estimating passengers’ path choices using automated...
TRANSCRIPT
Estimating Passengers’ Path Choices Using Automated Collection Data in
Urban Rail Systems
Zhenliang (Mike) Ma
Baichuan Mo, Haris N. Koutsopoulos, Yiwen Zhu, Yunqing Chen
2
Crowding
• Issues and challenges
‒ Near capacity operations
‒ Passengers’ denied boarding
‒ Safety
• Passengers’ perception of crowding‒ In-vehicle: 1 min standing (up to 10 min) = 2.04 min uncrowded seating*
*Zheng Li, David A. Hensher, Crowding and public transport: A review of willingness to pay evidence, Transport Policy, 20113
Building Blocks for Transit Data Analytics
• Customer experience• Denied boarding• Reliability
• System performance• Crowding
Monitoring
• Route choice• Habitual• Interventions • Disruptions• Information
Behavior • Real-time prediction• Demand• Individual mobility
• Strategic planning• TDM • NPM (assignment)• Micro-simulation
Prediction & Planning
4
Problem Definition
• General methodology to estimate the route choice fractions of OD pairs and discrete choice model parameters at different time periods considering crowding impacts using AVL and AFC data in the system
AFCAVL
Network
1 2
330%
70%
𝛽𝑡𝑟𝑎𝑣𝑒𝑙_𝑡𝑖𝑚𝑒
𝛽𝑤𝑎𝑖𝑡_𝑡𝑖𝑚𝑒
𝛽𝑡𝑟𝑎𝑛𝑠𝑓𝑒𝑟_𝑡𝑖𝑚𝑒
……5
Mo
nito
ring
Plan
nin
g
• Platform crowding / denied boarding
• Route choice behavior
Impacts of Crowding on Passengers
A
C
E D
B
6
Passenger OD Journey Time Distribution
A
C
B
7
Challenges
8
Run times + Walking times
Pro
bab
ilit
y
Waiting times
Pro
bab
ilit
y
Journey times
Pro
bab
ilit
y
• A passenger with a long journey time – chose a longer route, or – denied boarding multiple times on a shorter route
Observed
9
Approach Data Level Applications Characteristics
Passenger-to-Itinerary-Assignment(Zhu et.al. 2018)
AFC (tap-in & out)AVLWalk distance/speed
Station Performancemeasurement
Needs access/egress time distributions
Sensitive to model parameters
Unsupervised learning
5-10 mins to run
Structured Mixture Model(Ma et.al. 2019)
AFC (tap-in & out)AVL
Station Performance measurement
No external data neededUnsupervised learning2-5 seconds to run
Regression(Miller et.al. 2018)
AFC (tap-in)AVLDenied boarding observations
Station Performancemeasurement
Prediction
Requires observations of denied boarding for calibration Supervised learning
NetworkAssignment(Ma et.al. 2019)
OD flows Path choice fractionsAVLCapacity
Network Performancemeasurement
Planning
Applied at the network level
Various crowding metrics
Requires capacity
Overview: Denied Boarding Approaches
10
Overview: Path Choice Approaches
Model Data Applications Characteristics
AFC AVL Monitor Planning OD path
fraction
Individual
choice
Crowding Probabilistic
model
Intera
ctions
Sun et al (2016)
J. Zhao et al (2017)
√ √ √ √ √ √
Sun et.al. (2015)
Zhang et.al. (2018)
√ √ √ √ √ √
Hörcher, et al. (2017) √ √ √ √ √ √ √
OD Journey time based
approach
√ √ √ √ √ √ √ √
Network based
approach
√ √ √ √ √ √ √ √ √
Denied Boarding Estimation
11
• AFC and AVL data for a given OD pair (with no transfer and no route choice).• The origin is the station of interest for which denied boarding metrics will be
estimated
Ma et. al, 2019
Structured
Path Choice Estimation: MLE Formulation
maxmize𝜷
𝐿𝐿 𝑥1, 𝑥2, … , 𝑥𝑁; 𝜷, 𝑻 = 𝑙𝑜𝑔
𝑛∈𝑁
𝑃 𝑥𝑛; 𝜷, 𝑻
=
𝑛∈𝑁
𝑙𝑜𝑔
𝑚∈𝑀𝑛
𝑃 𝑥𝑛|𝑚; 𝑻 × 𝜋ℎ𝑚
where ∶𝜋ℎ
𝑚 : Path fraction for route m at time period h
𝑥𝑛 : Observation of journey time of a passenger𝑚 : Path 𝑚 for passenger 𝑛𝒛ℎ𝑚: Attribute vector for path 𝑚 at time period h (e.g. travel time, crowd, etc.)
T : Train run times (i.e. arrival and departure time from platform)𝜷 = 𝛽1, 𝛽2, … , 𝛽𝑀 : Unknown parameters to be estimated
𝜋ℎ𝑚 =
𝑒𝑥𝑝 𝜷𝒛ℎ𝑚
𝑚′=1𝑀 𝑒𝑥𝑝 𝜷𝒛ℎ
𝑚′
12
Probabilistic Process
𝑃 𝑥𝑛|𝑚; 𝑻
13
Time-Space Diagram and Itineraries
Entry gate Origin platform Destination Platform
Tap-in
Access time
Space
Exit gate
Tap-out
Egress timeLine 1
Transfer Platform
Transfer time
Line 2Time
14
Likelihood of an Observation Given a Path
𝑃 𝑥𝑛|𝑚; 𝑻 = P 𝑡𝑖𝑛, 𝑡𝑜𝑢𝑡|path =
itinerary
P 𝑡𝑖𝑛, 𝑡𝑜𝑢𝑡 𝑖𝑡ℎ itinerary
=
Itinerary
P 𝑎𝑐𝑐𝑒𝑠𝑠 𝑖𝑡ℎ itinerary ∗
𝐽
P 𝑡𝑟𝑎𝑛𝑠𝑓𝑒𝑟𝑗 𝑖𝑡ℎ itinerary ∗ P 𝑒𝑔𝑟𝑒𝑠𝑠 𝑖𝑡ℎ itinerary
𝑙=0
𝐿
P 𝐷𝑒𝑛𝑖𝑒𝑑𝑙| 𝑖𝑡ℎ itenerary ∗ P 𝑡𝑙𝑡𝑟𝑎𝑛𝑠𝑓𝑒𝑟
𝑘=0
𝐾
P 𝐷𝑒𝑛𝑖𝑒𝑑𝑘| 𝑖𝑡ℎ itenerary ∗ P 𝑡𝑘𝑎𝑐𝑐𝑒𝑠𝑠
P 𝑡𝑒𝑔𝑟𝑒𝑠𝑠| 𝑖𝑡ℎ itenerary
The denied boarding, access time, transfer time and egress time distributions can be pre-calculated based on AFC and AVL data or from survey data. (Zhu, 2017) 15
Model Characteristics
maxmize𝜷
𝐿𝐿 𝑥1, 𝑥2, … , 𝑥𝑁; 𝜷, 𝑻 = 𝑙𝑜𝑔
𝑛∈𝑁
𝑃 𝑥𝑛; 𝜷, 𝑻
=
𝑛∈𝑁
𝑙𝑜𝑔
𝑚∈𝑀𝑛
𝑃 𝑥𝑛|𝑚; 𝑻 × 𝜋ℎ𝑚
=
𝑛∈𝑁
𝑙𝑜𝑔
𝑚∈𝑀𝑛
𝑐𝑚,𝑛
𝑒𝑥𝑝 𝜷𝒛ℎ𝑚
𝑚′∈𝑀𝑛𝑒𝑥𝑝 𝜷𝒛ℎ
𝑚′
=
𝑛∈𝑁
𝑙𝑜𝑔1
𝑚′∈𝑀𝑛𝑒𝑥𝑝 𝜷𝒛ℎ
𝑚′
𝑚∈𝑀𝑛
𝑐𝑚,𝑛𝑒𝑥𝑝 𝜷𝒛ℎ𝑚
Model Characteristics
𝑛∈𝑁
𝑙𝑜𝑔1
𝑚′∈𝑀𝑛𝑒𝑥𝑝 𝜷𝒛ℎ
𝑚′
𝑚∈𝑀𝑛
𝑐𝑚,𝑛𝑒𝑥𝑝 𝜷𝒛ℎ𝑚
=
𝑛∈𝑁
−𝑙𝑜𝑔
𝑚′∈𝑀𝑛
𝑒𝑥𝑝 𝜷𝒛ℎ𝑚′
+ 𝑙𝑜𝑔
𝑚∈𝑀𝑛
𝑐𝑚,𝑛𝑒𝑥𝑝 𝜷𝒛ℎ𝑚
LogSumExp Function is convex-LogSumExp Function is concave
• This problem is known as DC (Difference of Convex) Programming• DCP can be solved globally by methods such as branch and bound, which may be
slow in practice. A locally optimal (approximate) solution can be found through the many techniques of general nonlinear optimization. (Shen et. al 2016)
Implementation
• Select OD pairs
• Prepare inputs• Denied boarding distribution (Ma et.al, 2019)
• Approximated walking distance (station layout)
• Walking speed distribution (Zhu et.al, 2017)
• Path attributes (network)
• Solve the MLE problem
18
Case Study Using Synthetic Data
52
3
14
67
3 m
in
2 min2 min2 min
3 m
in3
min
3 m
in
• Network– Walk distance 30-50 m (access, egress,
transfer)• Train operations
– Headway 2 min– Headway deviation (-10, 10) sec– Trave time deviation (-20, 20) sec
• Passengers– Walk speed distribution, logn(0.1, 0.4)
– Mean 1.2m/s, std 0.25m/s– Denied boarding at station 2
– Distribution (0.2, 0.5, 0.3)
19
Setting
Jin, F., Yao, E., Zhang, Y., & Liu, S. (2017). Metro passengers' route choice model and its application considering perceived transfer threshold. PloS one, 12(9)
Variables Beta values
In_Veh_Time -0.246
Out_Veh_Time -0.439
No_of_Transfers -1.707
Denied_Waiting -0.480
52
3
14
67
9 OD Pairs:{1, 5, 4} -> {3, 6, 7}
20
𝑉𝑖 = 𝛽1𝐼𝑛_𝑉𝑒ℎ_𝑇𝑖𝑚𝑒𝑖 + 𝛽2𝑂𝑢𝑡_𝑉𝑒ℎ_𝑇𝑖𝑚𝑒𝑖 + 𝛽3𝑁𝑜_𝑜𝑓_𝑇𝑟𝑎𝑛𝑠𝑓𝑒𝑟𝑖 + 𝛽4𝐷𝑒𝑛𝑖𝑒𝑑_𝑊𝑎𝑖𝑡𝑖𝑛𝑔𝑖
0,00
0,20
0,40
0,60
0,80
1,00
1_3 5_3 4_3 1_6 5_6 4_6 1_7 5_7 4_7
Path_1 Path_2
True path share for OD pairs
Results: Denied Boarding Estimation
21
0
0,1
0,2
0,3
0,4
0,5
0,6
0 1 2
Pro
abil
ity
Denied boarding times
TRUE
ESTIMATE
Results: Discrete Choice Model Parameters
Variables Beta values (true) Beta values (estimated)
In_Veh_Time -0.246 -0.201
Out_Veh_Time -0.439 -0.411
No_of_Transfers -1.707 -1.548
Crowd_Waiting -0.48 -0.525
0,00
0,20
0,40
0,60
0,80
1,00
1_3 5_3 4_3 1_6 5_6 4_6 1_7 5_7 4_7
Pat
h_1
Fra
ctio
n
OD Pairs
TRUE
ESTIMATION
Sensitivity Analysis: Parameter Initialization
23
-1,8
-1,6
-1,4
-1,2
-1
-0,8
-0,6
-0,4
-0,2
0E
stim
ated
par
amet
ers
Difference between initialization and true parameters
beta_tt
beta_ovt
beta_not
beta_cw
0 0.5 1.0 1.5 2.0 2.5 3.0
Sensitivity Analysis: Denied Boarding
24
0,00
0,10
0,20
0,30
0,40
0,50
0,60
0,70
0,80
0,90
1,00
1_3 5_3 4_3 1_6 5_6 4_6 1_7 5_7 4_7
Pat
h_1
fra
ctio
n
OD pairs
TRUE
SET1
SET2
SET3
Denied Set_1 Set_2 Set_3
0 0.2 0.2 1
1 0.5 0.6 0
2 0.3 0.2 0
Sensitivity Analysis: Walking Speed
25
0,00
0,10
0,20
0,30
0,40
0,50
0,60
0,70
0,80
0,90
1,00
1_3 5_3 4_3 1_6 5_6 4_6 1_7 5_7 4_7
Pat
t_1
fra
ctio
n
OD pairs
TRUE
SET1
SET2
Mean Std
Set_1 1.2m/s 0.5m/s
Set_2 1.4m/s 1m/s
Summary
• Probabilistic model for path choice estimation considering denied boarding at key stations• Flexible to incorporate different variables and model structures
• Robust to parameter initialization and small errors in inputs
• Neglecting denied boarding can lead to biased estimation of path fractions
• Future work• Real-world data testing
• Learning behaviour under intervention (e.g. network expansion)
26
Zhenliang(Mike) Ma
https://www.monash.edu/engineering/mikema
27
Network Based Path Choice Estimation
28
Initial parameters and OD entry demand
NPM Engine (Assignment)
OD exit flows
Difference between model and “true” OD exit flows is small enough
No
, up
dat
ep
aram
eter
s
Path choice parameters
Simulation based optimization
Preliminary results (Bayesian method)
29
Variable Beta (true) Beta (Estimate)
In vehicle time -0.0663 -0.0798
# of transfer -0.438 -0.293
Transfer time -0.183 -0.210
Commonality Factor -0.941 -0.996
Map distance -0.0767 -0.0671
• Synthetic data generated using MTR network
Discrete choice model provided by MTR using 2012 survey
Preliminary results (Bayesian method)
30
Best point
‘Brute-force’
Analytical Model
31
𝛽 is the prior parameters for the choice model
𝑞𝑟𝑖𝑚𝑗𝑛 is the observed OD
entry-exit flow.
Nonlinear equation constraints
Non-analytical constraints
Final Model Formulation
32
Preliminary Test (Small Network)
33