Dart2013_presentation_cristian_lai

A Web Portal for Reliability Diagnosis of BusRegularity

Dart 20137th International Workshop on Information Filtering and Retrieval

Benedetto Barabino1 Carlino Casari2 Roberto Demontis2

Cristian Lai2

Sara Mozzoni1 Antonio Pintus2 Proto Tilocca3

(1) Technomobility s.r.l. - Cagliari - Italy

(2) CRS4, Center for Advanced Studies, Research and Development in Sardinia -Pula (CA) - Italy

(3) CTM S.p.A. - Cagliari - Italy

December 6, 2013

Turin, December 6, 2013 1 / 32

Outline

l Objectives

l Motivation

l Methodology

l Web Portal

l Conclusion


Objectives

Research in the field of Transport Engineering

l Study of service quality, show when and where the service was or was notprovided as planned.

l The implementation of a methodology to evaluate regularity starting fromdata collected by Automatic Vehicle Location (AVL).

l Design and implementation of a Web based system specifically designedto support experts in transport engineering domain for evaluating servicequality, in particular regularity issues.


Outline

l Objectives

l Motivation

l Methodology

l Web Portal

l Conclusion


MotivationIssues

l Interest in the measurement of public transport service qualitym Reliability of service, the capability to meet the expectations.

l Bus regularity at a bus stop can be used as an indication of service quality.

l Means working on Big Data collected by Automatic Vehicle Location (AVL).l The need to support experts in transport engineering domain for evaluating

service qualitym Ability to process data and quickly present results;m Decision Support System (DSS).


MotivationRegularity

l The focus is on a particular busstop.

l In High frequency service,Regularity is measured by theHeadway Adherence (HA)between buses at bus stops.

l Headway is the time elapsedbetween two consecutive buses.

l Used as an indication of servicequality by both users and transitagencies


MotivationAVL

l In the transit industry is used to track where vehicles are in the field.

l AVL technology is installed in-busses: tracking + GPS.l Data recording

m actual arrival times at every bus stop.

l Data recorded during the transport service are downloaded and stored in acentral database when the vehicle finishes its service.

l Data used for off-line analysis.


Outline

l Objectives

l Motivation

l Methodology

l Web Portal

l Conclusion


MethodologyCoefficient of Variation of Headways

l Bus regularity can be measured by several indicators.l The Headway Adherence (HA) measured by the Coefficient of Variation of

Headways (Cvh) is a good indicatorm Ease of communication (understandable or easy−to−read)m Objectivity (impartial value to determine acceptance/rejections thresholds)m Orientation to customers (longer waiting time should be penalized)m Applicability requirements (such as regular scheduled headway)m Organization in regularity levels (to show where routes exhibits adequate

service levels and where not)


MethodologyMethod

The method as been discussed in previous works.The method addresses three main phases:

l Validate AVL data.

l Address criticality in AVL raw data.

l Determine the value of Cvh.

Then, the LoS can be determined.


MethodologyValidate AVL data

l Compare the numbers of actual and scheduled transits.l The aim is to discover lack of data for the following reasons:

m IOS, missed trips or unexpected breackdowns;m TF, AVL temporarily out of work;m BO, the succeeding bus passes its predecessor in the route.


MethodologyAddress criticality

Data validation is followed by the detection of criticalities

4. Address BO by ordering the sequence of actual transit times at bus stops.

5. Fill in IOS tables, reporting unpredicted missing trips and unexpectedbreakdowns and incorrect operation codes.

6. Generate a scheduled transits table with IOS. Match tables in STEP 5 andthe scheduled one.

7. Detect TF and IOS by GAPS. Match the table built in STEP 6 with the datavalidated in STEP 4.

8. Correct TF and IOS.


MethodologyExample of criticality

Correction of BO, by chronologically ordering actual transit times at bus stops

Correction of TF and IOS


MethodologyDetermine the value of Cvh

Given a generic bus stop j at time period t along the direction d of a route r :

C j ,t ,d ,rvh = σj ,t ,d ,r

hj ,t ,d ,r (1)

σ, the standard deviation of differences between actual and scheduled headwayh, the average scheduled headwayA LoS can be associated to each value of Cvh in different colors.


Outline

l Objectives

l Motivation

l Methodology

l Web Portal

l Conclusion


Web Portal

l A DSS, a Web based system specifically designed tom support transit industry experts;m managers are able to quickly prioritize actions to improve the service;m reduce the workload of transit agencies.

l Usually are used spreadsheets in a standards PC

l Handle AVL raw data for measuring the LoS at each bus stop.l Features:

m load raw data;m provides reposts on dashboards (tables, charts, maps).


Components and Technologies

l Entando, a Java Open Source portal-like platform for building information,cloud, mobile and social enterprise-class solutions. It natively combinesportal, web CMS and framework capabilities.

l PostgreSQL instance extended with spatial and geographic features,PostGIS.

l JasperServer, a BI engine and a reporting and analytics platform. Createsingle reports and dashboards.


GIS dataShape files

For creating the representation in a map we have two shape files:l routesl bus stops

Provided in ED50 and transformed in WGS84 and stored in the geoJSONformat.


GIS dataShape files, overlap

Overlapping the shape files we obtain the complete geo representation of thetransport network.


AVL dataCSV file

Raw AVL data are first downloaded, then separated according to routes. RawAVL data are imported as CSV file.Headers: date, route-bus-trip, stop, real, estimated, . . . , code route


Implementationof the method

Four modules:l data import

m the raw data of a month are imported and validated;

l data processingm difference between actual and scheduled headways between two consecutive

buses as measure

l data pre-aggregationm generates the Regularity facts table that contains the regularity measures

evaluated over three distinct type of day aggregations: by week (4 measures),by day of week (7 measures) and by the entire month

l data managementm Entando/JasperServer procedures to manage data and reports


Data importAVL table


Data importGeo table


Data processing

All the modules use PostgreSQL functions in PL/pgSQL language to performdatabase’s tasks. Data are separated by month in a dedicated databaseschema.The public schema contains common data, such as routes.


Data pre-aggregation


Data managementFile jrxml


Data managementDesigner


Reliability Diagnostic Tools

The items located on the dashboards include: regularity table, regularity linechart, mapper.The executive may select a route, the direction, the time slot, the time period.


Outline

l Objectives

l Motivation

l Methodology

l Web Portal

l Conclusion


Conclusion

l In bus transit operators the measure of regularity is a major requirement forhigh frequency public transport services.

l It is possible to handle huge AVL data sets for measuring bus routeregularity and understand whether a missing data point is a technicalfailure or an incorrect operation in the service.

l We have implemented:m a methodology to evaluate regularity starting from data collected by AVL;m a web portal as an environment designed to support bus transit operators

experts in evaluating regularity issues.


Future Work

l Implement a real case study, in order to exploit the complete set ofavailable data.

l Extend the web portal for both operators and users, then at a later stage fortransit agencies and passengers.

l Possible cause of low level of service will be investigated, in order to putthe bus operator in the position of selecting the most appropriate strategiesto improve regularity.


Thanks!

Q&A



Technology

Dart2013_presentation_cristian_lai