DTA 2014 5th International Symposium on Dynamic Traffic Assignment June 17–19, Salerno, Italy, EU

SCHEDULE-BASED DYNAMIC TRANSIT ASSIGNMENT INCLUDING INDIVIDUAL TRAVELLER INFORMATION

A. Nuzzolo, U. Crisalli, L. Rosati {nuzzolo,crisalli, rosati}@ing.uniroma2.it

Department of Enterprise Engineering Tor Vergata University of Rome

DTA 2014 – Salerno

Summary ü Introduction ü Traveller information and path choice behaviour ü Dynamic demand-supply interaction models ü Application test ü Conclusions and further developments

Tor Vergata Nuzzolo et al. - Schedule-based dynamic transit assignment including individual traveller information University of Rome

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DTA 2014 – Salerno

Introduction

APTS (Advanced Public Transportation Systems) apply telematics technologies in order to improve network performances both from users and operators perspective Current vehicles location

MONITORING Passengers boarding and alighting

OPERATIONS CONTROL CENTER (OCC)

Forecast bus arrival time

Forecast bus occupancy

REAL-TIME FLEET MANAGEMENT AND TRAVELLER INFORMATION

USERS

OPERATORS

- Real-time information to travellers ATIS (Advanced Traveller Information Systems)

Tor Vergata Nuzzolo et al. - Schedule-based dynamic transit assignment including individual traveller information University of Rome

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Real-time information systems

DTA 2014 – Salerno

Classification Shared info ü at-stops

(e.g. updated waiting times of arriving vehicles)

ü on-board (e.g. vehicle position)

Advanced individual info (related to O-D) ü pre-trip (e.g. travel alternatives and actual arrival/departure times)

ü  en-route (e.g. updated travel alternatives and arrival/departure times) Tor Vergata Nuzzolo et al. - Schedule-based dynamic transit assignment including individual traveller information University of Rome

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DTA 2014 – Salerno

Individual Traveller Information Systems Path generation criteria

trip planners/advisors

(individual information based on real-time data)

rule based min travel time subject to: •  min transfers •  min walking time •  preferred mode-services

utility theory based

weighted time based min function of weighted time components

(access, waiting, on-board,…)

utility function including

average/aggregate parameters

personal parameters TVPTA

Tor Vergata Nuzzolo et al. - Schedule-based dynamic transit assignment including individual traveller information University of Rome

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DTA 2014 – Salerno

Individual-info path choice modelling

Example of pre-trip and en-route path utility function od ,τ TT ,t



i

[ k ] = β ED ⋅ EDk + β AE ⋅ AEk + βTW ⋅TWk + βTB ⋅TBk + βTC ⋅TCk + β NT ⋅ NTk + βCFW ⋅CFWk + ηk

where: U is the Utility of path k EDk is the Early or Late arrival time AEk is the sum of access and egress times (only in pre-trip choice); TWk is the waiting time of transit service m; TBk is the on-board time; TCk is the transfer time; NTk is the number of transfers; CFWk is the on-board crowding degree (ratio between on-board load and vehicle capacity); ηk is the random term; βi are the model parameters.

(pre-trip and en-route with different β) Tor Vergata Nuzzolo et al. - Schedule-based dynamic transit assignment including individual traveller information University of Rome

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DTA 2014 – Salerno

Learning mechanism on attributes Exponential filter (adapted from Cantarella and Cascetta, 1995): ex fo X τfo,t = ξ ⋅ X τinfo + (1ξ )⋅ [ γ ⋅ X + ( 1− γ )⋅ X ] ,t τ ,t-1 τ ,t-1

where fo ü  X τ ,t is the attributes forecast on day t ü  X info is the attribute provided by the information system on day t τ ,t ex ü  X τ ,t-1 is the attribute experimented on day t-1 fo ü  X τ ,t-1 is the attribute forecast on day t-1 ü  ξ ∈]0,1] is the weight given by the user to the information provided on day t ü γ ∈]0,1] is the weight given by the user to the attribute realised on day t-1 Tor Vergata Nuzzolo et al. - Schedule-based dynamic transit assignment including individual traveller information University of Rome

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DTA 2014 – Salerno

Summary ü Introduction ü Traveller information and path choice behaviour ü Dynamic demand-supply interaction models ü Application test ü Conclusions and further developments

Tor Vergata Nuzzolo et al. - Schedule-based dynamic transit assignment including individual traveller information University of Rome

8

DTA 2014 – Salerno

Dynamic demand-supply interaction Schedule-based transit modelling

Dynamic real-time transit path choice and assignment models require a schedule-based approach (Nuzzolo et al., 2001) : ü Target-time segmentation of demand, as user’s departure or arrival target time distribution has to be taken into account; ü Run-based supply models, as single runs with explicit departure/arrival times at stops have to be considered; ü Schedule-based path choice models, with explicit within-day time-dependencies.

Tor Vergata Nuzzolo et al. - Schedule-based dynamic transit assignment including individual traveller information University of Rome

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SCHEDULE-BASED TRANSIT MODELLING

DTA 2014 – Salerno

Target-time demand segmentation The demand is characterised by times in which users desire start or end their trips (user target times, TT): ü  Desired Departure Times (DDT) which represent the times in which users would departure from origin,

ü  Desired Arrival Times (DAT), which represent the times in which users would arrive at destination !

2 1 11 1 dτ 1

8:10 7:30

21 d τ 2! D1 ! τD1!

12

dτ D1 dτ22 D1 ! 8:15 7:45

2 1 11 1 dτ D 2

12

dτ D 2

dτ22 D2 21 d 2! τ D 2 ! ! τD2! 8:20 8:00

2 1 11 1 dτ D 3

12

dτ D 3

dτ22 D3 21 d 2! τ D 3 ! ! 8:15 τD3! 8:25

2 1 11 1 dτ D 4

dτ12 D4

dτ22 D4 21 d 2! τ D 4 ! ! τD4! 8:30 8:30

Tor Vergata Nuzzolo et al. - Schedule-based dynamic transit assignment including individual traveller information University of Rome

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SCHEDULE-BASED TRANSIT MODELLING

DTA 2014 – Salerno

Run-based models

temporal centroids

Diachronic graph

temporal centroids

τd

p

b rs a

b rs

τDis

τDi

stop axis s’

stop axis s

stop s

stop s’ centroid

centroid

Tor Vergata Nuzzolo et al. - Schedule-based dynamic transit assignment including individual traveller information University of Rome

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SCHEDULE-BASED TRANSIT MODELLING

example of DAT paths

Late Departure (due to oversaturation) τD7

DTA 2014 – Salerno

Late Delay (due to oversaturation)

Early Delay (due to oversaturation)

τD6 τD5

Tor Vergata Nuzzolo et al. - Schedule-based dynamic transit assignment including individual traveller information University of Rome

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DTA 2014 – Salerno

Within-day dynamic network loading “Packets” or “platoons” of travellers characterised by the same OD pair od and target time τTTi are propagated on the network using a meso-simulation approach. l i n e   1 8

34000 33000

018001

32000

018002 018003

30000

018004

29000

018005

28000

018006

27000

018007

26000

018008

25000

018009 31

28

25

22

19

16

13

10

7

4

24000 1

1.  At each time step τ, the forecasted arrival and departure times at stops for all runs are real-time updated according to the current and forecasted transit operations and traffic conditions. The service graph and the space-time paths are accordingly modified 2.  The packets are propagated on the new graph, according the new path choices probabilities

time  ( sec.)

31000

st op s

018010 018011

stop axis B

stop axis C

stop axis A space

Tor Vergata Nuzzolo et al. - Schedule-based dynamic transit assignment including individual traveller information University of Rome

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DTA 2014 – Salerno

Within-day dynamic network loading “Packets” propagation is simulated by: 1.  loading the supply network from origins to access stops on the basis of traveller pre-trip choices 2.  defining the contribution to the on-board load of run r belonging to path k according to the path choice updating at stops and the bottlenecks defined through the capacity of arriving vehicles 3.  moving between stops with the transit average speeds 4.  loading the egress links from final stops to the destinations

Tor Vergata Nuzzolo et al. - Schedule-based dynamic transit assignment including individual traveller information University of Rome

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DTA 2014 – Salerno

Within-day dynamic network loading 1. Network Loading from origins to access stops od ,τ TT ,t

d s ,τ

i

Ds

od ,τ TT ,t

[ k ] = pτ

i

[k ]⋅d

od ,τ TT ,t i

∀k' ≠ k ,k' ∈ K[ τ ,bτ ,t ]

where od ,τ TT ,t

d s ,τ

i

Ds

[ k ] is the number of travellers of the “packet” moving at time τD from o to d with target time τTTi on day t, which arrive at stop s at time τDs following path k

od ,τ TT ,t



i

d

[ k ] is the probability of choosing path k calculated at time τ of day t

od ,τ TT ,t i

is the number of travellers on the OD pair od with target time τTTi on day t

Tor Vergata Nuzzolo et al. - Schedule-based dynamic transit assignment including individual traveller information University of Rome

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DTA 2014 – Salerno

Within-day dynamic network loading 2. On-board loads updating (at-stops) od ,τ TT ,t

hs ,τ

i

Ds ,k

s τ ,t

od ,τ TT ,t

[ r ] = q [ r ] ⋅ d s ,τ

i

Ds

(1/2)

[k]

where

od ,τ TT ,t

hs ,τ

i

Ds ,k

[ r ] is the contribution to the on-board load of run r belonging to path k given by the travellers of the “packet” moving from o to d with target time τTTi on day t, arrived at stop s at time τDs following path k

qτs ,t [ r ] is the probability of boarding run r at stop s arriving at time τ on day t od ,τ TT ,t

d s ,τ

i

Ds

[ k ] is the number of travellers of the “packet” moving at time τD from o to d with target time τTTi on day t, which arrive at stop s at time τDs following path k

Tor Vergata Nuzzolo et al. - Schedule-based dynamic transit assignment including individual traveller information University of Rome

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DTA 2014 – Salerno

Within-day dynamic network loading 2. On-board loads updating (at-stops)

(2/2)

Travellers boarding run r departing from stop s on day t: od ,τ TT ,t

t s

h [ r ] = ∑∑∑ hs ,τ od τ TTi k

i

Ds ,k

[r]

If an explicit vehicle capacity constraint is considered, boarding travellers are constrained to capr,s (residual capacity of run r at stop s)

hst [ r ] = min( hst [ r ];capr ,s ) The enabling of the vehicle capacity limit implies a redistribution of part of od ,τ ,t d s,τ TTi [k] on the other runs r’, arriving at times τr’>τr, updating the path choice Ds od ,τ TT ,t for the part of d s,τ Ds i [k] that failed-to-board, using in a recursive way the above loading process and assuming a FIFO rule at stops to board the arriving runs Tor Vergata Nuzzolo et al. - Schedule-based dynamic transit assignment including individual traveller information University of Rome

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DTA 2014 – Salerno

Providing real-time information about on-board loads Given the time τ of day t:

f τ ,t = Δ ⋅ Q τ ,t [ X τ ,t ( f τ ,t )] ⋅ d where:

f τ ,t is the vector of link loads predicted at time τ on day t

Δ

is the link-path incidence matrix, made of δl ,k elements

Q τ ,t is the path choice probability matrix, made of qτs ,t [ r ] elements X τ ,t is the vector of path attributes (depending on link loads)

d

od ,τ TT ,t

is the demand vector, made of d s ,τ

i

Ds

[ k ] elements

Tor Vergata Nuzzolo et al. - Schedule-based dynamic transit assignment including individual traveller information University of Rome

18

DTA 2014 – Salerno

Summary ü Introduction ü Traveller information and path choice behaviour ü Dynamic demand-supply interaction models ü Application test ü Conclusions and further developments

Tor Vergata Nuzzolo et al. - Schedule-based dynamic transit assignment including individual traveller information University of Rome

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APPLICATION TEST

DTA 2014 – Salerno

Test network 2

Typical workday (7:00-9:00) ü  11 traffic zones ü  11 transit lines ü  245 runs Tor Vergata Nuzzolo et al. - Schedule-based dynamic transit assignment including individual traveller information University of Rome

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APPLICATION TEST

DTA 2014 – Salerno

Path choice model parameters Logit family

Tor Vergata Nuzzolo et al. - Schedule-based dynamic transit assignment including individual traveller information University of Rome

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APPLICATION TEST

DTA 2014 – Salerno

Example of on-board loads Line 2 - Run 205 vs Run 222

Tor Vergata Nuzzolo et al. - Schedule-based dynamic transit assignment including individual traveller information University of Rome

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APPLICATION TEST

DTA 2014 – Salerno

ATIS assessment

Benefits of providing advanced individual information using TVPTA (Tor Vergata Personal Travel Advisor) total total total total avg on-board Early/Late waiting time on-board time transfer time travel time load at stop schedule delay (hours) (hours) (hours) (hours) (pass) (hours*) WITH 417.4 1699.5 26.5 2143.4 55.7 1433.1 456.3 1715.5 31.8 2203.6 61.9 1451.3 MEDIUM WITHOUT -8.5% -0.9% -16.8% -2.7% -10.1% -1.3% DIFF (%) WITH 397.2 1682.0 62.2 2141.5 59.6 1667.2 729.3 1717.0 48.7 2494.9 75.4 1887.0 HIGH WITHOUT -45.5% -2.0% 27.8% -14.2% -20.9% -11.6% DIFF (%) (*) Late hours are weighted 3 times w.r.t. early ones

Service personal irregularity information

WITHOUT scenario is carried out according to: Nuzzolo et al. (2012) A schedule-based assignment model with explicit capacity constraints for congested transit networks, Transportation Research C, Elsevier. Tor Vergata Nuzzolo et al. - Schedule-based dynamic transit assignment including individual traveller information University of Rome

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DTA 2014 – Salerno

Conclusions This paper presented a new schedule-based dynamic real-time assignment model for congested transit networks, which takes into account traveller behaviour in presence of real-time individual information provided by ATIS. It includes: ü a path choice behaviour that considers initial at origin pre-trip choice of path that includes the access stop using ATIS ü the updating of such pre-trip choices at stops on the basis of the advanced individual information on level-of-service attributes and on-board congestion provided in real-time at single path level by ATIS ü a learning process on path choice attributes that includes traveller experiences plus info ü a real-time schedule-based network loading using a meso-simulation approach, and including explicit vehicle capacity constraints

Preliminary results of some applications on a real-size test network show a positive response in terms of ATIS effectiveness. Tor Vergata Nuzzolo et al. - Schedule-based dynamic transit assignment including individual traveller information University of Rome

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DTA 2014 – Salerno

Future developments

Open issues and future developments: ü  theoretical properties of the demand-supply interaction process (in particular of on-board load forecasting procedure) ü specification of more advanced path choice models using single user dynamic network loading, personal preference path utility parameters and machine learning processes ü  new empirical evidences

Tor Vergata Nuzzolo et al. - Schedule-based dynamic transit assignment including individual traveller information University of Rome

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U Crisalli.pdf

Forecast. bus. occupancy. REAL-TIME. FLEET. MANAGEMENT. AND. TRAVELLER. INFORMATION. USERS. OPERATORS. - Real-time information to travellers.

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