BICYCLE AS A LOCAL FEEDER TO REGIONAL PUBLIC TRANSPORT
Asa Rystam
Department of Traffic Planning And Engineering
Lund Institute of Technology
University of Lund
Lund, Sweden
1.
BACKGROUND
To create a sustainable transport system there is a need to increase trips made by
"public" transport modes (bus, train, walk, bicycle, taxi etc) and decrease trips made
by the car. The majority of passenger trips in urban areas combine more than one mode
such as Park and Ride, Bike and Ride etc. The idea is to create more attractive
intermodal "public" transport system that can compete with the car for the whole doorto-door trip. This demands faster and more comfortable public transport modes
together with attractive local feeders. This paper deals with the issue of intermodality
between local feeders (walk, bicycle, bus, car) and regional trains.
Up till now the bicycle has been more or less forgotten in the planning process and few
traffic models treat the bicycle as a separate mode. Specific measures to increase
bicycle use are of great interest since the amount of people using the bicycle, especially
in combination with locdregional public transport, continues to rise i many countries
all over the world. The bicycle is used by persons in all age classes and for many
purposes, for schooVwork trips as well as for leisure trips.
Together with fast IocaYregional public transport the bicycle has shown to be a
competitive option to the car. The faster public transport the greater number of
bicyclists. A Danish study in Copenhagen showed that the bicycle can compete within
a distance of 5 km from the IocaVregional train stations, Gvo Jensen J, 1995 (I). A
similar study in south Sweden showed that 75% of the total number of bicycle trips can
be found within a distance of 3 km from the station with a concentratim of 1-1.5 km.
In the same part of Sweden 26% of the car trips, Nzlsson A, 1995 (2) and 50% of the
local bus trips can be found within a distance of 3 !a,Rystam r f , 1996 (3).
In average, 15-35% of the passengers use the bicycle as a feeder to regional trains in
northern Europe, Rystum A, 1992 (4). The highest figures can be found in dense
populated urban areas. i.e. in The Netherlands and in the county of Malmohuslan in
south Sweden. In the county of Malmohuslan, Sweden, minimum 30% (max 55%) of
the feeder trips to regional trains in the home part of the journey is made by bicycle and
up to 25% in the away part of the journey, Rystam, 1996 (3). In the home part the
bicycle users are mostly commuters and in the away part mostly they are leisure-,
shopping- or other cathegories of passengers. Danish and Swedish study show that
2-10% of the passengers use the bicycle in both ends of a multimodal trip, the more
commuters the higher share, G o JensenJ, 1995 (I) and Rystam, 1996 (3).
*
All these fact makes the need of measures to improve intermodality even more
important!!! In order to be able to do the correct mesures there is a need of knowing
what factors that influence the bicycle use and to what extent! ! !
This paper deals with factors directly influencing the mode choice such as transport
system factors, socioeconomic factors and trip related factors and factors indirectly
influencing the mode choice; attitudinal factors.
2.
PLANNING
Planning intermodal transport systems is not a very easy question. To make models that
describes the reality is even more complex but very important for making prognosis.
The models has to describe the relation between transport network design as well as
the assignment of the transport system resources (vehicle, personnel etc) and the
assignment of the traffic demand (passengers).
The planning can be divided into two levels; the micro and the macro level, described
by Amarin, Hansen and Rystam, 1995, (5). The micro level are normally used to
analyse the physical design of the transport system separately from the assignment of
system resourceshravel demand. This is done with a local value, using counter or other
indirect meters such as interview studies. The macro methodology analyses the relation
between the assignment of system resources/travel demand and the physical design of
the transport system.
When analysing changes in modal split due to different measures of the transport
system (in this case intermodal facilities) a lot of assumptions has to be made to
simplify the modelling: The location and the network transport system design are to be
considered as tixed and the system resource assignment given. Even having done these
assumption the modelling procedure is difficult.
A multimodal traffic planning model, describing the traffic demand assignment, must
consider the demand and the modal choice simultaneously in an "individual" choice
model, with the route choice and the mode choice either implicitly or explicitly treated.
In this case the route choice is assumed to be treated explicitly from the mode choice.
This means that the multimodal trips can be treated as new modes so the "pure" and
the combined modes are chosen according with a mode choice function.
The work in this report deals with mode choice analysis. In this case logit analysis,
which gives the proportion of trips made on each mode, so that the demand assigned
at each mode is obtained multiplying the demand by the proportion, has been used.
WHY PEOPLE CHOOSE A CERTAIN MODE IS OFTEN FORGOTTEN
WHEN PLANNING!!!
3.
To what extent the different factors influence the bicycle use is of great interest but still
not very good understood or examined. There is a lot of reason to if people choose to
bike or not and many of them are not even considered when modelling.
Traditionally quantitative models, used to describe model split, normally only consider
the mode choice to be a function of factors related directly to the transport system (time
and cost attributes). However the mode choice is influenced by many other things, in
addition to time and cost attributes, such as attributes indirectly related to the transport
system (comfort, information, bicycle parking etc), characteristic of the user (sex, age,
income, errand, travel frequency etc), attitudes and perceptions towards the transport
system etc. Some of these factors may be very important for understanding the longer
potential of cycling as a transportation mode. Combining traditional theory with
behavioral theory could lead to progresses in explaining mode choice by i.e.
differentiating the models for different users. The issues is to describe not only how
people travel but also why the travel. This issue is discussed in detail by Katz R (6)
1995 and Henscher & Stopher (7) 1979. This report deals with importance of these
factors !!!!!!
4.
CASESTUDY
4.1
Aim
The aim with the research area is to describe the bicycle role as a local feeder to
regional public transport and to suggest measures to enhance the use of bicycle in
intermodal transport.
The research area deals with issues analysing physical measures of the transport system
(micro analysis) as well as studying the relation between the travel demand and
measures of the transport system (macro level-model split analysis). The research will
result in a Dr-thesis in 1997.
This paper (macro analysis) focus on the bicycle use in relation to;
1) factors directly influencing the mode choice;
- transport system factors
- socioeconomic factors
- trip related factors,
2) factors indirectly influencing the mode choice;
- attitudinal factors.
4.2
Method
Interviews were made with regional train passengers in south Sweden. 150 passengers
where asked about a specific trip they had made to the station. The passengers were
asked questions about the actual mode chosen and other available modes. The
questionnaire was divided into two parts;
1) traditionally factors
* transport system factors
* socioeconomic factors
* trip related factors
time, cost. car parking, bicycle parking
sex. age, car access
errand, travel direction, travel frequency
time period, baggage, stop
2)
attitudinal factors
(The passengers were asked to rate, on a scale between 0 and 100. the importance of different
factors in relation to their mode choice.)
* transport system factors
*personal factors
- being fresh at arrival
- cost
-time
- health
- bicycle parking- number of places
- exercise
- bicycle parking- access to platform
- pollution
- nice to bicycle
- bicycle parking- weather shield
- bicycle parking- simpleness to park
- forced to use a certain mode
- environmental mode
- safety
* other factors
- mode flexibility
- weather
- nice closelhair at arrival
- topograph
4.3
Results
The results presented in this paper is based on modelling the travel demand with logit
analysis. The chapter in divided into the following part;
4.3.1 Mode choice - the importance of time and cost factors.
4.3.2 Mode choice - the importance of socioeconomic factors, trip related factors,
transport system related factors.
4.3.3 Mode choice - the importance of attitudinal factors.
4 . 3 . 4 Mode choice - the importance of attitudinal factors compared to the
importance of time factors.
4.3.1 Mode choice - the importance of time and cost factors
This chapter aim at presenting a "base" planning model, consisting on time an cost
attributes, to describe the mode choice for the local feeder traffic to regional trains. In
the analysis only three modes are included: bike, walk and bus. The car was omitted
from the analysis due to the fact that only 3 % of the feeder trips is made with a car.
Table 1 show the estimated parameters of the variables together with standard errors
and T-values. The work, when constructing the model or the quality analysis of the
model function or the parameters estimated, will not be discussed more than very
hrietly. This work is described in detail, see Rystum. 1996 (3).
BIKE
constant
4.018
2.37
I .7
WALKTIME
BIKETIME
BUSTIME
Parameter
std.rrr
T-value
WALK
Constant
5.717
2.34
2.4
-0.3765
0.802E-01
-4.7
-0.3592
0.138
-2.5
-0.9365E-01
0.128
-0.7
Factor
BUSHOME
BUSSTA
WABISTA
BUSCOST
Parameter
-0.4684
0.185
-2.5
-0.4800E-01
0.703E-01
-0.7
-0.35 13
0.170
-2.1
-0.5968E-0I
0.143
-0.4
Factor
std.eIT
T-value
A "likelihood ratio-test'' showed that the model is significant on the 99.999-level. The
R2-value of the final model is 0.33 which was less than the =-value in the initial
model (contained more variables). After a lot of work a final "base" model was found.
The parameters for attributes describing the bus alternative are not significant (T-value
< 1.6) due to the lack in the observations for the bus alternative. The size of the two
constants, BIKE and WALK, at an average distance of 2 km explain 30.35% of the
modeI.
The result show that the assessment of BIKETIME (time to bicycle) and WALKTIME
(time to walk) is almost equal which was expected due to results from earlier studies.
Every extra minute walking and cycling decrease the probability of choosing walk or
bicycle with 0.37 units. The combined waiting/walkingtime/
parkingtime at the station for pedestrians and cyclists (WABISTA) is valued 90-95 %
of the walk/bike time which is an interesting high figure. A deeper analyse showed that
it is the time to park the bicycle that is very highly assessed which in turn explain the
high assessment of WABISTA.
The bus time (BUSTIME) is assessed to 1/3-1/4 of the bikelwalk time (parameter is
not significant). This means that passengers using the bus alternatives have much lower
time values than pedestrians and cyclists. This can be explained by the fact that these
persons belong to different travelling groups (sex, errands, age), i.e. men bikes more
than women who instead use the bus to a greater extent. The combined
waitinglwalking time at the bus stop (BUSHOME) is valued 4.5 times than the bus
time which is a rather high assessment, however not impossible. The walk/waiting time
at the station (BUSSTA) for bus passengers is assessed to half of the bus time which
at first might seem a bit low. This low time value can be explained by the fact that the
connections between local buses and regional train is good.
The assessment of the bus cost (BUSCOST) is very low. This result is very interesting
but has to be further investigated due to the fact that the assessment is not significant.
If we consider the estimation to be correct the explanation ought to be the fact that the
cost for local bus trips is marginal compared to the cost for the regional trip. That
passengers have low value of cost for local trips has been shown earlier by Holrnberg,
I994 (8) and by Copenhagen Transport, Denmark (HT). The result from the study
gives us rather high time values; 94-121 SKR/hour for the bus alternative and 360-390
SKR/hour for the bike/walk alternative. These figures should not be used in general
due to the fact that the estimation is very insecure and probably somewhat to high!!
4.3.2
Mode choice - the importance of socioeconomic factors, trip related
factors and transport related factors
This chapter describes bicycle use in relation to;
*
socioeconomic factors
* trip specific factors
* transport
system factors
(sex. age class, car access
(errand, travel frequency, travel direction,
baggage, stop)
(time period, bicycle parking at the station,
car parking at the station)
The constant BIKE in the "base" model was segmented so that the bicycle use in
different groups (segments) could be analysed. The analysis is based on a normal case
with average times and costs. The results, presented in table 2, show differences
between the groups in all cases, however no significant differences.
In column 1 the different groups (segment) for every analysed factor are defined,
maximum 4 groups. In column 2 the bicycle share of group 1 is presented; in the first
case we can see that 32% of the men bikes to the station. In the third column the
relation in bicycle share, between group 2, 3 and 4 and group 1, is presented; in the
first case we can see that 6% less women than men bikes.
Group 1
Relation between
Group 2, 3, 4
and Group I
percantage
bicycle share
percentage
Sex; I . man
2. woman
Age class: I . 0- 17 years
2 . 18-24 years
3. 25-54 Years
4. 54 years or more
I
Car Access in the family I . 0 car
2. one car
3. two cars
4. three or more cars
Errand; I . worklschool
2. leisureishoppinglother
I Travel freauencv:~. I . often
+20%
+109%
+252%
40 %
-19%
I
33%
130%
2 seldom
Travel Direction: I . heading away (home part). 20%
2 heading home (not home part)
RaEKdge: I yes
397%
I
-60%
-51%
I
-18%
18%
167%
I
Group 1
bicycle share
percentage
Stop: 1. yes
Group 2, 3, 4
aud Group I
percantage
41 %
2. no
Time period: 1.hours;. 05-09
2. hours; 09-12
3. hours; 12-15
Relation between
-33 %
60 %
-2 %
9%
-65 %
4. hours: 15-19
Bicycle parking; 1 .easy to park the bike
2. difficult to park
48 %
Car Parking 1. easy to park the bike
2. difficult to park
17%
-31%
+16%
The result show that
*
Men bikes more than women
Persons in the age from 25 years and above bikes more than younger persons
persons with access to 0 or 1 car bikes more than persons with
access fa 2 or more cars.
* worWschoo1 passengers bikes more than leisure/shopping/other passengers
* Passengers who travel seldom bikes more than passengers that travel often
* passengers heading away bikes more than passengers heading home
* passengers with baggage bikes less than baggage without baggage
* passengers that make stops bikes more than passengers without baggage.
* passengers during time hours 12-15 bike most, during 15-19 lest
* passengers that find it easy to park the bicycle at the station bikes more than those how find
it difficult to park the bicycle
* passengers who fmd it difficult to park the car at the station bikes more than those who find
it easy to park the car.
*
*
This result gives as an general idea of that the bike user in local feeder traffic is a man,
above 25 year, heading for work/school and travels occasionally by train. The bikers
can be found during the time hour 12-15, he don't carry baggage and or make any
stops. The bike users have a high assessment of the bicycle parking and a low
assessment of the car parking. The bike trips are preferably made in the home part.
4.3.3
Mode choice - the importance of attitudinal factors
This chapter describes the importance of attitudinal factors when making a mode
choice. Attitudes towards the following factors was analysed;
* transport system factors
-
cost
time
bicycle parking- number of places
- bicycle parking- access to platform
- bicycle parking- weather shield
- bicycle parking- simpleness to park
- environmental mode
- safety
- mode flexibility
- nice closelhair at arrival
- being fresh at arrival
-
-
* personal factors
- health
exercise
pollution
nice to bicycle
- forced to use a certain mode
-weather
- topography
-
* other factors
The passengers were asked to rate, on a scale between 0 and 100, the importance of
different factors in relation to their mode choice. The passengers was given the
following question; "On a scale between 0 and 100, How importance is the factor when
You make Your mode choice'?.
In the analyse the passengers were dived into two groups;
I ) passengers that think the factor is important (55-loo), referred to as the HIGH
assessment group
2) passengers that think the factor is not important (0-50). referred to as the LOW
assessment group
The constant BIKE in the "base" model was segmented so that the bicycle use in
different groups (HIGH respectively LOW) could be analysed. The analysis is based
on a normal case with average times and costs.
The results. presented in table 3, show differences between the two groups in all cases,
however no significant differences. In column 1 the factors studied is presented. In the
second and the third columns the bicycle share in the two groups, LOW and HIGH,
is presented. In the fourth column the relation between the bicycle share between the
groups are presented; in the first case, the factor Cost, we can see that the bicycle use
is 1 I % less in groups with HIGH assessment of the cost compared to the group with
LESS assessment.
Assumption were made that in some cases HIGH assessment of a factors (the factor is
important to the mode choice) would increase bicycle use and in other cases LOW
assessment would decrease the bicycle use. The assumption were made in relation to
available data of the real conditions. The fact that the study concerned only short trips,
maximum 5-7 km from the station, was considered in the analysis. The following
assumptions were made;
transport svstem factors
1 cost
2. time
3. bicycle parking - space
4. bicycle parking - access to platform
5. bicycle parking - weather shields
6 . bicycle parking - simpleness to park
7. environmental mode
8. mode safety
9. mode flexibility.
high assessment increase bicycle use?
high assessment increase bicycle use?
high assessment decrease bicycle use?
high assessment decrease bicycle use?
high assessment decrease bicycle use?
high assessment increase bicycle use?
high assessment increase bicycle use?
high assessment decrease bicycle use?
high assessment increase bicycle use?
personal factors
9. nice clothedhair at arrival
10. being fresh at arrival
11. health
12. exercise
13. pollution
14. nice to bike
15. forced to use a certain mode
high assessment decrease bicycle use?
high assessment decrease bicycle use?
high assessment increase bicycle use?
high assessment increase bicycle use?
high assessment decrease bicycle use?
high assessment increase bicycle use?
high assessment increase bicycle use?
other factors
16. weather
17. topography
high assessment increase bicycle use?
high assessment decrease bicycle use?
Bicycle share
I cost
I Time
LOW
HIGH
I
I
Relation
HIGH to LOW
-9%
132%
129%
I 14%
134%
bicycle parking - number of places
37%
31%
-16%
bicycle parking - access to platform
35%
31%
-11%
bicycle parking - weather shields
37%
30%
-19%
bicycle parking - simpleness to park
26%
34%
+31%
environmental modes
38%
25%
-34%
safety
57%
30%
-41%
flexible modes
22%
31%
+41%
I
+143%
~~
Bicycle share
I
Relation
HIGH to LOW
lLOW
lHIGH
nice closelhair at arrival
33%
26%
-21%
being fresh at arrival
28%
35%
+25%
health
35%
26%
-26 %
exercise
36%
25%
-31%
pollution
44%
19%
-56 %
nice to bicycle
43%
30%
-30%
forced to use a certain mode
30%
33%
+7%
weather
30%
38%
+27%
topography
32%
30%
-6%
I
i
The result show that in some cases the assumptions were correct, especially those
regarding time and the factors describing the standard of the bicycle parking, and in
some case the assumptions were wrong, especially in cases regarding personal aspects.
However the size of the differences are in some cases not particularly big which must
be noticed when doing to much conclusions; 1. Cost, 2. Bicycle Parking -access to
platform, 3. Forced to use a certain mode and 4. Topography, However some
interesting results have been found!!!
The result show that assumptions regarding factors concerning time and the standard
of the bicycle parking was correct. The time is very important to the bicycle user which
seem to be reasonable since the bike user to regional train often is a commuter that
travel often and would rather than anything else like to have a quick trip to the station.
The standard of the bicycle parking is also important to the bicycle user. If it is easy to
park the bicycle the person will use the bike more and if the number of places at the
parking place is to few or the access to platform is bad the person will cycle less.
The topography seems to influence the use of bicycles rather much, but the differences
between group with low and high assessment of the factors is very little. If the
topography is more important to the bicycle user this might not be a to strange result
due to the fact that people tends to buy more comfortable bicycle with more gears.
Whether the mode is environmental or not is not an important factor for the bike user.
The pedestrians and bus users have higher assessment of this factor.!!!!
The modes flexibility is very important to the bicycle user. This seem to be reasonable
since bike users make more stop than other passengers. To be forced to use a certain
mode don’t show any differences in bicycle use between the groups. This might lead
to the conclusion that availability to different modes is not a big issue today!!!
The assumption that the cost should have great importance of bicycle use was wrong.
However the differences in bicycle use between groups with low an high assessment
of cost is very little. The result might be wrong due to the fact that bicycle users,
mostly commuters and men, have high time values, but it might also be correct due to
the fact that the cost of the local feeder trip is only marginal compared to the cost for
the regional trip. This could lead to that the passengers might have a hard time
evaluating whether the cost is important or not! !.
Assumption regarding personal factors showed more or less to be incorrect. You don't
choose the bike to get exercise, or because it is good for Your health, or because You
don't like pollutions when biking or because it is nice to bike. You might do this for
longer trips but for short trips like feeder trips to public transport other things, like the
time factors, is much more important. The same thing is with the weather. You don't
use the bicycle less because it bad weather, You just put on other more weather
protective clothes!!!
4.3.4
Mode choice -the importance of attitudinal factors compared to the
importance of time factors
The main issue with this chapter was to compare the importance of attitude factors
compared to time factors on the mode choice, in this case measured by diffrences in
bicycle use. The question to be answered was
* How big effect of the bicycle use has
- a decrease in the bicycle time (BIKETIME) with 25% and/or
- a decrease in the station time for the cyclists (BIWATIME) with 33 %
compared with
*
An attitude change between LOW and HIGH assessment?
The attitude factors considered was
- time
- bicycle parking - closeness to platform
- evironmental mode
The result show that the at an average distance from the station (2 km) the effect of
changes in time attribute (bicycle time -25% or station time -33%) are in general
greater than the effect of attitude changes (LOW/HIGH) except for the attitude factor
TIME which seem to be an important factor. The greater distances the more
importance the attitudinal factors have and vice versa. The station time has greater
influence than the bicycle time due the higher assessment of this factor. The fact that
attitudinal factors don't seem to influence mode choice little compared to the influences
of timdcosts attributs has been shown earlier by Widlert, 1992 (9).
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