Real-time Trip-Planner in Urban Public Transport ... · real-time trip-planner tool, which will...

INSTITUTO SUPERIOR TÉCNICO

Universidade Técnica de Lisboa

Real-time Trip-Planner in Urban Public Transport

Specifications and Preliminary Tests for Lisbon

David Manuel de Oliveira Alves

Department of Civil Engineering, Instituto Superior Técnico, Lisbon Technical University,

Avenida Rovisco Pais, Lisbon 1049-001, Portugal

Abstract

The strong economic and social changes that have occurred in developed and emergent cities in recent decades led to an increase of urbanized areas and diversification of mobility. It has been observed that, public transport systems haven’t been able to absolve this mobility increase, which is reflected on the increase of private transport usage. A common argument for low usage of public transport by citizens who are able to choose their transport mode is poor reliability of travel time. This dissertation intends to develop a model for a real-time trip-planner tool, which will allow users to plan their immediate trips on public transport with reliable information about the upcoming supply. This may potentially regain their confidence in the service provided. This tool will present the best options in terms of optimized route, optimized travel time and possible delays caused by accidents or incidents. The tool will have the potential to be applied to mobile device applications, based on real-time data exchange between those devices (e.g. smartphones) and the public transport network with the required data processing being remotely done by a central system. Several methodologies have been studied in order to compute the best possible real-time travel time predictions. A combination of classification trees with multiple regression analysis was introduced in an agent-based simulation environment in order to estimate the trip parameters. Although a larger and more complex test to the model is required, the obtained results are very promising. Nonetheless, the information already retrieved by the model, as well as the speed of computation of all the possible solutions, show a great potential of this application for a future real world system in the city of Lisbon or in other cities around the world.

Keywords: Real-time traffic, Public transport, Travel time predicions;Agent-based modeling

1. Introduction

As mobility is perceived in modern societies as a key element to ensure the access of citizens to activities and goods, the growth of urban areas led to a significant increase in the complexity of the transport systems to ensure safe and efficient mobility. These facts, along with the democratization of car ownership, are producing a steady increase of the impacts of urban mobility in modern cities (Banister 2008).

Although a great effort in increasing the quality of public transport supply has been carried out worldwide to fight this fact, especially in the European context, the demand for collective transport modes has been globally decreasing in the last decades in urban areas (Zegras & Gakenheimer 2006).

This trend might be explained by increasing urban sprawl, changes in lifestyles and activity patterns, and the growth of car ownership rates. All these factors play an important role on the difficulty of public transport to deal efficiently with a disperse time-space demand, especially for low density urban areas.

David Manuel de Oliveira Alves/ Real-time Trip Planner in Urban Public Transport

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These issues have been acknowledged by the main policy institutions, which have been trying to invert this tendency through the introduction of measures in three different fronts:

• Increase the attractiveness and competiveness of public transport supply by bringing in new transport alternatives and introducing new Intelligent Transport Systems (ITS) to support the system operation and upgrade the information of users from the system (Taylor, Nozick & Meyburg 1997); (Transport Demand Management - TDM);

• Introduce constraints to private car use through parking regulation and pricing, as well as new road charging schemes (also within the scope of TDM measures) (Viegas, J. M. 2001);

• Create more sustainable land use patterns, demanding less car intensive use and greater public transport accessibility (Cervero et al. 2004) (Transport Oriented Development – TOD).

Public transport use has also been affected by a perception bias of car users towards travel costs. Especially after purchasing a car, only variable costs like fuel and parking fees are taken into account, while public transport costs are always internalized. The neglect of fixed expenses, like the purchase of the car and insurance, may bias considerably a direct comparison between the charges of using a car versus a public transport service (Henley et al. 1981; Viegas, José M. 2010).

With the significant advances in data collection techniques, developments and proliferation of innovative technologies, public transport users begin to have more ability to access real-time information that helps the selection of routes in advance or during a trip. With accurate and reliable information, travelers can make decisions to avoid network segments that are congested, or in the context of public transport, choose the set of lines that allow reaching the destination in the shortest time.

This study intends to develop a model for a real-time information tool based on the Lisbon bus network operated by the company Carris, which will allow users to plan their immediately subsequent journeys through reliable information about the public transport supply, presenting the best options in terms of optimized route, optimized travel time and possible delays caused by accidents or incidents.

2. State of the practice: Information devices and applications around the world

Currently, the most common means of communicating real-time public transport information to users are: Dynamic Message Signs (DMSs), Interactive Voice Response (IVR) and Internet & Mobile devices.

A DMS is a panel (usually light devices) that can show words, numbers or symbols, dynamically changed from a remote location. As the message type can be variable, DMSs can be used to display different posts. In public transport systems, DMSs are normally used to provide information on expected arrival and departure time of buses and rails at stops or stations.

Interactive Voice Response (IVR) is a technology that allows a computer to detect voice and Dual-Tone Multi-Frequency (DTMF) during a phone call. IVR systems talk to callers following a recorded script. It prompts a response from the client to respond either verbally or by pressing a touchtone key and supplies the customer with information based on pre-recorded responses (Human Resources Software 2007).

A 2005 survey from Washington State Department of Transportation (WSDOT) showed the success of its Washington State’s 511 IVR systems. In the study became clear that 21% of the callers changed their behavior, which is a considerable high number that may have led to considerable benefits in highly congested areas.

With the widespread of Internet & smart mobile devices there are several emerging information systems that provide real-time information online. Some case studies research (see Table 1) lead to notice that several cities around the world already provide real-time information on buses, trams, light rail and other surface vehicles. Each vehicle uses the global positioning satellites and transmits its location and speed to a database. The information is then processed and made available at bus and tram stops with DMSs and on the Internet


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becoming accessible by computers and handheld devices such as tablet computers or cell phones. While there are, in some cases, real-time information available in none of them exist travel time predictions.

City Next

Vehicle Real-Time traffic

Info Owner Mode DTTP Mobile

Athens No No Athens Urban Transport Organization S.A. Multi No No Berlin Yes 3rd party Berliner Verkehrsbetriebe Multi No Yes Bogota No No Transmilenio Multi No No Boston Yes Yes Massachusetts Bay Transp. Authority Multi No Yes Brussels Yes Yes Société des T. Inter. de Bruxelles Multi No Yes Chicago Yes Yes Chicago Transit Authority Multi No Yes Curitiba No No Urbanização de Curitiba S/A Bus No No Helsinki Yes Partial Helsinki Region Transport Multi No Yes Hong-Kong No 3rd party CityBus Limited Bus No No Lausanne Yes Yes Transports publics de région Lausannoise Multi No N

Lisboa Yes No EFACEC Bus No Yes Yes No Metropolitano de Lisboa Subway No No No No IMTT Multi No No

London Yes Yes Transport for London Multi No Yes

Madrid Yes No Empresa Municipal de Transp. de Madrid Bus No Yes No No Metro de Madrid Sub No No

Melbourne Yes Yes Metlink Victoria Pty Ltd Multi No Yes Milan Yes Partial Trasporti Milanesi S.p.A. Multi No Yes Munich No No Münchner Verkehrs Multi No Yes New York Yes Yes Metropolitan Transpot Authority Bus No Yes S. do Chile No No Transantiago Informa Multi No No S. Francisco Yes Yes NextBus INC Bus No Yes

Singapore Yes Yes Quantum Inventions Multi No No Yes Yes Land Transport Authority of Singapore Bus No Yes

Stockholm Yes No Storstockholms Lokaltrafik Multi No Yes Thessaloniki Yes Yes Org. Urb. Transports Thessaloniki Multi No Yes Tokyo Yes Yes Metropolitan Expressway Company Ltd Multi No Yes Vienna Yes Yes Wiener Linien Multi No No Zurich No No Zürcher Verkehrsverbund Multi No Yes

Table 1 – State of the practice summary

3. State of the art: Current Methodologies for speeds and travel time predictions´

Some methods used in pattern recognition, data series prediction and optimization problems were analyzed in order to evaluate the accomplishments made in predicting speeds and travel times in the transport sector.

Neural networks, decision trees and Bayesian inference were selected due to their consideration as potential methodologies to estimate arrival times of buses and even travel time predictions.

While there are already applications using those methodologies in traffic forecast systems, they are mainly applied to the road sector in which only circulation speed disturbances are taken into consideration. These algorithms alone do not provide solutions to public transport systems that need to manage vehicles dispatch in order to avoid schedule delays and bus bunching. These characteristics of public transports increase the complexity of the problems due to the interdependence that exists among the network road sections.

To include the above mentioned public transport constraints in the model to be developed, it was considered necessary to integrate in the prediction system algorithms that not only predict by replicating previously observed patterns, but also incorporate intelligence to change the system’s behavior to “new” or uncertain operational conditions.


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4. Case Study

Lisbon is Portugal’s Capital city and the westernmost city in Europe’s mainland. It lies in the Iberian Peninsula on the Atlantic Ocean and stands beside Tagus River estuary. Lisbon observed a significant population growth in the last century, although since the 70’s observed a significant urban sprawl phenomenon producing a loss of population for the new sub-urban centralities. Presently Lisbon has 545,245 inhabitants within a 84.6 km2 area (INE 2011).

There is a single bus and tram operator in Lisbon, Carris. It operates 78 regular bus lines (667 km of service length), 5 tram lines (48 km of service length) and 4 lifts using a fleet of 745 buses, 57 trams and 4 elevators (2010). Carris already has 350 operational DMSs, Internet and SMS services to provide information in real-time based on information transmitted by buses when arriving to stops at earlier moments. The system provides the real-time information countdown forecast based exclusively upon historical data.

5. Data preparation

The database received was a raw log file and as such had no conditions to create a traffic predicting model. It was necessary to use some techniques to filter outlier records and to introduce some variables that were deducted from the ones provided. The whole data mining was very extensive, using as programming language Visual Basic found within Microsoft Excel software.

Figure 1 – Data mining procedures summary

The first two data mining processes undertaken were the split of the original files in a file for each day of records, and a transformation of coordinates, performed according to the Hayford-Gauss Datum Lisboa system. The second routine was made aiming to create the complete list of all 2,298 stops with bus stop records in the database. The third process aggregated all stops distanced less than 30 m into groups. The fourth one created a set of new variables deducted from the original ones (e.g. distance between stops, auxiliary variable representing day split in 5 minutes periods, time in sections, etc.). The Outliers Filtration routine was created to eliminate sections considered abnormal by appearing in all records less than 10 times. The last routine established the typical routes performed for each Carris bus line.

After this first data mining process, sections with similar speed profiles (split in four different percentiles: 0.25, 0.5, 0.75 and 0.9) for each 5 minute period of the day were grouped into clusters following the agglomerative Ward’s Method.

The main findings obtained from the data mining process undertaken are summarized in Table 2, where a classification of the different types of sections is presented. In the average speed profiles resulted into five different categories of sections that were labeled according to the speed distribution during the day and spatial location within the city.

Cluster Label Number of sections Average Speed [km/h]

1 High Speed 345 34.2 2 Unsteady 191 19.1 3 Bus Lane* sections 481 17.7 4 High Hierarchy Sections 945 22.1 5 Low Hierarchy Sections 818 12.1

Table 2 – Cluster Summary

David Manuel de Oliveira Alves/ Real

6. Research Methodology

This paper will present the framework of a simulation model ofLisbon, introducing a travel time prediction statistical procedure

ABS incorporates Multi-agent systems (MAS) that are systems composed of multiple interacting computer elements, known as agents and in a common intrinsically linked with the notion of emergence

The model presented here was written in the JAVA Programming Language, using AnyLogiclibrary of JAVA classes for creating, running, displaying and collecenvironments.

The general framework of the model is shown

For the development of this model, two main classes of class and object class. The agent class describes the behaviors and characteriagents and it is largely simulation-specific. The infrastructure parts of an AnyLogic simulation.

Figure 2 – General framework of the

In this model, the environment is defined by the road network model set in the simulation by the geographic configuration of the Carris service during 2008. The compatibility between this information and the existing logfile (for the years 2009 and 2010) was accessed and some minor corrections had to be introduced as explained below.

Since there were some changes to the service provided to public, lines 1 and 204 were deleted from the records and not considered in the analysis. After this filter, the model was built with 175 routes (87 operating in both ways and 2 circular routes).

There are four main objects that define the environmentNetwork. These objects collect and retrieve information on the systems behavior and interact with the two main agents of the system: the bus Services and the

Alves/ Real-time Trip Planner in Urban Public Transport

of a simulation model of the operation of the bus and tram networkstatistical procedure.

agent systems (MAS) that are systems composed of multiple interacting computer and in a common environment. Therefore, the concept of agent-based models is

ed with the notion of emergence (Martínez 2010).

The model presented here was written in the JAVA Programming Language, using AnyLogic, which provides a library of JAVA classes for creating, running, displaying and collecting data from complex simulation

shown in Figure 2.

For the development of this model, two main classes of objects available in AnyLogic libraries were used: class describes the behaviors and characteristics (states, capabilities) of

specific. The object class sets up and controls both the representational and infrastructure parts of an AnyLogic simulation.

General framework of the simulation model

is defined by the road network model set in the simulation by the geographic configuration of the Carris service during 2008. The compatibility between this information and the existing log

rs 2009 and 2010) was accessed and some minor corrections had to be introduced as explained

Since there were some changes to the service provided to public, lines 1 and 204 were deleted from the records s filter, the model was built with 175 routes (87 operating in both

environment: Section, Common Section, Street Path and collect and retrieve information on the systems behavior and interact with the two main

and the User.

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network of

agent systems (MAS) that are systems composed of multiple interacting computer based models is

provides a ting data from complex simulation

available in AnyLogic libraries were used: agent stics (states, capabilities) of

class sets up and controls both the representational and

is defined by the road network model set in the simulation by the geographic configuration of the Carris service during 2008. The compatibility between this information and the existing log-

rs 2009 and 2010) was accessed and some minor corrections had to be introduced as explained

Since there were some changes to the service provided to public, lines 1 and 204 were deleted from the records s filter, the model was built with 175 routes (87 operating in both

Transport collect and retrieve information on the systems behavior and interact with the two main


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Figure 3 - Description of the simulation environment configuration

Type of feature

Feature Name Description

Active Object

Route Information required to generate the services for each Carris line Stop Describe the real bus stops’ locations Group Agglomeration of stops distanced less than 30 m from one another

Common Section Represent an overlap between sections and merge information to compute travel times for the Street Paths

Street Path Assembles all the information of the transportation infrastructure and services Transfers Aggregation of each possible transfer in the final network (Transport Network).

Zone Aggregation of historical speed records for each section depending on the geographical location

Census Block Spatial unit for the origins and destinations of Users trips Connectors Represents the abstract connection between Census Blocks and the Pedestrian Network Pedestrian Network Network where Users can move by walking Nodes Transport Network

Source and destination of the dynamic shortest path algorithm (Dijkstra).

Transport Network Assembles all the information of the transportation infrastructure

Agents

Service Virtual representation the bus operation process User Represents the possible clients of bus and tram system of the simulation

Section Generates travel times and construct a prediction model based on live virtual regressions.

Functions

Bus arrivals Retrieves the data for each service operation Data percentile Creates a structure to assess data from the speed percentiles of each section Next Buses Timetable of the observed passages a bus in a Street Path Record travel time Retrieves the records of travel times for each Street Path Regression history Records data generated from the regression model for each Section Timetable Collects and retrieves information on the departure times of the Services of each Route Vehicle Entity that represents each bus running in the model

Table 3 – Description of the main simulation features

7. Simulation Model of Bus & Tram Operations

There are different types of data that have to be loaded depending on the type of simulation to be run. There are objects that represent the physical networks included in the model and historical speed records and are loaded independently of the type of simulation to be performed.


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Firstly, when there is no real data to measure the practiced speeds and travel times in the network (typically very early hours of operation), the model generates a synthetic day of operation of the Carris network. Secondly, when the model is run with registers from a real day of operation, there is no need to generate synthetically travel times in sections and therefore the features are directly imported to the model from the Carris log-file

In the first case, the generation of travel times in the simulation environment was based on the cyclic combination of historical speed percentile profiles for each Section and with the last six travel times (green arrows in Figure 4).

In the second case, the system collects data from the buses or trams passages at stops, the Speeds at each Section are estimated in an inverse process (blue arrows in Figure 4).

Figure 4 - Speed generation/load schema

To predict speeds and travel times, a routine was created in order to repeatedly (every 5 minutes) evaluate the accuracy of the prediction and act according to the results (see Figure 5).

• When the relative error is under 5%, the model preserves the estimates from the previous time period and projects the estimates for the next time periods;

• When the relative error is between 5% and 20%, the model computes a correction factor to the estimates to match the registered speed in the previous period and uses the same regression estimates with the correction factor to project for the next time periods;

• When the relative error is between 20% and 50%, a new regression of the model is triggered and the coefficients of each independent variable in the speed model are re-estimated;

• When the relative error is above 50%, the model triggers a build-up incident function.

Figure 5 - Prediction moment flowchart


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8. Trip-planner

The prediction model described has a potential use in a real world application. Therefore, a tip-planner has been built in order to test the model for a pre-set of queries performed by the agent User. This system of queries triggers the prediction model that using and adapted Dijkstra algorithm with node and section schedule, computes the equivalent shortest path to complete the desired trip.

In order to assess the quality of the trip-planner estimates, a small test was performed in 19 locations in the city of Lisbon (see Figure 6), analyzing the following indicators:

• Relative error of the estimate of the error;

• Correlation coefficients between the estimated travel times and observed travel times;

• Relative deviation on the time spent on transfers;

• Average number of transfers required by linear km of distance between stops;

• Average and standard deviation on walking time at the origin and destination.

The results are summarized in the following table:

Indicator Observed Value

Relative error 0.13 Correlation coefficient 0.70 Relative deviation on the time spent on transfers 1.84 min Average number of transfers 0.34 Average and standard deviation on walking time at the origin and destination 8.13 min / 11.93 min

Table 4 – Test Summary

Figure 6 - Test Source/Destination Stops


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9. Conclusions and Future Developments

This study presents the formulation of an ambitious Trip-Planner tool for the bus and tram system of the city of Lisbon. An extensive review showed that this type of real-time application of travel time predictions is not already available in large cities around the world that present very complex and multimodal public transport systems.

This work represents a first step on the development of this tool with the development of a complex simulation tool that allows testing the proposed real-time information and prediction system, and an innovative rule-based decision model to calibrate and recalibrate speed and travel time estimates for a 30 minutes time window.

The definition of the data mining process to analyze the available data and to create input information for the prediction model, proved to be a decisive step in the development of a tool of this kind. The spatial and speed configuration of the different services operated showed that there are some operational patterns of the system that are similar even in very distant streets.

The classified and processed data was then included in a comprehensive simulation model using an Agent-based formulation. This simulation tool aims to recreate the real system operation in a computer based scenario, allowing the construction of different operation settings, as well as road network behaviors that can affect the services operation. This artificial laboratory permitted the development of an agents’ interaction environment and the central control of a speed and travel time and forecast model.

The prediction model was formulated in a rule-base approach with four possible triggering solutions depending on the accuracy of the estimates from the previous period: not change the prediction model; calibrate a multivariate regression, produce a slight correction to the multivariate regression estimates, or create a build-up function for delay in sections, when a incident is detected.

Finally, after the design and programming of the ABM simulation, the Trip-Planner tool was introduced by discussing the concepts behind this service, its objectives and the main potential features. A small test-bed example was then conduced to prove the value-added of this new formulation. For that purpose, a small set of notable points in the city were selected to assess their possible connections at different hours of the day, but with the same user attributes specification for the Dijkstra parameters (waking speed and willingness to accept an extra transfer).

Although a larger and more complex test to the model is required, the obtained results are very promising. Nonetheless, the information already retrieved by the model as well as the speed of computation of all the possible solutions, show the great potential of this application for a future real world system in the city of Lisbon or in other cities around the world.

One of the key questions that remain unanswered is the impact that a system like this would have on the perception of users or potential users towards the public transport system. Does the introduction of this system create the momentum for a possible modal alternation of some private car users to the public transport system? Is really information one of the triggers in the equation of mode selection or just a necessary condition but not sufficient?

This study was based exclusively in Carris operational network, it would be desirable to include in future iterations of the model different transport modes (e.g. subway, taxis, etc.). As in the model developed the historical data remained static, this might be a constraint for future applications of the model. It should be evaluated how historical data could be updated with information regarding new travel time measurements using a Bayesian Statistical Inference procedure.

This tool has the potential to be customizable by declaration of the users’ preferences (for instance minimize transfers even if trip duration is increased by no more than 10 minutes), on the basis of which the small set of suggestions would be ranked by decreasing order of preference.


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Acknowledgments

I start by showing my appreciation to Carris and especially Eng. José Maia for providing the data used in this study to the MIT-Portugal program. This dissertation is included in the same program projects SCUSSE and CityMotion and therefore I would like to give my personal thank to Professor Carlos Bento (FCT-UC) and Dr. António Amador (INEGI-UP).

Main References

Banister, D 2008, 'The sustainable mobility paradigm', Transport Policy, vol. 15, no. 2, pp. 73-80. Carris 2010, Indicadores de Actividade, viewed August 25 2011, <http://www.carris.pt/pt/governo-societario/>. Cervero, R, Murphy, S, Ferrell, C, Goguts, N, Yu-Hsin, T, B., AG, John, B, Smith-Heimer, J, Golem, R, Peninger, P, Nakajima, E, Chui, E, Dunphy, R, Myers, M, Mckay, S & Witenstein, N 2004, Transit-Oriented Development in the United States: Experiences, Challenges, and Prospects, TCRP Report 102, Transit Cooperative Research Program - The Federal Transit Administration, Washington D.C. Henley, DH, Levin, IP, Louviere, JJ & Meyer, RJ 1981, 'Changes in Perceived Travel Cost and Time for the Work Trip during a Period of Increasing Gasoline Costs', Transportation, vol. 10, no. 1, pp. 23-34. Human Resources Software 2007, Interactive Voice Response, viewed August 21 2011, <http://www.hr-software.net/pages/216.htm>. INE 2011, Census 2011 - Resultados Preliminares, Instituto Nacional de Estatística, viewed September 3 2011, <http://www.ine.pt/scripts/flex_v10/Main.html>. Martínez, LM 2010, 'Activities, transportation networks and land prices as the key factors of location choices: an agent-based model for the Lisbon Metropolitan Area (LMA)', in 12th World Conference on Transport Research, Lisbon. Taylor, C, Nozick, L & Meyburg, A 1997, 'Selection and Evaluation of Travel Demand Management Measures', Transportation Research Record: Journal of the Transportation Research Board, vol. 1598, no. -1, pp. 49-60. Viegas, JM 2001, 'Making urban road pricing acceptable and effective: searching for quality and equity in urban mobility', Transport Policy, vol. 8, no. 4, pp. 289-94. Viegas, JM 2010, Improving urban mobility through intermediate transport modes: the search for “double second-best” solutions, CESUR - Instituto Superior Técnico. Zegras, PC & Gakenheimer, R 2006, Driving Forces in Developing Cities' Transportation Systems: Insights from Selected Cases, Massachusetts Institute of Technology, Cambridge.

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