Cycle propensity modelling: An extreme cycling uptake study

Billy Brazil1,2, Karen Whitaker1, Barry Colleary1,2, Robert Egan2, Brian Caulfield2

1 National Transport Authority, Dublin, Ireland

2 Trinity College Dublin, Dublin, Ireland
lncs@springer.com

Abstract. Expanding on the four-stage model, the NTA Regional Modelling System (RMS) comprises the most complete and extensive modelling system in Ire-land. Mode choice within the model is governed by parameters including generalised cost sensitivity terms and alternative specific constants (ASCs). The ASCs represent the unquantified elements of utility or preference associated with each mode, such as comfort, perceived safety, exposure to weather etc. For this study, the constant values estimated for walking are applied to the cycling utility equation for a future year 2028 test case. This test also accounts for the impact of increased access to electric bikes, with the speed assigned to all cycling network links within the model increasing by 4 kph. These changes create a modelled scenario wherein cycling is viewed as favourably as walking by trip makers, while retaining the speed advantage and increased range of possible destinations that cycling (and e-bikes) provides. This test provides an insight into the maximum demand available to cycling. Results show a very large shift to cycling, when measured by mode share; however, this shift is not universal across the regions, and is dependent on factors such as trip length, competing modes, and user class.

Keywords: Cycling, Electric Bikes, Choice Modelling, Sustainable Transport, Mode Shift, Emissions.

Introduction

Modelling Cycling in Strategic Models

Many traditional four-stage strategic transport models attempt to capture the disutility associated with specific modes of transport via the formulation of generalised cost functions, where generalised cost represents a composite cost of travel (including factors such as travel time, trip distance, and monetary costs). While the formulation of these functions can involve the collection of large amounts of data and the robust estimation of parameters, mode choice cannot always be fully explained by these parameters, and it may be influenced by a variety of factors outside of the generalised cost specification.

These additional factors are accounted in choice models through the estimation an alternative specific constant term (ASC) for each mode. These terms also captures unmeasured – or potentially extremely difficult to quantify – elements of travel, such as perceptions of specific modes and individual user experiences and biases. Changes to such perceptions and biases cannot be captured by strategic models under current specifications as the ASC value is not linked to a specific input, such as network speed travel cost. This study was undertaken to provide an understanding of a potential upper-bound for cycling within an Irish context by adjusting the cycling ASC term. This research aims to inform discussions around issues such as sustainable transport, carbon emissions reductions, increased mobility, and the respective role of various alternative modes of transport. Specifically, this study estimates the highest achievable levels of cycling possible, in a scenario where Ireland’s population is as amenable to cycling as it currently is to walking (but all other transport elements remain constant). It is important to note that this study does not employ naïve mode share reassignment assumptions. Rather, it is based upon a well-established and robust mode choice framework within the RMS, accounting for factors such as: trip purpose, competing modes, trip length and attraction density, availability of parking and park and ride, and user type-specific values of time.

Methods

Regional Modelling System

The National Transport Authority’s RMS is a suite of strategic transport models maintained for transport scheme appraisals, climate action planning, transport strategy formation, and other policy and investment tasks. The RMS is based upon the traditional four-stage transport model, with mode and destination choice undertaken via a combined multinomial logit model (MNL). This model operates by first estimating the probability of selecting a given mode to access each of the destinations within the modelled region (1907 in the eastern region and 822 in the southwestern region), and then using a logsum of mode choice utilities to estimate destination choice. The mode choice element of the model is of primary interest to this work and is described by:

〖Vt〗_m=〖ASC〗_m+ β_m* 〖GenCost〗_m+Intrazonal (0_not intra,1_intra	(1)

Vtm is the utility derived from travelling to destination d by mode m.

ASCm is the mode specific utility constant for mode m.

βm is the mode specific generalised cost sensitivity parameter.

GenCostm is the generalised cost in minutes experienced travelling from a given origin zone to destination d via mode m.

Intrazonal is the intrazonal constant that only enters the equation if the origin and destination are the same zone.

To understand what would happen if people were as amenable to cycling as they are to walking, cycling was assigned the ASC values currently used for walking, plus an additional penalty of five generalised minutes. These additional five generalised minutes account for factors like locking a bike, retrieving the bike from storage or parking, and other generally accepted required elements at the start and end of a cycling trip that are not required for walking (e.g., putting on/taking off a helmet). To justify such a change in ASC, the test must assume a future in which cycling has become as safe as walking. To account for this (and the increased penetration of e-bikes) all cycling links in the network are increased in speed by 4kph. As speed is a proxy for cycling infrastructure quality in the RMS, this accounts for the required network improvements needed to justify such an increase in the ASC. Note: The increase of the cycle ASC also assumes that infrastructural investment will have induced cultural changes that further normalize cycling, hence making it as accessible as walking. It should be further noted that these changes are region wide parameter adjustments, rather than being related to any specific investment, but as the purpose of this study is to understand and identify cycling potential, rather the impact of a given measure, this is deemed acceptable. The study areas of the eastern and south western modelling regions were selected, as they contain Ireland’s two largest urban areas (Cork and Dublin) while also containing considerable rural areas, allowing the modelling of cycling modelling in different land use environments. Results are compared to the NTA’s reference case scenarios, a set of RMS model runs with agreed future schemes maintained for comparative purposes.

Results

Mode Share by Trips

Table 1 presents the 24-hour level mode shares for each of the respective scenarios and regions. In the Eastern Region, cycling increases from 3.5% in the reference case to 38.8% in the extreme cycling scenario. This is accounted for with reductions in the other three respective modes, with car trips reducing from 58.4% to 46.1%, walking trips dropping from 23% mode share to 8.4%, and public transport (PT) trips falling from 15.1% to only 6.7%. The same general trends are seen in the South West Region, but with slightly smaller magnitudes of modal swift. Here cycling increases to 19.2% from a reference case share of only 1.7%, while decreases are evident across the other modes, with the most significant occurring for walking, where mode share decreases by nearly 10 percentage points.

Table 1. All purposes 24-hour mode shares by region and scenario

Mode	ERM Ref	ERM Extreme	SWRM Ref	SWRM Extreme
Car	58.4%	46.1%	72.6%	67.9%
Walk	23.0%	8.4%	17.0%	7.6%
Cycle	3.5%	38.8%	1.7%	19.2%
PT	15.1%	6.7%	8.7%	5.3%

Mode Share by Trip Kilometres

Table 2 outlines the kilometres travelled by both car and bicycle, for the respective regions and scenarios. Despite considerable reductions in car trips/car mode share, reductions in vehicle kilometres travelled are only 7.8% and 2.75% for the Eastern and South Western regions, respectively. These results indicate the small impact that large shifts to cycling when viewed from a trips perspective are likely to have on kilometres driven. For both regions, over a ten-fold increase is seen in the number of kilometres cycled, compared to the reference scenarios, but in both cases this amount is still considerably smaller than the distance travelled by car.

Table 2. AM Trip kilometres by region

Mode	ERM Ref	ERM Extreme	SWRM Ref	SWRM Extreme
Car	11,749,387	10,830,143	4,204,429	4,086,465
Cycle	319,573	3,314,106	36,935	378,494

Cycle Flows

Fig. 1 and Fig. 2 and outline AM period cycle flows for the Dublin area for both scenarios. The figures are scaled to the extreme cycling scenario value range, hence potential impression of very little cycling in the reference case. These figures clearly demonstrate that, while the ASC changes were applied to the entire region, high levels of cycling uptake is only seen in the major urban areas, and remains low in more rural areas.

Fig. 1. Dublin AM Cycle Flows Ref. Case.

Fig. 2. Dublin AM Cycle Flows Ref. Case.

Trip Length Distribution

Fig. 3 presents the trip length distribution for cycle trips for both scenarios in the East-ern Region. This shows that while there is a huge increase in the number of trips cycled under the extreme cycling scenario, the effect of this increase has largely dissipated once trips exceed 20 kilometres. This is likely due to an inability to compete with car trips beyond this distance; journey times, even by e-bike, are simply not competitive enough to induce a modal shift.

Fig. 3. Trip length distribution for cycling Eastern Region

Discussions and Conclusions

This study examined potential levels of cycling if people in Ireland were as favorably disposed to cycling as they currently are to walking via the transfer of a utility equation ASC. Results demonstrate that there is considerable potential demand for cycling within Ireland, with cycling trip mode shares of 38% and 19% estimated for the Eastern and Southwestern regions, respectively. While this can be considered encouraging from the perspective of cycling uptake, it is clear that the majority of these trips are occurring at short to medium distances and hence resulting in a considerable shift from walking and public transport. As the distance range of cycling (even with the addition of e-bikes) appears limited to trips of 20 kilometres and below, the impact on number of kilometres driven is not proportional to the respective mode share changes. While the results of this study indicate that there is a large potential for cycling within an Irish context, it must be acknowledged that this demand is largely contained within the large urban areas, and is limited by the journey lengths in more rural area, which themselves are ultimately a result of dispersed land use and settlement patterns.