1 Estimating cycling potential to rail stations

The goal of this repo is to explore methods for calculating cycling potential to public transport nodes, rail stations in the first instance.

2 OD data

The input data consists of origin-destination pairs. These can be obtained from a range of sources. We will use open OD data from the 2011 UK Census to demonstrate the methods. A random sample of OD pairs from the national dataset is shown below.

geo_code1	geo_code2	all	from_home	light_rail	train	bus	taxi	motorbike	car_driver	car_passenger	bicycle	foot	other	geo_name1	geo_name2	la_1	la_2
E02004474	E02000916	2	0	0	0	0	0	0	2	0	0	0	0	Castle Point 002	Waltham Forest 022	Castle Point	Waltham Forest
E02000512	E02003707	2	0	0	0	0	0	0	2	0	0	0	0	Hillingdon 019	Wycombe 012	Hillingdon	Wycombe
E02000371	E02000735	1	0	0	0	0	0	0	0	0	1	0	0	Hackney 027	Newham 022	Hackney	Newham

The case study region of West Yorkshire is used to subset the dataset of 2402201 OD pairs to records representing trips originating in the region (95915 rows). In a further subsetting stage only OD pairs with more than a threshold number of trips were kept to focus the analysis on desire lines in which large numbers of people travel by train. Setting this threshold to 10 people by results in 447 rows in the case study region. These rail trips are illustrated in Figure 2.1 below.

Figure 2.1: Illustration of major commute desire lines originating in West Yorkshire by any mode (black) and by rail (blue).

3 Rail station data

Data on rail station locations was obtained from the naptan.app.dft.gov.uk website. The multi-stage trips from home to work via rail stations is shown in Figure 3.1 below. This graphic assumes simplistically that the first stage of rail journeys was to the nearest station, that the rail journey went to the station closes to their destination, and that trips involve travelling in a straight line (an assumption we will remove in the next section).

Figure 3.1: Illustration of desire lines with high numbers of rail trips, focussing on a sample of 5, assuming straight line travel (left) and assuming trips travel via the nearest station to the origin and destination, showing desire lines from home locations to the nearest stations (right).

The distribution of total trip distances and trip distances to and from stations is shown in Figure 3.2.

4 Public transport routing

The route that people will take is not necessarily the one that goes to the closest rail station to their home. It will usually be the route that minimises total journey time.

The total journey time can be calculated as the sum of the origin, public transport stage, and destination stages:

T_j = T_o + T_p + T_d

The time taken for each stage varies depending on the origin and destination station. In this example we will focus only on the choice of the origin station. We can find the three nearest stations to each origin as follows:

nearest_stations = nngeo::st_nn(origin, rail_stations, k = 3, progress = FALSE)
nearest_stations
#> [[1]]
#> [1] 1256 1266 1198

Based on this example, we can plot the three route options and show their associated times:

	distance_m	distance_text	duration_s	duration_text
1-1	23684	23.7 km	2758	46 mins
1-11	21881	21.9 km	2406	40 mins
1-12	19656	19.7 km	2296	38 mins

5 Cycle routing

6 Scaling the methods

7 Discussion