First Mile/Last Mile Problems in Smart and Sustainable Cities: A Case Study in Stockholm County

Figures & data

Figure 1. Definition of first and last mile relating to different stages of trips

Table 1. Reported values for willingness to walk to public transport (in meters and for the average individual)

Study	Study Area	Mode	Meters	Note
(El-Geneidy et al., 2014)	Montreal, Canada	Bus	484	85^th percentile, STM bus
			556	85^th percentile, RTL bus
			601	85^th percentile, STL bus
			897	85^th percentile, CIT bus
		Metro	873	85^th percentile
		Train	1259	85^th percentile
(Hartoft-Nielsen, 2002)	Copenhagen, Denmark	Metro/train	1000	Within walking distance
(Ker and Ginn, 2003)	Perth, Australia	Bus	1400	85^th percentile, free bus
		Train	1250-4250	Almost 50% walked ≥1000 m, with large variations between stations.
(Krygsman et al., 2004)	Amsterdam, Netherlands	Bus	833	70% of individuals, bus or train
(Brand et al., 2017)	Amsterdam	Bus	1000	85^th percentile, R-net, only bus lines included, estimated from a graph
		Bus	1250	85^th percentile, Comfortnet, estimated from a graph
(Rietveld, 2000)	Netherlands	Train	1200	Dominant from residence
		Train	2200	Dominant activity end
(Zuo et al., 2018)	Ohio, USA	Bus	950	85^th percentile, home end
		Bus	547	85^th percentile, activity end
Mean		Bus	791
		Metro/train	1489
Median		Bus	833
		Metro/train	1230

Figure 2. Stockholm County in Sweden

Figure 3. The spatial structure map of development areas established in RUFS 2050 (Region Stockholm 2018d)

Table 2. First mile/last mile (FM/LM) threshold distances (meters) for the average population and the elderly and elderly impaired subgroups

	Bus Services	Train Services
Average population	1,200	2,000
Elderly	600	900
Elderly impaired	200	200

Table 3. Data used for modeling in Geographic Information System (GIS)

Description of Data	Type of Data	Source
Public transport stops and stations	Point	(Region Stockholm, 2018b)
Buildings (commercial and residential)	Polygon	(Lantmäteriet, 2017)
Holiday home areas	Polygon	(Statistics Sweden, 2015)
Road network	Polyline	(Lantmäteriet, 2017)
Future public transport lines	Polyline	(Region Stockholm, 2018c)
Future metro stations	Constructed point from an approximate position on the map	(Region Stockholm, 2019)
Future stops and stations other than metro	Constructed point using the average distance between stops per line	(AB Storstockholms Lokaltrafik, 2009; Region Stockholm, 2018c)
Spatial structure map of development areas	Polygon	(Region Stockholm, 2018d)

Figure 4. Service areas with access to public transport (left) and buildings lacking access to public transport (right) in the 2019 scenario shown on top of each other in each population group

Figure 5. Services areas with access to public transport (left) and buildings lacking access to public transport (right) in the 2019 scenario, showing central areas of Stockholm City

Figure 6. Areas lacking access in 2019 that will have gained access to public transport by 2035

Figure 7. Buildings lacking access in 2019 that will have gained access to public transport by 2035. The figure shows central areas in Stockholm City

Figure 8. Network service area for the average population (left) and for population subgroups (right) as an overlay on the planned development areas. Areas in red rectangles are areas lacking access to public transport. For the elderly and elderly impaired, areas highlighted reflect problem areas that remain in the Euclidean service area

Figure 9. Close-up of potential problem areas south of Stockholm city center, shown in red rectangles for the population subgroups in the 2035 scenario

Table 4. Changes with future development in public transport access up until 2035 for the average population and the elderly and elderly impaired subgroups

	Average Population	Elderly	Elderly Impaired
Network service area	+2.4%	+3.9%	+4.8%
No. of individuals without access to public transport*	–0.6%	–30.7%	–14.0%
No. of problem buildings	–0.5%	–31.4%	–14.8%

* Assuming all individuals in Stockholm county belong to one of the population groups, e.g., all are elderly

A. El-Geneidy, M. Grimsrud, R. Wasfi, P. Tétreault, and J. Surprenant-Legault, “New Evidence on Walking Distances to Transit Stops: Identifying Redundancies and Gaps Using Variable Service Areas,” Transportation 41: 1 (2014) 193–210.

 Web of Science ®Google Scholar

P. Hartoft-Nielsen, Stationsnærhedspolitikken i hovedstadsområdet – baggrund og effekter, By- og Landsplanserien (Copenhagen: Skov and Landskab, 2002) <https://videntjenesten.ku.dk/filer/rapporter/planlaegning-og-friluftsliv/bogl18.pdf > Accessed February 28, 2019.

 Google Scholar

I. Ker and S. Ginn, “Myths and Realities in Walking Catchments: The Case of Walking and Transit,” Road and Transport Research 12: 2 (2003) 69–80.

 Google Scholar

S. Krygsman, M. Dijst, and T. Arentze, “Multimodal Public Transport: An Analysis of Travel Time Elements and the Interconnectivity Ratio,” Transport Policy 11: 3 (2004) 265–275.

 Google Scholar

J. Brand, S. Hoogendoorn, N. van Oort, and B. Schalkwijk, “Modelling Multimodal Transit Networks Integration of Bus Networks with Walking and Cycling,” presented at the 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (Naples, Italy: IEEE, 2017) 750–755.

 Google Scholar

P. Rietveld, “The Accessibility of Railway Stations: The Role of the Bicycle in The Netherlands,” Transportation Research Part D: Transport and Environment 5: 1 (2000) 71–75.

 Web of Science ®Google Scholar

T. Zuo, H. Wei, and A. Rohne, “Determining Transit Service Coverage by Non-Motorized Accessibility to Transit: Case Study of Applying GPS Data in Cincinnati Metropolitan Area,” Journal of Transport Geography 67 (2018) 1–11.

 Web of Science ®Google Scholar

Region Stockholm, SL Spårstationer (Stockholm, Growth and Regional Planning Administration, 2018d).

 Google Scholar

Region Stockholm, Beslutade investeringsåtgärder transportinfrastrukturen (Stockholm: Region Stockholm, 2018b) <http://rufs.se/kartor/rufs-2050> Accessed February 14, 2019.

 Google Scholar

Lantmäteriet, “GSD-Fastighetskartan”, 2017.

 Google Scholar

Statistics Sweden, Fritidshusområden 2015 (Statistics Sweden: 2015) <https://www.scb.se/hitta-statistik/regional-statistik-och-kartor/geodata/oppna-geodata/fritidshusomraden/> Accessed May 11, 2019.

 Google Scholar

Region Stockholm,: Plankarta Stockholms län 2050 (Stockholm: Region Stockholm, 2018c) <http://rufs.se/kartor/rufs-2050> Accessed April 1, 2019.

 Google Scholar

AB Storstockholms Lokaltrafik,: Trafikplan 2020 (Stockholm: 2009) <http://www1.sl.se/pdf/trafikplan%202020_klickbar.pdf > Accessed April 23, 2019.

 Google Scholar