Crossrail S106 the Transport Case

8/14/2019 Crossrail S106 the Transport Case

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Crossra i l Sec t ion 106 Cont r ibut ions

Transport for London

December 2008


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Crossrail Section 106 Contributions

Crossrai l Sec t ion 106 Cont r ibut ions

10 Eastbourne Terrace,

London,

W2 6LG

Tables

Table 2.1: Distribution of London Underground crowding costs by location andtime of day (all costs given as % of morning peak total crowding) 9

Table 2.2: Distribution of trip times (minutes) by level of crowding morningpeak hour 11

Table 2.3: Crowded passenger hours in morning peak hour 11Table 2.4: Railplan model results of 1000 additional destinations (2001 base)11Table 2.5: Rail mode share for travel to work 13Table 3.1: TRAVL Trip Generation Rates 15Table 3.2: Rail mode share for travel to work 16Table 3.3: TRAVL Rail Mode Shares 17Table 3.4: Calculation of impact for office developments by location 18Table 4.1: Trip generation rates to retailers 20Table 4.2: Morning peak rail retail trip rates 20Table 4.3: Evening peak rail retail trip rates 20


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Table 4.4: Retail - Impact from employee trips (morning peak) 20Table 4.5: Retail - impact from shoppers (morning peak) 21Table 4.6: Retail - impact from shoppers (evening peak) 21Table 4.7: London underground exits by time period 21Table 5.1: Calculation of impact for hotel developments for employees and

guests 24Table 6.1: Residential trip generation rates per morning peak period per 100

square metres 26Table 6.2: Trip generation residential developments 27Table 6.3: TRAVL rail mode shares for residential trips morning peak 27Table 6.4: Proportion of travel to work trips by rail 28Table 6.5: Travel to work trips by destination 29Table 6.6: Crowding cost assumptions 29Table 6.7: Residential impact imposed on rail network morning peak 29Table 7.1: Rail mode share, Crossrail buffer region outside of Z1 against all

non-Z1 31Table 7.2: Impact to the rail network from office developments local to

Crossrail stations 31Table 7.3: Impact to the rail network from retail developments local to

Crossrail stations 32Table 7.4: Impact to the rail network from residential developments local to

Crossrail stations 32Table 8.1: Summary table impact indices 33Table 8.2: Impact Index using London Travel Report mode share 33Table 8.3: Alternative crowding costs 34Table 8.4: Alternative impact index for Residential developments 34Table 8.5: Impact index commercial developments 35Table 8.6: Impact index retail developments 35Table 9.1: Relative crowding impact by destination type and location Error!

Bookmark not defined.

Figures

Figure 2.1: London underground crowding by time of day 9Figure 2.2: Crowding curve 10


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Figure 2.3: 1987 passenger crowding v 1987-2000 demand growth, fittedstraight line represents linear regression of growth on crowding12

Figure 2.4: Rail mode share by LSOA 13Figure 3.1: Rail Mode share by LSOA 17Figure 4.1: Distribution of trips to office and retail development in the morning

peak 22Figure 6.1: Average floor space by household size, England 2001

(sqm/person) 26Figure 6.2: Rail mode share residential land use 28Figure 7.1: Buffer Zones in West London 30Figure A1: Isle of Dogs bus map 38Figure A2: Bank bus map 38Figure A3: Isle of Dogs morning peak travel by mode of transport (source:

London Travel Report, p 7) 39Figure A4: Peak morning travel into central London, by mode of transport

(source: London Travel Report, p 5) 39


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1 In t roduc t ion

1.1 Background

1.1.1 This note sets out the methodology that has been used to assess the impact thatdevelopment in London has on the rail network (that is, on both the LondonUnderground and national rail networks) by the nature and location of thatdevelopment. The results from this analysis have been used to determine if, whereand how much contributions under S106 of the Town and Country Planning Act 1990should be sought to mitigate the impact of these developments on the rail network.

1.1.2 Planning obligations secured through S106 are private agreements usually negotiatedin the context of a planning application between local authorities and developers. Theyare intended to make acceptable development which would otherwise beunacceptable in planning terms.

1.1.3 Guidance on developer contributions is outlined in ODPM Circular 05/2005 PlanningObligations which sets out the Secretary of States five policy tests for planningobligations, namely:

relevant to planning; necessary to make the proposed development acceptable in planning terms; directly related to the proposed development; fairly and reasonably related in scale and kind to the proposed development;

and reasonable in all other respects.

1.1.4 The guidance states that planning obligations should only be sought where they meetall of the five tests. This note relates particularly to point 3 and partially to point 4, thatis, demonstrating how any S106 contribution sought is directly related to the proposed

development and related to the scale and kind of development.

1.1.5 This report sets out

how to determine the impact of development on the rail network; and how that impact varies by development type and location.

1.1.6 The analysis used is consistent with the transport modelling for Crossrails businesscase and with the economic appraisal of Crossrail.


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2 Deter min ing impac t on deve lopment on t he

ra il ne tw ork

2.1 Determin ing a measure of impac t

2.1.1 The analysis uses the crowding costs imposed on other rail users as the indicator ofimpact for new developments. This is to meet the conditions set out in Circular05/2005.

2.1.2 The main reasons for choosing rail passenger crowding costs are:

crowding on the rail network is an indicator of an imbalance between the supplyof rail capacity and passenger demand;

rail services in parts of London suffer from very high levels of crowding; crowding imposes a major disbenefit to users which can be quantified in

monetary terms in line with Department for Transport guidance; rail passenger crowding is a key reason why new rail capacity is provided in

London; crowding relief on the rail network accounts for a third of Crossrails benefits; crowding relief is also taken into consideration within the Wider Economic

Benefits (that is, the quantification of agglomeration benefits) the reduction incrowding removes a constraint on future central London development posed bythe lack of rail capacity, again calculated in line with Department for Transportguidance ;

impacts on rail passenger crowding were available from the Crossrail modellingwork and is consistent with the Crossrail economic appraisal;

2.1.3 A number of alternatives to using crowding costs as the measure of impact of

development were considered as follows:2.1.4 Additional jobs it would have been possible to levy a charge on the increase in

employment across London. But changes in employment do not necessarily imposeany impact on the rail network it depends where that development takes place. So adevelopment employing 100 people adjacent to the M25 might have no-one travellingto work by rail while a similar sized development in central London may have 90% ofits employees using the rail network. The nature of the development will also lead todifferent impacts in terms of timing of trips and hence its impact on the rail network.

2.1.5 Additional square metres of development this faces the same problems as theadditional jobs measure above. The size and nature of the new development mayhave no direct link to the impact imposed on the rail network, which is a necessarycondition for seeking a S106 contribution to mitigate that impact.

2.1.6 The change in accessibility provided by Crossrail this was considered as anindicator but would have been working backwards from the impact of Crossrail ratherthan from the impact caused by additional developments.

2.1.7 The change in capacity provided by Crossrail again this was considered butagain it is a measure of the impact of Crossrail not a measure of the impact ofdevelopment on Crossrail.

2.1.8 Our conclusion is that only the cost of rail crowding provides an appropriatemeasure of the impact caused by new developments to the rail network.

2.1.9 The main implications of using rail passenger crowding as the measure of impact areto:

enable differences in impact according to geographic location of developmentsto be assessed;


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enable differences in impact according to type of developments to be assessed;and

enable account to be taken of differences in impact according to time period,with rail crowding being largely a peak period issue.

2.1.10 In summary, the impact that development has on the rail network is to generateadditional trips which may result in increased levels of crowding depending on thelocation and nature of that development. This in turn leads to requirements to increasecapacity. One of Crossrails key objectives is to provide additional rail capacity intocentral London and the Isle of Dogs to enable future demand to be met and mitigateprojected increased crowding on existing rail lines.

2.2 Determin ing the impac t o f development

2.2.1 Determining the impact that development has on the rail network requires a number oftasks to be undertaken. These include:

defining crowding; assessing the present level of crowding; assessing the cost of crowding and demonstrating the impact that crowding has

on patronage; and demonstrating how new development increases rail usage by location and type

of development.

2.2.2 By its very nature the methodology and analysis is based on the impact of the averagedevelopment by type of development in broad spatial areas across London. In doingso it is line with the Governments objective of having in place standard formulae toassess impacts and contributions.

2.3 Def in ing crow ding and assess ing present leve ls o f

c rowd ing2.3.1 Defining transport capacity and hence crowding is a complex task. On the national rail

network, capacity is defined by standards rather than the physical carrying capacity ofthe train. Train operating companies are expected to provide sufficient capacity toprevent passengers having to stand involuntarily for more than 20 minutes. So in thecase of trains which are timetabled to run non-stop for 20 minutes or more capacity isdeemed to be equal to the seating capacity. For other services capacity is usuallytaken as 135% of the number of seats available.

2.3.2 Passengers in excess of capacity (PIXC) is expressed as the percentage of peakperiod passengers who are standing in excess of planned capacity of the trains onwhich they are travelling. Trains are assumed to run with the programmed number ofcarriages, irrespective of whether they actually do at the time of the count. Whilst

spare capacity on other trains is disregarded so there is no netting off of heavilyloaded trains against others. The peak periods are defined as 07:00-09:59 intoLondon and 16.00-18.59 out of London. Train operating companies are requiredwhere practical to ensure they operate at PIXC below 4.5% in any one peak or 3% onaverage over both peaks. Average PIXC across all train companies for morning peakservices into London is 4.8% (Office of Rail Regulation National Rail Trends Yearbook2006-2007 6 September 2007).

2.3.3 The standard measure of capacity used by TfL and London Underground is planningguidance capacity (PGC). This is defined as

PGC = (seating capacity +60% of crush standing capacity)* 0.67 * 0.98

2.3.4 Crush capacity is the maximum number of people that it is physically possible to

squeeze onto a train. Account is then taken of the fact that trains are not equally


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loaded along their length and are not equally crowded (the 0.67 factor) and servicereliability is not 100% (but 98% hence the 0.98 factor).

2.3.5 TfL apply three standard categorisations of crowding on rail links. At 80% of PGC on-train conditions are described as busy, at 100% as crowdedand at 125% as verycrowded. London Underground advises that beyond 150% of PGC services becomeimpossible to operate effectively. At very high loadings, the dwell times at stationsincrease to the point at which it is impossible to maintain service frequency. LondonUnderground also advises that in recent years passengers have tended to stop usingservices before that extreme level of crowding is reached.

2.3.6 How passengers experience crowding on London Underground and national rail canbe measured in various ways. The most relevant measure is to use select link analysis(SLA). This measures the level of crowding experienced on each link of a publictransport journey from origin to destination. Thereby stating what proportion of apersons journey will be at levels of crowding over some predetermined level to a

defined destination. That is, it is possible to state what proportion of journeys and forwhat length of time, are in crowded conditions to a destination. This data is obtainedfrom the Railplan model which is a morning peak assignment model of Londoncovering all public transport modes.

2.3.7 Crowding is very largely a peak period issue. There is some crowding at other times ofthe day, but much smaller in scale and in any event off peak crowding could generallybe resolved by running some additional trains. In the peak periods there is little scopeto run additional services and so new infrastructure is required to provide a solution.

2.3.8 The distribution of passenger crowding by time of day on London Undergroundservices

1, based on observed data for 2002 is shown in figure 2.1. This shows that

crowding (as shown by the yellow line) is very peaked occurring largely in the morningand evening peak periods. The morning shows a higher peak, but across a shorter

time period whereas the evening peak is lower but broader.

1Similar data is not available for national rail services


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Figure 2.1: London underground crowding by time of day

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

1,600,000

1,800,000

2,000,000

0500-

0515

0545-

0600

0630-

0645

0715-

0730

0800-

0815

0845-

0900

0930-

0945

1015-

1030

1100-

1115

1145-

1200

1230-

1245

1315-

1330

1400-

1415

1445-

1500

1530-

1545

1615-

1630

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1715

1745-

1800

1830-

1845

1915-

1930

2000-

2015

2045-

2100

2130-

2145

2215-

2230

2300-

2315

2345-

2400

Passengers(linkflows)

0

2,000

4,000

6,000

8,000

10,000

12,000

Crowding(hours)

Sum of Flow Sum of PGC Sum of Crowded Hours

Source: CB analysis of RODS data for CLRL

Table 2.1: Distribution of London Underground crowding costs by locationand time of day (all costs given as % of morning peak totalcrowding)

early morningpeak

inter-peak eveningpeak

late

central 0 68 10 43 6inner 0 25 1 15 3outer 0 7 0 5 0total 1 100 11 63 10

2.3.9 Table 2.1 shows the same information as figure 2.1 but in a different format, showing

both the total crowding by time period and the distribution of that crowding splitbetween central, inner and outer London. It should be noted that the geographicdistribution of crowding relates to where that crowding takes place and not to wherethe passengers who experience that crowding are travelling to. Most crowding withinthe Inner area in the morning peak period is associated with trips heading to thecentral zone.

2.3.10 It is also worth mentioning that demand on London Underground has increasedsignificantly since 2002 and that growth has been greater in the inter-peak and latetime periods than in the peaks. The data above is illustrative in order to show the sortof distribution that we might expect to find.


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2.4 Assessment o f c row d ing cos ts

2.4.1 Every additional trip on a crowded rail service imposes a cost on other rail users

(crowding is largely an external cost imposed on others rather than an internal costborne by the additional passenger). Crowding costs and changes in the level ofpassenger crowding form a key component of rail scheme economic appraisal. Railinfrastructure requirements in London are largely driven by morning peak perioddemand when rail crowding is at its worst. The models of public transport in Londontherefore focus on the morning peak period.

2.4.2 Crowding costs are determined by applying a crowding factor (CF) to the actualjourney time on each link that is crowded. This (CF) is determined by a formularelating passenger demand to a combination of seating and standing capacity. Theformula used within the Railplan model (RP) is a highly conservative representation ofcrowding and assumes a straight line relationship between demand and the CF, withno capacity constraint imposed on any line. The RP crowding curve is shown in figure

2.2.Figure 2.2: Crowding curve

1

1.2

1.4

1.6

1.8

2

2.2

0.22 0.44 0.66 0.88 1.1 1.32 1.54 1.76 1.98

Demand to PGC ratio

crowding cost factor

generalisedcost factor

2.4.3 Two approaches have been tested to understand the scale of the crowding costsgenerated by additional rail trips:

the first is based on Select Link Analysis (SLA) and looks at the proportion oftime trips to the central zone spend under particular levels of crowding.

the second is derived from model tests undertaken by Crossrail where 1,000additional rail trips were added to overall demand in the first case with thosetrips having destinations in the central zone and in the second with destinationsin outer London.

2.5 Select L ink Analys is resul ts

2.5.1 Using SLA it is possible to determine the number of rail trips; to each destination; theamount of time spent at each level of crowding; and the monetised value of thedisbenefit caused by that crowding.

2.5.2 Table 2.2 below shows the results of the SLA undertaken by CB for CLRL in 2004 and

consistent with the economic appraisal of Crossrail produced at that time. The data is


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for a 2001 base year. The table shows the number of rail trips to each destination areaand the amount of time spent on average within different levels of crowding. Crowdingin excess of 1.0 is beyond TfLs targets, but accounts for 28% of all time on thenetwork in the morning peak hour. Around 1.5 PGC is the point at which LondonUnderground suggest that service operations break down.

Table 2.2: Distribution of trip times (minutes) by level of crowding morningpeak hour

PTTrips

SL>0.00 SL>0.30 SL>0.75 SL>0.80 SL>1.00 SL>1.25 SL>1.5

Isle of Dogs 17,831 446,549 429,429 277,750 265,040 152,875 49,495 4,105

City 93,863 2,687,311 2,474,912 1,916,269 1,718,380 956,689 357,182 38,384

Central 397,894 10,115,539 9,204,902 6,909,497 6,323,561 3,835,590 1,425,767 110,991

Westminster 199,392 4,753,719 4,347,259 3,218,763 2,971,295 1,875,030 695,869 48,893

London 850,961 16,180,771 13,848,178 9,788,431 8,930,012 5,302,703 1,949,020 150,599

All Zones 930,622 19,520,347 14,961,387 10,179,974 9,284,052 5,481,881 2,019,459 156,646

2.5.3 By applying the Railplan crowding curve shown in figure 2.2 it is possible to determinethe total crowding cost (in generalised minutes) and by dividing by the number ofpassengers it is possible to determine the average cost per public transport trip.Working through that process produces the following findings:

Table 2.3: Crowded passenger hours in morning peak hour

Trips to centralzone

Trips to rest ofLondon

Number of trips 416,000 515,000

Crowded hours 155,000 65,000Crowded hours per trip 0.37 0.13Central: Rest of London 3.0

2.5.4 Thus on average rail trips to central zone produce about three times as muchcrowding as trips to the rest of London.

2.6 Resul ts o f Rai lp lan model runs

2.6.1 In addition to the SLA approach additional Railplan tests have been run to test theimpact of adding 1,000 additional commuting trips to central zone and the samenumber to outer London. The results of those tests are summarised in Table 2.4.

Table 2.4: Railplan model results of 1000 additional destinations (2001 base)

Trips to centralzone

Trips to rest ofLondon

Number of trips 1,000 1,000Crowded hours 430 159Crowded hours per trip 0.43 0.16Central: Rest of London 2.7

2.6.2 Since we are interested in the marginal impact of development, table 2.4 above canbe used to show that crowding costs for trips to central zone are about 2.7 times largerthan those to the rest of London.


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2.7 Crow d ing and pa t ronage growt h

2.7.1 There is a clear link between passenger crowding and demand growth. Figure 2.2shows this relationship which arises from research undertaken for Crossrail.2

Figure 2.3: 1987 passenger crowding v 1987-2000 demand growth, fittedstraight line represents linear regression of growth on crowding

2.7.2 The analysis shows that links with a crowding level of around 75% of PGC and abovestart to experience negative growth, illustrating that passengers are not prepared totravel at these levels of crowding.

2.8 Crowding by locat ion

2.8.1 Crowding on the rail network varies by location and hence the impact of newdevelopment is also likely to vary by location. There are two main elements to this;crowding levels by location and mode choice by location.

2.8.2 Taking mode choice first, rail mode share (both underground and national rail) bygeographic zone was taken from analysis of the 2001 Population Census. Rail modeshare isavailable by area of residence and workplace, by lower super output area(LSOA)

3level. Residents working from home or currently not working have been taken

out of the rail mode share calculations.

2.8.3 London was split into the Central Activity Zone (CAZ) and the three super output areaswhich broadly make up the business zone in the Isle of Dogs (for simplicity thecombined area is referred to as the central zone), as designated in the London Plan(please refer to policy 5G.1 p.353 of the 2008 London Plan). Inner London includes all

2While the analysis was done a number of years ago there is no reason to suppose the relationship shown

is not still valid.3

LSOA are spatial areas defined by the Office for National Statistics with a mean population of 1,500


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boroughs within inner London, excluding the CAZ and the Isle of Dogs business areadefined above, and outer London incorporates the remaining outer boroughs. The railmode share for each zone is shown in table 2.5 and figure 2.4.

Table 2.5: Rail mode share for travel to work

Boundary Rail Mode ShareCentral Zone 71%Inner London 29%Outer London 11%Inner/outer London 17%

Figure 2.4: Rail mode share by LSOA

2.8.4 Turning to crowding, using SLA, it is possible to determine the number of rail trips toeach destination area and the amount of time spent on average within different levelsof crowding. By dividing the number of crowded trips by the crowded hours it ispossible to estimate crowded hours per trip in the central zone, compared to the restof London.

2.9 Other issues

2.9.1 The results above are for the morning peak period and therefore represent an averageacross three hours (07.00-10.00).

2.9.2 This analysis considers rail trips to the central zone for whatever journey purpose, thetime and direction of the trips are more important in determining crowding costsimposed than any variations of trip length or patterns associated with different journeypurposes. Having said that it is possible that trips to the central zone for retail (forexample) have very different distributions and hence different crowding costs thanaverage.

2.9.3 Crowding increases over time from 2001 until the opening of Crossrail in 2017, despitethe assumed delivery of the PPP enhancements, the Thameslink enhancement and aseries of national rail capacity enhancements. The 2001 results are 17% lower thanthose for the 2016 without Crossrail modelled scenario in terms of average crowded


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time per passenger, so the additional crowding cost would be expected to increase by1% per annum even without allowing for increases in the value of time.

2.10 Conclusions

2.10.1 It seems reasonable to conclude that rail trips to the central zone generate about threetimes as much crowding costs on average as rail trips to all other parts of London.


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3 Of f ic e deve lopments

3.1 In t roduc t ion

3.1.1 This chapter considers the appropriate levels of S106 charges that could be applied tooffice developments. That analysis depends on bringing together:

the crowding costs by destination area described in Chapter 2; appropriate trip generation rates for office developments, concentrating on the

morning peak; and analysis of the likely rail mode share for those generated trips.

3.1.2 Those three factors combine to produce an indicator of impact or crowding costsimposed on the rail network by office developments in different locations.

3.2 Tr ip generat ion ra tes

3.2.1 A number of alternative sources of trip generation rates have been reviewed by thestudy team. These comprise:

general assumptions on employment density and trip rates TRAVL which is a database based on observed trips rates and mode shares to

developments

The general approach

3.2.2 A typical transport planning approach to this would be:

each office development will have an average of one employee per 19 squaremetres (source: Employment Densities, English Partnerships and RegionalDevelopment Authorities, Arup, 2001); and,

each employee generates 0.85 inbound trips per morning peak period allowingfor periods of annual and sick leave.

3.2.3 Thus each 100 square metres of office space generates 4.4 inbound trips per morningpeak period.

TRAVL/TRICS

3.2.4 The TRAVL and TRICS databases contain details of trip generation rates and modeshares of trips to different development types. TRICS is UK-wide whilst TRAVL isLondon specific. Due to the limitations of the TRICS database, we have not used it inthe analysis. For commercial office space TRAVL gives the results shown in Table3.1.

Table 3.1: TRAVL Trip Generation Rates

Area TRAVL all modetripsper 100 sq metres

Greater London 4.6central zone 4.2inner 4.6outer 5.5

3.2.5 The TRAVL database results appear to be broadly consistent with the generalapproach described in 3.2.2. TRAVL shows a pattern of trips reducing by floor areaas development is located more centrally, but this is based on an extremely lowsample of sites and therefore needs to be treated with caution. The number of central


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zone offices within the TRAVL database is 8 and the total sample of offices within thewhole database is only 50 (only half of which have been surveyed since 2000).

3.2.6 Given the limitations of the TRAVL database we propose to apply a standard measureof 4.4 inbound trips per 100 square metres of office development space and hold thatconstant for all areas of London.

3.3 Rai l mode shares

3.3.1 Mode share data for journeys to work is available from a number of sources:

census data TRAVL London Travel Report 2007 (TfL)

Census data

3.3.2 The table below sets out rail mode share by central, inner and outer London zones.Inner London refers to all boroughs within inner London, excluding those in the centralzone and outer London incorporates all other zones in Greater London. Rail modeshare was taken from the census 2001 at LSOA level. Population working from homeand currently not working have been taken out of the rail mode share calculations.

Table 3.2: Rail mode share for travel to work

Boundary Rail mode sharecentral zone 71%inner London 29%outer London 11%inner/outer London 17%

3.3.3 The map below sets out rail mode share by LSOA within the central zone, inner and

outer London zones. It clearly shows the reduction in rail mode share when movingaway from central zone.


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Figure 3.1: Rail Mode share by LSOA

TRAVL data3.3.4 The TRAVL data suffers from the same issues of low sample sizes as before.

However, the results from TRAVL are broadly comparable to those from the census.

Table 3.3: TRAVL Rail Mode Shares

Land use Area TRAVL rail % modal shareGreater London 40central 74inner 37

Office

outer 10

London Travel Report, 2007

3.3.5 The London Travel Report 2007 provides the main mode of travel to work for 2006. Asfor the census data set this represents all journeys to work across all time periodsrather than morning peak period data. The rail mode shares to central, inner and outerLondon are as follows:

central zone 68% rest of inner London 35% outer London 10%

3.3.6 These figures are broadly comparable to the 2001 census figures. The remainder ofthis analysis uses the rail mode shares from the census data, but the estimation of animpact index using rail mode share from the London Travel Report has been testedas a sensitivity in section seven of this report.


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3.4 Quant i fy ing impac t to the ra i l ne tw ork

3.4.1 The calculation of total impact to the rail network from office developments in the

morning peak period is determined simply by multiplying trip generation rates by railmode share by a crowding cost per trip indicator, to produce an index of impact. Theresults of this are shown in table 3.4.

Table 3.4: Calculation of impact for office developments by location

area trips per 100sqm

rail modeshare

crowding costper trip

total impact(index)

central zone 4.5 71% 3 9.4inner 4.5 29% 1 1.3outer 4.5 11% 1 0.5inner/outer 4.5 17% 1 0.8

3.4.2 The index is to show what the relative charges would need to be to try and best reflectthe differing amount of impact caused to the rail network (or other rail users). Thus thecharge to office developments in the central zone area would need to be twelve timeshigher than the charge to a combined inner/outer (or rest of London) charge. If a threezone system were to be used then the central zone charge would need to be seventimes higher than that for inner London and nineteen times higher than that for outerLondon.


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4 Ret ai l developm ent s


4.1.1 This chapter considers the appropriate levels of S106 charges that could be applied toretail developments. The analysis is the same as applied in Chapter 3 for officedevelopments and depends on bringing together:

the crowding costs by destination area described in Chapter 2; appropriate trip generation rates for retail developments, covering both

employees and shopping trips for the morning and the evening peak; and analysis of the likely rail mode share for these trips.

4.1.2 For retail developments there are two distinct types of trips, those related to retailemployees and their journeys to work and those related to shoppers.


4.2.1 A number of alternative sources of trip generation rates have been reviewed by thestudy team. These comprise:

general assumptions on employment density and trip rates TRAVL which is a database based on observed trips rates and mode shares

The approach for retail employees

4.2.2 A typical transport planning approach to this would be:

each retail development will have an average of one employee per 19 squaremetres (source: Employment Densities, English Partnerships and RegionalDevelopment Authorities, Arup, 2001); and,

each employee generates 0.85 inbound trips per morning peak period allowingfor periods of annual and sick leave.

4.2.3 Thus each 100 square metres of retail space generates 4.4-4.5 inbound trips permorning peak period, exactly the same as for office developments.

4.2.4 However, there is a significant issue that may serve to reduce the rail trip generationrate for retail employees. That is that retail employees are more likely to work shiftsand hence generate less peak period commuting than office developments. 44% ofretail workers in Westminster are part time compared to only 20% in business servicesand 8% in public administration. We are unable to accurately identify an appropriatefactor for this effect at present but for the moment have assumed a one third reductionin peak period commuting rates compared to office developments. The morning peaktrip generation rates for employees are therefore assumed to be 3 per 100 square

metres.

4.2.5 No other information is available distinguishing commuting trips to retailers.

The approach for shopping trips

4.2.6 The TRAVL database contains details of trip generation rates and mode shares oftrips to different development types. TRICS is UK-wide whilst TRAVL is Londonspecific. For retail space they give the results shown in table 4.1.


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Table 4.1: Trip generation rates to retailers

Area TRAVL All ModeTripsPer 100 Sq MetresGreater London 19central zone 56.8inner 18.7outer 9.6

4.2.7 The main problem with TRAVL retail data is that these numbers do not represent pureshopping trips but are largely trips being undertaken for other purposes which includevisiting a shop along the way. In addition the number of central zone retail sites withinthe TRAVL database is very low, at five and the total sample of retail sites within thewhole database is only 128.

4.2.8 In conclusion we do not want to rely on either data source for retail trip rates. Insteadwe have calculated retail trip rate as the number of retail trips (from the London AreaTransport Survey factored up to total rail trips in London) divided by the retail floorarea (as per Commercial and Industrial Floor space Rateable Value Statistics,Neighbourhood Statistics, 2005).

Table 4.2: Morning peak rail retail trip rates

morning peakperiod

retail trips (lats) retail floor area(000 sq metres)

trip rate per 100sq metres

central 3,841 2,348 0.16inner 4,442 5,018 0.09

outer 12,142 8,219 0.15

Table 4.3: Evening peak rail retail trip rates

evening peakperiod

retail trips (lats) retail floor area(000 sq metres)

trip rate per 100sq metres

central 40,211 2,348 1.71inner 18,585 5,018 0.37outer 21,316 8,219 0.26

4.3 Quant i fy ing impac t to the ra i l ne tw ork4.3.1 The calculation of total impact to the rail network from retail developments in the

morning peak period would therefore come in two parts as shown in tables 4.4 and4.5.

Table 4.4: Retail - Impact from employee trips (morning peak)

area trips per100 sqm

rail modeshare

crowdingcost per trip

total impact(index)

central 3 71% 3.0 6.9inner 3 29% 1.0 1.0outer 3 11% 1.0 0.4inner/outer 3 17% 1.0 0.6


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Table 4.5: Retail - impact from shoppers (morning peak)

area trips per100 sqm rail modeshare crowdingcost per trip total impact(index)central 0.16 71% 3.0 0.34inner 0.09 29% 1.0 0.03outer 0.15 11% 1.0 0.02inner/outer 0.12 17% 1.0 0.02

Table 4.6: Retail - impact from shoppers (evening peak)

area trips per100 sqm

rail modeshare

crowdingcost per trip

total impact(index)

central 1.71 71% 3.0 3.61

inner 0.37 29% 1.0 0.11outer 0.26 11% 1.0 0.03inner/outer 0.31 17% 1.0 0.05

4.3.2 The analysis above reflects movements during the peak period. For example, themorning peak period is defined as 7am-10am. However, the number of peopletravelling over that time period is not constant. Data from London Underground stationexits by 15 minute time slots shows the peak number of exits is between 08.15 and09.15 accounting for almost half of morning peak period exits.

Table 4.7: London underground exits by time period

0700-0715 0715-0730 0730-0745 0745-0800 0800-0815 0815-0830 0830-0845 0845-0900 0900-0915 0915-0930 0930-0945 0945-1000

24387 34288 48816 61649 72973 85294 103094 111012 103687 85031 70775 58187

3% 4% 6% 7% 8% 10% 12% 13% 12% 10% 8% 7%

Source 2005 exit counts LUL

4.3.3 In order to determine the impact of retail trips on rail crowding in the morning peak, ananalysis of retail trips and their distribution during the morning peak was carried out,and compared to the distribution of commercial trips, using data taken from theTRAVL datasets. The trips to each development are for all purposes (ie employees,visitors, shoppers etc and are not available by journey purpose.)

4.3.4 The data is in half hour rather than quarter hour slots but the chart clearly shows thedifference in the distribution of trips to office and retail developments in the morningpeak. Almost two thirds of morning peak trips to retail outlets are in the period 9-10amcompared to just 18% of trips to office developments. This would imply that trips toretail developments tend to take place far more at the shoulder of the morning peakrather than at the peak of the peak that is the busiest hour on the network.


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Figure 4.1: Distribution of trips to office and retail development in themorning peak

0%

5%

10%

15%

20%

25%

30%

35%

40%

07:00 07:30 08:00 08:30 09:00 09:30 10:00

retail commercial

Source TRAVL database using major office and retail developments in London

4.4 Reta i l conc lus ions

4.4.1 The main impact of retail developments on the rail network comes from the journeys towork of employees. In the morning peak in particular, which is the basis for themodelling and appraisal work on rail schemes, the impact of shoppers themselves isminimal.

4.4.2 Adding the morning peak impacts together would give a total impact index of:

7.23 for retail developments in central zone 0.98 for retail developments in inner London 0.38 for retail developments in outer London

4.4.3 However, retail development generated trips tend to occur at the shoulder of the

morning peak period.


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5 Hote ls


5.1.1 This chapter considers the appropriate levels of S106 charges that could be applied tohotel developments. The calculation of impact to the rail network of hoteldevelopments has taken into account trips during the morning peak generated byhotel workers as well as hotel guests, and follows the same methodology as the oneapplied to office and retail developments.

5.1.2 The analysis only investigates impact to the network of developments within centralLondon, as it is unlikely that hotel developments in inner and outer London will have asignificant impact on the rail network: it is most probable that business visitors choosehotels closer to their work destinations where they will not have to use therail/underground system. Similarly leisure visitors staying in hotels in inner and outer

London are unlikely to travel into central London during the peak periods to avoid peakfares and congestion.

5.1.3 There is a general lack of robust data available regarding trip patterns and modeshare for hotel developments. The results of the analysis therefore need to beinterpreted with care.


5.2.1 Trip generation rates have been derived from the TRAVL database, which containstrip information for twelve hotels within Greater London. Using TRAVL, the morningpeak trip rate for hotel developments reaches 1.4 trips per room, which amounts to 3.7trips per 100 sqm gross floorspace (using an average of 38 sqm per room also derivedfrom the TRAVL database).

5.2.2 In order to determine trips undertaken by hotel employees during the morning peak,the following assumptions were made:

an average of 0.5 employees per bedroom4

( equivalent to 1.3 trips per 100sqm floorspace); and

one third reduction in morning peak work trips to take into account shift work(same approach used for retail developments)

5.2.3 Hotel developments therefore generate roughly 1 trip per 100 sqm during the morningpeak period, which means 2.7 trips are undertaken by hotel guests.

5.2.4 Hotel guests on business trips are more likely to use rail/underground than leisurevisitors during the morning peak period. Therefore total guests have been split

between business and leisure visitors, assuming business visitors represent 25% oftotal guests (assumption based on table 7.3.2 of the London Travel Report). Howeverit is possible that during the morning peak period the proportion of business tripsincreases.

5.3 Rai l mo de share

5.3.1 Rail mode share for central London has been taken from the census data and appliedto work trips and guests on business trips. However leisure visitor trips in centralLondon are less likely to use rail during the morning peak (to avoid peak congestionand fares). There is no robust data available to estimate rail mode share for leisurevisitors during the morning peak, however analysis of the LATS data seems to

4Employment Densities: A full guide, English Partnerships,2001


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suggest low levels of rail use for leisure trips during the morning peak period (around3%, but based on a very small sample size). Taking this into account, a rail modeshare for these trips has been assumed to be around 10%.

5.4 Quant i fy ing impac t to the ra i l ne tw ork

5.4.1 The table below sets out the impact of hotel developments on the rail network incentral London. It shows that the impact index for hotel developments is 4.1, which islower than office and retail developments in central London (approximately 40% and60% of the office and retail impact index respectively).

Table 5.1: Calculation of impact for hotel developments for employees andguests

central zonecrowding cost

trips per 100 sqmduring the morning

peak

rail modeshare

impactindex

Employees 3 1.0 71% 2

Visitors 2.7

business 3 0.7 71% 1.5

leisure 3 2.0 10% 0.6

TOTAL 3.7 4.1


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6 Resident ia l developm ent s


6.1.1 This chapter looks at applying the same approach to residential developments. Beforedoing that however there are a number of issues specific to residential developmentsthat need to be taken into account.

6.1.2 There is an important issue of causation for justifying a S106 charge on residentialdevelopments. In broad terms one would expect that the increase in employment inLondon would be matched by a corresponding increase in residential developmentwithin Londons employment catchment area (which extends rather further thanGreater London). If not there would need to be a balancing increase in participationrates from the existing population, but the London Plan expects a significant increasein both population and employment. In theory therefore the additional crowding costs

imposed by the new residential developments would be exactly the same as theadditional costs imposed by the new office and retail developments but just attributingthe cause to the trip origin rather than the destination.

6.1.3 On an average basis it would be possible to show that each house in inner/outerLondon generates one commute trip per day (for example) and that 30% of them userail as their main mode and hence calculate the crowding costs associated with that.The alternative view is that the houses themselves do not generate trips to centralzone, it is the location of office premises in the central zone that create those trips.Residential developments are likely to generate significant numbers of local trips andlocal costs which would be picked up within the normal S106 negotiations but in termsof their impact on the strategic rail network it is not clear that they should bear any ofthose costs.

6.1.4 If you look at it the other way around, if all the expected office developments were tobe located in outer London around the M25 then the impact of future residentialgrowth on the strategic rail network would be minimal, although the impacts on thehighway network would be disastrous. Thus although a new office development in thecentral zone must attract inward commuters by rail, the same is not true of newresidential developments, no matter where they are located.

6.1.5 The rest of this chapter derives an impact per residence in a similar fashion to theprevious chapters, despite the reservations expressed above. It follows the sameformat as that adopted for office developments:

how many outbound commuter trips per residence/100sqm of residential spacein am peak period?

what proportion of commuter trips go to central zone from each origin area? what is rail mode share of those trips?

6.1.6 The analysis focuses on work trips from residential areas as these will have the mostimpact on rail congestion during the morning peak.

6.2 Tr ip generat ion for res ident ia l development s

6.2.1 There are fewer obvious data sources for trip generation from residentialdevelopments. Table 6.1 shows the results available from TRAVL.


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Table 6.1: Residential trip generation rates per morning peak period per 100square metres

Land Use Area TRAVLGreater London 0.35central zone 1.2inner 1.2

Residential

outer 0.2

6.2.2 Whereas the TRAVL results for the central zone and inner London appear sensible,those for outer London seem extraordinary. In four out of five houses in London no-one leaves the house before 10am (the average sized property is just under 100sqm).Again the very low sample sizes seem to indicate that this data has limitations.

The general approach6.2.3 A more general approach would say that:

there are 3.19 million workers in London there are 3.02 million households, giving an average of 1.05 worker per

household average house size in London is 93 square metres 85% of employees commute to work each day so the commuting trip generation rate per 100 square metres would be 0.96

6.2.4 It is possible that the commuting trip generation rate is slightly lower in outer Londonthan in central zone or inner London, but it is not reasonable to draw that conclusionbased on the TRAVL data. Hence a broadly constant 1 trip per 100 square metres ofresidential space seems a reasonable assumption.

Census data

6.2.5 It is possible to extrapolate trip generation rates for central, inner and outer Londonusing the census data and assumptions on average floorspace per person, taken froma University of Oxford report exploring energy consumption in households

5, and

shown in the figure below.

Figure 6.1: Average floor space by household size, England 2001(sqm/person)

540% House, University of Oxford, 2005


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6.2.6 By combining the information with the number of people per household, total residentseconomically active by LSOA and assuming a trip generation of 0.85 per employee, atrip generation rate by zone has been estimated and is shown in the table below. Itshows that trip generation rates are higher in outer London than for residential areasin the central zone, which may seem counter-intuitive, but is due to the lowereconomic activity rates within the central zone.

Table 6.2: Trip generation residential developments

Area Trips Per 100 sqmcentral zone 0.73

Inner 0.75

Outer 0.87

Inner/outer 0.82

6.3 Rai l mode shares for res ident ia l

6.3.1 Rail mode shares for trips from residential land uses are available from TRAVL. Theyare much flatter than rail mode shares by destination

Table 6.3: TRAVL rail mode shares for residential trips morning peak

Land Use Area Travl Rail % Modal ShareGreater London 27central zone 39inner 31

Residential

outer 22

6.3.2 The map below sets out rail mode share by LSOA for residential land uses, taken fromthe census data. It clearly shows that rail mode share is the highest for residents ofinner London.


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Figure 6.2: Rail mode share residential land use

6.3.3 It is possible to derive rail mode shares by destination for residential land uses. The

tables below set out the proportion of travel to work trips from central

6

, inner and outerLondon for each destination.

Table 6.4: Proportion of travel to work trips by rail

workplaceresidenceCentral Inner Outer

central zone 35% 31% 41%

Inner 61% 36% 32%

Outer 82% 64% 7%

6.4 Proport ion of t r ips to t he cent ra l zone6.4.1 In addition to the information on rail mode shares, the distribution of commuting trip

destinations also needs to be taken into account. Trips from outer to outer London willgenerate almost no crowding costs whereas those to the central zone will impose farhigher costs.

6.4.2 Information on the proportion of residents in each of central zone, inner and outerLondon who commute to the different zones has been extracted from census data,and is shown in the table below.

6Origin Destination data for travel to work trips is only available at ward and Borough level. Therefore for

this analysis Central London is defined as Westminster and City of London Boroughs.


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Table 6.5: Travel to work trips by destination

workplaceresidence central zone Inner Outer

central zone 63% 28% 6%inner 27% 59% 10%outer 13% 19% 59%

6.5 Conclusions

6.5.1 The crowding costs assumed for travel to the different zones are shown in table 6.6.All travel to the central zone has a crowding cost of 3, whereas travel to inner andouter London does not generate crowding. These assumptions are tested in thesensitivity analysis.

Table 6.6: Crowding cost assumptions

DestinationOrigincentral zone Inner Outer

central zone 3 1 1

inner 3 1 1

outer 3 1 1

6.5.2 The results for residential trip generation rates and quantification of impact imposedon the rail network are shown in table 6.7, derived from trip per 100 sqm, rail modeshare, destinations and crowding cost assumptions.

Table 6.7: Residential impact imposed on rail network morning peak

destinationorigincentral zone inner outer

total impact index

central zone 0.48 0.06 0.02 0.56inner 0.37 0.16 0.02 0.56outer 0.28 0.11 0.04 0.42

6.5.3 The results seem to indicate that developments within the central zone and innerLondon should be charged more than in outer London. This has the slightly strangeconclusion that residential developments in the central zone should be charged the

same as residential developments in inner London, even though those trips in thecentral zone have many more options of walk, cycle and bus available to them andtheir rail journeys are much shorter. However, the combination of trip destination andrail mode share by destination seems to highlight that residential developments in thecentral zone and inner London have the same impact on the rail network.

6.5.4 There is also the issue of possibly double-counting rail trips and overestimating theimpact index for developments: a new residential development may generate trips thatare already taken into account for a new office development from where the demandfor that work trip originates. Moreover due to the relatively low impact indices forresidential developments compared to other land uses, our opinion is that it isprobably inappropriate to place a S106 charge for strategic rail infrastructure onresidential developments.


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7 Crossra i l s ta t ions outs ide t he Cent r a l Area

Zone


7.1.1 In extending the S106 charges beyond the central zone they could cover whole areasof inner and outer London as described in the previous chapters, or they could be setup to cover specific areas around Crossrail stations, defined by 400m or 800m or1,000 metre circles.

7.1.2 This chapter is based on data for journeys to work only and focuses on officedevelopments. As shown in the previous chapters the potential charges on officedevelopments would be significantly higher than those for retail and much higher thanthose for residential.

7.1.3 The questions which we seek to answer within this chapter are:

are the zones around the future Crossrail stations different to the rest of innerand outer London?

would those differences result in a significantly different impact to the railnetwork from new developments?

would that justify different S106 charges?

7.2 Analys is o f ra i l mode shares

7.2.1 This section finds that trips to work located within a 400m, 800m or 1000m buffer ofCrossrail stations outside of zone 1 do have higher rail modal share than trips to workgenerally (outside of zone 1). The figure below shows the buffer zones at Crossrail

stations used for the analysis.Figure 7.1: Buffer Zones in West London

7.2.2 The table below shows rail modal share for trips in the buffer region as compared to

commuter trips to the outside of zone 1 generally.


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Table 7.1: Rail mode share, Crossrail buffer region outside of Z1 against allnon-Z1

Area rail modal share oftrips to work

400m buffer around non-central zone Crossrailstations

39%


34%


25%

All LSOAs outside of Zone 1 18%

7.2.3 This supports the argument that the areas around the Crossrail stations would

generate additional rail trips and therefore additional impact to the rail network. Itshould be borne in mind however that the higher rail mode shares around stationswould apply generally to areas around stations in inner and outer London and is in noway specific to those stations that Crossrail will serve in the future. The overallaverage mode shares include areas with very poor rail access so it might be possibleto put forward an argument that such a charge should apply to areas around allstations rather than simply those that will in the future be Crossrail stations. The railmode share of trips to work by buffer zones for all rail stations are broadly similar tothe Crossrail stations, although lower for the 400m and 800m buffer zones. Thereforeimpact to the rail network if including all rail stations outside of central zone would beoverall lower.

7.3 Impac t on the ra i l ne tw ork

7.3.1 Table 7.2 shows how the impact might change for office developments in inner andouter London by applying the ratio of the rail mode shares to overall averages shownin Table 7.1.

Table 7.2: Impact to the rail network from office developments local toCrossrail stations

Area Total impact(Index)

400m 800m 1,000m Trip

Mode Share ratio 2.2 2.6 1.4Inner 1.30 2.8 3.4 1.8Outer 0.50 1.1 1.3 0.7

Inner/Outer 0.78 1.7 2.0 1.1

7.3.2 Thus it might be possible to justify some sort of local premium for office developmentsaround Crossrail stations either against a general charge for inner and outer Londonor as the only charge in inner and outer London. The charges would still besignificantly lower than those applicable to the central zone.

7.3.3 The tables below sets out impact to the rail network from retail and residentialdevelopments local to Crossrail stations.


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Table 7.3: Impact to the rail network from retail developments local toCrossrail stations

Area Total impact(Index)

400m 800m 1,000m Trip

mode share ratio 2.2 1.9 1.5inner 0.98 2.1 1.8 1.5outer 0.38 0.8 0.7 0.6inner/outer 0.59 1.3 1.1 0.9

Table 7.4: Impact to the rail network from residential developments local toCrossrail stations

Area Total impact

(Index)

400m 800m 1,000m Trip

mode share ratio 2.2 1.9 1.5inner 0.55 1.2 1.0 0.8outer 0.42 0.9 0.8 0.6

7.4 Conc lus ions s ta t ions outs ide cent ra l zone

7.4.1 It might be possible to implement a charge here, but our initial thoughts are that thiswould raise a number of issues and would not be the most appropriate approach. Themain reasons we see for that are:

the leakage factor in outer London will be very high, particularly where

developers can move just a few hundred metres and avoid the charge; deterring developments from taking place around Crossrail stations would be a

strange thing to do. A strong argument could be made for encouraging newdevelopments around Crossrail stations to take advantage of the additionalcapacity provided;

the arguments for a charge on retail and residential developments would beweaker than those on office developments.


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8 Sens i t iv i ty ana lys is


8.1.1 This section sets out a number of tests carried out to determine the impact of certainassumptions and use of different datasets on the final impact index results for office,residential and retail developments.

8.1.2 The table below shows a summary of the impact indices calculated for office,residential and retail developments for central, inner and outer London. The sensitivitytests will be compared to these indices.

Table 8.1: Summary table impact indices

central

zone

Inner Outer I/O 800 M

Office 9.43 1.230 0.50 2.00

Retail 7.23 0.98 0.38 1.1

Residential 0.56 0.56 0.42 0.9

8.2 Rai l mo de share

8.2.1 The impact indices for office and retail developments have been based on rail modeshare figures taken from the census data. Using the morning peak mode share fromthe London Travel Report slightly reduces the differences in impact indices betweencentral zone and inner/outer London.

Table 8.2: Impact Index using London Travel Report mode share

centralzone

Inner Outer

Office 9.00 1.55 0.44

Retail 6.901.16

0.34

8.3 Crowding cost s for res ident ia l land use

8.3.1 The assessment of crowding costs have shown that on average, rail trips to centralzone produce three times as much crowding as trips to the rest of London. However, it

is unclear what the crowding cost of travel to zones other than central zone is. Forresidential developments, we have assumed that all trips to central zone have acrowding cost that is three times greater than travel to inner and outer London (shownin table 6.6).

8.3.2 However, it is probable that crowding costs vary depending on the origin of travel. Forexample, rail trips within the central zone may not contribute as much to crowding astrips to the central zone from inner London areas. The table below sets out alternativeassumptions on crowding cost, keeping the overall assumption of crowding intocentral zone having three times more impact on crowding than travel to inner andouter London.


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Table 8.3: Alternative crowding costs

DestinationOrigin central zone Inner Outer

central zone 1.8 0.7 0.7

Inner 3.5 1.1 1.1

Outer 3.7 1.3 1.3

Average 3.0 1.0 1.0

8.3.3 When applying these alternative crowding ratios, the impact index for residentialdevelopments significantly changes, with impact being more important in inner Londonzones than in central zone. This would seem to produce more realistic results,comparable to rail mode use ratios presented in table 6.4. More research intocrowding costs by trip origin would help refine the impact index of residentialdevelopments on the rail network.

Table 8.4: Alternative impact index for Residential developments

centralzone

Inner Outer

Baseimpactindex

0.56 0.56 0.42

Alternativeimpactindex

0.35 0.64 0.53

8.4 Tr ip generat ion and dens i t ies assumpt ions

Office land use

8.4.2 Office densities are assumed to be the same in all of London. However, research hasshown that office densities are higher in the central zone

7. Our analysis has also

assumed that densities in new buildings will increase in time. By assuming thatdensities in new build located in inner and outer London remain constant, tripgeneration for central zone offices will be higher than offices in the rest of London.

8.4.3 The impact to the rail network of central zone office developments would therefore be8.5 times greater than developments located in inner London, and 23 times greaterthan developments in outer London (compared to 7 and 19 times respectively).

7Employment Densities: a full guide, English partnerships, July 2001


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Table 8.5: Impact index commercial developments

Area Trips Per100 Sqm Railmodeshare

Crowdingcost pertrip

Totalimpact(Index)

central zone 4.5 71% 3 9.4

Inner 3.7 29% 1 1.1

Outer 3.7 11% 1 0.4

Inner/Outer 3.7 17% 1 0.6

Retail land use

8.4.4 The analysis of impact from retail developments has been based on morning peakshopping patterns. However, the impact of shopping trips on rail crowding may best bebased on overall shopping trips during the day. By applying an average of morning

and evening trip generation factors for shopping trips, the impact index of retaildevelopments increases, to a level comparable to commercial developments for thecentral zone. Developments in the central zone produce a crowding impact that is 8.5times greater than developments in inner London, compared to 7 times using morningpeak shopping patterns.

Table 8.6: Impact index retail developments

central zone Inner OuterRetail base 7.23 0.98 0.38Retail new tripgeneration forshopping trips

8.86 1.02 0.39

8.5 Conclusion

8.5.1 The sensitivity analysis has shown that a number of factors potentially affect therelative impact levels caused to the rail network for different types of land uses, whichhighlights the uncertainty of certain datasets and assumptions used which need to beaccounted for when informing decisions on the structure of a S106 charge forCrossrail. However although the scale of the relative differences in impact in central,inner and outer London may vary, the overall relativity between those zones remainrelatively consistent.


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9 Conclusions

9.1 Relat ive va lues of impac t

9.1.1 This report has calculated the relative crowding costs imposed on the rail network bydifferent types of development in different locations within London.

9.1.2 These values are summarised in Table 9.1.

Table 9.1: Relative crowding impact by destination type and location

centralzone

Inner Outer I/O 800m

Commercial 9.43 1.30 0.49 2.00Retail 7.23 0.98 0.38 n/a

Residential 0.56 0.56 0.42 n/a

9.1.3 The relative values have been used to

Determine appropriate charges for different land uses in different geographicareas; and

test a number of alternative scenarios to be developed with different land usesand different geographic areas included or excluded.

9.1.4 The indicator of impact is taken to be a general impact on crowding on the railnetwork. That is a fairly broad brush measure depending on three key variables:

The level of trip generation in the morning peak period The rail mode share appropriate for that development type and that geographic

locations The crowding impact associated with rail trips to different destinations within

London

9.1.5 There is detailed analysis underlying all of these variables, but there are also someinconsistencies in the data available and its accuracy which need to be understoodand borne in mind.


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Appendix : Is le of Dogs analys is

In t roduc t ion

This section sets out the reasons why it maybe appropriate to have different levels of S106contributions between the Isle of Dogs and CAZ.

Three factors have been assessed:

the extent of the rail capacity constraint on growth and how that varies betweenthe Isle of Dogs and CAZ;

the reliability/resilience of the public transport networks serving both areas; and, the scale of the change in public transport capacity that would be provided by

Crossrail.

The Capac i t y Constra in t On Growt hIt is clear that the constraint imposed by public transport capacity (without Crossrail) to the Isle ofDogs will be much more severe than that imposed on CAZ overall. That arises because:

employment is expected to grow much faster (proportionately) on the Isle of Dogs than it is in theCAZ and while rail capacity rises faster (proportionately) on the Isle of Dogs, it is not sufficient tocounteract the employment growth

access to the Isle of Dogs by bus, walk and cycle modes is restricted because of the lack of roadcrossings from south of the river

Employment Growth

The Isle of Dogs had some 44,000 jobs in 2001. That total has risen to 100,000 by 2008 and is

expected to exceed 200,000 by 2020 although the London Plan gives a date of 2026 before thislevel of jobs is reached. CAZ employment growth is much lower.

Table A1: Employment growth, CAZ and Isle of Dogs

Year Isle of DogsEmployment

CAZ Employment

2001 44,000 1,348,0002008 (estimate) 100,000 1,451,0002020 (forecast) 200,000 1,579,000

% growth 2001 to 2020 +454% +17%

Access By Bus, Walk And Cycle

The Isle of Dogs is much less accessible by bus than the CAZ. Bus use in the Isle of Dogs isrestricted to access from the North, and the number of bus routes which serve the area comparedto central London is low ( Figures 4.1 and 4.2, taken from the TfL website, show the difference inthe density of the bus network in the Isle of Dogs compared to Bank).


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Figure A1: Isle of Dogs bus map

Figure A2: Bank bus map

Bus trips to the Isle of Dogs have been relatively stable over the past 15 years, but this is in the

context of massive growth in the number of trips by all modes, therefore the percentage of trips to


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the Isle of Dogs by bus has fallen significantly, as shown in Figure 4.3, accounting for only around5% of total trips to the area. In comparison, bus trips in central London have slightly increased inthe past 10 years, along with the share of trips undertaken by bus compared to other modes oftransport.

Figures 4.3 and 4.4 show travel mode shares for the Isle of Dogs and central London, taken fromthe 2007 London Travel Report.

Figure A3: Isle of Dogs morning peak travel by mode of transport (source:London Travel Report, p 7)

Figure A4: Peak morning travel into central London, by mode of transport(source: London Travel Report, p 5)


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Thus, whereas the bus mode share to central London is increasing, that to the Isle of Dogs isdecreasing. A higher proportion of future employment growth in the Isle of Dogs is likely to rely onrail than is the case for central London.

Changes To Rail Capacity

Crossrail has a bigger impact on rail capacity to the Isle of Dogs than it does on central London.Table 4.2 shows that Crossrail increases public transport capacity to the Isle of Dogs by 53%,compared to only 6% into zone 1. The reasons for this are:

existing rail capacity to the Isle of Dogs is much smaller comprising only the JLEand DLR

all Crossrail capacity to the Isle of Dogs is additional to the base situation,whereas most Crossrail capacity into central London (defined as Zone 1)replaces existing rail services (although there is additional capacity through trainlengthening/ increasing frequencies)

Table A2: Rail capacity in morning peak hour (PGC, 000s)

Isle of Dogs Zone 12001 30 4482016 (no Crossrail) 51 5172016 (w Crossrail) 78 550Crossrail (% inc) +53% +6%

Nb capacity is defined as PGC, tfls standard measure of capacity that takes intoaccount the number of seats on a train as well as an allowance for standing capacity atan acceptable density)

The future year capacities used in Table 4.2 are consistent with the Crossrail economic appraisal.They assume significant increases in rail capacity prior to the introduction of Crossrail, including:

the full PPP network growth assumptions on LUL; Thameslink 2000, East London Line Extension and CTRL domestic services

implemented in full; DLR extensions to Woolwich Arsenal and Stratford International; significant capacity increases (train lengthening) on National Rail (NR) services;

and bus services maintained at 2004 levels of supply.

Rail services into the Isle of Dogs in 2016 without Crossrail would be more crowded than railservices in central London (according to the CLRL modelling results which are based on only144,000 jobs on the Isle of Dogs). Table 4.3 shows that without Crossrail, in 2016, trips to the Isleof Dogs would spend 50% of their time at crowding levels over 100% of PGC, compared to 45% forcentral London, and that 94% of the Isle of Dogs trips would be affected by crowding levels over

100% of PGC, compared to 82% of central London trips.Table A3: Comparison of crowding levels in the Isle of Dogs and CAZ

without Crossrail

Percentage timespent at crowdinglevels greater thanPGC

Percentage of tripsaffected by crowdinglevels greater thanPGC

Isle of Dogs 50.2% 94.0%

Central London 45.2% 82.3%

Source: Crossrail Select Link Analysis. Nb this modelling is based on 144,000 jobs onIsle of Dogs, much less than currently expected.


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In addition, the rail capacity constraint on the Isle of Dogs is in many ways more important than it isto central London, notably because:

there are no alternative rail schemes being planned to increase capacity to theIsle of Dogs; and

the Isle of Dogs is less accessible by walk/cycle modes because of its islandnature

Reliabi l i ty /Resi l ience

Regarding the reliability and resilience of public transport services to the Isle of Dogs and CAZ, it isclear that rail services to the Isle of Dogs are much less resilient to problems occurring on anysingle line.

Currently the Isle of Dogs is served by JLE and DLR, with roughly 61% of rail users on the JLE and39% on the DLR. If one of those lines goes down in the peak period, particularly if it is the JLE,then there are major problems getting people in and out of the Isle of Dogs. The 100,000employees on the Isle of Dogs are largely dependent on just two lines and therefore highlysensitive to problems on either one. Whereas CAZ was able to cope with the Central Line being outof action for three months in 2003 by reassigning across a range of other lines, this would not bepossible on the Isle of Dogs. It seems likely that this is already an issue with large employers andwill increase in importance as employment grows.

By providing a third line into the Isle of Dogs Crossrail significantly reduces that risk: with threelines the impact of any one of them failing is greatly reduced. Central London of course is servedby all the LUL lines and all the main Network Rail commuter services, so dependence (as adestination) on any one service is much less.

It is possible to show this effect by thinking about the length of time it would take to enable allemployees in the CAZ and the Isle of Dogs to board trains. The numbers are illustrative based ongross rail capacity (not allowing for through passengers or non-commuters) assuming 70% ofemployees commute in the peak and assuming that all commuters travel by rail. On that basisTable 4.4 below shows the time taken to accommodate all employees.

Table A4: Comparative resilience of central London and the Isle of Dogs toone line being out of action

Central London Isle of DogsTime

(hours)%

changeTime

(hours)% change

Without CrossrailWith all lines 2.4 3.1

Without one line (Northern /JLE)

2.7 10.3% 9.1 198%

Without one line (Northern /DLR)

2.7 10.3% 4.6 50%

With CrossrailWith all lines, using grosscapacity

2.3 2.0

Without one line (Northern /JLE)

2.5 9.7% 3.5 76%

Without one line (Northern /DLR)

2.5 9.7% 2.5 28%

Thus removing one line from central London only increases the time taken to load all commuters by10% in central London, whereas on the Isle of Dogs the time increases three-fold if the Jubilee Line


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was out of action. With Crossrail the Isle of Dogs is still much more sensitive to one line being outof service, but the time taken increases by 75% rather than 200%.

It is worth noting that the problem applies almost equally to stations as it does to lines. Closure ofCanary Wharf JLE station would have a similar effect to closure of the JLE itself. Closure of Bankstation on the Central line would still allow access within reasonable walking distance at St Pauls orLiverpool Street but those options are not possible from the Isle of Dogs.

Conclus ions

It seems clear that development on the Isle of Dogs is more directly constrained by a lack of publictransport capacity without Crossrail, than is the case in the CAZ. That is the case because:

the rail capacity constraint will bite harder on the Isle of Dogs because of thescale of planned employment growth;

the Isle of Dogs is less easy to serve by bus, cycle and walk modes because ofits island nature, again increasing reliance on additional rail capacity; and

the dependence on only two lines provides reduced resilience to operationalproblems and reduced potential to reassign away from very crowded links.

Documents

Crossrail S106 the Transport Case