Through the Bus Driver’s Eye

Linking Operational Data and Driver Perspectives
to Bus Services Monitoring and Planning

Author Gonçalo André Ferreira Matos

Thesis to obtain the Master of Science Degree in Transportation Systems

Supervisors Dr. Rosa Félix and Prof. Filipe Moura

Juri Prof. José Neves and Prof. Tiago Farias

1. Motivation

OP CONSTRAINTS

Lowest commercial speed registered in 2024

Alemão (2025); Soldado (2024)

NEGATIVE PERCEPTION

Bus is perceived as late and unreliable

Ramos et al. (2019); Rocha et al. (2023); Ribeiro, Fonseca, and Santos (2020)

LOW MODAL SHARE

Public transit share is only 16.2%

INE (2021)

It is crucial to study solutions to improve its efficiency and reliability

1. Motivation + 2. Literature Review

How are these problems being addressed?

Literature focuses on passenger satisfaction and performance-driven indicators

1. Motivation + 2. Literature Review

How are these problems being addressed?

Literature focuses on passenger satisfaction and performance-driven indicators

Drivers are overlooked by most studies

1. Motivation + 2. Literature Review

How are these problems being addressed?

Literature focuses on passenger satisfaction and performance-driven indicators

Drivers are overlooked by most studies

1. Motivation + 2. Literature Review

How are these problems being addressed?

Literature focuses on passenger satisfaction and performance-driven indicators

Drivers are overlooked by most studies

Considered as an output of the system

How can the driver’s knowledge be used to monitor and plan a bus network?





1. Research Question

3. Methodology


Mixed-methods approach to capture both the 📊 explicit and 🧠 tacit knowledge

3. Methodology


Mixed-methods approach to capture both the 📊 explicit and 🧠 tacit knowledge

3. Methodology


Mixed-methods approach to capture both the 📊 explicit and 🧠 tacit knowledge

3. Methodology


Mixed-methods approach to capture both the 📊 explicit and 🧠 tacit knowledge

3. Methodology


Mixed-methods approach to capture both the 📊 explicit and 🧠 tacit knowledge

3. Methodology


Mixed-methods approach to capture both the 📊 explicit and 🧠 tacit knowledge

3. Methodology

State-of-the-Practice

Structured interviews with operational control professionals from several cities

3. Methodology

Case Study

Lisbon public bus operator, part of Navegante multimodal system

102

Routes

1,658

Drivers

100 M

Pax/Year

3. Methodology

Case Study

Carris faces growing challenges caused by 🚗 conflicts with private vehicles and
🚧 construction works (Carris 2024)

Demonstrated by Carris and computed indicators
Yellow indicators were computed by the author, considering operational events for May 2025

13.71

Km/h

7.5 %

Bus bunching

4.9 %

Short turns

95 %

Regularity

3. Methodology

Focus Group Method and Analysis

1 session with 8 drivers, sampled by operator with diversity criteria

3. Methodology

Focus Group Method and Analysis

Held at Pontinha Garage, lasting 90 minutes

3. Methodology

Focus Group Method and Analysis

Moderated by research team, following a semi-structured discussion
(Hennink, Hutter, and Bailey 2020; Tracy 2013)

3. Methodology

Focus Group Method and Analysis

Content analysed and reported based on thematic analysis
(Braun and Clarke 2006)

3. Methodology

Exploratory Data Analysis

Dataset from XTraN, the operational assistance system used by Carris

👮‍♀️ Controllers




🧑‍✈️ Drivers

3. Methodology

Exploratory Data Analysis: Messages

Text communication between drivers (on-board tablet) and controllers (Operational Control Center)

📅 From Jan to May 2025 (132 days) 🖇️️+ 270k records

{
  "DriverMec": "189944",
  "DriverName": null,
  "DriverStationCode": "3739",
  "SenderID": 2937,
  "OperatorName": 23554,
  "OriginType": 1,
  "GeometryPoint": "{\"type\":\"Point\",\"coordinates\":[-9.1473,38.74355]}",
  "GpsQuality": 3,
  "Id": 9736723,
  "Date": "/Date(1749431120000)/",
  "ProcessedDate": "/Date(1749431130883)/",
  "VehicleNumber": 796,
  "VehiclePlate": "",
  "BusNumber": 1761,
  "RouteNumber": "207",
  "PlateNumber": "1Z207",
  "PlateNumberText": "207-1",
  "MessageText": "ACIDENTE ENTRE TERCEIROS",
  "Status": 4,
  "FirmGroup": null,
  "FontColor": "#2b2b2b",
  "BackgroundColor": "#d9cbfb"
}

3. Methodology

Exploratory Data Analysis: Messages

🧑‍✈️ 2.78 messages per driver/day (SD = 2.35)

3. Methodology

Exploratory Data Analysis: Messages

🧑‍✈️ 2.78 messages per driver/day (SD = 2.35)

67.51% are procedural

3. Methodology

Exploratory Data Analysis: Messages

🧑‍✈️ 2.78 messages per driver/day (SD = 2.35)

67.51% are procedural

👮‍♀️ 61.4% by controllers

3. Methodology

Exploratory Data Analysis: Messages

🧑‍✈️ 2.78 messages per driver/day (SD = 2.35)

67.51% are procedural

👮‍♀️ 61.4% by controllers

⚠️ 71.8% are free text messages

3. Methodology

Exploratory Data Analysis: Messages

👮‍♀️ 61.4% by controllers

⚠️ 71.8% are free text messages

Text mining processing using Latent Dirichlet Allocation topic modeling and bigrams analysis
(adapted from Valença, Moura, and Morais de Sá 2023)

Spatial dynamics of messages categories to reveal geographical and temporal patterns

3. Methodology

Exploratory Data Analysis: Events

Logs of automatic vehicle positions

📅 May 2025 (31 days) 🖇️️+ 32M records

{
  "Id": 2545991349,
  "DriverName": null,
  "DriverMec": "189294",
  "DriverStationCode": "3779",
  "BusNumber": "4627",
  "VehicleNumber": 168,
  "PlateNumber": "4Z736",
  "PlateNumberText": "736-4",
  "RouteNumber": "736",
  "Direction": "DESC",
  "VoyageNumber": 4,
  "TotalKm": 702783,
  "EventTime": "/Date(1749457228000)/",
  "EventReceptionTime": "/Date(1749457291137)/",
  "GpsQuality": 3,
  "Latitude": 38.75392,
  "Longitude": -9.15257,
  "Speed": 14,
  "BusStopNumber": 5704,
  "Description": "Detectada a paragem Campo Grande - Av. Brasil"
}

⚠️ Normalization issues

Algorithm to identify events associated to each trip planned

3. Methodology

Exploratory Data Analysis

4. Empirical Findings and
Thematic Analysis


4. Empirical Findings and Thematic Analysis

State-of-the-Practice

Interviews with controllers from 4 cities

Lisbon 🇵🇹

Porto 🇵🇹

Braga 🇵🇹

Portland 🇺🇸

4. Empirical Findings and Thematic Analysis

State-of-the-Practice

3/4 use on-board operational assistance systems

Portland presents most comprehensive setup

Portland reports highest engagement

4. Empirical Findings and Thematic Analysis

State-of-the-Practice

3/4 use on-board operational assistance systems

Portland presents most comprehensive setup

Portland reports highest engagement

All value previous driving experience for controller recruiting

Only Lisbon and Portland report structured participation

Portland provides feedback through interactive dashboard

4. Empirical Findings and Thematic Analysis

State-of-the-Practice

3/4 use on-board operational assistance systems

Portland presents most comprehensive setup

Portland reports highest engagement

All value previous driving experience for controller recruiting

Only Lisbon and Portland report structured participation

Portland provides feedback through interactive dashboard

None reported to include drivers in planning team

Most have channels for driver input

Portland stands out with direct channel

4. Empirical Findings and Thematic Analysis

State-of-the-Practice

3/4 use on-board operational assistance systems

Portland presents most comprehensive setup

Portland reports highest engagement

All value previous driving experience for controller recruiting

Only Lisbon and Portland report structured participation

Portland provides feedback through interactive dashboard

None reported to include drivers in planning team

Most have channels for driver input

Portland stands out with direct channel

Overall, drivers’ experience is recognized as valuable, but structured inclusion of their knowledge in monitoring and planning remains limited in Portugal 🇵🇹

4. Empirical Findings and Thematic Analysis

Focus Group Thematic Analysis

18 codes, grouped in 6 categories

4. Empirical Findings and Thematic Analysis

Focus Group Thematic Analysis

Connect passengers from A to B on time, with comfort, safety and no complaints

Drivers miss guidance on how to balance competing objectives

Factor improves operational performance | Factor damages operational performance

4. Empirical Findings and Thematic Analysis

Focus Group Thematic Analysis

 

Illegal parking

Bus lanes and stops occupied by ride-hailing and private vehicles

Touristic pressure

 

Factor improves operational performance | Factor damages operational performance

4. Empirical Findings and Thematic Analysis

Focus Group Thematic Analysis

 

XTraN lacks updates, feedback and conection reliability

Highway patrol discourages road code infractions

 

Factor improves operational performance | Factor damages operational performance

4. Empirical Findings and Thematic Analysis

Focus Group Thematic Analysis

 

Unrealistic schedules (traffic, urban changes, touristic demand)

Ticketing improvements with contactless

 

Factor improves operational performance | Factor damages operational performance

4. Empirical Findings and Thematic Analysis

Focus Group Thematic Analysis

 

Network outdated with urban development + missing toilets

Intersections and traffic lights as bottlenecks

Unattractive career

 

Factor improves operational performance | Factor damages operational performance

4. Empirical Findings and Thematic Analysis

Focus Group Thematic Analysis

Drivers express pride in Carris mission and their role

But also a sense of not being considered in decisions processes

Factor improves operational performance | Factor damages operational performance

4. Empirical Findings and Thematic Analysis

Focus Group Thematic Analysis

Drivers tacit knowledge goes beyond their functional role and hold valuable
system-wide perspectives

Potential to complement explicit knowledge of planners

4. Empirical Findings and Thematic Analysis

Comparing Findings With Previous Studies

Results are aligned with the literature

Factor Subfactor Source
Weather Impact on operations 📖 👥
Other vehicles Traffic 📖 👥
Passengers Irregular loads; Traveler behaviour 📖 👥
Service planning Network design; Schedule quality 📖 👥
Vehicle Modernization 📖 👥
Stop design Location; Traffic flow segregation; Number of berths 📖 👥
Stop operation Boarding conditions; Schedule design; Traffic lights; Payment system 📖 👥
Bus lane Impacts; Design 📖 👥
Drivers Experience; Stress; Fatigue and drowsiness 📖 👥

📖 Literature Review | 👥 Focus Group

4. Empirical Findings and Thematic Analysis

Comparing Findings With Previous Studies

Results are aligned with the literature and extend it

Factor Subfactor Source
Weather Impact on operations 📖 👥
Other vehicles Traffic 📖 👥
Passengers Irregular loads; Traveler behaviour 📖 👥
Service planning Network design; Schedule quality 📖 👥
Vehicle Modernization 📖 👥
Stop design Location; Traffic flow segregation; Number of berths 📖 👥
Stop operation Boarding conditions; Schedule design; Traffic lights; Payment system 📖 👥
Bus lane Impacts; Design 📖 👥
Drivers Experience; Stress; Fatigue and drowsiness 📖 👥
Service operation Operating assistance system; Enforcement ➖👥
Governance Working conditions; Company commitment on stakeholder engagement ➖👥

📖 Literature Review | 👥 Focus Group

5. Operational Data
Analysis Outcomes


5. Operational Data Analysis Outcomes

Free Text Messages Categorization

Text mining identified 7 new categories and mapped to 7 pre-existing ones

⚠️ 71.8% are free text messages

64,522 re-categorized

10% free text messages

5. Operational Data Analysis Outcomes

Free Text Messages Categorization

Text mining identified 7 new categories and mapped to 7 pre-existing ones

⚠️ 71.8% are free text messages

64,522 re-categorized

10% free text messages

Suggests that XTraN system is misaligned with operational needs

5. Operational Data Analysis Outcomes

Messages Spatial Dynamics

5. Operational Data Analysis Outcomes

Messages Spatial Dynamics

Wheelchair passengers 🦽

5. Operational Data Analysis Outcomes

Messages Spatial Dynamics

Wheelchair passengers 🦽

5. Operational Data Analysis Outcomes

Messages Spatial+Temporal Dynamics

 

Full bus 👥

Ticket fraud 🚨

 

5. Operational Data Analysis Outcomes

Messages Spatial+Temporal Dynamics

 

Full bus 👥

Ticket fraud 🚨

 

There is a high potential to convert tacit observations into explicit knowledge to support decision-making

5. Operational Data Analysis Outcomes

Linking Messages to Trip Performance

Considering average values, in minutes

Message N Departure delay Travel time diff Arrival delay
Full 6621 0.3 3.4 3.7
Proceed 2606 -0.1 2.1 2.0
Short turning 2043 -0.3 5.8 5.5
Traffic 1782 0.4 4.6 4.9
Overtake 173 0.4 -0.3 0.1
Crash 167 0.5 3.5 4.0
Schedule warning 61 -0.4 3.4 3.0
Control driving 52 -0.7 -4.2 -5.0
Reserved 4 0.5 4.0 4.5

5. Operational Data Analysis Outcomes

Linking Messages to Trip Performance

Considering average values, in minutes

Message N Departure delay Travel time diff Arrival delay
Full 6621 0.3 3.4 3.7
Proceed 2606 -0.1 2.1 2.0
Short turning 2043 -0.3 5.8 5.5
Traffic 1782 0.4 4.6 4.9
Overtake 173 0.4 -0.3 0.1
Crash 167 0.5 3.5 4.0
Schedule warning 61 -0.4 3.4 3.0
Control driving 52 -0.7 -4.2 -5.0
Reserved 4 0.5 4.0 4.5

5. Operational Data Analysis Outcomes

Linking Messages to Trip Performance

Considering average values, in minutes

Message N Departure delay Travel time diff Arrival delay
Full 6621 0.3 3.4 3.7
Proceed 2606 -0.1 2.1 2.0
Short turning 2043 -0.3 5.8 5.5
Traffic 1782 0.4 4.6 4.9
Overtake 173 0.4 -0.3 0.1
Crash 167 0.5 3.5 4.0
Schedule warning 61 -0.4 3.4 3.0
Control driving 52 -0.7 -4.2 -5.0
Reserved 4 0.5 4.0 4.5

Most categories tend to be sent during trips that take longer than scheduled

5. Operational Data Analysis Outcomes

Linking Messages to Trip Performance

Considering average values, in minutes

Message N Departure delay Travel time diff Arrival delay
Full 6621 0.3 3.4 3.7
Proceed 2606 -0.1 2.1 2.0
Short turning 2043 -0.3 5.8 5.5
Traffic 1782 0.4 4.6 4.9
Overtake 173 0.4 -0.3 0.1
Crash 167 0.5 3.5 4.0
Schedule warning 61 -0.4 3.4 3.0
Control driving 52 -0.7 -4.2 -5.0
Reserved 4 0.5 4.0 4.5

Most categories tend to be sent during trips that take longer than scheduled

Delayed arrivals are related to traffic, crashes, crowding, reserved and short turns

5. Operational Data Analysis Outcomes

Linking Messages to Trip Performance

Considering average values, in minutes

Message N Departure delay Travel time diff Arrival delay
Full 6621 0.3 3.4 3.7
Proceed 2606 -0.1 2.1 2.0
Short turning 2043 -0.3 5.8 5.5
Traffic 1782 0.4 4.6 4.9
Overtake 173 0.4 -0.3 0.1
Crash 167 0.5 3.5 4.0
Schedule warning 61 -0.4 3.4 3.0
Control driving 52 -0.7 -4.2 -5.0
Reserved 4 0.5 4.0 4.5

Most categories tend to be sent during trips that take longer than scheduled

Delayed arrivals are related to traffic, crashes, crowding, reserved and short turns

Overtake seems to be effective on restoring schedule regularity, unlike control driving

5. Operational Data Analysis Outcomes

Linking Messages to Trip Performance

Considering average values, in minutes

Message N Departure delay Travel time diff Arrival delay
Full 6621 0.3 3.4 3.7
Proceed 2606 -0.1 2.1 2.0
Short turning 2043 -0.3 5.8 5.5
Traffic 1782 0.4 4.6 4.9
Overtake 173 0.4 -0.3 0.1
Crash 167 0.5 3.5 4.0
Schedule warning 61 -0.4 3.4 3.0
Control driving 52 -0.7 -4.2 -5.0
Reserved 4 0.5 4.0 4.5

Messages patterns could be incorporated in automated real-time alarms or responses to enhance operational response time and mitigate impacts of disruptions

6. Conclusion


6. Conclusion

Main Findings

Results demonstrate that drivers have relevant insights about all planning levels, which can meaningfully contribute to decision making

6. Conclusion

Main Findings

Results demonstrate that drivers have relevant insights about all planning levels, which can meaningfully contribute to decision making

There is an untapped potential to turn experience into organizational knowledge, that can ultimately improve service reliability and overall system performance

REDUCE OP CONSTRAINTS

NEGATIVE IMPROVE PERCEPTION

LOW INCREASE MODAL SHARE

6. Conclusion

Main Findings

Results demonstrate that drivers have relevant insights about all planning levels, which can meaningfully contribute to decision making

There is an untapped potential to turn experience into organizational knowledge, that can ultimately improve service reliability and overall system performance

Technological innovation alone is not sufficient without serious institutional commitment to involve stakeholders and promote continuous improvement

6. Conclusion

Policy recommendations for Carris


  1. Improve messages categories to meet operational needs
  1. Improve messages functionality to increase usability
  1. Incorporate on-board automatic detection of recurring disruptions
  1. Evolve XTraN from reactive to predictive decision-support tool
  1. Create structured channels for driver feedback
  1. Institutional commitment for particited decision framework

6. Conclusion

Policy recommendations for Carris


  1. Improve messages categories to meet operational needs
  1. Improve messages functionality to increase usability
  1. Incorporate on-board automatic detection of recurring disruptions
  1. Evolve XTraN from reactive to predictive decision-support tool
  1. Create structured channels for driver feedback
  1. Institutional commitment for participated decision framework

6. Conclusion

Policy recommendations for Carris


  1. Improve messages categories to meet operational needs
  1. Improve messages functionality to increase usability
  1. Incorporate on-board automatic detection of recurring disruptions
  1. Evolve XTraN from reactive to predictive decision-support tool
  1. Create structured channels for driver feedback
  1. Institutional commitment for particited decision framework

6. Conclusion

Policy recommendations for Carris


  1. Improve messages categories to meet operational needs
  1. Improve messages functionality to increase usability
  1. Incorporate on-board automatic detection of recurring disruptions
  1. Evolve XTraN from reactive to predictive decision-support tool
  1. Create structured channels for driver feedback
  1. Institutional commitment for particited decision framework

6. Conclusion

Policy recommendations for Carris


  1. Improve messages categories to meet operational needs
  1. Improve messages functionality to increase usability
  1. Incorporate on-board automatic detection of recurring disruptions
  1. Evolve XTraN from reactive to predictive decision-support tool
  1. Create structured channels for driver feedback
  1. Institutional commitment for particited decision framework

6. Conclusion

Policy recommendations for Carris


  1. Improve messages categories to meet operational needs
  1. Improve messages functionality to increase usability
  1. Incorporate on-board automatic detection of recurring disruptions
  1. Evolve XTraN from reactive to predictive decision-support tool
  1. Create structured channels for driver feedback
  1. Institutional commitment for particited decision framework

6. Conclusion

Methodological Contributions

Replicable framework to assess how tacit and explicit knowledge coexists within organizations

Code available at gmatosferreira/Through-bus-drivers-eyes

Acknowledgements


 

 

References

References

Alemão, Samuel. 2025. “A Passo de Caracol. Autocarros Da Carris Com a Mais Baixa Velocidade de Sempre.” PÚBLICO. https://www.publico.pt/2025/04/15/local/noticia/passo-caracol-autocarros-carris-baixa-velocidade-2129894.
Borda, Jean-Charles de. 1781. “Mémoire Sur Les Élections Au Scrutin.” Histoire de l’Académie Royale Des Sciences.
Braun, Virginia, and Victoria Clarke. 2006. “Using Thematic Analysis in Psychology.” Qualitative Research in Psychology 3 (2): 77–101. https://doi.org/10.1191/1478088706qp063oa.
Carris. 2024. “Relatório e Contas 2024.” https://www.carris.pt/media/ikvdezmc/relat%C3%B3rio-e-contas-carris-2024.pdf.
Cordera, Rubén, Margarita Novales, Alfonso Orro, Borja Alonso, and Luigi dell’Olio. 2024. “Good Practices on Transit Operation Design: Bus Drivers’ Perspective.” European Transport Research Review 16 (1): 36. https://doi.org/10.1186/s12544-024-00661-1.
Desaulniers, Guy, and Mark D. Hickman. 2007. “Chapter 2 Public Transit.” In Handbooks in Operations Research and Management Science, edited by Cynthia Barnhart and Gilbert Laporte, 14:69–127. Transportation. Elsevier. https://doi.org/10.1016/S0927-0507(06)14002-5.
European Commission. 1998. “QUATTRO – Final Report.” http://trimis.ec.europa.eu/project/quality-approach-tendering-urban-public-transport-operations.
Hennink, Monique, Inge Hutter, and Ajay Bailey. 2020. Qualitative Research Methods. SAGE.
Hu, Wen Xun, and Amer Shalaby. 2017. “Use of Automated Vehicle Location Data for Route- and Segment-Level Analyses of Bus Route Reliability and Speed.” Transportation Research Record 2649 (1): 9–19. https://doi.org/10.3141/2649-02.
INE. 2021. “Indicador: População residente que vive no alojamento a maior parte do ano (N.o) por local de residência à data dos censos [2021].” 2021. https://tabulador.ine.pt/indicador/?id=0011704.
NACTO. 2017. “Better Boarding, Better Buses.” NACTO, February. https://nacto.org/publication/better-boarding-better-buses/.
Nakanishi, Yuko, and Kittelson & Associates, Inc. 2003. A Guidebook of Developing a Transit Performance-Measurment System. Transit Cooperative Research Program Report 88. Washington, D.C: Transportation Research Board. https://onlinepubs.trb.org/onlinepubs/tcrp/tcrp_report_88/guidebook.pdf.
Nguyen, Kien, Jingyun Yang, Yijun Lin, Jianfa Lin, Yao-Yi Chiang, and Cyrus Shahabi. 2018. “Los Angeles Metro Bus Data Analysis Using GPS Trajectory and Schedule Data (Demo Paper).” In Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 560–63. SIGSPATIAL ’18. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3274895.3274911.
Ramos, Sara, Paula Vicente, Ana M. Passos, Patrícia Costa, and Elizabeth Reis. 2019. “Perceptions of the Public Transport Service as a Barrier to the Adoption of Public Transport: A Qualitative Study.” Social Sciences 8 (5): 150. https://doi.org/10.3390/socsci8050150.
Ribeiro, Paulo, Fernando Fonseca, and Paulo Santos. 2020. “Sustainability Assessment of a Bus System in a Mid-Sized Municipality.” Journal of Environmental Planning and Management 63 (2): 236–56. https://doi.org/10.1080/09640568.2019.1577224.
Rocha, Hudyeron, Manuel Filgueiras, José Pedro Tavares, and Sara Ferreira. 2023. “Public Transport Usage and Perceived Service Quality in a Large Metropolitan Area: The Case of Porto.” Sustainability 15 (7): 6287. https://doi.org/10.3390/su15076287.
Soldado, Camilo. 2024. “Em Mais de 15 Anos, Autocarros de Lisboa e Porto Nunca Estiveram Tão Lentos.” PÚBLICO. https://www.publico.pt/2024/06/20/local/noticia/15-anos-autocarros-lisboa-porto-tao-lentos-2094651.
Tracy, Sarah J. 2013. Qualitative Research Methods: Collecting Evidence, Crafting Analysis, Communicating Impact, 2nd Edition Wiley. 1st ed. Wiley-Blackwell. https://www.wiley.com/en-be/Qualitative+Research+Methods%3A+Collecting+Evidence%2C+Crafting+Analysis%2C+Communicating+Impact%2C+2nd+Edition-p-9781119390800.
Valença, Gabriel, Filipe Moura, and Ana Morais de Sá. 2023. “How Can We Develop Road Space Allocation Solutions for Smart Cities Using Emerging Information Technologies? A Review Using Text Mining.” International Journal of Information Management Data Insights 3 (1): 100150. https://doi.org/10.1016/j.jjimei.2022.100150.
Van Oort, Niels. 2011. “Service Reliability and Urban Public Transport Design.” PhD Thesis, Netherlands TRAIL Research School Delft, The Netherlands. https://repository.tudelft.nl/file/File_243bbe9b-0d7c-4bc5-8ec0-9b9aaeb6afba.

Thank you!



“Esta é uma das primeiras profissões que conhecemos ainda na infância e, sem darmos conta, os motoristas de transportes públicos acompanham-nos ao longo das várias etapas das nossas vidas, da primeira à última viagem. Ainda assim, pouco ou nada sabemos sobre eles.”

— Raquel Albuquerque, Eles Que Nos Levam: Histórias de motoristas de transportes públicos (2024)

Extra: LR

Extra

How are bus networks monitored and planned?

Process that covers several management levels with distinct but interconnected goals

Extra

How are bus networks monitored and planned?

Process that covers several management levels with distinct but interconnected goals

Management levels (adapted from Desaulniers and Hickman 2007)

Operational instruments improving reliability requiring enabling conditions at the planning stages (adapted from Van Oort 2011)

Extra

How are bus networks monitored and planned?

Process that covers several management levels with distinct but interconnected goals

Management levels (adapted from Desaulniers and Hickman 2007)

Operational instruments improving reliability requiring enabling conditions at the planning stages (adapted from Van Oort 2011)

Extra

What factors impact performance?

Bus operation takes place inside a complex and dynamic urban system
(Desaulniers and Hickman 2007)

Affected by internal

SERVICE PLANNING

VEHICLES

STOP DESIGN

STOP OPERATION

BUS LANES

DRIVERS

URBAN ENVIRONMENT

(Cordera et al. 2024, extended)



…and external factors



WEATHER

TRAFFIC

PASSENGERS

(Van Oort 2011, extended)

Understanding these interactions is essential for an effective planning

Extra

QUATTRO quality loop

QUATTRO quality loop (Adapted from European Commission 1998; Nakanishi and Kittelson & Associates, Inc 2003)

Extra

Summary and Research Gap

Bus network monitoring and planning is a well-established field of research

Annual growth rate of literature for bus planning and monitoring on Web Of Science (produced using bibliometrix)

Extra

Summary and Research Gap

Bus network monitoring and planning is a well-established field of research

Annual growth rate of literature for bus planning and monitoring on Web Of Science (produced using bibliometrix)

Extra

Focus group transcription

turboscribe.ai dashboard for one of the focus group transcripts

Extra: Methodology

Extra

Focus group TA codes

interface for codes and themes management

Extra

Focus group TA manual categorization

interface for coding textual transcript

Extra

Focus Group Method and Analysis

Drivers’ priorities ranked and evaluated using Borda Count method
(Borda 1781)

Extra: Results

Extra

Focus Group Ranking Analysis

Ranking of 11 operational factors prioritize Tactical Planning (Borda Score = 61.4) and Strategic (Borda Score = 54) to Operational (Borda Score = 49.7)

Extra

Focus Group Ranking Analysis

Ranking of 11 operational factors prioritize Tactical Planning (Borda Score = 61.4) and Strategic (Borda Score = 54) to Operational (Borda Score = 49.7)

Further supports that drivers tacit knowledge goes beyond their functional role and hold valuable system-wide perspectives

Extra

Better Boarding, Better Buses (NACTO 2017)

Stops with +150 boardings/day

Fare evasion decrease

Travel time reduction

📁 PDF

Extra

Free Text Messages Categorization

LDA produced 12 topics that were manually reviewed and interpreted

Extra

Free Text Messages Categorization

Complemented by bigrams that reinforced and extended previous findings

Extra

Messages Spatial Dynamics

Wheelchair passengers 🦽

Extra

Messages Spatial Dynamics

Blocked road 🚫

Extra

Messages Spatial Dynamics

Full bus 👥

Extra

Messages Spatial Dynamics

Ticket fraud 🚨

Extra

Messages Spatial Dynamics

Bus bunching 🚌🚌

Extra: Events

Extra

Events Spatial Dynamics

Bus bunching 🚌🚌

Extra

Reliability Buffer Index

Reliability Buffer Index (RBI) determines how much higher is the 95th percentile of speed/travel time than the mean (Hu and Shalaby 2017), quantifying the extra time a passenger should budget for to ensure arriving on-time 95% of the times. It was computed per route variant.



\[ RBI(\%) = \frac{\text{95th percentile travel time} - \text{average travel time}}{\text{average travel time}} \]

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Reliability Buffer Index

RBI histogram

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Reliability Buffer Index

RBI variation through the week

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Reliability Buffer Index

RBI variation through the day

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Bus Bunching

Bus bunching was determined using the indicator documented by Nguyen et al. (2018), that evaluates the percentage of stop services in which more than one bus arrived in the -1 and +5 minutes window.

A stop service is considered to be the event of a bus arriving, picking up and dropping off passengers, and departing from a stop. In each trip, a bus will have as many stop services as the number of stops of its route.



\[ \text{Bus bunching} (\%) = \frac{N_{\text{bunching events}}}{N_{\text{total stop services}}} \times 100 \]

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Bus Bunching

Bus bunching histogram

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Bus Bunching

Bus bunching variation through the week

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Bus Bunching

Bus bunching variation through the day

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Short Turnings

Short turnings were evaluated by comparing the monitored trips with the planned ones, considering a trip to be short turned if there is a short turn event registered for its id. It was computed per route.



\[ \text{Short turnings} (\%) = \frac{N_{\text{short-turned trips}}}{N_{\text{total trips}}} \times 100 \]

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Short Turnings

Short turnings histogram

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Short Turnings

Short turnings variation through the week

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Short Turnings

Short turnings variation through the day

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Correlations

Variable Description
pct_bunched % of services bunched
pct_bunched_per_km % of services bunched/km
pct_bus_lane % of route with bus lane
total_length_m route length
gtfs_stops nr of stops
mean_speed_kmh commercial speed
headway_morning_peak mean headway in morning peak
reliability_buffer_index_real RBI real
difference_real_planned RBI difference planned vs real
pct_shortened_vs_planned % of services short turned

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Correlations matrix (Day + Bairro)

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Correlations matrix (Day)

Extra: Conclusions

6. Conclusion

Methodological Contributions

Replicable framework to assess how tacit and explicit knowledge coexists within organizations

Code available at gmatosferreira/Through-bus-drivers-eyes

Limitations

Small interview sample, single focus group and need to standardize data

6. Conclusion

Methodological Contributions

Replicable framework to assess how tacit and explicit knowledge coexists within organizations

Code available at gmatosferreira/Through-bus-drivers-eyes

Limitations

Small interview sample, single focus group and need to standardize data

Future Work

Broaden participation to extend findings

Enhance of operating-assistance data through machine learning and decision-support tools