Public transport travel time reliability across modes and...

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


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


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


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


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


Characterization

Conclusions


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%



pairs

Travel

Demand

Percentage

(OD Pairs/Demand)

At least one


At least one

observation in bus or

combination

7.328 1.289.621 80,69% / 96,90%



pairs

Travel

Demand

Percentage

(OD Pairs/Demand)

At least one


At least one


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%



pairs

Travel

Demand

Percentage

(OD Pairs/Demand)

At least one


At least one


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


( ) ( ) 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


; 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


𝜽𝑻𝑻;𝐁𝐮𝐬 -0,058 0,00

𝜽𝑻𝑻;𝐌𝐞𝐭𝐫𝐨 -0,024 0,00

𝜃𝑇𝐸;All -0,156 0,00


𝜃𝑇𝑟𝑇 -0,042 0,00





𝐴𝑆𝐶𝐵𝑢𝑠 0,205 0,33


Travel time is perceived worse in bus than in metro

𝑇𝑀𝑆 𝑇𝑇(Bus), 𝑇𝑇 Metro =−0,058

−0,024= 2,42

Results


𝜃𝑇𝑇;Bus -0,058 0,00

𝜃𝑇𝑇;Metro -0,024 0,00

𝜽𝑻𝑬;𝐀𝐥𝐥 -0,156 0,00


𝜃𝑇𝑟𝑇 -0,042 0,00





𝐴𝑆𝐶𝐵𝑢𝑠 0,205 0,33


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


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





𝑨𝑺𝑪𝑩𝒖𝒔 0,205 0,33


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


𝜃𝑇𝑇;Bus -0,058 0,00

𝜃𝑇𝑇;Metro -0,024 0,00

𝜃𝑇𝐸;All -0,156 0,00


𝜽𝑻𝒓𝑻 -0,042 0,00





𝐴𝑆𝐶𝐵𝑢𝑠 0,205 0,33


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


𝜃𝑇𝑇;Bus -0,058 0,00

𝜃𝑇𝑇;Metro -0,024 0,00

𝜃𝑇𝐸;All -0,156 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


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


Characterization

Conclusions


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

Public transport travel time reliability across modes and...

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