Understanding the trip and user characteristics of the combined bicycle and transit mode

Several cities around the world are facing mobility related problems such as traffic congestion and air pollution. Although limited individually, the combination of bicycle and transit offers speed and accessibility; by complementing each other’s characteristics the bicycle and transit combination can compete with automobiles. Recognising this, several studies have investigated policies that encourage integration of these modes. However, empirical analysis of the actual users and trips of the combined mode is largely missing. This study addresses this gap by (i) reviewing empirical findings on related modes, (ii) deriving user and trip characteristics of the bicycle and transit mode in the Netherlands, and (iii) applying latent class cluster analysis to discover prototypical users based on their sociodemographic attributes. Most trips by this mode are found to be for relatively long commutes where transit is in the form of trains, and bicycle and walking are access and egress modes respectively. Furthermore, seven user groups are identified and their spatial and temporal travel behaviour is discussed. Transport authorities may use the empirical results in this study to further streamline integration of bicycle and transit for its largest users as well as to tailor policies to attract more travellers.Institute of Transport and Logistics Studies. Faculty of Economics and Business. The University of Sydne

A Markov-chain Activity-based Model for Pedestrians in Office Buildings

As the number of people working in office buildings increases, there is an urgent need to improve building services, such as lighting and temperature control, within these buildings to increase energy efficiency and well-being of occupants. A pedestrian behaviour model that simulates office occupants’ movements and locations can provide the high spatial and temporal resolution data required for the testing, evaluation, and optimization of these control systems. However, since most studies in pedestrian research focus on modelling specific actions at the operational level or target situations where movement schedules do not have to modelled, a pedestrian behaviour model that can simulate complex situations over long time periods is missing. Therefore, this paper proposes a tactical level model to generate occupant movement patterns in office buildings. The Markov-chain activity-based model proposed here is data parsimonious, flexible in accepting different levels of information, and can produce high resolution output. The mathematical properties of the methodology are analyzed to understand their impact on the final results. Finally, the tactical level pedestrian behaviour model is face validated using a case study of an imaginary office with a simple layout

Developing an Integrated Pedestrian Behaviour Model for Office Buildings

As an increasing number of people work in office buildings and new sophisticated sensor technologies become available there is, both, a need and potential, to develop more complex building service controls that increase the energy efficiency of buildings as well as the well-being of employees. The data required for the testing and evaluation of such control systems is usually in the form of movements and locations of office building occupants collected over long periods of time. However, such data is generally difficult to obtain for reasons ranging from the need to evaluate un-commissioned buildings to privacy concerns related to data sharing. Therefore, this study develops a pedestrian behaviour model that can simulate office occupants’ movements and locations thereby acting as a research platform that produces data for external applications. The model is integrated as it simulates not only the movements of occupants between different locations in the building but also decisions that drive the movements such as which activities occupants want to carry out throughout the day, and when and where they want to perform these activities. Furthermore, the model is based on the guidelines of (i) flexibility – the model is able to simulate movements in different building plans and represent movement patterns of different organizations; (ii) extensibility – the model uses a modular framework that enables easy adoption of more complex components and integration with other workplace related studies as and when required; and (iii) data parsimony – the model has low and simple data requirements itself

Analysing the trip and user characteristics of the combined bicycle and transit mode

Several cities around the world are facing mobility related problems such as traffic congestion and air pollution. Although limited individually, the combination of bicycle and transit offers speed and accessibility that can compete with automobiles by complementing each other's characteristics. Recognising the potential benefits with regard to accessibility, health, and sustainability, several studies have investigated policies that encourage integration of these modes. However, the actual users and trips of the combined bicycle and transit mode have not been extensively studied empirically. This study addresses this gap by (i) reviewing empirical findings on related modes, (ii) deriving user and trip characteristics of the combined bicycle and transit mode in the Netherlands, and (iii) applying latent class cluster analysis to discover prototypical users based on their socio-demographic attributes. Most trips by this combined mode are found to be for relatively long commutes where transit is in the form of trains, and bicycle and walking are access and egress modes respectively. Furthermore, seven user groups are identified and their travel behaviour is discussed. Transport authorities may use these empirical results to further streamline integration of bicycle and transit for its largest users as well as to tailor policies to attract more travellers

Traveller behaviour in public transport in the early stages of the COVID-19 pandemic in the Netherlands

With a few exceptions, public transport ridership around the world has been hit hard by the COVID-19 pandemic. Travellers are now likely to adapt their behaviour with a focus on factors that contribute to the risk of COVID-19 transmission. Given the unprecedented spatial and temporal scale of this crisis, these changes in behaviour may even be sustained after the pandemic. To evaluate travellers’ behaviour in public transport networks during these times and assess how they will respond to future changes in the pandemic, we conduct a stated choice experiment with train travellers in the Netherlands at the end of the first infection wave. We specifically assess behaviour related to three criteria affecting the risk of COVID-19 transmission: (i) crowding, (ii) exposure duration, and (iii) prevalent infection rate. Observed choices are analysed using a latent class choice model which reveals two, nearly equally sized latent traveller segments: ‘COVID Conscious’ and ‘Infection Indifferent’. The former has a significantly higher valuation of crowding, accepting, on average 8.75 min extra waiting time (average total travel time in the choice scenarios was about 40 min) to reduce one person on-board. Moreover, this class indicates a strong desire to sit without anybody in the neighbouring seat and is quite sensitive to changes in the prevalent infection rate. By contrast, the Infection Indifferent class has a value of crowding (1.04 waiting time minutes/person) that is only slightly higher than pre-pandemic estimates and is relatively unaffected by infection rates. We find that older and female travellers are more likely to be COVD Conscious while those reporting to use the trains more frequently during the pandemic tend to be Infection Indifferent. Further analysis also reveals differences between the two segments in attitudes towards the pandemic and self-reported rule-following behaviour. We believe that the behavioural insights from this study will not only contribute to better demand forecasting for service planning but will also inform public transport policy decisions aimed at curbing the shift to private modes

Calibrating Route Choice Sets for an Urban Public Transport Network using Smart Card Data

Identifying the set of alternatives from which travellers choose their routes is a crucial step in estimation and application of route choice models. These models are necessary for the prediction of network flows that are vital for the planning of public transport networks. However, choice set identification is typically difficult because while selected routes are observed, those considered are not. Approaches proposed in literature are not completely satisfactory, either lacking transferability across networks (observation-driven methods) or requiring strong assumptions regarding traveller behaviour (uncalibrated choice set generation methodologies (CSGM)). Therefore, this study proposes a constrained enumeration CSGM that applies the non-compensatory decision model, elimination-by-Aspects, for choice set formation. Subjective assumptions of traveller preferences are avoided by calibrating the decision model using observed route choice behaviour from smart card data, which is becoming increasingly available in public transport systems around the world. The calibration procedure also returns two key insights regarding choice set formation behaviour: (i) the ranking of different attributes by their importance, and (ii) the acceptable detours for each attribute. To demonstrate the methodology and investigate choice set formation behaviour, the tram and bus networks of The Hague, Netherlands are used as a case study

Understanding the Role of Cycling to Urban Transit Stations through a Simultaneous Access Mode and Station Choice Model

Governments worldwide are aiming to increase sustainable mode use to increase sustainability, livability, and accessibility. Integration of bicycle and transit can increase catchment areas of transit compared with walking and thus provide better competition to non-sustainable modes. To achieve this, effective measures have to be designed that require a better understanding of the factors influencing access mode and station choice. At the national/regional level this has been thoroughly studied, but there is a knowledge gap at the urban level. This study aims to investigate which factors influence the joint decision for tram access mode and tram station choice. The joint investigation can identify trade-offs between the access and transit journeys. Furthermore, the effect of each factor on the bicycle catchment area is investigated. Using data from tram travelers in The Hague, Netherlands, a joint simultaneous discrete choice model is estimated. Generally, walking is preferred to cycling. The findings of this study suggest that access distance is one of the main factors for explaining the choice, where walking distance is weighted 2.1 times cycling distance. Frequent cyclists are more likely also to cycle to the tram station, whereas frequent tram users are less inclined to cycle. Bicycle parking facilities increase the cycling catchment area by 234 m. The transit journey time has the largest impact on the catchment area of cyclists. Improvements to the system, such as fewer stops, higher frequency (like light rail transit), or both, therefore would result in a much longer accepted cycling distance