High Speed Rail Passenger Demand Forecastingonlinepubs.trb.org › ... › Tuesday › HighSpeedRail...
20
1 Dr. Xiushan Jiang Associate Professor School of Traffic and Transportation Beijing Jiaotong University Dr. Lei Zhang Yijing Lu Associate Professor Graduate Research Assistant Department of Civil and Environmental Engineering 1173 Glenn Martin Hall, University of Maryland High Speed Rail Passenger Demand Forecasting: A New Approach Based on Network Scale with Real-world Applications in China 2014 ITM Presentation 4/29/2014
Transcript of High Speed Rail Passenger Demand Forecastingonlinepubs.trb.org › ... › Tuesday › HighSpeedRail...
1
Dr. Xiushan Jiang Associate Professor
School of Traffic and Transportation
Beijing Jiaotong University
Dr. Lei Zhang Yijing Lu
Associate Professor Graduate Research Assistant
Department of Civil and Environmental Engineering
1173 Glenn Martin Hall, University of Maryland
High Speed Rail Passenger Demand Forecasting:
A New Approach Based on Network Scale with Real-world
Applications in China
2014 ITM Presentation
4/29/2014
Outline
2
1. Introduction
2. Objectives
3. Methodology
4. Application
5. Conclusion
2
3 3
Introduction
The HSR network in China is rapidly expanding…
4
Introduction
The HSR travel demand forecasting is a vital issue.
From the view of macroscopic planning perspective:
Evaluate the feasibility of construction project
Foresee the overall trend of the rail network.
From the view of microscopic operation perspective:
Determining the technical index
Rail track length
Rail network operational scheme
HSR revenue management and so on.
5
Develop an improved four-step demand
model for HSR operators and planning
authorities.
Demonstrate the methods through case
studies in China
Explore the transferability of the model
to the other countries.
6
Objectives
7
Methodology
Comprehensively consider the characteristics
of HSR passenger flows: Temporal dynamics
and spatial network effects.
Build economic-population model to predict
induced travel demand.
Use RP/SP joint survey of modal split.
Assign travel demand to multiple paths based
on utility function.
The Improved Four-Step Demand Model
8
Determine travel demand of each OD
in the base year
Trend passenger flow
model
Economy – population
induced passenger flow
models
Total travel demand of each OD in different
developing stages
HSR travel demand split rates
of each OD in different developing stages
Traffic zone division and
determination of OD
Traffic generation and
distribution in different
Developing stages
multiple paths traffic assignment model based on
utility function
passenger travel demand along each path in
different developing stages
Traffic modes division
Traffic assignment based
on utility function
9
Application: A case study
Traffic Analysis Zones and Base Year ODs by Travel Modes
10
Application: Data
The highway data was computed according to
passenger departure schedules. The airline data
was obtained from China's Traffic Yearbook. The
HSR data was deprived from the Ticketing and
Reservation System.
Regional economic data was employed to acquire
the trend and the induced passenger demand.
Mode preference survey at different mode stations
in Beijing, Tianjin, Shanghai, Guangzhou.
11
Results: Trip generation and distribution
0.00
500.00
1000.00
1500.00
2000.00
2500.00
3000.00Ti
anjin
Qin
hu
angd
ao
Shiji
azh
uan
g
Jin
an
Qin
gdao
Hef
ei
Xu
zho
u
Suzh
ou
Yan
gzh
ou
Han
gzh
ou
Nin
gbo
Dat
on
g
Shan
xin
abu
Luo
yan
g
Yich
ang
Yin
gtan
Zhu
zho
u
Fuzh
ou
Shan
tou
Zhu
hai
Jin
zho
u
Dal
ian
Sip
ing
Nei
men
ggu
Nin
gxia
Ch
on
gqin
g
Nan
nin
g
Ch
engd
u
Qin
ghai
Xiz
ang
2011 2015 2020
0.00
500.00
1000.00
1500.00
2000.00
2500.00
3000.00
2010 2015 2020
12
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
Tian
jin
Can
gzh
ou
Qin
hu
angd
ao
Shiji
azh
uan
g
Dez
ho
u
Jin
an
Zib
o
Qin
gdao
Ben
gbu
Hef
ei
Xu
zho
u
Nan
jing
Shan
ghai
Han
gzh
ou
Nin
gbo
Wen
zho
u
Taiy
uan
Zhe
ngz
ho
u
Luo
yan
g
Wu
han
Yic
han
g
Yin
gtan
Ch
angs
ha
Zhu
zho
u
Fuzh
ou
Xia
me
n
Shan
tou
Gu
angz
ho
u
She
nzh
en
Jin
zho
u
Shen
yan
g
Dal
ian
Ch
anch
un
Hae
rbin
Xia
n
Ch
on
gqin
g
Gu
iyan
g
Ku
nm
ing
Ch
engd
u
Lan
zho
u
2020 2015 2011
Results: High speed rail mode split rates
13
Results: Comparison
0
100000
200000
300000
400000
500000
600000
2009 2011 2015 2020
Predicted Rail Trips The Observed or the official predicted Rail Trips
thousand
14
2011
15
The HSR Route Choice Model 2015
16
The HSR Route Choice Model 2020
Conclusions
The HSR travel demand will continue increasing
with reduced growth rate.
With the growth of the HSR network, The induced
passenger demand becomes more important.
The trips are concentrated in the developed regions
with large population and high employment. In
different HSR developing stages, passenger
demand increases with different growth rates.
17
Future steps
Further modify the calibration method of the
parameters in order to improve prediction
accuracy.
Establish utility functions specific to traffic
zones.
Consider complementary effects and substitute
effects among multiple modes.
18
Traditional model
Historical Average model
Exponential smoothing model
Grey forecasting
Autoregressive integrated moving average (ARIMA)
Elastic coefficient method
Box - Jenkins approach and so on.
The modal demand method
The logit model
Four step model
20
Introduction
