Title: Experimental Evaluation of Real-Time Information Services in Transit Systems from the Perspective of Users
1Experimental Evaluation of Real-Time Information
Services in Transit Systems from the Perspective
of Users
- Antonio Mauttone
- Operations Research Department, Universidad de la
República, Uruguay - Ricardo Giesen
- Department of Transport Engineering and
Logistics, Pontificia Universidad Católica de
Chile, Chile - Matías Estrada, Emilio Nacelle, Leandro Segura
- Undergraduate Program in Computer Engineering,
Universidad de la República, Uruguay - CASPT 2015, Rotterdam, The Netherlands, 19-23
July 2015
2Contents
- Introduction, motivation and goals
- Proposed model
- Simulation experiments
- Conclusions and future work
3Introduction, motivation and goals
4Introduction and motivation
- Advances on ICT.
- Real time information (RTI) services for transit
users. - Updated arrival time of buses to stops, available
through internet, mobile devices and screens at
the stops. - Large investments.
- Influence over the performance of the system.
5Existing models and studies
- Evaluations based in observed data Brakewood et
al., 2014 Watkins et al., 2011. - Analytical models Hickman and Wilson, 1995
Gentile et al., 2005 Chen and Nie, 2015. - Simulation models Coppola and Rosati, 2010 Cats
et al., 2011. - General characteristics and conclusions
- Methodologies transit assignment, random
utility, discrete event simulation. - Improvements measured in terms of travel time.
- Results highly depends on the particular
hypothesis. - Statistical significance, even across different
cases. - Sophisticated models are computationally costly.
6Research goals
- Evaluate the impact of RTI over transit systems
from the perspective of users. - Based on detailed modeling of interactions
between passengers and buses. - Focused on travel time, at both aggregated and
non-aggregated levels. - Scenario of small city, low frequency, high
regularity. - Different levels of information availability.
7Proposed model
8Model components
- Transit system representation.
- Passenger behavior model.
- Discrete event simulation.
9Transit system representation
Destination centroid
Origin centroid
Street node
Bus stop
- Demand model
- Each passenger is generated randomly at origin
centroids, using a negative exponential
distribution with mean value taken from an
OD-matrix. - Service model (lines)
- Sequence of network links. The bus travel time is
truncated normally distributed with mean taken
from the arc attribute. - Forward and backward directions and circular
lines. - Frequency and timetable.
10Passenger behavior
- Critical aspect of the model direct influence on
performance measures (travel time). - Dynamic characteristic given by RTI availability.
- Passengers plan their trips in terms of single
paths, using timetable information. - Schedule-based approach detailed modeling of
each passenger and each bus run. - Network representation line-database.
- Passengers maximize utility shortest paths.
11Proposed passenger behavior models
- All-or-nothing assignment with dynamic
rescheduling no transfers. - Six model variants (scenarios)
- RTI-always Real time information available
during the whole trip. - RTI_at_origin Real time information available only
at the origin centroid. - RTI-1Line Real time information of a single line
during the whole trip. - STT Static timetable only no RTI available.
- RTI_at_stops Real time information available only
at the bus stop. - FBA Frequency based, no timetables nor real time
information. - Particular characteristics
- Models 1 to 4 schedule departure from origin.
- Models 2 and 4 do not change the line selected at
origin. - Models 3, 5 and 6 use the frequency to estimate
waiting time. - Model 6 takes the first line that leads to
destination.
12Discrete event simulation model
- Bus
- Created at the initial node, moves according to
the timetable and disposed at the final node. - We do not simulate fleet management and control.
- Passengers
- Generated according to a given OD-matrix.
- Plan the trip at the origin centroid and may
change the selected line at the bus stop (in some
variants). - RTI is broadcasted immediately to passengers.
- Model implemented in C and EOSimulator library.
13Simulation experiments
14Methodology and goals
- Case study city of Rivera, Uruguay, 65,000
inhabitants. - Transit system 13 lines, low frequency (1/20 to
1/60) and high regularity. - Model 84 zone centroids, 378 OD-pairs, averaged
demand over 12 hours. Size about 500 nodes and
1500 arcs. - Execution time simulation of 6 hours of the real
system takes 18 seconds in a Core i7 computer.
15Methodology and goals
- Evaluation of the transit systems performance,
comparison among the six models. - Aggregated measure total travel time, averaged
over all passengers. - Non-aggregated measures time by travel component
and by OD-pair. - Several independent executions.
- Sensitivity analysis
- Higher frequencies.
- Higher irregularity.
16Current system aggregated values
Model Mean travel time (secs.)
1. RTI-always 2589
2. RTI_at_origin 2612
3. RTI-1Line 2625
4. STT 2693
5. RTI_at_stops 2960
6. FBA 3778
- Reasonable values for an average trip in the case
study 43 - 63 minutes. - RTI usage improves total travel time.
- RTI-always, RTI_at_origin and RTI-1Line exhibit
similar results. - STT is a bit higher.
- RTI_at_stops is higher because users do not schedule
departure. - FBA is significantly higher.
17Non-aggregated values by travel component
1. RTI-always 2. RTI_at_origin 3. RTI-1Line 4.
STT 5. RTI_at_stops 6. FBA
- RTI-always, RTI_at_origin and RTI-1line exhibit
similar results, even by travel component. - Main differences are in waiting time
- RTI_at_stops seems to be not very useful.
- FBA is significantly higher (due to on-board
travel time).
18Non-aggregated values by OD-pair
1. RTI-always 2. RTI_at_origin 3. RTI-1Line 4.
STT 5. RTI_at_stops 6. FBA
- Different characteristics geographic distance
between OD and service availability (lines,
frequencies). - Closest and farthest pairs three randomly
selected pairs. - The tendency already observed also holds for
different OD-pairs.
19Non-aggregated values waiting time
1. RTI-always 4. STT 6. FBA
- Why waiting time? Main differences among the
different models, the most onerous component. - Extreme models (RTI-always and FBA) and
intermediate model (STT). - Passengers using static timetables experience
similar waiting time with respect to those who
use RTI always. - Valid for low frequencies and high regularity.
20Sensitivity analysis higher frequencies
1. RTI-always 2. RTI_at_origin 3. RTI-1Line 4.
STT 5. RTI_at_stops 6. FBA
- Headways 20 to 60 minutes -gt 5 to 15 minutes
- Differences among models 1 to 4 are very small.
- RTI influence is less useful, when compared to
static timetables.
21Sensitivity analysis higher irregularity
- Model higher standard deviation in the parameter
of the bus travel time over the network links.
Model Mean travel time (secs.) increase w.r.t. current system
1. RTI-always 2972 15
2. RTI_at_origin 3037 16
3. RTI-1Line 2979 13
4. STT 3159 17
5. RTI_at_stops 3219 9
6. FBA 4189 11
- Mean travel time increased 14 in average, w.r.t.
current system mainly due to waiting time. - Models where decisions are not updated using RTI
(RTI_at_origin and STT) present the highest increase
w.r.t. the current system.
22Conclusions and future work
23Conclusions
- Simple model with six variants concerning
passenger behavior. - Small cities, low frequencies, high regularity.
- Improvements w.r.t. worst model (FBA), in terms
of - Total travel time 29 using static timetables
and 31 using RTI. - Waiting time 37 using static timetables and 48
using RTI. - Using STT is a reasonable and cheap alternative,
even for a scenario of higher frequencies. - RTI turns itself more relevant for a scenario of
high irregularity.
24Future work
- Study additional cases, including
- Bigger cities.
- Less regular services.
- More complex travel patterns.
- Include other atributes on route selection
- Transfers.
- Crowdiness, etc.
- Implement a visualization tool.
25Thanks for your attention!