🛣️ Do bus lanes have an influence?
Increase lwd of bus lanes on map (width) to 3 or 5
🛣️ Do bus lanes have an influence?
Increase lwd of bus lanes on map (width) to 3 or 5
Submit to GET 2026?
Submit both to GET: * GTFShift * Master dissertation
Write methodology and results of EDA
Focus on EDA messages only and messages + events results
Add bus bunching to case-study
10
Disclaim that only 10
Overcome
Overtake
Kernel density maps (Wickham 2016)
Use grid instead of heatmap!
route/day
Add indicator per trip
in the middle of the week and Saturdays
Shortenings can complement bus bunching analysis and should be related with them, so no need for week/hour evolution.
Actually, there should be a chart with % shortenings vs % bus bunching, to study correlation between the two variables.
ALTERNATIVE: Difference between days is residual. If y starts on 0 and line is smoother, there won't be a visual difference throughout the week.
actually data suggests the opposite occurs
Since there is no statistical evidence of a tendency, the chart would be better explored in 4 quadrants (P50 dividing them), and exploring how the 4 groups are characterized
For each quadrant, choose some routes to explore in the text as examples, and for those provide chart with nr of buses bunched (y, comulative) per stop number (x)
ALTERNATIVE: try to normalize per route extension (m). Y would be % of services bunched per km and then there should be a tendency
8.38% of the services are bunched
This is a good value, since Carris average is around 7. Having a value close to the average means that the edge cases happen outside the bus lanes.
Also, compute for other big axis that do not have bus lanes, to validate that bus bunching occurs more there: * Morais Soares * Avenida do Brasil
Stops with highest bus bunching index
It's ok to show only worst cases, but specify (top 5 or top 10)
Complement with histogram of % per stop (to affirm that for 25% of stops, 1 in every 10 buses is bunched)
7.5% of stop services
Also disclaim % of stops (without services dimension)
Greatest impact on business days…
Distinguish weekends of week days
Make line smoother
Start y axis on 0.0, and units to % (instead of decimal values)
… and during peak hours
Again: weekends vs week days
Examples of routes with high RBI²
Add columns with route extension and commercial speed (extension/duration), for easier interpretation
Show also routes with low values
Add histogram with all routes, for people to have the big picture
monitored (65.1% of planned offer)
CLARIFY: analysed, corresponding to
Examples of routes with high RBI²
Add columns with route extension and commercial speed (extension/duration), for easier interpretation
monitored
CLARIFY: analysed, corresponding to
Business Intelligence platform
Data driven mngmt
Conclusions
improve their decision-making
Retirar ónus da decisão do motorista
Tipificar e automatizar respostas a disrupcoes no serviço como redundância á falta de resposta da central
relevant
And unexpected
That in some circunstances can be Critical (add examples)
Linking Operational Data and Drivers’ Perspectives to Network Monitoring and Planning
Teste
Linking Operational Data and Drivers’ Perspectives to Network Monitoring and Planning
Teste :)