The document discusses modal split analysis in urban transportation, emphasizing its role in predicting travel mode choices among various transportation options like walking, biking, and motor vehicles. It covers the factors affecting modal split, different modeling techniques, and the implications for urban planning and traffic management. The document also highlights the importance of understanding trip characteristics and network dynamics in improving transportation efficiency.

1. Modal Split Analysis
• Presented By :-
H. A. Patel
1
Design of Urban Transportation System
Department of Civil Engineering
M. Tech Sem

2. What is Modal Split?
• The third stage in travel demand modeling is modal split.
• Mode split involves separating (splitting) the predicted trips from each
origin zone to each destination zone into distinct travel modes (e.g.,
walking, bicycle, driving, train , bus).
• Usually expressed as a fraction, ratio or percentage of the total number of
trips.
2
Modal Split In
Indian Cities
Source:-
India Indicators
By EMBARQ

3. Example of different scenarios
within one journey
3
Example of different types of multimodal
journey within one journey
Modal split can be calculated on the city
level or it can be calculated between the
zones in one urban area. After the
modal split calculation the traffic
experts and transport planners can
plan the land use in a more efficient way.
E.g. with the high density of origin and
destination spots between zones 2 and
8 (Picture), one can assume that users
could use the better infrastructure for
non-motorised traffic thus stimulating
walking and cycling in “green” zones.

4. Mode Split Model Applications
4
• Route or Service changes
– Effect of changes in cost, frequency,
transfer system, more or less service
and routes
– Not usually modeled with Travel
demand forecasting
– Major investment studies, e.g. New
rail or other capital investment
project design
• Policy changes
– Urban growth
boundaries
– Parking
– congestion pricing
Choosing a Mode Split Technique
• Application
• Time and budget constraints
• Project costs
• Existing data availability
• Existing service?
 Future transportation patter can only be perfect forecasted id transportation
modes can be analyzed.

5. Mode Choice
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 Public transport modes make use of road space more
efficiently
 There will be less congestion on the roads and the
accidents will be less.
 Travel with low cost. In addition, the fuel is used more
efficiently.
 Main characteristics of public transport is that they will
have some particular schedule, frequency etc.
 Private transport is highly flexible. It provides more
comfortable and convenient travel. It has better
accessibility also.
 Increase in Air Pollution, Number of Road Accidents
 The issue of mode choice, therefore, is probably the
single most important element in transport planning
and policy making. It affects the general efficiency with
which we can travel in urban areas.

6. Factors Affecting Modal Split
• Characteristics of the trip
– Trip Purpose
– Trip length
– Time
• Household Characteristics
– Income
– Car Ownership
– Household Size
– Life Cycle
• Network Characteristics
 Travel Time Ratio = Tpt / Tct
Where, Tpt & Tct = Total Travel time by Public
Transport System & Private car system
 Travel Cost Ratio
 Frequency & Congestion
• Zonal Characteristics
– Residential Density
– Sidewalk / Pedestrian facility
– Distance to CBD
– Parking
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7. Classification of Modal Split
7
Modal Split
Process
Pre distribution
(Trip end modal
Split)
At Trip Generation
Stage
Post distribution
(Trip Interchange
modal Split)
After Trip distribution
(before trip Assignment
stage)
Trip End Modal Split model:-
If modal split is considered at the trip generation stage itself, it is
necessary to derive separate multiple linear regression equation for each mode
of transportation like wise car, public transport, rail etc.
Trip Interchange Modal Split model:-
This modal applied after the trip distribution stage . That is possible to include
the characteristics of the journey.

8. Sequence Activity of Modal Split
8
Land Use
Trip Generation equation
Modal Split model
Trip end by mode
Trip distribution Model
O-D Volumes by mode
Trip distribution models
O-D volumes
Modal Split model
O -D Volumes by mode
Sequence activity for
Trip- end type model
Sequence activity for Trip-
interchange type model

9. 9
Difference between types of modal Split Models
Trip End Modal Split Model Trip Interchange Modal Split Model
That is modal split is applied at the
stage of trip generation.
That is modal split is applied after the
trip distribution stage.
These models could be very accurate in
the short run.
This is beneficial for long term
modeling.
Model considers length of journey by
car and public transport.
Model ignores length of journey by car
and public transport.
Factors:- Car Ownership, Residential
density, Income, Distance of Zone
Factors:- Relative Travel time and Cost
ratio
Less difficult More Complex and costly
Performed in medium & small size city Performed in large size city
Exmphasize on transit captives Exmphasize on choice transit captives
e.g. Various studies and models. e.g. Toronto Model

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The probability of the occurrence of an event varies with respects to function F(x) as a
sigmoid curve called the logistic curve.
𝑷 𝟏 =
𝟏
𝟏 + 𝒆 𝑮(𝒙)
Logit Model Analysis
Where, 𝑷 𝟏 = Probability of an individual choosing mode 1
G (x) = 𝒂 𝟏 (𝑪 𝟏 - 𝑪 𝟐) + 𝒂 𝟐 (𝒕 𝟏 - 𝒕 𝟐) + 𝒂 𝟑 () + ….
𝒂 𝟏, 𝒂 𝟐, 𝒂 𝟑 = modal parameters
𝒄 𝟏 , 𝒄 𝟐 = Cost of travel by mode 1 & 2
𝒕 𝟏, 𝒕 𝟐 = Time of travel by mode 1 & 2
Affecting Factors:-
Characteristics of the trip and trip maker behavior
Transportation system
Note:- Probit models based directly on the normal distribution of the stochastic term
and require integrations. Also this is take more time in analysis.
Whereas, logit models follow weibull distribution and no need integration. Therefore,
logit analysis can be preferred.

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Binary logit model is the simplest form of mode choice, where the travel choice between
two modes is made.
The traveler will associate some value for the utility of each mode. if the utility of one
mode is higher than the other, then that mode is chosen. But in transportation, we have
disutility also. The disutility here is the travel cost. This can be represented as
Binary Logit Model
Where, 𝒕𝒊𝒋
𝒗
is the in-vehicle travel time between i and j,
𝒕𝒊𝒋
𝒘
is the walking time to and from stops, 𝑡𝑖𝑗
𝑡
is the waiting time at stops,
𝒏𝒊𝒋 𝑛𝑜. 𝑜𝑓 𝑡𝑟𝑎𝑛𝑠𝑓𝑟𝑒𝑠 , 𝑭𝒊𝒋 is the fare charged to travel between i and j,
∅𝒋 is the parking cost, and δ is a parameter representing comfort and convenience.
 This relationship is normally expressed by a logit curve
𝑷𝒊𝒋
𝟏
= 𝑻𝒊𝒋
𝟏
/𝑻𝒊𝒋 =
𝒆−𝜷𝒄𝒊𝒋
𝟏
𝒆−𝜷𝒄𝒊𝒋
𝟏
+ 𝒆−𝜷𝒄 𝒊𝒋
𝟐
where 𝐜𝐢𝐣 is called the generalized cost and β is the parameter for calibration. the
proportion of trips by mode (𝐓𝐢𝐣
𝟏
/𝐓𝐢𝐣) against cost difference.
Where, 𝑷𝒊𝒋 = Probability of an individual choosing mode 1
G (x) = 𝒂 𝟏 (𝑪 𝟏 - 𝑪 𝟐) + 𝒂 𝟐 (𝒕 𝟏 - 𝒕 𝟐) + 𝒂 𝟑 () + ….
𝒂 𝟏, 𝒂 𝟐, 𝒂 𝟑 = modal parameters
𝒄𝒊𝒋
𝟏
&𝒄𝒊𝒋
𝟐
,
= Cost of travel by mode 1 & 2

12. Multinomial Logit model
• The binary model can easily be extended to multiple modes. The equation
for such a model can be written us :
𝑷𝒊𝒋
𝟏
=
𝒆−𝜷𝒄 𝒊𝒋
𝟏
∑𝒆−𝜷𝒄𝒊𝒋
𝒎
Where,
𝑷𝒊𝒋
𝟏
= probability of choosing mode
e = exponential base
𝒄𝒊𝒋 generalized cost of travel modes
β is the parameter for calibration
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Basic Example of Modal Spilt

13. Toronto Model
13
Toronto Model

14. References:-
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1. Dr. H. R. Varia , P. J. Gundaliya at Urban Transportation Engineering
2. Tom V. Mathew and K.V. Krishna Rao (2007), Introduction to
transportation engineering , NPTEL, Modal split Chapter .

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