Mode specific accessibility and car ownership

1Max Bohnet, 2Carsten Gertz TU Hamburg-Harburg, Institut für Verkehrsplanung und Logistik 1 2

Abstract This paper analyses the links between mobility tool ownership (cars, bicycles public transport passes) and mode specific accessibility, using data from the Region. An utility based indicator is proposed, which captures accessibility by different modes differentiating persons with and without car availability. Incorporating accessibility indicators in discrete choice car ownership models helps to better explain spatial influences on car ownership. The analysis revealed that, when controlling for socio-economic variables, car ownership levels double in areas with a poor accessibility to destinations for persons without cars.

1. Introduction In the last decades motorisation levels heavily increased in most countries. At the same time, car-oriented land use patterns and spatial relationships emerged – not only in North American cities – that are hardly accessible without private cars. Persons which do not (anymore) have access to a car find themselves in an accessibility trap. This is an issue of major importance looking at a rapidly aging suburban population.

A Atlanta A 700 AHouston Perth A A A A A A A a 600 A San Francisco

t A A

i A p A AAA a A A A A c 500 A A A Munich

A A 0 A A A A ANew York A 0 AA A A A 0 A . A A 400 A A A A Berne 1 A A A Barcelona A A o A r A A A p 300 AA Tokyo A A Amsterdam A

s A A A A A r A A Copenhagen a AAA c 200 A A AA A AAA Lisbon A A A A Singapore 100 A AAA A AA A Hong Kong AAAA AA 10000 20000 30000 40000 50000 GDP per capita (US$)

Figure 1: Car-ownership and GDP per capita in 100 metropolitan areas (Source: Data from the Millennium Cities Database [1])

However, some cities and regions, where car ownership levels have remained relatively low despite high income levels, could preserve a transportation system and urban structure where walking, cycling and public transport provide a relatively competitive accessibility compared to accessibility by car. In these places, other mobility tools like bicycles and public transport passes are often attractive alternatives to owning a (second) car.

Car ownership is a major decision for households not only because of the economic impact on the household budget. It is an important link between long and short term mobility decisions (Figure 2).

On the one hand, car availability determines short term mode and destination decisions for daily activities. It influences which destinations (e.g. shops) a person visits and by which mode the trip is carried out. On the other hand long term job and residential location decisions depend on the availability of mobility tools. Households only take locations into account that they perceive accessible by their available modes.

Figure 2: The ‘land use-transport feedback cycle’ [2]

The feedback mechanism of accessibility on car ownership decisions has rarely been studied. The purpose of this paper therefore is twofold:

Section 2 discusses mode specific accessibility and proposes utility based mode specific accessibility indicators to assess the competitiveness of walking, cycling and public transport compared to car use. In Section 3 these indicators are used to estimate discrete choice models of car ownership. The impact of accessibility on car ownership decisions of households and persons are analysed, controlling for socio-economic variables. Finally the results are discussed and some indications on further research needs given.

2. Accessibility in the The Hanover Region metropolitan area has 1.1 Mio. inhabitants. Half of them are living in the city of Hanover, the other half in the 22 suburban municipalities.

Neustadt

Burgdorf

Lehrte Hannover

Barsinghausen cars per 1.000 capita

Wennigsen Airport 451 - 500 250 - 300 501 - 550

Springe 301 - 350 551 - 600 351 - 400 601 - 612 401 - 450 Rail Network Light-Rail Network Figure 3 and 4: Mobility tool ownership in the Hanover Region. motorisation (left, Data: [3]) and public transport passes (right, Data: [4])

Hanover traditionally has relatively low car ownership levels [5]. The motorisation is highest in suburban municipalities lacking (good) rail access (Figure 3), while in central locations in Hanover only one out of 4 persons owns a private car. Highest rates of public transport pass owners are found in a 500 m-radius around the (light-) rail stops in Hanover and some suburban municipalities (Figure 4). Pass ownership drops by 25% in a distance of 500 - 1.000 m and by 50% in a distance of more than 1.000 m to a rail stop.

Data sources As socio-economic characteristics play an important role in mobility decisions, they have to be controlled for when analysing the link between land use and mobility. This study analysed the data of the German Mobility Survey (MiD) 2002 [6]. The survey includes information on 25.800 households with 61.700 persons throughout . For the Hanover Region, detailed spatial information on residential location and on trip origins and destinations are available for a 4.581-household sample. A transportation network model [7] and a GIS- database of jobs and shopping facilities were used to calculate travel times and accessibility indicators. The transportation model of the Hanover Region (863 zones) and a GIS-database of jobs and shopping facilities were used to calculate travel times and accessibility indicators. Despite the growing significance of car sharing (2.800 users in the Hanover Region) no data was available in the MiD data, so the influence of car sharing as an alternative to private car ownership could not be studied.

Perceived accessibility in the Hanover Region In the MiD 2002 the respondents were asked to assess the “accessibility to usual destinations” by car and public transport. The perceived accessibility by car is good or very good for most respondents. Only in Hanover’s inner neighbourhoods a significant share (13%) assesses the accessibility as moderate to very poor (Figure 5). This might reflect scare parking facilities in the inner neighbourhoods.

The accessibility by public transport is rated very good by 62% in the inner neighbourhoods of Hanover. This share drops to 22% in rural areas (Figure 6). Compared to other German regions, accessibility by public transport is generally rated better in the Hanover Region. This might be due to the extensive light and commuter rail system in Hanover City, that covers 71% of the Cities population within 500 m and 94% within 1.000 m of a light or commuter rail station. In the suburban municipalities, 19% live within 500 m and 41% within 1.000 m of a light or commuter rail station.

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Hanover inner neighbourhood Hanover inner neighbourhood Hanover outer neighbourhood Hanover outer neighbourhood first suburban ring first suburban ring very good mid-level centre very good mid-level centre good good moderate basic centre moderate basic centre poor rural area poor rural area very poor very poor

Hanover Region Hanover Region Germany: monocentric conurbations Germany: monocentric conurbations Germany: cities over 500.000 inh. Germany: cities over 500.000 inh. Germany Germany

Figure 5: perceived accessibility by car (N=7.403, left) and by public transport (N=7.677, right) in the Hanover Region and in Germany. Data: [6]

An utility based mode specific accessibility indicator There are many ways to measure accessibility, ranging form simple, infrastructure based to more complex utility based indicators [8]. A person without car availability might access some destinations by foot, some by bicycle and others by public transport. To assess an intermodal accessibility, the question arises, how to combine the accessibilities by various modes.

This objective was to develop an utility based indicator based upon an simultaneous destination and mode choice model. The indicator is able to capture the availability of mobility tools and integrates the accessibility of destinations by different available modes of

transport. As the parameters are estimated from MiD survey travel data, it reflects the respondents perception of distance decay.

Choice

Destination 1 (...)Destination j (...) Destination J

Public Car Trans- Cycle Walk port

Figure 6: Nested logit model of mode and destination choice

A nested logit model for mode and destination choice (destination choice at nest level, mode choice at the bottom level) was estimated (Figure 6), taking up the model proposed in [9: p. 18 ff.]. A model for J zones has 4*J alternatives. The utility formulation is written in (1):

U = C + ln (A f (gc ))+ ε (1) ijm mav j m mij i

The utility U ijm of mode m and destination j for person i increases with the attractivity A j of destination j, measured by number of jobs or retail floorspace. It decreases with rising generalised costs gc ij (travel time, costs, transfers…) between the residential location of person i and destination j. It is possible to relate the travel costs to the income of person i.

This has not been done within this study. εi represents a stochastic term, which is assumed to be gumbel distributed and reflects the unobserved preferences of observation i.

100% Walking 90% Cycling 80% Public Transport 70% Car 60%

50%

40%

30%

20%

10%

0% 0 10 20 30 40 50 60 70 80 90 generalised costs

Figure 7: Distance decay functions for different modes

The shape of the mode specific distance decay function f m(gc mij ) describes, how fast the utility of destinations decreases with increasing generalised costs (Figure 7). Different distance decay functions (exponential function, Box Turkey transformation, loglogistic, EVA) were tested. Best fits were obtained with the EVA function (2).

( + F ()1−w/ G ) f ()()w = /1 1+ w 1 e (2).

Cmav is a constant term for mode m. It reflects the general preferences for mode m and its availability for person i (mode car: always, temporal or never available). For a person 1 with temporal car availability, C mav is lower than for a person 2 with a car always available. This effects, that the accessibility by car of a destination contributes less to the overall accessibility of person 1 than for person 2, because person 1 cannot access this destination

all the time by car. The model could also capture the availability of other mobility tools (bicycle, transit pass) by incorporating corresponding constants.

The probability for person i on the given relation to destination j (within nest j) to choose mode m from the set of available modes M is written in (3). λ is the nest-parameter capturing the covariance of the error terms of the alternatives within the nest.

λ U ijm / = e P λ (3) U / λ ij m M U ijm / = ijm ∑e Iij ln ∑e (4) ∈ m∈M m M

The log of the denominator in (3) is written as I ij in (4). It reflects the utility that destination j contributes to the overall utility of the alternatives. It is used to estimate the probability to choose destination j out of the set of all destinations J (5).

λ e I ij = λ P λ (5) Iij i j J I ij = ∑e ac i ln ∑ e (6) ∈ ∈ j J j J The log of the denominator in (5) can be interpreted as the accessibility ac i for person i to all destinations J with all available modes M (6). As the utility formulation of the model (1) includes the term C mav which depends on the availability of mode m of person i, the accessibility indicator ac i reflects mode availability.

The model was estimated with the MiD-Survey data (1.889 journey to work trips). To each chosen alternative (destination j, mode m) 47 non-chosen alternatives were sampled. In order to reduce estimation time, 11 non-chosen destinations have been selected as a stratified random sample: for each k ∈[1..11], one destination j k within the subset S k. S k is the choice set of all destinations in a distance class k from the residential zone of person i. If S k is empty, an additional destination from distance S k+1 is chosen. The information on the generalised costs of all 48 alternatives and the attractiveness A jk , weighted with the inverse probability of zone j k to be chosen from the choice set S k were added to the dataset in order to estimate the model parameters. The model parameters were estimated using biogeme [10].

Accessibility 9,1 - 9,5 Railway Accessibility 9,1 - 9,5 Railway All modes 9,6 - 10,0 Freeway without car 9,6 - 10,0 Freeway 5,4 - 6,0 10,1 - 10,5 Federal Highway 5,4 - 6,0 10,1 - 10,5 Federal Highway 6,1 - 7,0 10,6 - 11,0 6,1 - 7,0 10,6 - 11,0 7,1 - 8,0 11,1 - 11,5 7,1 - 8,0 11,1 - 11,5 8,1 - 9,0 11,6 - 12,5 8,1 - 9,0 11,6 - 12,5

Figure 8: Accessibility with all modes (left) without car availability (right)

Figures 8 show accessibility measured by this accessibility indicator. To compare and interpret utility based accessibility indicators, they have to be scaled with a ‘marginal utility of income’ parameter λ [8: p.63]. Using parameters derived from Table 1 below ( λ=exp( βEVAnie / β peinkln ) = exp(2.961)=1.40) , an increase of one indexpoint of this accessibility indicator represents a 40% better accessibility.

The left map in Figure 8 shows the job accessibility indicator for a person with all modes available. It ranges from index values of 8.3 in peripheral areas up to 12.1 in central Hanover and along some motorway exits. This represents an 3.5 times better accessibility in central locations for car owners.

The right map in Figure 8 shows the job accessibility without car availability. It is lower than the accessibility by car, ranging from 5.4 at the periphery to 10.5 in central Hanover. However it can be seen that persons in central Hanover have a higher job accessibility than persons in peripheral areas at the fringe of the region, even if they don’t have access to a car. In Figure 8 the light-rail tracks and the areas around the railway stations can be clearly identified as areas with substantial higher accessibility than in the surrounding.

High job accessibility for persons without cars is also found in some mid-range centres like Laatzen, and Wunstorf with a fast rail access to Hanover and a good local job supply accessible by walking and cycling.

The accessibility gap of persons with no car availability In areas, where accessibility by car is much higher than accessibility by other modes are particularly car dependent. Persons without car availability have clear disadvantages compared to car owners, they face a big accessibility gap. Accessibilty gap indicators are proposed by [11] and [12]. This study proposes the difference between the presented accessibility indicator for persons with and without a car availability as “accessibility gap” indicator. The advantage is, that it covers the accessibility by different modes (walking, cycling, public transport) as alternatives to car use. A low accessibility gap indicates areas where walking, cycling and public transport are relatively competitive to cars (Figure 9).

Accessibility 2,4 - 2,5 Railway Gap without car 2,6 - 2,8 Freeway 1,3 - 1,5 2,9 - 3,0 Federal Highway 1,6 - 1,8 3,1 - 3,3 1,9 - 2,0 3,4 - 4,0 2,1 - 2,3 4,1 - 5,0

Figure 9: Job accessibility gap without car compared to persons with car available

For commute trips, the accessibility gap is highest (5 indexpoints) in peripheral areas but also in many areas close to Hanover with a fast road access and a poor public transport supply. It is low in central Hanover (1.25 indexpoints) and along the rail stations and in some secondary centres.

The indicator also could be calculated to assess the accessibility gains provided by other ∆ = − mobility tools, such as bicycles or public transport passes as ac m ac m ac 0 , whereas ac m is the accessibility indicator with m, a combination of mobility tools (e.g. car available and transit pass ownership) and ac 0 the accessibility with reference alternative 0 (e.g. car never available, no transit pass).

The study focused to job accessibility. For an overall picture, not only job accessibility also accessibility to other activities like shopping, schools or services should be considered.

3. Car ownership and accessibility without a car In this section the utility based accessibility indicator is used to analyse the relation between accessibility and household car ownership levels. Car ownership is widely determined by socio-economic characteristics like household size, income, or age [13]. To control for these variables, a discrete choice model of household car ownership was estimated.

Household car ownership model The dependent variable is a set of discrete alternatives with an ordinal structure: 0, 1, 2, 3 or more cars per household. For that reason an ordinal logit model has been chosen (7):  P   n  = α − + ε ln   n βX i (7) 1− P  n It estimates the probability P n for a household to own n cars or less. X is a vector of the independent variables, (characteristics of household i at its residential location) and β is a vector of the parameters of X. αn is a constant (threshold value) for the probability to own n cars or less. εi reflects the unobserved preferences of household i. It is a stochastic term, which is assumed to be gumbel distributed (an ordinal probit model assuming normal distribution of εI delivers nearly the same results). The model has been estimated using the ‘ologit’ (ordinal logit) model of Stata10 [14].

First, a basic model only including socio-economic variables was estimated. As there are several non-linear relationships, the following specification of the significant variables was found to fit the model best: • the categorical variable “net household income” (8 classes) has been transformed into a continuous variable log of “net personal equivalent income”, taking the number and the age of household members into account [15]. • number of adults (1, 2, 3, 4 or more as dummys) • number of children (1, 2, 3 or more as dummys) • the sum of the (age - 65) of all men older than 65 years and • the sum of the (age - 65) of all women older than 65 years The results are displayed in the column ‘basic model’ of Table 1. As expected, the probability to own more cars increases with income and number of adults in a household, and less strongly with the number of children. It decreases with the age of senior persons in the household, particularly among women.

COEFFICIENT LABELS basic model including including accessibility mobility tools FRAU_65 women sum of -0.110*** -0.118*** -0.127*** years over 65 (-0.012) (-0.012) (-0.013) MANN_65 men sum of -0.0427*** -0.0424*** -0.0567*** years over 65 (-0.014) (-0.014) (-0.014) peink_ln log(equivalent 1.762*** 1.836*** 1.805*** income) (-0.098) (-0.1) (-0.1) _IAnzErw_2 2 adults 2.420*** 2.322*** 2.319*** (-0.11) (-0.11) (-0.13) _IAnzErw_3 3 adults 4.057*** 3.961*** 4.198*** (-0.18) (-0.18) (-0.21) _IAnzErw_4 4 and 5.747*** 5.559*** 6.011*** more adults (-0.3) (-0.29) (-0.33) _IAnzKinder_1 1 child 0.772*** 0.707*** 0.732*** (-0.12) (-0.12) (-0.13) _IAnzKinder_2 2 children 0.943*** 0.781*** 0.767*** (-0.13) (-0.13) (-0.13) _IAnzKinder_3 3 and 1.000*** 0.921*** 0.851*** more children (-0.26) (-0.27) (-0.27) ZPP750LN log(m² retail -0.0276** -0.0327** floorspace in 750 m) (-0.013) (-0.013) EVAnie accessibility wihout -0.620*** -0.509*** car available (-0.048) (-0.049) fahrrad number of 0.300*** bicycles (-0.071) _Izeitkarte_1 1 public -1.354*** transport pass (-0.12) _Izeitkarte_2 2 public -1.832*** transport passes (-0.24) Constant 1 car 12.41*** 7.013*** 7.611*** (-0.7) (-0.8) (-0.81) Constant 2 cars 16.10*** 10.96*** 11.81*** (-0.74) (-0.83) (-0.85) Constant 3 cars 18.91*** 13.97*** 14.94*** (-0.76) (-0.84) (-0.86) Observations 3872 3872 3862 Pseudo R-squared 0.2602 0.2998 0.3344 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Table 1: Estimated model parameters for household car ownership

To this basic model, two accessibility indicators are added: • EVAnie : The job accessibility without car availability derived from the joint destination and mode choice model, using an EVA-distance decay function, as described in section 2. • ZPP750LN: the log of retail floorspace (for periodic shopping purposes) within in 750 m walking distance. Including these variables, the model can be improved significantly. These variables have a strong negative influence on car ownership. The model can be used to estimate the

probabilities for a household to own 0, 1, 2 or more cars, depending on the accessibility without a car at the residential location. Figure 10 displays the car ownership probability for a household with 2 adults under 65 years without children with an average income (1.850 € net monthly household income, which corresponds to 1.230 € personal equivalent net income), depending on the accessibility without cars at the residential location. The probability to own no car increases from 1 % at the least accessible location to 17 % at the most accessible location of the Hanover region. Meanwhile the probability to own 2 or 3 cars decreases from 70% to 10%.

100 Three Cars 90

80 Two Cars 70

60

% 50

40

30 One Car 20

10 worst accessibility worst

No car accessibiltiy best 0 5 6 7 8 9 10 Accessibility Index without car availability

Figure 10: Probability of car ownership of a 2-adult household with a net household income of 1.850 € by accessibility without car at residential location

This method can be applied for a micro-simulation of car ownership of a synthetic population, as described in [16]. In Figure 11 the modelled average number of cars for such household of 2 adults under 65 years without children with a net monthly household income of 1.850 € are displayed. It shows that the motorisation of this household type is nearly twice as high in peripheral locations and that within suburban areas it is clearly lower around railway stations and close to the secondary centres. So an average 2-adult household spends yearly 4.000 € on fixed car costs in poorly accessible areas instead of 2.000 € well accessible locations (calculated with cost rates from [17]). In addition, in poorly accessible areas they drive much more kilometres and bear several times higher variable vehicle costs [18].

The last column of Table 1 displays the results of another model specification which includes the ownership of other mobility tools. A strong negative correlation between car ownership and public transport pass ownership is found, which indicates that public transport passes might serve as an alternative to a (2 nd ) car for many households. For bicycles, a positive correlation was found between households which own more cars and own more bicycles. Modelling the mobility tool decisions in a joint model could help to better understand these interactions [19].

Railway 1,31 - 1,40 Cars per household 1,41 - 1,50 0,94 - 1,00 1,51 - 1,60 1,01 - 1,10 1,61 - 1,70 1,11 - 1,20 1,71 - 1,85 1,21 - 1,30

Figure 11: Expected average number of cars per household for a 2-adult household with a net monthly household income of 1.850 € in the Hanover Region

Outlook The results show, that there are significantly lower car ownership levels at locations well accessible without car, even when controlling for socio-economic household characteristics. The study reveals, that households living at central locations with an attractive public transport are using other mobility tools like public transport passes as a substitute to a (second) car and save a high amount of mobility costs.

However, the question of self-selection remains open: to what extent households with preferences for an automobile lifestyle choose locations well accessible by car and households with preferences for intermodal mobility move to locations well accessible by foot, bicycle and public transport? To what extent do households adjust their motorisation when their accessibility changes due to land use or infrastructure changes, or after relocation? To analyse these interactions between accessibility, car ownership and location choice, longitudinal panel data analysis can provide valuable insights [20]. Further research along these lines is needed for a better understanding of the land use - transportation interaction.

Acknowledgements I would like to thank the Hanover Region (particularly Tanja Göbler and Klaus Geschwinder) and the üstra (Harald Paul) for providing the data and Carsten Gertz, Matthias Winkler and Andrea Broaddus for their helpful comments on this paper.

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