Section 2.3 in this chapter illustrates the importance of walking distances to stops.
Next to population densities, the acceptable walking distance is an important factor that determines the potential of public transport infrastructure11. The
11 There are numerous factors that influence the use of public transport infrastructure, such as pricing, the frequency of the transport service, the quality of the public transport system in a city, and more.
literature in this section investigates how the character of the urban walking environment around stops influences acceptable walking distances to stops.
The study of Peperna (1982) was previously presented at the beginning of Section 2.3. The author was one of the first researchers to measure the substantial effect of environmental characteristics on accepted walking distances to stops. His methodology allowed to measure the environmental effect separately from some previously described contextual conditions such as the travel purpose, available transport options, and the quality of the public transport service.
Peperna uses four indicators to determine the quality of the urban surroundings for walking trips to public transport stops: firstly, the typology of the footpath network, such as existing of radials, a grid-like network or specific typologies;
secondly, the quality of footpaths by accounting for the amount of car traffic along pavements, greening, and traffic calming measures; thirdly, the visual appearance of the urban surroundings, comprising building types and styles, facades, and the design of the public space; fourthly, the existence of shops, schools, and workplaces around public transport stops. From these four indicators, the researcher defines walking environments that are either pedestrian-oriented or car-dominated (pp. 50–52).
Sporadic travellers walk 216 metres to stops in unattractive car-dominated environments. Accepted walking distances increase in the same group by 20 percent in pedestrian-oriented surroundings12. Most interesting appear results for journeys to work. Fifty percent accept walking distances over 376 metres in pedestrian-oriented environments, decreasing to 218 metres in car-dominated urban areas. Walking distances in attractive urban surroundings remain 73 percent longer after the effect of available transport alternatives is filtered out (p. 65). On the basis of these findings, Peperna suggests not generalising on accepted walking distances to public transport stops without considering the influence of the urban environment (p. 65).
Knoflacher (1996) points to the substantial effect, which the study of Peperna indicates, for the potential of public transport. With a 70 percent increased walking distance, the spatial size of the (theoretical) radial catchment area around stops would triple in size. Figure 19 illustrates this effect graphically. Pedestrian-oriented
12 Due to limitations of the data set, the analysis did not filter out the effect of available travel options.
urban environments hence triple the number of urban inhabitants within acceptable walking distances to public transport systems13, at least theoretically.
Lam and J. F. Morrall (1982) analyse data from 2400 interviews on walking routes to bus stops in Calgary (Canada). The study questions the difference between summer and winter seasons. Public transport hubs and stops where passengers change are excluded. The researchers use detailed maps to trace accurate walking routes to stops, including detours. Table 2 shows the average length of routes to stops in three different urban areas. Distances vary with season but more extensively with the environmental characteristics, as the reduction in industrial areas shows. Accepted walking distances decrease here by 46-48 percent as
13 Factors such as the density of stops along a public transport line, the density of the public transport network, and further factors influence such a simplified theoretical consideration.
Figure 19: Tripled size of the catchment area with 70 percent longer walking distances
Table 2: Average walking distance dependent on season, frequency of bus service, and type of urban environment (Lam and J. F. Morrall 1982, p. 416)
Stop environment Frequency bus
service, minutes Walking distance
summer, metres Walking distance winter, metres
Central business district 5-8 min 292 m 355 m
Suburban residential 5-8 min 373 m 348 m
Industrial area 30 min 173 m 211 m
compared to suburban stops, and by 39 – 42 percent compared to stops in the centre (p. 411). To what extent lower bus service frequencies in industrial areas influence walking distances remains unclear.
The shorter walking distances in the central business district is somewhat surprising. That walking environments in suburban areas are more pedestrian orientated than the central business district of Calgary appears to be a scarcely convincing explanation. Further, walking distances drop unexpectedly during the summer. The authors explain the phenomenon with the possibility of reduced reliability of car transport with snow and ice on the streets during the cold winter in Calgary (p. 416). The effect of these non-investigated factors remains unclear though. The study demonstrates that there are a number of factors involved when measuring walking distances.
In the previously reported study of O’Sulivan and Morrall (1996), researchers asked interviewees to estimate the time they spent walking to and from light rail stops. The researchers calculated the time spent walking from the measured distance of walking trips (assuming an average walking speed of 80 metres per minute). Walking distances are with 651 meters at suburban stops longer as in the central business district with 326 metres (p. 25). Comparing the calculated duration for the walk to the stop with peoples’ time estimates uncovers a difference.
Walking trips to and from suburban stops are on average underestimated. The quality of the walking environment could cause the effect, the authors assume (p.
25).
Faster walking speeds in suburban environments would also explain the phenomenon. Differences in average waiting times for street crossings may provide further explanations. Without more detailed knowledge on the background of the study that O’Sulivan and Morrall report, the difference between calculated distance and time estimates remains unknown. Similarly, longer walking distances to suburban stops are difficult to explain with the character of the walking environment.
Maghelal (2009) investigates whether the percentage of public transport users that walk to light rail stops varies with the features of the built environment. Even though this is not a direct investigation of accepted walking distances, varying percentages indicate changing accepted walking distances to light rail. With the help of Geographic Information Systems (GIS), the author analyses four features of the pedestrian environment around 20 light rail stops in Dallas county: (1)
vehicle oriented design, (2) residential density, (3) land use diversity, and (4) walking oriented design (p. 48). Only density had a significant effect (p. 58).
Unexpectedly, higher densities decreased the percentage of walking trips to stops (p. 61). The study does not provide a convincing explanation for the found effect.
Chapter 4 discusses the challenges of the applied methodology of the study, Maghelal reports.
The previously presented study of Guo (2009) identifies five environmental features that influence the travel route of public transport users. These features also influence the time spent walking to final destinations after the public transport ride and, accordingly, the walking distance:
1. One more parcel with shops and services per 100-metre walking route increases walking time by 5 percent
2. One more footpath intersection per 100-metre route increases walking time by 3 percent
3. Pavements widened by 1.8 meters (6 feet) increases walking time by 5 percent
4. Walking routes through the park, Boston Common, increase walking time by 29 percent
5. Walking routes through the hilly area of Beacon Hill decrease walking time by 35 percent
The five presented environmental conditions can vary in parallel with other non-included factors. For example, wider pavements and more shops can correspond with generally more pleasant and stimulating surroundings. I assume that it is not the pavement width alone that caused the observed effect but the environmental conditions that correspond with wider pavements.
The influence of topography appears extensive in Guo’s study. We have to remember that the enquiry shows the effect of an urban area with noticeable topography. Here, investigated pedestrians walk up- and downhill. Weidmann (1993) finds walking uphill on a 10 to 12 percent incline to increase the energy consumption by about 80 percent (p. 24). The data of the previously discussed research of Brändli et al. (1978) uncovers sloping terrain to reduce acceptable walking distances by 32 to 43 percent (p. 43). The findings of Guo appear plausible.
The decision to depart from the subway stop through a park is likely to be a conscious decision. Such depends on individual preferences, as Guo (p. 350) admits. I consider results not to show that parks generally increase accepted
walking distances. However, the investigation shows that the environment can influence route choice and the time travellers accept to walk after the public transport ride. This is an important contribution to the discourse.
One of the most interesting studies was present by Yang et al. (2012). The authors investigate factors that influence the accepted walking distance to 19 rapid bus transit14 stations in the City of Jinan in China. They use data from 1233 interviews regarding the walking trip to the stop (p. 6). The study defines three rough characteristics for the pedestrian environment.
An ordinary least square regression isolates the effect of the urban environment on accepted walking distances from other factors. Variables such as income, occupation, age, and travel purpose have a very low influence on accepted walking distances. Conversely, all environmental variables remain significant (p. 11). The average walking distance is 600 metres. More shops, smaller block sizes and trees along pavements increase accepted walking distances by about 25 percent. At terminal stations (at the end of lines) the average walking distance increases by 67 percent (p. 13). With high land-use densities, the average walking distance decreases by 25 percent (p. 13). The authors explain the reduction with higher density by the lower number of people that have to walk far to reach the stop.
Further, the distance between the stop and the city centre influences how far people are willing to walk to the stop. With each kilometre increased distance between city centre and stop, the accepted walking distance to stops rises by 75 metres (p. 12). According to these results, the average walking distance of 600 metres doubles at stops of eight kilometres distance from city centres. This appears to be a very extensive effect.
The authors point to some shortcomings of their study. First, they could only measure the land use density very roughly. Second, they investigate only walking trips to reach stops before the ride on the means of transport. As they point out, the walking route after the ride on the bus could also influence accepted walking distances. Third, they do not investigate the character of the walking environment along each individual walking route. Instead, they use the characteristics of the public transport corridor as a proxy for the urban environment along the walking route (p. 13). Despite these shortcomings, the study shows well the potential of the environment to increase accepted walking distances to public transport stops.
14 Rapid bus transit public transport systems consist of buses that drive on dedicated carriageways with priority over other vehicles on street.
This research focuses on the effect of urban environments on pedestrian access to public transport stops. How well do the six presented studies explain the environmental influence on pedestrian access to stops? The findings of Lam and J. F. Morrall (1982), O'Sulivan and J. Morrall (1996), and Maghelal (2009) remain somewhat ambivalent. It is likely that the results of these studies are influenced by the combined effect of (1) the population density around stops, and (2) the applied methodology to derive average walking distances. All three studies measure walking distances of a random number of pedestrians that access stops. With high population densities directly around the stop, the data sample must contain more observations from pedestrians who only have to walk short distances to the stop.
Inversely, a highly dense residential development at 250 metres distance from the stop would increase the average walking distance, as more public transport users arrive from the dense development at some distance from the stop. Average measured walking distances are influenced by the spatial distribution of population densities around stops, as Figure 20 illustrates. The described phenomenon influences the average walking distance (in a random data sample) to stops independently from environmental characteristics.
Figure 20: Different average walking distances as result of dissimilar spatial locations of urban density (indicated by grey fields)
The described effect of the spatial urban density distribution on the results of average walking distances could well explain longer walking distances to stops in less dense suburban areas in the studies of Lam and J.F. Morrall and O’Sulivan and J. Morrall. Similarly, the phenomenon in Figure 20 explains shorter walking distances with high urban densities in the study of Maghelal. The survey of Peperna (1982) is less likely to be influenced by the described effect15. Yang et al.
(2012) recognise the effect of density and filter it out. Density distributions remains irrelevant for the methodology Guo (2009) applies. Walther (1973) was the first author who described and accounted for the density effect (p. 53), but his study does not investigate the influence of environmental characteristics on walking distances to stops.
The study of Peperna (1982) provides a good orientation on the extent of the environmental effect. However, the investigated environmental characteristics remain rough and still do not provide detailed information for planning and designing pedestrian-friendly environments around stops.
Guo (2009) presents applicable findings. Attractive parks increase accepted walking distances. The general effect may, however, remain lower than the reported 30 percent increase. The average effect of topography appears valid, reducing accepted walking distances by 35 percent. The unit chosen for the density of shops and footpath intersections requires a detailed measure for a specific walking route. Both variables describe features of an attractive walking environment. More shops and a denser footpath network with more intersections are likely to increase accepted walking distances.
Congruent with Guo, Yang et al. also find that more shops and smaller street block sizes (resulting in a denser footpath network with more footpath intersections) increase accepted walking distances by 25 percent. These results do not appear unreasonable but may depend on further environmental features. More shops and a denser footpath network are also likely to increase social activity, possibly the design of footpaths, the size of buildings, and so on. Environmental characteristics that vary together with the footpath network and the number of shops may jointly influence the uncovered effect.
Yang et al. find longer accepted walking distances at terminal stops. The effect may result from overlapping catchment areas, as Figure 21 shows. The terminal stop overlaps 50 percent less with other catchment areas. Eclipses close to the
15 The maps that Peperna provides for the urban areas around the studied public transport stops indicate an evenly distributed dense city structure around stops.
radial borders of the catchment area can reduce the number of people that have to walk longer distances to stops. Additionally, the previously discussed phenomenon of different density distributions may be another reason for longer walking distances at terminal stops.
Possibly terminal stops are more important as local centres in suburban parts of the city. This centrality effect may decline around stops along the