Due to increasing urbanisation rates worldwide combined with growing transportation demand, liveability of the urban environment is under pressure (UN, 2018). In response, many governments worldwide have set goals for increasing the share of trips made using sustainable modes of transport, such as walking and cycling. The use of active modes (i.e. walking and cycling) provides health benefits for individuals due to increased activity levels, and on a network level these modes (standalone or in combination with public transport) can potentially reduce traffic jams and the associated externalities (including air and noise pollution) when substituting the car. To achieve the desired increase in active mode shares, targeted policies need to be implemented. This requires a better understanding of who currently uses these modes, who could be persuaded to switch to active modes, and which determinants are driving active mode choice.
This intended change towards active modes requires an adequate representation of walking and cycling in the transportation planning models in order to assess the effect of active mode policies on modal shares and distribution over the network. However, this is often not the case. Moreover, integration of active modes in these models occurs very slowly. Walking and cycling are often missing in transportation planning models, treated as a ‘rest’ category, or combined into slow/active modes, all of which result in incorrect estimates of the active mode shares, making it impossible to correctly identify the impact of potential policy measures on active mode shares. Examples of these policy measures are introduction of new infrastructure or changes to existing infrastructure, which impact route choice and distribution over the network, and reimbursement of using the bicycle to go to work, which impacts the mode choice of individuals.
Investigating mode and route choice of active mode users increases the knowledge on active mode choice behaviour. By bridging this gap, the transportation planning models can potentially be improved. The objective of this thesis is ‘to understand and model mode and route choice behaviour of active mode users’. We identify six topics that are imperative to travel choices. First, we investigate the daily mobility patterns of individuals in relation to attitudes towards modes, because attitudes are considered to influence travel behaviour (Chapter 2). Afterwards, we zoom in on individual trips. We aim to understand which determinants drive the choice to walk or cycle (Chapter 3). In this topic we define the mode choice set as all feasible modes per individual and trip. However, not all feasible modes are used by individuals. Therefore, the third topic focuses on modes used over a long period of time, which we coin the experienced choice set. We investigate which determinants are relevant for including or excluding modes in this choice set (Chapter 4). Regarding cyclists’ route choice, we investigate the determinants influencing this choice (Chapter 5). This research is based on the experienced choice set. Accordingly, we compare this method to frequently used choice set generation methods to identify the added value of the experienced choice set (Chapter 6). Finally, we perform a literature review on how mode and route choice can be modelled simultaneously (Chapter 7).
TRAIL Thesis Series no. T2019/12, the Netherlands Research School TRAIL
- active modes
- route choice determinants
- mode choice determinants
- discrete choice analysis
- choice set formation
- experienced choice set