Experimental 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

Contents

Introduction, motivation and goals

Proposed model

Simulation experiments

Conclusions and future work

Introduction, motivation and goals

Introduction 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.

Existing 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.

Research 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.

Proposed model

Model components

Transit system representation.

Passenger behavior model.

Discrete event simulation.

Transit system representation

Origin centroid Destination centroid

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.

Street node

Passenger 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.

Proposed passenger behavior models

All-or-nothing assignment with dynamic rescheduling; no transfers.

Six model variants (scenarios):

1. RTI-always: Real time information available during the whole trip.

2. RTI@origin: Real time information available only at the origin centroid.

3. RTI-1Line: Real time information of a single line during the whole trip.

4. STT: Static timetable only; no RTI available.

5. RTI@stops: Real time information available only at the bus stop.

6. 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.

Discrete 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.

Simulation experiments

Methodology 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.

Methodology and goals

Evaluation of the transit system’s 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.

Current system: aggregated values

Reasonable values for an average trip in the case study: 43 - 63 minutes.

RTI usage improves total travel time.

RTI-always, RTI@origin and RTI-1Line exhibit similar results.

STT is a bit higher.

RTI@stops is higher because users do not schedule departure.

FBA is significantly higher.

Model Mean travel time (secs.)

1. RTI-always 2589

2. RTI@origin 2612

3. RTI-1Line 2625

4. STT 2693

5. RTI@stops 2960

6. FBA 3778

Non-aggregated values: by travel component

RTI-always, RTI@origin and RTI-1line exhibit similar results, even by travel component.

Main differences are in waiting time

RTI@stops seems to be not very useful.

FBA is significantly higher (due to on-board travel time).

1. RTI-always2. RTI@origin3. RTI-1Line4. STT5. RTI@stops6. FBA

Non-aggregated values: by OD-pair

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.

1. RTI-always2. RTI@origin3. RTI-1Line4. STT5. RTI@stops6. FBA

Non-aggregated values: waiting time

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.

1. RTI-always4. STT6. FBA

Sensitivity analysis: higher frequencies

Headways: 20 to 60 minutes -> 5 to 15 minutes

Differences among models 1 to 4 are very small.

RTI influence is less useful, when compared to static timetables.

1. RTI-always2. RTI@origin3. RTI-1Line4. STT5. RTI@stops6. FBA

Sensitivity analysis: higher irregularity

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@origin and STT) present the highest increase w.r.t. the current system.

Model Mean travel time (secs.) % increase w.r.t. current system

1. RTI-always 2972 15

2. RTI@origin 3037 16

3. RTI-1Line 2979 13

4. STT 3159 17

5. RTI@stops 3219 9

6. FBA 4189 11

Model: higher standard deviation in the parameter of the bus travel time over the network links.

Conclusions and future work

Conclusions

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.

Future 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.

Thanks for your attention!