choice typically make use of behavioural models either derived from stated preference choice experiments or calibrated to observed average mode shares. Whilst these models have played and continue to play a key role in economic, social, and environmental assessments of transport investments, there is growing need to gain a deeper understanding of how people interact with transport services, through exploiting available but fragmented data on passenger movements and transport networks. This thesis contributes to this need through developing a novel approach for urban mode choice prediction and applying it to historical trip records in the Greater London area. The new approach consists of two parts: (i) a data generation framework which combines multiple data-sources to build trip datasets containing the likely mode-alternative options faced by a passenger at the time of travel, and (ii) a modelling framework which makes use of these datasets to fit, optimise, validate, and select mode choice classifiers. This approach is used to compare the relative predictive performance of a complete suite of Machine Learning (ML) classification algorithms, as well as traditional utility-based choice models. Furthermore, a new assisted specification approach, where a fitted ML classifier is used to inform the utility function structure in a utility-based choice model, is then explored. The results identify three key findings. Firstly, the Gradient Boosting Decision Trees (GBDT) model is the highest performing classifier for this task. Secondly, the relative differences in predictive performance between classifiers are far smaller than has been suggested by previous research. In particular, there is a much smaller performance gap identified between Random Utility Models (RUMs) and ML classifiers. Finally, the assisted specification approach is successful in using the structure of a fitted ML classifier to improve the performance of a RUM. The resulting model achieves significantly better performance than all but the GBDT ML classifier, whilst maintaining a robust, interpretable behavioural model.Funding provided by UK Engineering and Physical Sciences Research Council via the Future Infrastructure and Built Environment Centre for Doctoral Training (EP/L016095/1)

Geographies of (dis)advantage in walking and cycling: Perspectives on equity and social justice in planning for active transportation in U.S. cities

In recent years, cities across the U.S. have increasingly invested in programs, policies, and infrastructure to support active transportation. Some have suggested that these investments could help to address health disparities observed by race and socioeconomic status (SES) in the U.S., given that walking and cycling are physically active and low-cost modes of transportation. Despite this potential, there is emerging evidence that active transportation investments have been inequitably distributed across communities of varying sociodemographic composition. For instance, cycling advocates have recently argued that low-income and minority populations have disproportionately low access to safe, convenient infrastructure such as bike lanes. At the same time, some active transportation projects have recently faced opposition in several large U.S. cities due to concerns about gentrification. Limited research has considered the distribution of active transportation infrastructure and potential associations between cycling investment and sociodemographic change. I address this gap through three related analyses. First, I examine how different sociodemographic groups are distributed across space with respect to built environment characteristics in Birmingham, Chicago, Minneapolis, and Oakland. I find that low-SES and minority populations tend to live in more walkable neighborhoods, but are less likely to be distributed across a full range of neighborhood types. Second, I examine cross-sectional associations between bike lane access and area-level sociodemographic characteristics in 22 large U.S. cities. I find that even after adjusting for traditional indicators of cycling demand, access to bike lanes is lower in areas with lower educational attainment, higher proportions of Hispanic residents, and lower SES. Third, I examine longitudinal associations between new bike lane infrastructure and sociodemographic change between 1990 and 2015 in Chicago, Minneapolis, and Oakland. I find evidence that new bike lanes occurred disproportionately in areas that were either already advantaged or increasing in advantage over time. These analyses reveal sociodemographic differences in access to environments and infrastructure that support active transportation, often suggesting lower access among disadvantaged populations. Addressing these disparities, however, is complicated by associations between infrastructure investment and sociodemographic change. Efforts to expand active transportation infrastructure should recognize concerns about gentrification and carefully consider the social context of infrastructure investment.Doctor of Philosoph