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Information Prof. Dr. Fabiano Baldo fabiano.baldo@udesc.br Short - - PowerPoint PPT Presentation

Feb 28, 2023 346 likes •807 views

Department of Computer Science Graduate Program in Applied Computing Proposition of Mobility Indicators Based on Traffic Information Prof. Dr. Fabiano Baldo fabiano.baldo@udesc.br Short CV Associate professor of Santa Catarina State

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Proposition of Mobility Indicators Based on Traffic Information

  • Prof. Dr. Fabiano Baldo

fabiano.baldo@udesc.br

Department of Computer Science Graduate Program in Applied Computing

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Short CV

  • Associate professor of Santa Catarina State

University;

  • Department of Computer Science;
  • Graduate program of Applied Computing
  • Research Interest:

– Trajectory data analysis; – Stream data mining; – Vehicle routing problem optimization.

2

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Agenda

  • Introduction;
  • Problem;
  • Objective;
  • First Results;
  • Next Steps;
  • Team;
  • Past Experiences.

3

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Introduction

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  • Joinville
  • Almost 600 thousand

habitant in 2019

  • Population increases 14%

in 10 years

  • Joinville had 1,827 km road

network in 2017

(IBGE, 2019)

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Introduction

  • Amount of vehicles in Joinville:
  • An increment of 56% of cars in 10 years;
  • Rate of vehicle per habitant is 0.77;

– Florianópolis has 0.72.

5

2009

263,667 vehicles 170,978 cars

2019

414,837 vehicles 266,958 cars

X

(DETRAN-SC, 2019)

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Introduction

  • Ways of transportation in Joinville

6

Way % Foot 23% Car 35% Public Transportation 24% Motorcicle 6% Bycicle 11% Others 1%

(SEPUD-Joinville, 2016)

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Introduction

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  • Widespread territorial
  • ccupation;
  • People live in the

south and work in the north;

– 30km from south to north;

  • Scarce budget to

invest the mobility.

(SEPUD-Joinville, 2016)

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Introduction

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  • Jams level 4 in Waze scale of one day 08/June/2018

Jams concentration Alternative road

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Problem

  • Which are the regions or streets that should

have the mobility investments prioritized?

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Objective

  • To build mobility indicators that allow the

identification of the regions in the city with critical mobility problems based on the analysis of traffic information.

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Smart Mobility Methodology

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(SEPUD-Joinville, 2018)

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Data Sources

  • Waze Connected Citizens Program

– It provides accidents and congestion on reports within 2” of time interval.

  • Speed limit radars

– They count the number of vehicles within 15’’ of time interval.

  • Accidents report

– More detailed information about an accident in provide by Joinville firefighters.

  • These data are collected since 2017.

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Data Sources

13

(SEPUD-Joinville, 2018)

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Mobility Indicators

  • Possible mobility indicators:

– Geolocation of jams regarding:

  • Jams’ average speed;
  • Jams’ average frequency;
  • Jams’ average duration;
  • Jams’ average length.

– Spatio-temporal correlation between jams; – Spatio-temporal correlation between jams and alerts; – Spatio-temporal jams and alerts patterns.

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Data Sources

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(CARDOSO, 2018)

  • Waze

Database Schema

Irregularities Jams Alerts

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First Results

  • Traffic jam alerts reported by users

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First Results

  • Accident alerts reported by users

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First Results

  • Jams level 4 (highest level)

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First Results

  • Streets with highest jams’ length

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Next Steps

  • Propose an appropriated data schema;
  • Construct a suitable data index model;
  • Apply data mining techniques;
  • Propose mobilities indicators;
  • Design intuitive dashboards.

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Team

  • Professors:

– Prof. Dr. Elisa Henning – Prof. Msc. Éverlin Fighera Costa Marques – Prof. Dr. Fabiano Baldo – Prof. Dr. Omir Correia Alves Jr. – Prof. Dr. Rebeca Schroeder Freitas – Prof. Dr. Ana Mirthes Hackenberg

  • Students:

– Master graduate students – Undergraduate students

21

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Work in progress

  • Indexing Traffic Events

22

Duarte M. M. G., Schroeder R., Hara, C. S. (2019). An Indexing Framework for Traffic Events. In Workshop de Teses e Dissertações em Banco de Dados (WTDBD – SBBD).

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Past Experiences

  • Generation of road maps

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Costa, G. H., & Baldo, F. (2015). Generation of road maps from trajectories collected with smartphone–a method based on genetic algorithm. Applied Soft Computing, 37, 799-808.

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  • Generation of road maps

Past Experiences

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  • Generation of road maps

Past Experiences

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𝑄

1,1

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Past Experiences

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𝑄

1,1

Buffer

  • Generation of road maps
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Past Experiences

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accuracy 𝑄

1,1

Buffer

  • Generation of road maps
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Past Experiences

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accuracy 𝑄

1,1

Buffer Selected ones

  • Generation of road maps
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  • Generation of road maps

Past Experiences

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Past Experiences

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  • 𝐷𝑦: coordinate candidate
  • 𝑇: set of near points
  • 𝑜′′: size of 𝑇 set
  • 𝐽𝑈 𝑇𝑗 : time influence
  • 𝐽𝐵 𝑇𝑗 : accuracy influence
  • 𝐽𝐸 𝐷𝑦, 𝑇𝑗 : distance influence

𝐺𝐽𝑈𝑂𝐹𝑇𝑇 𝐷𝑦, 𝑇 = ෍

𝑗=1 𝑜′′

𝐽𝑈 𝑇𝑗 ∙ 𝑁𝑈 + 𝐽𝐵 𝑇𝑗 ∙ 𝑁𝐵 + 𝐽𝐸 𝐷𝑦, 𝑇𝑗 ∙ 𝑁𝐸 𝑜′′

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Past Experiences

  • Suggest Alternative Routes

31

Schmitt, J. P., & Baldo, F. (2018). A Method to Suggest Alternative Routes Based

  • n Analysis of Automobiles' Trajectories. In 2018 XLIV Latin American Computer

Conference (CLEI) (pp. 436-444). IEEE.

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Past Experiences

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  • Group:

○

Euclidean distance

○

D is a parameter

○

Groups are created dynamically to comport all candidates

  • Standard and

alternative groups

○

Parameter K = number of standard groups

  • Suggest Alternative Routes
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Past Experiences

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  • Route:

○

Represents a path between start and end regions.

○

Used to suggest alternative directions.

○

First step: ○ Route segmentation by distance.

  • Suggest Alternative Routes
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Past Experiences

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  • Route:

○

Second step: ○ Route segmentation by angle.

  • Suggest Alternative Routes
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Past Experiences

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Parameters:

  • SH = Start Hour
  • EH = End Hour
  • SR = Start Region
  • ER = End Region
  • I = Interpolation
  • SD = Std. Dev.
  • σ = Sigma
  • D = Distance
  • ϴ = Angle
  • KS = Standards groups

Recovering Grouping Separation Segmentation

  • Suggest Alternative Routes
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Past Experiences

  • Guarding action recognition

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Past Experiences

  • Guarding action recognition

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Past Experiences

  • Guarding Coefficient

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Spatial proximity Position in the referential area Direction similarity Velocity similarity

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Past Experiences

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Past Experiences

  • Vehicle routing problem

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100 200 300 400 500 600 700 800 6000 7000 8000 9000 10000 11000 1 864 1727 2590 3453 4316 5179 6042 6905 7768 8631 9494 10357 11220 12083 12946 13809 14672 15535 16398 17261 18124 18987 19850 20713 21576 22439 23302 24165 Temperatura TC Iteração

C) Problema dinâmico (a priori 50%), curva de TC vs Temperatura (LRC1_4_1_q_0_0.5)

TC - Solução melhor global TC - Solução corrente Temperatura

Schmitt, J. P., Baldo, F., & Parpinelli, R. S. (2018). A MAX-MIN Ant System with Short-Term Memory Applied to the Dynamic and Asymmetric Traveling Salesman Problem. In 2018 7th Brazilian Conference on Intelligent Systems (BRACIS) (pp. 1-6). IEEE.

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Past Experiences

  • Vehicle

Routing Problem

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Past Experiences

  • Vehicle routing problem

– Green VRP (GVRP); – Dynamic VRP (DVRP); – Fractional or Split Delivery VRP (SDVRP); – Bi dimensional Vehicle Routing Problem with time windows constraints.

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Gauer, V. P., Weiss, F., & Alves, O. C. (2019). Meta Heuristics Applied to VRP problem with Heterogeneity and Simultaneous Picking and Delivery. In 2019 LI Brazilian Symposium on Operational Research. Peripolli A., Alves, O.C. (2020). Bi dimensional VRP Problem with TW constraints. In Brazilian Symposium on Information Systems. (submitted in September 2019).

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Past Experiences

  • Data Management of large datastores:
  • Partitioning (RDF)
  • Query processing (RDF)
  • Data mining

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Schroeder R., Hara C. S. (2015). Partitioning Templates for RDF. In Advances in Databases and Information Systems (ADBIS). Penteado R. R. M., Schroeder R., Hara C. S. (2016). Exploring Controlled RDF Distribution. In IEEE International Conference on Cloud Computing Technology and Science (CloudCom). Menezes S. L., Schroeder R., Parpinelli R. S. (2016). Mining of Massive Databases Using Hadoop MapReduce and Bio-inspired algorithms: A Systematic Review. RITA, v. 23(1).

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Proposition of Mobility Indicators Based on Traffic Information

Thank you!

  • Prof. Dr. Fabiano Baldo

fabiano.baldo@udesc.br

Department of Computer Science Graduate Program in Applied Computing