public transport travel time reliability across modes and...
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Public transport travel time reliability across modes and space
Jaime Soza-Parra
Advisors:
Juan Carlos Muñoz
Sebastián Raveau
Morning peak
452 pax/week WE99 pax/week EW
115 pax/week WE31 pax/week EW
WE: Bus ~ 20% demand
EW: Bus ~ 24% demand
7.5
min
12 min WE
7.3 min EW
Morning peak
45 pax/week WE74pax/week EW
398 pax/week WE370 pax/week EW
WE: Bus ~ 90% demand
EW: Bus ~ 83% demand
25 min
2
transfers
27 min
0
transfers
Travel time variability, waiting times, and headwayregularity are harder to analyse
Objective:
Estimate an aggregate mode-choice model considering those attributes
Outline
Databases and methodology
Characterization
Conclusions
Aggregate mode-choice analysis
Smartcard Database
Tap-in only system
Alights are inferred
Source: Munizaga y Palma (2012); Directorio de Transporte Metropolitano
Smartcard Database
1 week in April 2017
Buses
Travel demandBoarded bus/bus-stop – inferred alighting bus-stop for every paid travelTransfer times
Metro
Travel demandBoarded metro-station – inferred alighting metro-station for every paid travelNo information about boarded train – average travel times are considered
Metro Santiago Database
Buses
Travel Demand
Travel times
Transfer times
Headways
Metro
Travel Demand
Travel times
Transfer times
Headways
Metro Santiago Database
Arrival and departure times for every train in a specific line for the same week of analysis
+ Walking times in transfer stations
However, for the same pair of stations there is more than one reasonable route
We assigned a choice probability for each route (Raveau et al., 2014)
𝑓𝑚𝑒𝑡𝑟𝑜𝐴𝐵 𝑘 = 𝑟 𝑝𝑟 ∙ 𝑓𝑚𝑒𝑡𝑟𝑜
𝑟,𝐴𝐵 𝑘
Metro Santiago Database
Buses
Travel Demand
Travel times
Transfer times
Headways
Metro
Travel Demand
Travel times
Transfer times
Headways
Buses Arrivals Database
Estimated arrival times for some buses
Easy to process headway variability per bus-stop
Hard to process travel time for every bus-stop pair
Buses Arrivals Database
Buses
Travel Demand
Travel times
Transfer times
Headways
Metro
Travel Demand
Travel times
Transfer times
Headways
Outline
Databases and methodology
Characterization
Conclusions
Aggregate mode-choice analysis
Outline
Databases and methodology
Characterization
Conclusions
Aggregate mode-choice analysis
Buffers
For every bus stop, the closer metro station was assigned
Only bus stops with metro stations closer to 750 mts were considered
Observations Aggregate Database
Criteria Number of OD
pairs
Travel
Demand
Percentage
(OD Pairs/Demand)
At least one
observation in metro9.082 1.330.896 100% / 100%
Observations Aggregate Database
Criteria Number of OD
pairs
Travel
Demand
Percentage
(OD Pairs/Demand)
At least one
observation in metro9.082 1.330.896 100% / 100%
At least one
observation in bus or
combination
7.328 1.289.621 80,69% / 96,90%
Observations Aggregate Database
Criteria Number of OD
pairs
Travel
Demand
Percentage
(OD Pairs/Demand)
At least one
observation in metro9.082 1.330.896 100% / 100%
At least one
observation in bus or
combination
7.328 1.289.621 80,69% / 96,90%
At least six
observations in every
alternative
2.315 669.232 25,49% / 50,28%
Observations Aggregate Database
Criteria Number of OD
pairs
Travel
Demand
Percentage
(OD Pairs/Demand)
At least one
observation in metro9.082 1.330.896 100% / 100%
At least one
observation in bus or
combination
7.328 1.289.621 80,69% / 96,90%
At least six
observations in every
alternative
2.315 669.232 25,49% / 50,28%
Presence of headway
observations in Bus
DB for buses or
comination
2.264 662.063 24,93% / 49,75%
Cross-Nested Logit
Two modes
Three alternatives
Modeling considerations
Only Metro Combination Only Bus
, 1m m , 1b b c,bc,m
m b
c, c, 1m b
1
Modeling considerations
( ) ( ) Transfers ln2
iim i TV i TE CV h i TrT i Tr i i im im
hU ASC TT CV h TrT
By mode
By alternative
By alternativeBy type of transfers
Modeling considerations
; comb combASC TrBMB
= combcomb bus
comb comb
TVbusASC ASC
TVbus TVmetro
Strong correlation between
Then
Results
Parametre Value p-value
𝜃𝑇𝑇;Bus -0,058 0,00
𝜃𝑇𝑇;Metro -0,024 0,00
𝜃𝑇𝐸;All -0,156 0,00
𝜃𝐶𝑉 ℎ ;Bus,Comb -0,619 0,00
𝜃𝑇𝑟𝑇 -0,042 0,00
𝜃𝑇𝑟𝑀𝑀;Metro -0,879 0,00
𝜃𝑇𝑟𝑀𝑀;Comb -0,652 0,00
𝜃𝑇𝑟𝐵𝐵;Bus,Comb -1,390 0,00
𝜃𝑇𝑟𝐵𝑀𝐵;Comb -0,777 0,00
𝐴𝑆𝐶𝐵𝑢𝑠 0,205 0,33
𝐴𝑆𝐶𝑀𝑒𝑡𝑟𝑜 0 -
Loglikelihood = -2766,72
𝜇𝑀𝑒𝑡𝑟𝑜 = 1,81 ; 𝜇𝐵𝑢𝑠 = 1,0
𝛼𝐶𝑜𝑚𝑏−𝐵𝑢𝑠 = 0,19 ; 𝛼𝐶𝑜𝑚𝑏−𝑀𝑒𝑡𝑟𝑜 = 0,81
Best specification criteria: Loglikelihood ratio
“Combination” alternative relates more to metro
Results
Parametre Value p-value
𝜽𝑻𝑻;𝐁𝐮𝐬 -0,058 0,00
𝜽𝑻𝑻;𝐌𝐞𝐭𝐫𝐨 -0,024 0,00
𝜃𝑇𝐸;All -0,156 0,00
𝜃𝐶𝑉 ℎ ;Bus,Comb -0,619 0,00
𝜃𝑇𝑟𝑇 -0,042 0,00
𝜃𝑇𝑟𝑀𝑀;Metro -0,879 0,00
𝜃𝑇𝑟𝑀𝑀;Comb -0,652 0,00
𝜃𝑇𝑟𝐵𝐵;Bus,Comb -1,390 0,00
𝜃𝑇𝑟𝐵𝑀𝐵;Comb -0,777 0,00
𝐴𝑆𝐶𝐵𝑢𝑠 0,205 0,33
𝐴𝑆𝐶𝑀𝑒𝑡𝑟𝑜 0 -
Travel time is perceived worse in bus than in metro
𝑇𝑀𝑆 𝑇𝑇(Bus), 𝑇𝑇 Metro =−0,058
−0,024= 2,42
Results
Parametre Value p-value
𝜃𝑇𝑇;Bus -0,058 0,00
𝜃𝑇𝑇;Metro -0,024 0,00
𝜽𝑻𝑬;𝐀𝐥𝐥 -0,156 0,00
𝜃𝐶𝑉 ℎ ;Bus,Comb -0,619 0,00
𝜃𝑇𝑟𝑇 -0,042 0,00
𝜃𝑇𝑟𝑀𝑀;Metro -0,879 0,00
𝜃𝑇𝑟𝑀𝑀;Comb -0,652 0,00
𝜃𝑇𝑟𝐵𝐵;Bus,Comb -1,390 0,00
𝜃𝑇𝑟𝐵𝑀𝐵;Comb -0,777 0,00
𝐴𝑆𝐶𝐵𝑢𝑠 0,205 0,33
𝐴𝑆𝐶𝑀𝑒𝑡𝑟𝑜 0 -
Average waiting time is perceived larger than travel time, es expected.
The effect on metro users is more than two times higher. Why? “Very low” waits
𝑇𝑀𝑆 𝑇𝐸, 𝑇𝑇(Bus) =−0,156
−0,058= 2,69
𝑇𝑀𝑆 𝑇𝐸, 𝑇𝑇(Metro) =−0,156
−0,024= 6,50
Results
Parametre Value p-value
𝜃𝑇𝑇;Bus -0,058 0,00
𝜃𝑇𝑇;Metro -0,024 0,00
𝜃𝑇𝐸;All -0,156 0,00
𝜽𝑪𝑽 𝒉 ;𝐁𝐮𝐬,𝐂𝐨𝐦𝐛 -0,619 0,00
𝜃𝑇𝑟𝑇 -0,042 0,00
𝜃𝑇𝑟𝑀𝑀;Metro -0,879 0,00
𝜃𝑇𝑟𝑀𝑀;Comb -0,652 0,00
𝜃𝑇𝑟𝐵𝐵;Bus,Comb -1,390 0,00
𝜃𝑇𝑟𝐵𝑀𝐵;Comb -0,777 0,00
𝑨𝑺𝑪𝑩𝒖𝒔 0,205 0,33
𝐴𝑆𝐶𝑀𝑒𝑡𝑟𝑜 0 -
The coefficient of variation of headways only affects Bus and Combination users.
Metro operation is very regular, as it is operated by a central control system.
𝑇𝑀𝑆 𝐶𝑉(ℎ), 𝑇𝑇(Bus) =−0,619
−0,058= 10,67 min
Bus constant is not significative
Headway variability might be a key difference between bus and metro services
Results
Parametre Value p-value
𝜃𝑇𝑇;Bus -0,058 0,00
𝜃𝑇𝑇;Metro -0,024 0,00
𝜃𝑇𝐸;All -0,156 0,00
𝜃𝐶𝑉 ℎ ;Bus,Comb -0,619 0,00
𝜽𝑻𝒓𝑻 -0,042 0,00
𝜃𝑇𝑟𝑀𝑀;Metro -0,879 0,00
𝜃𝑇𝑟𝑀𝑀;Comb -0,652 0,00
𝜃𝑇𝑟𝐵𝐵;Bus,Comb -1,390 0,00
𝜃𝑇𝑟𝐵𝑀𝐵;Comb -0,777 0,00
𝐴𝑆𝐶𝐵𝑢𝑠 0,205 0,33
𝐴𝑆𝐶𝑀𝑒𝑡𝑟𝑜 0 -
Transfer times are perceived differently between bus and metro users
𝑇𝑀𝑆 𝑇𝑟𝑇, 𝑇𝑉(Bus) =−0,042
−0,058= 0,72
𝑇𝑀𝑆 𝑇𝑟𝑇, 𝑇𝑉(Metro) =−0,042
−0,024= 1,75
Results
Parametre Value p-value
𝜃𝑇𝑇;Bus -0,058 0,00
𝜃𝑇𝑇;Metro -0,024 0,00
𝜃𝑇𝐸;All -0,156 0,00
𝜃𝐶𝑉 ℎ ;Bus,Comb -0,619 0,00
𝜃𝑇𝑟𝑇 -0,042 0,00
𝜽𝑻𝒓𝑴𝑴;𝐌𝐞𝐭𝐫𝐨 -0,879 0,00
𝜽𝑻𝒓𝑴𝑴;𝐂𝐨𝐦𝐛 -0,652 0,00
𝜽𝑻𝒓𝑩𝑩;𝐁𝐮𝐬,𝐂𝐨𝐦𝐛 -1,390 0,00
𝜽𝑻𝒓𝑩𝑴𝑩;𝐂𝐨𝐦𝐛 -0,777 0,00
𝐴𝑆𝐶𝐵𝑢𝑠 0,205 0,33
𝐴𝑆𝐶𝑀𝑒𝑡𝑟𝑜 0 -
Transfers have an important impact on users’ decisions
𝑇𝑀𝑆 𝑇𝑟𝑀𝑀, 𝑇𝑉(Metro) =−0,879
−0,024= 36,63 min
𝑇𝑀𝑆 𝑇𝑟𝐵𝐵, 𝑇𝑉(Bus) =−1,390
−0,058= 23,97 min
Combination
𝑇𝑀𝑆 𝑇𝑟𝐵𝑀, 𝑇𝑉(Bus) =−0,777
−0,058= 13,40 min
𝑇𝑀𝑆 𝑇𝑟𝑀𝐵, 𝑇𝑉(Metro) =−0,777
−0,024= 32,38 min
Outline
Databases and methodology
Characterization
Conclusions
Aggregate mode-choice analysis
Conclusions
Significant differences among travel time dispersions by mode
This dispersion also increases with travel length for every mode. However, the dispersion is always smaller than 5 minutes for metro
Significant impact of headway variability in user’s alternative decision. This attribute decreases ASC significance
Heavy impact of transfers. Crowding should be incorporated in the analysis
Big data analysis is helpful to understand modal differences
Public transport travel time reliability across modes and space
Jaime Soza-Parra
Advisors:
Juan Carlos Muñoz
Sebastián Raveau