Extending activity-based models of travel demand to represent activity-travel behaviour of children: some descriptive results

This paper presents the research and analysis process showing that transport system customers have a specific perception of service quality, as an indicator of transport system. Determining satisfactory level of service quality implies knowledge of travel demand and travel behaviour. There are a lot of elements that define the transport system quality. The goal of this paper is to identify the public transport system’s service quality elements that should be primarily acted on, in order to increase the level of service quality from transport system users’ (public transport users’ and non-users’) point of view, with minimal investment. The paper describes a specifically defined research methodology for determining service quality elements that should be primarily acted on, from the transport system users’ point of view. Methodology involves the use of Importance Performance Analysis (IPA) which is upgraded with the state preferences analysis. Presented methodology, which is used to determine user perception of service quality, can be considered to be universal. This methodology can be applied in other cities, with additional research that must precede its use. The methodology was tested on transport system users in Belgrade.

Download Full-text

Commuting mode choice in the context of travel demand management (TDM) policies: an empirical investigation in Edmonton, Alberta

Canadian Journal of Civil Engineering ◽

10.1139/l11-015 ◽

2011 ◽

Vol 38 (4) ◽

pp. 433-443 ◽

Cited By ~ 5

Author(s):

Hamid Zaman ◽

Khandker M. Nurul Habib

Keyword(s):

Travel Demand ◽

Mode Choice ◽

Demand Management ◽

Travel Behaviour ◽

Data Set ◽

Travel Demand Management ◽

Depth Analysis ◽

Private Car ◽

Commuting Mode ◽

The City

Travel demand management (TDM) for achieving sustainability is now considered one of the most important aspects of transportation planning and operation. It is now a well known fact that excessive use of private car results inefficient travel behaviour. So, from the TDM perspective, it is of great importance to analyze travel behaviour for improving our understanding on how to influence people to reduce car use and choose more sustainable modes such as carpool, public transit, park & ride, walk, bike etc. This study attempts an in-depth analysis of commuting mode choice behaviour using a week-long commuter survey data set collected in the City of Edmonton. Using error correlated nested logit model for panel data, this study investigates sensitivities of various factors including some specific TDM policies such as flexible office hours, compressed work week etc. Results of the investigation provide profound understanding and guidelines for designing effective TDM policies.

Download Full-text

Energy and Transport: Strategies for Energy Conservation in Urban Passenger Transport

Environment and Planning A Economy and Space ◽

10.1068/a110767 ◽

1979 ◽

Vol 11 (7) ◽

pp. 767-780 ◽

Cited By ~ 1

Author(s):

Kirsten Schou

Keyword(s):

Energy Conservation ◽

Public Transport ◽

Alternative Fuels ◽

Travel Demand ◽

Fuel Efficiency ◽

Travel Behaviour ◽

Passenger Transport ◽

Fuel Savings ◽

Energy Constraints ◽

Transport Modes

Before proceeding to explore potential strategies for energy conservation in urban passenger transport, this paper presents some evidence on energy efficiencies of various transport modes and on travel behaviour under energy constraints. Knowledge of the relative energy efficiencies of different modes of transport is evidently necessary for analysing and developing policies for fuel conservation. Although the automobile does appear to be significantly more energy-intensive than public transport modes, this does not automatically indicate that a policy to attract people to public transport would lead to the maximum possible fuel savings. Available evidence on travel behaviour under energy constraints indicates that the elasticity of travel demand is very small. Increasing prices, within the range expected, are not likely to result in satisfactory fuel savings, and it is therefore necessary to consider alternative strategies. The strategies to be considered here may be outlined as follows: (1) improving fuel efficiency of automobiles by modifying driving habits, reducing speeds, improving traffic flows, and keeping vehicles properly maintained; (2) increasing efficiency of automobile travel by promoting higher occupancies; (3) attracting car travellers to public transport; (4) shifting to smaller, more fuel-efficient vehicles, changing vehicle and engine designs such as to improve the inherent fuel efficiency of the automobile; (5) technological change: new propulsion systems, alternative fuels, and rapid personal transport; (6) reducing travel needs by changing land-use patterns and improving communications. These strategies are discussed in turn and, given the available information about travel patterns and behaviour, an attempt is made to assess their likely impact. Clearly those strategies should be selected which offer the maximum potential fuel savings and which can be introduced with minimum sacrifice.

Download Full-text

Generative Modelling and Machine Learning Methods for Travel Behaviour Analysis

10.32920/ryerson.14664273 ◽

2021 ◽

Author(s):

Melvin Wong

Keyword(s):

Machine Learning ◽

Discrete Choice ◽

Learning Process ◽

Travel Demand ◽

Travel Behaviour ◽

Generative Model ◽

Choice Modelling ◽

Behaviour Analysis ◽

Discrete Choice Modelling ◽

Generative Modelling

The dissertation outlines novel analytical and experimental methods for discrete choice modelling using generative modelling and information theory. It explores the influence of information heterogeneity on large scale datasets using generative modelling. The behaviorally subjective psychometric indicators are replaced with a learning process in an artificial neural network architecture. Part of the dissertation establishes new tools and techniques to model aspects of travel demand and behavioural analysis for the emerging transport and mobility markets. Specically, we consider: (i) What are the strengths, weaknesses and role of generative learning algorithms for behaviour analysis in travel demand modelling? (ii) How to monitor and analyze the identiability and validity of the generative model using Bayesian inference methods? (iii) How to ensure that the methodology is behaviourally consistent? (iv) What is the relationship between the generative learning process and realistic representation of decision making as well as its usefulness in choice modelling? and (v) What are the limitations and assumptions that have needed to develop the generative model systems? This thesis is based on four articles introduced in Chapters 3 to 6. Chapters 3 and 4 introduces a restricted Boltzmann machine learning algorithm for travel behaviour that includes an analysis of modelling discrete choice with and without psychometric indicators. Chapter 5 provides an analysis of information heterogeneity from the perspective of a generative model and how it can extract population taste variation using a Bayesian inference based learning process. One of the most promising applications for generative modelling is for modelling the multiple discretecontinuous data. In Chapter 6, a generative modelling framework is developed to show the process and methodology of capturing higher-order correlation in the data and deriving a process of sampling that can account for the interdependencies between multiple outputs and inputs. A brief background on machine learning principles for discrete choice modelling and newly developed mathematical models and equations related to generative modelling for travel behaviour analysis are provided in the appendices.

Download Full-text

Impacts of interpersonal distancing on-board trains during the COVID-19 emergency

European Transport Research Review ◽

10.1186/s12544-021-00474-6 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Pierluigi Coppola ◽

Francesco De Fabiis

Keyword(s):

Public Transport ◽

High Speed ◽

Structural Changes ◽

Travel Demand ◽

Demand Forecasting ◽

Travel Behaviour ◽

Free Access ◽

Strong Impact ◽

High Speed Rail ◽

Interpersonal Distancing

Abstract Introduction The COVID-19 emergency and the cities lockdown have had a strong impact on transport and mobility. In particular, travel demand has registered an unprecedented overall contraction, dramatically dropping down with peaks of - 90%-95% passengers for public transport (PT). During the re-opening phase, demand is gradually resuming the levels before the crisis, although some structural changes are observed in travel behaviour, and containment measures to reduce the risk of contagion are still being applied, affecting transport supply. Objective This paper aims at assessing to what extent keeping a one-meter interpersonal distancing on-board trains is sustainable for public transport companies. Method The analysis is based on travel demand forecasting models applied to two case-studies in Italy: a suburban railway line and a High-speed Rail (HSR) line, differentiated by demand characteristics (e.g. urban vs. ex-urban) and train access system (free access vs. reservation required). Results In the suburban case, the results show the need of new urban policies, not only limited to the transport domain, in order to manage the demand peaks at the stations and on-board vehicles. In the ex-urban case, the outputs suggest the need for public subsidies in order for the railways undertakings to cope with revenue losses and, at the same time, to maintain service quality levels.

Download Full-text

How does the Built Environment Influence Public Transit Choice in Urban Villages in China?

Sustainability ◽

10.3390/su11010148 ◽

2018 ◽

Vol 11 (1) ◽

pp. 148 ◽

Cited By ~ 3

Author(s):

Le Yu ◽

Binglei Xie ◽

Edwin Chan

Keyword(s):

Built Environment ◽

Low Income ◽

Traffic Congestion ◽

Travel Demand ◽

Public Transit ◽

Multinomial Logistic Regression ◽

Large Population ◽

Travel Behaviour ◽

New Perspective ◽

Urban Villages

With growing traffic congestion and environmental issues, the interactions between travel behaviour and the built environment have drawn attention from researchers and policymakers to take effective measures to encourage more sustainable travel modes and to curb car trips, especially in urbanising areas where travel demand is very complicated. This paper presents how built environmental factors affect public transit choice behaviour in urban villages in China, where a large population of low-income workers are accommodated. This location had a high demand for public transit and special built environmental characteristics. Multinomial logistic regression was employed to examine both the determinants and magnitude of their influence. The results indicate that the impacts of built environments apply particularly in urban villages compared to those in formal residences. In particular, mixed land use generates an adverse effect on public transit choice, a surprising outcome which is contrary to previous common conclusions. This study contributes by addressing a special type of neighbourhood in order to narrow down the research gap in this domain. The findings help to suggest effective measures to satisfy public transit demand efficiently and also provide a new perspective for urban regeneration.

Download Full-text

Generative Modelling and Machine Learning Methods for Travel Behaviour Analysis

10.32920/ryerson.14664273.v1 ◽

2021 ◽

Author(s):

Melvin Wong

Keyword(s):

Machine Learning ◽

Discrete Choice ◽

Learning Process ◽

Travel Demand ◽

Travel Behaviour ◽

Generative Model ◽

Choice Modelling ◽

Behaviour Analysis ◽

Discrete Choice Modelling ◽

Generative Modelling

The dissertation outlines novel analytical and experimental methods for discrete choice modelling using generative modelling and information theory. It explores the influence of information heterogeneity on large scale datasets using generative modelling. The behaviorally subjective psychometric indicators are replaced with a learning process in an artificial neural network architecture. Part of the dissertation establishes new tools and techniques to model aspects of travel demand and behavioural analysis for the emerging transport and mobility markets. Specically, we consider: (i) What are the strengths, weaknesses and role of generative learning algorithms for behaviour analysis in travel demand modelling? (ii) How to monitor and analyze the identiability and validity of the generative model using Bayesian inference methods? (iii) How to ensure that the methodology is behaviourally consistent? (iv) What is the relationship between the generative learning process and realistic representation of decision making as well as its usefulness in choice modelling? and (v) What are the limitations and assumptions that have needed to develop the generative model systems? This thesis is based on four articles introduced in Chapters 3 to 6. Chapters 3 and 4 introduces a restricted Boltzmann machine learning algorithm for travel behaviour that includes an analysis of modelling discrete choice with and without psychometric indicators. Chapter 5 provides an analysis of information heterogeneity from the perspective of a generative model and how it can extract population taste variation using a Bayesian inference based learning process. One of the most promising applications for generative modelling is for modelling the multiple discretecontinuous data. In Chapter 6, a generative modelling framework is developed to show the process and methodology of capturing higher-order correlation in the data and deriving a process of sampling that can account for the interdependencies between multiple outputs and inputs. A brief background on machine learning principles for discrete choice modelling and newly developed mathematical models and equations related to generative modelling for travel behaviour analysis are provided in the appendices.

Download Full-text

Exploring the Factors that Impact on Transit Use through an Ordered Probit Model: the Case of Metro of Madrid

Libro de Actas CIT2016. XII Congreso de Ingeniería del Transporte ◽

10.4995/cit2016.2016.3205 ◽

2016 ◽

Author(s):

Carmen Forciniti ◽

Laura Eboli ◽

Gabriella Mazzulla ◽

Francisco Calvo

Keyword(s):

Land Use ◽

Urban Areas ◽

Travel Demand ◽

Probit Model ◽

Travel Behaviour ◽

Transportation System ◽

Ordered Probit ◽

Ordered Probit Model ◽

Economic Activities ◽

Transit Use

The configuration of urban areas is the result of a cyclic relationship between land use and transportation system: the changes in transportation system arrangements influence the localisation of residence and economic activities, as well as the changes in land use affect transportation system characteristics. In this context, by operating on land use, travel demand can be shift from the individual transportation modes to transit systems. In the literature, many conceptual models were proposed to describe the complex relationship between land use and travel behaviour. In addition to spatial variation, the study of travel demand shows the categorical variation of variables. This work aims to analyse the influence of the categorical variation of variables impacting on transit use. An ordered probit model is proposed for evaluating how transit use depends on variables related to socio-economic characteristics of population, territorial features, accessibility, and transportation system. The study case is Madrid metro network (Spain). The results show a strong influence of characteristics of population and land use variables on daily trips made using metro system and highlighted the aspects that mainly impact on the choice to travel by metro, providing useful suggestions for shifting people from individual transportation mode to transit systems.DOI: http://dx.doi.org/10.4995/CIT2016.2016.3205

Download Full-text