Evidence on performance

By concentrating a mix of pedestrian-oriented development around public transport nodes, residents and workers are more likely to catch a train or a bus for out-of-neighbourhood trips, and walk or bike for shorter within-neighbourhood trips (Cervero et al, 2005). Evidence on how transit-oriented development (TOD) has influenced travel and environmental quality comes mainly from the US. There, studies show that TOD housing produced considerably less traffic than is generated by conventional development (UN HABITAT, 2013, citing Arrington and Cervero 2008). TODs can reduce car use per capita by half, thus saving households around 20 per cent of their income since they have, on average, one less car or often none (UN HABITAT, 2013, citing Cervero, 2008). Typically, TOD residents in the US commute by transit four to five times more than the average commuter in a region (UN HABITAT, 2013, citing Lund et al, 2006; Arrington and Cervero 2008). Similar ridership bonuses have been recorded for TOD projects in Toronto, Vancouver, Singapore and Tokyo (UN HABITAT, 2013, citing Cervero, 1998; Yang and Lew, 2009; Chorus, 2009). In China, a recent study found smaller differentials of around 25 per cent in rail commuting between those living near versus away from suburban rail stations (UN HABITAT, 2013, citing  Cervero and Day, 2008). While TOD planning tends to focus on residences, experience from the US shows that concentrating jobs around rail stops in well-designed, pedestrian-friendly settings can exert even stronger influences on the choice of travel mode (UN HABITAT, 2013, citing Cervero, 2007; Kolko 2011; Guerra et al, 2012). The location of TOD in a region and the quality of connecting public transport services can strongly influence the choice of travel mode. A TOD as an island in a sea of auto-oriented development will have little influence on travel (UN HABITAT, 2013)

Policy contribution

Though there are many case studies of schemes intended to encourage public transport use by land use planning there are few, if any, case studies which have quantified the real effect. The main reason for this is the difficulty of comparing before and after conditions for an instrument that takes so long to implement and for effects to be felt.

A range of such schemes, though without evaluation, are described in VTPI (2002).

Contribution to objectives

Objective	Scale of contribution	Comment
		By reducing motor traffic
		By higher standard development
		By reducing motor traffic
		Through increased accessibility
		By reducing motor traffic and creating less car-intensive neighbourhoods
		By developing more attractive locations
	-	Uncertain effect

	= Weakest possible positive contribution,		= strongest possible positive contribution
	= Weakest possible negative contribution		= strongest possible negative contribution

= No contribution

Contribution to problems

Contribution to allevaition of key problems
Problem	Scale of contribution	Comment
Congestion		By encouraging modal shift to public transport and so reducing motor traffic
Community impacts		By encouraging modal shift to public transport and so reducing motor traffic
Environmental damage		By encouraging modal shift to public transport and so reducing motor traffic and congestion in local centres. Includes reduction in "cold starts" from fewer car trips.
Poor accessibility		By encouraging a situation for more viable public transport and by easier movement in newly-designed centres
Social and geographical disadvantage		By encouraging a situation for more viable public transport, social and geographical groups are made more "accessibility equal"
Accidents		By encouraging modal shift to public transport and so reducing motor traffic in local centres (pedestrian safety) in particular
Economic growth		If land use planning increases accessibility, the area should become more attractive; the accompanying improved amenity will help enhance this effect

	= Weakest possible positive contribution,		= strongest possible positive contribution
	= Weakest possible negative contribution		= strongest possible negative contribution

= No contribution